International Journal of Information Management最新文献

While the positive aspects of the flow state have been widely researched, the effects of being aware of this state and the negative consequences still need to be explored. The loss of awareness that flow state may bring can lead to the purchase of products that consumers afterward regret and want to return them. This research aims to understand flow consciousness's effect on consumer post-purchase regret from a dual perspective (process and outcome regret). The relevance of these relationships is also examined by analyzing the differences between 2 groups: maximizers and satisficers consumers. Through a structural equation model based on covariances tested with EQS 6.4, the results show that flow consciousness generates process regret but not outcome regret. However, the differences found between the two groups are worth noting. The research sheds light on the effect of flow consciousness on regret and provides insight into the mechanism of product return through post-purchase regret. The theoretical and managerial implications for e-commerce retailers are discussed.

While data availability and access used to be a major challenge for information systems research, the growth and ease of access to large datasets and data analysis tools has increased interest to use such resources for publishing. Such publications, however, seem to offer weak theoretical contributions. While big data-driven studies increasingly gain popularity, they rarely introspect why a phenomenon is better explained by a theory and limit the analysis to data descriptive by mining and visualizing large volumes of big data. We address this pressing need and provide directions to move towards theory building with Big Data. We differentiate based on inductive and deductive approaches and provide guidelines how may undertake steps for theory building. In doing so, we further provide directions surrounding common pitfalls that should be avoided in this journey of Big-Data driven theory building.

This paper examines how alternative food networks (AFNs) cultivate engagement on a social media platform. Using the method proposed in Kar and Dwivedi (2020) and Berente et al. (2019), we contribute to theory through combining exploratory text analysis with model testing. Using the theoretical lens of relationship cultivation and social media engagement, we collected 55,358 original Weibo posts by 90 farms and other AFN participants in China and used Latent Dirichlet Allocation (LDA) modeling for topic analysis. We then used the literature to map the topics with constructs and developed a theoretical model. To validate the theoretical model, a panel dataset was constructed on Weibo account and year level, with Chinese city-level yearly economic data included as control variables. A fixed effects panel data regression analysis was performed. The empirical results revealed that posts centered on openness/disclosure, sharing of tasks, and knowledge sharing result in positive levels of social media engagement. Posting about irrelevant information and advertising that uses repetitive wording in multiple posts had negative effects on engagement. Our findings suggest that cultivating engagement requires different relationship strategies, and social media platforms should be leveraged according to the context and the purpose of the social cause. Our research is also among the early studies that use both big data analysis of large quantities of textual data and model validation for theoretical insights.

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly with inconsistent results. In this research, we attempt to bridge the gap in the literature by examining the impacts of user-, time-, and content-based characteristics that affect the virality of real versus misinformation during a crisis event. Using a big data-driven approach, we collected over 42 million tweets during Hurricane Harvey and obtained 3589 original verified real or false tweets by cross-checking with fact-checking websites and a relevant federal agency. Our results show that virality is higher for misinformation, novel tweets, and tweets with negative sentiment or lower lexical density. In addition, we reveal the opposite impacts of sentiment on the virality of real news versus misinformation. We also find that tweets on the environment are less likely to go viral than the baseline religious news, while real social news tweets are more likely to go viral than misinformation on social news.

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.

Recent events have renewed attention to how organizations rely on digital resources in response to exogenous shock. Though the literature on organizational resilience indicates that this is best understood as a process through which organizational actors respond to a specific shock, most IS research attends to resilience as an outcome. Against that backdrop, we present a case study of how a university shifted to virtual teaching in response to a government-imposed lockdown during the COVID-19 pandemic. Adopting a digital resourcing perspective allowed us to reveal the organizational resilience process and the way digital resources shaped it. We found that the resilience process unfolded in stages as educators, assisted by students, managers, and IT personnel pivoted, adapted, and normalized into teaching virtually. Across these stages, digital resources took on specific roles as the resilience process progressed from the organization’s pre-shock accumulation of digital resources into its continued digitalization efforts. Based on these findings, we contribute to existing literature by advancing and empirically substantiating a process view of the role of digital resources in organizational resilience.

As consumers increasingly engage in online brand communities, the demand of brand-related activities (e.g., finding relevant product information and sharing product information) also increases. Consumers may feel overwhelmed by these brand-related activities. To provide an understanding of this phenomenon, we conceptualize it as brand overload and then empirically examine its antecedents and consequences based upon the Stimulus-Organism-Response (SOR) framework in the context of online brand community. Using two rounds of surveys to collect data from 409 American consumers, our results show that consumer-generated content and social interaction quality are the primary drivers of information and social overload. Consumer-generated content quality has a quadratic effect on information overload following an inverted U shape. Social interaction quality also significantly affects social overload following an inverted U-shaped relationship. Information and social overload are both positively related to brand overload, which in turn leads to brand disloyalty as the negative consequence. In addition, to supplement the quantitative study, a qualitative study was used to test and substantiate the proposed research model. With its mixed-method design, our study contributes to the literature by proposing the concept of brand overload and empirically investigating its antecedents and consequences from the SOR perspective. Practically, our study delivers insights for companies on how to use online brand communities strategically and efficiently to avoid negative impacts.

Although organizational factors related to big data analytics (BDA) and its performance have been studied extensively, the number of failed BDA projects continues to rise. The quality of BDA information is a commonly cited factor in explanations for such failures and could prove key to improving project performance. Using the resource-based view (RBV) lens, data analytics literature, business strategy control, and an empirical setup of two studies based on marketing and information technology managerial data, we draw on the dimensions of the balanced scorecard (BSC) as an integrating framework of BDA organizational factors. Specifically, we tested a model –from two different perspectives– that would explain information quality through analytical talent and organizations' data plan alignment. Results showed that both managers have a different understanding of what information quality is. The characteristics that make marketing a better informer of information quality are identified. In addition, hybrid (embedded) type analyst placements are seen to achieve better performance. Moreover, we add greater theoretical rigour by incorporating the moderating effect of the use of big data analytics in companies. Finally, the BSC provided a greater causal understanding of the resources and capabilities within a data strategy.

Owing to the pervasive use of accounting information systems (AIS), financial workers are experiencing increased levels of technostress. While technostress has been conceptualized as overwhelmingly harmful in the IS literature, the challenge–hindrance stressor model suggests that employees can also experience “good stress”. This study instantiates the challenge–hindrance stressor model and coping theory in the AIS context using a two-step mixed-method approach. A qualitative study was first conducted to conceptualize the AIS characteristics associated with technostress appraisals. This study then builds a research model that posits perceived challenge and hindrance technostressors and coping responses as key mechanisms linking AIS characteristics and job burnout. The model was tested by collecting longitudinal data from 285 accountants. The results revealed that several AIS characteristics are associated with technostressor appraisals, and such appraisals lead to proactive and reactive coping responses, which further influence job burnout. The theoretical and practical implications are also discussed.

As individuals age, mental and physical impairment can emerge and impact their well-being. Previous research on social participation in older adults has typically focused on specific activities or failed to distinguish between different forms of participation. In contrast, we propose that social participation is a resource optimization process for older adults, where various forms of participation must be optimized in order to improve well-being outcomes. Drawing on Selective Optimization with Compensation theory, we develop hypotheses on how older adults with cognitive decline select and optimize their social participation profile and how multiple modes of participation synergistically affect their perceived loneliness. Using data from the Canadian Longitudinal Study on Aging (CLSA), we test a polynomial model and find that online social participation has a U-shaped effect on loneliness, while personal-oriented offline social participation mitigates this effect. We also find that the impact of social participation modes varies considerably between younger and older groups. Our findings suggest implications for the design of integrative aging support programs and the use of ICT to promote mental well-being in different stages of advanced age.