International Journal of Information Management最新文献

Large language models (LLMs) have proven capable of assisting with many aspects of organizational decision making, such as helping to collect information from databases and helping to brainstorm possible courses of action ahead of making a choice. We propose that broad adoption of these technologies introduces new questions in the study of decision support systems, which assist people with complex and open-ended choices in business. Where traditional study of decision support has focused on bespoke tools to solve narrow problems in specific domains, LLMs offer a general-purpose decision support technology which can be applied in many contexts. To organize the wealth of new questions which result from this shift, we turn to a classic framework from Herbert Simon, which proposes that decision making requires collecting evidence, considering alternatives, and finally making a choice. Working from Simon’s framework, we describe how LLMs introduce new questions at each stage of this decision-making process. We then group new questions into three overarching themes for future research, centered on how LLMs will change individual decision making, how LLMs will change organizational decision making, and how to design new decision support technologies which make use of the new capabilities of LLMs.

Traditional adoption models explain the intention to use information technology (IT). These models draw on theories that relate perceptions of IT to its actual use. To advance the IT adoption literature, we direct attention away from individual perceptions of IT towards understanding the drivers of individuals' decisions as they use IT. This approach may offer richer explanations of individual IT-enabled performance. Using five decisions about IT acceptance and the theoretical lens of automaticity as proposed in previous work, we develop the construct of Information Technology Acceptance as being comprised of the five decisions that users make (i.e., to receive, to grasp, to assess, to be given, and to submit) and validate an instrument with data collected from 524 technology users in three organizations. The results support the model, suggesting that the five acceptance decisions are constant across organizational and technological contexts. We conclude with propositions to shape adoption research further using this new construct.

In this study, we examine how individuals react to an ostracism experience in the metaverse. Based on the temporal need-threat model and virtual identity discrepancy model, two experimental studies and a critical incident study were conducted to investigate how metaverse ostracism affects individuals’ need for belonging, mood, avatar–self discrepancy, and behavioral intentions, as well as how avatar customization moderates this relationship. The results of Study 1 revealed that ostracized participants perceived a higher threat to the need for belonging, a less positive mood, and a higher avatar–self discrepancy than non-ostracized participants. The results of Study 2 revealed that experiencing ostracism led to a greater perception of avatar–self discrepancy only when participants interacted with others using assigned avatars. The results of a moderated mediation analysis showed that the ostracism experience with assigned avatars increased avatar–self discrepancy, thereby reducing intention to continue using the avatars and revisit the metaverse. The mediation effect was not significant when participants customized their avatars. Study 3 replicated the results of Studies 1 and 2 with a larger sample and provided further insight through a qualitative analysis. The theoretical and practical implications are highlighted.

This paper explores the dynamics between value-added services, intermediary brands, and consumer privacy concerns in shaping attitudes toward blockchain-enabled consumer services. Grounded in the Antecedents-Privacy Concerns-Outcomes (APCO) framework, we develop a theoretical model that we test in three experimental studies with a total of 1613 participants, utilizing verbal scenarios featuring blockchain applications for international money transfer and hotel booking. Our research reveals that complete disintermediation via pure peer-to-peer blockchain transactions is unlikely. Consumers prefer blockchain applications offering supplementary services like call centers, password assistance, and cancellation options. As consumers become familiar with blockchain technology, privacy concerns intensify due to its distributed and immutable storage. The fears of data breaches are more pronounced when blockchain applications are offered by unknown startups as opposed to well-known Big Tech companies. However, privacy-conscious consumers also value the prospect of distancing themselves from big-data ecosystems by embracing blockchain solutions from startups. Our research extends the APCO framework by clarifying how privacy concerns, brand-based heuristics, and technological attributes interact. For managerial implications, blockchain applications necessitate re-intermediation to meet consumer preferences. Potential intermediaries, including Big Tech firms, startups, and industry incumbents, face unique challenges in developing and marketing blockchain-enabled consumer services.

Omitting relevant variables in research models is a significant challenge in academic research involving non-experimental research methods. Omitted variables may bias the empirical findings and lead to erroneous conclusions about relationships between factors underlying information systems phenomena. Using data coded from 128 samples reported in 105 prior studies, this study applies meta-regression methods to quantify the extent to which omitted variables bias the reported effect sizes between behavioral intention and system use. Since a direct examination of omitted variables is not possible, this study quantifies four measures: independent variables common to both behavioral intention and system use, moderators for the relationship between behavioral intention and system use, moderators for relationships between other independent variables and system use, and control variables for system use. Meta-regression results show that the effect size for the relationship between behavioral intention and system use decreases when independent variables common to behavioral intention and system use or moderators for the relationship between behavioral intention and system use are included in research models. This implies that the non-inclusion of relevant variables distorts the effect size for the relationship between behavioral intention and system use resulting in a biased understanding of the relationships. It is crucial for research models to include relevant variables such that the omitted variables bias can be effectively handled. Several mitigation strategies and research implications are described.

The emergence of data as a critical asset and the prevalence of technologies such as the Artificial Intelligence of Things (AIoT) on the one hand, and the importance of collaborations for value creation on the other hand have given rise to a new breed of ecosystems known as data ecosystems. While data ecosystems provide new business opportunities, proposing and capturing value in those ecosystems is challenging, and the extant literature provides little guidance in this regard. Our research encompasses two studies that address this limitation and establish a framework for business-model archetypes in the context of AIoT data ecosystems. In the first study, exploratory qualitative research on 28 leading AIoT data ecosystem actors leads to the identification of value propositions and value-capture mechanisms in these ecosystems. We identify eight possible value propositions and eight possible value-capture mechanisms. The second, qualitative study centers on 19 expert interviews. Our analysis leads to the identification of two dimensions – control and customization – that guide the conceptualization and formation of business-model archetypes. Using these dimensions, we develop a framework for business-model archetypes in AIoT data ecosystems. Our findings contribute to the discourse on data ecosystems and offer new perspectives valuable for both researchers and industry practitioners.

Entrepreneurship is one of the most common strategies that are followed by companies. In parallel, each company needs to effectively manage information and knowledge to successfully implement its strategy. Thus, this study focuses on the points where knowledge, information, and entrepreneurial management meet, with special attention paid to the relationship between information management (IM) and knowledge management (KM) as well as their mediating roles in shaping firm performance. This study aims to identify the role of information management (IM) and knowledge management (KM) in shaping performance in entrepreneurial firms. Moreover, the study aims to examine the causal relationship between IM and KM. Our sample consisted of 150 small and medium-sized firms that manufacture furniture in Poland. This is a mix-method study; it uses structural equitation modeling (SEM), fuzzy-set qualitative comparative analysis (fsQCA), and interview-based qualitative analysis to examine the associations among entrepreneurial orientation (EO), IM, KM, and firm performance. In particular, three types of outcomes are analyzed; namely, firm competitiveness (FC), firm growth (FG), and financial performance (FP). The results unveiled the impact of IM on KM; the findings also showed the positive role of EO in shaping IM and KM as well as firm performance. In general, IM and KM impact firm performance; however, their individual impacts depend on the performance type (when they interact, they constitute a sufficient condition for achieving a high level of performance regardless of the type). These findings contribute to the IM and KM literature as well as entrepreneurship and small business research.

The ongoing scientific discourse surrounding the replication crisis in behavioral research, including management information systems (MIS) research, underscores the importance of innovative and rigorous approaches to theory development and validation. This article proposes the EP-mixed framework, which addresses the necessity of an ontological distinction between explanation and prediction in MIS theories, along with the epistemological challenges associated with conflating exploratory and confirmatory research during the design of robust, replicable theories. EP-mixed refers to theories that explain and predict (i.e., EP theories) developed using a mixed mode that combines the strengths of both exploratory and confirmatory research. The EP-mixed framework guides researchers in selecting appropriate analytical approaches based on their research goals and the type of theory being developed. While it can be applied in conjunction with a broad spectrum of statistical methods to enhance the robustness and replicability of MIS theories, we elaborate on the predictive analytic tools available in partial least squares structural equation modeling (PLS-SEM) as an exemplar for operationalizing the framework.

On digital platforms, thumbnails are omnipresent promotion tools for hedonic media goods. However, thumbnail designs still constitute a black box regarding their effects on consumption. To address this research gap, we apply artificial intelligence (AI) based imagery analysis to quantify and analyze almost 500,000 thumbnails drawn from two international digital media platforms – an advertisement-based video-sharing platform and a sales-oriented e-commerce one. Through multiple linear regression analyses, we provide quantitative evidence on the relationship between thumbnail designs and the consumption of hedonic media goods. For both digital media platforms, we find that several classes of visual cues and more faces especially with negative emotions foster consumption, while more text decreases consumption. We also find several distinct impacts on consumption across the two digital media platforms that we explain with the different underlying business models. We contribute to the literature on visual information processing and choice, AI-based design, and platform and e-commerce strategies. Further, we supplement the inventory of Information Systems (IS) research methods by adding AI-based imagery analysis. We provide implications for practice concerning AI-based thumbnail design and strategic pathways for digital platforms and offer directions for future research. We conclude with some key insights and a brief outlook.

Social media can be an effective tool to foster values and lifestyles such as physical activity. However, it is still largely unknown what type of social media information helps to promote physical activity practices. This research aims to (1) identify the combination of variables that explain social media engagement in physical activity (SME-PA) and its relationship with physical activity behavior and (2) determine what type of social media content posted on social media could be most effective in promoting physical activity behaviors. Using a quasi-experimental and an experimental study, the findings showed that the conditions of high levels of information availability, social ties, trust in social media information, and opportunity seeking were present and combined in three of the four solutions (77% of the cases). SME-PA was correlated with the intention to improve physical fitness, and the physical shape, the type of benefit associated with physical activity practice and the gender of the person in the photo practicing physical activity were found to be relevant variables for improving the physical activity-related behavior of social media users. Finally, several implications are proposed to improve the administration of social media content for SME-PA.