# NG_writers

slug: patrick-banks
writer_no: CEW-000001
image: writer-files/patrick-banks-1727876549.webp
name: Patrick Banks
education: PhD
profession: Teacher
status: Online
online: Online
competences: English, Classic English Literature, History
works: English, Classic English Literature, History
orders: 723
review: 1663
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4h+
subjects: 3+
experience: 7+
created_at: 2024-10-10 12:24:39
updated_at: 2024-10-10 12:24:39
about:
  A wordsmith with a PhD in English and 7+ years of experience in academic writing. He crafts compelling narratives that captivate and inspire.

reviews:
  [ { "name": "Patrick Banks", "customer_no": "CEW-234984", "image": null, "reviews": "Patrick is an exceptional writer! He greatly helped me with English essay writing. His clear insights and writing style proved helpful. I highly recommend this writer!", "type": null, "date": "29-Dec-20" }, { "name": "Patrick Banks", "customer_no": "CEW-457321", "image": null, "reviews": "Amazing Writer! Patrick delivered a top-notch History assignment to me. His expertise in the subject is reflected throughout his work. Strongly recommend!", "type": null, "date": "15-Mar-22" }, { "name": "Patrick Banks", "customer_no": "CEW-576934", "image": null, "reviews": "I am highly impressed with Patrick's deep knowledge of English literature. He handled my assignment with attention to detail. Thank You for your writing service!", "type": null, "date": "20-Sep-23" } ]


slug: declan-rose
writer_no: CEW-000002
image: writer-files/declan-rose.webp
name: Declan Rose
education: PhD
profession: Teacher
status: Online
online: Online
competences: Education, English, History
works: English, Classic English Literature, History
orders: 567
review: 1342
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4h+
subjects: 3+
experience: 5+
created_at: 2024-10-10 12:24:39
updated_at: 2024-10-10 12:24:39
about:
  An expert writer with a passion for education that knows no bounds. He has a PhD in Education and over 5 years of experience in academic writing.

reviews:
  [ { "name": "Declan Rose", "customer_no": "CEW-142867", "image": null, "reviews": "Declan impressed me with his insightful case study writing. His expertise is evident in his thorough analysis and clear presentation. Truly appreciate his quality writing!", "type": null, "date": "05-Jun-20" }, { "name": "Declan Rose", "customer_no": "CEW-259801", "image": null, "reviews": "I recently bought an English essay from Declan, and it was perfect! He delivered top-notch content that educates and engages. If you ask for his assistance, you won�t be disappointed!", "type": null, "date": "27-Jan-21" }, { "name": "Declan Rose", "customer_no": "CEW-282389", "image": null, "reviews": "He greatly assisted in history essay writing. He made complex subjects engaging. His in-depth knowledge truly elevates the writing process. Thanks, Declan!", "type": null, "date": "05-Feb-24" } ]


slug: noah-c-lorenzen
writer_no: CEW-000003
image: writer-files/noah-c-lorenzen.webp
name: Noah C. Lorenzen
education: PhD
profession: Teacher
status: Online
online: Online
competences: Economics, Finance, Business Studies
works: English, Classic English Literature, History
orders: 645
review: 1528
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4h+
subjects: 3+
experience: 7+
created_at: 2024-10-10 12:24:39
updated_at: 2024-10-10 12:24:39
about:
  An accomplished academic writer with over 7 years of experience specialising in Finance and Business Studies. With a strong foundation in research methodologies and analytical frameworks, he produces high-quality content.

reviews:
  [ { "name": "Noah C. Lorenzen", "customer_no": "CEW-134698", "image": null, "reviews": "Noah is an expert writer! He did my business studies assignment, which was highly impactful. His work meets the standards of academia. I highly recommend him!", "type": null, "date": "21-Aug-19" }, { "name": "Noah C. Lorenzen", "customer_no": "CEW-452301", "image": null, "reviews": "Noah's case study writing is exceptional! As a PhD in Finance, his knowledge shines through in his clear, insightful analysis and thorough research. Strongly suggest his high-quality writing service!", "type": null, "date": "18-Apr-22" }, { "name": "Noah C. Lorenzen", "customer_no": "CEW-534698", "image": null, "reviews": "This writer delivered the highest-quality Economics essay. The essay was well-structured and engaging. He was also responsive, which made the writing process smooth. Thank you, Noah, for your efforts!", "type": null, "date": "21-Mar-24" } ]


slug: mollie-hayes
writer_no: CEW-000004
image: writer-files/mollie-hayes.webp
name: Mollie Hayes
education: PhD
profession: Teacher
status: Online
online: Online
competences: Philosophy, History, English
works: English, Classic English Literature, History
orders: 792
review: 1757
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4h+
subjects: 3+
experience: 4+
created_at: 2024-10-10 12:24:39
updated_at: 2024-10-10 12:24:39
about:
  She holds a Master's in Philosophy and brings over 4 years of experience in academic writing. Passionate about exploring complex ideas and invaluable contributor to scholarly write-ups.

reviews:
  [ { "name": "Mollie Hayes", "customer_no": "CEW-339870", "image": null, "reviews": "Mollie's writing is an extraordinary writer! She has a profound ability to articulate complex ideas clearly and engagingly. Highly recommend her for philosophical topics!", "type": null, "date": "09-May-21" }, { "name": "Mollie Hayes", "customer_no": "CEW-415349", "image": null, "reviews": "I purchased an English assignment from her, and I was thoroughly impressed with the quality and depth of analysis. I am grateful for her assistance, Thanks Mollie!", "type": null, "date": "25-Sep-23" }, { "name": "Mollie Hayes", "customer_no": "CEW-775124", "image": null, "reviews": "Mollie is an amazing writer with a keen understanding of historical concepts. She delivered a case study which was well-researched and engaging. A go-to writer for anyone who needs assistance in history topics!", "type": null, "date": "11-Apr-24" } ]


slug: graham-sutherland
writer_no: CEW-000005
image: writer-files/graham-sutherland.webp
name: Graham Sutherland
education: PhD
profession: Teacher
status: Online
online: Online
competences: Marketing, Finance, Business Studies
works: English, Classic English Literature, History
orders: 598
review: 1820
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4h+
subjects: 3+
experience: 6+
created_at: 2024-10-10 12:24:39
updated_at: 2024-10-10 12:24:39
about:
  A professional writer with more than 6 years of experience in academic writing, specialising in Marketing, Finance, and Business Studies. He is dedicated to helping students achieve their educational goals.

reviews:
  [ { "name": "Graham Sutherland", "customer_no": "CEW-491843", "image": null, "reviews": "Graham is an excellent writer! This is the third time I asked for his assistance in a marketing project. He crafted a compelling case study, and I strongly recommend him!", "type": null, "date": "17-Feb-20" }, { "name": "Graham Sutherland", "customer_no": "CEW-634987", "image": null, "reviews": "He is highly capable of writing complex narratives of finance into clear and engaging content. He offers smooth communication throughout the writing process. Thank you Graham for your service!", "type": null, "date": "08-Apr-22" }, { "name": "Graham Sutherland", "customer_no": "CEW-556478", "image": null, "reviews": "Graham is an amazing go-to writer! He delivered an assignment for my business subject. It was well-researched and met deadlines. Highly recommend!", "type": null, "date": "22-Sep-24" } ]


slug: tyler-burns
writer_no: CEW-000006
image: writer-files/tyler-burns.webp
name: Tyler Burns
education: PhD
profession: Teacher
status: Online
online: Online
competences: Nursing, Healthcare, Social Work
works: English, Classic English Literature, History
orders: 771
review: 1476
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4h+
subjects: 3+
experience: 5+
created_at: 2024-10-10 12:24:39
updated_at: 2024-10-10 12:24:39
about:
  A seasoned academic writer with over five years of experience. Committed to delivering high-quality work in the fields of Nursing, Healthcare, and Social Work.

reviews:
  [ { "name": "Tyler Burns", "customer_no": "CEW-641577", "image": null, "reviews": "Tyler's writing on nursing effectively balances clinical insight with compassionate storytelling. Her assignment provided a thorough understanding of nursing practices while highlighting the ethical dimensions of patient care. I highly recommend her!", "type": null, "date": "22-Jun-20" }, { "name": "Tyler Burns", "customer_no": "CEW-698052", "image": null, "reviews": "Her essay writing assistance for nursing is outstanding. I have taken her help many times. Each piece showcases clarity and attention to detail. Trust me she is a real gem!", "type": null, "date": "08-Oct-22" }, { "name": "Tyler Burns", "customer_no": "CEW-779832", "image": null, "reviews": "I got Tyler's writing for social work was exceptional. Her delivered case study was insightful and compassionate. She skillfully balances theoretical frameworks with real-world applications. She is my go-to writer!", "type": null, "date": "28-Dec-23" } ]


slug: sam-duffy
writer_no: CEW-000007
image: writer-files/sam-duffy.webp
name: Sam Duffy
education: PhD
profession: Teacher
status: Online
online: Online
competences: Finance, Management, Economics
works: English, Classic English Literature, History
orders: 612
review: 1959
rating: 5
success_rate: 99
on_time_rate: 99
delivery: 4h+
subjects: 3+
experience: 7+
created_at: 2024-10-10 12:24:39
updated_at: 2024-10-10 12:24:39
about:
  With over 7 years of experience, he is an expert academic writer. His expertise in delivering high-quality content in Finance, Management, and Economics is extraordinary.

reviews:
  [ { "name": "Sam Duffy", "customer_no": "CEW-779832", "image": null, "reviews": "Sam provides exceptional research paper help for my finance degree. With a keen understanding of complex financial concepts, he delivered insightful analysis and well-structured papers. Highly recommended for academic support!", "type": null, "date": "28-Jul-20" }, { "name": "Sam Duffy", "customer_no": "CEW-657320", "image": null, "reviews": "Thank you, Sam, for the perfect essay! With a keen understanding of management principles, he delivered well-researched essays that exceeded my expectations.", "type": null, "date": "12-Oct-22" }, { "name": "Sam Duffy", "customer_no": "CEW-787534", "image": null, "reviews": "I recently bought my assignment from Sam and it was a great experience! His ability to meet deadlines while ensuring high-quality work sets him apart. I am highly grateful for him!", "type": null, "date": "12-Mar-24" } ]


slug: esli-quartel
writer_no: CEW-000008
image: writer-files/esli-quartel.webp
name: Esli Quartel
education: Masters
profession: Teacher
status: Online
online: Online
competences: Marketing, Management, Business Studies
works: English, Classic English Literature, History
orders: 736
review: 1234
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4h+
subjects: 3+
experience: 6+
created_at: 2024-10-10 12:24:39
updated_at: 2024-10-10 12:24:39
about:
  She is a highly dedicated academic writer with over 6 years of experience specialising in Business Studies. She has contributed valuable insights to the field. 

reviews:
  [ { "name": "Esli Quartel", "customer_no": "CEW-438219", "image": null, "reviews": "Esli is an exceptional writer! She delivered an assignment that reflected her keen understanding of marketing principles. I highly recommend her high-quality writing!", "type": null, "date": "09-May-21" }, { "name": "Esli Quartel", "customer_no": "CEW-567321", "image": null, "reviews": "She helped me with dissertation writing for business studies subject. She delivered well-researched content that met academic standards. Her timely work met deadlines effortlessly. Thank You, Esli!", "type": null, "date": "09-May-21" }, { "name": "Esli Quartel", "customer_no": "CEW-539219", "image": null, "reviews": "Esli is an exceptional writer! I found her expert in management-related essays. Her work not only informs but inspires. A reliable choice for anyone seeking a high-quality management essay. Highly recommend!", "type": null, "date": "28-Nov-23" } ]


slug: hans-brandt
writer_no: CEW-000009
image: writer-files/hans-brandt.webp
name: Hans Brandt
education: PhD
profession: Teacher
status: Online
online: 1
competences: Psychology, Education, Healthcare
works: English, Classic English Literature, History
orders: 736
review: 1959
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4h+
subjects: 3+
experience: 4+
created_at: 2024-10-10 12:24:39
updated_at: 2025-04-17 15:22:40
about:
  He is an experienced academic writer with over four years of expertise. He is a professional in producing high-quality and research-based content in psychology, education, and healthcare.

reviews:
  [ { "name": "Esli Quartel", "customer_no": "CEW-438219", "image": null, "reviews": "Esli is an exceptional writer! She delivered an assignment that reflected her keen understanding of marketing principles. I highly recommend her high-quality writing!", "type": null, "date": "09-May-21" }, { "name": "Esli Quartel", "customer_no": "CEW-567321", "image": null, "reviews": "She helped me with dissertation writing for business studies subject. She delivered well-researched content that met academic standards. Her timely work met deadlines effortlessly. Thank You, Esli!", "type": null, "date": "09-May-21" }, { "name": "Esli Quartel", "customer_no": "CEW-539219", "image": null, "reviews": "Esli is an exceptional writer! I found her expert in management-related essays. Her work not only informs but inspires. A reliable choice for anyone seeking a high-quality management essay. Highly recommend!", "type": null, "date": "28-Nov-23" } ]


slug: mark-collier
writer_no: CEW-000157
image: writer-files/markcollier-1745307706.webp
name: Mark Collier
education: PhD
profession: Teacher
status: Active
online: 1
competences: Microeconomics, Macroeconomics, Finance
works: Teacher
orders: 369
review: 129
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 3H
subjects: 6+
experience: 8+
created_at: 2025-04-22 12:41:46
updated_at: 2025-04-22 12:47:49
about:
  Collier writes engaging macroeconomics essays that break down complex theories in a simple way. His research is spot-on, and his ability to explain tough topics is really impressive!


slug: jonathan-ranger
writer_no: CEW-000169
image: writer-files/jonathan-ranger-1745309158.webp
name: Jonathan Ranger
education: PhD
profession: Professional Lawyer
status: Active
online: 1
competences: Criminal Law, Family Law, International Law
works: Essay, Case study, Assignment
orders: 587
review: 169
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 3H
subjects: 6+
experience: 8+
created_at: 2025-04-22 13:05:58
updated_at: 2025-04-22 13:05:58
about:
  Jonathan’s law essays are well-structured and insightful. He has a remarkable ability to handle legal issues and back up his arguments with thorough research. So he makes complex legal concepts easy to grasp.


slug: catherine-kelly
writer_no: CEW-000172
image: writer-files/catherine-kelly-1745313830.webp
name: Catherine Kelly
education: Master
profession: Teacher
status: Active
online: 1
competences: English, Classical Literature, History
works: Essay, Assignment, Coursework
orders: 629
review: 152
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4H
subjects: 8+
experience: 8+
created_at: 2025-04-22 14:19:46
updated_at: 2025-04-22 14:23:50
about:
  Kelly’s literary essays are always thoughtful and full of fresh insights. She has a real knack for interpreting classic texts that make her essays enjoyable and thought-provoking.


slug: florence-miller
writer_no: CEW-000782
image: writer-files/florence-miller-1745313809.webp
name: Florence Miller
education: PhD
profession: Teacher
status: Active
competences: Child Psychology, Adult Psychology, Mental Health
works: Case study, Essay, Homework
orders: 377
review: 122
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4H
subjects: 8+
experience: 8+
created_at: 2025-04-22 14:22:23
updated_at: 2025-04-22 14:23:29
about:
  Her psychology essays are clear, compassionate, and well-researched. She has a talent for explaining mental health concepts in an approachable way. Hence, her psychology essays, even on complex topics, are easy to understand.


slug: matthew-hamilton
writer_no: CEW-000675
image: writer-files/matthew-hamilton-1745314918.webp
name: Matthew Hamilton
education: PhD
profession: Economics Expert
status: Active
online: 1
competences: Banking, Finance, Accounting
works: Dissertation, Assignment, Coursework
orders: 477
review: 125
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 5H
subjects: 8+
experience: 8+
created_at: 2025-04-22 14:41:58
updated_at: 2025-04-22 14:41:58
about:
  Matthew crafts dissertations that blend rigorous research with engaging arguments. His approach simplifies complex financial theories that help students to complete finance dissertations with peace of mind.


slug: finley-glover
writer_no: CEW-000523
image: writer-files/finley-glover-1745315062.webp
name: Finley Glover
education: PhD
profession: Practising Lawyer
status: Active
online: 1
competences: Family Law, International Law, Constitutional Law
works: Dissertation, Case study, Assignment
orders: 426
review: 127
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 3H
subjects: 8+
experience: 8+
created_at: 2025-04-22 14:44:22
updated_at: 2025-04-22 14:44:22
about:
  He crafts law dissertations with clarity and a friendly tone. His legal expertise breaks down complex family law issues and also he provides practical guidance on sensitive law topics.


slug: nicole-davies
writer_no: CEW-000567
image: writer-files/nicole-davies-1745315246.webp
name: Nicole Davies
education: PhD
profession: Certified Nurse
status: Active
online: 1
competences: Nursing, Healthcare, Medicine
works: Dissertation, Assignment, Coursework
orders: 679
review: 388
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4H
subjects: 8+
experience: 6+
created_at: 2025-04-22 14:47:26
updated_at: 2025-04-22 14:47:26
about:
  Nicole delivers dissertations that combine compassionate healthcare insights with rigorous research. Her friendly style demystifies medical theories that help PhD candidates to excel in their academic pursuits.


slug: luke-grant
writer_no: CEW-000687
image: writer-files/luke-grant-1745315427.webp
name: Luke Grant
education: PhD
profession: Software Engineer
status: Active
online: 1
competences: IT, Computer Science, Programming
works: Dissertation, Coursework, Case Study
orders: 395
review: 275
rating: 5
success_rate: 100
on_time_rate: 100
delivery: 4H
subjects: 10+
experience: 6+
created_at: 2025-04-22 14:50:27
updated_at: 2025-04-22 14:50:27
about:
  Luke crafts the best IT dissertation with his technical expertise in his field. He loves to handle coding concepts and challenges. He has a proven record of helping students to secure distinction with their dissertations.


# NG_samples

slug: Pain Management Plans For Post-Surgery Patients-1
title: Pain Management Plans For Post-Surgery Patients-1
academic_level: O Level
subject: Education
type_of_paper: Essay
citation_style: APA
image: sample-files/design-a-data-schema-based-on-the-fields-shown-in-the-picture-1749787483.docx
meta_title: Pain Management Plans For Post-Surgery Patients-1
meta_description: Pain Management Plans For Post-Surgery Patients-1
canonical_url: https://education.test/samples/add
meta_robots: noindex,nofollow
created_at: 2025-06-13 09:04:43
updated_at: 2025-06-25 15:04:34
deleted_at: 2025-06-25 15:04:34
status: published
content:
  <p dir="ltr">Assessing the Efficacy of AI-Driven OSINT Approaches in Cybersecurity: Uncovering Literature Gaps for Enhanced Threat&nbsp;Detection</p>
  <p dir="ltr">Abstract: As connectivity increases across the digital landscape of the modern world, it opens up growing opportunities for cybercriminals. It is often the case that traditional security efforts need to be faster in reacting to new and complex threats as they emerge. When it comes to identifying emerging risks, OSINT from the public internet is the way to go about it. Whenever implemented into defensive systems, the proper implementation of the acquired OSINT data ensures the ability to detect proactively. However, manual analysis cannot be applied to a firehose of information in the social media domain, which overloads analysts. There is significant literature investigating AI and OSINT integration. They have focused on the specific functions essential to security operations, such as real-time threat identification. This study aims to assess the current state of AI-powered OSINT applications with systematic literature review (SLR) as a methodology to analyse existing literature and identify gaps in knowledge relating to strengths, limitations and optimisation of OSINT techniques when operationalized with AI. The findings also make informed recommendations for researchers to understand future industry trends and design their studies accordingly. Using SLR, this study surveys peer-reviewed articles and conference papers to evaluate how existing research has established technical feasibility and reported limitations. Special attention is paid to the literature addressing real-world deployment issues and the application of AI-powered OSINT in real life. Knowledge gaps have been extracted and organised by common themes. Finally, this study proposed solutions and directions for future work to strengthen the protection of critical systems and data through intelligence-guided protection strategies. This work provides valuable insight for both researchers developing new applications and practitioners seeking to apply AI-driven OSINT cautiously. By shedding light on areas requiring further work, this study ultimately aims to enable more effective and responsible operationalisation of these promising capabilities for cyber defence.</p>
  <p dir="ltr">KEYWORDS:&nbsp; AI-Driven OSINT, Cybersecurity, Threat&nbsp;Detection, cyber defence</p>
  <h1 dir="ltr"><strong><br><br></strong></h1>
  <h1 dir="ltr">1. Introduction</h1>
  <p dir="ltr">The digitisation of critical infrastructure and widespread internet connectivity have transformed the landscape of modern society. However, greater reliance on digital technologies has also expanded the attack surface for cyber threats. Cybercriminals are exploiting vulnerabilities at an alarming rate, with many organisations struggling to detect and mitigate sophisticated intrusions in a timely manner [1]. Traditional security protocols often need to catch up with the rapidly evolving methods of cyber attackers. Furthermore, the sheer volume of data that must be analysed to glean practical threat intelligence presents a significant challenge. Cyber threat intelligence aims to provide visibility into emerging risks through analysis of indicators gathered from diverse open sources [2]. Open-source intelligence (OSINT) has emerged as a valuable approach for uncovering clues about potential threats from publicly available information online.&nbsp;</p>
  <p dir="ltr">While following hackers' activity and discussions, dark web trading platforms, and vulnerable facilities, the tool allows for the identification of existing or planned exploits. If information is incorporated into security systems, timely OSINT data can help prevent specific threats known to the system as they attempt to execute their intents before the damage has been done. Nevertheless, it is unreasonable to conduct a manual analysis of a large number of available resources due to the lack of time and resources in security teams [3]. AI techniques are effective for unsupervised extraction and correlation of threat indicators at a mass scale. Trading algorithms are also better able to present patterns and peculiarities that are difficult for analysts to discover and uncover new links between parties. Natural language processing is beneficial in feeding cyber threat intelligence from basically virtually any unstructured text-based OSINT source with moderate simplicity [4]. In partnership with data visualisation, Artificial Intelligence solutions seek to turn OSINT data into intelligent information for the improvement of cybersecurity.</p>
  <p dir="ltr">While initial studies demonstrate the potential of integrating AI and OSINT, many technical and practical challenges must still be addressed. Questions still need to be answered regarding the effectiveness, accuracy, and limitations of these tools when applied to real-world complex cybersecurity problems. Adversarial adaptation may undermine classifier performance over time. Bias in training datasets could negatively impact results or propagate unfair outcomes [5]. Moreover, practical implementation barriers such as infrastructure requirements, model maintenance costs, privacy and ethical concerns require deeper examination. Empirical research evaluating AI-driven OSINT approaches for real-time threat detection is limited.</p>
  <p dir="ltr">Further exploration of techniques, best practices, and refinement is needed to optimise these evolving solutions [6]. Cyber-related crimes have become more and more frequent and diverse in the recent past. Advanced persistent threats operate beyond the physical boundary that has been established, while supply chain compromises begin beyond the secured network perimeter. At the same time, a new generation of commodity malware, along with the global availability of hacking tools, reduces entry barriers to sporadic cybercrime on an industrial scale. As the digital transformation exercises introduce more services and infrastructure to the online environment than ever before, the exposed attack surface remains vast [7]. Such dynamics have outcompeted the ability of security teams to cope effectively with them using traditional measures only. There is a clear imperative to find new approaches and tools that could address today's reality in the cybersecurity environment.</p>
  <p dir="ltr">Open-source intelligence (OSINT) harvesting from the public internet holds promise for gaining valuable threat insights through analysis of technical forums, malware databases, and vulnerable systems. When incorporated into defensive systems, OSINT data fuels proactive detection and rapid remediation by uncovering indicators of new exploits, botnets, and planned intrusions. However, the sheer quantity of publicly available information online far surpasses what can reasonably be scrutinised by human analysts within security operations centres [8]. Automated processing is required to extract meaningful intelligence from this firehose of OSINT sources at the scale and speed demanded in modern cyber defence.</p>
  <p dir="ltr">Artificial intelligence (AI) shows potential for extracting actionable threat intelligence from vast troves of OSINT at machine speeds. Techniques like natural language processing and machine learning can uncover patterns, relationships, and anomalies across structured and unstructured data that would be nearly impossible for people to discern alone or in teams. However, while initial studies highlight AI and OSINT's technical feasibility, many knowledge gaps still need to be discovered regarding practical challenges, limitations, and real-world efficacy for critical functions like real-time threat detection [9]. Other issues remain open regarding model fitness, adversarial attacks, bias, needed resources, costs of maintenance, and ethics, as these all need to be confirmed with experiments.</p>
  <p dir="ltr">The current research landscape leaves security practitioners needing comprehensive guidance on standardising and operationalising effective, responsible AI-driven OSINT methodologies. This limitation undermines organisations' ability to harness these promising approaches for proactive cyber defence and leaves a crucial area of cybersecurity techniques relatively underdeveloped. Clearly defined knowledge gaps must be addressed to optimise AI and OSINT integration as envisioned [10]. This study aims to assess the efficacy of AI-driven OSINT approaches in cybersecurity, focusing on identifying gaps in the current literature and evaluating how these tools can be optimised for enhanced real-time threat detection. The objectives proposed in this manner are provided below:</p>
  <ul>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">To analyse the current state of AI-driven OSINT tools and their application in cybersecurity.</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">To evaluate the strengths and limitations of integrating AI with OSINT for real-time threat detection.</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">To identify critical gaps in existing research on AI and OSINT in cybersecurity, particularly in relation to practical implementation.</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">To propose potential solutions and future research directions for improving the efficacy of AI-driven OSINT approaches in cybersecurity.</p>
  </li>
  </ul>
  <p dir="ltr">The purpose of this research is to evaluate the current status of incorporating artificial intelligence techniques in open-source intelligence collection for cyber threat detection. More specifically, the study will be designed to uncover lacunae in extant scholarship in relation to widely acknowledged applications and limitations of AI and OSINT for operational functions like threat detection in real-time. By evaluating the strengths and limitations of these approaches as described in previous works, this study seeks to uncover areas in need of further exploration and empirical validation. Figure 1 below shows the trend of OSINT and AI literature over the years. It can be determined that the trend of OSINT and AI literature has increased over the years due to the increase in the use of these technologies.&nbsp;</p>
  <p dir="ltr"><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXcs4HdeTPVz07nrFgQpKxfNCzQC3z465y38zIdYy0MLa5WtLbnTHqhRsV0oWLXLuYpJSI73cOFiPPf8epgfY7sDToIXnQZmwX_Z0zHVtJEniMFHx2pA1kHB7m3Kqv82U-XRMknIB2iXLKWyfNDozjw?key=DWlgdn3Nz9k2tXXTZA_6Bw" alt="OSINT literature over the years (1992&ndash;2021)" width="304" height="226"></p>
  <p dir="ltr">Figure 1 OSINT and AI literature over the years (1992&ndash;2021)</p>
  <p dir="ltr">Source: [11]</p>
  <p dir="ltr">The identified areas of knowledge have then been used to formulate recommendations for enhancing the future developments of AI-based OSINT procedures to enhance its prospects as it will be practised inside a security operation environment. Recommendations may involve best practices for addressing challenges, such as data bias, continuous adversarial evasion, suitable infrastructures depending on the organisation's demands, and more, as well as methods of consistent model management. The purpose is to offer information that can benefit both researchers in creating subsequent applications and practitioners in deciding where and when to implement AI-based OSINT into their cybersecurity plans. Finally, this is expected to improve the protection of systems and data by including intelligence-driven methods.</p>
  <p dir="ltr">With the threats in cyberspace changing constantly and more opportunities for attacks being created all the time, intelligence-led protection methodologies have become crucial. It is only where these areas of knowledge are currently lacking that such enhancement concerns appear relevant at all to integrating AI and OSINT as techniques that can transform cybersecurity from a one-off process utilising on-average solid and scalable best practices into one that is capable of moving at the speed of change necessitated by the threatscape. This research also seeks to improve current cyber security measures by identifying numerous limitations in previous studies and offering workable recommendations to mitigate risks for developers and end users of AI-based OSINT tools. In sum, this work aims to provide new insights toward the more responsible implementation of these instead of promising technologies for the benefit of society.</p>
  <h1 dir="ltr">2. Methods</h1>
  <h2 dir="ltr">2.1 Research Design&nbsp;</h2>
  <p dir="ltr">Research design is the blueprint of a research process which explains all the methodological decisions and choices made that led to the completion of the study. There is a range of research designs adopted in the research community, but the choice is dependent upon the main research aim. This study aims to assess the current state of AI and OSINT integration in cybersecurity contexts. By identifying gaps in existing literature, recommendations can be made to inform further development and standardisation of effective AI-empowered OSINT methodologies for enhanced proactive cyber defence. Since this study is focused on reviewing the literature, the systematic literature review (SLR) design was deemed most suitable.</p>
  <p dir="ltr">SLR is a methodology that allows researchers to conduct a critical appraisal of existing literature by systematically selecting studies, i.e. a search strategy. Unlike traditional narrative reviews, SLR enhances the reliability and validity of the review. SLR is often used to identify the best available evidence and develop best practices. However, SLR is also appreciated for its ability to synthesise existing literature and identify research gaps [13].</p>
  <p dir="ltr">In this study, the SLR started with defining a search strategy by identifying keywords and databases and then continued to filter search results using inclusion and exclusion criteria. All of these steps are explained and discussed in the following sections.</p>
  <h2 dir="ltr">2.2 Search Strategy&nbsp;</h2>
  <p dir="ltr">A search strategy is a set of steps that SLR follows to identify and select studies from available evidence for the final review. A search strategy begins with identifying keywords and databases based on research aims and objectives [14]. Both of these choices are critical because they affect the overall reliability and validity of the review. The researchers must select relevant and authentic databases or libraries to ensure that they access the relevant literature. Similarly, researchers must choose keywords in a way that the most pertinent studies match the requirement of the research question [15]. To make sure that keywords were relevant, phrases from research objectives were selected. The following table summarises the keywords and databases selected,</p>
  <p dir="ltr">Table 1: Databases and Keywords</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="154"><col width="148"></colgroup>
  <tbody>
  <tr>
  <td>
  <p dir="ltr">Databases</p>
  </td>
  <td>
  <p dir="ltr">Keywords</p>
  </td>
  </tr>
  <tr>
  <td>
  <ul>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">IEEE</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">Google Scholar</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">Springer&nbsp;</p>
  </li>
  </ul>
  </td>
  <td>
  <ul>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">AI AND* OSINT</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">AI AND OSINT AND Cybersecurity</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">AI Applications AND Cybersecurity</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">OSINT AND Cybersecurity&nbsp;</p>
  </li>
  </ul>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p dir="ltr">* AND is a Boolean Operator often used to combine keywords during search strategy and refine search results&nbsp;</p>
  <p dir="ltr">When the keywords and their combinations were applied in the databases selected, the following results were obtained (see Table 2)</p>
  <p dir="ltr">Table 2: Initial Search Results</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="116"><col width="49"><col width="67"><col width="70"></colgroup>
  <tbody>
  <tr>
  <td>
  <p dir="ltr">Keywords and Combinations&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">IEEE</p>
  </td>
  <td>
  <p dir="ltr">Google Scholar&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Springer&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">AI AND OSINT</p>
  </td>
  <td>
  <p dir="ltr">26</p>
  </td>
  <td>
  <p dir="ltr">9,090</p>
  </td>
  <td>
  <p dir="ltr">191</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">AI AND OSINT AND Cybersecurity</p>
  </td>
  <td>
  <p dir="ltr">9</p>
  </td>
  <td>
  <p dir="ltr">4,300</p>
  </td>
  <td>
  <p dir="ltr">82</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">AI Applications AND Cybersecurity</p>
  </td>
  <td>
  <p dir="ltr">956</p>
  </td>
  <td>
  <p dir="ltr">250000</p>
  </td>
  <td>
  <p dir="ltr">6472</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">OSINT AND Cybersecurity</p>
  </td>
  <td>
  <p dir="ltr">51</p>
  </td>
  <td>
  <p dir="ltr">8580</p>
  </td>
  <td>
  <p dir="ltr">228</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Total</p>
  </td>
  <td>
  <p dir="ltr">1042</p>
  </td>
  <td>
  <p dir="ltr">271970</p>
  </td>
  <td>
  <p dir="ltr">6973</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p dir="ltr">&nbsp;</p>
  <p dir="ltr">The results above clearly indicate that there is vast literature available related to the subject and research question of this study, and it is only possible to review some of the evidence retrieved from the databases above. Hence, there is a need to refine the search results and narrow them down to a feasible number of studies. For this purpose, the SLR design allows researchers to use inclusion and exclusion criteria, which can be used to reduce the studies using a set of criteria systematically.&nbsp;</p>
  <p dir="ltr">Inclusion criteria are used to make sure that studies that fulfil these criteria are included in the review. The inclusion criteria ensure the specificity of the search strategy. Specificity implies that the review has relevant literature and does not miss vital evidence to answer the research question. On the other hand, exclusion criteria are used to ensure the sensitivity of the search strategy. Sensitivity refers to the ability of the search strategy to exclude irrelevant evidence and thus enable the researcher to achieve feasibility in the review [16]. Table 3 below provides the inclusion and exclusion criteria applied to reduce the number of studies retrieved in Table 2.</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="150"><col width="152"></colgroup>
  <tbody>
  <tr>
  <td>
  <p dir="ltr">Inclusion Criteria&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Exclusion Criteria&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Articles focused on OSINT and AI in the specific context of cyber security must be included.&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Literature published earlier than 2020 must be excluded to maintain the latency of evidence.&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Articles analysing the benefits and limitations of AI and OSINT must be included.&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Literature reviews must be excluded.&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td rowspan="2">
  <p dir="ltr">Articles analysing the application of AI and OSINT in cyber security must be included.&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Articles other than those in the English language must be excluded.&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Replication studies be excluded.&nbsp;</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <h1 dir="ltr"><strong>&nbsp;</strong></h1>
  <p dir="ltr">Once inclusion and exclusion criteria were identified, the researcher started sorting and funnelling the search results. Since multiple databases were included, therefore, there was duplication of articles. The researcher consequently started manual perusal of titles and abstracts of articles. The researcher continued to apply inclusion and exclusion criteria and finally selected 24 articles for review. The entire search strategy and results are summarised using the PRISMA flow chart as follows.</p>
  <h2 dir="ltr">2.3 PRISMA Flowchart</h2>
  <p dir="ltr">A PRISMA flow chart is a commonly adopted standard that helps researchers demonstrate how they executed the search strategy by summarising all steps [15]. The following chart shows that initially, total search was 279985. The researcher started manual perusal of titles and abstracts and applied inclusion and exclusion criteria. A total of 505 articles were found to be duplicated, and 278982 were removed after applying exclusion criteria. The majority of the studies were removed when the publication year filter was used in Google Scholar, which reduced the search results significantly. Finally, after applying inclusion criteria, a total of 24 articles were selected for final review. The search strategy was a lengthy process, but it helped manage the review of a feasible number of studies.&nbsp;</p>
  <h1 dir="ltr"><strong><br><br></strong></h1>
  <p dir="ltr">&nbsp;</p>
  <h1 dir="ltr"><strong><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br></strong></h1>
  <p dir="ltr">&nbsp;</p>
  <h2 dir="ltr">2.4 Data Extraction and Analysis Technique&nbsp;</h2>
  <p dir="ltr">Once studies were selected, the researcher started applying data extraction and analysis techniques. This stage is often called the synthesis of literature [17]. In order to conduct a thorough analysis of the literature, thematic analysis was selected. Thematic analysis is a widely acknowledged data extraction and synthesis technique because it enables analysis of large pieces of text(s) (articles in cases of this review) using categories and themes [18]. Thematic analysis is a commonly used qualitative data analysis technique in social sciences. It can be used to identify recurring and common patterns or themes in large datasets [19]. The thematic analysis is executed by carefully reading and interpreting the data under analysis [20]. The reviewer notes common categories and themes and derives meaningful results from the data to answer the research question [21].</p>
  <p dir="ltr">This review started the thematic analysis by carefully reading the abstracts of the articles selected and noting key categories. The reviewer then continued to conduct an in-depth and detailed reading of the main body of the articles and noted common themes with a specific focus on research gaps. The first category identified was streams of OSINT literature in the context of cyber security. This category classifies applications and implementation of OSINT in cybersecurity. A number of themes emerged in this category, which included techniques and tools in cyber security, and a range of different tools were identified. The researcher noted down all the research gaps identified in the selected studies. The second category was strengths and limitations of AI-integrated OSINT in cyber security. This category also included a number of themes, such as anomaly detection behaviour analysis and automation of security processes via AI. This category was also explicitly focused on identifying research gaps in the context of OSINT applications in cyber security. The third and final section focused on identifying future patterns in the OSINT industry and searching for potential future research opportunities. All of the themes and information were explicitly focused on identifying existing research gaps and searching for potential research areas.</p>
  <h2 dir="ltr">2.5 Key Questions to Address in the Literature</h2>
  <p dir="ltr">To assess the efficacy of AI-driven Open Source Intelligence (OSINT) approaches in cybersecurity and uncover gaps for enhanced threat detection, it is essential to structure the literature review around key research questions that align with the proposed objectives. The following questions were designed to guide the analysis:</p>
  <h1 dir="ltr"><strong>&nbsp;</strong></h1>
  <ul>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">What are the streams of AI-driven OSINT tools and their application in cybersecurity?</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">What are the key strengths and limitations of integrating AI with OSINT for real-time threat detection?</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">What gaps exist in the current research on AI and OSINT integration for cybersecurity?</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">What potential solutions and future research directions as per industrial trends could improve the efficacy of AI-driven OSINT approaches?</p>
  </li>
  </ul>
  <h3 dir="ltr">2.5.1 What are the streams of AI-driven OSINT tools and their application in cybersecurity?</h3>
  <p dir="ltr">This question has helped determine what are the most commonly used AI-driven OSINT tools in cybersecurity and how these tools are being deployed for threat detection, threat intelligence gathering, and cyber risk management. In addition, it also determined how these tools compare with traditional OSINT approaches in terms of accuracy, efficiency, and scope.</p>
  <h3 dir="ltr">2.5.2 What are the key strengths and limitations of integrating AI with OSINT for real-time threat detection?</h3>
  <p dir="ltr">This question helped to determine what AI techniques (e.g., machine learning, natural language processing) are most effective when applied to OSINT data for cybersecurity purposes and how AI enhance the speed, scalability, and precision of OSINT in identifying and mitigating cyber threats in real-time. In addition, it also provided insights into the benefits of automation in AI-driven OSINT for detecting emerging threats and trends. Moreover, it also helped to determine the main technical, ethical, and operational challenges associated with AI-driven OSINT approaches, along with the issues related to data privacy, biases, and misinformation when applying AI to OSINT in the context of cybersecurity.</p>
  <h3 dir="ltr">2.5.3 What gaps exist in the current research on AI and OSINT integration for cybersecurity?</h3>
  <p dir="ltr">The third question was developed to determine where the literature falls short in addressing the practical implementation of AI-driven OSINT in threat detection and whether there are any underexplored areas related to specific types of cyber threats (e.g., nation-state actors, ransomware, zero-day vulnerabilities). Moreover, it also helped determine how well existing studies account for real-world deployment challenges, such as the scalability of AI models and integration with security operations.</p>
  <h1 dir="ltr"><strong>&nbsp;</strong></h1>
  <h3 dir="ltr">2.5.4 What potential solutions and future research directions as per industrial trends could improve the efficacy of AI-driven OSINT approaches?</h3>
  <p dir="ltr">The last question developed for the literature was related to the emerging AI technologies or methodologies that could be integrated into OSINT to enhance threat detection capabilities. In addition, it also determined how the current limitations of AI-driven OSINT tools can be addressed to improve their reliability and scalability.&nbsp;</p>
  <p dir="ltr">These questions have helped structure the literature review, providing a comprehensive understanding of the current state of AI-driven OSINT while also identifying areas where more research is needed.</p>
  <h1 dir="ltr"><strong>&nbsp;</strong></h1>
  <h1 dir="ltr">3. Different Aspects of Literature Review&nbsp;</h1>
  <h2 dir="ltr">3.1 Introduction&nbsp;</h2>
  <p dir="ltr">In order to gain an understanding of the state of the art of AI-powered OSINT and its application in cybersecurity, it is critical to reiterate the basic concepts of OSINT and current and future trends in the open source intelligence (OSINT) market. OSINT is information obtained by processing and analysing public data sources such as television and radio, social media, and websites. These sources provide data in text, video, image, and audio formats. The evolution of OSINT is based on the increasing demand for fast and 360&deg; intelligence, which is generated by disparate data sources. These are critical to address public safety. OSINT enables the government and security agencies, as well as private organisations, to take swift and effective actions against cyber threats while also gaining competitiveness. Commercial organisations have the benefit of monitoring and analysing market data to gain insights into market trends and competition [22].</p>
  <p dir="ltr">Many of the OSINTs' tools and techniques are integrating Artificial Intelligence (AI). OSINT involves collecting video, audio, and text data from publicly available sources and analysing it to extract information from various channels. The analysis is based on AI and machine learning algorithms and deep neural networks that allow the system to learn from the data and achieve and refine the recognition of patterns, trends, and relationships [23]. For example, in the case of a TV interview, OSINT can identify both the interviewer and the interviewee (video analysis), the key topics of discussion (voice and text analysis), how viewers react on social media (text analysis) and automatically provide, for example, opinion clusters, trends and viewer sentiments [24].</p>
  <p dir="ltr">The value of the Open Source Intelligence (OSINT) Tools market was USD 9.74 Billion in 2024 and is projected to reach USD 43.72 Billion by 2032. The CAGR is 20.65% [25].</p>
  <p dir="ltr"><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXc1DsFfdK-xQE3wdN3WRJ43fdfpDAbHRgQOE8ugqQkzGKIhgTOpft7jEBj4odoEd2bNgteL3Ad7-R6jGt5xrRjSgTgqohiUaHkozZzbXxjOKpHduiSCrqKBEoTg2iPNZyd9sp3fljM3FKONQJZkuj4?key=DWlgdn3Nz9k2tXXTZA_6Bw" width="271" height="139"></p>
  <p dir="ltr">Figure 2 OSINT market source [25]</p>
  <p dir="ltr">The products, i.e. tools in the OSINT market, include diverse programs as well as hardware which are designed and tailored to meet the individual needs of users. However, standard functions include collecting, examining, and deciphering data from open sources that are publicly available. Some of the familiar sources of data and information for these tools include massive volumes generated by social media platforms, government publications, news sources, forums, and other online websites. These can be easily acquired and examined [26]. The common purpose of OSINT tools is to extract useful information from the data and provide valuable insights for cybersecurity systems which are deployed for law enforcement, corporate intelligence, national security, and other competitive analysis [27].</p>
  <p dir="ltr">As mentioned, there are diverse products catering to unique individual requirements related to competitive intelligence, threat intelligence, and social media that define the OSINT tools market. These are cutting-edge technologies and techniques that can sort massive data sets, identify patterns, and derive meaningful information. Some of the commonly used algorithms in these OSINTs are natural language processing (NLP), data mining, and other machine learning techniques. The capabilities of OSINT technologies increase significantly when AI is integrated, and they provide more accuracy and effectiveness [28].</p>
  <p dir="ltr">Both established cybersecurity companies and cutting-edge organisations dominate the OSINT tools market. These companies offer a variety of solutions, from comprehensive intelligence platforms to specialised applications targeting specific industries or data types. The market is driven by the growing need for better decision-making tools in government and business operations, increased cyber risks, and the increasing need for real-time intelligence [29].</p>
  <h2 dir="ltr">3.2 Streams of OSINT Literature on Application and Implementation in Cybersecurity</h2>
  <p dir="ltr">From the cyber intelligence and intelligence perspective, data represent relationships both jointly and in isolation in such a way that they give a specific value to information under defined contexts [30]. Precisely Cyber intelligence focuses on protection and intelligence in the search for risks or threats in computer systems, which are used as primary source assets available in information systems (databases and information networks). The use of open sources through the assets available in information systems allows intelligence and counterintelligence to be carried out at a low cost and with limited risk, being the essence of the application of OSINT techniques and tools [31].</p>
  <p dir="ltr">It is essential to emphasise the role that OSINT plays in intelligence. Intelligence here is defined as the process by which information relevant to national security is required, collected, analysed and processed for decision-making. Based on this definition, it can be stated that information collected from open sources through OSINT becomes Intelligence when it is collected, analysed, exploited and disseminated in response to a specific Intelligence requirement under a case study or investigation [32].</p>
  <p dir="ltr"><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXenfwZyiA3G2HZ1fgpEwlkObKEBLPkQuuhi7P68WVkk9s2HitA25s8QaR-r4gpyCFWEHM-UMJR4y8k3SI2U6ipI_a3C4M_3aqcUFyccRZ6WDKS1eKOn7F8kqupI2mUdpc5J_hz6TgjMKbMM9ZN0gAw?key=DWlgdn3Nz9k2tXXTZA_6Bw" width="294" height="203"></p>
  <h3 dir="ltr">3.2.1 Techniques and Tools of OSINT in Cybersecurity&nbsp;</h3>
  <p dir="ltr">Given the constant increase in Internet consumption and all its interconnected services, as well as the growth of insecurity, the need arises to take extreme protection measures. From a social point of view, it is necessary to analyse the different scenarios that could occur; this is where several techniques for data collection appear [33]. The history and use of OSINT have been spreading over the years. There are several events worldwide known as "hackathons", in which participants from various places meet to show their skills in the field of research using OSINT techniques [34].</p>
  <p dir="ltr">In May 2022, the first exclusive OSINT and social engineering conference called "Osintom&aacute;tico Conference 2022" was organised and held in Madrid, Spain. There were 25 presentations attended by 550 attendees at the event. The activities were focused on imparting knowledge and raising awareness among participants. As a highlight of the event, there was an activity called CTF (Capture The Flag), which is nothing more than a contest in which the people who participate collect points by meeting goals or objectives; for this, they make use of OSINT tools and their particular knowledge. The theme of the contest was to search for clues about missing people in real life.</p>
  <p dir="ltr">It is critical to note that OSINT tools and techniques do not serve as or are alternatives to firewalls and antiviruses; instead, they are focused on gathering and providing intelligence. More specifically, OSINT is used to search and extract data (from open sources), analyse patterns and traces and draw conclusions from incidents to enable effective decision-making for cyber security [35].&nbsp;</p>
  <p dir="ltr">Search engines are the most common and primary sources of data and information for OSINT. A very effective technique for OSINT consists of performing searches through the different search engines that can be found on the web. As examples and among the best known, we have the search engines Google, Yahoo, Bing, Dogpile, Yandex, DuckDuckGo, and Ask [35]. Most search engines work similarly and allow you to search for specific words and people by name and photo, search within a particular domain, search for social media profiles, search for files, even search for passwords or dictionaries for brute force attacks, etc. [36].</p>
  <p dir="ltr">Google's search engine is one of the most famous around the world; in this search engine, there is something known as "dorks" that help searches to be as precise as possible. Citing some "dorks" for a better understanding that is available [37]. Search for specific terms when quotation marks are used, search within a particular domain citing terms and the URL, search for file type specifying the extension, search for the title of the web page, search with wildcards that allow, for example, to search for incomplete texts using the asterisk wildcard [38].</p>
  <p dir="ltr">In reference to image search, this has been very useful in recent years; facial recognition is shown as a broad resource with the appearance and use of security cameras, excellent quality cameras in mobile phones, and the consequent exposure on the Internet creates the perfect environment to make comparisons through artificial intelligence to find someone or at least know more accurate data that allows their location [39]. The search engines mentioned can be used as tools. However, it should be noted that there are search engines specialised in the collection of more specific data, such as the Shodan search engine, which, with its filters, is capable of finding IP addresses of various devices with specific ports open on the Internet [40].</p>
  <p dir="ltr">It is essential to know that any of the OSINT techniques is a good complement when considering implementing an Information Security Management System based on the ISO/IEC 27001 standard. In various studies such as [41], the application of multiple techniques, including Google Dorking, is observed to expose the security flaws present in multiple case studies.&nbsp;</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="62"><col width="153"><col width="87"></colgroup>
  <tbody>
  <tr>
  <td colspan="3">
  <p dir="ltr">Streams of OSINT Literature on Application and Implementation in Cybersecurity</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Studies</p>
  </td>
  <td>
  <p dir="ltr">Purpose</p>
  </td>
  <td>
  <p dir="ltr">Methodology</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Abu, et al., [31], Khurana, et al., [41], Mittal,&nbsp; et al. [33], Kanta, et al., [32]</p>
  </td>
  <td>
  <p dir="ltr">Handling extensive data from open sources</p>
  </td>
  <td>
  <p dir="ltr">case study</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Pastor-Galindo, et al. [35], Ghazi et al., [36], Riebe et al., [39], Bezas and Filippidou [40]</p>
  </td>
  <td>
  <p dir="ltr">OSINT Tools and techniques in Cyber Security for searching and extracting data to identify traces and patterns of threats (OSINT do not serve like firewalls or antiviruses)</p>
  </td>
  <td>
  <p dir="ltr">Application Design</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Miller [30], Ficco, and Palmieri, [34], et al., Ranade, [37], Alves, et al., [38]</p>
  </td>
  <td>
  <p dir="ltr">use of search engine</p>
  </td>
  <td>
  <p dir="ltr">using large data sets such as reviews, images, pictures, audio, etc.</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <h1 dir="ltr"><strong>&nbsp;</strong></h1>
  <p dir="ltr">The table above summarises the first category and sub-themes identified in selected studies. It is important to note that this table is brief and does not cover all the tools and techniques; however, it provides a bird's eye view of the different streams of literature on AI and OSINT being used in cyber security.</p>
  <h2 dir="ltr">3.3 Strengths and Limitations of AI Integration with OSINT&nbsp;</h2>
  <p dir="ltr">Artificial intelligence can do more than generate photorealistic images and written content. It is a powerful tool that has found its way into many fields, from medicine to finance, e-commerce, manufacturing, and, unsurprisingly, cybersecurity. At first glance, AI Cybersecurity seems like the perfect solution. After all, AI tools are known for their speed, efficiency and accuracy, which means that they can deal with any cybersecurity threat with the greatest of ease [42]. However, there are some drawbacks to using AI in OSINT for accurate time threat detection. In this section, the focus of the discussion is to identify the benefits and risks of AI-based OSINT for accurate time threat detection and find out whether AI should be integrated into this field or not [43].</p>
  <p dir="ltr">Firstly, it is critical to understand the way AI fits into OSINT for accurate time threat detection and the modern cybersecurity landscape. Artificial intelligence is not a new concept in cybersecurity, as it has been around since the early 1950s [44]. However, it is only recently that AI tools have become "smart" and powerful enough to be used in many applications, including cybersecurity. For example, many antivirus programs rely on AI to detect malware and other anomalies that could threaten a network and system. The main reason is that these programs can learn and improve over time with the help of AI and machine learning, which is a significant benefit [45].</p>
  <p dir="ltr">However, there are also limitations, such as the fact that it might take a while for an AI-based cybersecurity program to figure out how to deal with a specific computer virus when it first comes into contact with it. However, the next time it does, it will know what to do to fix the problem in no time. The more a network security AI tool comes into contact with various malicious programs, the more it learns about them and the more effectively it removes them from the system [47]. Focusing on the benefits of AI in cybersecurity, general literature indicates that artificial intelligence and cybersecurity often go hand in hand for several good reasons. Following are just some of the advantages of using AI for system and network security.</p>
  <h3 dir="ltr">3.3.1 Anomaly detection and behaviour analysis over time&nbsp;</h3>
  <p dir="ltr">With AI security, professionals can deploy advanced protection against all threats, including computer viruses, worms, botnets, ransomware, adware, and other malware. That's because AI monitors the systems and user and program behaviours at all times and can quickly identify anything out of the ordinary. It immediately flags any anomalies in network traffic and instantly indicates risks or threats to security. It can detect a threat that would take human hours (if not days) to detect before it has enough time to cause any damage [48].</p>
  <h3 dir="ltr">3.3.2 Analyse large amounts of data</h3>
  <p dir="ltr">Another advantage of combining cybersecurity and AI is that the latter can process and analyse large amounts of data quickly. An AI-based cybersecurity tool can simultaneously monitor different devices, networks, and endpoints, keeping a close eye on everything that happens and detecting threats in real time [49]. Not even an entire team of IT professionals could process such vast amounts of data as quickly and efficiently as AI.</p>
  <h3 dir="ltr">3.3.3 Streamlining cybersecurity processes and operations</h3>
  <p dir="ltr">In theory, the central role of AI in cybersecurity (or any other field) is to streamline and automate all processes. For example, when trying to secure a network, experts would have to do a million little things if they were to approach the task manually [50]. They would have to check emails for spam, scan all the devices that connect to the system and its nodes, check the websites the users visit, assess the devices connected to the network, and manually update all apps and software, among many other manual tasks. Using AI experts can streamline these and other processes [51]. They can continuously monitor your network and efficiently perform time-consuming, repetitive tasks.</p>
  <h3 dir="ltr">3.3.4 Proactive responses</h3>
  <p dir="ltr">As stated above, the main driver of AI's power is its ability to learn and improve over time. This feature can be valuable in the face of evolving cybersecurity threats. Therefore, an AI-based cybersecurity solution can learn to identify even novel threats at an early stage and build defences against them before they can cause damage. It can develop proactive responses, protecting the systems at all times [52]. Rather than helping experts fight off a viral infection, it can prevent it from occurring in the first place. Considering that most AI cybersecurity solutions monitor the network and all activity on it at all times, it's no surprise that they can do more than detect threats. They can also keep an eye out for any potential vulnerabilities that a cybercriminal could exploit. AI solutions can quickly detect how malware exploits existing vulnerabilities in the system and suggest solutions. In many cases, they might even fix them [53].</p>
  <h3 dir="ltr">3.3.5 Reduction of human errors</h3>
  <p dir="ltr">An often overlooked but highly valuable advantage is that AI-based cybersecurity solutions can significantly reduce the risk of human error. They can scan the network and its systems, monitor user and program behaviours, analyse all incoming and outgoing traffic, and automate tedious processes. In this way, AI solutions can up the ante on cyber defences and prevent experts from making mistakes due to fatigue. However, there are also limitations and risks of using AI in cybersecurity. Artificial intelligence is not everything. The use of AI in cybersecurity poses specific risks [54].&nbsp;</p>
  <p dir="ltr">Artificial intelligence can improve the defences against all cyber attacks, from malware to hacking attempts. However, just as security experts can use AI to strengthen their defences, cybercriminals can also use it to enhance their attacks. Criminals can harness the power of AI technology to modify malware and make it more resistant to the defences [53]. For example, they can use AI to create more believable phishing scams or deepfake videos for ransomware attacks. They can use it to find ways to shield malware from detection and make it easier for it to infect and corrupt the files. Once the files get corrupted or lost after a malware attack that an AI-based antivirus program has not stopped, the system owner incurs costs to recover the files [51].</p>
  <p dir="ltr">The entire discussion above is summarised in the following table.</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="84"><col width="110"><col width="108"></colgroup>
  <tbody>
  <tr>
  <td colspan="3">
  <p dir="ltr">Strengths and Limitations of AI Integration with OSINT&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Studies</p>
  </td>
  <td>
  <p dir="ltr">Strengths&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Limitations</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Evangelista, et al. [42], Vegesna and Adepu, Sarker, [43], Sarker, [44], Martineau, et al. [45], Aragon&eacute;s Lozano, et al. [46], Riebe, [47]</p>
  </td>
  <td>
  <p dir="ltr">Anomaly detection and behavior analysis over time&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Learning/training period for AI algorithm</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Chen, et al. [49]</p>
  </td>
  <td>
  <p dir="ltr">Analyse large amounts of data</p>
  </td>
  <td>
  <p dir="ltr">Lack of accurate data to train algorithms</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Pavanello, et al. [50], Petrović, and M. Jovanović, [51]</p>
  </td>
  <td>
  <p dir="ltr">Streamlining cybersecurity processes and operations</p>
  </td>
  <td>
  <p dir="ltr">Continuous change in data&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Evangelista, et al. [52], Torres, et al. [53]</p>
  </td>
  <td>
  <p dir="ltr">Proactive responses</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Petrović, and Jovanović, [51], Torres, et al. [53], Iturbe, et al. [54]</p>
  </td>
  <td>
  <p dir="ltr">Reduction of human errors</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  </tbody>
  </table>
  </div>
  <h1 dir="ltr"><strong>&nbsp;</strong></h1>
  <h2 dir="ltr">3.4 Research Gaps in Research on Practical Implementation</h2>
  <p dir="ltr">As per the third objective identified in this study, this section is organised to analyse the state of the art of AI and OSINT with a specific focus on implementation and identify potential research gaps that can be used to make recommendations for future research in this field. As discussed earlier, AI and OSINT have spread to various industries and have gained a foothold in different fields; therefore, the practical applications are diverse. Hence, this section retrieves information from diverse studies which focus on the implementation of AI and OSINT and provides current research following the research gap in each area.&nbsp;</p>
  <h3 dir="ltr">3.4.1 Leveraging AI and Machine Learning to Improve Security</h3>
  <p dir="ltr">One of the areas of implementation for AI and OSINT is security improvements. A review of recent studies shows that the digital age is ever-evolving, and organisations (both commercial and non-commercial) are facing innumerable challenges in security, which are credible threats to their survival. Integrating AI and ML into security measures can be a vital protection against threats [51]. The majority of the studies discuss the way these technologies have automated the process of detecting potential risks. There are also studies focusing on how AI based on security measures such as OSINT are able to learn and adapt to emerging threats with more agility than traditional processes [42]. The ML can react and trigger countermeasures when a threat is detected [44]. For instance, AI and ML can enable OSINT to automatically isolate infected segments of the network and prevent malware from spreading to the entire system. In this area, the central joint research gap identified in studies is the need for more research on the efficacy of AI-powered OSINT in blocking diverse threats.</p>
  <p dir="ltr">However, the authors also identify that there are research gaps. In this area, one of the joint research gaps is the need for more research on how cybercriminals are countering security measures. There is also a need to research how AI can improve OSINT in monitoring the evolution of security attacks.&nbsp;</p>
  <h3 dir="ltr">3.4.2 Predictive threat analysis</h3>
  <p dir="ltr">Another typical implementation of AI in cybersecurity is the potential of algorithms to handle large data sets (which humans cannot handle) for prediction and identification of potential breaches in security [43]. For instance, AI-enabled systems are able to monitor traffic in the network on a timely basis and flag abnormal patterns, which could be a cyber attack(s) such as a distributed denial of service (DDoS) [36]. The central research gap in this area is in the use of sophisticated algorithms and large data sets. As the amount of data grows, there will be ample research opportunities to find new insights and predictive models.</p>
  <h3 dir="ltr">3.4.3 Behavioral Analysis</h3>
  <p dir="ltr">User behaviour analyses are also standard in research studies. Researchers show that AI can detect anomalies and make suggestions about compromised security mechanisms [51]. For instance, if one of the authenticated users accesses the system from an unusual location, AI-based OSINT can detect the incidence and impose additional measures for authentication [45]. The research gap in this area is to evaluate the effectiveness of different additional measures for authentication to ultimately find the most effective measure(s).</p>
  <h3 dir="ltr">3.4.4 Intelligent Threat Hunting and Improved Incident Response</h3>
  <p dir="ltr">AI using ML algorithms can uncover hidden threats and thus enable a proactive approach to threat hunting. This is in line with analysing large datasets where threats are more accessible to hide [38]. For instance, AI-based OSINT is able to identify subtle and persistent threats that may evade basic security mechanisms [46]. The potential research area in this field is conducting experiments on virtual systems with mock hidden threats and training algorithms for practical purposes.&nbsp;</p>
  <p dir="ltr">Incidence response is also a common research area for AI-based OSINT tools. These tools aim to assist in the agility of incident response as they can analyse the causes of a breach faster and suggest standardised, automated solutions and evidence-based courses of action [55]. Ultimately, the downtime will be reduced [47]. In this aspect, the research gaps identified include evaluating downtime periods, evaluating agility, and measuring success rates of algorithms.&nbsp;</p>
  <h3 dir="ltr">3.4.5 Security Orchestration</h3>
  <p dir="ltr">Unlike humans, AI can coordinate simultaneously with multiple security tools and use them in unison, thus streamlining response against threats [37]. This can be visualised as a symphony which is produced by an orchestra using different instruments in harmony [57]. So, AI can be an orchestra that creates a symphony of security mechanisms to counter threats [48]. The research gap in this context is related to developing sophisticated algorithms that can manipulate mechanisms and test them against known threats.&nbsp;</p>
  <p dir="ltr">In the dynamic world of cybersecurity, incorporating OSINT threat intelligence into security operations is not only beneficial but also imperative for the survival of technology users [56]. This integration allows organisations to proactively identify, assess, and mitigate potential threats before they can impact business operations [32]. By leveraging data-driven insights, security teams can shift from a reactive to a proactive stance, adapting their defence mechanisms to the ever-evolving threat landscape [48]. A number of studies have focused on the potential of OSINT in this aspect of cyber security and identified various opportunities for more research.</p>
  <h3 dir="ltr">3.4.6 Contextual awareness for Real-time threat analysis</h3>
  <p dir="ltr">OSINT can improve detection capabilities by adding context to raw data. This could involve correlating threat data with internal logs to identify suspicious patterns [49]. For example, a threat intelligence feed indicates an increase in phishing attempts targeting a specific industry. In that case, security operations can increase scrutiny of incoming emails and educate employees accordingly [50]. In this context, the researchers have the opportunity to conduct research on different industries and provide generic and customised solutions. OSINT can use threat feeds in real-time to analyse potential threats before and during they emerge. For instance, if a new malware variant is reported, security operations can immediately check their systems for related indicators of compromise (IoCs) and take preventative action [49]. In this regard, researchers have designed IoCs and recommend research to improve current and develop new IoCs.</p>
  <h3 dir="ltr">3.4.7 Strategic Planning and Collaborative Defence</h3>
  <p dir="ltr">AI uses ML and historical threat intelligence to inform strategic security planning. Analysing past incidents and trends can help predict future attacks and guide the development of solid security policies [33]. For example, if data breaches in an industry are often related to weak authentication protocols, implementing multi-factor authentication becomes a priority [51]. Similar to previous research gaps, there are opportunities to find standard and industry-specific weak policies and make relevant suggestions. Unlike humans, AI has the potential to participate in large, complex information-sharing platforms with other organisations to gain a collective defence advantage [48]. When a similar company encounters a new attack vector, sharing this information allows organisations to strengthen their defences against similar tactics [38]. Similarly, AI can integrate threat intelligence with security automation tools to enable a rapid response to identified threats. Automation can speed up the quarantine of affected systems, as seen when a ransomware attack is detected, minimising potential damage [52].</p>
  <p dir="ltr">Based on the discussion above, this category and relevant themes are presented in the following table.</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="58"><col width="96"><col width="148"></colgroup>
  <tbody>
  <tr>
  <td colspan="3">
  <p dir="ltr">Research Gaps in Research on Practical Implementation</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Studies</p>
  </td>
  <td>
  <p dir="ltr">Practical implementation</p>
  </td>
  <td>
  <p dir="ltr">Research Gap</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Petrović and Jovanović, [51], Evangelista, et al. [42], Sarker, [44]</p>
  </td>
  <td>
  <p dir="ltr">Automation</p>
  </td>
  <td>
  <p dir="ltr">Evaluate counter-security measures of criminals</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Vegesna and Adepu [43], Ghazi et al. [36]</p>
  </td>
  <td>
  <p dir="ltr">Predictive threat analysis</p>
  </td>
  <td>
  <p dir="ltr">using sophisticated algorithms and new large data sets (as they emerge)</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Martineau, et al. [45], Petrović and Jovanović, et al. [51]</p>
  </td>
  <td>
  <p dir="ltr">Behavioural Analysis</p>
  </td>
  <td>
  <p dir="ltr">effectiveness of different additional measures triggered by OSINT&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Alves, et al. [38], Aragon&eacute;s Lozano, et al. [46], Riebe, [47], Kamal, [55]</p>
  </td>
  <td>
  <p dir="ltr">Intelligent Threat Hunting for improved incidence response</p>
  </td>
  <td>
  <p dir="ltr">studies to train algorithms and evaluate downtime, agility, and success rates&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Kanta, et al. [32], Ranade, et al. [37], Rodriguez and Okamura, [48], Song, et al. [56], Sarker, et al. [57]</p>
  </td>
  <td>
  <p dir="ltr">Security Orchestration</p>
  </td>
  <td>
  <p dir="ltr">develop new algorithms for orchestration</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Chen, et al. [49], Pavanello, et al. [50]</p>
  </td>
  <td>
  <p dir="ltr">Contextual awareness for Real-time threat analysis</p>
  </td>
  <td>
  <p dir="ltr">customised algorithms for industries, new indicators of compromise (IoCs)</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Mittal, et al. [33], Alves, et al. [38], Rodriguez and Okamura, [48], Petrović and Jovanović, [51], Evangelista, [52]</p>
  </td>
  <td>
  <p dir="ltr">Strategic Planning and Collaborative Defence</p>
  </td>
  <td>
  <p dir="ltr">common and industry-specific weaknesses&nbsp;</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <h1 dir="ltr"><strong>&nbsp;</strong></h1>
  <h2 dir="ltr">3.5 Potential Solutions and Research Directions as per Future Industrial Trends&nbsp;</h2>
  <p dir="ltr">Based on the discussion of various research gaps in the previous section, it is clear that there are diverse opportunities for OSINT researchers to conduct more studies. However, in the last section, research gaps and research opportunities were based on an analysis of the implementation of AI-powered OSINT. In this section, the research opportunities are presented within the context of future trends in security threat intelligence as an industry. In the ever-evolving realm of cybersecurity, staying ahead of potential threats is not just a matter of strategy but of survival; businesses are constantly vulnerable, particularly if they have limited resources to allocate to security. The threat intelligence landscape is changing, with advances in technology and shifts in attacker tactics driving new trends that businesses need to be aware of to safeguard their future.</p>
  <h3 dir="ltr">3.5.1 Automation and Machine Learning</h3>
  <p dir="ltr">A significant trend in the OSINT industry is that cyber threats will continue to become increasingly sophisticated, and the use of automated systems and machine learning algorithms for detection and response to threats will also spread by leaps and bounds [42]. For instance, anomaly detection systems will be developed to identify abnormal patterns that may indicate a breach [39]. Machine learning will be helpful in predicting and preventing zero-day attacks as it can analyse large data sets and recognise potential vulnerabilities before cyber criminals can exploit them [52]. Therefore, it is recommended that researchers focus on these areas and design new ways of automation using machine learning and integrating OSINT in these systems.</p>
  <h3 dir="ltr">3.5.2 Threat Intelligence Platforms (TIPs)</h3>
  <p dir="ltr">Tips are also a significant trend in this industry. These platforms are becoming more user-friendly and increasingly integrated to allow organisations to leverage intelligence from shared information accumulated from various sources [34]. A TIP can correlate data from diverse sources, offering a more comprehensive perspective of the threat landscape [46]. For instance, a company can use TIP to combine threat data specific to the industry and compare it with global threat intelligence to formulate a tailored security response [53]. This is an essential trend in the future of this industry, and hence, researchers are encouraged to endeavour creative ideas and innovative methods to design new TIPs.</p>
  <h3 dir="ltr">3.5.3 Collaborative Security and Insider Threat</h3>
  <p dir="ltr">As discussed earlier, collaborative security is an essential feature of the future vision of the OSINT industry. In the future, there will be an increase in collaborative efforts by organisations, even their competitors, who will be sharing threat intelligence for mutual benefit [36]. This collective strategy for defence can be seen in various sectors already, including but not limited to financial services, where business organisations are sharing indicators of compromise (IoCs) to fight widespread fraud [54]. A relevant trend to regulatory compliance is insider threat detection using AI-based OSINT. As remote work becomes the norm, the risk of insider threats increases. Organisations will need to invest in solutions that monitor user behaviour and access patterns to identify potential insider threats [47]. For example, an employee accessing sensitive data outside of regular business hours could trigger an alert for further investigation [56]. There is scant literature on this trend, but rigorous research is required to integrate psychology, behaviour, and cyber technology.&nbsp;</p>
  <h3 dir="ltr">3.5.4 Cloud security</h3>
  <p dir="ltr">With the shift to cloud services, organisations need to focus on securing their cloud infrastructure. This includes using cloud-native security tools that provide visibility into cloud environments and protect against misconfigurations, which are a common cause of cloud data breaches [57]. In this area, researchers need to realise that there is scant evidence about the integration of OSINT in cloud security, and compatibility issues are frequent. There are ample research opportunities in this regard.</p>
  <h3 dir="ltr">3.5.5 Regulatory Compliance</h3>
  <p dir="ltr">The regulatory environment is evolving, and regulations such as GDPR and CCPA are emerging, with strict guidelines and rigorous data protection requirements being imposed [43]. The organisations foresee the need to align their systems for threat intelligence with legal requirements [41]. This compliance is aimed not only at protecting customer data but also to make sure that threat intelligence systems comply with privacy laws [55]. In this area, the foremost research opportunity lies in collaboration with legal experts and defining rules for compliance during the design of OSINT.</p>
  <p dir="ltr">The table below summarises the discussion of themes and relevant suggestions in this category.</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="58"><col width="115"><col width="129"></colgroup>
  <tbody>
  <tr>
  <td colspan="3">
  <p dir="ltr">Potential Solutions and Research Directions as per Future Industrial Trends&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Studies</p>
  </td>
  <td>
  <p dir="ltr">OSINT Industry trend</p>
  </td>
  <td>
  <p dir="ltr">Research Direction</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Riebe, et al, [39], Evangelista, et al, [42], Evangelista, et al, [52]</p>
  </td>
  <td>
  <p dir="ltr">Automation and machine learning</p>
  </td>
  <td>
  <p dir="ltr">design new ways of automation</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Ficco and Palmieri, [34], Aragon&eacute;s Lozano, et al, [46], Torres, et al, [53]</p>
  </td>
  <td>
  <p dir="ltr">Threat Intelligence Platforms (TIPs)</p>
  </td>
  <td>
  <p dir="ltr">new and innovative TIPs</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Ghazi, et al, [36], Riebe, [47], Iturbe, et al, [54], Kamal, [56]</p>
  </td>
  <td>
  <p dir="ltr">Collaborative Security and Insider Threat</p>
  </td>
  <td>
  <p dir="ltr">design new IoCs and integration of psychology in cyber behaviour&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Sarker, et al, [57]</p>
  </td>
  <td>
  <p dir="ltr">Cloud Security</p>
  </td>
  <td>
  <p dir="ltr">scant evidence about the integration of OSINT in cloud security and compatibility issues&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Khurana, et al, [41], Vegesna and Adepu, [43], Kamal, [55]</p>
  </td>
  <td>
  <p dir="ltr">Regulatory Compliance</p>
  </td>
  <td>
  <p dir="ltr">integrate legal rules and compliance in the design of OSINT</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p dir="ltr">&nbsp;</p>
  <h2 dir="ltr">3.6 Chapter Summary&nbsp;</h2>
  <p dir="ltr">Based on the analyses above, there is ample research about the state of implementation and application of AI and OSINT in cybersecurity and a diverse set of tools and techniques have been developed. Each of these tools and techniques has unique strengths and limitations which are applicable in specific contexts. The main strength identified in the discussion above is the ability of AI and OSINT to handle large datasets, automate, and have faster speed than humans. The main limitation is that these tools and techniques are still evolving, and therefore, more research and experiments are required. Another standard limitation is that with the evolution of security measures, counter-security and cyber attacks are also evolving. Based on the discussion of strengths and limitations, by harnessing the power of AI and machine learning, organisations can not only strengthen their defences but also gain a strategic advantage in the threat landscape. The key lies in intelligently layering these technologies within their security infrastructure to create a resilient and responsive system. As these technologies continue to mature, they will become indispensable tools in the arsenal of any startup looking to navigate the treacherous waters of cyber threats.</p>
  <p dir="ltr">Furthermore, based on a discussion of future trends for research, by integrating these elements into the fabric of security operations, organisations can not only navigate but also anticipate the threat landscape, turning intelligence into a powerful tool for cyber resilience. The key is not just to gather intelligence but to integrate it in a way that improves the overall security posture, making the organisation not only aware but prepared and responsive to the threats it faces. By understanding the trends in the OSINT industry, organisations and researchers can develop proactive and dynamic approaches to security threat intelligence, ensuring they are prepared for the cyber threat challenges of tomorrow. The key is not just to embrace new technologies but to foster a culture of security awareness and collaboration that can adapt to the changing tides of the digital world.</p>
  <h1 dir="ltr">4. Discussion</h1>
  <h2 dir="ltr">4.1 Introduction&nbsp;</h2>
  <p dir="ltr">This study aimed to assess the efficacy of AI-driven OSINT approaches in cybersecurity. The purpose of the review was to focus on identifying gaps in the current literature and to evaluate how these tools can be optimised for enhanced real-time threat detection. In order to achieve this aim, this study identified various objectives and conducted a comprehensive review of the literature to accomplish each of these objectives individually, as presented in the previous chapter. This chapter presents a discussion of the results and shows how these objectives have been achieved, referring to the results and interpreting the implications of the results. Similar to the results chapter, this chapter is also organised in accordance with the research objectives and starts with a discussion on the results of the first objective, followed by a discussion of the second objective, and ends with a summary of the debate.&nbsp;</p>
  <h2 dir="ltr">4.2 Discussion&nbsp;</h2>
  <p dir="ltr">The results of the first objective (see section 4.2) show that the application of AI in OSINT in cybersecurity has demonstrated significant advances and has also raised critical concerns. The results showed that AI-powered tools such as OSINT can help improve network security, simulate cybersecurity scenarios, and promote cybersecurity education [58]. However, this same capability can be exploited by cybercriminals to develop malicious software, create fake websites, and carry out more sophisticated social engineering attacks. This duality is mentioned in various research [59] where it is highlighted that AI in OSINT can be exploited to distribute false information on social media and manipulate public opinion, increasing cybersecurity challenges [60].</p>
  <p dir="ltr">The results also show that AI in OSINT approaches can reproduce real scenarios, facilitating the development of advanced tools to detect zero-day vulnerabilities and perform pen-testing. However, human oversight remains essential to ensure accuracy and address false positives and negatives [61]. Additionally, the increasing sophistication of AI in OSINT in creating ransomware [62], as well as phishing scams and deepfakes, poses a significant challenge, as conventional detection techniques are becoming obsolete, as discussed in [63]. Furthermore, the ability of AI in OSINT to analyse malicious code and detect malicious behaviours and characteristics, even when employing obfuscation and encryption techniques, is a promising application [64]. AI in OSINT can also automatically generate new security vulnerabilities and attack scenarios, facilitating simulation and improving defence and response capabilities [65].</p>
  <p dir="ltr">The results related to the second objective (see section 4.3) focus on the strengths and limitations of applications and techniques. The results show that the applications of AI in OSINT in cybersecurity are varied and promising. According to the review, using text and code analysis and generation capabilities, automatic encryption and secure transmission of information can be achieved [66]. Furthermore, OSINT tools can analyse large volumes of threat intelligence data, helping to anticipate future threats to network security [67].&nbsp;</p>
  <p dir="ltr">AI-powered OSINTs enable faster identification of vulnerabilities and automation of test scenario generation, reducing the need for manual intervention and enabling more thorough assessment [68]. It can simulate the behaviour of real attackers by learning from patterns and adapting to new tactics, providing a more realistic understanding of how adversaries may act [70].</p>
  <p dir="ltr">Malware identification has been improved by research methodologies based on Generative Adversarial Networks. Various tools mentioned in the reviewed studies provide automated network security, malware protection, and privacy compliance monitoring services [71]. OSINTs are also used to generate email messages that trick scammers, wasting their resources and prolonging their interaction with them. Generative AI can analyse large amounts of data to identify patterns and anomalies that indicate the presence of threats, generating reports and alerts based on these analyses [72]. AI in OSINT enables defenders to accelerate and automate the incident response process, analyse cybersecurity incidents, generate reports on incidents and threats, and make strategic recommendations [73]. Generating persuasive honeypots to lure attackers and analyse their methods is another significant application of AI in OSINT [74].</p>
  <p dir="ltr">However, the review also shows that there are significant risks and limitations. The implementation of Generative Artificial Intelligence in cybersecurity faces numerous ethical challenges, risks, and limitations. The exposure of personal information and threats to social security are critical concerns. Generative models can increase the risks of information and political manipulation, affecting electoral processes [75]. The use of AI in OSINT raises ethical and legal issues, including the possibility of biased results if models are trained with non-representative data, which can lead to unfair test scenarios or failure to detect specific vulnerabilities [76].</p>
  <p dir="ltr">In addition, the use of large amounts of data to train AI models raises concerns about privacy and data security, as sensitive information can be exposed. Collecting large volumes of data to train AI models raises concerns as sensitive information can be exposed. AI systems require a large amount of computational resources, which can have an adverse effect on the ecosystem. Challenges also include the possibility of adversaries injecting perturbations into the input data to replicate models or impair performance [77]. Privacy concerns include model theft and poisoning, as well as the persistence of biases present in training data. Lack of transparency and control by companies and users is also a significant challenge, which can result in unfair or discriminatory decisions. Large-scale language models (LLMs) present several challenges and limitations in cybersecurity. These models focus on generating responses but do not always prioritise accuracy, which can result in the generation of incorrect or misleading responses, known as "hallucinations" [78].</p>
  <p dir="ltr">Protecting Personally Identifiable Information and other sensitive data is vital to maintaining user security and trust. Finally, researchers, developers, and policymakers must work together to address these challenges and ensure the integrity and security of AI-based systems. The implementation of emerging AI security frameworks guides security teams on best practices, controls, recommendations, and procedures [79].</p>
  <p dir="ltr">This review is an extensive and comprehensive evaluation of the literature and shows the state of generative artificial intelligence-based approaches for OSINTs in cybersecurity. Although previous studies [systematic reviews] have conducted thorough reviews, these have focused exclusively on the security and privacy of generative AI. Other studies, such as [80], have focused on specific tools such as ChatGPT. In contrast, research such as [81] has explored applications in particular fields, such as cybersecurity threats, pen testing, ransomware, and digital forensics [82]. This research covers a broader perspective, providing a comprehensive overview of the applications, techniques, ethical challenges, risks, and limitations of generative AI in cybersecurity. This work is limited to answering the research questions posed, providing an overview of the current state, applications, techniques, ethical challenges, risks, and limitations of generative AI in cybersecurity and presenting research gaps. However, there are particular areas where generative AI is at its peak, such as the Internet of Things (IoT), network physical layer, pen-testing, ransomware, and digital forensics, which have yet to be explored in depth in this review.</p>
  <p dir="ltr">In the third objective, the main requirement was to identify critical gaps in the literature with a specific focus on the implementation of AI-powered OSINTs. A significant trend identified in OSINT is security improvements [83]. Based on the review, the main research gaps and opportunities are to increase research attempts focusing on analysing and countering the security measures of cyber criminals. It is further asserted that there is a need to investigate the way AI can make improvements in OSINT in terms of monitoring and understanding the trends in security attacks.</p>
  <p dir="ltr">One of the standard implementation aspects of AI and OSINT in cybersecurity is using algorithms to handle massive data sets that are beyond the capacity of humans [84]. The algorithms can predict and identify potential sources of breaches and suggest improvements in security [85]. In this area, the central research gap is in conducting studies that can apply very sophisticated algorithms to handle large data sets. The amount of digital data will continue to grow, offering ample opportunities for researchers to use predictive models and find new insights [86]. The researchers can apply AI and ML to enable the security system to react and trigger countermeasures based on their analysis of hidden threats in the data [87]. Similarly, a research gap has also been identified in these types of current studies. More research is needed to evaluate the efficacy of AI-powered OSINT in blocking diverse threats.</p>
  <p dir="ltr">Another significant stream of literature identified from the review is studies focusing on user behaviour [88]. These studies have contributed comprehensive scientific evidence, but there is a gap in this area that there needs to be more evaluation of the effectiveness of additional measures that OSINT trigger to enhance authentication mechanisms and compare them with other security measure(s) [89]. One of the purposes of AI-based OSINT is to detect and uncover hidden threats and design and implement a proactive system to manage threats [90]. There is a potential research opportunity in this field that only scant studies are available experimenting with virtual systems and using mock hidden threats to train algorithms and then deploy them in practical scenarios. Another common trend in OSINT studies is the studies on Incidence response [91]. Potential research opportunities in this aspect are to evaluate changes in downtime periods, agility, and success rates of OSINT and AI algorithms.</p>
  <p dir="ltr">AI can aid humans in cybersecurity because they are able to simultaneously coordinate with multiple tools for security and manage them in unison [92]. This way, OSINT can streamline response against threats [93]. Based on the review, there is a research gap in this context, so they can develop practical algorithms and manipulate security mechanisms while testing them against known threats. There are also studies which use OSINT to analyse threat feeds in real-time and assess potential threats in real-time [94]. The studies in this regard designed IoCs but also acknowledge that improvements are needed in current IoCs.&nbsp;</p>
  <p dir="ltr">Another stream of OSINT literature is focused on improving detection capabilities from large raw datasets. The studies in this context are generic and theoretical and lack practical case studies [95]. Therefore, the gap in this regard is in conducting industry-specific case studies and offering customised and tailored solutions. Some studies focus on strategic security planning and the use of AI and ML to analyse historical threat intelligence [96]. The research gap in this context is similar to the previously mentioned lack of case studies, which industry-specific case studies can fill.</p>
  <p dir="ltr">The fourth objective of this study was to propose solutions and suggest future research opportunities focusing on major trends in the OSINT industry. Based on the review, it can be observed that a vital industry trend is that the sophistication of cyber threats and criminals will likely increase in response to developments in the security industry. The reports on industry trends also show that there will be an increase in the automation of systems and machine learning algorithms with the aim of detecting and countering threats [98]. Hence, researchers are encouraged to focus on this trend and develop creative new designs for automation.</p>
  <p dir="ltr">A significant trend is the emergence and likely strong foothold in TIPs. Recent studies show that these platforms have improved in terms of user-friendliness and are welcome to be integrated into existing security systems. The organisations welcoming TIPs aim to leverage the intelligence platforms by sharing information and gaining mutual benefits. It is also a critical future trend in this industry and, therefore, presents ample research opportunities where researchers can use their creative ideas to design new innovative methods in TIPs.&nbsp;</p>
  <p dir="ltr">The review also showed that one of the future trends is the increase in collaborative efforts. Disparate organisations will share disparate information input data sets for security OSINTs, and this sharing will provide mutual benefits. Researchers can explore the ways in which information can be shared, filtered, and protected to encourage collaborative efforts. Another important trend in this industry will have a significant effect on the development of the regulatory environment and regulations (recent examples are CCPA and GDPR). It is expected that stricter guidelines and data protection regulations will be created across the globe at the regional and state level. Hence, the primary research opportunity in this regard is for researchers to collaborate with legal experts to understand and define how AI-based OSINTs can ensure compliance. There is also the risk factor that as remote working becomes the new normal, the insider threat risk will increase [98]. There are only limited studies on insider threat management using AI and OSINT. Therefore, rigorous research is needed to address this concern and apply AI and OSINT to this area of cybersecurity.</p>
  <h2 dir="ltr">4.3 Summary&nbsp;</h2>
  <p dir="ltr">This study has conducted a systematic literature review on the current state of the art in the application of generative artificial intelligence in the cybersecurity field, providing a comprehensive overview of the current state, promising applications, ethical challenges, risks and associated limitations. The main findings highlight that AI in OSINT is being used both in the implementation of defensive strategies and in the execution of offensive actions in the cybersecurity field. Its ability to generate complex content improves the simulation of security scenarios and the detection of vulnerabilities. Still, cybercriminals can also exploit it to develop advanced social engineering techniques and generate malicious code, increasing privacy and data security risks. This review and elaborated research contribute to the understanding of how AI in OSINT can transform the cybersecurity field. By addressing both beneficial applications and risks and challenges, a solid foundation is provided for future research that can explore more specific areas and develop theoretical and normative frameworks for the responsible use of AI. There are various streams of literature, but the common aim is to apply AI and OSINTs in the cybersecurity area and make improvements. The studies in this area are very diverse in terms of aims and methodologies, and therefore, there are ample research opportunities in each stream, as discussed in this chapter. Thus, researchers are encouraged to use this review to identify research gaps and essential trends in this study industry and apply their skills and creativity to contribute towards literature as well as practical implementation.</p>
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slug: uncovering-literature-gaps-for-enhanced-threat-detection
title: Uncovering Literature Gaps for Enhanced Threat Detection
academic_level: Undergraduate
subject: Education
type_of_paper: Essay
citation_style: APA
meta_title: Uncovering Literature Gaps for Enhanced Threat Detection
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created_at: 2025-06-13 11:25:57
updated_at: 2025-06-25 15:19:34
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status: published
content:
  <h2 dir="ltr">Assessing the Efficacy of AI-Driven OSINT Approaches in Cybersecurity: Uncovering Literature Gaps for Enhanced Threat&nbsp;Detection</h2>
  <p dir="ltr">Abstract: As connectivity increases across the digital landscape of the modern world, it opens up growing opportunities for cybercriminals. It is often the case that traditional security efforts need to be faster in reacting to new and complex threats as they emerge. When it comes to identifying emerging risks, OSINT from the public internet is the way to go about it. Whenever implemented into defensive systems, the proper implementation of the acquired OSINT data ensures the ability to detect proactively. However, manual analysis cannot be applied to a firehose of information in the social media domain, which overloads analysts. There is significant literature investigating AI and OSINT integration. They have focused on the specific functions essential to security operations, such as real-time threat identification. This study aims to assess the current state of AI-powered OSINT applications with systematic literature review (SLR) as a methodology to analyse existing literature and identify gaps in knowledge relating to strengths, limitations and optimisation of OSINT techniques when operationalized with AI. The findings also make informed recommendations for researchers to understand future industry trends and design their studies accordingly. Using SLR, this study surveys peer-reviewed articles and conference papers to evaluate how existing research has established technical feasibility and reported limitations. Special attention is paid to the literature addressing real-world deployment issues and the application of AI-powered OSINT in real life. Knowledge gaps have been extracted and organised by common themes. Finally, this study proposed solutions and directions for future work to strengthen the protection of critical systems and data through intelligence-guided protection strategies. This work provides valuable insight for both researchers developing new applications and practitioners seeking to apply AI-driven OSINT cautiously. By shedding light on areas requiring further work, this study ultimately aims to enable more effective and responsible operationalisation of these promising capabilities for cyber defence.</p>
  <p dir="ltr">KEYWORDS:&nbsp; AI-Driven OSINT, Cybersecurity, Threat Detection, cyber defence</p>
  <h3 dir="ltr">1. Introduction</h3>
  <p dir="ltr">The digitisation of critical infrastructure and widespread internet connectivity have transformed the landscape of modern society. However, greater reliance on digital technologies has also expanded the attack surface for cyber threats. Cybercriminals are exploiting vulnerabilities at an alarming rate, with many organisations struggling to detect and mitigate sophisticated intrusions in a timely manner [1]. Traditional security protocols often need to catch up with the rapidly evolving methods of cyber attackers. Furthermore, the sheer volume of data that must be analysed to glean practical threat intelligence presents a significant challenge. Cyber threat intelligence aims to provide visibility into emerging risks through analysis of indicators gathered from diverse open sources [2]. Open-source intelligence (OSINT) has emerged as a valuable approach for uncovering clues about potential threats from publicly available information online.&nbsp;</p>
  <p dir="ltr">While following hackers' activity and discussions, dark web trading platforms, and vulnerable facilities, the tool allows for the identification of existing or planned exploits. If information is incorporated into security systems, timely OSINT data can help prevent specific threats known to the system as they attempt to execute their intents before the damage has been done. Nevertheless, it is unreasonable to conduct a manual analysis of a large number of available resources due to the lack of time and resources in security teams [3]. AI techniques are effective for unsupervised extraction and correlation of threat indicators at a mass scale. Trading algorithms are also better able to present patterns and peculiarities that are difficult for analysts to discover and uncover new links between parties. Natural language processing is beneficial in feeding cyber threat intelligence from basically virtually any unstructured text-based OSINT source with moderate simplicity [4]. In partnership with data visualisation, Artificial Intelligence solutions seek to turn OSINT data into intelligent information for the improvement of cybersecurity.</p>
  <p dir="ltr">While initial studies demonstrate the potential of integrating AI and OSINT, many technical and practical challenges must still be addressed. Questions still need to be answered regarding the effectiveness, accuracy, and limitations of these tools when applied to real-world complex cybersecurity problems. Adversarial adaptation may undermine classifier performance over time. Bias in training datasets could negatively impact results or propagate unfair outcomes [5]. Moreover, practical implementation barriers such as infrastructure requirements, model maintenance costs, privacy and ethical concerns require deeper examination. Empirical research evaluating AI-driven OSINT approaches for real-time threat detection is limited.</p>
  <p dir="ltr">Further exploration of techniques, best practices, and refinement is needed to optimise these evolving solutions [6]. Cyber-related crimes have become more and more frequent and diverse in the recent past. Advanced persistent threats operate beyond the physical boundary that has been established, while supply chain compromises begin beyond the secured network perimeter. At the same time, a new generation of commodity malware, along with the global availability of hacking tools, reduces entry barriers to sporadic cybercrime on an industrial scale. As the digital transformation exercises introduce more services and infrastructure to the online environment than ever before, the exposed attack surface remains vast [7]. Such dynamics have outcompeted the ability of security teams to cope effectively with them using traditional measures only. There is a clear imperative to find new approaches and tools that could address today's reality in the cybersecurity environment.</p>
  <p dir="ltr">Open-source intelligence (OSINT) harvesting from the public internet holds promise for gaining valuable threat insights through analysis of technical forums, malware databases, and vulnerable systems. When incorporated into defensive systems, OSINT data fuels proactive detection and rapid remediation by uncovering indicators of new exploits, botnets, and planned intrusions. However, the sheer quantity of publicly available information online far surpasses what can reasonably be scrutinised by human analysts within security operations centres [8]. Automated processing is required to extract meaningful intelligence from this firehose of OSINT sources at the scale and speed demanded in modern cyber defence.</p>
  <p dir="ltr">Artificial intelligence (AI) shows potential for extracting actionable threat intelligence from vast troves of OSINT at machine speeds. Techniques like natural language processing and machine learning can uncover patterns, relationships, and anomalies across structured and unstructured data that would be nearly impossible for people to discern alone or in teams. However, while initial studies highlight AI and OSINT's technical feasibility, many knowledge gaps still need to be discovered regarding practical challenges, limitations, and real-world efficacy for critical functions like real-time threat detection [9]. Other issues remain open regarding model fitness, adversarial attacks, bias, needed resources, costs of maintenance, and ethics, as these all need to be confirmed with experiments.</p>
  <p dir="ltr">The current research landscape leaves security practitioners needing comprehensive guidance on standardising and operationalising effective, responsible AI-driven OSINT methodologies. This limitation undermines organisations' ability to harness these promising approaches for proactive cyber defence and leaves a crucial area of cybersecurity techniques relatively underdeveloped. Clearly defined knowledge gaps must be addressed to optimise AI and OSINT integration as envisioned [10]. This study aims to assess the efficacy of AI-driven OSINT approaches in cybersecurity, focusing on identifying gaps in the current literature and evaluating how these tools can be optimised for enhanced real-time threat detection. The objectives proposed in this manner are provided below:</p>
  <ul>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">To analyse the current state of AI-driven OSINT tools and their application in cybersecurity.</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">To evaluate the strengths and limitations of integrating AI with OSINT for real-time threat detection.</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">To identify critical gaps in existing research on AI and OSINT in cybersecurity, particularly in relation to practical implementation.</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">To propose potential solutions and future research directions for improving the efficacy of AI-driven OSINT approaches in cybersecurity.</p>
  </li>
  </ul>
  <p dir="ltr">The purpose of this research is to evaluate the current status of incorporating artificial intelligence techniques in open-source intelligence collection for cyber threat detection. More specifically, the study will be designed to uncover lacunae in extant scholarship in relation to widely acknowledged applications and limitations of AI and OSINT for operational functions like threat detection in real-time. By evaluating the strengths and limitations of these approaches as described in previous works, this study seeks to uncover areas in need of further exploration and empirical validation. Figure 1 below shows the trend of OSINT and AI literature over the years. It can be determined that the trend of OSINT and AI literature has increased over the years due to the increase in the use of these technologies.&nbsp;</p>
  <p dir="ltr"><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXcs4HdeTPVz07nrFgQpKxfNCzQC3z465y38zIdYy0MLa5WtLbnTHqhRsV0oWLXLuYpJSI73cOFiPPf8epgfY7sDToIXnQZmwX_Z0zHVtJEniMFHx2pA1kHB7m3Kqv82U-XRMknIB2iXLKWyfNDozjw?key=DWlgdn3Nz9k2tXXTZA_6Bw" alt="OSINT literature over the years (1992&ndash;2021)" width="304" height="226"></p>
  <p dir="ltr">Figure 1 OSINT and AI literature over the years (1992&ndash;2021)</p>
  <p dir="ltr">Source: [11]</p>
  <p dir="ltr">The identified areas of knowledge have then been used to formulate recommendations for enhancing the future developments of AI-based OSINT procedures to enhance its prospects as it will be practised inside a security operation environment. Recommendations may involve best practices for addressing challenges, such as data bias, continuous adversarial evasion, suitable infrastructures depending on the organisation's demands, and more, as well as methods of consistent model management. The purpose is to offer information that can benefit both researchers in creating subsequent applications and practitioners in deciding where and when to implement AI-based OSINT into their cybersecurity plans. Finally, this is expected to improve the protection of systems and data by including intelligence-driven methods.</p>
  <p dir="ltr">With the threats in cyberspace changing constantly and more opportunities for attacks being created all the time, intelligence-led protection methodologies have become crucial. It is only where these areas of knowledge are currently lacking that such enhancement concerns appear relevant at all to integrating AI and OSINT as techniques that can transform cybersecurity from a one-off process utilising on-average solid and scalable best practices into one that is capable of moving at the speed of change necessitated by the threatscape. This research also seeks to improve current cyber security measures by identifying numerous limitations in previous studies and offering workable recommendations to mitigate risks for developers and end users of AI-based OSINT tools. In sum, this work aims to provide new insights toward the more responsible implementation of these instead of promising technologies for the benefit of society.</p>
  <h3 dir="ltr">2. Methods</h3>
  <h4 dir="ltr">2.1 Research Design&nbsp;</h4>
  <p dir="ltr">Research design is the blueprint of a research process which explains all the methodological decisions and choices made that led to the completion of the study. There is a range of research designs adopted in the research community, but the choice is dependent upon the main research aim. This study aims to assess the current state of AI and OSINT integration in cybersecurity contexts. By identifying gaps in existing literature, recommendations can be made to inform further development and standardisation of effective AI-empowered OSINT methodologies for enhanced proactive cyber defence. Since this study is focused on reviewing the literature, the systematic literature review (SLR) design was deemed most suitable.</p>
  <p dir="ltr">SLR is a methodology that allows researchers to conduct a critical appraisal of existing literature by systematically selecting studies, i.e. a search strategy. Unlike traditional narrative reviews, SLR enhances the reliability and validity of the review. SLR is often used to identify the best available evidence and develop best practices. However, SLR is also appreciated for its ability to synthesise existing literature and identify research gaps [13].</p>
  <p dir="ltr">In this study, the SLR started with defining a search strategy by identifying keywords and databases and then continued to filter search results using inclusion and exclusion criteria. All of these steps are explained and discussed in the following sections.</p>
  <h4 dir="ltr">2.2 Search Strategy&nbsp;</h4>
  <p dir="ltr">A search strategy is a set of steps that SLR follows to identify and select studies from available evidence for the final review. A search strategy begins with identifying keywords and databases based on research aims and objectives [14]. Both of these choices are critical because they affect the overall reliability and validity of the review. The researchers must select relevant and authentic databases or libraries to ensure that they access the relevant literature. Similarly, researchers must choose keywords in a way that the most pertinent studies match the requirement of the research question [15]. To make sure that keywords were relevant, phrases from research objectives were selected. The following table summarises the keywords and databases selected,</p>
  <p dir="ltr">Table 1: Databases and Keywords</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="154"><col width="148"></colgroup>
  <tbody>
  <tr>
  <td>
  <p dir="ltr">Databases</p>
  </td>
  <td>
  <p dir="ltr">Keywords</p>
  </td>
  </tr>
  <tr>
  <td>
  <ul>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">IEEE</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">Google Scholar</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">Springer&nbsp;</p>
  </li>
  </ul>
  </td>
  <td>
  <ul>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">AI AND* OSINT</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">AI AND OSINT AND Cybersecurity</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">AI Applications AND Cybersecurity</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">OSINT AND Cybersecurity&nbsp;</p>
  </li>
  </ul>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p dir="ltr">* AND is a Boolean Operator often used to combine keywords during search strategy and refine search results&nbsp;</p>
  <p dir="ltr">When the keywords and their combinations were applied in the databases selected, the following results were obtained (see Table 2)</p>
  <p dir="ltr">Table 2: Initial Search Results</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="116"><col width="49"><col width="67"><col width="70"></colgroup>
  <tbody>
  <tr>
  <td>
  <p dir="ltr">Keywords and Combinations&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">IEEE</p>
  </td>
  <td>
  <p dir="ltr">Google Scholar&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Springer&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">AI AND OSINT</p>
  </td>
  <td>
  <p dir="ltr">26</p>
  </td>
  <td>
  <p dir="ltr">9,090</p>
  </td>
  <td>
  <p dir="ltr">191</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">AI AND OSINT AND Cybersecurity</p>
  </td>
  <td>
  <p dir="ltr">9</p>
  </td>
  <td>
  <p dir="ltr">4,300</p>
  </td>
  <td>
  <p dir="ltr">82</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">AI Applications AND Cybersecurity</p>
  </td>
  <td>
  <p dir="ltr">956</p>
  </td>
  <td>
  <p dir="ltr">250000</p>
  </td>
  <td>
  <p dir="ltr">6472</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">OSINT AND Cybersecurity</p>
  </td>
  <td>
  <p dir="ltr">51</p>
  </td>
  <td>
  <p dir="ltr">8580</p>
  </td>
  <td>
  <p dir="ltr">228</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Total</p>
  </td>
  <td>
  <p dir="ltr">1042</p>
  </td>
  <td>
  <p dir="ltr">271970</p>
  </td>
  <td>
  <p dir="ltr">6973</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p dir="ltr">&nbsp;</p>
  <p dir="ltr">The results above clearly indicate that there is vast literature available related to the subject and research question of this study, and it is only possible to review some of the evidence retrieved from the databases above. Hence, there is a need to refine the search results and narrow them down to a feasible number of studies. For this purpose, the SLR design allows researchers to use inclusion and exclusion criteria, which can be used to reduce the studies using a set of criteria systematically.&nbsp;</p>
  <p dir="ltr">Inclusion criteria are used to make sure that studies that fulfil these criteria are included in the review. The inclusion criteria ensure the specificity of the search strategy. Specificity implies that the review has relevant literature and does not miss vital evidence to answer the research question. On the other hand, exclusion criteria are used to ensure the sensitivity of the search strategy. Sensitivity refers to the ability of the search strategy to exclude irrelevant evidence and thus enable the researcher to achieve feasibility in the review [16]. Table 3 below provides the inclusion and exclusion criteria applied to reduce the number of studies retrieved in Table 2.</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="150"><col width="152"></colgroup>
  <tbody>
  <tr>
  <td>
  <p dir="ltr">Inclusion Criteria&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Exclusion Criteria&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Articles focused on OSINT and AI in the specific context of cyber security must be included.&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Literature published earlier than 2020 must be excluded to maintain the latency of evidence.&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Articles analysing the benefits and limitations of AI and OSINT must be included.&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Literature reviews must be excluded.&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td rowspan="2">
  <p dir="ltr">Articles analysing the application of AI and OSINT in cyber security must be included.&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Articles other than those in the English language must be excluded.&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Replication studies be excluded.&nbsp;</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p><strong>&nbsp;</strong></p>
  <p dir="ltr">Once inclusion and exclusion criteria were identified, the researcher started sorting and funnelling the search results. Since multiple databases were included, therefore, there was duplication of articles. The researcher consequently started manual perusal of titles and abstracts of articles. The researcher continued to apply inclusion and exclusion criteria and finally selected 24 articles for review. The entire search strategy and results are summarised using the PRISMA flow chart as follows.</p>
  <h4 dir="ltr">2.3 PRISMA Flowchart</h4>
  <p dir="ltr">A PRISMA flow chart is a commonly adopted standard that helps researchers demonstrate how they executed the search strategy by summarising all steps [15]. The following chart shows that initially, total search was 279985. The researcher started manual perusal of titles and abstracts and applied inclusion and exclusion criteria. A total of 505 articles were found to be duplicated, and 278982 were removed after applying exclusion criteria. The majority of the studies were removed when the publication year filter was used in Google Scholar, which reduced the search results significantly. Finally, after applying inclusion criteria, a total of 24 articles were selected for final review. The search strategy was a lengthy process, but it helped manage the review of a feasible number of studies.&nbsp;<strong><br></strong></p>
  <h4 dir="ltr">2.4 Data Extraction and Analysis Technique&nbsp;</h4>
  <p dir="ltr">Once studies were selected, the researcher started applying data extraction and analysis techniques. This stage is often called the synthesis of literature [17]. In order to conduct a thorough analysis of the literature, thematic analysis was selected. Thematic analysis is a widely acknowledged data extraction and synthesis technique because it enables analysis of large pieces of text(s) (articles in cases of this review) using categories and themes [18]. Thematic analysis is a commonly used qualitative data analysis technique in social sciences. It can be used to identify recurring and common patterns or themes in large datasets [19]. The thematic analysis is executed by carefully reading and interpreting the data under analysis [20]. The reviewer notes common categories and themes and derives meaningful results from the data to answer the research question [21].</p>
  <p dir="ltr">This review started the thematic analysis by carefully reading the abstracts of the articles selected and noting key categories. The reviewer then continued to conduct an in-depth and detailed reading of the main body of the articles and noted common themes with a specific focus on research gaps. The first category identified was streams of OSINT literature in the context of cyber security. This category classifies applications and implementation of OSINT in cybersecurity. A number of themes emerged in this category, which included techniques and tools in cyber security, and a range of different tools were identified. The researcher noted down all the research gaps identified in the selected studies. The second category was strengths and limitations of AI-integrated OSINT in cyber security. This category also included a number of themes, such as anomaly detection behaviour analysis and automation of security processes via AI. This category was also explicitly focused on identifying research gaps in the context of OSINT applications in cyber security. The third and final section focused on identifying future patterns in the OSINT industry and searching for potential future research opportunities. All of the themes and information were explicitly focused on identifying existing research gaps and searching for potential research areas.</p>
  <h4 dir="ltr">2.5 Key Questions to Address in the Literature</h4>
  <p dir="ltr">To assess the efficacy of AI-driven Open Source Intelligence (OSINT) approaches in cybersecurity and uncover gaps for enhanced threat detection, it is essential to structure the literature review around key research questions that align with the proposed objectives. The following questions were designed to guide the analysis:</p>
  <p><strong>&nbsp;</strong></p>
  <ul>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">What are the streams of AI-driven OSINT tools and their application in cybersecurity?</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">What are the key strengths and limitations of integrating AI with OSINT for real-time threat detection?</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">What gaps exist in the current research on AI and OSINT integration for cybersecurity?</p>
  </li>
  <li dir="ltr" aria-level="1">
  <p dir="ltr" role="presentation">What potential solutions and future research directions as per industrial trends could improve the efficacy of AI-driven OSINT approaches?</p>
  </li>
  </ul>
  <h5 dir="ltr">2.5.1 What are the streams of AI-driven OSINT tools and their application in cybersecurity?</h5>
  <p dir="ltr">This question has helped determine what are the most commonly used AI-driven OSINT tools in cybersecurity and how these tools are being deployed for threat detection, threat intelligence gathering, and cyber risk management. In addition, it also determined how these tools compare with traditional OSINT approaches in terms of accuracy, efficiency, and scope.</p>
  <h5 dir="ltr">2.5.2 What are the key strengths and limitations of integrating AI with OSINT for real-time threat detection?</h5>
  <p dir="ltr">This question helped to determine what AI techniques (e.g., machine learning, natural language processing) are most effective when applied to OSINT data for cybersecurity purposes and how AI enhance the speed, scalability, and precision of OSINT in identifying and mitigating cyber threats in real-time. In addition, it also provided insights into the benefits of automation in AI-driven OSINT for detecting emerging threats and trends. Moreover, it also helped to determine the main technical, ethical, and operational challenges associated with AI-driven OSINT approaches, along with the issues related to data privacy, biases, and misinformation when applying AI to OSINT in the context of cybersecurity.</p>
  <h5 dir="ltr">2.5.3 What gaps exist in the current research on AI and OSINT integration for cybersecurity?</h5>
  <p dir="ltr">The third question was developed to determine where the literature falls short in addressing the practical implementation of AI-driven OSINT in threat detection and whether there are any underexplored areas related to specific types of cyber threats (e.g., nation-state actors, ransomware, zero-day vulnerabilities). Moreover, it also helped determine how well existing studies account for real-world deployment challenges, such as the scalability of AI models and integration with security operations.</p>
  <h5 dir="ltr">2.5.4 What potential solutions and future research directions as per industrial trends could improve the efficacy of AI-driven OSINT approaches?</h5>
  <p dir="ltr">The last question developed for the literature was related to the emerging AI technologies or methodologies that could be integrated into OSINT to enhance threat detection capabilities. In addition, it also determined how the current limitations of AI-driven OSINT tools can be addressed to improve their reliability and scalability.&nbsp;</p>
  <p dir="ltr">These questions have helped structure the literature review, providing a comprehensive understanding of the current state of AI-driven OSINT while also identifying areas where more research is needed.</p>
  <h3 dir="ltr">3. Different Aspects of Literature Review&nbsp;</h3>
  <h4 dir="ltr">3.1 Introduction&nbsp;</h4>
  <p dir="ltr">In order to gain an understanding of the state of the art of AI-powered OSINT and its application in cybersecurity, it is critical to reiterate the basic concepts of OSINT and current and future trends in the open source intelligence (OSINT) market. OSINT is information obtained by processing and analysing public data sources such as television and radio, social media, and websites. These sources provide data in text, video, image, and audio formats. The evolution of OSINT is based on the increasing demand for fast and 360&deg; intelligence, which is generated by disparate data sources. These are critical to address public safety. OSINT enables the government and security agencies, as well as private organisations, to take swift and effective actions against cyber threats while also gaining competitiveness. Commercial organisations have the benefit of monitoring and analysing market data to gain insights into market trends and competition [22].</p>
  <p dir="ltr">Many of the OSINTs' tools and techniques are integrating Artificial Intelligence (AI). OSINT involves collecting video, audio, and text data from publicly available sources and analysing it to extract information from various channels. The analysis is based on AI and machine learning algorithms and deep neural networks that allow the system to learn from the data and achieve and refine the recognition of patterns, trends, and relationships [23]. For example, in the case of a TV interview, OSINT can identify both the interviewer and the interviewee (video analysis), the key topics of discussion (voice and text analysis), how viewers react on social media (text analysis) and automatically provide, for example, opinion clusters, trends and viewer sentiments [24].</p>
  <p dir="ltr">The value of the Open Source Intelligence (OSINT) Tools market was USD 9.74 Billion in 2024 and is projected to reach USD 43.72 Billion by 2032. The CAGR is 20.65% [25].</p>
  <p dir="ltr"><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXc1DsFfdK-xQE3wdN3WRJ43fdfpDAbHRgQOE8ugqQkzGKIhgTOpft7jEBj4odoEd2bNgteL3Ad7-R6jGt5xrRjSgTgqohiUaHkozZzbXxjOKpHduiSCrqKBEoTg2iPNZyd9sp3fljM3FKONQJZkuj4?key=DWlgdn3Nz9k2tXXTZA_6Bw" width="271" height="139"></p>
  <p dir="ltr">Figure 2 OSINT market source [25]</p>
  <p dir="ltr">The products, i.e. tools in the OSINT market, include diverse programs as well as hardware which are designed and tailored to meet the individual needs of users. However, standard functions include collecting, examining, and deciphering data from open sources that are publicly available. Some of the familiar sources of data and information for these tools include massive volumes generated by social media platforms, government publications, news sources, forums, and other online websites. These can be easily acquired and examined [26]. The common purpose of OSINT tools is to extract useful information from the data and provide valuable insights for cybersecurity systems which are deployed for law enforcement, corporate intelligence, national security, and other competitive analysis [27].</p>
  <p dir="ltr">As mentioned, there are diverse products catering to unique individual requirements related to competitive intelligence, threat intelligence, and social media that define the OSINT tools market. These are cutting-edge technologies and techniques that can sort massive data sets, identify patterns, and derive meaningful information. Some of the commonly used algorithms in these OSINTs are natural language processing (NLP), data mining, and other machine learning techniques. The capabilities of OSINT technologies increase significantly when AI is integrated, and they provide more accuracy and effectiveness [28].</p>
  <p dir="ltr">Both established cybersecurity companies and cutting-edge organisations dominate the OSINT tools market. These companies offer a variety of solutions, from comprehensive intelligence platforms to specialised applications targeting specific industries or data types. The market is driven by the growing need for better decision-making tools in government and business operations, increased cyber risks, and the increasing need for real-time intelligence [29].</p>
  <h4 dir="ltr">3.2 Streams of OSINT Literature on Application and Implementation in Cybersecurity</h4>
  <p dir="ltr">From the cyber intelligence and intelligence perspective, data represent relationships both jointly and in isolation in such a way that they give a specific value to information under defined contexts [30]. Precisely Cyber intelligence focuses on protection and intelligence in the search for risks or threats in computer systems, which are used as primary source assets available in information systems (databases and information networks). The use of open sources through the assets available in information systems allows intelligence and counterintelligence to be carried out at a low cost and with limited risk, being the essence of the application of OSINT techniques and tools [31].</p>
  <p dir="ltr">It is essential to emphasise the role that OSINT plays in intelligence. Intelligence here is defined as the process by which information relevant to national security is required, collected, analysed and processed for decision-making. Based on this definition, it can be stated that information collected from open sources through OSINT becomes Intelligence when it is collected, analysed, exploited and disseminated in response to a specific Intelligence requirement under a case study or investigation [32].</p>
  <p dir="ltr"><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXenfwZyiA3G2HZ1fgpEwlkObKEBLPkQuuhi7P68WVkk9s2HitA25s8QaR-r4gpyCFWEHM-UMJR4y8k3SI2U6ipI_a3C4M_3aqcUFyccRZ6WDKS1eKOn7F8kqupI2mUdpc5J_hz6TgjMKbMM9ZN0gAw?key=DWlgdn3Nz9k2tXXTZA_6Bw" width="294" height="203"></p>
  <h4 dir="ltr">3.2.1 Techniques and Tools of OSINT in Cybersecurity&nbsp;</h4>
  <p dir="ltr">Given the constant increase in Internet consumption and all its interconnected services, as well as the growth of insecurity, the need arises to take extreme protection measures. From a social point of view, it is necessary to analyse the different scenarios that could occur; this is where several techniques for data collection appear [33]. The history and use of OSINT have been spreading over the years. There are several events worldwide known as "hackathons", in which participants from various places meet to show their skills in the field of research using OSINT techniques [34].</p>
  <p dir="ltr">In May 2022, the first exclusive OSINT and social engineering conference called "Osintom&aacute;tico Conference 2022" was organised and held in Madrid, Spain. There were 25 presentations attended by 550 attendees at the event. The activities were focused on imparting knowledge and raising awareness among participants. As a highlight of the event, there was an activity called CTF (Capture The Flag), which is nothing more than a contest in which the people who participate collect points by meeting goals or objectives; for this, they make use of OSINT tools and their particular knowledge. The theme of the contest was to search for clues about missing people in real life.</p>
  <p dir="ltr">It is critical to note that OSINT tools and techniques do not serve as or are alternatives to firewalls and antiviruses; instead, they are focused on gathering and providing intelligence. More specifically, OSINT is used to search and extract data (from open sources), analyse patterns and traces and draw conclusions from incidents to enable effective decision-making for cyber security [35].&nbsp;</p>
  <p dir="ltr">Search engines are the most common and primary sources of data and information for OSINT. A very effective technique for OSINT consists of performing searches through the different search engines that can be found on the web. As examples and among the best known, we have the search engines Google, Yahoo, Bing, Dogpile, Yandex, DuckDuckGo, and Ask [35]. Most search engines work similarly and allow you to search for specific words and people by name and photo, search within a particular domain, search for social media profiles, search for files, even search for passwords or dictionaries for brute force attacks, etc. [36].</p>
  <p dir="ltr">Google's search engine is one of the most famous around the world; in this search engine, there is something known as "dorks" that help searches to be as precise as possible. Citing some "dorks" for a better understanding that is available [37]. Search for specific terms when quotation marks are used, search within a particular domain citing terms and the URL, search for file type specifying the extension, search for the title of the web page, search with wildcards that allow, for example, to search for incomplete texts using the asterisk wildcard [38].</p>
  <p dir="ltr">In reference to image search, this has been very useful in recent years; facial recognition is shown as a broad resource with the appearance and use of security cameras, excellent quality cameras in mobile phones, and the consequent exposure on the Internet creates the perfect environment to make comparisons through artificial intelligence to find someone or at least know more accurate data that allows their location [39]. The search engines mentioned can be used as tools. However, it should be noted that there are search engines specialised in the collection of more specific data, such as the Shodan search engine, which, with its filters, is capable of finding IP addresses of various devices with specific ports open on the Internet [40].</p>
  <p dir="ltr">It is essential to know that any of the OSINT techniques is a good complement when considering implementing an Information Security Management System based on the ISO/IEC 27001 standard. In various studies such as [41], the application of multiple techniques, including Google Dorking, is observed to expose the security flaws present in multiple case studies.&nbsp;</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="62"><col width="153"><col width="87"></colgroup>
  <tbody>
  <tr>
  <td colspan="3">
  <p dir="ltr">Streams of OSINT Literature on Application and Implementation in Cybersecurity</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Studies</p>
  </td>
  <td>
  <p dir="ltr">Purpose</p>
  </td>
  <td>
  <p dir="ltr">Methodology</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Abu, et al., [31], Khurana, et al., [41], Mittal,&nbsp; et al. [33], Kanta, et al., [32]</p>
  </td>
  <td>
  <p dir="ltr">Handling extensive data from open sources</p>
  </td>
  <td>
  <p dir="ltr">case study</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Pastor-Galindo, et al. [35], Ghazi et al., [36], Riebe et al., [39], Bezas and Filippidou [40]</p>
  </td>
  <td>
  <p dir="ltr">OSINT Tools and techniques in Cyber Security for searching and extracting data to identify traces and patterns of threats (OSINT do not serve like firewalls or antiviruses)</p>
  </td>
  <td>
  <p dir="ltr">Application Design</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Miller [30], Ficco, and Palmieri, [34], et al., Ranade, [37], Alves, et al., [38]</p>
  </td>
  <td>
  <p dir="ltr">use of search engine</p>
  </td>
  <td>
  <p dir="ltr">using large data sets such as reviews, images, pictures, audio, etc.</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p><strong>&nbsp;</strong></p>
  <p dir="ltr">The table above summarises the first category and sub-themes identified in selected studies. It is important to note that this table is brief and does not cover all the tools and techniques; however, it provides a bird's eye view of the different streams of literature on AI and OSINT being used in cyber security.</p>
  <h4 dir="ltr">3.3 Strengths and Limitations of AI Integration with OSINT&nbsp;</h4>
  <p dir="ltr">Artificial intelligence can do more than generate photorealistic images and written content. It is a powerful tool that has found its way into many fields, from medicine to finance, e-commerce, manufacturing, and, unsurprisingly, cybersecurity. At first glance, AI Cybersecurity seems like the perfect solution. After all, AI tools are known for their speed, efficiency and accuracy, which means that they can deal with any cybersecurity threat with the greatest of ease [42]. However, there are some drawbacks to using AI in OSINT for accurate time threat detection. In this section, the focus of the discussion is to identify the benefits and risks of AI-based OSINT for accurate time threat detection and find out whether AI should be integrated into this field or not [43].</p>
  <p dir="ltr">Firstly, it is critical to understand the way AI fits into OSINT for accurate time threat detection and the modern cybersecurity landscape. Artificial intelligence is not a new concept in cybersecurity, as it has been around since the early 1950s [44]. However, it is only recently that AI tools have become "smart" and powerful enough to be used in many applications, including cybersecurity. For example, many antivirus programs rely on AI to detect malware and other anomalies that could threaten a network and system. The main reason is that these programs can learn and improve over time with the help of AI and machine learning, which is a significant benefit [45].</p>
  <p dir="ltr">However, there are also limitations, such as the fact that it might take a while for an AI-based cybersecurity program to figure out how to deal with a specific computer virus when it first comes into contact with it. However, the next time it does, it will know what to do to fix the problem in no time. The more a network security AI tool comes into contact with various malicious programs, the more it learns about them and the more effectively it removes them from the system [47]. Focusing on the benefits of AI in cybersecurity, general literature indicates that artificial intelligence and cybersecurity often go hand in hand for several good reasons. Following are just some of the advantages of using AI for system and network security.</p>
  <h5 dir="ltr">3.3.1 Anomaly detection and behaviour analysis over time&nbsp;</h5>
  <p dir="ltr">With AI security, professionals can deploy advanced protection against all threats, including computer viruses, worms, botnets, ransomware, adware, and other malware. That's because AI monitors the systems and user and program behaviours at all times and can quickly identify anything out of the ordinary. It immediately flags any anomalies in network traffic and instantly indicates risks or threats to security. It can detect a threat that would take human hours (if not days) to detect before it has enough time to cause any damage [48].</p>
  <h5 dir="ltr">3.3.2 Analyse large amounts of data</h5>
  <p dir="ltr">Another advantage of combining cybersecurity and AI is that the latter can process and analyse large amounts of data quickly. An AI-based cybersecurity tool can simultaneously monitor different devices, networks, and endpoints, keeping a close eye on everything that happens and detecting threats in real time [49]. Not even an entire team of IT professionals could process such vast amounts of data as quickly and efficiently as AI.</p>
  <h5 dir="ltr">3.3.3 Streamlining cybersecurity processes and operations</h5>
  <p dir="ltr">In theory, the central role of AI in cybersecurity (or any other field) is to streamline and automate all processes. For example, when trying to secure a network, experts would have to do a million little things if they were to approach the task manually [50]. They would have to check emails for spam, scan all the devices that connect to the system and its nodes, check the websites the users visit, assess the devices connected to the network, and manually update all apps and software, among many other manual tasks. Using AI experts can streamline these and other processes [51]. They can continuously monitor your network and efficiently perform time-consuming, repetitive tasks.</p>
  <h5 dir="ltr">3.3.4 Proactive responses</h5>
  <p dir="ltr">As stated above, the main driver of AI's power is its ability to learn and improve over time. This feature can be valuable in the face of evolving cybersecurity threats. Therefore, an AI-based cybersecurity solution can learn to identify even novel threats at an early stage and build defences against them before they can cause damage. It can develop proactive responses, protecting the systems at all times [52]. Rather than helping experts fight off a viral infection, it can prevent it from occurring in the first place. Considering that most AI cybersecurity solutions monitor the network and all activity on it at all times, it's no surprise that they can do more than detect threats. They can also keep an eye out for any potential vulnerabilities that a cybercriminal could exploit. AI solutions can quickly detect how malware exploits existing vulnerabilities in the system and suggest solutions. In many cases, they might even fix them [53].</p>
  <h5 dir="ltr">3.3.5 Reduction of human errors</h5>
  <p dir="ltr">An often overlooked but highly valuable advantage is that AI-based cybersecurity solutions can significantly reduce the risk of human error. They can scan the network and its systems, monitor user and program behaviours, analyse all incoming and outgoing traffic, and automate tedious processes. In this way, AI solutions can up the ante on cyber defences and prevent experts from making mistakes due to fatigue. However, there are also limitations and risks of using AI in cybersecurity. Artificial intelligence is not everything. The use of AI in cybersecurity poses specific risks [54].&nbsp;</p>
  <p dir="ltr">Artificial intelligence can improve the defences against all cyber attacks, from malware to hacking attempts. However, just as security experts can use AI to strengthen their defences, cybercriminals can also use it to enhance their attacks. Criminals can harness the power of AI technology to modify malware and make it more resistant to the defences [53]. For example, they can use AI to create more believable phishing scams or deepfake videos for ransomware attacks. They can use it to find ways to shield malware from detection and make it easier for it to infect and corrupt the files. Once the files get corrupted or lost after a malware attack that an AI-based antivirus program has not stopped, the system owner incurs costs to recover the files [51].</p>
  <p dir="ltr">The entire discussion above is summarised in the following table.</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="84"><col width="110"><col width="108"></colgroup>
  <tbody>
  <tr>
  <td colspan="3">
  <p dir="ltr">Strengths and Limitations of AI Integration with OSINT&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Studies</p>
  </td>
  <td>
  <p dir="ltr">Strengths&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Limitations</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Evangelista, et al. [42], Vegesna and Adepu, Sarker, [43], Sarker, [44], Martineau, et al. [45], Aragon&eacute;s Lozano, et al. [46], Riebe, [47]</p>
  </td>
  <td>
  <p dir="ltr">Anomaly detection and behavior analysis over time&nbsp;</p>
  </td>
  <td>
  <p dir="ltr">Learning/training period for AI algorithm</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Chen, et al. [49]</p>
  </td>
  <td>
  <p dir="ltr">Analyse large amounts of data</p>
  </td>
  <td>
  <p dir="ltr">Lack of accurate data to train algorithms</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Pavanello, et al. [50], Petrović, and M. Jovanović, [51]</p>
  </td>
  <td>
  <p dir="ltr">Streamlining cybersecurity processes and operations</p>
  </td>
  <td>
  <p dir="ltr">Continuous change in data&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Evangelista, et al. [52], Torres, et al. [53]</p>
  </td>
  <td>
  <p dir="ltr">Proactive responses</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Petrović, and Jovanović, [51], Torres, et al. [53], Iturbe, et al. [54]</p>
  </td>
  <td>
  <p dir="ltr">Reduction of human errors</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  </tbody>
  </table>
  </div>
  <h4 dir="ltr">3.4 Research Gaps in Research on Practical Implementation</h4>
  <p dir="ltr">As per the third objective identified in this study, this section is organised to analyse the state of the art of AI and OSINT with a specific focus on implementation and identify potential research gaps that can be used to make recommendations for future research in this field. As discussed earlier, AI and OSINT have spread to various industries and have gained a foothold in different fields; therefore, the practical applications are diverse. Hence, this section retrieves information from diverse studies which focus on the implementation of AI and OSINT and provides current research following the research gap in each area.&nbsp;</p>
  <h5 dir="ltr">3.4.1 Leveraging AI and Machine Learning to Improve Security</h5>
  <p dir="ltr">One of the areas of implementation for AI and OSINT is security improvements. A review of recent studies shows that the digital age is ever-evolving, and organisations (both commercial and non-commercial) are facing innumerable challenges in security, which are credible threats to their survival. Integrating AI and ML into security measures can be a vital protection against threats [51]. The majority of the studies discuss the way these technologies have automated the process of detecting potential risks. There are also studies focusing on how AI based on security measures such as OSINT are able to learn and adapt to emerging threats with more agility than traditional processes [42]. The ML can react and trigger countermeasures when a threat is detected [44]. For instance, AI and ML can enable OSINT to automatically isolate infected segments of the network and prevent malware from spreading to the entire system. In this area, the central joint research gap identified in studies is the need for more research on the efficacy of AI-powered OSINT in blocking diverse threats.</p>
  <p dir="ltr">However, the authors also identify that there are research gaps. In this area, one of the joint research gaps is the need for more research on how cybercriminals are countering security measures. There is also a need to research how AI can improve OSINT in monitoring the evolution of security attacks.&nbsp;</p>
  <h5 dir="ltr">3.4.2 Predictive threat analysis</h5>
  <p dir="ltr">Another typical implementation of AI in cybersecurity is the potential of algorithms to handle large data sets (which humans cannot handle) for prediction and identification of potential breaches in security [43]. For instance, AI-enabled systems are able to monitor traffic in the network on a timely basis and flag abnormal patterns, which could be a cyber attack(s) such as a distributed denial of service (DDoS) [36]. The central research gap in this area is in the use of sophisticated algorithms and large data sets. As the amount of data grows, there will be ample research opportunities to find new insights and predictive models.</p>
  <h5 dir="ltr">3.4.3 Behavioral Analysis</h5>
  <p dir="ltr">User behaviour analyses are also standard in research studies. Researchers show that AI can detect anomalies and make suggestions about compromised security mechanisms [51]. For instance, if one of the authenticated users accesses the system from an unusual location, AI-based OSINT can detect the incidence and impose additional measures for authentication [45]. The research gap in this area is to evaluate the effectiveness of different additional measures for authentication to ultimately find the most effective measure(s).</p>
  <h5 dir="ltr">3.4.4 Intelligent Threat Hunting and Improved Incident Response</h5>
  <p dir="ltr">AI using ML algorithms can uncover hidden threats and thus enable a proactive approach to threat hunting. This is in line with analysing large datasets where threats are more accessible to hide [38]. For instance, AI-based OSINT is able to identify subtle and persistent threats that may evade basic security mechanisms [46]. The potential research area in this field is conducting experiments on virtual systems with mock hidden threats and training algorithms for practical purposes.&nbsp;</p>
  <p dir="ltr">Incidence response is also a common research area for AI-based OSINT tools. These tools aim to assist in the agility of incident response as they can analyse the causes of a breach faster and suggest standardised, automated solutions and evidence-based courses of action [55]. Ultimately, the downtime will be reduced [47]. In this aspect, the research gaps identified include evaluating downtime periods, evaluating agility, and measuring success rates of algorithms.&nbsp;</p>
  <h5 dir="ltr">3.4.5 Security Orchestration</h5>
  <p dir="ltr">Unlike humans, AI can coordinate simultaneously with multiple security tools and use them in unison, thus streamlining response against threats [37]. This can be visualised as a symphony which is produced by an orchestra using different instruments in harmony [57]. So, AI can be an orchestra that creates a symphony of security mechanisms to counter threats [48]. The research gap in this context is related to developing sophisticated algorithms that can manipulate mechanisms and test them against known threats.&nbsp;</p>
  <p dir="ltr">In the dynamic world of cybersecurity, incorporating OSINT threat intelligence into security operations is not only beneficial but also imperative for the survival of technology users [56]. This integration allows organisations to proactively identify, assess, and mitigate potential threats before they can impact business operations [32]. By leveraging data-driven insights, security teams can shift from a reactive to a proactive stance, adapting their defence mechanisms to the ever-evolving threat landscape [48]. A number of studies have focused on the potential of OSINT in this aspect of cyber security and identified various opportunities for more research.</p>
  <h5 dir="ltr">3.4.6 Contextual awareness for Real-time threat analysis</h5>
  <p dir="ltr">OSINT can improve detection capabilities by adding context to raw data. This could involve correlating threat data with internal logs to identify suspicious patterns [49]. For example, a threat intelligence feed indicates an increase in phishing attempts targeting a specific industry. In that case, security operations can increase scrutiny of incoming emails and educate employees accordingly [50]. In this context, the researchers have the opportunity to conduct research on different industries and provide generic and customised solutions. OSINT can use threat feeds in real-time to analyse potential threats before and during they emerge. For instance, if a new malware variant is reported, security operations can immediately check their systems for related indicators of compromise (IoCs) and take preventative action [49]. In this regard, researchers have designed IoCs and recommend research to improve current and develop new IoCs.</p>
  <h5 dir="ltr">3.4.7 Strategic Planning and Collaborative Defence</h5>
  <p dir="ltr">AI uses ML and historical threat intelligence to inform strategic security planning. Analysing past incidents and trends can help predict future attacks and guide the development of solid security policies [33]. For example, if data breaches in an industry are often related to weak authentication protocols, implementing multi-factor authentication becomes a priority [51]. Similar to previous research gaps, there are opportunities to find standard and industry-specific weak policies and make relevant suggestions. Unlike humans, AI has the potential to participate in large, complex information-sharing platforms with other organisations to gain a collective defence advantage [48]. When a similar company encounters a new attack vector, sharing this information allows organisations to strengthen their defences against similar tactics [38]. Similarly, AI can integrate threat intelligence with security automation tools to enable a rapid response to identified threats. Automation can speed up the quarantine of affected systems, as seen when a ransomware attack is detected, minimising potential damage [52].</p>
  <p dir="ltr">Based on the discussion above, this category and relevant themes are presented in the following table.</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="58"><col width="96"><col width="148"></colgroup>
  <tbody>
  <tr>
  <td colspan="3">
  <p dir="ltr">Research Gaps in Research on Practical Implementation</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Studies</p>
  </td>
  <td>
  <p dir="ltr">Practical implementation</p>
  </td>
  <td>
  <p dir="ltr">Research Gap</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Petrović and Jovanović, [51], Evangelista, et al. [42], Sarker, [44]</p>
  </td>
  <td>
  <p dir="ltr">Automation</p>
  </td>
  <td>
  <p dir="ltr">Evaluate counter-security measures of criminals</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Vegesna and Adepu [43], Ghazi et al. [36]</p>
  </td>
  <td>
  <p dir="ltr">Predictive threat analysis</p>
  </td>
  <td>
  <p dir="ltr">using sophisticated algorithms and new large data sets (as they emerge)</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Martineau, et al. [45], Petrović and Jovanović, et al. [51]</p>
  </td>
  <td>
  <p dir="ltr">Behavioural Analysis</p>
  </td>
  <td>
  <p dir="ltr">effectiveness of different additional measures triggered by OSINT&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Alves, et al. [38], Aragon&eacute;s Lozano, et al. [46], Riebe, [47], Kamal, [55]</p>
  </td>
  <td>
  <p dir="ltr">Intelligent Threat Hunting for improved incidence response</p>
  </td>
  <td>
  <p dir="ltr">studies to train algorithms and evaluate downtime, agility, and success rates&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Kanta, et al. [32], Ranade, et al. [37], Rodriguez and Okamura, [48], Song, et al. [56], Sarker, et al. [57]</p>
  </td>
  <td>
  <p dir="ltr">Security Orchestration</p>
  </td>
  <td>
  <p dir="ltr">develop new algorithms for orchestration</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Chen, et al. [49], Pavanello, et al. [50]</p>
  </td>
  <td>
  <p dir="ltr">Contextual awareness for Real-time threat analysis</p>
  </td>
  <td>
  <p dir="ltr">customised algorithms for industries, new indicators of compromise (IoCs)</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Mittal, et al. [33], Alves, et al. [38], Rodriguez and Okamura, [48], Petrović and Jovanović, [51], Evangelista, [52]</p>
  </td>
  <td>
  <p dir="ltr">Strategic Planning and Collaborative Defence</p>
  </td>
  <td>
  <p dir="ltr">common and industry-specific weaknesses&nbsp;</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p><strong>&nbsp;</strong></p>
  <h4 dir="ltr">3.5 Potential Solutions and Research Directions as per Future Industrial Trends&nbsp;</h4>
  <p dir="ltr">Based on the discussion of various research gaps in the previous section, it is clear that there are diverse opportunities for OSINT researchers to conduct more studies. However, in the last section, research gaps and research opportunities were based on an analysis of the implementation of AI-powered OSINT. In this section, the research opportunities are presented within the context of future trends in security threat intelligence as an industry. In the ever-evolving realm of cybersecurity, staying ahead of potential threats is not just a matter of strategy but of survival; businesses are constantly vulnerable, particularly if they have limited resources to allocate to security. The threat intelligence landscape is changing, with advances in technology and shifts in attacker tactics driving new trends that businesses need to be aware of to safeguard their future.</p>
  <h5 dir="ltr">3.5.1 Automation and Machine Learning</h5>
  <p dir="ltr">A significant trend in the OSINT industry is that cyber threats will continue to become increasingly sophisticated, and the use of automated systems and machine learning algorithms for detection and response to threats will also spread by leaps and bounds [42]. For instance, anomaly detection systems will be developed to identify abnormal patterns that may indicate a breach [39]. Machine learning will be helpful in predicting and preventing zero-day attacks as it can analyse large data sets and recognise potential vulnerabilities before cyber criminals can exploit them [52]. Therefore, it is recommended that researchers focus on these areas and design new ways of automation using machine learning and integrating OSINT in these systems.</p>
  <h5 dir="ltr">3.5.2 Threat Intelligence Platforms (TIPs)</h5>
  <p dir="ltr">Tips are also a significant trend in this industry. These platforms are becoming more user-friendly and increasingly integrated to allow organisations to leverage intelligence from shared information accumulated from various sources [34]. A TIP can correlate data from diverse sources, offering a more comprehensive perspective of the threat landscape [46]. For instance, a company can use TIP to combine threat data specific to the industry and compare it with global threat intelligence to formulate a tailored security response [53]. This is an essential trend in the future of this industry, and hence, researchers are encouraged to endeavour creative ideas and innovative methods to design new TIPs.</p>
  <h5 dir="ltr">3.5.3 Collaborative Security and Insider Threat</h5>
  <p dir="ltr">As discussed earlier, collaborative security is an essential feature of the future vision of the OSINT industry. In the future, there will be an increase in collaborative efforts by organisations, even their competitors, who will be sharing threat intelligence for mutual benefit [36]. This collective strategy for defence can be seen in various sectors already, including but not limited to financial services, where business organisations are sharing indicators of compromise (IoCs) to fight widespread fraud [54]. A relevant trend to regulatory compliance is insider threat detection using AI-based OSINT. As remote work becomes the norm, the risk of insider threats increases. Organisations will need to invest in solutions that monitor user behaviour and access patterns to identify potential insider threats [47]. For example, an employee accessing sensitive data outside of regular business hours could trigger an alert for further investigation [56]. There is scant literature on this trend, but rigorous research is required to integrate psychology, behaviour, and cyber technology.&nbsp;</p>
  <h5 dir="ltr">3.5.4 Cloud security</h5>
  <p dir="ltr">With the shift to cloud services, organisations need to focus on securing their cloud infrastructure. This includes using cloud-native security tools that provide visibility into cloud environments and protect against misconfigurations, which are a common cause of cloud data breaches [57]. In this area, researchers need to realise that there is scant evidence about the integration of OSINT in cloud security, and compatibility issues are frequent. There are ample research opportunities in this regard.</p>
  <h5 dir="ltr">3.5.5 Regulatory Compliance</h5>
  <p dir="ltr">The regulatory environment is evolving, and regulations such as GDPR and CCPA are emerging, with strict guidelines and rigorous data protection requirements being imposed [43]. The organisations foresee the need to align their systems for threat intelligence with legal requirements [41]. This compliance is aimed not only at protecting customer data but also to make sure that threat intelligence systems comply with privacy laws [55]. In this area, the foremost research opportunity lies in collaboration with legal experts and defining rules for compliance during the design of OSINT.</p>
  <p dir="ltr">The table below summarises the discussion of themes and relevant suggestions in this category.</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="58"><col width="115"><col width="129"></colgroup>
  <tbody>
  <tr>
  <td colspan="3">
  <p dir="ltr">Potential Solutions and Research Directions as per Future Industrial Trends&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Studies</p>
  </td>
  <td>
  <p dir="ltr">OSINT Industry trend</p>
  </td>
  <td>
  <p dir="ltr">Research Direction</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Riebe, et al, [39], Evangelista, et al, [42], Evangelista, et al, [52]</p>
  </td>
  <td>
  <p dir="ltr">Automation and machine learning</p>
  </td>
  <td>
  <p dir="ltr">design new ways of automation</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Ficco and Palmieri, [34], Aragon&eacute;s Lozano, et al, [46], Torres, et al, [53]</p>
  </td>
  <td>
  <p dir="ltr">Threat Intelligence Platforms (TIPs)</p>
  </td>
  <td>
  <p dir="ltr">new and innovative TIPs</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Ghazi, et al, [36], Riebe, [47], Iturbe, et al, [54], Kamal, [56]</p>
  </td>
  <td>
  <p dir="ltr">Collaborative Security and Insider Threat</p>
  </td>
  <td>
  <p dir="ltr">design new IoCs and integration of psychology in cyber behaviour&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Sarker, et al, [57]</p>
  </td>
  <td>
  <p dir="ltr">Cloud Security</p>
  </td>
  <td>
  <p dir="ltr">scant evidence about the integration of OSINT in cloud security and compatibility issues&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p dir="ltr">Khurana, et al, [41], Vegesna and Adepu, [43], Kamal, [55]</p>
  </td>
  <td>
  <p dir="ltr">Regulatory Compliance</p>
  </td>
  <td>
  <p dir="ltr">integrate legal rules and compliance in the design of OSINT</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <h3 dir="ltr">3.6 Chapter Summary&nbsp;</h3>
  <p dir="ltr">Based on the analyses above, there is ample research about the state of implementation and application of AI and OSINT in cybersecurity and a diverse set of tools and techniques have been developed. Each of these tools and techniques has unique strengths and limitations which are applicable in specific contexts. The main strength identified in the discussion above is the ability of AI and OSINT to handle large datasets, automate, and have faster speed than humans. The main limitation is that these tools and techniques are still evolving, and therefore, more research and experiments are required. Another standard limitation is that with the evolution of security measures, counter-security and cyber attacks are also evolving. Based on the discussion of strengths and limitations, by harnessing the power of AI and machine learning, organisations can not only strengthen their defences but also gain a strategic advantage in the threat landscape. The key lies in intelligently layering these technologies within their security infrastructure to create a resilient and responsive system. As these technologies continue to mature, they will become indispensable tools in the arsenal of any startup looking to navigate the treacherous waters of cyber threats.</p>
  <p dir="ltr">Furthermore, based on a discussion of future trends for research, by integrating these elements into the fabric of security operations, organisations can not only navigate but also anticipate the threat landscape, turning intelligence into a powerful tool for cyber resilience. The key is not just to gather intelligence but to integrate it in a way that improves the overall security posture, making the organisation not only aware but prepared and responsive to the threats it faces. By understanding the trends in the OSINT industry, organisations and researchers can develop proactive and dynamic approaches to security threat intelligence, ensuring they are prepared for the cyber threat challenges of tomorrow. The key is not just to embrace new technologies but to foster a culture of security awareness and collaboration that can adapt to the changing tides of the digital world.</p>
  <h3 dir="ltr">4. Discussion</h3>
  <h4 dir="ltr">4.1 Introduction&nbsp;</h4>
  <p dir="ltr">This study aimed to assess the efficacy of AI-driven OSINT approaches in cybersecurity. The purpose of the review was to focus on identifying gaps in the current literature and to evaluate how these tools can be optimised for enhanced real-time threat detection. In order to achieve this aim, this study identified various objectives and conducted a comprehensive review of the literature to accomplish each of these objectives individually, as presented in the previous chapter. This chapter presents a discussion of the results and shows how these objectives have been achieved, referring to the results and interpreting the implications of the results. Similar to the results chapter, this chapter is also organised in accordance with the research objectives and starts with a discussion on the results of the first objective, followed by a discussion of the second objective, and ends with a summary of the debate.&nbsp;</p>
  <h4 dir="ltr">4.2 Discussion&nbsp;</h4>
  <p dir="ltr">The results of the first objective (see section 4.2) show that the application of AI in OSINT in cybersecurity has demonstrated significant advances and has also raised critical concerns. The results showed that AI-powered tools such as OSINT can help improve network security, simulate cybersecurity scenarios, and promote cybersecurity education [58]. However, this same capability can be exploited by cybercriminals to develop malicious software, create fake websites, and carry out more sophisticated social engineering attacks. This duality is mentioned in various research [59] where it is highlighted that AI in OSINT can be exploited to distribute false information on social media and manipulate public opinion, increasing cybersecurity challenges [60].</p>
  <p dir="ltr">The results also show that AI in OSINT approaches can reproduce real scenarios, facilitating the development of advanced tools to detect zero-day vulnerabilities and perform pen-testing. However, human oversight remains essential to ensure accuracy and address false positives and negatives [61]. Additionally, the increasing sophistication of AI in OSINT in creating ransomware [62], as well as phishing scams and deepfakes, poses a significant challenge, as conventional detection techniques are becoming obsolete, as discussed in [63]. Furthermore, the ability of AI in OSINT to analyse malicious code and detect malicious behaviours and characteristics, even when employing obfuscation and encryption techniques, is a promising application [64]. AI in OSINT can also automatically generate new security vulnerabilities and attack scenarios, facilitating simulation and improving defence and response capabilities [65].</p>
  <p dir="ltr">The results related to the second objective (see section 4.3) focus on the strengths and limitations of applications and techniques. The results show that the applications of AI in OSINT in cybersecurity are varied and promising. According to the review, using text and code analysis and generation capabilities, automatic encryption and secure transmission of information can be achieved [66]. Furthermore, OSINT tools can analyse large volumes of threat intelligence data, helping to anticipate future threats to network security [67].&nbsp;</p>
  <p dir="ltr">AI-powered OSINTs enable faster identification of vulnerabilities and automation of test scenario generation, reducing the need for manual intervention and enabling more thorough assessment [68]. It can simulate the behaviour of real attackers by learning from patterns and adapting to new tactics, providing a more realistic understanding of how adversaries may act [70].</p>
  <p dir="ltr">Malware identification has been improved by research methodologies based on Generative Adversarial Networks. Various tools mentioned in the reviewed studies provide automated network security, malware protection, and privacy compliance monitoring services [71]. OSINTs are also used to generate email messages that trick scammers, wasting their resources and prolonging their interaction with them. Generative AI can analyse large amounts of data to identify patterns and anomalies that indicate the presence of threats, generating reports and alerts based on these analyses [72]. AI in OSINT enables defenders to accelerate and automate the incident response process, analyse cybersecurity incidents, generate reports on incidents and threats, and make strategic recommendations [73]. Generating persuasive honeypots to lure attackers and analyse their methods is another significant application of AI in OSINT [74].</p>
  <p dir="ltr">However, the review also shows that there are significant risks and limitations. The implementation of Generative Artificial Intelligence in cybersecurity faces numerous ethical challenges, risks, and limitations. The exposure of personal information and threats to social security are critical concerns. Generative models can increase the risks of information and political manipulation, affecting electoral processes [75]. The use of AI in OSINT raises ethical and legal issues, including the possibility of biased results if models are trained with non-representative data, which can lead to unfair test scenarios or failure to detect specific vulnerabilities [76].</p>
  <p dir="ltr">In addition, the use of large amounts of data to train AI models raises concerns about privacy and data security, as sensitive information can be exposed. Collecting large volumes of data to train AI models raises concerns as sensitive information can be exposed. AI systems require a large amount of computational resources, which can have an adverse effect on the ecosystem. Challenges also include the possibility of adversaries injecting perturbations into the input data to replicate models or impair performance [77]. Privacy concerns include model theft and poisoning, as well as the persistence of biases present in training data. Lack of transparency and control by companies and users is also a significant challenge, which can result in unfair or discriminatory decisions. Large-scale language models (LLMs) present several challenges and limitations in cybersecurity. These models focus on generating responses but do not always prioritise accuracy, which can result in the generation of incorrect or misleading responses, known as "hallucinations" [78].</p>
  <p dir="ltr">Protecting Personally Identifiable Information and other sensitive data is vital to maintaining user security and trust. Finally, researchers, developers, and policymakers must work together to address these challenges and ensure the integrity and security of AI-based systems. The implementation of emerging AI security frameworks guides security teams on best practices, controls, recommendations, and procedures [79].</p>
  <p dir="ltr">This review is an extensive and comprehensive evaluation of the literature and shows the state of generative artificial intelligence-based approaches for OSINTs in cybersecurity. Although previous studies [systematic reviews] have conducted thorough reviews, these have focused exclusively on the security and privacy of generative AI. Other studies, such as [80], have focused on specific tools such as ChatGPT. In contrast, research such as [81] has explored applications in particular fields, such as cybersecurity threats, pen testing, ransomware, and digital forensics [82]. This research covers a broader perspective, providing a comprehensive overview of the applications, techniques, ethical challenges, risks, and limitations of generative AI in cybersecurity. This work is limited to answering the research questions posed, providing an overview of the current state, applications, techniques, ethical challenges, risks, and limitations of generative AI in cybersecurity and presenting research gaps. However, there are particular areas where generative AI is at its peak, such as the Internet of Things (IoT), network physical layer, pen-testing, ransomware, and digital forensics, which have yet to be explored in depth in this review.</p>
  <p dir="ltr">In the third objective, the main requirement was to identify critical gaps in the literature with a specific focus on the implementation of AI-powered OSINTs. A significant trend identified in OSINT is security improvements [83]. Based on the review, the main research gaps and opportunities are to increase research attempts focusing on analysing and countering the security measures of cyber criminals. It is further asserted that there is a need to investigate the way AI can make improvements in OSINT in terms of monitoring and understanding the trends in security attacks.</p>
  <p dir="ltr">One of the standard implementation aspects of AI and OSINT in cybersecurity is using algorithms to handle massive data sets that are beyond the capacity of humans [84]. The algorithms can predict and identify potential sources of breaches and suggest improvements in security [85]. In this area, the central research gap is in conducting studies that can apply very sophisticated algorithms to handle large data sets. The amount of digital data will continue to grow, offering ample opportunities for researchers to use predictive models and find new insights [86]. The researchers can apply AI and ML to enable the security system to react and trigger countermeasures based on their analysis of hidden threats in the data [87]. Similarly, a research gap has also been identified in these types of current studies. More research is needed to evaluate the efficacy of AI-powered OSINT in blocking diverse threats.</p>
  <p dir="ltr">Another significant stream of literature identified from the review is studies focusing on user behaviour [88]. These studies have contributed comprehensive scientific evidence, but there is a gap in this area that there needs to be more evaluation of the effectiveness of additional measures that OSINT trigger to enhance authentication mechanisms and compare them with other security measure(s) [89]. One of the purposes of AI-based OSINT is to detect and uncover hidden threats and design and implement a proactive system to manage threats [90]. There is a potential research opportunity in this field that only scant studies are available experimenting with virtual systems and using mock hidden threats to train algorithms and then deploy them in practical scenarios. Another common trend in OSINT studies is the studies on Incidence response [91]. Potential research opportunities in this aspect are to evaluate changes in downtime periods, agility, and success rates of OSINT and AI algorithms.</p>
  <p dir="ltr">AI can aid humans in cybersecurity because they are able to simultaneously coordinate with multiple tools for security and manage them in unison [92]. This way, OSINT can streamline response against threats [93]. Based on the review, there is a research gap in this context, so they can develop practical algorithms and manipulate security mechanisms while testing them against known threats. There are also studies which use OSINT to analyse threat feeds in real-time and assess potential threats in real-time [94]. The studies in this regard designed IoCs but also acknowledge that improvements are needed in current IoCs.&nbsp;</p>
  <p dir="ltr">Another stream of OSINT literature is focused on improving detection capabilities from large raw datasets. The studies in this context are generic and theoretical and lack practical case studies [95]. Therefore, the gap in this regard is in conducting industry-specific case studies and offering customised and tailored solutions. Some studies focus on strategic security planning and the use of AI and ML to analyse historical threat intelligence [96]. The research gap in this context is similar to the previously mentioned lack of case studies, which industry-specific case studies can fill.</p>
  <p dir="ltr">The fourth objective of this study was to propose solutions and suggest future research opportunities focusing on major trends in the OSINT industry. Based on the review, it can be observed that a vital industry trend is that the sophistication of cyber threats and criminals will likely increase in response to developments in the security industry. The reports on industry trends also show that there will be an increase in the automation of systems and machine learning algorithms with the aim of detecting and countering threats [98]. Hence, researchers are encouraged to focus on this trend and develop creative new designs for automation.</p>
  <p dir="ltr">A significant trend is the emergence and likely strong foothold in TIPs. Recent studies show that these platforms have improved in terms of user-friendliness and are welcome to be integrated into existing security systems. The organisations welcoming TIPs aim to leverage the intelligence platforms by sharing information and gaining mutual benefits. It is also a critical future trend in this industry and, therefore, presents ample research opportunities where researchers can use their creative ideas to design new innovative methods in TIPs.&nbsp;</p>
  <p dir="ltr">The review also showed that one of the future trends is the increase in collaborative efforts. Disparate organisations will share disparate information input data sets for security OSINTs, and this sharing will provide mutual benefits. Researchers can explore the ways in which information can be shared, filtered, and protected to encourage collaborative efforts. Another important trend in this industry will have a significant effect on the development of the regulatory environment and regulations (recent examples are CCPA and GDPR). It is expected that stricter guidelines and data protection regulations will be created across the globe at the regional and state level. Hence, the primary research opportunity in this regard is for researchers to collaborate with legal experts to understand and define how AI-based OSINTs can ensure compliance. There is also the risk factor that as remote working becomes the new normal, the insider threat risk will increase [98]. There are only limited studies on insider threat management using AI and OSINT. Therefore, rigorous research is needed to address this concern and apply AI and OSINT to this area of cybersecurity.</p>
  <h4 dir="ltr">4.3 Summary&nbsp;</h4>
  <p dir="ltr">This study has conducted a systematic literature review on the current state of the art in the application of generative artificial intelligence in the cybersecurity field, providing a comprehensive overview of the current state, promising applications, ethical challenges, risks and associated limitations. The main findings highlight that AI in OSINT is being used both in the implementation of defensive strategies and in the execution of offensive actions in the cybersecurity field. Its ability to generate complex content improves the simulation of security scenarios and the detection of vulnerabilities. Still, cybercriminals can also exploit it to develop advanced social engineering techniques and generate malicious code, increasing privacy and data security risks. This review and elaborated research contribute to the understanding of how AI in OSINT can transform the cybersecurity field. By addressing both beneficial applications and risks and challenges, a solid foundation is provided for future research that can explore more specific areas and develop theoretical and normative frameworks for the responsible use of AI. There are various streams of literature, but the common aim is to apply AI and OSINTs in the cybersecurity area and make improvements. The studies in this area are very diverse in terms of aims and methodologies, and therefore, there are ample research opportunities in each stream, as discussed in this chapter. Thus, researchers are encouraged to use this review to identify research gaps and essential trends in this study industry and apply their skills and creativity to contribute towards literature as well as practical implementation.</p>
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meta_description:
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slug: to-what-extent-does-the-volatility-in-the-us-market-correlate-with
title: To What Extent Does the Volatility in the U.S Market Correlate With
academic_level: Graduate
subject: Economics
type_of_paper: Dissertation
citation_style: APA
meta_title: To What Extent Does the Volatility in the U.S Market Correlate With
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created_at: 2025-06-16 14:21:43
updated_at: 2026-01-14 15:52:24
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content:
  <h2>To What Extent Does the Volatility in the U.S Market Correlate With and Influence Key Economic Indicators, Namely GDP, Unemployment Rates and Inflation?</h2>
  <h3>Abstract&nbsp;</h3>
  <p><strong>Aim:</strong> The aim of this research paper is to determine the impact of stock market volatility over the economic indicator, namely GDP.</p>
  <p><strong>Method:</strong> The data for stock market volatility (S&amp;P 500) was considered from investing.com while the macroeconomic indicators were obtained from World Data Bank. The dependent variable which was considered is GDP of USA. On the other hand, stock market volatility was the independent variable of this study. In addition, the FDI to GDP ratio, export of goods and services to GDP ratio, unemployment and inflation was considered as the control variables of this study. The log for GDP values was taken to normalise the values. The unit root testing was carried out using the ADF. After the determination and confirmation regarding the presence of unit root in the time series data, the ARDL model was applied due to the presence of unit root.</p>
  <p><strong>Findings:</strong> The results determined that there is significant influence of stock market volatility, and unemployment rate over the GDP of USA. While, the effect of FDI/ GDP ratio, exports of goods and services/GDP ratio and inflation was determined to be insignificant over the GDP of the country.</p>
  <p><strong>Key Words:</strong> GDP, unemployment rate, inflation, foreign direct investment (FDI), ARDL, ADF</p>
  <h3>Introduction&nbsp;</h3>
  <p>Alongside other public entities, the stock exchanges functions as the forum for security&rsquo;s exchange from semi-governmental and governmental bodies (Chauhan, Gupta and Shridhar, 2023). In this manner, they serve to enable the transfer of bonds and equities that have been issues by organisations with shared ownership. It has been argued in the study of Gulyamov (2021) that employing various mechanisms such as over the counter sales and free competition, the stock market serves as a platform for exchange of shares. In contrast to this, the transactions might also be applicable to the government securities. On the other hand, agreements are established at valuations by the individuals comprising the stock exchanges those are considered equitable on the basis of underlying tenets of demand and supply within the realm of commerce. The securities market is also considered as the financial marketplace which allows the exchange of various bonds, debentures, shares and equities. As per the study conducted by Ravikumar and Saraf (2020), across diverse international stock exchanges, equities are exchanged on diurnal basis.&nbsp;</p>
  <p>In terms of promoting the economic development and growth, the procurement of sufficient liquidity is regarded as the pivotal factor (Aripin, Wibowo and Ariyanti, 2024). The stock market forum is open for the public, in which the mechanism of vending and purchasing allows trading of corporate equities at a consensually determined valuation. The stock market of USA can have influence over the global and domestic economic conditions, investor sentiment, political developments and corporate earnings (Sun, Bao, and Lu, 2021). It is due to the reason that the stock market volatility can result in opportunities and risks for the investors. Thus, it is important for the investors to analyse the company fundamentals, thorough research, conduct risk management strategies and track market trends when investing in major stock indices such as S&amp;P 500. It is due to the reason that key economic indicator such as GDP, take centre stage in this exploration, providing a comprehensive overview of economic performance.</p>
  <h3>Literature Review</h3>
  <h4>Impact on GDP</h4>
  <p>It has been argued in the study of Fatmawati (2022) that the total value of services and goods those are produced in the country over a specific period is known as GDP. The study of Giri and Joshi (2017) determined positive correlation among the economic growth and stock prices. On the other hand, the study by Alexius and Sp (2018) also pointed out the relationship between stock prices and GDP. In contrast, Huy et al. (2021) argued that there is no positive relationship between economic growth and stock prices. Therefore, the following hypothesis is developed to test the effect of stock market volatility on US GDP:</p>
  <p>H1: There is a significant influence of stock market volatility over the GDP of USA.</p>
  <p>On the other hand, an increase in a country's exports leads to a positive trade balance and reduces imports. In this way, the value of currency is increased due to the increase in exports of the country. As per the study of Okyere and Jilu (2020), export of goods and services has a positive association with the GDP of the country. In this manner, considering the evidence on FDI and export of goods and service over the GDP of the country, several hypothesis have been developed and presented below:</p>
  <p>H2a There is a significant influence of FDI over the GDP of USA.</p>
  <p>H3a: There is a significant influence of export of goods and services over the GDP of USA.</p>
  <p>It has been argued in the study of Angelina and Nugraha (2020) that inflation is considered as the rate at which there has been an increase in general level of services and goods. Indicating a growing economy, moderate inflation is usually desirable while the purchasing power is eroded due to high inflation and has effect on GDP. On the other hand, the value of money is also reduced due to high inflation rate and can lead to economic instability as the GDP is reduced. In this manner, the stock market prices should provide hedge against the inflation as the profits and revenues of the company must grow after the period of adjustment with inflation which will also result in increase of GDP (Moyo and Tursoy, 2020). In contrast to this, the varying influence of the inflation over the stocks tend to improve the risk premium and equity market volatility and overall GDP of the country (Basri et al., 2022). Therefore, the effect of inflation should be determined over the GDP of USA in order to recognise the varying impact and association among both the variables.&nbsp;</p>
  <p>In addition to the stock market volatility, the FDI can also have huge effect over the inflation rate of the country and negatively influence the GDP. As per the study conducted by Fernandez and Joseph (2020), the negative association of the FDI with inflation is on the basis of the fact that it acts as a tax on investment as there is increase in cost of transportation, capital and labour due to inflation which results in less profitable investment for the foreign investors. The study of Mostafa (2020) argued that there is negative association among the inflation and GDP of the country. In this manner, the effect of inflation is essential to be determined over the GDP of USA. On the other hand, the export of goods and services can have influence over the inflation because of the supplies pattern of the domestic services and goods to the household consumers (Olusola et al., 2022). It has been argued in the study of Stievany and Jalunggono (2022) that increase in the exports of the country results in the reduction in inflation rate while increasing the GDP. Thus, considering the theoretical evidence with respect to the effect of the inflation over GDP, following hypothesis have been developed:</p>
  <p>H4a: There is a significant influence of inflation over the GDP of USA.</p>
  <p>The study of Alenda-Demoutiez and M&uuml;gge (2020) defined the unemployment rate as the one which measures the percentage of labour force which is actively seeking employment or is jobless. The strong labour market is denoted by the lower unemployment rates which leads to healthier economy or GDP and higher consumer spending. The stock market volatility can have uncertainty in the profits and revenues of the company due to which company&rsquo;s employee turnover rate is increased. In this manner, the unemployment rate in the country is increased due to higher employee turnover while reducing the GDP. It has been argued in the study of Moradi et al. (2021) that stock market volatility can have negative influence over the unemployment rate of the country.&nbsp;</p>
  <p>The FDI results in the increase in investment and employment opportunities for the country. As per Vasa and Angeloska (2020), there is a negative relationship among the FDI and unemployment rate of the country. It is due to the reason that with the increased FDI, the unemployment rate of the country is expected to reduce which contributes positively towards the GDP of the country. On the other hand, the study of Edeh et al. (2020) argued that export of goods and services results in creating more employment opportunities as increase in exports results in the increase of production of goods and services due to which more labour force is required. In this manner, following hypothesis have been developed to determine the influence of unemployment rate over the GDP of USA:</p>
  <p>H5a: There is a significant influence of unemployment over the GDP of USA.</p>
  <h4>Conceptual Framework</h4>
  <p>The below figure 1 shows the conceptual framework of the study. In this manner, it can be determined that stock market volatility is the independent variable while the dependent variable include GDP. However, the control variables include unemployment rate, inflation FDI and export of goods and services which have been tested along the stock market volatility in all there models.&nbsp;</p>
  <p><img style="display: block; margin-left: auto; margin-right: auto;" src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXdNrssKVKeR1snfXP3QPj5lcG9J6N4hhwzTy2LDOUqqRq-e5OhWa6bQIIxDaHGM89MHcEMz1BTMDVkRaUsVcDRQjzVMeMZ2A-e81rN2fgDbMrboOuY4tLzSAe1fEK3PXDixyqybCAn0HZdQN8T7kBM?key=Cms_IR_sj_7wxDWOyPVWmg" alt="Conceptual Framework" width="624" height="362"></p>
  <p>Figure 1 Conceptual Framework</p>
  <h4>Theoretical Framework</h4>
  <p>The prediction of returns is crucial for the investors which can be done in association with the macroeconomic indicators in order to get the broader picture. Therefore, arbitrage pricing theory is adopted which is a pricing model through which a return is predicted using the association among macroeconomic factors and expected returns (Dewri and Noyel, 2024). As per the study conducted by Shah, Raza and Hashmi (2020), the arbitrage pricing theory is the asset pricing theory which posits that the returns of the assets can be forecasted through the linear relationship of expected returns of an asset and the macroeconomic factors that influence the risk of an asset. In this way, the arbitrage pricing theory model is considered in this study for determining the influence of stock market volatility over the GDP.&nbsp;</p>
  <h3>Research Questions/Hypotheses</h3>
  <p>The research question which has been developed in this research paper is provided below:</p>
  <p>To what extent does the volatility in the U.S market correlate with and influence key economic indicator, namely GDP?</p>
  <p>In terms of the hypotheses, independent variable is stock market volatility while the dependent variables is GDP. In addition to this, several control variables have also been included in the model which involve inflation, unemployment rate, FDI and export of goods and services.&nbsp;</p>
  <div dir="ltr" align="center">
  <table><colgroup><col width="47"><col width="680"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>S.No</p>
  </td>
  <td>
  <p>Hypotheses</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>1</p>
  </td>
  <td>
  <p>H1a: There is a significant influence of stock market volatility over the GDP of USA.</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>2</p>
  </td>
  <td>
  <p>H2a There is a significant influence of FDI over the GDP of USA.</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>3</p>
  </td>
  <td>
  <p>H3a: There is a significant influence of export of goods and services over the GDP of USA.</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>4</p>
  </td>
  <td>
  <p>H4a: There is a significant influence of inflation over the GDP of USA.</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>5</p>
  </td>
  <td>
  <p>H5a: There is a significant influence of unemployment over the GDP of USA.</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p><strong>&nbsp;</strong></p>
  <h3>Methodology</h3>
  <p>This study adopted quantitative research design for assessing the influence of stock market volatility over the GDP, inflation and unemployment rate of USA. The previous literature was used for the purpose of developing the hypotheses and variables for this study include stock market volatility, GDP, inflation, Unemployment, FDI and export of goods and services. Pertaining to the approach, the researcher adopted the deductive approach to test the hypothesis in context of USA in this study. The time series data has been gathered from the secondary sources of information and has been tested for presence of unit root using the Augmented Dickey-Fuller (ADF). As per Roza, Violita and Aktivani (2022), Augmented Dickey-Fuller (ADF), is used for the purpose of testing the presence of unit root in the time series data. After the determination of the unit root, the analysis has been carried out using the Autoregressive Distributed Lag (ARDL) model. The study of Rahman et al. (2020) also confirmed that the ARDL model can be used if there is presence of unit root in the data.&nbsp;</p>
  <h3>Data Description&nbsp;</h3>
  <p>The time series data has been gathered from the secondary sources of information. The stock market volatility (S&amp;P 500) has been gathered from Investing.com from 2004 till 2022. The monthly returns were converted to yearly observations. On the other hand, the data related to GDP, inflation, unemployment rate, FDI and export of goods and services was obtained from World Data Bank from 2004 till 2022.&nbsp;</p>
  <h3>Empirical Findings</h3>
  <h4>Descriptive Statistics&nbsp;</h4>
  <p>The descriptive statistics of the variables from this study is provided below. It can be determined that average stock market volatility was 13.8% as mean for stock volatility is determined to be 0.138 and is expected to deviate from 6.1%. In addition, the average FDI/GDP ratio for USA from 1983 to 2022 was 0 which is expected to deviate from 0.007. On the other hand, the average for export of goods and services/GDP ratio was determined to be 0.115 which is expected to deviate from 0.012. Moreover, the average for log GDP was determined to be 13.024 which is expected to deviate from 0.238. Furthermore, the average for unemployment rate was 6% which is expected to deviate from 1.6%. Lastly, the average for inflation was 2.8% which will deviate from 1.5%.</p>
  <div dir="ltr" align="center">
  <table><colgroup><col width="246"><col width="126"><col width="118"><col width="126"><col width="126"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>Variable</p>
  </td>
  <td>
  <p>Mean</p>
  </td>
  <td>
  <p>Std. Dev.</p>
  </td>
  <td>
  <p>Min</p>
  </td>
  <td>
  <p>Max</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Stock Volatility</p>
  </td>
  <td>
  <p>0.138</p>
  </td>
  <td>
  <p>0.061</p>
  </td>
  <td>
  <p>0.039</p>
  </td>
  <td>
  <p>0.306</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>FDI/GDP</p>
  </td>
  <td>
  <p>0.000</p>
  </td>
  <td>
  <p>0.007</p>
  </td>
  <td>
  <p>-0.017</p>
  </td>
  <td>
  <p>0.013</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Exports of goods and services/GDP</p>
  </td>
  <td>
  <p>0.115</p>
  </td>
  <td>
  <p>0.012</p>
  </td>
  <td>
  <p>0.090</p>
  </td>
  <td>
  <p>0.132</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Log GDP</p>
  </td>
  <td>
  <p>13.024</p>
  </td>
  <td>
  <p>0.238</p>
  </td>
  <td>
  <p>12.560</p>
  </td>
  <td>
  <p>13.406</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Unemployment</p>
  </td>
  <td>
  <p>0.060</p>
  </td>
  <td>
  <p>0.016</p>
  </td>
  <td>
  <p>0.037</p>
  </td>
  <td>
  <p>0.096</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Inflation</p>
  </td>
  <td>
  <p>0.028</p>
  </td>
  <td>
  <p>0.015</p>
  </td>
  <td>
  <p>-0.004</p>
  </td>
  <td>
  <p>0.080</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>Table 1 Descriptive Statistics</p>
  <p><strong>&nbsp;</strong></p>
  <h4>Augmented Dickey-Fuller (ADF)</h4>
  <p>On the basis of the previous patterns, the unit root testing is essential aspect for the purpose of making predictions and calculating values. The study of Paparoditis and Politis (2018) argued that the historical data makes it challenging to assess the future value using traditional inferential statistics. Therefore, the ADF has been employed for the purpose of determining the unit root presence in the data.&nbsp;&nbsp;</p>
  <p><strong>&nbsp;</strong></p>
  <div dir="ltr" align="center">
  <table><colgroup><col width="322"><col width="146"><col width="110"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>Augmented Dickey-Fuller test statistic</p>
  </td>
  <td>
  <p>t-Statistic</p>
  </td>
  <td>
  <p>Prob.*</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Stock Volatility</p>
  </td>
  <td>
  <p>-4.420</p>
  </td>
  <td>
  <p>0.0003</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Foreign direct investment/GDP</p>
  </td>
  <td>
  <p>-4.254</p>
  </td>
  <td>
  <p>0.0005</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Exports of goods and services/GDP</p>
  </td>
  <td>
  <p>-1.237</p>
  </td>
  <td>
  <p>0.6575</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Log GDP</p>
  </td>
  <td>
  <p>-2.644</p>
  </td>
  <td>
  <p>0.0843</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Unemployment</p>
  </td>
  <td>
  <p>-2.958</p>
  </td>
  <td>
  <p>0.0390</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Inflation</p>
  </td>
  <td>
  <p>-2.242</p>
  </td>
  <td>
  <p>0.1913</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>Table 2 Augmented Dickey-Fuller (ADF)</p>
  <p>The results of ADF can be determined from table 2 with respect to the variables of this study. The null hypothesis of the ADF is grounded on the fact that there is presence of unit root in the data. It is evident that apart from the FDI, stock volatility and unemployment, all the variables have sig value above the threshold of 0.05 which suggest that null hypothesis has been failed to reject and there is presence of unit root in the data. Due to the presence of unit root in the time series data, ARDL model has been employed (Rahman et al., 2020).&nbsp;</p>
  <h4>Autoregressive Distributed Lag (ARDL)</h4>
  <p>The below table shows the results of ARDL model with respect to GDP. It can be determined that GDP is not dependent on its first lag as B= -0.009, p= 0.161&gt; 0.1. In the long run, there is no significant effect of stock volatility, FDI.GDP ratio, exports of goods and services/ GDP ratio, unemployment rate and inflation over the GDP of USA. On the other hand, with respect to the short term, the effect of stock volatility was determined on its second lag as B= -0.025, p= 0.051&lt; 0.1. In addition to this, the unemployment rate was also determined to have significant effect over the GDP of USA in the first and second lag as well due to the reason that B= -0.435, p= 0.000&lt; 0.01 and B= 0.223, p= 0.089&lt; 0.1.&nbsp;</p>
  <div dir="ltr" align="center">
  <table><colgroup><col width="236"><col width="85"><col width="64"><col width="64"><col width="64"><col width="112"><col width="113"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>D.Log GDP</p>
  </td>
  <td>
  <p>Coef.</p>
  </td>
  <td>
  <p>Std.Err.</p>
  </td>
  <td>
  <p>t</p>
  </td>
  <td>
  <p>P&gt;|t|</p>
  </td>
  <td>
  <p>[95%</p>
  </td>
  <td>
  <p>Conf. Interval]</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>ADJ</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Log GDP</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>L1.</p>
  </td>
  <td>
  <p>-0.009</p>
  </td>
  <td>
  <p>0.006</p>
  </td>
  <td>
  <p>-1.460</p>
  </td>
  <td>
  <p>0.161</p>
  </td>
  <td>
  <p>-0.023</p>
  </td>
  <td>
  <p>0.004</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>LR</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Stock Volatility</p>
  </td>
  <td>
  <p>1.510</p>
  </td>
  <td>
  <p>2.550</p>
  </td>
  <td>
  <p>0.590</p>
  </td>
  <td>
  <p>0.560</p>
  </td>
  <td>
  <p>-3.809</p>
  </td>
  <td>
  <p>6.829</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>FDIGDP</p>
  </td>
  <td>
  <p>7.185</p>
  </td>
  <td>
  <p>18.813</p>
  </td>
  <td>
  <p>0.380</p>
  </td>
  <td>
  <p>0.707</p>
  </td>
  <td>
  <p>-32.058</p>
  </td>
  <td>
  <p>46.427</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Exports GDP</p>
  </td>
  <td>
  <p>-11.818</p>
  </td>
  <td>
  <p>14.043</p>
  </td>
  <td>
  <p>-0.840</p>
  </td>
  <td>
  <p>0.410</p>
  </td>
  <td>
  <p>-41.110</p>
  </td>
  <td>
  <p>17.474</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Unemployment rate</p>
  </td>
  <td>
  <p>-18.203</p>
  </td>
  <td>
  <p>13.450</p>
  </td>
  <td>
  <p>-1.350</p>
  </td>
  <td>
  <p>0.191</p>
  </td>
  <td>
  <p>-46.258</p>
  </td>
  <td>
  <p>9.853</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Inflation consumer prices annual</p>
  </td>
  <td>
  <p>10.595</p>
  </td>
  <td>
  <p>14.067</p>
  </td>
  <td>
  <p>0.750</p>
  </td>
  <td>
  <p>0.460</p>
  </td>
  <td>
  <p>-18.747</p>
  </td>
  <td>
  <p>39.938</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>SR</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Log GDP</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>LD.</p>
  </td>
  <td>
  <p>0.195</p>
  </td>
  <td>
  <p>0.210</p>
  </td>
  <td>
  <p>0.930</p>
  </td>
  <td>
  <p>0.364</p>
  </td>
  <td>
  <p>-0.242</p>
  </td>
  <td>
  <p>0.632</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Stock Volatility</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>D1.</p>
  </td>
  <td>
  <p>-0.027</p>
  </td>
  <td>
  <p>0.018</p>
  </td>
  <td>
  <p>-1.560</p>
  </td>
  <td>
  <p>0.134</p>
  </td>
  <td>
  <p>-0.064</p>
  </td>
  <td>
  <p>0.009</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>LD.</p>
  </td>
  <td>
  <p>-0.025*</p>
  </td>
  <td>
  <p>0.012</p>
  </td>
  <td>
  <p>-2.070</p>
  </td>
  <td>
  <p>0.051</p>
  </td>
  <td>
  <p>-0.051</p>
  </td>
  <td>
  <p>0.000</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>FDIGDP</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>D1.</p>
  </td>
  <td>
  <p>-0.170</p>
  </td>
  <td>
  <p>0.159</p>
  </td>
  <td>
  <p>-1.070</p>
  </td>
  <td>
  <p>0.296</p>
  </td>
  <td>
  <p>-0.501</p>
  </td>
  <td>
  <p>0.161</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>LD.</p>
  </td>
  <td>
  <p>0.003</p>
  </td>
  <td>
  <p>0.124</p>
  </td>
  <td>
  <p>0.020</p>
  </td>
  <td>
  <p>0.981</p>
  </td>
  <td>
  <p>-0.255</p>
  </td>
  <td>
  <p>0.261</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Exports GDP</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>D1.</p>
  </td>
  <td>
  <p>0.130</p>
  </td>
  <td>
  <p>0.200</p>
  </td>
  <td>
  <p>0.650</p>
  </td>
  <td>
  <p>0.522</p>
  </td>
  <td>
  <p>-0.287</p>
  </td>
  <td>
  <p>0.547</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>LD.</p>
  </td>
  <td>
  <p>-0.194</p>
  </td>
  <td>
  <p>0.206</p>
  </td>
  <td>
  <p>-0.940</p>
  </td>
  <td>
  <p>0.356</p>
  </td>
  <td>
  <p>-0.623</p>
  </td>
  <td>
  <p>0.235</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Unemployment total of total&nbsp;</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>D1.</p>
  </td>
  <td>
  <p>-0.435***</p>
  </td>
  <td>
  <p>0.091</p>
  </td>
  <td>
  <p>-4.800</p>
  </td>
  <td>
  <p>0.000</p>
  </td>
  <td>
  <p>-0.624</p>
  </td>
  <td>
  <p>-0.246</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>LD.</p>
  </td>
  <td>
  <p>0.223*</p>
  </td>
  <td>
  <p>0.125</p>
  </td>
  <td>
  <p>1.790</p>
  </td>
  <td>
  <p>0.089</p>
  </td>
  <td>
  <p>-0.037</p>
  </td>
  <td>
  <p>0.484</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Inflation consumer prices annual</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>D1.</p>
  </td>
  <td>
  <p>-0.017</p>
  </td>
  <td>
  <p>0.105</p>
  </td>
  <td>
  <p>-0.170</p>
  </td>
  <td>
  <p>0.870</p>
  </td>
  <td>
  <p>-0.236</p>
  </td>
  <td>
  <p>0.201</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>LD.</p>
  </td>
  <td>
  <p>0.047</p>
  </td>
  <td>
  <p>0.090</p>
  </td>
  <td>
  <p>0.520</p>
  </td>
  <td>
  <p>0.610</p>
  </td>
  <td>
  <p>-0.141</p>
  </td>
  <td>
  <p>0.235</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>_cons</p>
  </td>
  <td>
  <p>0.156</p>
  </td>
  <td>
  <p>0.086</p>
  </td>
  <td>
  <p>1.810</p>
  </td>
  <td>
  <p>0.085</p>
  </td>
  <td>
  <p>-0.024</p>
  </td>
  <td>
  <p>0.336</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>R-squared</p>
  </td>
  <td>
  <p>0.9318</p>
  </td>
  <td>
  <p>&nbsp;</p>
  </td>
  <td>
  <p>&nbsp;</p>
  </td>
  <td>
  <p>&nbsp;</p>
  </td>
  <td>
  <p>Adj R-squared</p>
  </td>
  <td>
  <p>0.8739</p>
  </td>
  </tr>
  <tr>
  <td colspan="7">
  <p>Significant at 10% "*", 5% "**", 1% "***"</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>Table 3 ARDL for GDP</p>
  <h3>Discussion&nbsp;&nbsp;</h3>
  <div dir="ltr" align="center">
  <table><colgroup><col width="56"><col width="539"><col width="85"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>S.No</p>
  </td>
  <td>
  <p>Hypotheses</p>
  </td>
  <td>
  <p>Status</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>1</p>
  </td>
  <td>
  <p>H1a: There is a significant influence of stock market volatility over the GDP of USA.</p>
  </td>
  <td>
  <p>Accepted&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>2</p>
  </td>
  <td>
  <p>H2a There is a significant influence of FDI over the GDP of USA.</p>
  </td>
  <td>
  <p>Rejected</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>3</p>
  </td>
  <td>
  <p>H3a: There is a significant influence of export of goods and services over the GDP of USA.</p>
  </td>
  <td>
  <p>Rejected</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>4</p>
  </td>
  <td>
  <p>H4a: There is a significant influence of unemployment rate over the GDP of USA.</p>
  </td>
  <td>
  <p>Accepted</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>5</p>
  </td>
  <td>
  <p>H5a: There is a significant influence of inflation over the GDP of USA.</p>
  </td>
  <td>
  <p>Rejected</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>Table 6 Hypothesis Assessment Summary</p>
  <p>The results of the study determined that there is a significant effect of stock market volatility over the GDP of USA. Similarly, Giri and Joshi (2017) determined positive correlation among the economic growth and stock prices. Also, Alexius and Sp (2018) presented that there is association among the stock market prices and the GDP. However, the effect of FDI was determined to be insignificant over the GDP of USA. The results can be opposed based on the study by Sengupta, and Puri (2020) that FDI has a direct association with the GDP of the country. Moreover, the effect of exports of goods and services was also determined to be insignificant over the GDP of USA. The results do not support the findings of Okyere and Jilu (2020) that export of goods and services has a positive association with the GDP.&nbsp; With respect to the Inflation, there is no significant influence of inflation over the GDP of USA. The results opposed the findings of Moyo and Tursoy (2020) that inflation has huge influence over the GDP. With respect to the unemployment rate, there was significant influence of unemployment rate determined over the GDP of USA. The results supported the findings of Moradi et al. (2021) that unemployment rate can have negative influence over the GDP of the country.&nbsp;</p>
  <h3>Conclusion</h3>
  <p>The stock market forum is open for the public, in which the mechanism of vending and purchasing allows trading of corporate equities at a consensually determined valuation. The stock market of USA can have influence over the global and domestic economic conditions, investor sentiment, political developments and corporate earnings. Therefore, this study has been conducted to determine the influence of stock market volatility over the GDP of USA. Apart from these, four control variables such as unemployment, inflation, FDI/GDP ratio and export of goods and services/GDP ratio were also considered. The data for stock market volatility (S&amp;P 500) was considered from investing.com while the macroeconomic indicators were obtained from World Data Bank. The unit root testing was carried out using the ADF. After the determination and confirmation regarding the presence of unit root in the time series data, the ARDL model was applied due to the presence of unit root. The results determined that there is significant influence of stock market volatility, and unemployment rate over the GDP of USA. While, the effect of FDI/ GDP ratio, exports of goods and services/GDP ratio and inflation was determined to be insignificant over the GDP of the country.</p>
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  <li>Rahman, H.U., Ghazali, A., Bhatti, G.A. and Khan, S.U., 2020. Role of economic growth, financial development, trade, energy and FDI in environmental Kuznets curve for Lithuania: evidence from ARDL bounds testing approach.&nbsp;Engineering Economics,&nbsp;31(1), pp.39-49.</li>
  <li>Ravikumar, S. and Saraf, P., 2020, June. Prediction of stock prices using machine learning (regression, classification) Algorithms. In&nbsp;2020 International Conference for Emerging Technology (INCET)&nbsp;(pp. 1-5). IEEE.</li>
  <li>Raza, S.A., Shah, N. and Arif, I., 2021. Relationship between FDI and economic growth in the presence of good governance system: Evidence from OECD Countries.&nbsp;Global Business Review,&nbsp;22(6), pp.1471-1489.</li>
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  The aim of this research paper is to determine the impact of stock market volatility over the economic indicator, namely GDP.


slug: the-impact-of-technology-on-formative-assessment-instruments
title: The Impact of Technology on Formative Assessment Instruments
academic_level: Graduate
subject: English
type_of_paper: Annotated Bibliography
citation_style: APA
meta_title: The Impact of Technology on Formative Assessment Instruments
meta_keywords: Impact of Technology, Formative Assessment Instruments
canonical_url: https://www.cheap-essay-writing.co.uk/
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created_at: 2025-06-16 14:32:07
updated_at: 2026-01-14 15:52:19
status: published
content:
  <h2>The Impact of Technology on Formative Assessment Instruments Evaluating Learners&rsquo; Speaking Skills<strong><br></strong></h2>
  <p>Student Name:</p>
  <p>Student Number</p>
  <p>Date:</p>
  <p>&nbsp;</p>
  <h3>Bibliography</h3>
  <p><strong>Article no. 1: Estrada-Araoz, E. G., Sayed, B. T., Niyazova, G. G., &amp; Lami, D. (2023). Comparing the effects of computerized formative assessment vs. computerized dynamic assessment on developing EFL learners&rsquo; reading motivation, reading self-concept, autonomy, and self-regulation. Language Testing in Asia, 13(1), 39.</strong></p>
  <p>The purpose of the study is to investigate the impact of technology on formative assessment instruments evaluating learners for speaking skills, and this study suits the research topic best. According to (Estrada-Araoz et al. 2023), the effects of computerized dynamic assessment (CDA) and computerized formative assessment (CFA) on reading self-concept, self-motivation, self-regulation, and autonomy among students are investigated in this study. Around 87 Bangladeshi students from a total of 123 Bangladeshi students were selected for the study based on their Preliminary English Test (PET) scores. Students were categorized into three equal groups; two experimental groups were formative and dynamic, and one was a control group comprising 29 students each, using convenience sampling. All three groups completed pre-tests to assess their reading self-concept, autonomy, self-regulation, and motivation before getting different treatments. The first experimental group goes CDA, and the second experimental group goes with CFA. However, the control group experienced traditional assessment. All groups post-test measure the same variables after the treatment. ANOVA test and post hoc Scheffe test allow for the analysis of data, which shows significant differences among the three groups in post-test results. In terms of reading self-concept, autonomy, self-regulation, and reading motivation, the CDA group showed greater performance than the CFA group. The findings highlighted that EFL students significantly enhanced their reading motivation, autonomy, reading self-concept, and self-regulation. A discussion of the implications and limitations of the study, along with the future recommendations, concluded the study.</p>
  <p><strong>Article no. 2: Faber, J. M., &amp; Visscher, A. J. (2018). The effects of a digital formative assessment tool on spelling achievement: Results of a randomized experiment. Computers &amp; education, 122, 1-8.</strong></p>
  <p>Similarly, another study conducted by Faber et al. (2018) was found relevant to the research topic. A randomized clinical trial was utilized to evaluate the spelling achievement of eight to nine-year-old third-grade students for the evaluation of the impact of the digital formative assessment tool. The sample comprised 39 control schools (n=986) and 30 experimental schools (n=619). The control group employed regular materials and spelling instruction, while the experimental group continued with digital formative assessment tools. Pre and post-test scores of standard achievements, percentage of adaptive assignments, and the tasks completed by a total number of the students were necessary for the data collection process. The outcomes of the study indicated that digital formative assessment tools had a moderate influence on spelling achievement. Several crucial areas for future research were highlighted for further exploration.</p>
  <p><strong>Article no. 3: Granberg, C., Palm, T., &amp; Palmberg, B. (2021). A case study of formative assessment practice and the effects on students' self-regulated learning. Studies in Educational Evaluation, 68, 100955</strong></p>
  <p>Another study conducted by Granberg et al. (2021) showed relativity with the selected topic. The research study explores the use of formative assessment on students' self-regulated learning (SRL), which one mathematics teacher carries out. The students participating in the study were about 13-14 years of age and belonged to various social and cultural diversities. The two control groups have 18 and 19 students, respectively, whereas the intervention class has 18 students. Nine students from each control group, along with fourteen students from the intervention class, have shown interest and agreed to participate in the study. An impressive suitable SLR effect on students' motivational beliefs was observed in contrast to the two control classes. Exponential improvement in students' SLR behaviour in the classroom was seen through qualitative data. The integration of multiple features of formative assessment was demonstrated by the analysis of teachers' formative assessment applications, which gave factual results on how efficient formative assessment can be arranged with development models of SLR and enhance SLR efficacy.</p>
  <p><strong>Article no. 4: Hagos, T., &amp; Andargie, D. (2023). Effects of Technology-Integrated Formative Assessment on Students' Conceptual and Procedural Knowledge in Chemical Equilibrium. Journal of Education and Learning (EduLearn), 17(1), 113-126.</strong></p>
  <p>This study was conducted by Hagos, T., &amp; Andargie, D. (2023) and focuses on exploring the student's knowledge and concepts about chemical equilibrium after introducing a technology-integrated formative assessment application. A qualitative method was integrated with the Quasi-experimental pre and post-test approach in this study. About 132 eleventh-grade students from public secondary schools are involved in the study, categorized into three groups: one comparative and two experimental. For the treatment groups and one for the comparative group, random sampling was adopted for the selection of intact classes. A chemical equilibrium conceptual test, class observations, and a procedural test were utilized for the data collection. Inferential statistics like one-way ANOVA MANOVA and Pearson product-moment correlation, along with descriptive statistics like standard and mean deviation, play an essential role in the analysis of the data.</p>
  <p>In contrast to traditional methods and formative assessment strategies alone, technology-integrated formative assessment techniques impressively enhance students&rsquo; knowledge and concepts for apprehension of chemical equilibrium. The student seems more motivated to learn, and the teacher maintains higher excellence through technology-integrated formative assessment strategies that are acknowledged through classroom observations. Chemical equilibrium understanding among students was significantly improved through these techniques as compared to other approaches.</p>
  <p><strong>Article 5: Zheng, C., Wang, L., &amp; Chai, C. S. (2023). Self-assessment first or peer-assessment first: effects of video-based formative practice on learners&rsquo; English public speaking anxiety and performance. Computer Assisted Language Learning, 36(4), 806-839.</strong></p>
  <p>The purpose of this study is to propose a particular sequence of formative assessment practice in computer-assisted language learning (CALL). Less attention should be paid to the effects of different levels of formative assessment strategies on the cognitive and affective aspects of students' public speaking performance. In this mixed-method study, data were collected from multiple sources to examine and describe the impact of two approaches of integrating video-based formative assessment into the teaching and learning of public speaking among English language learners. There were two intact classes in a 16-week English public speaking course, with 25 students in the self-assessment-initiated (SAI) group and 26 students in the peer-assessment-initiated (PAI) group. After two different sequences of formative practice, both groups were expected to deliver three formal English public speeches. Quantitative and qualitative data analysis revealed that learners in the SAI group had lower public speaking anxiety than the learners in the PAI group, and the learners in the PAI group had better public speaking performance.</p>
  <p>Furthermore, the study revealed that SAI formative practice assisted the learners in focusing on their delivery and anxiety in public speaking. In contrast, PAI formative practice may have assisted the learners in the correct usage of the English language. This research thus showed that the order of using the different modalities of assessment was relevant. With the use of video technology and CALL tools, various sequences in getting other types of feedback may influence learners' public speaking anxiety and performance. This study offers more specific insights into the processes of using video technology in formative practice to reduce learners' public speaking anxiety and enhance their performance.</p>
  <p><strong>Article no. 6: Song, Y., &amp; Sparks, J. R. (2019). Building a game-enhanced formative assessment to gather evidence about middle school students&rsquo; argumentation skills. Educational Technology Research and Development, 67, 1175-1196.</strong></p>
  <p>The purpose of this paper is to design and assess a new formative assessment to collect information on middle school students' argumentation abilities. More specifically, the game-enhanced scenario-based assessment used in this study (Seaball&mdash;Semester at Sea) consists of a series of argumentation exercises within an elaborate scenario in which students discuss the question of whether junk food should be made available to students. These activities were developed based on argumentation learning progressions (i.e., conjectures about the developmental stages that students move through as they become more elaborate in constructing arguments) used to define the skills, levels, and sequences of the activities. Feedback on performance is also given in the assessment. Researchers carried out a pilot study to assess the performance of students and the effectiveness of the tasks in measuring argumentation skills. Over one hundred middle school students participated in this assessment and other measures of argumentation in a pre/post design. Summary statistics of student achievement in the activities, item indices, and interrelationships are presented. Findings showed that total scores of students were significantly associated with measures of argumentation skills and with students' reading and writing performance on state assessments.</p>
  <p>Furthermore, the students obtained higher average scores in a post-test of argumentation skills after they participated in the Seaball activities. Last, the provision of feedback about students' task performance was observed to be helpful for students who scored "Below" or "Approaching" at the proficient level on the state reading and writing test. In the last section, researchers discuss the implications of the current study for the assessment of argumentation and argumentative learning.</p>
  <p><strong>Article no. 7: Wang, P. (2015). The effect of dynamic assessment on the listening skills of lower-intermediate EFL learners in Chinese Technical College: A pilot study. Journal of Language Teaching &amp; Research, 6(6).</strong></p>
  <p>The purpose of this study is to find out if dynamic assessment can help integrate listening comprehension assessment and instruction and improve students' listening skills. The participants of the study were five second-year English majors from a technical college in an underdeveloped area of a coastal province in China. The assessment used the cake format, where participants were first presented with an audio clip and required to respond to questions and explain how they understood the material. The researcher then stepped in to facilitate the task. The audio material was replayed to the participants, and they were asked to narrate what they had heard. This went on until the listener was able to get the relevant information from the audio material. From the notes taken by the researcher, notes taken by the students, verbal reports by the students, and reflective reports, it was realized that dynamic assessment helps both the researcher and the participants to understand the problems in listening better. The data also showed that the researcher's intervention and mediation aimed at participants' issues supported the building of the mediated learning experience for the participants. Therefore, the integration of listening comprehension assessment and instruction is possible through dynamic assessment and the development of students' listening study at macro and micro levels.</p>
  <p><strong>Article no.8:&nbsp; White, L. (2019). The Impact of Technology on the Implementation of Formative Assessment Lessons in Eighth-Grade Math Classrooms</strong></p>
  <p>The purpose of this study was to determine whether there is a statistical difference between the post-lesson assessment results of students who participated in a formative assessment lesson in which the teacher used technology and those of students who participated in a formative assessment lesson in which the teacher did not use technology. The teachers gave the students pre- and post-lesson assessments after the formative assessment lesson enactments. The whole class introduction and discussion were delivered to the control groups by the teacher using technology. The entire class introduction and whole class discussion were done in this experiment without the teacher utilising any form of technology to teach the lesson. This study also sought to determine teacher and student attitudes toward the use of technology in mathematics teaching and learning. The teachers were asked to share their thoughts regarding any consequences stemming from the use of technology during the formative assessment lesson. The students were required to fill out a survey to state their opinions on the incorporation of technology in a formative assessment lesson. The mixed quantitative results also needed to provide a clearer picture of whether technology use in formative assessment lessons affected student performance on post-lesson assessments. The findings of this research may provide doubts about the need to spend money on technology purchases. From the findings of this study, it can be concluded that the integration of technology in a formative assessment lesson does not affect student learning.</p>
  <p><strong>Article no. 9: K&ouml;roğlu, Z. &Ccedil;. (2021). Using digital formative assessment to evaluate EFL learners&rsquo; English speaking skills. GIST&ndash;Education and Learning Research Journal, (22), 103-123.</strong></p>
  <p>A study was conducted by K&ouml;roğlu, Z. &Ccedil;. (2021) to evaluate English speaking skills. For the evaluation of English speaking skills among 52 upper-intermediate EFL learners, this study compared the traditional speaking tests and digital formative assessments using a mixed-method approach. Qualitative data collection was performed through interviews, and quantitative data was collected through achievement tests. Study findings indicated that participants acknowledged the close integration, preparation time, and refined materials, and their performance increased on digital formative tests. Apart from positive outcomes, some technical complications disturbed the participants which occurred during the digital formative tests.&nbsp;</p>
  <p><strong>Article no.10: Karay, Y., Reiss, B., &amp; Schauber, S. K. (2020). Progress testing anytime and anywhere&ndash;Does a mobile-learning approach enhance the utility of a large-scale formative assessment tool?. Medical Teacher, 42(10), 1154-1162.</strong></p>
  <p>Another study was performed by Karay et al. (2020) to explore the impact of mobile learning effectiveness among students. The study includes the total number of students (n=1701) from 2-10 semesters divided into two groups. This study evaluated the impact of mobile-formative progress tests on students' responses, validity, and creditability. Students received tests and were allowed to select their desired place and time for performing the test. Data was gathered consisting of test scores, semester performance, student evaluations, and time spent on the test. After the analysis of the findings, it was found that the results did not show a significant difference in the test results among groups. However, groups using the mobile phone often utilized external sources and took more time. The mobile test format was highly favored by the students due to their motivation and acceptance of this approach, but the performance stayed unaffected.&nbsp;</p>
  <p><strong>Article no. 11: Zainuddin, Z., Shujahat, M., Haruna, H., &amp; Chu, S. K. W. (2020). The role of gamified e-quizzes on student learning and engagement: An interactive gamification solution for a formative assessment system. Computers &amp; education, 145, 103729.</strong></p>
  <p>This study is performed by Zainuddin et al. (2020), highlighting student engagement and learning enhancement through gamified e-quizzes. The differences in students learning and engagement during science class along with the comparison of three intervention groups utilizing two types of pedagogical methods including gamified instruction with e-quizzes as formative assessments and traditional instruction with paper-based quizzes are explored in this research study. Three applications were employed through gamified instructions; iSpring Leam LMS, socrative, and quizizz. The effects Formative quizzes, personal interviews, and post-questionnaire surveys were used to measure the effects of instructional interventions (n=94). The findings highlight the effectiveness of both paper-based quizzes and gamified e-quizzes in the learning performance of students. For engaging students and enhancing the learning environment in the classroom, incorporating gamified elements as innovative tools plays a vital role in student academic progress.</p>
  <p><strong>Article no. 12: McCallum, S., &amp; Milner, M. M. (2021). The effectiveness of formative assessment: student views and staff reflections. Assessment &amp; Evaluation in Higher Education, 46(1), 1-16.</strong></p>
  <p>This study was conducted by McCallum &amp; Milner, (2021) which explores the effectiveness of formative e-assessments in first-year courses and focuses on evaluating their efficacy depending upon the staff reflections and students' feedback. Participation of students takes place in these assessments and data gathered through questionnaires have highlighted the effectiveness of e-assessment in students learning and understanding, encouraged them to pursue further studies, and helped them in tracking their progress. The results helped the faculty staff to focus on the benefits of using formative e-assessments to enhance student involvement and allow early development of strategies. The study highlights the advantages of integrating formative assessments in first-year courses.</p>
  <p><strong>Article no. 13: Elmahdi, I., Al-Hattami, A., &amp; Fawzi, H. (2018). Using Technology for Formative Assessment to Improve Students' Learning. Turkish Online Journal of Educational Technology-TOJET, 17(2), 182-188.</strong></p>
  <p>This paper focuses on the role of the formative assessment technology known as the Plickers, a classroom response system that is a tool used in classroom learning. In this study, a descriptive mixed-method research design was employed. Data was collected from one hundred and sixty-six students from Bahrain Teachers College, University of Bahrain, through the use of a developed self-administered questionnaire. The study established that students hold formative assessment and getting feedback as valuable and this was in line with the application of Plickers. Furthermore, the study revealed that Plickers for formative assessment assists in learning with the following benefits: an increase in students' engagement, time-saving, equal opportunities for all the learners, and fun appeals to learners. The conclusions also call upon the teachers to incorporate technology tools like Plickers in class to assist them in determining the utility of this tool in evaluating their performance as well as the learning achievements of their learners.</p>
  <p><strong>Article no. 14: V&aacute;squez, A., Nussbaum, M., Sciarresi, E., Mart&iacute;nez, T., Barahona, C., &amp; Strasser, K. (2017). The impact of the technology used in formative assessment: The case of spelling. Journal of educational computing research, 54(8), 1142-1167.</strong></p>
  <p>The purpose of this study is to show how the particular technology addressed to support formative assessment in spelling influences learning outcomes. Observational assessment is an example of a formative assessment that is a set of student-centered practices that do not necessarily yield the best outcomes. Moreover, various technologies are more capable when it comes to performing specific tasks than when it comes to others. The study employed a Design-Based Research method and was conducted in Chile in two phases. The first part of the study identifies a formative assessment approach to teach spelling. In a later step, it becomes imperative to examine the effects of varying technologies on this strategy. This is achieved by comparing two different technologies such as Tablet PCs and the interpersonal computer, The findings of this study show that the Self-Paced Formative Assessment using Tablets is a more effective form of instruction than using an Interpersonal Computer for teaching spelling to the primary school students. This therefore explains how technology affects learning when one has to embrace a formative assessment strategy.</p>
  <p><strong>Article no 15: Dalby, D., &amp; Swan, M. (2019). Using digital technology to enhance formative assessment in mathematics classrooms.&nbsp;British journal of educational technology,&nbsp;50(2), 832-845.</strong></p>
  <p>This study examines the contexts within which six mathematics teachers and their classes utilize iPads in formative assessments in two secondary comprehensive schools located in the Midlands of England. In the first instance, a design research methodology is adopted to create and test six lessons, each of which incorporates iPads in formative assessment practices in different ways. Researchers then identified during lesson observation, video analysis, and interviews with the teachers how this technology supports these processes, the roles iPad plays as well as the division of roles for the main actors that include the teacher, technology, and the student. An analytical model is created that describes these interactions and illustrates how the technology both serves communicative purposes and also functionalities that are more actively involved which change the part assumed by the teacher in certain formative processes. The study provides an understanding of how iPad technology can support students' learning through assessment and presents an analytic view that may benefit the following research.</p>

meta_description:
  The purpose of the study is to investigate the impact of technology on formative assessment instruments evaluating learners for speaking skills, and this study suits the research topic best.


slug: assessing-the-reshaping-of-retail-consumer-patterns-in-england
title: Assessing the Reshaping of Retail Consumer Patterns in England
academic_level: Graduate
subject: Business
type_of_paper: Dissertation
citation_style: APA
meta_title: Assessing the Reshaping of Retail Consumer Patterns in England
meta_keywords: Retail Consumer Patterns, Consumer Patterns
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created_at: 2025-06-16 15:08:35
updated_at: 2026-01-14 15:52:09
status: published
content:
  <h2>Topic: Assessing the Reshaping of Retail Consumer Patterns in England: A Comprehensive Study on the Long-term Effects of the Covid-19 Pandemic on Consumer Behaviour</h2>
  <p><strong>&nbsp;</strong></p>
  <h3>TABLE OF CONTENTS</h3>
  <p>CHAPTER 1 1</p>
  <p>INTRODUCTION 1</p>
  <p>1.1 Background 1</p>
  <p>1.2 Problem Statement 2</p>
  <p>1.3 Research Objectives 3</p>
  <p>1.4 Research Questions 3</p>
  <p>1.5 Significance of Study 4</p>
  <p>1.6 Justification of the Study 5</p>
  <p>1.7 Scope and Limitations 5</p>
  <p>1.7 Structure of the Dissertation 6</p>
  <p>CHAPTER 2 7</p>
  <p>LITERATURE REVIEW 7</p>
  <p>2.1 Introduction 7</p>
  <p>2.2 Virus SARS-CoV-2 (COVID-19) 7</p>
  <p>2.3 Consumer Behavior 9</p>
  <p>2.3 Factors affect retail consumers 10</p>
  <p>2.4 Short-Term Impacts of COVID-19 on Consumer Behavior 11</p>
  <p>2.5 Long-Term Effects 12</p>
  <p>2.6 Emerging Trends and Future Directions 13</p>
  <p>2.5 Literature Gap 13</p>
  <p>CHAPTER 3 15</p>
  <p>METHODOLOGY 15</p>
  <p>3.1 Introduction 15</p>
  <p>3.2 Research Design 15</p>
  <p>3.3 Data Generation Methods 16</p>
  <p>3.4 Data Analysis Methods 17</p>
  <p>3.6 Ethical Considerations 18</p>
  <p>References 20</p>
  <p>Appendix A 25</p>
  <p>Survey question 26</p>
  <p>Appendix B 27</p>
  <p>Interview Question 27</p>
  <h3>CHAPTER 1:&nbsp;INTRODUCTION</h3>
  <h4>1. Background</h4>
  <p>The development of new purchasing behaviour during and post-pandemic is no longer the habit of shopping by people but by the complex forces of society, economy, and technology that guide people in making a decision. The catalyst function of the pandemic implied consumers reconsidering the cores and edges of their lives, which led to the formulation of the rules that were used for basic needs. UV factors like this that the public has never seen before are the innovation in online purchasing and cashless payments that are fueled by security and hygiene. The digital rise came as a necessity as soon as the lockdowns. Consumers are buying more and more online as the new lifestyle dictates (Nanda et al., 2021). The pandemic also generated a silver lining in terms of businesses' resilience and adaptability. These innovations made them change policies towards service delivery and supply chain logistics. Economy unrest has resulted in changes in consumption behaviours among people, and consequently, all types of spending (discretion and non-discretion) have been influenced to some extent. The changing patterns and practices in consumption behaviours because of the revolutionary and long-term effects of the remote work styles, the lost social interaction and the changed consumption mode are solid proof that consumption pattern is primarily shaped by social force. The shifting patterns of consumption behaviour influenced by the COVID-19 epidemic are a result of the interdependent health issues, tech progress and economic growth, which caused the social attitude to alter (Filimonau et al., 2022).</p>
  <p>The Corona SARS-CoV-2 virus, also known as COVID-19, has quickly and extensively disseminated worldwide since March 2020, resulting in a pandemic. The COVID-19 pandemic has resulted in a significant loss of life in several regions around the globe. The global dissemination of the 2020 COVID-19 pandemic commenced during the initial quarter of that year. Various nations have imposed territorial restrictions to manage the transmission of the COVID-19 pandemic (Jiang and Stylos, 2021). Health precautions are being enforced to mitigate the continued escalation of the COVID-19 pandemic. Every person must adhere to the social distance protocol as a component of their health regimen. The COVID-19 pandemic has impacted various sectors, including the economic sector. Due to the pandemic, the global economy saw a complete collapse. The combination of social isolation and geographical lockdowns exerts a significant influence on individuals' behaviour. Individuals rarely venture outside their residences, mainly engage in remote work and education, and infrequently engage in social interactions.&nbsp;</p>
  <p>The COVID-19 pandemic that occurred in 2020 has marked a significant milestone in the history of human civilisation. This happens when customers behave in such a manner. Typically, individuals engage in many activities related to online buying. Individuals allocate a significant amount of time within their residences, so affording them ample occasions to do Internet investigations on various goods (Or&icirc;ndaru et al., 2021). Specific consumers will continue to adhere to digital purchasing methods even after being exposed to information from digital media (completely online shopping). Particular individuals opt to make their purchases in person following the receipt of digital information (online browsing) in order to observe the functionality of the products personally.&nbsp;</p>
  <h5>1.1 Problem Statement</h5>
  <p>The following study focuses on the central issue of COVID-19's impact on consumer behavior in the English retail market long term. Though the existing research focuses on short-term impacts, variation in consumption patterns in the long term life is left neglected. These two notions are further strengthened by empirical data from sources like Morgan (2020) and V&aacute;zquez-Mart&iacute;nez et al. (2021). The practical problem that the pandemic left people developed behaviours in which tactical marketing or business vending, policy-making, and investment decisions should carry out a reappraisal of the customer behaviour in the period after the pandemic. Such a gap, in theory, must be taken into consideration because it shows that there is a lack of longitudinal studies that could allow us to record the long-term consequences. The goal of this paper is to close these gaps, resulting in valuable arguments for the post-COVID-19 retail world. This study is not only practical but also a theoretical contribution to the relevant science.&nbsp;</p>
  <h5>1.2 Research Objectives</h5>
  <p>The research objectives of this study are as follows:</p>
  <ol>
  <li>To investigate the transformations that have characterised consumer shopping habits in England since the onset of the COVID-19 pandemic.</li>
  <li>To examine the notable shifts in the frequency and modalities of consumer shopping in the post-pandemic landscape and to understand how these changes have manifested.</li>
  <li>To analyse consistent alterations in consumer spending patterns across diverse retail sectors and identify discernible trends.</li>
  <li>To assess the extent of migration from conventional physical retail establishments to online shopping channels and to determine the enduring nature of these shifts.</li>
  </ol>
  <h5>1.3 Research Questions</h5>
  <ol>
  <li>What transformations have characterised consumer shopping habits in England since the advent of the COVID-19 pandemic?</li>
  <li>In the post-pandemic landscape, what notable shifts have occurred in the frequency and modalities of consumer shopping, and how have these changes manifested?</li>
  <li>Across diverse retail sectors, have there been consistent alterations in consumer spending patterns, and if so, what are the discernible trends?</li>
  <li>To what extent have consumers migrated from conventional physical retail establishments to online shopping channels, and what is the enduring nature of these shifts?</li>
  <li>Are there visible demographic or geographic influencers shaping the preference for either online or offline shopping?</li>
  <li>How have consumer behaviours transformed within distinct retail sectors, such as groceries, fashion, and electronics, in the aftermath of the COVID-19 pandemic?</li>
  <li>Do these behavioural shifts exhibit uniformity across various sectors, or are there nuanced distinctions that warrant specific attention and exploration?</li>
  </ol>
  <h5>1.4 Significance of the Study</h5>
  <p>Regarding the impact which the COVID-19 pandemic will have on retail consumer behaviour in England and the context and implications for retail, this is a highly important and relevant matter. This research adds to the existing knowledge base by providing a holistic examination of the complex changes in consumer behaviours. On the part of companies, these insights are used to make tactical deliberations as the organisation adapts and innovates. Policymakers can rely on, as well as investors develop a better understanding of both short-term dynamics and long-run processes. Consumers will gain in trade-off with enhanced well-being and contentment. The core of the economy lies in facilitating resilience; the paper helps to create a passage that leads businesses successfully through the post-pandemic retail environment.</p>
  <h5>1.5 Justification of the Study</h5>
  <p>The decision to focus this study on retail consumer behaviour after the criticality and topical nature of the issue drives COVID-19. The development of new ways of consumption that COVID-19 has caused highlighted the importance of identifying the true extent of the changes in consumers' behaviour during the pandemic.&nbsp;One of the reasons this subject is vital in England&rsquo;s context is that it is a very socially and economically diverse country, and retail plays a significant role in the UK economy.&nbsp;Investigating these changes offers legitimate, timely insight for retailers, public authorities, and investors who adapt to the changing retailing environment.&nbsp;The study's importance lies in the possible straightforward remedies and tactical suggestions that can be helpful to the stakeholders in the retail industry in England.</p>
  <h5>1.6 Scope and Limitations</h5>
  <p>This study provides a concentrated examination of customer shopping patterns in the retail sector within this particular setting, as its geographical coverage is limited to England. The era after COVID-19 is included in the period to make sure the information is relevant to the existing retail scene. Limitations may arise due to possible restrictions on the availability of data. Both the need for extensive datasets and the lengthier duration are being significantly impacted by these constraints. The study acknowledges that consumer behaviour is inherently dynamic and that some components may evolve as the research progresses (Lashgari and Shahab, 2022). This study aims to contribute significantly to the continuing conversation about post-pandemic consumer behaviour in England's retail sector by undertaking a comprehensive investigation within the defined scope.</p>
  <h5>1.7 Structure of the Dissertation</h5>
  <p>The dissertation is structured to facilitate a systematic exploration of retail consumer behaviour post-COVID-19.&nbsp;</p>
  <ul>
  <li>Chapter 1: The introduction establishes the background, problem statement, and research objectives.&nbsp;</li>
  <li>Chapter 2: the literature review critically examines existing research, providing the theoretical framework for understanding consumer behaviour shifts.</li>
  <li>Chapter 3 details the methodology, outlining the research design, data generation methods, and analysis techniques.&nbsp;</li>
  <li>Chapter 4 presents the results and engages in a comprehensive discussion of findings, exploring trends and patterns.&nbsp;</li>
  <li>Chapter 5 synthesises the research, draws conclusions, and offers practical implications, contributing to the ongoing discourse on post-pandemic consumer behaviour in England's retail sector.</li>
  </ul>
  <p>&nbsp;</p>
  <h3>CHAPTER 2:&nbsp;LITERATURE REVIEW</h3>
  <h4>2.1 Introduction&nbsp;</h4>
  <p>The literature review is a crucial part of this dissertation, with the goal of offering a thorough insight into post-COVID-19 consumer behaviour in the retail sector in England. The main goal is to thoroughly analyse current academic literature to establish a solid theoretical basis. Exploring different theories and models related to consumer behaviour, the literature review places the study within the educational landscape and highlights the changing dynamics in retail. Highlighting the significant effects of the COVID-19 pandemic, this section will analyse and evaluate research to provide a detailed examination of both immediate effects and lasting shifts in consumer behaviour.&nbsp;</p>
  <h4>2.2 Virus SARS-CoV-2 (COVID-19)&nbsp;</h4>
  <p>COVID-19, caused by the SARS-CoV-2 coronavirus, was first identified in Wuhan, China. The news quickly reached numerous other countries. In December 2019, Wuhan experienced significant anxiety levels due to the large number of fatalities from the virus. In early 2020, a global pandemic was declared as the virus rapidly spread to many countries. Many countries have imposed restrictions on travel within their borders, implemented measures to prevent the spread of the COVID-19 pandemic, and suspended classes and workdays due to the significant number of fatalities (Sayyida et al., 2021). This global pandemic has impacted various sectors, including the economy. Online shopping has significantly increased due to higher sales during the lockdown period. During the Movement Control Order period, online retailers like Happy Fresh and Lazada saw a 10% to 15% increase in sales. To control the virus and stop it from spreading, everyone must steer clear of individuals who are infected (H&uuml;seyin Erbil &Ouml;zy&ouml;r&uuml;k and Esra Civelek Yilmaz, 2022). Given the ongoing COVID-19 pandemic, it is impossible to predict when it will come to an end. In 2020, amidst the global spread of the COVID-19 pandemic, a new civilisation is beginning to take shape. People need to change their actions in order to control the spread of the infection. This change in viewpoint also happens when customers buy from brick-and-mortar stores.</p>
  <p><img style="display: block; margin-left: auto; margin-right: auto;" src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXdWAagLeRxrKT0q6NOSgCyFCqNYOSjzUlztWW2ZYdgRaEiSSkucVm31HmD1JLm54DILsz_GFWF-MUaBcziKCgmGtHHFzyFNgDN6BLycVDapwLikqhxeeDAhKWUBKR4N8pd-u1BjZuqrm4MP-PQ3qpw?key=rdqfMRMxvWQ5dcKKbibkBg" alt="COVID Accelerates Declining Retailed Industry&nbsp;(Roper, 2020)" width="624" height="420"></p>
  <p>Figure 1: COVID Accelerates Declining Retailed Industry&nbsp;(Roper, 2020)</p>
  <p>Companies like J. C. Penny, Neiman Marcus and J. Crewe are also some of the household brands which filed bankruptcy in the last 4 weeks, and this is a clear indicator that the COVID limits we are seeing now are a real disaster for businesses.&nbsp;Recent forecasts estimate that a significant number of retail outlets could probably be forced to shut their doors within the next five years.&nbsp;It will be throughout the country. Experts' forecast for 2025 introduces a gloomy prediction that more than 100,000 stores will have shut down by that time because people craved online shopping and the overall decrease in in-store shopping.&nbsp;The Wall Street Journal could obtain the data after its release from UBS. The industry has suffered immensely, and most are in charge of office supplies. The retailers of this branch are predicting that due to fierce competition, around 50% of their number will be gone in five years (Roper, 2020). Manifold physical retail chains decide to close their shops very soon because of the quick interest rate upturn. This is the main reason why most retailers have high debt levels all the time. Numerous people rush into the re-opened stores as the social distancing requirements are lifted, causing a short-term increase in foot traffic at brick-and-mortar stores.&nbsp;While the coronavirus epidemic was the primary reason behind the decline in the shopping mall&rsquo;s prosperity, this process began much earlier.&nbsp;The failure resulted from the competition with ever-growing new players like Amazon and eBay (Roper, 2020).</p>
  <h4>2.3 Consumer Behavior&nbsp;&nbsp;</h4>
  <p>In the digital age, people's actions can be categorised into four distinct groups: in-store purchasing, exclusive online shopping, showrooming, and webrooming. There are two particular purchasing habits: pure physical shopping and pure online shopping. Both of these buying habits involve the customer only utilising a single channel to complete the transaction. "Pure offline shopping" refers to the act of seeking information and making purchases exclusively in physical retail stores. "Pure offline shopping" refers to the act of conducting all shopping activities in physical stores or locations without utilising online or mobile platforms (Purcărea et al., 2022). The process of purchasing items conventionally&nbsp;is referred to as "pure offline shopping." On the contrary, pure online shopping refers to the exclusive use of digital media throughout the entire purchase process. "Pure online shopping" refers to the act of purchasing items through digital platforms, as well as utilising digital media to gather information about potential purchases.&nbsp;</p>
  <p>According to Chang, Green and Cummins (2021), individuals who exclusively engage in internet shopping are referred to as "pure online shoppers," while those who solely engage in offline shopping are referred to as "pure offline shoppers." "Pure online" is the common term used to describe just people who buy only via online platforms. Nowadays, webrooming and showrooming are becoming increasingly common consumer behavioural trends that were revealed by the results of the research made by Kursan Milaković in 2021. Customers conduct two types of purchase activities, known by the names of showrooming and webrooming, each time they visit the store.&nbsp;Consumer behaviour in the modern world entails a combination of physical goods as well as virtual payment methods.&nbsp;In reality, before ordering an online product from an e-retailer, it is sometimes called "showrooming." In the words of Kulshrestha et al. (2022), "webrooming" consists of researching the product specs online and later purchasing in a physical store.&nbsp;Webrooming involves the use of the Internet to navigate the different profiles of other people.&nbsp;Other than webroomers and showroom workers who are focused on keeping their items in good condition and saving money.&nbsp;In 2018, approximately 51% of clients looked for web rooming, which means that they have researched products online before buying them in a physical store online (Li, 2019).&nbsp;&nbsp;</p>
  <h4>3. Factors affect retail consumers.</h4>
  <p>Consumer behaviour is the epitome of many complex factors that give a good insight into the intricate nature of the decision-making process.&nbsp;The economy is the factor that induces powerful responses because consumers' ability to purchase products depends on their income level, current employment status, and cost of living (Smith, 2002).&nbsp;The psychological components, which include perceptions, attitudes, and motivation, have a stronger effect on consumers' choices (Solomon, 2020). Knowing the psychological factors which drive consumers allows companies to adjust their marketing strategies accurately. From the perspective of social and cultural factors, the most crucial elements are grouped into cultural backgrounds, reference groups, social class, and subcultures, which, in turn, positively affect consumers' values and preferences (Hofstede, 2001). Technology integration into the retail arena, thanks to digitalisation and having information in fingerprints, influences how we interact with retail greatly (Park, 2020). Demographic features, such as age, gender, and family structure, make the composition of the market structure more complex, reflecting the need to differentiate amongst a variety of consumer segments (Schiffman et al., 2013). Sustainability, along with ethical practices, are now the determining criteria that consumers search for before they brand a company as one that is true to their values (Belz and Peattie, 2012). Private factors consist of lifestyle selections and personal characteristics. On this premise, the behaviour of the customer is a very elaborate subject (Engel et al., 1995). The mixing of these myriad elements sets the tone for which companies must strategies to keep up with things like audience analytics, audience attraction, and audience retention. While retailers need to be conversant with the complex nature of consumer behaviours to be able to make strategies that connect perfectly with their audience and enhance loyalty, they need to be keen to change with the market evolution.</p>
  <h4>2.4 Short-Term Impacts of COVID-19 on Consumer Behavior</h4>
  <p>These days, customers' attitudes are more centred on the buying of necessities because of the new patterns of consumption that the pandemic has brought in. Zwanka's study in 2022 emphasises the shift in consumers' buying habits due to the rapid move to online platforms following the shutdown of brick-and-mortar stores. Businesses must stay flexible to adapt to changing customer preferences, as indicated by the Future Consumer Index, which reports a significant rise in trust in online shopping (Mehta et al., 2020). Several studies have suggested that the increased dependence on online shopping led to substantial changes in the retail industry during the lockdown period. The consultancy also notes an increased focus on physical store safety. In 2020, retail safety expectations changed due to a significant study that impacted customer behaviour in the UK. This body of work highlights a drastic shift in consumer behaviour. It focuses on the requirement of consumer trust in online and physical stores, the development of shopping environments and an increase in essential expenditure (Hesham et al., 2021).</p>
  <h4>2.5 Long-Term Effects</h4>
  <p>While the COVID-19 outbreak was taking place, researchers worked hard on dispelling the long-term impacts of the pandemic on consumer behaviour. Joshi and Brahme (2021) reported long-term changes that persisted throughout the entire duration of the epidemic, according to a thorough research study that they conducted. Consumers keep making their decisions around health and safety - soon to be the ultimate determinant of their purchase intentions. The retail industry is retracing its steps in accordance with the increasing Compliance demands brought about by these statistics in the face of changing customer patterns as time moves on. A recent study, specifically by Rahmanov et al. (2021), revealed that UK market customers are inclined toward online platforms rather than the old style where shoppers buy from local markets. This is a pointer to a shifting customer preference behaviour.&nbsp;The overall results of this study not only contributed to the gap in knowledge but also helped the concept of the nature of consumer behaviour patterns that emerged after the COVID-19 crisis (Tien et al., 2021). They illustrate the interaction between irreversible transformations and precise reshaping, which is evident in retail.</p>
  <h4>2.6 Emerging Trends and Future Directions</h4>
  <p>Due to the emergence of the coronavirus, consumers are changing the way they act, which is beneficial for the retail industry and may promote further growth. The most interesting conclusion is that e-commerce is still a preferred medium (Mehta et al., 2020). According to Alsharif et al. (2023), people are heading to the convenience and security of virtual places more and more. The wide-ranging effects of this trend prove how vital it is for companies to have a robust online presence and e-commerce strategies to adapt to the changing market. Research shows consumers assign higher value to ecology in their decision-making process. People are more and more willing to purchase things that are good not only for the environment but for society too. This demonstrates that people are no longer oblivious towards social and environmental problems&mdash;some shops sense and respond to how customer preferences evolve (Gursoy et al., 2022). Researchers who are interested in this topic should look into how complicated customer attitudes and behaviours are, especially when it comes to hybrid shopping experiences that include visiting stores and shopping online. Himeur et al. (2021) say that it would be interesting to look into how technologies like augmented reality (AR) and virtual reality (VR) affect how people make decisions. Staying on top of new trends helps the retail business stay relevant and adapt to the changing consumer landscape post-epidemic. The dynamic interplay among technology, sustainability, and shifting consumer values offers numerous research and strategic possibilities in the retail sector.</p>
  <h4>2.7 Conceptual Framework</h4>
  <p><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXfih5dV4JcIEphjXQjRSWtoorCT5Yp1gGxnI5N_7pqXfm7ytlZQvmWex2lKHb5PIC542NZYen7exrEc5cohzOkk2EY4aOeetxqmQME3dj61CgWxf89PIs0kpzrZafeTR96wlOwrHw7WEPRuT1HZMUg?key=rdqfMRMxvWQ5dcKKbibkBg" alt="Conceptual Framework" width="624" height="281"></p>
  <h4>2.8 Literature Gap</h4>
  <p>COVID-19 Research in the consumer behaviour literature has focused on the immediate effects; perspective is not available from the existing literature, and there is limited understanding of changes in consumer habits (Belz and Peattie, 2012; Solomon, 2020). The initial studies have some knowledge of the sharp movements. The literature does not provide research to systematically understand stable trends after the pandemic and the changes in consumer behaviour post-pandemic in the retail sector in the UK. In fulfilling the gap with this research, I will provide a panoramic of the over a longer period thus and offer, I hope, to what extent consumer has reverted to the pandemic. Fulfilling this gap research provides an understanding to business policymakers and investors that will be useful for lasting managing the consequences of the pandemic.</p>
  <h3>CHAPTER 3:&nbsp;METHODOLOGY</h3>
  <h4>3.1 Introduction&nbsp;</h4>
  <p>This methodology section aims to give an overall strategic direction and shape for the study that is going to examine the long-term impacts of the COVID-19 pandemic on retail consumer behaviour in England.&nbsp;This research will utilise a mixed-method design incorporating both quantitative surveys and qualitative interviews in order to measure statistics as well as sense perceptions.&nbsp;This mixed-strategical basis in which current trends and objectives will be researched is a better fit for the complex changes in consumer behaviors.&nbsp;For the quantitative surveys, which are completed in the Likert scale format, figures are received that can be processed with the help of statistics, detecting the trends and the patterns.&nbsp;Qualitative interviews are helpful for the substantial exploration of specific experiences on an individual level, which is a valuable addition to the quantitative findings.&nbsp;This methodological decision will set a solid research undertaking that is in line with the overall objective, which is to discover the long-lasting retail consumer behaviour changes in the post-COVID-19 era.</p>
  <h4>3.2 Research Design&nbsp;</h4>
  <p>A mixed study that is built upon the pragmatism philosophy and its principle, i.e., inductive, provides an extra boost to the research project. The researcher may thereby critique the design thoroughly and truthfully explain why the design of the study was chosen. Particularly, the integration of both qualitative and quantitative techniques of the hybrid research design is employed, thus leading to the in-depth analysis of different aspects of consumer behaviours after the COVID-19 crisis in the English retailing sector. The thesis addresses a mixture of qualitative and quantitative techniques used to track emerging trends and patterns and, ultimately, the overview consisting of the complexities of post-pandemic consumer behaviours (Rybaczewska et al., 2021). Post-consumption surveys and interviews are fruitful tools that we will use in order to have a chance to cover various consumer views, opinions, habits and their change over time. A descriptive approach can capture the characteristics of change in consumption patterns over an elongated period by being detailed so as to support the researchers in interpreting and comprehending the societal changes elaborately.</p>
  <h4>3.3 Data Generation Methods</h4>
  <p>The research involves the use of different methods of data collection to examine the long-lasting influence of the COVID-19 pandemic on UK retail sector consumer behaviour in a detailed way.</p>
  <ul>
  <li>Surveys and Questionnaires (Quantitative Approach)</li>
  </ul>
  <p>Surveys and questionnaires will be designed which would gather objective data from 30 respondents. A Likert scale type of question structure will explore changes in shopping behaviours and the preferences for online versus in-store environments as well as safety measures perceptions in the retail industry (Ahmadian et al., 2020). The diversity of the demography and the strategy employed in sampling is intended to ensure that the insights are sufficiently comprehensive and representative.</p>
  <ul>
  <li>In-Depth Interviews (Qualitative Approach)</li>
  </ul>
  <p>The qualitative insights will be done through 5 in-depth interviews, which will be carried out with purposefully selected target consumers. The semi-structured interviews allow the capture of different angles on the emotional and practical changes during the post-COVID-19 experience by shopping (Tenny et al., 2017)</p>
  <h4>3.4 Data Analysis Methods</h4>
  <p>The analysis approach will include both descriptive and inductive methods, thus providing a holistic perspective on the implications of COVID-19 pandemic development for consumer behaviour in the British retail sector.</p>
  <ul>
  <li>Quantitative Data Analysis: Regression Analysis</li>
  </ul>
  <p>The regression analysis is going to be used to discover the nature of the relationships and dependencies among variables.&nbsp;The study will focus on using regression models that will identify any relationships between the specific factors which may change, including age, location, and purchasing behaviour, the shift in consumer intent, the choice between channels (online and offline), and other relevant aspects.&nbsp;The approach was statistical, thus allowing for a more profound analysis of the quantitative survey data in order to locate the statistically significant predictors and patterns within the dataset (Krishnamurthy et al., 2017).</p>
  <ul>
  <li>Qualitative Data Analysis: Coding and Systematic Categorisation</li>
  </ul>
  <p>The qualitative data collected from the deep dive interviews and customers will be analysed by carefully coding process. This is done by putting together related responses and pinpointing common themes or problems. At the same time, qualitative data analysis can be accessible through programming that implies meaningful outputs (Li et al., 2020). The order of the narration can play a crucial role in the objectivity and coherence of our conclusions, drawing out valuable consumer patterns.</p>
  <h4>3.6 Ethical Considerations&nbsp;</h4>
  <p>This study is buttressed by a solid ethical framework so as to uphold the maximum standards of professionalism and transparency and honour all participants who will be involved in the investigation of the prolonged impacts of the COVID-19 pandemic on consumer behaviour in England (Hasan, 2023). Informed consent obtained from each participant and information to them about the research purpose, procedures, as well as risks or rewards is the mainstay of ethical behaviour.&nbsp;Hence, in order to safeguard the first of all, the person can leave the program at any stage without harm, even after many years of being there, the guarantee of their autonomy is provided, and the possibility of any external coercion presence is rendered minimal.&nbsp;The privacy of participants should be maintained at the utmost level, and proper measures should be taken to ensure that all the information is anonymously collected and secured with advanced data storage techniques. Only the research team members are to be issued the passcodes to access the raw data, and they will be permitted to access them. The data the team members use to report findings will be made meaningful in ways that the identity of the individuals who participated in the study is not compromised.&nbsp;</p>
  <p>The privacy protection of the competitors and the fact that there is no topic related to socially sustainable concerns will be the main focus. The subjects are the pattern of consumption and perception classes, which would be handled with much care to uphold the dignity of the participants. We envision truth-based reporting as the foundation of our mission and plan to offer undistorted facts and uncorrupted information only. Studies have proven that researchers in this field are well aware of the possibility that sometimes symptoms and side effects can occur and that they try their best to avoid any negative influence on the subjects of their experiment. The aspects of ethical evaluations and oversight are some of the most essential elements of research. The issues that might not be taken into consideration while collecting data scientifically will be immediately reviewed by the ethical review boards and adequately addressed by the appropriate authorities. The study makes a silent promise on ethics, which is of fundamental implication to the identification and understanding of the different forces that cause a shift in society's consumption patterns.</p>
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  <p>&nbsp;</p>
  <h3>Appendix A</h3>
  <p><img style="display: block; margin-left: auto; margin-right: auto;" src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXf0Qiya-CugQvbUHgNKPzKpXN5tUxtwg-nkzSzDXCQD-JYEMEbTiMj7Q_oeQGS8_2Dz237FqSFeVsvRG7-TnxSp9fv5eL7xbRWMv19co7wM3ICmFoQw5r3ah1E2-jlJQOODWTfZWa3PqYae5cxX-ro?key=rdqfMRMxvWQ5dcKKbibkBg" alt="Theory Of Planned Behavior &amp; Purchase Intentions" width="607" height="369"></p>
  <p><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXfih5dV4JcIEphjXQjRSWtoorCT5Yp1gGxnI5N_7pqXfm7ytlZQvmWex2lKHb5PIC542NZYen7exrEc5cohzOkk2EY4aOeetxqmQME3dj61CgWxf89PIs0kpzrZafeTR96wlOwrHw7WEPRuT1HZMUg?key=rdqfMRMxvWQ5dcKKbibkBg" alt="Retail Consumer Behaviors" width="624" height="281"></p>
  <h3>Survey question</h3>
  <div dir="ltr" align="left">
  <table><colgroup><col width="273"><col width="78"><col width="57"><col width="67"><col width="74"><col width="74"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>Question</p>
  </td>
  <td>
  <p>Strongly Agree (0)</p>
  </td>
  <td>
  <p>Agree (1)</p>
  </td>
  <td>
  <p>Neutral (2)</p>
  </td>
  <td>
  <p>Disagree (3)</p>
  </td>
  <td>
  <p>Strongly Disagree (4)</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>I intend to make more purchases online in the next six months.</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Brand loyalty plays a significant role in my purchase decisions.</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>I prefer shopping in physical retail stores over online shopping.</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Online shopping offers greater convenience compared to in-store shopping.</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>I am satisfied with my overall shopping experiences in the past six months.</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>I am likely to switch brands if I find a better deal or offer elsewhere.</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>I trust online retailers to provide secure payment and delivery options.</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>I believe the COVID-19 pandemic has influenced my shopping behaviors.</p>
  </td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>&nbsp;</p>
  <h3>Appendix B:&nbsp;Interview Question</h3>
  <div dir="ltr" align="left">
  <table><colgroup><col width="96"><col width="528"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>Question Number</p>
  </td>
  <td>
  <p>Interview Question</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>1</p>
  </td>
  <td>
  <p>Can you describe your shopping habits before the COVID-19 pandemic and how they have changed since the pandemic began?</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>2</p>
  </td>
  <td>
  <p>What factors influence your decision to shop either online or in physical retail stores?</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>3</p>
  </td>
  <td>
  <p>How important is brand reputation to you when making purchasing decisions?</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>4</p>
  </td>
  <td>
  <p>Have you encountered any challenges or concerns related to online shopping security or product quality?</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>5</p>
  </td>
  <td>
  <p>In your opinion, how do you think the retail industry has adapted to meet consumer needs during and after the pandemic?</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>6</p>
  </td>
  <td>
  <p>Can you share any experiences or observations regarding shifts in consumer behaviors within specific retail sectors?</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>

meta_description:
  The following study focuses on the central issue of COVID-19's impact on consumer behavior in the English retail market long term.


slug: exploring-ways-to-improve-nurses-ability-to-identify-sepsis
title: Exploring Ways to Improve Nurse’s Ability to Identify Sepsis
academic_level: Graduate
subject: Nursing
type_of_paper: Dissertation
citation_style: APA
meta_title: Exploring Ways to Improve Nurse’s Ability to Identify Sepsis
meta_keywords: Identify Sepsis
canonical_url: https://www.cheap-essay-writing.co.uk/
meta_robots: index,follow
created_at: 2025-06-16 15:28:24
updated_at: 2026-01-14 15:52:01
status: published
content:
  <h2>Exploring Ways to Improve Nurse&rsquo;s Ability to Identify Sepsis in Patients in Emergency Department</h2>
  <p>[Name of Student]</p>
  <p>[Date]</p>
  <p>&nbsp;</p>
  <h3>1. Abstract</h3>
  <p>Aim: The objective of this research is to investigate ways to improve the ability of nurses to diagnose sepsis in patients presenting to the Emergency Department (ED), thus screening techniques such as the Systemic Inflammatory Response Syndrome (SIRS), quick Sepsis-related Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) were examined, along with the development of a service improvement plan.</p>
  <p>Background: Taking into account the significant effects of sepsis on patient outcomes, this study examines six important publications from various geographical areas, therefore this systematic literature review offers a thorough overview of the sepsis screening methods currently in use by exploring the sensitivity, specificity, and usefulness of SIRS, qSOFA, and NEWS across a range of sepsis severities.</p>
  <p>Methodology: The approach entails a thorough review of the literature that includes research from Norway, Singapore, Switzerland, the United Kingdom, and the United States, moreover the performance indicators of the screening instruments are assessed rigorously in order to establish the foundation for the suggested service improvement strategy, hence the strategy incorporates NEWS and qSOFA's advantages into the NHS strategic model of project management.</p>
  <p>Results: The results are summarised in the critical discussion, which also highlights the benefits and drawbacks of each screening instrument, thus the purpose, rationale, goals, and quality outcomes of the suggested service improvement plan are all outlined and supported by the information from the literature review, thus a six-phase project plan is provided for methodical execution that follows the NHS structure.</p>
  <p>Conclusion: This study's finding emphasises how crucial the suggested service change is to improving sepsis diagnosis and care in the ED, in addition to highlighting personal growth, the reflective paragraph assesses the benefits and drawbacks of the capstone project and highlights how it might enhance the treatment given to patients who are at risk of sepsis.</p>
  <p>Keywords: Emergency Department, Healthcare Delivery, Nursing Ability, Service Improvement, Sepsis.</p>
  <h3>2. Introduction</h3>
  <p>Patients with sepsis pose a formidable challenge in the emergency room on account of the atypical onset of symptoms and the critical requirement for immediate intervention, therefore the primary concern is the possibility of delayed identification, which may have detrimental effects on elderly individuals who are more vulnerable to the deleterious effects of sepsis (Reyes et al., 2018). This dissertation's main goal is to look into and suggest practical ways to improve emergency room nurses' capacity to recognize sepsis in elderly patients quickly, hence the dissertation endeavours to improve patient outcomes, lower rates of morbidity and death, and improve healthcare procedures in emergency departments by tackling this crucial issue, and this will ultimately improve the overall quality of care for this susceptible population.</p>
  <p>Having worked in the medical field for a while, I have a strong personal interest in finding strategies to help nurses recognise sepsis in elderly patients in the emergency room, and having worked in a variety of hospital settings, I have direct experience with the difficulties nurses encounter when tasked with the difficult duty of diagnosing sepsis in elderly patients (Drahnak et al., 2016). These encounters have increased my drive to learn more about this important healthcare topic since I genuinely want to improve patient care, therefore in the last several months of my work, I have seen cases where the particular difficulties adult people face when presenting with sepsis have caused delays in diagnosis and treatment planning. My understanding of the criticality of diagnosing sepsis has increased as a result of these observations, which have also highlighted the need for specific enhancements in the skills of healthcare professionals, especially nurses who work in the hectic emergency room setting (Porter et al., 2021).</p>
  <p>Understanding the history and contextual details pertaining to the diagnosis of sepsis in elderly patients presenting to the emergency room is crucial to fully appreciate the complexities of this healthcare dilemma, thus the diagnosis of sepsis in the elderly population is more challenging due to the distinct physiological changes and comorbidities that this group shows; usually healthcare practitioners face challenges in accurately and promptly diagnosing atypical symptom presentations due to age-related immune response changes and the presence of chronic diseases. The identification process is further complicated by the emergency department's environment, thus emergency situations are dynamic and high-pressure environments that require quick decisions, which could impede comprehensive evaluations, hence time restrictions and the sheer number of patients may make it more difficult to do the thorough assessment needed to correctly diagnose sepsis in adult people, moreover the importance of focused tactics to increase the accuracy and effectiveness of sepsis detection in emergency care settings is highlighted by this setting (Rababa et al., 2022).</p>
  <p>Caring for elderly patients in the emergency room comes with its own set of problems that have a big effect on the diagnosis of sepsis, this is because emergency rooms are usually very busy and demanding, which makes it hard to do the full evaluation that is needed for a correct diagnosis, especially when it comes to elderly patients (Garnacho-Montero et al., 2014). Time restraints and the need to attend to urgent medical issues may unintentionally obscure the subtle and unusual symptoms that are frequently linked to sepsis in the elderly, in addition, the sheer number of patients requiring emergency care increases the difficulties faced by medical professionals. According to Jones et al. (2015), because of the need to properly allocate resources and prioritise cases, it may not be possible to do the in-depth evaluations that are needed for elderly patients, therefore this could lead to delayed or missed signs of sepsis.</p>
  <p>Identifying sepsis is harder in adult people because they are more likely to be hurt in emergencies because their immune systems are weaker and they already have health problems, because of this, healthcare professionals need to be more aware of these weaknesses and take specific steps to deal with the difficulties of treating sepsis in adult people in emergency care settings (Kane-Gill et al., 2015; Sloane et al., 2018). UK's approach to sepsis treatment is based on the National Institute for Health and Care Excellence (NICE), a group that gives healthcare workers advice based on evidence, thus the NICE guidelines for adult sepsis are the main source for this information. Make it clear how important it is to diagnose sepsis quickly and use tools like the National Early Warning Score (NEWS) to find people who may get worse, thus realising this is also important for starting quick treatments and keeping sepsis from getting worse (Nieves, 2018).</p>
  <h3>3. Methodology</h3>
  <h4>3.2 Research Question</h4>
  <p>"How can the proficiency of emergency department nurses in identifying sepsis in patients be improved, considering the unique challenges posed by the adult population and the demanding environment of emergency care, with a focus on aligning practices with national guidelines and addressing the potential risks associated with misdiagnosis?"</p>
  <h4>3.2 Structured Search Strategy using PICO and MeSH.</h4>
  <p>Selecting the PICO (Population, Intervention, Comparison, and Outcome) framework as the methodology for this capstone project offers a structured and systematic approach to framing research questions. PICO improves the clarity of a research issue by making it easier to conduct a targeted literature search and critical evaluation. PICO guarantees clarity, comparability, and relevance in the formulation of research inquiries by clearly defining the Population, Intervention, Comparison, and Outcome. According to Ebai, (2017) this facilitates the identification of pertinent evidence and the synthesis of findings more efficiently, therefore this methodological decision strengthens the capstone project's overall robustness by encouraging rigour and coherence in the literature evaluation.</p>
  <h5>3.2.1 Population (P)</h5>
  <p>The target population consists of adult patients and emergency department nurses who present with possible symptoms of sepsis, the following terms will be included as synonyms and related designations for "adult patients": "geriatric," "adult," and "ageing population", thus variations such as "emergency department nurses" and "ED nurses" will be integrated to designate nurses.</p>
  <h5>3.2.2 Intervention (I)</h5>
  <p>The intervention under consideration comprises educational programmes, strategies, or interventions designed to improve the ability of emergency department nurses to recognise sepsis in elderly patients, thus "interventions," "educational programmes," "training," and "strategies" are all keywords.</p>
  <h5>3.2.3 Comparison (C)</h5>
  <p>The search will look at any comparative aspects, even if not all studies have a direct comparison group, this includes comparing different interventions or how well interventions work compared to standard practices.</p>
  <h5>3.2.4 Outcome (O)</h5>
  <p>The main objective is to enhance the competence of emergency department nurses in recognising sepsis in elderly patients, this encompasses results associated with precise and prompt identification of sepsis, compliance with established protocols, and ultimately, enhanced patient outcomes.</p>
  <h5>3.2.5 MeSH</h5>
  <p>A controlled vocabulary known as MeSH (Medical Subject Headings) terms is utilised to index articles in PubMed, thus by standardising and classifying publications according to their subjects, they facilitate the process of finding pertinent literature. The MeSH terms that were used in the search strategy for the literature review are "Sepsis," "Sepsis Syndrome," "Septic Shock," "Early Diagnosis," "Diagnosis, Early," "Diagnostic Techniques and Procedures," "Patient Care," "Patient Care Management," "Patient Care Planning," "Education, Nursing," "Nursing Education Research," "Education, Nursing, Continuing," "Emergency Medical Services," "Emergency Service, Hospital," "Triage," "Interdisciplinary Communication," "Interprofessional Relations," "Health Communication," "Quality Improvement," "Total Quality Management," "Quality Assurance, Health Care," etc.</p>
  <h4>3.3 Search Strategy and Justification</h4>
  <p>The search had been conducted using a combination of controlled vocabulary phrases, specifically MeSH terms, and free text terms, therefore utilising both methods guarantee a thorough investigation, as certain articles may be categorised differently or have not yet been assigned MeSH keywords. An example of a search query in PubMed may be: ("emergency nursing"[Mesh] OR "emergency nurses" OR "ED nurses") AND ("sepsis"[Mesh] OR "sepsis" OR "septicemia") AND ("aged"[Mesh] OR "geriatric" OR "adults") AND ("education"[Mesh] OR "intervention" OR "training"). The choice to incorporate both MeSH terms and free-text terms is justified by evidence from the literature, which suggests that relying exclusively on MeSH terms could result in the omission of pertinent publications that employ alternative language.</p>
  <h4>3.4 Critical Appraisal Using CASP</h4>
  <p>Selected studies in this literature review will have their methodological quality and reliability rigorously evaluated using the Critical Appraisal Skills Programmed (CASP) instrument, therefore CASP offers a methodical framework for analysing research findings, guaranteeing a comprehensive assessment of each study's applicability and validity (Enongene, 2022). Selecting papers according to predetermined inclusion criteria is the first stage, hence articles that satisfy the requirements will next go through a thorough CASP examination, moreover studies that have been published within the last ten years and that concentrate on interventions or methods to improve emergency department nurses' ability to identify sepsis in adult patients are included in the inclusion criteria (Ebai, 2017).</p>
  <p>CASP is made up of particular questions designed for various study methods, including qualitative research, cohort studies, and randomized controlled trials (RCTs), thus questions about study design, sample representativeness, data collection techniques, and statistical analysis will be examined about quantitative studies. Aspects such as research design, data collecting, and the rigor of qualitative analysis will be the emphasis of qualitative investigations, therefore the detection of potential biases in research, including selection bias, information bias, and confounding factors, is guided by CASP, furthermore it is essential to assess the internal validity of each study to make sure that the therapies or methods under evaluation are responsible for the observed outcomes rather than methodological flaws (Burke and Conway, 2023).</p>
  <p>CASP encourages evaluation of the external validity or generalizability of study findings in addition to internal validity assessment, this is especially important when putting interventions into practice in various emergency department settings, therefore the process of adopting CASP for critical assessment will result in a summary of each study's advantages and disadvantages (Aitken et al., 2011). In order to facilitate a more nuanced view of the efficacy and application of treatments targeted at enhancing the skill of emergency department nurses in identifying sepsis in patients who are adult, this synthesis will serve as a guide for the interpretation of the available data.</p>
  <h4>3.5 Data Organization Methods</h4>
  <p>My dissertation research relied heavily on Thematic Analysis, which provided a methodological framework for extracting significant insights from the wide range of papers that were found, therefore with this method, studies were thoroughly categorised according to recurrent themes and patterns that emerged throughout the exhaustive evaluation procedure. Finding and comprehending the common themes throughout various studies was the main goal in order to provide a comprehensive and nuanced understanding of the larger field of research, hence every study was thoroughly evaluated to find similarities and differences. I sought to identify the core ideas and recurrent motifs that were present across the literature by organising studies according to common themes (Vanzant and Schmelzer, 2011). Through this technique, the study subject was able to be explored in greater detail, bringing to light not only the prevalent tendencies but also the subtle variations and distinctive viewpoints found throughout the wide range of literature, hence the process of classifying studies according to emergent themes made it easier to recognise significant trends and differences among various research projects. This nuanced understanding made a substantial contribution to the dissertation's literature review section's construction of a coherent narrative, thus it enhanced the study with a thorough picture of the state of the field by enabling a more methodical presentation of the combined insights drawn from the numerous investigations (MacPherson-Dias, 2014).</p>
  <h4>3.6 Analysis Methods and Synthesis into Themes</h4>
  <p>A comprehensive analysis had been carried out in the next literature review to extract pertinent data from the chosen research; thus, the selected analytical approach had entailed a critical appraisal utilizing the CASP (Critical Appraisal Skills Programme) instrument, guaranteeing an exhaustive assessment of the methodological excellence of every research project. This methodical technique had raised the literature review's credibility and general quality; therefore, the studies had been categorized as part of the analysis according to important factors such as study design, sample size, and intervention techniques (Casianos, 2023). For quantitative research, quantitative data such as effect sizes and statistical significance had been extracted and condensed; hence, finding recurrent themes, patterns, and concepts had been the main goal of qualitative research.</p>
  <p>A synthesis of the information that had been gathered was done after the analysis. The goal of this study had been to combine the results of several investigations into logical themes; therefore, to create a cohesive narrative that encapsulated the main ideas found in the literature, the narrative synthesis technique had been utilized. In order to provide a thorough understanding of the interventions aimed at enhancing emergency department nurses' competence in diagnosing sepsis in adult patients, this procedure had entailed analyzing similarities, differences, and linkages between studies.</p>
  <h4>3.7 Summary of Literature Search Results (PRISMA)</h4>
  <p><strong><br><br><img src="data:image/png;base64,[encoded data, 299132 characters]" width="100%"><br></strong></p>
  <h3>4. Findings</h3>
  <h4>4.1 Overview</h4>
  <p>Early detection and treatment of sepsis by ward-based nurses is a critical and well-researched topic in the healthcare industry, therefore patient outcomes are seriously threatened by sepsis, and ward nurses play a critical role in identifying it and acting quickly to treat it. The fact that sepsis is becoming more well recognised as a major cause of death and morbidity shows how relevant this topic is, hence by examining the efficacy of sepsis training programmes for nurses and addressing knowledge, skill, and attitude gaps regarding sepsis screening and management, the chosen papers add to the corpus of current knowledge (Usman, Usman and Ward, 2019). The evaluated publications include research from several nations, which contributes a valuable global perspective to our understanding of sepsis management strategies, thus the fact that studies from Norway and the UK are included shows how widespread the problem of sepsis in healthcare is, moreover the incorporation of research from many geographic regions augments the generalizability and relevance of the results, acknowledging that sepsis is a ubiquitous issue necessitating context-specific strategies.</p>
  <p>The chosen articles use a range of study designs in their research approaches to examine how ward-based nurses manage sepsis, these consist of questionnaire-based evaluations, cross-sectional surveys, and before-and-after intervention studies, therefore a thorough investigation of the subject is made possible by the variety of study approaches, which capture both the quantitative and qualitative facets of nurses' attitudes, abilities, and knowledge of sepsis. The utilisation of various research methodologies enhances the comprehensive comprehension of the intricacies associated with executing sepsis education initiatives and their influence on nursing practices (Nguyen et al., 2014), thus the evaluated publications' diverse study approaches, international representation, and well-established topic all enhance the depth and breadth of understanding regarding the management of sepsis by ward-based nurses.</p>
  <h4>4.2 Thematic Findings</h4>
  <h5>4.2.1 Impact of Sepsis Training Programs&nbsp;</h5>
  <p>The examined articles demonstrated the potential for focused educational interventions to dramatically improve the knowledge, abilities, and attitudes of ward-based nurses regarding sepsis, one key theme that emerged was the impact of sepsis training programmes. Studies carried out in the UK and Norway have consistently shown that structured sepsis training has a favourable impact, therefore significant contributions to the discussion on early detection and treatment of severe sepsis and septic shock have been made by Keep et al. and Torsvik et al. The effectiveness of the NEWS in triaging patients to the ED is the main emphasis of Keep et al, which shows an impressive area under the curve (AUC) of 0.89 in identifying patients at risk for severe sepsis, hence Torsvik et al. highlights the importance of ward nurses in identifying sepsis early on and the study's statistically significant results include an odds ratio (OR) of 2.7 for 30-day survival in the post-intervention group.</p>
  <p>In Norway, the implementation of an intervention in an emergency community hospital led to a significant improvement in the frequency of vital sign observation and the overall quality of ward nurses' care for patients with sepsis, as reported by Torsvik et al. The post-intervention group showed reduced risk of serious organ failure, a shorter duration of stay in intensive care and high dependency units, and increased odds of living for 30 days, therefore these results imply that a thorough sepsis-specific triage that incorporates a flow chart alert and treatment system improves patient outcomes (Keep et al., 2015). Improvements in sepsis therapy, early warning signals, and pathophysiology were made possible in large part by the training nurses received, a noteworthy consequence of the new strategy is that patients receiving care in the intensive care unit (ICU) and high dependency unit (HDU) had lower lengths of stay (LOS), moreover this suggests that by shortening the amount of time patients need intensive care, the simplified approach to sepsis management maximises resource utilisation while also improving patient outcomes.</p>
  <p>Similarly, a research carried out by Keep et al. in an NHS teaching hospital in London demonstrated the beneficial effects of sepsis training on nurses' attitudes, knowledge, and abilities, thus the nurses who took part in the sepsis training demonstrated increased knowledge of the SIRS criteria and the NEWS for sepsis screening, significantly, these nurses showed a more upbeat attitude towards sepsis management and screening, demonstrating the impact of training on their outlook and method of treating sepsis. Keep et al support NEWS as a useful tool for identifying patients during ED triage who are at risk for severe sepsis and septic shock, therefore the authors suggest using a NEWS score of three or higher to initiate a systematic screening process for sepsis, with a 92.6% sensitivity and a 77% specificity. On the other hand, Torsvik et al. present a thorough analysis that emphasises a multimodal approach, encompassing training for doctors and nurses as well as a flow chart, SIRS, and organ failure triage (SOF-Triage) system, hence using NEWS as a physiological observation tool, Keep et al. emphasise the significance of vital signs and observations in the detection of sepsis. They propose that patients at risk can be successfully identified during ED triage if they have a NEWS score of three or higher, with a positive predictive value of 18.7% and a negative predictive value of 99.5%, similar to this, Torsvik et al. emphasise the value of vital sign observations by putting in place a bundle that motivates ward nurses to make better observations more frequently in order to enhance patient outcomes and sepsis identification.</p>
  <p>According to Keep et al., using NEWS at ED triage may result in early diagnosis and treatment, which could enhance patient outcomes, on the other hand, according to Torsvik et al., the post-intervention group experienced favourable outcomes such as a 3.7-day average reduction in length of stay, a 2.7-day gain in 30-day survival, and a 0.7-day decrease in the likelihood of severe organ failure. In order to improve sepsis management, both research emphasise the necessity of proactive involvement in healthcare settings, therefore Keep et al emphasise the significance of consciousness in ED triage, especially with reference to physiological observations, hence to ensure that all staff members are knowledgeable and helping to improve sepsis outcomes, Torsvik et al. go one step further and design a training session for nurses and nursing students.</p>
  <h5>4.2.2 Challenges in Translating Knowledge to Practice&nbsp;</h5>
  <p>Eirian and Lorelei effectively addresses the issue of problems in converting knowledge to practice, the study explores the knowledge, abilities, and attitudes of ward-based nurses on sepsis, illuminating the challenges associated with integrating theoretical understanding into clinical practice. The assessment of sepsis knowledge, abilities, and attitudes among registered nurses working in acute surgical and medical wards in the UK is the main focus of Eirian and Lorelei's (2021) research, therefore by attempting to ascertain how sepsis training affects these factors, the study reveals difficulties in converting information into useful abilities.</p>
  <p>The study implies that it is still difficult to apply theoretical knowledge to practical practice, the study reveals a significant gap in the practical use of skills, even if sepsis training programmes have enhanced nurses' theoretical awareness and confidence, in particular, the study pinpoints fields where theoretical knowledge is not as advanced as actual competence, such as peripheral cannulation and venipuncture (Horng et al., 2017). According to the study, ward nurses frequently lack the hands-on abilities required for key aspects of sepsis therapy, like peripheral cannulation and venipuncture, hence a significant percentage of nurses stated they did not use these skills in their clinical practice, even among those who had received training in them. Because of this competence gap, implementing sepsis care procedures effectively and promptly may be significantly hampered, this is because relying on other healthcare providers to obtain blood cultures and perform required interventions may cause delays.</p>
  <h5>4.2.3 Nurses' knowledge and confidence levels</h5>
  <p>Chua et al.'s study examines the contributing elements as well as registered nurses' knowledge and confidence levels about sepsis, this cross-sectional survey was carried out across multiple sites in Singapore, comprising nurses working in emergency rooms and inpatient units. The average score of 10.56 out of 15 nurses who participated in the study indicated that they knew a decent amount about sepsis, likewise, with a mean score of 18.46 out of 25, their confidence in identifying and treating septic patients was reasonably good, therefore the fact that only 52.0% of the nurses could correctly describe sepsis, however, raised alarms as it suggested a basic knowledge gap. The study finds that employment grade, nursing education level, and clinical work area are important determinants of nurses' knowledge of sepsis, thus sepsis knowledge scores were higher among nurses employed in acute care settings, with advanced degrees, and in higher job grades, moreover this emphasises how nurses' understanding of sepsis may be influenced by their educational background and clinical experience.</p>
  <h5>4.2.4 Comparative Analysis of Sepsis Screening Tools in the Emergency Department</h5>
  <p>With a focus on SIRS, qSOFA, and NEWS, Usman, Usman and Ward, and Durr et al. advanced our knowledge of the efficacy of sepsis screening methods, aiming to enhance sepsis detection and treatment in the ED as swiftly as possible, thus the assessment of the relative efficacy of the sepsis screening instruments emerged as the central theme in both articles. In the study by Usman, Usman and Ward, where SIRS, qSOFA, and NEWS were examined, NEWS emerged as the most reliable method for identifying septic shock and severe sepsis (SS/SS), on the other hand Durr et al directly compared NEWS and qSOFA, revealing that NEWS outperforms qSOFA in terms of sensitivity for sepsis identification, ICU admission, and 28-day mortality when the cutoff is &ge;5, hence there was a consensus between the two trials regarding NEWS's superiority in early sepsis identification.</p>
  <p>Both publications delved into the sensitivity and specificity of the screening tools, Usman, Usman and Ward examined the trade-offs between sensitivity and specificity for NEWS&ge;4, SIRS&ge;2, and qSOFA&ge;2, on the other hand Durr et al presented numerical data demonstrating that NEWS&gt;5 has better sensitivity for sepsis detection than qSOFA&ge;2, underscoring the significance of sensitivity in the early detection of sepsis cases. The evaluation of utility across different sepsis severities emerged as a recurring theme, with Durr et al demonstrating NEWS's superiority to qSOFA in predicting outcomes across all sickness severities, while Usman, Usman and Ward found that qSOFA improves with illness severity, therefore this thematic analysis underscored the importance of considering the range of sepsis severity when evaluating the effectiveness of screening technologies.</p>
  <h5>4.2.5 CASP Analysis of Keep et al.</h5>
  <p><strong>Research Question</strong></p>
  <p>The impact of sepsis training programmes on nurses' knowledge, attitudes, and abilities as well as patient outcomes was the explicit emphasis of the study question.</p>
  <p><strong>Study Design</strong></p>
  <p>A retrospective observational study design was utilised, potentially constraining the capacity to establish causality but still yielding significant insights.</p>
  <p><strong>Recruitment Strategy</strong></p>
  <p>Although the recruitment technique is not stated clearly, the fact that the study was carried out in a London NHS teaching hospital suggests that it is relevant to the research subject.</p>
  <p><strong>Data Collection</strong></p>
  <p>Pre- and post-intervention evaluations of nurses' sepsis-related knowledge, attitudes, and skills were probably included in the data, which is consistent with the study question.</p>
  <h4>4.3 Synthesis of Strengths and Limitations&nbsp;</h4>
  <h5>4.3.1 Early Detection Strategies:</h5>
  <p>The study by Keep et al has a strong retrospective observational design and a sizable sample size of 500 consecutive adult patients who did not experience trauma, therefore the effectiveness of the NEWS in detecting individuals at risk for severe sepsis is indicated by its high area under the curve (AUC) of 0.89. An extensive retrospective analysis of 130,595 adult ED visits is provided by Usman, Usman and Ward., which also includes a detailed comparison of several grading schemes, thus the study's strength is its capacity to quantify several sepsis endpoints using measurements of sensitivity, specificity, and area under the receiver-operating characteristic (AUROC). Keep et al.'s study is retroactive, which could lead to selection bias, and its single-center design restricts generalizability, furthermore, the dynamic character of sepsis development during hospitalisation may not be adequately captured by relying solely on discharge diagnoses, however the retrospective nature of Usman, Usman and Ward.'s study may introduce bias, and the fact that visits with insufficient clinical evaluation (11.4%) were excluded from the analysis may limit the applicability of the results to the whole ED population.</p>
  <h5>4.3.2 Role of Observations and Vital Signs</h5>
  <p>Understanding the significance of observations and vital signs is deepened by Keep et al.'s attention on the connection between the diagnosis of severe sepsis and the first NEWS at ED triage, a precise quantitative evaluation is given by the study's statistical specifics, such as computations of sensitivity and specificity. The significance of ward-level vital sign measurements is emphasised by Torsvik et al and the study's strength lies in its before-and-after intervention design and in the utilisation of an established registry that dates back to 1994, therefore these features show how enhanced observation frequency can be used in real-world settings. The study recognises the difficulty of striking a balance between sensitivity and specificity, particularly in light of the low occurrence of severe sepsis in the ED, and Keep et al. claim that NEWS is a useful tool, although useful, the observational design of the study by Torsvik et al lacks the control provided by randomised controlled trials (RCTs), and the retrospective nature of the investigation may create biases.</p>
  <h5>4.3.3 Intervention Strategies and Outcomes</h5>
  <p>Potential improvements in patient outcomes may result from the Keep et al. study's proposal to use a NEWS score of three or higher as a trigger for systematic sepsis screening and from the emphasis on early detection and treatment, however according to Torsvik et al.'s before-and-after intervention design, the post-intervention group saw improved 30-day survival and a lower risk of catastrophic organ failure. The study by Keep et al. focuses mainly on the correlation between NEWS and the diagnosis of severe sepsis and does not directly intervene, because Torsvik et al relied on historical controls and did not use randomization, there is a chance that confounding variables will be introduced and affect the results that were observed.</p>
  <h5>4.3.4 Training and Education</h5>
  <p>The research conducted by Keep et al supports the need for education in sepsis recognition and advocates for increased awareness at ED triage, particularly with relation to physiological findings, however the study by Chua et al. identifies the variables that affect nurses' knowledge and confidence in diagnosing and treating sepsis, offering insightful information on their understanding. The sample is more diversified because to the multi-site cross-sectional design, on the other hand the study by Keep et al. emphasises awareness, although it does not directly analyse how training and education affect the early detection of sepsis, because Chua et al.'s self-reported data were used in the study, response bias could be introduced, and the cross-sectional design makes it difficult to determine causality.</p>
  <h5>4.3.5 Consideration of Prevalence and Specificity:</h5>
  <p>Keep et al provides a nuanced viewpoint on the interpretation of NEWS by admitting the low occurrence of severe sepsis in the ED and analysing the trade-off between sensitivity and specificity, however Durr et al. provide light on the role that EMS transport plays in sepsis detection and advance our knowledge of the trade-offs that exist between sensitivity and specificity, particularly when comparing NEWS and qSOFA. The study by Keep et al does not explore the possible influence on patient outcomes, instead concentrating on sensitivity and specificity, however the findings' generalisability may be limited by Durr et al.'s retrospective nature and single-center strategy.</p>
  <h5>4.3.6 Need for Further Research</h5>
  <p>Keep et al expressed their dedication to continuing research by recommending a prospective study to verify the relationship between NEWS, triage categories, and the final diagnosis of severe sepsis, similarly according to Torsvik et al, promoting continuous sepsis registries and assessing flow chart developments helps to enhance sepsis management over time. Because Keep et al.'s study is retrospective in nature, more research is required to validate its findings prospectively, the acknowledgement of steady mortality rates implies the need for additional research, and the design of the Torsvik et al. study does not offer convincing proof.</p>
  <h4>4.4 Interplay between Studies and Implications for Practice</h4>
  <h5>4.4.1 Support and Build:</h5>
  <p>The investigations by Keep et al and Durr et al stress how crucial early detection is in the ED, while Durr et al. compares NEWS and qSOFA for sepsis detection, Keep et al. suggests using a NEWS of 3 or above as a threshold for systematic sepsis screening. The focus on NEWS is echoed by Durr et al., who point out that it has a higher sensitivity for identifying sepsis patients in the ED, therefore this alignment serves to support the idea that NEWS is an effective early detection tool, despite concentrating on distinct environments (wards versus EDs), Torsvik et al and Keep et al both stress the significance of vital signs and observations in the treatment of sepsis. In addition to Keep et al.'s focus on NEWS during ED triage, Torsvik et al. show improved 30-day survival with increased observation frequency at the ward level, collectively.</p>
  <h5>4.4.2 Refute and Challenge:</h5>
  <p>There is a disparity in the results between the investigations by Durr et al and Usman, Usman and Ward with reference to the scoring system comparability, while Durr et al contend that qSOFA has a higher specificity than NEWS, Usman, Usman and Ward contend that NEWS is the most accurate scoring system, moreover this disparity calls into doubt the generalizability of scoring systems in various healthcare settings, necessitating additional study to resolve these conflicts and identify the best instrument for particular situations. Keep et al.'s emphasis on NEWS is made more complex by Chua et al.'s study on nurses' skills and confidence in managing sepsis, according to Chua et al., there is a knowledge gap among nurses, suggesting that ineffective sepsis therapy may occur even with the suggested trigger (NEWS &ge; 3), therefore this disproves the notion that a NEWS-based trigger by itself is adequate and emphasises the significance of simultaneous instructional activities.</p>
  <h5>4.4.3 Methodological Quality:</h5>
  <p>Generalisability might be restricted by Keep et al.'s retrospective, single-center design, it is difficult to demonstrate causation when there is no direct intervention because bias may be introduced by relying solely on discharge diagnosis, and however, the study's robustness is increased by the sizable sample size and thorough statistical analysis (Askim et al., 2017). Although Usman, Usman and Ward.'s retrospective research with a large sample size increases statistical power, external validity may be impacted by the exclusion of visits with insufficient clinical evaluation, since the study is focused on ED visits, its relevance to other healthcare settings may be limited, and causality cannot be established due to the study's methodology. Potential biases are introduced by Durr et al.'s retrospective, single-center design, however, the findings are given particular context by the application of the Sepsis-3 consensus criteria and the emphasis on EMS transfer, thus the study adds to the current conversation on sepsis detection technologies while also acknowledging its limits.&nbsp;</p>
  <p>The study's external validity is improved by Chua et al.'s multi-site cross-sectional design, however, causal inference is limited by the cross-sectional design and dependence on self-reported data, therefore the analysis gains depth with the addition of workplace and nursing components. The study's internal validity is enhanced by Torsvik et al.'s before-and-after intervention design, potential biases are introduced, nonetheless, by the retrospective nature and absence of randomization, thus utilising a reputable register that has been in place since 1994 gives the results historical context (Haydar et al., 2017). The strength of a study by Eirian and Lorelei is how well it examines how sepsis training affects nurses' attitudes, knowledge, and abilities, potential biases are introduced.</p>
  <h5>4.4.4 Applicability/Transferability</h5>
  <p>The emphasis on ED triage in the Keep et al study is relevant to emergency situations everywhere, however, generalisability to other healthcare settings might be limited due to the unique environment of the metropolitan university hospital, and on the other hand the application of the Usman, Usman and Ward, the study might be limited to ED settings in sizable academic centres, hence the study's external validity is impacted by the removal of visits without a clinical evaluation. The study by Durr et al. may not be as generalisable because it only looks at patients who were sent by emergency medical services to a particular hospital in Switzerland, the results might apply more readily to environments with comparable EMS procedures (Bhattacharjee, Edelson and Churpek, 2017). The study's transferability to comparable healthcare clusters in Singapore is improved by Chua et al.'s multi-site design, however, generalisability to various nations may be impacted by organisational and cultural differences, and similarly the study conducted by Torsvik et al. has limited generalisability due to its emphasis on a particular hospital in Norway, however, comparable contexts may take into account the ward nurses' emphasis on early recognition.</p>
  <h4>4.5 Summary of Findings</h4>
  <p>The thorough analysis of the research on the early detection and treatment of sepsis highlights important takeaways that are essential for improving patient outcomes, with studies like Keep et al and Durr et al proving its sensitivity in identifying patients at risk for severe sepsis and septic shock, NEWS emerges as a potential tool, especially in the emergency environment. Nonetheless, disparities in results, like those between Durr et al and Usman, Usman and Ward, highlight the necessity of taking context-specific factors into account when putting scoring systems into place, on the other hand as highlighted by Torsvik et al., the importance of expanding attention from the emergency room to the ward levels offers a comprehensive viewpoint on sepsis care. Furthermore, the significance of continuing education and training is shown by the crucial role that healthcare personnel play, as demonstrated by Chua et al. and Eirian and Lorelei, therefore together, these data highlight the difficulty of managing sepsis and call for a multifaceted, nuanced approach that integrates efficient scoring methods, ongoing education, and context-specific factors in a range of healthcare settings.</p>
  <h3>5. Critical Discussion</h3>
  <p>The results of the literature evaluation offer insightful information that can help guide an endeavour to improve services aimed at improving the early detection and treatment of sepsis, acknowledging the merits and demerits of the examined research, it is apparent that a comprehensive strategy is needed to tackle the intricacies of sepsis in various hospital environments (McCoy and Das, 2017). The critical discussion will examine important aspects of a service improvement proposal, utilising the literature's evidence and examining larger contextual variables, thus the usefulness of early warning scores, in particular the NEWS, in identifying patients at risk for severe sepsis and septic shock, as shown by Keep et al and Durr et al, is one important point that the literature emphasises. This demonstrates how important it is to include NEWS into standard clinical procedures (Saeed et al., 2019), particularly in the ED, it is imperative to recognise the disparities in sensitivity and specificity documented in various research, signifying the need of customising the implementation to the distinct attributes and requirements of every healthcare environment.</p>
  <p>Usman, Usman and Ward and Durr et al.'s comparison of various scoring systems, including SIRS, qSOFA, and NEWS, highlights how difficult it is to select the best instrument, thus the proposed service enhancement ought to encompass a meticulous assessment of current procedures and the implementation of a grading scheme that corresponds with the distinct requirements of the medical facility (Goulden et al., 2018). Furthermore, taking into account the results of Torsvik et al., it is essential to shift the focus from the ED to ward-level monitoring, and improving patient outcomes can be greatly aided by training ward nurses and putting early sepsis recognition protocols into practice, therefore the role of healthcare practitioners is crucial when it comes to improving services, as demonstrated by Chua et al. and Eirian and Lorelei.</p>
  <h4>5.1 Improvement in Future Practice</h4>
  <p>The thorough examination of the literature on the diagnosis and treatment of sepsis has greatly improved my comprehension of the vital role nurses play in early detection and intervention, upon contemplation of how this novel understanding will augment my forthcoming nursing practice, a number of important revelations surface, providing a basis for enhanced patient care and augmenting the wider framework of nursing practice (Wallgren et al., 2021; Okafor et al., 2015). Keep et al and Durr et al.'s findings supporting the usefulness of early warning scores especially the NEWS in identifying patients at risk for septic shock and severe sepsis will radically alter the way I approach patient assessment, therefore it will become imperative to integrate NEWS into regular practice, particularly in the ED (Ulla et al., 2013; Liu et al., 2019; Singer et al., 2017). In order to facilitate timely interventions and enable early identification of patients who are worsening.</p>
  <p>The intricacy of choosing the best instrument for sepsis detection is shown by Usman, Usman and Ward and Durr et al.'s evaluation of several scoring systems, including SIRS, qSOFA, and NEWS, in my future work, I will push for a careful analysis of current procedures and the implementation of a scoring system that takes into account the particular needs and features of the medical facility (Downing et al., 2019). Working together with interdisciplinary teams may be necessary to provide a comprehensive and situation-specific approach to sepsis screening, as highlighted by Torsvik et al., broadening the scope to include ward-level observations will help me in my support of an all-encompassing sepsis recognition programme.</p>
  <h4>5.2 Supporting Rationale for the Service Improvement Plan (SIP)</h4>
  <p>Adopting NEWS in the ED in accordance with Keep et al and Durr et al.'s recommendations is in line with the data showing how well it may identify patients who are at risk for septic shock and severe sepsis, thus using NEWS at ED triage with a trigger threshold of three or higher can be a useful screening tool to initiate additional sepsis assessment, which will ultimately result in early diagnosis and treatment. The results of Torsvik et al.'s study highlight the beneficial effects on patient outcomes that occur when ward nurses identify sepsis early, in order to ensure a smooth transition of care from the emergency department to inpatient wards, the SIP incorporates initiatives to encourage interdisciplinary teamwork. Transparent communication, cooperative training sessions, and the inclusion of sepsis screening in standard nursing assessments are all part of this teamwork, thus the necessity of providing nursing personnel with specialised continuing education programmes to enhance their knowledge, abilities, and attitudes about sepsis is highlighted by Chua et al and Eirian and Lorelei.</p>
  <h3>6. A Service Improvement Proposal Plan</h3>
  <p><strong>Appendix 2: </strong>Capstone Project Service improvement template to be submitted with the service improvement&nbsp;</p>
  <p>Year 3 Capstone Project Service Improvement Proposal Form</p>
  <p>Title: Enhancing Sepsis Recognition and Management in Emergency and Inpatient Settings</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="123"><col width="499"></colgroup>
  <tbody>
  <tr>
  <td colspan="2">
  <p>Six stage project management guide (2010) NHS institute for innovation and improvement</p>
  </td>
  </tr>
  <tr>
  <td colspan="2">&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Stage</p>
  </td>
  <td>
  <p>Key steps Ensure all key steps are supported by relevant evidence</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>1. Start out</p>
  </td>
  <td>
  <p>Aim: The primary objective is to implement a comprehensive sepsis recognition and management program within the healthcare system.</p>
  <p>Service: The focus will be on improving sepsis recognition and management in both the ED and inpatient wards.</p>
  <p>Improvement: The key improvement goal is to enhance early identification and intervention for sepsis cases.</p>
  <p>Rationale: This initiative is grounded in evidence from the literature review, which underscores the effectiveness of specific strategies, therefore the NEWS emerges as a valuable tool for early identification of patients at risk for severe sepsis, additionally, tailored education programs for healthcare professionals have been shown to contribute significantly to improved sepsis outcomes.</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>2. Define and scope</p>
  </td>
  <td>
  <p>Objectives (SMART):</p>
  <ul>
  <li>
  Specific: Implement the NEWS in the ED triage process and conduct regular interdisciplinary training sessions for ED and inpatient ward staff, hence establish sepsis education programs targeted at enhancing awareness and skills among healthcare professionals.
  </li>
  <li>
  Measurable: Aim to achieve a 20% increase in sepsis recognition rates within the first six months of implementation, this will be measured through continuous monitoring of sepsis identification and intervention data.
  </li>
  <li>
  Achievable: The objectives are well-supported by the existing infrastructure and available resources within the healthcare system, therefore necessary tools, training materials, and support systems are already in place to facilitate the implementation of NEWS, training sessions, and educational programs.
  </li>
  <li>
  Realistic: The objectives are aligned with evidence-based practices, drawing from the literature review that emphasizes the effectiveness of NEWS and targeted education programs, hence the approach is tailored to the local context, taking into account the specific needs and challenges of the healthcare setting.
  </li>
  <li>
  Timely: The implementation plan is designed to be executed within a six-month timeframe, this timeframe allows for a focused and efficient rollout of NEWS in ED triage, regular training sessions, and the establishment of sepsis education programs..
  </li>
  </ul>
  <p>Stakeholders:</p>
  <p>To ensure a comprehensive and collaborative approach, the involvement of various stakeholders is crucial:</p>
  <ul>
  <li>
  ED Staff: Frontline healthcare professionals in the Emergency Department who play a key role in the initial identification and management of sepsis cases.
  </li>
  <li>
  Inpatient Ward Nurses: Healthcare professionals in the inpatient wards who are responsible for the ongoing care of patients, including those diagnosed with sepsis.
  </li>
  <li>
  Physicians: Medical professionals, including emergency physicians and attending physicians, who contribute to the diagnosis and treatment of sepsis cases.
  </li>
  <li>
  Educators: Individuals responsible for designing and conducting interdisciplinary training sessions and sepsis education programs.
  </li>
  </ul>
  </td>
  </tr>
  <tr>
  <td>
  <p>3. Measure and understand</p>
  </td>
  <td>
  <p>Baseline/Pre-change Measurements</p>
  <p>Current Sepsis Recognition Rates:</p>
  <ul>
  <li>
  Collect data on the existing rates at which sepsis cases are recognised within the ED and inpatient wards.
  </li>
  <li>
  Identify the number of sepsis cases accurately diagnosed during the initial assessment.
  </li>
  </ul>
  <p>Staff Knowledge Levels:</p>
  <ul>
  <li>
  Assess the baseline knowledge levels of healthcare professionals, including ED staff and inpatient ward nurses, regarding sepsis recognition and management.
  </li>
  <li>
  Use surveys, quizzes, or other assessment tools to gauge their understanding of sepsis symptoms, risk factors, and appropriate interventions.
  </li>
  </ul>
  <p>Patient Outcomes:</p>
  <ul>
  <li>
  Examine current patient outcomes related to sepsis, including mortality rates, lengths of hospital stay, and occurrences of severe complications.
  </li>
  <li>
  Analyse the data to understand the impact of the existing sepsis recognition and management practices on patient health.
  </li>
  </ul>
  <p>Success Measurement</p>
  <p>Increased Recognition Rates:</p>
  <ul>
  <li>
  Track and compare the rates of sepsis recognition after the implementation of the NEWS and educational programs.
  </li>
  <li>
  Look for a significant improvement in the identification of sepsis cases during the early stages of patient assessment.
  </li>
  </ul>
  <p>Reduced Time to Intervention:</p>
  <ul>
  <li>
  Measure the time taken from the identification of sepsis to the initiation of appropriate interventions.
  </li>
  <li>
  Aim for a noticeable reduction in the time it takes for healthcare professionals to intervene and provide necessary treatments for sepsis cases.
  </li>
  </ul>
  <p>Improved Patient Outcomes:</p>
  <ul>
  <li>
  Assess changes in patient outcomes, including reductions in sepsis-related mortality rates.
  </li>
  <li>
  Evaluate whether the implementation of NEWS and educational programs contributes to better overall health outcomes for patients diagnosed with sepsis.
  </li>
  </ul>
  <p>Enhanced Staff Knowledge:</p>
  <ul>
  <li>
  Conduct post-implementation assessments to measure the improvement in staff knowledge levels regarding sepsis.
  </li>
  <li>
  Look for increased awareness, accurate identification of symptoms, and adherence to evidence-based practices among healthcare professionals.
  </li>
  </ul>
  <p>Sustainability of Changes:</p>
  <ul>
  <li>
  Evaluate the sustainability of the implemented changes over an extended period.
  </li>
  <li>
  Monitor continued success in sepsis recognition rates, time to intervention, and patient outcomes to ensure lasting positive effects.
  </li>
  </ul>
  </td>
  </tr>
  <tr>
  <td>
  <p>4. Design and plan</p>
  </td>
  <td><br>
  <div dir="ltr" align="left">
  <table><colgroup><col width="158"><col width="326"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>Task</p>
  </td>
  <td>
  <p>Rationale</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Task 1: Implement NEWS in ED triage.</p>
  </td>
  <td>
  <p>Implementing the NEWS in the ED triage involves integrating this standardised scoring system into the initial assessment of patients, thus NEWS has shown remarkable sensitivity and specificity in identifying sepsis, making it a valuable tool for early recognition. This task includes educating ED staff on the NEWS criteria, configuring systems for its seamless integration, and ensuring consistent application during the triage process, hence by incorporating NEWS, the goal is to enhance the accuracy of identifying patients at risk of sepsis, thereby facilitating prompt and targeted interventions.</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Task 2: Conduct interdisciplinary training sessions.</p>
  </td>
  <td>
  <p>Conducting interdisciplinary training sessions is a crucial initiative aimed at fostering collaboration among healthcare professionals and ensuring uniform practices in sepsis recognition and management, therefore these sessions bring together ED staff, inpatient ward nurses, physicians, and educators to participate in comprehensive training programs.&nbsp;</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Task 3: Establish ongoing sepsis education programs.</p>
  </td>
  <td>
  <p>Establishing ongoing sepsis education programs addresses the identified knowledge gaps among nursing staff and contributes to continuous professional development, hence these programs go beyond one-time training sessions and involve the creation of a structured curriculum that encompasses regular updates, workshops, and resources. The content of these programs is tailored to the specific needs identified during the initial assessments of staff knowledge levels.</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  </td>
  </tr>
  <tr>
  <td>
  <p>5. Pilot and implement</p>
  </td>
  <td>
  <ul>
  <li>
  Pilot in one ED unit and one inpatient ward.
  </li>
  <li>
  Leadership Approach: Transformational leadership, fostering a culture of continuous improvement.
  </li>
  </ul>
  </td>
  </tr>
  <tr>
  <td>
  <p>6. Sustain and share</p>
  </td>
  <td>
  <p>Evaluation of Success:</p>
  <ul>
  <li>
  Monitor ongoing sepsis recognition rates, intervention times, and patient outcomes.
  </li>
  <li>
  Use feedback mechanisms for continuous improvement.
  </li>
  </ul>
  <p>Sustainability:</p>
  <ul>
  <li>
  Integrate sepsis protocols into routine practice; incorporate ongoing education into staff development programs.
  </li>
  </ul>
  <p>Sharing the Improvement:</p>
  <ul>
  <li>
  Present findings at departmental meetings.
  </li>
  </ul>
  <ul>
  <li>
  Develop educational materials for wider dissemination.
  </li>
  <li>
  Share outcomes through hospital-wide communication channels.
  </li>
  </ul>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>&nbsp;</p>
  <h3>7. Leading Change Lewin's 3 Stages of Change</h3>
  <p>In leading change to improve nurses' ability to identify sepsis in the ED, adopting effective leadership strategies is crucial for successful implementation, hence drawing upon Lewin's 3 stages of change can provide a structured framework for navigating the complexities of change management.</p>
  <h4>7.1 Leadership Strategies</h4>
  <ul>
  <li>
  Unfreezing: Involve stakeholders by working together and communicating effectively, express the need for change in a clear and concise manner, highlighting the advantages for patient outcomes and the significance of early sepsis detection, therefore establishing habits that are hard to break or doubting the efficacy of novel screening instruments can be the source of resistance to change (Harrison et al., 2021). To address these issues and foster a feeling of urgency among all parties, open communication, empathy, and active listening are necessary, hence according to Lewin, unfreezing entails lowering resistance and raising readiness in order to foster a change-friendly atmosphere, moreover make use of tactics like workshops, public discussion platforms, and opinion leader participation to speed up the unfreezing process.
  </li>
  <li>
  Changing: While the implementation phase is underway, give explicit guidance and assistance, thus team members can be empowered if they are included in decision-making, given access to resources, and given instruction on how to use new screening techniques. Overseeing multidisciplinary groups with disparate backgrounds and viewpoints may result in disagreements or coordination issues (Ratana, Raksmey and Danut, 2020), therefore encouraging a collaborative, respectful, and accountable culture is essential to effective leadership in order to overcome these obstacles and guarantee seamless execution. According to Lewin, in order to bring about the changes that are wanted, active action is required, hence make use of strategies like continuous monitoring, feedback mechanisms, and pilot testing to speed up the adoption of new procedures and quickly resolve any issues that arise.
  </li>
  <li>
  Refreezing: To guarantee long-term effects and sustainability, institutionalise the reforms, acknowledge and celebrate accomplishments, give reinforcement for desired conduct, and incorporate the new screening methods into routine operations, therefore it can be difficult to keep the momentum going and avoid going back to the old habits during the refreezing stage (Burnes, 2020). To avoid reverting, leadership must continue to be watchful, offer continuous assistance, and integrate the changes into the culture of the company, thus stabilising the modifications and incorporating them into the organisational structure are part of Lewin's refreezing stage, moreover to guarantee long-lasting change, use techniques like performance reviews, incentive schemes, and organisational reorganisation to reinforce the new procedures.
  </li>
  </ul>
  <h4>7.2 Challenges in a Team Environment:</h4>
  <ul>
  <li>
  Resistance to Change: Members of the team might object to changes to established practices, to tackle this, open communication is necessary, along with emphasising the advantages of the suggested modifications and incorporating employees in the decision-making process.
  </li>
  <li>
  Interprofessional Differences: Different backgrounds of professionals can lead to differing ideas on managing sepsis, a common understanding and cooperative approach will be facilitated by regular interdisciplinary training and discussion forums (Schuetz et al., 2017).
  </li>
  </ul>
  <h4>7.3 Change Management Theory</h4>
  <p>Applying the Kotter's 8-Step Change Model provides a structured approach:</p>
  <ul>
  <li>
  Create Urgency: Communicate the urgency of improving sepsis outcomes, supported by statistics from the literature review.
  </li>
  <li>
  Form a Powerful Coalition: Establish a leadership team with representatives from nursing, medicine, and education to drive the change collaboratively.
  </li>
  <li>
  Create a Vision for Change: Clearly articulate the vision of enhanced sepsis recognition and improved patient outcomes.
  </li>
  <li>
  Communicate the Vision: Utilize various channels for consistent communication to ensure all team members are well-informed.
  </li>
  <li>
  Remove Obstacles: Identify and address barriers proactively. This may involve providing additional resources, addressing concerns, and ensuring training opportunities.
  </li>
  <li>
  Create Short-term Wins: Celebrate small victories, such as successful pilot implementations, to build momentum and confidence.
  </li>
  <li>
  Build on the Change: Incorporate sepsis protocols into routine practice and continuously improve through feedback mechanisms.
  </li>
  <li>
  Anchor the Changes in Corporate Culture: Ensure that the changes become an integral part of the organizational culture through ongoing education and reinforcement.
  </li>
  </ul>
  <h3>8. Conclusion</h3>
  <p>This capstone project has been a journey of discovery and education, offering insightful information about the intricate field of sepsis identification in ED settings, therefore wwhen evaluating the endeavor's advantages and disadvantages, it becomes clear that the thorough literature analysis acted as a foundation for the next service enhancement plan. This thoughtful conclusion, which follows the project's path from conception to the suggested plan and, eventually, its possible influence on patient care, captures the heart of the capstone project, hence taking on this capstone project has changed me in both ways&mdash;professionally and personally. My grasp of important problems in emergency care has grown as a result of the in-depth study of sepsis screening techniques, which has also improved my ability to evaluate studies critically, thus by navigating the complex network of literature, I have improved my analytical skills and am now able to recognise subtle differences in study designs, methodology, and practical implications, moreover this experience has made me more aware of the value of evidence-based practice in nursing and has given me a renewed dedication to keeping up with the rapidly changing healthcare environment.</p>
  <p>This capstone project's main goal was to investigate strategies for improving nurses' capacity to recognise sepsis in ED patients, thus the effectiveness of screening methods, with an emphasis on SIRS, fast qSOFA, and NEWS, was investigated by a thorough assessment of the literature. The literature review, which included studies from various geographic regions, filled in gaps in the knowledge base and provided a solid framework for a service enhancement proposal. It also synthesised the body of current knowledge, therefore NEWS emerged as a frontrunner in sepsis diagnosis, according to a rigorous study of six major papers. Outperforming both SIRS and qSOFA, the investigations by Omar et al and Dimitri et al offered strong evidence supporting NEWS as a dependable and timely screening technique, hence analyses of sensitivity and specificity demonstrated the advantage of NEWS, particularly at a cutoff of &ge;5, establishing it as a useful tool for early detection of sepsis at different intensities, moreover these results highlight the critical importance of NEWS and provide a clear roadmap for improving sepsis screening practices in EDs.</p>
  <p>Keep et al. examined the usefulness of the NEWS in diagnosing severe sepsis and emphasised its use in triaging patients to the ED, conversely, Eirian and Lorelei investigated how sepsis training affected ward-based nurses' attitudes, knowledge, and abilities, however both studies agreed on the significance of NEWS despite the different circumstances; Keep et al. recommended its usage in ED situations, while Eirian and Lorelei emphasised its role after training. Eirian and Lorelei emphasised the significance of improved knowledge and skills while focusing on the effects of sepsis training on ward-based nurses, on the other hand, Torsvik et al. investigated ward nurses' early sepsis detection in an emergency and community hospital, although the two studies had different backgrounds, they agreed that it is important to increase nurses' capacity to identify sepsis; Eirian and Lorelei concentrated on training, while Torsvik et al. focused on better ward surveillance.</p>
  <p>Torsvik et al.'s study was carried out in a Norwegian emergency hospital, emphasising the benefits of early ward-level sepsis recognition, Chua et al., on the other hand, conducted a multi-site cross-sectional study in Singapore to evaluate nurses' confidence and knowledge regarding sepsis care. Both studies recognised that healthcare personnel need more education and training in sepsis management, despite working in different contexts, therefore the suggested service enhancement plan is greatly influenced by the results of the literature review, moreover the evidence synthesis has not only identified the benefits and drawbacks of the current screening instruments, but it has also brought attention to the urgent need for a more sophisticated strategy. The plan, which is based on the NHS strategic model of project management, combines the depth of qSOFA for severity assessment with the accuracy of NEWS for early identification in a strategic manner, hence this synergistic strategy has the potential to transform the way sepsis is identified in EDs, enabling a more thorough and prompt response to this life-threatening illness.</p>
  <h3>References</h3>
  <ul>
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  <li>Keep, J.W., Messmer, A.S., Sladden, R., Burrell, N., Pinate, R., Tunnicliff, M. and Glucksman, E., 2015. National early warning score at Emergency Department triage may allow earlier identification of patients with severe sepsis and septic shock: a retrospective observational study.&nbsp;Emergency Medicine Journal.</li>
  <li>Levin, S., Toerper, M., Hamrock, E., Hinson, J.S., Barnes, S., Gardner, H., Dugas, A., Linton, B., Kirsch, T. and Kelen, G., 2018. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index.&nbsp;Annals of emergency medicine,&nbsp;71(5), pp.565-574.</li>
  <li>Liu, Z., Meng, Z., Li, Y., Zhao, J., Wu, S., Gou, S. and Wu, H., 2019. Prognostic accuracy of the serum lactate level, the SOFA score and the qSOFA score for mortality among adults with Sepsis.&nbsp;Scandinavian journal of trauma, resuscitation and emergency medicine,&nbsp;27, pp.1-10.</li>
  <li>MacPherson-Dias, R., 2014.&nbsp;Development of a Sepsis Screening Tool for Use with Elderly Patients in Nursing Homes. University of California, Davis.</li>
  <li>Mao, Q., Jay, M., Hoffman, J.L., Calvert, J., Barton, C., Shimabukuro, D., Shieh, L., Chettipally, U., Fletcher, G., Kerem, Y. and Zhou, Y., 2018. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.&nbsp;BMJ open,&nbsp;8(1), p.e017833.</li>
  <li>McCoy, A. and Das, R., 2017. Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units.&nbsp;BMJ open quality,&nbsp;6(2).</li>
  <li>Mikkelsen, M.E., Shah, C.V., Meyer, N.J., Gaieski, D.F., Lyon, S., Miltiades, A.N., Goyal, M., Fuchs, B.D., Bellamy, S.L. and Christie, J.D., 2013. The epidemiology of acute respiratory distress syndrome in patients presenting to the emergency department with severe sepsis.&nbsp;Shock (Augusta, Ga.),&nbsp;40(5), p.375.</li>
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  <li>Nieves, A., 2018.&nbsp;Improving Nurses' Identification of Sepsis in the Hospitalized Elderly&nbsp;(Doctoral dissertation, Grand Canyon University).</li>
  <li>Okafor, N., Payne, V.L., Chathampally, Y., Miller, S., Doshi, P. and Singh, H., 2015. Using voluntary reports from physicians to learn from diagnostic errors in emergency medicine.&nbsp;Emergency Medicine Journal.</li>
  <li>Paul, R., Melendez, E., Stack, A., Capraro, A., Monuteaux, M. and Neuman, M.I., 2014. Improving adherence to PALS septic shock guidelines.&nbsp;Pediatrics,&nbsp;133(5), pp.e1358-e1366.</li>
  <li>Porter, T.K., Turner, K.M., McMillian-Bohler, J. and De Gagne, J.C., 2021. Improving care of skilled nursing patients: implementation of early sepsis recognition.&nbsp;Journal of Gerontological Nursing,&nbsp;47(8), pp.37-44.</li>
  <li>Rababa, M., Bani-Hamad, D., Hayajneh, A.A. and Al Mugheed, K., 2022. Nurses' knowledge, attitudes, practice, and decision-making skills related to sepsis assessment and management.&nbsp;Electronic Journal of General Medicine,&nbsp;19(6).</li>
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  </ul>
  <h3>Appendices</h3>
  <h4>Appendix 1. Conceptual Table - PICO Elements and Search Approach</h4>
  <p>Table 1. Conceptual Table - PICO Elements and Search Approach</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="169"><col width="236"><col width="208"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>PICO Element</p>
  </td>
  <td>
  <p>Description</p>
  </td>
  <td>
  <p>Search Approach</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Population</p>
  </td>
  <td>
  <p>Adults with suspected or confirmed sepsis in emergency department settings</p>
  </td>
  <td>
  <p>Database search focused on sepsis in adults, emergency department settings, including relevant keywords and synonyms</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Intervention/Exposure</p>
  </td>
  <td>
  <p>Various sepsis screening tools: National Early Warning Score (NEWS), quick Sepsis-related Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS)</p>
  </td>
  <td>
  <p>Comprehensive literature review exploring the effectiveness and applicability of NEWS, qSOFA, and SIRS in sepsis identification</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Comparison</p>
  </td>
  <td>
  <p>Comparative analysis of screening tools, including sensitivity, specificity, and outcomes</p>
  </td>
  <td>
  <p>Comparative analysis of studies evaluating NEWS, qSOFA, and SIRS, emphasizing differences in performance and clinical outcomes</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Outcome</p>
  </td>
  <td>
  <p>Early identification of sepsis, accuracy of screening tools, impact on patient outcomes</p>
  </td>
  <td>
  <p>Examination of studies reporting on the effectiveness of sepsis screening tools in early identification and improved outcomes</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p><strong><br><br></strong></p>
  <h4>Appendix 2. CASP Quality Appraisal</h4>
  <p>Table 2. CASP Quality Appraisal</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="84"><col width="78"><col width="78"><col width="78"><col width="78"><col width="78"><col width="78"><col width="78"><col width="78"><col width="78"><col width="108"></colgroup>
  <tbody>
  <tr>
  <td>
  <p>Study</p>
  </td>
  <td>
  <p>Consideration of Ethical Issues</p>
  </td>
  <td>
  <p>Rigorous Data Analysis</p>
  </td>
  <td>
  <p>Clear Statement of Findings</p>
  </td>
  <td>
  <p>Is the Research Valuable?</p>
  </td>
  <td>
  <p>Statement of Aims</p>
  </td>
  <td>
  <p>Appropriate Methodology</p>
  </td>
  <td>
  <p>Appropriate Research Design</p>
  </td>
  <td>
  <p>Appropriate Recruitment Strategy</p>
  </td>
  <td>
  <p>Appropriate Data Collection</p>
  </td>
  <td>
  <p>Consideration of Relationship between Researcher and Participants</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Keep et al.</p>
  </td>
  <td>
  <p>✗</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Chua et al.</p>
  </td>
  <td>
  <p>✗</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✗</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Usman, Usman and Ward.</p>
  </td>
  <td>
  <p>✗</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>&nbsp;</td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✗</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Torsvik et al.</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Durr et al.</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Eirian and Lorele</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  <td>
  <p>✓</p>
  </td>
  </tr>
  </tbody>
  </table>
  </div>
  <p><strong>&nbsp;</strong></p>
  <h4>Appendix 3. Potential Findings from Selected Studies</h4>
  <p>Table 3. Potential Findings from Selected Studies</p>
  <div dir="ltr" align="left">
  <table><colgroup><col width="96"><col width="106"><col width="117"><col width="118"><col width="104"><col width="134"><col width="121"><col width="126"><col width="115"></colgroup>
  <tbody>
  <tr>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Article</p>
  </td>
  <td>
  <p>Study Focus</p>
  </td>
  <td>
  <p>Objective</p>
  </td>
  <td>
  <p>Design</p>
  </td>
  <td>
  <p>Population</p>
  </td>
  <td>
  <p>Measurements</p>
  </td>
  <td>
  <p>Results</p>
  </td>
  <td>
  <p>Conclusion</p>
  </td>
  <td>
  <p>Limitations</p>
  </td>
  </tr>
  <tr>
  <td colspan="9">
  <p>Early Detection Strategies</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Keep et al.</p>
  </td>
  <td>
  <p>Early sepsis detection using NEWS</p>
  </td>
  <td>
  <p>Demonstrate effectiveness of NEWS</p>
  </td>
  <td>
  <p>Retrospective observational design</p>
  </td>
  <td>
  <p>500 consecutive adult patients</p>
  </td>
  <td>
  <p>Sensitivity, specificity, AUROC</p>
  </td>
  <td>
  <p>High AUC of 0.89 in detecting severe sepsis</p>
  </td>
  <td>
  <p>Effective for early detection; Single-center bias</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Usman, Usman and Ward.</p>
  </td>
  <td>
  <p>Comparative analysis of grading schemes</p>
  </td>
  <td>
  <p>Evaluate sensitivity, specificity, AUROC</p>
  </td>
  <td>
  <p>Comprehensive analysis with large sample size</p>
  </td>
  <td>
  <p>ED visits (population size not specified)</p>
  </td>
  <td>
  <p>Sensitivity, specificity, AUROC</p>
  </td>
  <td>
  <p>Comparative analysis of grading schemes</p>
  </td>
  <td>
  <p>Strength in quantifying multiple endpoints</p>
  </td>
  <td>
  <p>Exclusion of visits with insufficient evaluation</p>
  </td>
  </tr>
  <tr>
  <td colspan="9">
  <p>Role of Observations and Vital Signs</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Keep et al.</p>
  </td>
  <td>
  <p>Connection between severe sepsis diagnosis</p>
  </td>
  <td>
  <p>Demonstrate significance of vital signs</p>
  </td>
  <td>
  <p>Retrospective observational design</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Initial NEWS at ED triage</p>
  </td>
  <td>
  <p>Emphasizes role of observations and vital signs</p>
  </td>
  <td>
  <p>Robust retrospective design; Single-center bias</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Torsvik et al.</p>
  </td>
  <td>
  <p>Ward-level vital sign measurements</p>
  </td>
  <td>
  <p>Highlight importance of observations</p>
  </td>
  <td>
  <p>Before-and-after intervention design</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Before-and-after comparison</p>
  </td>
  <td>
  <p>Improved 30-day survival; Lower risk of organ failure</p>
  </td>
  <td>
  <p>Significance of ward-level observations</p>
  </td>
  <td>
  <p>Lack of RCT control; Retrospective biases</p>
  </td>
  </tr>
  <tr>
  <td colspan="9">
  <p>Intervention Strategies and Outcomes</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Keep et al.</p>
  </td>
  <td>
  <p>Systematic sepsis screening using NEWS</p>
  </td>
  <td>
  <p>Advocate early detection and treatment</p>
  </td>
  <td>
  <p>Retrospective observational design</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Proposal for systematic screening using NEWS</p>
  </td>
  <td>
  <p>Proposal for systematic sepsis screening</p>
  </td>
  <td>
  <p>Emphasis on early detection; Lack of direct intervention</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Torsvik et al.</p>
  </td>
  <td>
  <p>Post-intervention outcomes</p>
  </td>
  <td>
  <p>Evaluate impact on 30-day survival</p>
  </td>
  <td>
  <p>Before-and-after intervention design</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Post-intervention outcomes</p>
  </td>
  <td>
  <p>Improved 30-day survival; Lower risk of organ failure</p>
  </td>
  <td>
  <p>Positive outcomes post-intervention</p>
  </td>
  <td>
  <p>Lack of randomization; Potential confounding</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Eirian and Lorelei</p>
  </td>
  <td>
  <p>Challenges in Translating Knowledge</p>
  </td>
  <td>
  <p>Examine the challenges of applying theoretical knowledge into practice</p>
  </td>
  <td>
  <p>Cross-sectional study</p>
  </td>
  <td>
  <p>Registered nurses in acute surgical and medical wards in the UK</p>
  </td>
  <td>
  <p>Sepsis knowledge, abilities, attitudes</p>
  </td>
  <td>
  <p>Difficulty in applying theoretical knowledge</p>
  </td>
  <td>
  <p>Need for interventions focusing on practical skills</p>
  </td>
  <td>
  <p>Cross-sectional design, potential bias, self-reported data</p>
  </td>
  </tr>
  <tr>
  <td colspan="9">
  <p>Training and Education</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Keep et al.</p>
  </td>
  <td>
  <p>Increased awareness at ED triage</p>
  </td>
  <td>
  <p>Emphasize significance of training</p>
  </td>
  <td>
  <p>Retrospective observational design</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Not directly assessing training impact</p>
  </td>
  <td>
  <p>Emphasis on awareness; Lack of direct analysis</p>
  </td>
  <td>
  <p>Lack of direct analysis; Single-center bias</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Chua et al.</p>
  </td>
  <td>
  <p>Variables affecting nurses' knowledge</p>
  </td>
  <td>
  <p>Understand nurses' knowledge and confidence</p>
  </td>
  <td>
  <p>Multi-site cross-sectional design</p>
  </td>
  <td>
  <p>Nurses in emergency rooms and inpatient units</p>
  </td>
  <td>
  <p>Knowledge scores, confidence levels</p>
  </td>
  <td>
  <p>Knowledge gaps among nurses; Need for further training</p>
  </td>
  <td>
  <p>Identification of knowledge and skill gaps</p>
  </td>
  <td>
  <p>Cross-sectional limitations; Self-reported data</p>
  </td>
  </tr>
  <tr>
  <td colspan="9">
  <p>Consideration of Prevalence and Specificity</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Keep et al.</p>
  </td>
  <td>
  <p>Interpretation of NEWS considering</p>
  </td>
  <td>
  <p>Acknowledge low occurrence of severe</p>
  </td>
  <td>
  <p>Retrospective observational design</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Sensitivity, specificity, prevalence, and trade-offs</p>
  </td>
  <td>
  <p>Nuanced interpretation of NEWS; Trade-offs analysis</p>
  </td>
  <td>
  <p>Awareness of low prevalence in the ED; Single-center bias</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Durr et al.</p>
  </td>
  <td>
  <p>EMS transport's role in sepsis detection</p>
  </td>
  <td>
  <p>Explore factors influencing sensitivity</p>
  </td>
  <td>
  <p>Retrospective observational design</p>
  </td>
  <td>
  <p>Patients sent by EMS to a Swiss hospital</p>
  </td>
  <td>
  <p>Sensitivity, specificity, and trade-offs</p>
  </td>
  <td>
  <p>NEWS outperforms qSOFA; Consideration of EMS role</p>
  </td>
  <td>
  <p>Specific to EMS settings; Potential biases</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td colspan="9">
  <p>Need for Further Research</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Keep et al.</p>
  </td>
  <td>
  <p>Commitment to ongoing research</p>
  </td>
  <td>
  <p>Propose prospective study</p>
  </td>
  <td>
  <p>Retrospective observational design</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Recommendations for future research</p>
  </td>
  <td>
  <p>Call for prospective research; Acknowledge limitations</p>
  </td>
  <td>
  <p>Need for prospective validation; Acknowledge biases</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Torsvik et al.</p>
  </td>
  <td>
  <p>Emphasis on continuous research</p>
  </td>
  <td>
  <p>Promote continuous sepsis registries</p>
  </td>
  <td>
  <p>Before-and-after intervention design</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Continuous sepsis registries; Assessment of flow charts</p>
  </td>
  <td>
  <p>Call for continuous research; Acknowledge design limits</p>
  </td>
  <td>
  <p>Retrospective design; Need for ongoing assessment</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td colspan="9">
  <p>Interplay between Studies and Implications for Practice</p>
  </td>
  </tr>
  <tr>
  <td>
  <p>Keep et al. and Durr et al.</p>
  </td>
  <td>
  <p>Significance of early detection in ED</p>
  </td>
  <td>
  <p>Support for NEWS in early detection</p>
  </td>
  <td>
  <p>Retrospective observational and comparative designs</p>
  </td>
  <td>
  <p>Not specified</p>
  </td>
  <td>
  <p>Recommendations for NEWS use in ED triage</p>
  </td>
  <td>
  <p>Alignment on early detection using NEWS</p>
  </td>
  <td>
  <p>Discrepancy on scoring system comparability</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  <tr>
  <td>
  <p>Keep et al. and Chua et al.</p>
  </td>
  <td>
  <p>Effectiveness of NEWS in sepsis detection</p>
  </td>
  <td>
  <p>Support for heightened monitoring</p>
  </td>
  <td>
  <p>Retrospective observational and cross-sectional designs</p>
  </td>
  <td>
  <p>Nurses in emergency rooms and inpatient units</p>
  </td>
  <td>
  <p>Knowledge scores, confidence levels, and self-reported data</p>
  </td>
  <td>
  <p>Alignment on significance of vital signs; Discrepancy on NEWS effectiveness</p>
  </td>
  <td>
  <p>Lack of direct analysis on training impact; Methodological differences</p>
  </td>
  <td>&nbsp;</td>
  </tr>
  </tbody>
  </table>
  </div>

meta_description:
  The objective of this research is to investigate ways to improve the ability of nurses to diagnose sepsis in patients presenting to the Emergency Department (ED).


