Purpose This study investigates user churn in online mutual help communities (OMHCs) for truck drivers, identifying when and why disengagement occurs to enable early warning and targeted retention strategies. Design/methodology/approach Using large-scale behavioral logs from a truck-driver OMHC, this study models time-to-churn with Cox proportional hazards, estimates Kaplan-Meier survival curves across user groups and applies penalized variable selection (Lasso, Smoothly Clipped Absolute Deviation, Minimax Concave Penalty) to high-dimensional features capturing social capital, individual characteristics, and environmental factors. Model sparsity and predictive performance are compared across penalties. Findings Survival analysis reveals that retention declines over time, with pronounced heterogeneity across groups. Churn is slower for branch members, mutual-help insurance purchasers, “mutual-help pioneer” recipients, certified or license-verified users, higher point levels, and membership in higher-level branches. Penalized Cox models identify key factors influencing churn, with Lasso achieving the greatest predictive accuracy, MCP offering the most parsimonious model, and SCAD balancing accuracy and sparsity. Originality/value This study extends churn research to truck drivers' OMHCs by integrating community logs with external environmental data and structuring predictors into social capital, individual, and contextual domains. Methodologically, it advances survival analysis for high-dimensional community data through comparative penalized Cox frameworks, providing a replicable and interpretable approach for churn detection and retention optimization in road transportation-focused digital communities.
Purpose This study investigates the behavioral patterns, key influencing factors and configurational mechanisms of researchers' information verification behavior toward Generative AI-generated content in human-AI interaction contexts. Design/methodology/approach A mixed-methods design was adopted. First, qualitative interviews and grounded theory identified influencing factors and constructed a theoretical framework. Then, hypotheses were proposed based on coding results and tested using partial least squares structural equation modeling on 506 valid questionnaires. Finally, fuzzy-set qualitative comparative analysis identified multiple paths triggering high-level verification behavior from a configurational perspective of antecedent conditions. Findings The study revealed three patterns of information verification behavior: verification via authoritative sources, platform comparison and interactive optimization verification and social network verification. Structural Equation Modeling showed that information quality, perceived risk, cognitive evaluation, AI literacy, task importance and algorithmic transparency significantly affect verification behavior. Qualitative comparative analysis further identified five equivalent driving paths, with cognitive evaluation and AI literacy as core conditions across all paths. The study also found that social influence, platform reputation and interactive design limitations, while not having significant independent effects, emerged as key configurational elements, highlighting the complexity of multi-factor concurrent effects. Originality/value The widespread use of Generative AI has triggered a severe information credibility crisis, making researchers' verification behavior crucial. This study addresses this challenge, fills the gap in research on AIGC as a “black-box information source” and provides evidence for building trustworthy AI systems and usage guidelines.
PurposeChatbots are increasingly embodied in business and IS contexts to enhance customer and user experience. Despite wide interest in chatbots among business and IS academics, surprisingly, there are no current comprehensive reviews to reveal the knowledge structure of chatbot research in such areas. Design/methodology/approachThis study employed a mixed-method approach that combines systematic review and bibliometric analysis to provide a comprehensive synthesis of chatbot research. The sample was obtained in December 2023 after searching across six databases: EBSCOhost, PsycINFO, Web of Science, Scopus, ACM Digital Library and IEEE Computer Society Digital Library. FindingsThis study reveals the major trend in publication trends, countries, article performance and cluster distribution of chatbot research. We also identify the key themes of chatbot research, which mainly focus on how users interact with chatbots and their consequences, such as users’ cognition and behavior. Moreover, several important research agendas have been discussed to address some limitations in the current chatbot research in business and IS fields. Originality/valueThe present review is one of the first attempts to systematically reveal the ongoing knowledge map of chatbots in business and IS fields, which makes important contributions and provides useful resources for future chatbot research and practice.
Purpose The increasing use of the terms “fake data” and “fake information” in digital systems has led to conceptual ambiguity and inconsistent usage across the literature. This study aims to clarify the definitional boundaries and relationships between these terms by examining how they are used and interpreted in existing research. Design/methodology/approach A structured literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 334 studies published between 2009 and 2026 were selected after screening and eligibility assessment. Their abstracts were analyzed using thematic analysis, where recurring patterns were identified and encoded based on dimensions such as intent of creation, transformation processes and usage context. Findings The results reveal that neither “fake data” nor “fake information” is explicitly defined in the literature, with meanings typically inferred from context. The analysis also shows a marked increase in the use of these terms since 2020, with fake data more prevalent in technical domains such as cybersecurity and data science, and fake information more common in media and social contexts. Research limitations/implications The review is restricted to the computer science domain and its related fields (e.g. IT, data science and artificial intelligence), which may limit the generalizability of the findings to other disciplines. Despite these limitations, the study highlights the need for consistent terminology and provides a foundation for future research to refine and validate the proposed conceptual distinctions across broader contexts. Practical implications The proposed framework supports improved classification, risk assessment and intervention strategies by distinguishing between data-level manipulation and information-level distortion in digital systems. Originality/value This study introduces a data–information–knowledge-grounded, two-level framework that clarifies ambiguity in the use of fake data and fake information. The first level distinguishes between legitimate and deceptive intent, while the second differentiates based on what is being produced (data vs. information). This structure enables a clearer conceptual separation between fake data, fake information and synthetic data.
Purpose Despite exploratory search being a common information behaviour we engage in on a daily basis, it remains challenging to investigate. When people search for a book on a vaguely defined topic, they exhibit complex, multidimensional, and diverse search behaviours. To better understand the approach to such search tasks, a mixed-methods approach is encouraged. This article investigates the methodological benefits and drawbacks when utilising a triangulation design with the three methods used in our study.Design/methodology/approach The study was carried out in a live online bookstore, with a sample of 33 participants. In order to capture both observable interactions and participants' cognitive and emotional responses, we employed a triangulation design, combining screen recording, eye tracking, and concurrent think-aloud simultaneously. The analysis was conducted in three consecutive steps, with the explicit aim of comparing methodological insights first from each individual method and subsequently from their combination.Findings Screen recording and eye tracking offered complementary observational perspectives - one focusing on navigation paths and timing, the other on visual attention. Concurrent think-aloud added a qualitative, introspective layer, allowing us to access participants' reasoning and perceptions in real time. Combining insights from multiple methods improved the understanding of how the interface could better support exploratory book search, emphasising the importance of selecting and combining the right methods.Originality/value The paper's primary contribution is methodological, demonstrating how the combined use of the three methods strengthens research design and elevates the findings of exploratory search studies. Additionally, the case study revealed insights into user information behaviour during exploratory search in bibliographic information systems.
