Pub Date : 2025-10-01Epub Date: 2025-05-05DOI: 10.1016/j.ijinfomgt.2025.102911
A. Mohammed Abubakar , Ömer Turunç , Mohammad Soliman , Alexandre Sukhov
This research explores the socio-technical factors behind employees’ quiet quitting by investigating how social factors (boreout syndrome and digital cohorts) and technical factors (IS-induced depletion and social media usage) interact to encourage this behavior. A two-wave design across two independent studies was used with an expanded analytical approach. Study 1 used a symmetric variable-based analysis to identify IS-induced depletion, boreout, low social media usage, and digital immigrant status as key predictors of quiet quitting. In Study 2, boreout, social media usage, and digital immigrant status emerged as significant predictors. An asymmetric case-based analysis further demonstrated that quiet quitting results from combinations of conditions: in Study 1, IS-induced depletion and boreout were prevalent among digital immigrants with low social media usage. Similarly, in Study 2, IS-induced depletion and boreout occurred among digital natives or individuals with low social media usage. Contrary to the assumption that digital natives are more prone to quiet quitting, the findings indicate that digital immigrants are, in fact, more susceptible to this behavior when exposed to these stressors.
{"title":"Exploring the role of information systems-induced depletion, boreout syndrome, social media use on quiet quitting among digital cohorts","authors":"A. Mohammed Abubakar , Ömer Turunç , Mohammad Soliman , Alexandre Sukhov","doi":"10.1016/j.ijinfomgt.2025.102911","DOIUrl":"10.1016/j.ijinfomgt.2025.102911","url":null,"abstract":"<div><div>This research explores the socio-technical factors behind employees’ quiet quitting by investigating how social factors (boreout syndrome and digital cohorts) and technical factors (IS-induced depletion and social media usage) interact to encourage this behavior. A two-wave design across two independent studies was used with an expanded analytical approach. Study 1 used a symmetric variable-based analysis to identify IS-induced depletion, boreout, low social media usage, and digital immigrant status as key predictors of quiet quitting. In Study 2, boreout, social media usage, and digital immigrant status emerged as significant predictors. An asymmetric case-based analysis further demonstrated that quiet quitting results from combinations of conditions: in Study 1, IS-induced depletion and boreout were prevalent among digital immigrants with low social media usage. Similarly, in Study 2, IS-induced depletion and boreout occurred among digital natives or individuals with low social media usage. Contrary to the assumption that digital natives are more prone to quiet quitting, the findings indicate that digital immigrants are, in fact, more susceptible to this behavior when exposed to these stressors.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"84 ","pages":"Article 102911"},"PeriodicalIF":20.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01Epub Date: 2025-05-05DOI: 10.1016/j.ijinfomgt.2025.102916
Byron W. Keating , Rory Mulcahy , Aimee Riedel , Amanda Beatson , Kate Letheren
Service organizations are increasingly deploying generative AI (GenAI) chatbots to handle service failures, yet there is a critical gap in understanding how anthropomorphic AI design can improve service recovery outcomes. This study addresses that gap by investigating whether making AI agents more human-like can mitigate customers’ stress during service recovery and foster positive word-of-mouth (PWOM). Grounded in the Transactional Model of Stress and Coping, we propose that anthropomorphic cues in AI interactions reduce customers’ stress appraisals of service failures. A multi-study experimental design was employed, including a pilot study and three scenario-based experiments that manipulated AI anthropomorphism and service failure severity. The results show that anthropomorphized AI significantly lowers customer stress levels and, in turn, increases PWOM, with stress appraisals mediating the relationship between AI anthropomorphism and positive word-of-mouth. Notably, these benefits emerged mainly for low-severity service failures, and the stress-reduction effect of an anthropomorphic AI agent was most pronounced for customers with limited personal coping resources. These findings provide actionable insights for service managers and AI designers: incorporating human-like warmth and competence into AI service agents can enhance recovery experiences by alleviating customer stress, thereby encouraging PWOM and improving overall service recovery effectiveness.
{"title":"Designing AI to elicit positive word-of-mouth in service recovery: The role of stress, anthropomorphism, and personal resources","authors":"Byron W. Keating , Rory Mulcahy , Aimee Riedel , Amanda Beatson , Kate Letheren","doi":"10.1016/j.ijinfomgt.2025.102916","DOIUrl":"10.1016/j.ijinfomgt.2025.102916","url":null,"abstract":"<div><div>Service organizations are increasingly deploying generative AI (GenAI) chatbots to handle service failures, yet there is a critical gap in understanding how anthropomorphic AI design can improve service recovery outcomes. This study addresses that gap by investigating whether making AI agents more human-like can mitigate customers’ stress during service recovery and foster positive word-of-mouth (PWOM). Grounded in the <em>Transactional Model of Stress and Coping</em>, we propose that anthropomorphic cues in AI interactions reduce customers’ stress appraisals of service failures. A multi-study experimental design was employed, including a pilot study and three scenario-based experiments that manipulated AI anthropomorphism and service failure severity. The results show that anthropomorphized AI significantly lowers customer stress levels and, in turn, increases PWOM, with stress appraisals mediating the relationship between AI anthropomorphism and positive word-of-mouth. Notably, these benefits emerged mainly for low-severity service failures, and the stress-reduction effect of an anthropomorphic AI agent was most pronounced for customers with limited personal coping resources. These findings provide actionable insights for service managers and AI designers: incorporating human-like warmth and competence into AI service agents can enhance recovery experiences by alleviating customer stress, thereby encouraging PWOM and improving overall service recovery effectiveness.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"84 ","pages":"Article 102916"},"PeriodicalIF":20.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01Epub Date: 2025-04-30DOI: 10.1016/j.ijinfomgt.2025.102915
Mohsin Malik , Amir Andargoli , Kerem Pala , Guilherme Luz Tortorella
The extant theorising of digital transformation has seldom focused on the human agency and how the human agency is affected by the dynamic of human-technology that entails social conflicts. To address this, we provide a complementary explanation of digital transformations grounded in socio-cognitive and role theories. We suggest that employees’ cognitions and behaviours influence digital transformations, but this relationship is dampened by conflicts arising from the dynamic of human-technology. Survey data from 256 participants involved in digital transformations in Australia was used to test the suggested relationships. The statistical findings confirm that employees’ cognitive trust is the driving mechanism that affects digital transformation both directly and indirectly through the innovative behaviour of employees. Role conflict was found to weaken the relationships between trust and innovative behaviour and trust and digital transformation. The empirical validation of the integrative socio-cognitive and role theorising of digital transformations—highlighting the potential social conflicts arising from the human technology interactions—has important theoretical and managerial implications for the strategic decision-making governing digital transformations.
{"title":"Towards explaining the effects of the human-technology dynamic on human agency in digital transformations","authors":"Mohsin Malik , Amir Andargoli , Kerem Pala , Guilherme Luz Tortorella","doi":"10.1016/j.ijinfomgt.2025.102915","DOIUrl":"10.1016/j.ijinfomgt.2025.102915","url":null,"abstract":"<div><div>The extant theorising of digital transformation has seldom focused on the human agency and how the human agency is affected by the dynamic of human-technology that entails social conflicts. To address this, we provide a complementary explanation of digital transformations grounded in socio-cognitive and role theories. We suggest that employees’ cognitions and behaviours influence digital transformations, but this relationship is dampened by conflicts arising from the dynamic of human-technology. Survey data from 256 participants involved in digital transformations in Australia was used to test the suggested relationships. The statistical findings confirm that employees’ cognitive trust is the driving mechanism that affects digital transformation both directly and indirectly through the innovative behaviour of employees. Role conflict was found to weaken the relationships between trust and innovative behaviour and trust and digital transformation. The empirical validation of the integrative socio-cognitive and role theorising of digital transformations—highlighting the potential social conflicts arising from the human technology interactions—has important theoretical and managerial implications for the strategic decision-making governing digital transformations.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"84 ","pages":"Article 102915"},"PeriodicalIF":20.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-04-14DOI: 10.1016/j.ijinfomgt.2025.102908
Reza Marvi , Pantea Foroudi , Naja AmirDadbar
Given the scarcity of previous studies on employee–AI collaboration and its impact on employee behavior and user engagement, we investigated its potential to drive user engagement using a mixed-method approach. Grounded in qualitative findings from 27 participants in a healthcare setting, we propose a robust model that emphasizes the impact of AI–employee collaboration on AI mastery goal, user engagement, and a paradox mindset, as well as the moderating role of AI empathy and technological frames. Using a quantitative method, we collected data from 452 participants in a healthcare setting across two studies. Our findings showed that AI–employee collaboration can drive AI mastery goal and a paradox mindset. We also found empirical evidence that both AI mastery goal and the paradox mindset can mediate the relationship between employee–AI collaboration and user engagement. Moreover, our findings revealed interesting moderating results across two studies. In Study 1, significant effects were found for both employee–AI collaboration and AI mastery goal at low AI empathy, but not at high levels. In Study 2, while the interaction between employee–AI collaboration and AI empathy was not significant, the influence of AI mastery goal became significant at high empathy levels, and the paradox mindset showed a significant effect only at high levels of AI empathy. These findings provide managers with valuable insights into the essential operations dynamic of employee–AI collaboration, underscoring its important role in enhancing user engagement.
{"title":"Dynamics of user engagement: AI mastery goal and the paradox mindset in AI–employee collaboration","authors":"Reza Marvi , Pantea Foroudi , Naja AmirDadbar","doi":"10.1016/j.ijinfomgt.2025.102908","DOIUrl":"10.1016/j.ijinfomgt.2025.102908","url":null,"abstract":"<div><div>Given the scarcity of previous studies on employee–AI collaboration and its impact on employee behavior and user engagement, we investigated its potential to drive user engagement using a mixed-method approach. Grounded in qualitative findings from 27 participants in a healthcare setting, we propose a robust model that emphasizes the impact of AI–employee collaboration on AI mastery goal, user engagement, and a paradox mindset, as well as the moderating role of AI empathy and technological frames. Using a quantitative method, we collected data from 452 participants in a healthcare setting across two studies. Our findings showed that AI–employee collaboration can drive AI mastery goal and a paradox mindset. We also found empirical evidence that both AI mastery goal and the paradox mindset can mediate the relationship between employee–AI collaboration and user engagement. Moreover, our findings revealed interesting moderating results across two studies. In Study 1, significant effects were found for both employee–AI collaboration and AI mastery goal at low AI empathy, but not at high levels. In Study 2, while the interaction between employee–AI collaboration and AI empathy was not significant, the influence of AI mastery goal became significant at high empathy levels, and the paradox mindset showed a significant effect only at high levels of AI empathy. These findings provide managers with valuable insights into the essential operations dynamic of employee–AI collaboration, underscoring its important role in enhancing user engagement.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"83 ","pages":"Article 102908"},"PeriodicalIF":20.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-02-20DOI: 10.1016/j.ijinfomgt.2025.102888
Zewei Li , Zheng Zhang , Mingwei Wang , Qi Wu
With the rapid advancement of large language models (LLMs), the phenomenon of LLMs dependence has emerged and garnered significant attention. However, previous scales have been insufficient to measure the extent of individuals' dependence on LLMs. The current study aims to utilize the human-computer trust model and addiction theory to develop and validate the LLMs dependence scale (LDS) and to report its psychometric properties. An exploratory structural investigation of LLMs dependence was conducted with a sample of 421 LLMs users (Sample 1), which included items analysis, exploratory factor analysis, and network analysis. Additionally, a formal test was performed with a separate sample of 1030 LLMs users (Sample 2), with the data undergoing structural validation through confirmatory factor analysis, validity testing, and reliability testing. To mitigate the potential negative impacts of LLMs dependence, we employed the NodeIdentifyR algorithm for computational simulation interventions to identify critical intervention nodes within the LLMs dependence network. The results indicated that the LDS (18 items) exhibited a bifactor structure of functional dependence and existential dependence. The confirmatory factor model demonstrated a good fit and the LDS also showed good criterion-related validity. Subsequent simulated results of alleviating interventions suggested that users' existential dependence was primarily driven by their dependence on LLMs to handle tedious and boring tasks, while functional dependence was more influenced by users' belief in the omnipotence of LLMs. In summary, the factor structure of the LDS is clear, and its reliability and validity indices meet psychometric standards, making it an effective tool for measuring LLMs dependence.
{"title":"From assistance to reliance: Development and validation of the large language model dependence scale","authors":"Zewei Li , Zheng Zhang , Mingwei Wang , Qi Wu","doi":"10.1016/j.ijinfomgt.2025.102888","DOIUrl":"10.1016/j.ijinfomgt.2025.102888","url":null,"abstract":"<div><div>With the rapid advancement of large language models (LLMs), the phenomenon of LLMs dependence has emerged and garnered significant attention. However, previous scales have been insufficient to measure the extent of individuals' dependence on LLMs. The current study aims to utilize the human-computer trust model and addiction theory to develop and validate the LLMs dependence scale (LDS) and to report its psychometric properties. An exploratory structural investigation of LLMs dependence was conducted with a sample of 421 LLMs users (Sample 1), which included items analysis, exploratory factor analysis, and network analysis. Additionally, a formal test was performed with a separate sample of 1030 LLMs users (Sample 2), with the data undergoing structural validation through confirmatory factor analysis, validity testing, and reliability testing. To mitigate the potential negative impacts of LLMs dependence, we employed the NodeIdentifyR algorithm for computational simulation interventions to identify critical intervention nodes within the LLMs dependence network. The results indicated that the LDS (18 items) exhibited a bifactor structure of functional dependence and existential dependence. The confirmatory factor model demonstrated a good fit and the LDS also showed good criterion-related validity. Subsequent simulated results of alleviating interventions suggested that users' existential dependence was primarily driven by their dependence on LLMs to handle tedious and boring tasks, while functional dependence was more influenced by users' belief in the omnipotence of LLMs. In summary, the factor structure of the LDS is clear, and its reliability and validity indices meet psychometric standards, making it an effective tool for measuring LLMs dependence.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"83 ","pages":"Article 102888"},"PeriodicalIF":20.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-03-31DOI: 10.1016/j.ijinfomgt.2025.102903
Hamed Azad Moghddam , Gary Mortimer , Hormoz Ahmadi , Hamid Sharif-Nia
In the rapidly evolving digital landscape, livestream commerce has become a powerful tool for tourism providers. This study underscores the pressing need for businesses within the tourism sector to understand the nuances of this dynamic medium and align their marketing strategies accordingly. In responding to calls, this research utilises a rigorous mixed-methods approach to examine the interactions between streamers, viewers, and the broader online community. Inspiration theory and optimum stimulation level theory are applied in a tourism context to examine how livestream viewers’ individual differences influence their inspiration for impulse buying and cross-buying. Further, sociomateriality is adopted to examine how the interconnectedness of livestreaming has an impact on inspiration and behaviours. Findings from in-depth interviews and an online survey of social media users who had livestreaming commerce experience indicate that impulse buying and cross-buying are driven by antecedents such as two-way communications between streamers and individuals and online communities and individuals, as well as the perception of inspiration. These relationships are moderated by the optimum stimulation level. This study offers directions for further research and insights for managers, suggesting a focus on the dynamics of livestream commerce.
{"title":"How livestream engagement inspires tourist purchasing behaviour: A multi-study approach","authors":"Hamed Azad Moghddam , Gary Mortimer , Hormoz Ahmadi , Hamid Sharif-Nia","doi":"10.1016/j.ijinfomgt.2025.102903","DOIUrl":"10.1016/j.ijinfomgt.2025.102903","url":null,"abstract":"<div><div>In the rapidly evolving digital landscape, livestream commerce has become a powerful tool for tourism providers. This study underscores the pressing need for businesses within the tourism sector to understand the nuances of this dynamic medium and align their marketing strategies accordingly. In responding to calls, this research utilises a rigorous mixed-methods approach to examine the interactions between streamers, viewers, and the broader online community. Inspiration theory and optimum stimulation level theory are applied in a tourism context to examine how livestream viewers’ individual differences influence their inspiration for impulse buying and cross-buying. Further, sociomateriality is adopted to examine how the interconnectedness of livestreaming has an impact on inspiration and behaviours. Findings from in-depth interviews and an online survey of social media users who had livestreaming commerce experience indicate that impulse buying and cross-buying are driven by antecedents such as two-way communications between streamers and individuals and online communities and individuals, as well as the perception of inspiration. These relationships are moderated by the optimum stimulation level. This study offers directions for further research and insights for managers, suggesting a focus on the dynamics of livestream commerce.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"83 ","pages":"Article 102903"},"PeriodicalIF":20.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-02-22DOI: 10.1016/j.ijinfomgt.2025.102887
Ya-Ting Chuang , Hua-Ling Chiang , An-Pan Lin
Artificial intelligence (AI) has rapidly integrated into organizational workflows, sparking two debates: proponents argue that it increases productivity and decreases workloads, whereas opponents warn that it induces technostress (e.g., job replacement) and decreases employees' well-being. However, AI adoption by employees remains understudied, requiring both theoretical and empirical investigation to assess its positive and negative effects. This study employs the job demands–resources (JD–R) model as a guiding framework to examine the impact of AI demands (i.e., technostress) and resources (i.e., efficacy and generative AI) on employees' work and life domains (i.e., productivity, job satisfaction, and work–family conflict), with engagement and exhaustion as mediating factors. Data gathering through a three-wave survey involved 600 gender-balanced participants working with AI across diverse industries. Bayesian SEM results indicate that both AI efficacy and generative AI positively impact productivity, with AI efficacy also enhancing engagement and job satisfaction. In contrast, AI technostress increases exhaustion, exacerbates work–family conflict, and lowers job satisfaction, even though it may still contribute to productivity. These findings highlight the dual impact of AI on employees: AI technostress impairs well-being, while AI efficacy enhances it. Notably, generative AI mitigates the negative effects of technostress, a benefit not observed for AI efficacy as measured in this study. Overall, this study provides an empirical basis for understanding the resources and demands associated with AI adoption and its impact on employees' psychological processes, influencing both their work and life domains and leading to diverse outcomes.
{"title":"Insights from the Job Demands–Resources Model: AI's dual impact on employees’ work and life well-being","authors":"Ya-Ting Chuang , Hua-Ling Chiang , An-Pan Lin","doi":"10.1016/j.ijinfomgt.2025.102887","DOIUrl":"10.1016/j.ijinfomgt.2025.102887","url":null,"abstract":"<div><div>Artificial intelligence (AI) has rapidly integrated into organizational workflows, sparking two debates: proponents argue that it increases productivity and decreases workloads, whereas opponents warn that it induces technostress (e.g., job replacement) and decreases employees' well-being. However, AI adoption by employees remains understudied, requiring both theoretical and empirical investigation to assess its positive and negative effects. This study employs the job demands–resources (JD–R) model as a guiding framework to examine the impact of AI demands (i.e., technostress) and resources (i.e., efficacy and generative AI) on employees' work and life domains (i.e., productivity, job satisfaction, and work–family conflict), with engagement and exhaustion as mediating factors. Data gathering through a three-wave survey involved 600 gender-balanced participants working with AI across diverse industries. Bayesian SEM results indicate that both AI efficacy and generative AI positively impact productivity, with AI efficacy also enhancing engagement and job satisfaction. In contrast, AI technostress increases exhaustion, exacerbates work–family conflict, and lowers job satisfaction, even though it may still contribute to productivity. These findings highlight the dual impact of AI on employees: AI technostress impairs well-being, while AI efficacy enhances it. Notably, generative AI mitigates the negative effects of technostress, a benefit not observed for AI efficacy as measured in this study. Overall, this study provides an empirical basis for understanding the resources and demands associated with AI adoption and its impact on employees' psychological processes, influencing both their work and life domains and leading to diverse outcomes.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"83 ","pages":"Article 102887"},"PeriodicalIF":20.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-04-18DOI: 10.1016/j.ijinfomgt.2025.102909
Ron Chi-Wai Kwok , Surinder S. Kahai , Jin-Xing Hao
The theory of community of inquiry (COI) is widely used to explain students’ experiences in online learning. However, it is rarely used to develop structured, evidence-based teaching strategies for managing communities of inquiry. To extend COI theory, this study proposes a dual-element construct of teaching presence and a problem-driven online small-group learning model. The study aims to understand the effects of teaching presence (combining facilitation and group reward) on social presence (peer interactions) and cognitive presence (cognitive learning outcomes). It incorporates leadership style in facilitation (transformational vs. transactional) and individual accountability in group reward to define strategies for the dual-element approach to teaching presence. A controlled experiment with a 2 × 2 factorial design was conducted on a text-based platform to observe the effects of facilitation style and individual accountability in group reward. Results showed that both the transformational (vs. transactional) facilitation style and individual accountability in group reward promoted peer interaction and cognitive learning outcomes. They also interacted positively to enhance peer interaction and cognitive learning outcomes. The nuanced effects of the different elements of the dual-element approach to teaching presence are discussed, along with theoretical and practical implications.
{"title":"Managing online small-group learning: Effects of facilitation style and type of group reward","authors":"Ron Chi-Wai Kwok , Surinder S. Kahai , Jin-Xing Hao","doi":"10.1016/j.ijinfomgt.2025.102909","DOIUrl":"10.1016/j.ijinfomgt.2025.102909","url":null,"abstract":"<div><div>The theory of community of inquiry (COI) is widely used to explain students’ experiences in online learning. However, it is rarely used to develop structured, evidence-based teaching strategies for managing communities of inquiry. To extend COI theory, this study proposes a dual-element construct of teaching presence and a problem-driven online small-group learning model. The study aims to understand the effects of teaching presence (combining facilitation and group reward) on social presence (peer interactions) and cognitive presence (cognitive learning outcomes). It incorporates leadership style in facilitation (transformational vs. transactional) and individual accountability in group reward to define strategies for the dual-element approach to teaching presence. A controlled experiment with a 2 × 2 factorial design was conducted on a text-based platform to observe the effects of facilitation style and individual accountability in group reward. Results showed that both the transformational (vs. transactional) facilitation style and individual accountability in group reward promoted peer interaction and cognitive learning outcomes. They also interacted positively to enhance peer interaction and cognitive learning outcomes. The nuanced effects of the different elements of the dual-element approach to teaching presence are discussed, along with theoretical and practical implications.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"83 ","pages":"Article 102909"},"PeriodicalIF":20.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-03-26DOI: 10.1016/j.ijinfomgt.2025.102902
Paras Bhatt , Rohit Valecha , H. Raghav Rao
Cyber threat incidents are increasingly on the rise resulting in concern among the public. Recently, data breaches and ransomware attacks have emerged as two types of critical cyber threats in terms of impact to both organizations and individuals. As such, organizations and the public have started to discuss these threats in various forms. While the former discusses the threats in practitioner reports that are available for public consumption, social media platforms are the preferred avenue for the public. Though literature has started to examine the issues regarding such cyber threat incidents, research on cyber threats, its resultant discourse on social media and its potential for situational awareness and for extracting meaningful or actionable cyber intelligence is scarce. This paper makes a twofold contribution: first, it extracts multiple dimensions of cyber threats from an examination of theoretical, regulatory and domain specific literature. We term these dimensions, leak, laws, cause, and cost and use them for creating a cyber-threat impact framework. Second, by undertaking text mining for content analysis of large datasets from Verizon’s Data Breach Investigation Reports (DBIR) as well as social media discourses from Twitter, this paper investigates the practitioner-public discourses about the two types of cyber threat incidents to uncover relative significance of different dimensions for situational awareness. The paper finds that topical similarities and differences exist between data breach and ransomware attack incidents on different dimensions in the cyber-threat impact framework. The dual analysis of practitioner and public discourses allows situational awareness that policy makers can use for developing appropriate cyber intelligence and cyber threat defense policies.
{"title":"Situational awareness about data breaches and ransomware attacks: A multi-dimensional cyber threat impact framework and content analyses of practitioner-public discourses","authors":"Paras Bhatt , Rohit Valecha , H. Raghav Rao","doi":"10.1016/j.ijinfomgt.2025.102902","DOIUrl":"10.1016/j.ijinfomgt.2025.102902","url":null,"abstract":"<div><div>Cyber threat incidents are increasingly on the rise resulting in concern among the public. Recently, data breaches and ransomware attacks have emerged as two types of critical cyber threats in terms of impact to both organizations and individuals. As such, organizations and the public have started to discuss these threats in various forms. While the former discusses the threats in practitioner reports that are available for public consumption, social media platforms are the preferred avenue for the public. Though literature has started to examine the issues regarding such cyber threat incidents, research on cyber threats, its resultant discourse on social media and its potential for situational awareness and for extracting meaningful or actionable cyber intelligence is scarce. This paper makes a twofold contribution: first, it extracts multiple dimensions of cyber threats from an examination of theoretical, regulatory and domain specific literature. We term these dimensions, leak, laws, cause, and cost and use them for creating a cyber-threat impact framework. Second, by undertaking text mining for content analysis of large datasets from Verizon’s Data Breach Investigation Reports (DBIR) as well as social media discourses from Twitter, this paper investigates the practitioner-public discourses about the two types of cyber threat incidents to uncover relative significance of different dimensions for situational awareness. The paper finds that topical similarities and differences exist between data breach and ransomware attack incidents on different dimensions in the cyber-threat impact framework. The dual analysis of practitioner and public discourses allows situational awareness that policy makers can use for developing appropriate cyber intelligence and cyber threat defense policies.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"83 ","pages":"Article 102902"},"PeriodicalIF":20.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-03-07DOI: 10.1016/j.ijinfomgt.2025.102890
Dongmei Hu , Yuting Lan , Haolan Yan , Charles Weizheng Chen
Social companion AI, as a generative AI application with empathy and emotional support functions, is gradually becoming a new object of human emotional attachment. This study explores the formation framework of human-SCAI attachment through a two-stage mixed-method approach. In Study 1, using reviews of two films themed around human-AI intimate relationships (Her and M3GAN) as analysis data, semantic network analysis and topic modeling were conducted to identify seven potential concepts and propose the “Interpersonal & Human-AI Relationship Attitudes → Value Evaluation → Attachment Manifestation” framework for AI attachment formation. The study found that perception of AI agent personification and interpersonal dysfunction are driving factors for intimate human-SCAI interactions. Based on social exchange theory, it was discovered that the cost-benefit exchange mechanism in the interaction process influences the formation and varied manifestations of AI attachment. Building on the conclusions of Study 1, a research model was proposed and Study 2 was conducted, involving a survey of long-term users of AI companions and structural model testing using SmartPLS. This study provides insights into understanding human-AI intimate relationships and the mechanisms of AI attachment formation in the GenAI era, while also offering insights and recommendations regarding the potential risks of human-SCAI intimate relationships.
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