ABSTRACTThe increasing popularity of social networking for small-and-medium enterprises (SMEs) has resulted in a significant body of research. This study conducts a bibliometric analysis to identify the intellectual structure of the research domain and the major research themes within the domain. Based on a sample of 1710 prior studies published between 2010 and 2021, this study identifies the leading authors, institutions, countries, keywords, and co-authorship networks. Further, SME social media marketing, SME social media performance, and SME social media innovation are identified as the major themes of research. This study identifies the critical areas and potential directions for future research.KEYWORDS: Bibliometric analysissocial networkingSMEs decision makingco-citation analysisco-word analysisco-authorshipco-occurrences Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Social Networking Technologies in SMEs: A Bibliometric Analysis","authors":"Sandip Rakshit, Anand Jeyaraj, Tripti Paul, Sandeep Mondal","doi":"10.1080/08874417.2023.2273811","DOIUrl":"https://doi.org/10.1080/08874417.2023.2273811","url":null,"abstract":"ABSTRACTThe increasing popularity of social networking for small-and-medium enterprises (SMEs) has resulted in a significant body of research. This study conducts a bibliometric analysis to identify the intellectual structure of the research domain and the major research themes within the domain. Based on a sample of 1710 prior studies published between 2010 and 2021, this study identifies the leading authors, institutions, countries, keywords, and co-authorship networks. Further, SME social media marketing, SME social media performance, and SME social media innovation are identified as the major themes of research. This study identifies the critical areas and potential directions for future research.KEYWORDS: Bibliometric analysissocial networkingSMEs decision makingco-citation analysisco-word analysisco-authorshipco-occurrences Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"133 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-26DOI: 10.1080/08874417.2023.2270453
Satish Radhakrishnan, Lavanya Rajendran
ABSTRACTIn the digital age, information is drastically exchanged among users. This data exchange paved the way for unsolicited access by cybercriminals, which could lead to psychological and financial loss. In this study, through a pre- and posttest experimental design, 668 Indian teenagers aged between fifteen and nineteen were evaluated last year. The preliminary study revealed low performance by teenagers in e-mail practices, password management, software practices, social media usage, and privacy settings. Through a novel intervention, 36 teenagers were observed through a curated information security module. The pretest and posttest analyses significantly supported the effects of security training, and Cohen’s d effect size reiterated the importance of progressive outcomes in their security literacy and practices. The intervention focused on the importance of threat perception and coping appraisal for inculcating security parameters and behavioral change among the teenagers.KEYWORDS: Information security awarenesssecurity practicesemail securitypassword securitysecurity training AcknowledgmentsWe would like to thank all the candidates who have participated in this research and spent time with us in spite of their busy commitments.Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Information Security Practices and Intervention Among Teenagers","authors":"Satish Radhakrishnan, Lavanya Rajendran","doi":"10.1080/08874417.2023.2270453","DOIUrl":"https://doi.org/10.1080/08874417.2023.2270453","url":null,"abstract":"ABSTRACTIn the digital age, information is drastically exchanged among users. This data exchange paved the way for unsolicited access by cybercriminals, which could lead to psychological and financial loss. In this study, through a pre- and posttest experimental design, 668 Indian teenagers aged between fifteen and nineteen were evaluated last year. The preliminary study revealed low performance by teenagers in e-mail practices, password management, software practices, social media usage, and privacy settings. Through a novel intervention, 36 teenagers were observed through a curated information security module. The pretest and posttest analyses significantly supported the effects of security training, and Cohen’s d effect size reiterated the importance of progressive outcomes in their security literacy and practices. The intervention focused on the importance of threat perception and coping appraisal for inculcating security parameters and behavioral change among the teenagers.KEYWORDS: Information security awarenesssecurity practicesemail securitypassword securitysecurity training AcknowledgmentsWe would like to thank all the candidates who have participated in this research and spent time with us in spite of their busy commitments.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"493 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136376378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACTCybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements in cybercrime prediction, highlighting the relevant research. For this purpose, we reviewed more than 150 research articles and discussed 50 most recent and appropriate ones. We start the review with some standard methods cybercriminals use and then focus on the latest machine and deep learning techniques, which detect anomalous behavior and identify potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset. We then focus on active and reinforcement learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. This paper presents a holistic view of cutting-edge developments and publicly available datasets.KEYWORDS: Cybercrime predictionmachine learningcybersecurity AcknowledgmentsThe authors wish to acknowledge all those who contributed to the preparation and revision of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Recent Advancements in Machine Learning for Cybercrime Prediction","authors":"Elluri, Lavanya, Mandalapu, Varun, Vyas, Piyush, Roy, Nirmalya","doi":"10.1080/08874417.2023.2270457","DOIUrl":"https://doi.org/10.1080/08874417.2023.2270457","url":null,"abstract":"ABSTRACTCybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements in cybercrime prediction, highlighting the relevant research. For this purpose, we reviewed more than 150 research articles and discussed 50 most recent and appropriate ones. We start the review with some standard methods cybercriminals use and then focus on the latest machine and deep learning techniques, which detect anomalous behavior and identify potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset. We then focus on active and reinforcement learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. This paper presents a holistic view of cutting-edge developments and publicly available datasets.KEYWORDS: Cybercrime predictionmachine learningcybersecurity AcknowledgmentsThe authors wish to acknowledge all those who contributed to the preparation and revision of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"44 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135220216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.1080/08874417.2023.2270452
Sanjeev Shukla, Manoj Misra, Gaurav Varshney
ABSTRACTCyberattacks on e-mails are of different types, but the most pervasive and ubiquitous are spoofing attacks. Our approach uses memory forensics to extract e-mail headers from live memory to perform an e-mail header investigation to identify spoofing attacks. We have identified the research gaps and advanced our work to achieve better results. In this paper, we have made two significant improvements. First is URL validation module that uses a novel technique of checking each captured URL with an MX record and e-mail URL features. This scheme is fast, and reduces the total time from 35 sec to 27 sec. Second, spoofed e-mail detection is ameliorated by applying an ML model built using two novel e-mail header fields (BIMI and X-FraudScore) and four authentication header fields (SPF, DKIM, DMARC, and ARC). This enhances the spoofed e-mail detection accuracy from 96.15% to 97.57% with low false positives.KEYWORDS: Email spoofingemail attacksmemory forensicsemail forensicscyber-security Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request through email.
{"title":"Spoofed Email Based Cyberattack Detection Using Machine Learning","authors":"Sanjeev Shukla, Manoj Misra, Gaurav Varshney","doi":"10.1080/08874417.2023.2270452","DOIUrl":"https://doi.org/10.1080/08874417.2023.2270452","url":null,"abstract":"ABSTRACTCyberattacks on e-mails are of different types, but the most pervasive and ubiquitous are spoofing attacks. Our approach uses memory forensics to extract e-mail headers from live memory to perform an e-mail header investigation to identify spoofing attacks. We have identified the research gaps and advanced our work to achieve better results. In this paper, we have made two significant improvements. First is URL validation module that uses a novel technique of checking each captured URL with an MX record and e-mail URL features. This scheme is fast, and reduces the total time from 35 sec to 27 sec. Second, spoofed e-mail detection is ameliorated by applying an ML model built using two novel e-mail header fields (BIMI and X-FraudScore) and four authentication header fields (SPF, DKIM, DMARC, and ARC). This enhances the spoofed e-mail detection accuracy from 96.15% to 97.57% with low false positives.KEYWORDS: Email spoofingemail attacksmemory forensicsemail forensicscyber-security Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request through email.","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"6 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135567535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1080/08874417.2023.2267510
Peng Gao, Haotian Zhang, Ming Wang, Weiyong Yang, Xinshen Wei, Zhuo Lv, Zengzhou Ma
ABSTRACTInsider threats pose a significant concern for critical information infrastructures. Graph neural networks are widely used for detection due to their ability to model complex relationships among network entities. However, deep learning algorithms struggle with learning from business system data as anomalies are extremely rare. To tackle this challenge, we propose deep temporal graph infomax (DTGI), a new method for detecting insider threats in real-world scenarios with highly imbalanced data. DTGI utilizes an extended continuous-time dynamic heterogeneous graph network and a behavior context constraint anomaly sample generator. This generator incorporates attack behavior context constraints to augment attack samples and enhance the performance of the supervised model. Extensive experiments conducted on the CERT dataset, consisting of over one million records, demonstrate that DTGI surpasses state-of-the-art methods in terms of detection performance.KEYWORDS: Insider threatanomaly detectiondynamic graphgraph neural networkgraph contrastive learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by the State Grid Science and Technology Project [Project No.5108-202224046A-1-1-ZN].
{"title":"Deep Temporal Graph Infomax for Imbalanced Insider Threat Detection","authors":"Peng Gao, Haotian Zhang, Ming Wang, Weiyong Yang, Xinshen Wei, Zhuo Lv, Zengzhou Ma","doi":"10.1080/08874417.2023.2267510","DOIUrl":"https://doi.org/10.1080/08874417.2023.2267510","url":null,"abstract":"ABSTRACTInsider threats pose a significant concern for critical information infrastructures. Graph neural networks are widely used for detection due to their ability to model complex relationships among network entities. However, deep learning algorithms struggle with learning from business system data as anomalies are extremely rare. To tackle this challenge, we propose deep temporal graph infomax (DTGI), a new method for detecting insider threats in real-world scenarios with highly imbalanced data. DTGI utilizes an extended continuous-time dynamic heterogeneous graph network and a behavior context constraint anomaly sample generator. This generator incorporates attack behavior context constraints to augment attack samples and enhance the performance of the supervised model. Extensive experiments conducted on the CERT dataset, consisting of over one million records, demonstrate that DTGI surpasses state-of-the-art methods in terms of detection performance.KEYWORDS: Insider threatanomaly detectiondynamic graphgraph neural networkgraph contrastive learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by the State Grid Science and Technology Project [Project No.5108-202224046A-1-1-ZN].","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135825277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-05DOI: 10.1080/08874417.2023.2261010
Keng-Boon Ooi, Garry Wei-Han Tan, Mostafa Al-Emran, Mohammed A. Al-Sharafi, Alexandru Capatina, Amrita Chakraborty, Yogesh K. Dwivedi, Tzu-Ling Huang, Arpan Kumar Kar, Voon-Hsien Lee, Xiu-Ming Loh, Adrian Micu, Patrick Mikalef, Emmanuel Mogaji, Neeraj Pandey, Ramakrishnan Raman, Nripendra P. Rana, Prianka Sarker, Anshuman Sharma, Ching-I Teng, Samuel Fosso Wamba, Lai-Wan Wong
ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).KEYWORDS: Generative artificial intelligencemachine learninglarge language modelChatGPTBard Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions","authors":"Keng-Boon Ooi, Garry Wei-Han Tan, Mostafa Al-Emran, Mohammed A. Al-Sharafi, Alexandru Capatina, Amrita Chakraborty, Yogesh K. Dwivedi, Tzu-Ling Huang, Arpan Kumar Kar, Voon-Hsien Lee, Xiu-Ming Loh, Adrian Micu, Patrick Mikalef, Emmanuel Mogaji, Neeraj Pandey, Ramakrishnan Raman, Nripendra P. Rana, Prianka Sarker, Anshuman Sharma, Ching-I Teng, Samuel Fosso Wamba, Lai-Wan Wong","doi":"10.1080/08874417.2023.2261010","DOIUrl":"https://doi.org/10.1080/08874417.2023.2261010","url":null,"abstract":"ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).KEYWORDS: Generative artificial intelligencemachine learninglarge language modelChatGPTBard Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/08874417.2023.2259346
Kenan Degirmenci, Michael H. Breitner, Ferry Nolte, Jens Passlick
We investigate legal concerns in privacy calculus, which are currently not given enough attention in privacy research. Legal aspects can lead to liability issues in various information systems scenarios such as bring your own device (BYOD) in the workplace. To analyze the impact of legal concerns in privacy calculus, we conducted a quantitative study by surveying 542 employees from three countries: United States, Germany, and South Korea. Building on our research model to test our hypothesized relationships, structural equation modeling was employed. Our findings provide recommendations for multinational organizations to mitigate legal concerns in privacy calculus. A comparison of the three countries reveals that employees from the United States and South Korea place greater emphasis on legal concerns compared to German employees. We develop an understanding of employees’ concerns with liability issues, and how these affect their privacy calculus in a BYOD context.
{"title":"Legal and Privacy Concerns of BYOD Adoption","authors":"Kenan Degirmenci, Michael H. Breitner, Ferry Nolte, Jens Passlick","doi":"10.1080/08874417.2023.2259346","DOIUrl":"https://doi.org/10.1080/08874417.2023.2259346","url":null,"abstract":"We investigate legal concerns in privacy calculus, which are currently not given enough attention in privacy research. Legal aspects can lead to liability issues in various information systems scenarios such as bring your own device (BYOD) in the workplace. To analyze the impact of legal concerns in privacy calculus, we conducted a quantitative study by surveying 542 employees from three countries: United States, Germany, and South Korea. Building on our research model to test our hypothesized relationships, structural equation modeling was employed. Our findings provide recommendations for multinational organizations to mitigate legal concerns in privacy calculus. A comparison of the three countries reveals that employees from the United States and South Korea place greater emphasis on legal concerns compared to German employees. We develop an understanding of employees’ concerns with liability issues, and how these affect their privacy calculus in a BYOD context.","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"889 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135829915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-29DOI: 10.1080/08874417.2023.2260333
Priya Sharma, Vrinda Gupta, Sandeep Kumar Sood
ABSTRACTPost-quantum cryptography (PQC) is under development to guard against the threats of quantum computers by implementing a new class of cryptosystems. In this direction, much work has been done since 2006, which has led to many publications. Hence, this study presents an overview of PQC research through scientometric analysis of the data containing 1611 publications published from 2006 to 2023, retrieved from the Scopus database. The analysis identifies growth, trends, leading countries, and significant publications, providing insights into impactful PQC research. It also demonstrates a significant rise in publications after 2015, and the United States is a highly productive country. Furthermore, this study also discusses the managerial view, which can assist technology managers in understanding its impact on global and local markets. The findings of this analysis can be a valuable resource for researchers, policymakers, and stakeholders interested in the future of cryptography and other potential impacts of quantum computing.KEYWORDS: Post-quantum cryptography (PQC)scientometricVOSviewerpublication trendsmanagerial perspective Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data supporting the findings presented in this article is downloadable from Scopus database or can be made available by the corresponding author upon reasonable request.Notesa These classifications of economic status are done according to statistics prepared by the Economy Analysis and Policy Division (EAPD) of the Department of Economy and Social Affairs of the United Nations Secretariat. For more, readers are requested to refer toCitation50
{"title":"Post-Quantum Cryptography Research Landscape: A Scientometric Perspective","authors":"Priya Sharma, Vrinda Gupta, Sandeep Kumar Sood","doi":"10.1080/08874417.2023.2260333","DOIUrl":"https://doi.org/10.1080/08874417.2023.2260333","url":null,"abstract":"ABSTRACTPost-quantum cryptography (PQC) is under development to guard against the threats of quantum computers by implementing a new class of cryptosystems. In this direction, much work has been done since 2006, which has led to many publications. Hence, this study presents an overview of PQC research through scientometric analysis of the data containing 1611 publications published from 2006 to 2023, retrieved from the Scopus database. The analysis identifies growth, trends, leading countries, and significant publications, providing insights into impactful PQC research. It also demonstrates a significant rise in publications after 2015, and the United States is a highly productive country. Furthermore, this study also discusses the managerial view, which can assist technology managers in understanding its impact on global and local markets. The findings of this analysis can be a valuable resource for researchers, policymakers, and stakeholders interested in the future of cryptography and other potential impacts of quantum computing.KEYWORDS: Post-quantum cryptography (PQC)scientometricVOSviewerpublication trendsmanagerial perspective Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data supporting the findings presented in this article is downloadable from Scopus database or can be made available by the corresponding author upon reasonable request.Notesa These classifications of economic status are done according to statistics prepared by the Economy Analysis and Policy Division (EAPD) of the Department of Economy and Social Affairs of the United Nations Secretariat. For more, readers are requested to refer toCitation50","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-29DOI: 10.1080/08874417.2023.2259334
Katia Guerra, Chang Koh, Victor Prybutok, Vess Johnson
ABSTRACTInternet of Things (IoT) and blockchain are complex digital technologies characterized by a strong connection and dependency between individuals, data, and information technology (IT). A socio-technical perspective that captures the dynamic interaction of social, legal, and technical artifacts is essential to understanding the IoT and blockchain phenomena. This review provides direction for employing a social-technical perspective across IoT and blockchain studies. The findings lead to new insights for developing a theoretical framework, research model, and research agenda to investigate IoT and blockchain technology. Practitioners and institutions can use these findings to develop strategies to promote the adoption of IoT and blockchain technologies through specific technical, legal, and social strategies. Overall, this research is unique in its attempt to trace new trajectories for future IS scholars and practitioners facing the complexity of IoT and blockchain.KEYWORDS: Internet of thingsblockchainsocio-technical paradigmsocial, legal, and technical factors Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"IoT and Blockchain: A Review and a Technical-Legal-Social Acceptance Model","authors":"Katia Guerra, Chang Koh, Victor Prybutok, Vess Johnson","doi":"10.1080/08874417.2023.2259334","DOIUrl":"https://doi.org/10.1080/08874417.2023.2259334","url":null,"abstract":"ABSTRACTInternet of Things (IoT) and blockchain are complex digital technologies characterized by a strong connection and dependency between individuals, data, and information technology (IT). A socio-technical perspective that captures the dynamic interaction of social, legal, and technical artifacts is essential to understanding the IoT and blockchain phenomena. This review provides direction for employing a social-technical perspective across IoT and blockchain studies. The findings lead to new insights for developing a theoretical framework, research model, and research agenda to investigate IoT and blockchain technology. Practitioners and institutions can use these findings to develop strategies to promote the adoption of IoT and blockchain technologies through specific technical, legal, and social strategies. Overall, this research is unique in its attempt to trace new trajectories for future IS scholars and practitioners facing the complexity of IoT and blockchain.KEYWORDS: Internet of thingsblockchainsocio-technical paradigmsocial, legal, and technical factors Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-27DOI: 10.1080/08874417.2023.2255551
Sophia Xiaoxia Duan, Hepu Deng
This paper investigates whether personality traits matter in job performance in digital work. A conceptual model is developed within the background of the big five personality traits theory and the boundary theory. This model is then tested and validated using structural equation modeling of the survey data in Australia. The study shows that agreeableness, conscientiousness, and extraversion significantly influence job performance while neuroticism and conscientiousness have significant influence on work-life balance. It finds that individuals’ attitude toward digital work negatively moderates the influence of agreeableness on work-life balance and the impact of conscientiousness on job performance. The study reveals that work-life balance has a significant and direct influence on job performance. This study extends existing research on the relationship between job performance, work-life balance, and personality traits and enhances the knowledge of the interplay between digital technologies and individuals in digital work.
{"title":"Job Performance in Digital Work: Do Personality Traits Matter?","authors":"Sophia Xiaoxia Duan, Hepu Deng","doi":"10.1080/08874417.2023.2255551","DOIUrl":"https://doi.org/10.1080/08874417.2023.2255551","url":null,"abstract":"This paper investigates whether personality traits matter in job performance in digital work. A conceptual model is developed within the background of the big five personality traits theory and the boundary theory. This model is then tested and validated using structural equation modeling of the survey data in Australia. The study shows that agreeableness, conscientiousness, and extraversion significantly influence job performance while neuroticism and conscientiousness have significant influence on work-life balance. It finds that individuals’ attitude toward digital work negatively moderates the influence of agreeableness on work-life balance and the impact of conscientiousness on job performance. The study reveals that work-life balance has a significant and direct influence on job performance. This study extends existing research on the relationship between job performance, work-life balance, and personality traits and enhances the knowledge of the interplay between digital technologies and individuals in digital work.","PeriodicalId":54855,"journal":{"name":"Journal of Computer Information Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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