International Journal of Information Management最新文献

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.

Business model renewal is a key consideration for organizations, and AI (artificial intelligence) has been identified as a significant potential enabler for that renewal. However, while there are examples of emerging organizations using AI as a key basis of competitive advantage as well as examples of established organizations trialing AI technologies, there are relatively few examples of established organizations fundamentally transforming their business models through the use of AI. Through case studies underpinned by interviews with named executives of ten organizations and complemented by an applicability check involving 14 executives, advisors and practice-oriented academics, this paper presents an empirically supported set of factors linked to successful AI-enabled business model transformation as well as a model of interactions between these factors. Using a horizontal contrasting approach to articulate the difference between empirical findings and a literature based model, this paper moves from the language of potentially passive top management support towards the concept of proactive leadership and introduces tech-sensitive innovation culture, AI-sensitive risk tolerance and strategic process discipline into the dynamic capability lexicon. The insights of this paper can be used by managers to assess their readiness to move beyond traditional ‘proof-points’ and successfully undertake and accelerate AI-enabled business model transformation.

Traditional models that are dedicated to environment analysis and anticipation help deciders reinforce decision-making in both the academic and professional worlds. Nevertheless, as useful as these models are, it seems they are not effective, as shown by the lack of anticipation of the Russian invasion of Ukraine or of the different crisis observed in the African continent. Consequently, confrontation with reality made them incapable of predicting evolution, making those models unable to answer the new challenge induced by technology development, new phenomena and the developments that made the world increasingly volatile, uncertain, complex, and agile. This paper offers suggestions for a different approach that helps to embrace this complexity by proposing an inclusive model mixing both qualitative and quantitative analysis.

Concerns about the quality of care provided to patients have been recently revisited in the healthcare management literature, driven by the aftermath of the COVID-19 pandemic. Quality of care is a complex measure of healthcare performance that relies on optimizing healthcare knowledge. Knowledge management initiatives and practices in healthcare remain difficult to implement and develop – presenting a managerial challenge that must coexist in a competitive environment. This paper aims to study the contribution of knowledge-management systems, knowledge-sharing behavior, and competitive culture to the quality of care provided to patients. We use a mixed-methods approach to explore a research model in a sequence of two studies. Research data comes from a sample of 323 healthcare professionals in Portuguese healthcare organizations. Quantitative results show that both knowledge-management systems and a competitive culture positively influence the quality of care. Results also show a positive influence of knowledge-sharing behavior on the quality of care. Complementarily, qualitative results reveal two configurations leading to quality of care. The first highlights the role of knowledge-management systems alone as sufficient to provide quality of care. A second configuration involves cumulative contributions of competitive culture and individual knowledge sharing, leading to quality of care. Theoretical and practical implications are discussed, and a model for quality of care creation is offered. Limitations are acknowledged and future work directions are presented.

The frequent launch of new product upgrades and the explosion of online user reviews have profoundly impacted consumer purchase behavior in various product categories, especially digital ones. This research examines how the rapid innovation and frequent updates in digital product categories (such as fitness wearables) influence consumer demand and sales of new product generations varying in feature intensity and update frequency. Through a mixed-method approach, this study reveals that consumer feedback and expert feedback polarities differentially impact sales, and these effects are amplified for more complex, rapidly updated products. The results provide empirical validation for key premises argued in the literature that user-generated content helps overcome information asymmetry, but also introduce contingencies based on product characteristics. This study contributes unique evidence substantiating the strategic value of online opinions and interest metrics in an omnichannel context. The findings offer actionable guidance for marketers to leverage consumer insights and tailor digital product positioning amidst information overload.