International Journal of Information Management最新文献

Despite the debate on the potential effects of the adoption of generative artificial intelligence (AI) in modern societies in terms, there needs to be more clarity in the scholarly discourse and directions for the creative industries. In this editorial article, we discuss the potential impact of generative AI adoption on the creative industries and outline future research agendas. We argue that the successful adoption of generative AI in the creative industries lies in finding the delicate balance between maintaining human ingenuity and reaping the benefits of technological innovation. Unlike other industrial sectors, where AI primarily automates repetitive tasks, creative professionals can use generative AI as a collaborative tool to spark new avenues for creativity, streamline workflows, and accelerate creative processes. However, maintaining the human touch and authenticity that define the output of the creative industries remains a challenge for the industry and society. We examine these issues in the paper.

Regressive discontinuance is a form of IS discontinuance that occurs due to adoption failure. The phenomenon of regressive discontinuance takes place between the adoption and continuation stages. Among the other types of IS discontinuance, this phenomenon has received little attention earlier due to the sparse availability of IS solutions. However, there are abundant IS solutions in this digital age, and keeping a user committed to it for a longer duration is a challenge. This study has been developed to enhance the knowledge about the regressive discontinuance phenomenon by employing the exploratory sequential design of mixed methods research designs. With the help of information processing and experiential perspectives of consumption, we developed a conceptual model in phase one using qualitative research. The lens of complexity theory was further utilized to identify those composite conditions that result in regressive discontinuance. A contrarian analysis, necessary condition analysis, and sufficient condition analysis have been performed to deduce the core and peripheral conditions that affect a person’s early discontinuation decision. This study has been conducted for mobile social media applications and has implications for multiple stakeholders.

This research empirically studies whether recommendation agents are as effective on newer, mobile devices (i.e., tablets, smartphones) as they are on older, stationary ones (i.e., desktop computers). We analyze clickstream data from Airbnb with a novel econometric model based on beta and logit regressions and estimated within the Bayesian framework. The model controls for self-selectivity bias. Our empirical findings show that recommendation agents are less effective on mobile devices; the number of recommended alternatives clicked by smartphone (desktop computer) users is smaller (larger) than that of tablet users. This is managerially important as we also show that a consumer’s purchase likelihood is directly related to the number of recommended alternatives evaluated by them. Furthermore, we found that men and younger consumers rely less on recommendation agents. Our results highlight the importance of redesigning recommendation agents for mobile devices as well as identifying consumer segments that need stronger incentives to shop online.

Achieving impact from academic research is a challenging, complex, multifaceted, and interconnected topic with a number of competing priorities and key performance indicators driving the extent and reach of meaningful and measurable benefits from research. Academic researchers are incentivised to publish their research in high-ranking journals and academic conferences but also to demonstrate the impact of their outputs through metrics such as citation counts, altmetrics, policy and practice impacts, and demonstrable institutional decision-making influence. However, academic research has been criticized for: its theoretical emphasis, high degree of complexity, jargon-heavy language, disconnect from industry and societal needs, overly complex and lengthy publishing timeframe, and misalignment between academic and industry objectives. Initiatives such as collaborative research projects and technology transfer offices have attempted to deliver meaningful impact, but significant barriers remain in the identification and evaluation of tangible impact from academic research. This editorial focusses on these aspects to deliver a multi-expert perspective on impact by developing an agenda to deliver more meaningful and demonstrable change to how “impact” can be conceptualized and measured to better align with the aims of academia, industry, and wider society. We present the 4D model - Design, Deliver, Disseminate, and Demonstrate - to provide a structured approach for academia to better align research endeavors with practice and deliver meaningful, tangible benefits to stakeholders.

The study analyzes how tourism businesses satisfy customers through artificial intelligence innovation services. Based on the socio-technical system perspective and emotions, the research framework was proposed and analyzed using the partial least squares (PLS-SEM) approach. By collecting questionnaire data over two years (2022 and 2023 from Beijing, Hangzhou, Taipei, and Singapore) and conducting in-depth interviews with a well-known travel agency and AI service development company in Taiwan, this could thus provide a combination of quantitative and qualitative evidence to enhance the study's validity. Our research findings indicate that AI innovation and new product advantage significantly enhance functional benefits, strengthening tourist satisfaction and continued AI service usage intention. However, the moderating effects of positive user experience and perceived anthropomorphism are not supported. Building upon the previous research results, this study proposes relevant theoretical contributions, practical implications, and insightful suggestions for tourism businesses seeking to implement AI services to improve customer satisfaction.

Economic, social and ecological sustainability presents one of the greatest challenges of our society. As a result, there is growing pressure on a variety of organizations to implement sustainability programs, such as inclusion training related to social sustainability, cost management programs related to economic sustainability, and perhaps most importantly, green (information) systems related to environmental sustainability. Not only is multi-faceted sustainability most beneficial to organizations at large, but also there are several categorical thresholds that organizations must meet in order to be certified. For these reasons, companies are increasingly adopting information systems (IS) for sustainability in their operations. Specifically, programs designed to influence employee motivation, attitudes, beliefs, and behaviors are gaining attention. While there is a long tradition of research on technology adoption, such sustainability systems harbor several new phenomena, both with respect to individual and organizational adoption. Therefore, in this study, we explore the challenges of adopting sustainability systems in organizational contexts. Using activity theory and innovation diffusion theory, and with technology adoption theory as the backdrop of our investigation, we conduct a qualitative field study in four small- and medium- sized companies. Using both theory-guided deductive and inductive interpretation phases, we illuminate adoption challenges through combined intersections of adoption stages (knowledge, persuasion, decision, implementation, and confirmation) and activity domains (subject, instrument, object, community, rules). We then shed light on the severity and importance of these challenges through an expert validation study with green IS experts. In discussing the challenges in light of motivational and green IS design, we posit four dilemmas in green IS adoption that arise from tensions in the specific context of sustainability in organizations. Our findings expand existing knowledge in research on green IS, motivational IS design and sustainable behavior change interventions and yield several theoretical and practical avenues for future inquiry and endeavour.

Although the empowerment of technology is of great value to society, little is known about its downstream effects on consumers' decisions. This research draws on the expectation–confirmation theory and autonomy in artificial intelligence (AI) and investigates how AI (vs. autonomous choice) has detrimental effects on consumer outcomes, creating an autonomy-technology tension — i.e., the conflict arising from AI technology diminishing consumers' autonomy in their choices. Four studies using a mixed-method approach reveal that the use of AI recommendations in streaming platforms creates an autonomy-technology tension that reduces consumers' performance expectancy, thus lowering their satisfaction. However, such effects are contingent on the nature of the AI recommendations. While a mismatch between AI recommendations and consumer preferences might backfire, AI's negative effect is mitigated when choices match consumers' preferences. We make significant theoretical and practical contributions to empirical research on consumers' sense of autonomy while interacting with AI.

As the application of Blockchain Technology (BCT) grows across several industries, its inherent decentralized and immutable nature raises some conflicting issues concerning privacy regulation. The enforcement of privacy regulations like the General Data Protection Regulation (GDPR) exemplifies these challenges, as certain stringent privacy requirements within GDPR may conflict with BCT's features. However, little attention is given to such critical issues. This study draws on the Technology-Organization-Environment (TOE) theoretical lens as a mechanism to unfold the issue. The current research focuses on exploring the tensions and the opportunities for synergy through the lens of TOE to prevent privacy regulatory compliance failure. We analyzed 71 multidisciplinary research studies that highlight the areas of friction and present a unifying structure for research synthesis. In response to the identified areas of friction and potential opportunities for coexistence, we formulate nine research propositions centered around six prominent and contentious data privacy and protection requirements within the GDPR. This research contributes to the broader discourse on privacy regulatory-compliant blockchain-based solutions by providing a theoretical foundation for future investigation into this critical area. The study suggests that reconciling BCT and privacy regulation requirements will unlock BCT's full potential, creating a secure, privacy-conscious technology infrastructure and offering practical implications and insights for policymakers.

The concept of recommendation acceptance intention (RAI) refers to the willingness of an individual to adopt and use recommender systems. However, contemporary recommender systems, relying solely on accuracy as an evaluation metric have demonstrated subpar performance. The evolving landscape requires the integration of multiple objectives, including relevance, diversity, novelty, and coverage. This study investigates the influence of distinct product organization formats (similar vs. related products) on consumer reactions, aiming to reduce privacy concerns while achieving objectives such as relevance and variety. Grounded in the privacy calculus theory and personalization–privacy paradox framework, this study asserts that organization formats prompt consumers to evaluate a recommender system, considering both privacy risks and personalization benefits. Findings from three experiments and one qualitative study indicate that a related product organization format effectively decreases consumer perceptions of privacy violation while enhancing their perceptions of variety compared to a similar product organization format. This result contributes to RAI and shapes consumer preferences for recommender systems. Moreover, this research distinguishes the mechanisms underlying the influence of organization formats on RAI in the context of different product types, such as durable goods and consumables. This distinction enhances implications for designing recommender systems in e-commerce.

The continuous development of technology leads to stimuli-dense consumption environments for consumers. Although the literature primarily highlighted the advantages of adopting technologies to support consumers’ decision-making process, these systems may also require too much attention and excessive effort to be considered always rewarding. Accordingly, this special issue addresses the interplay between technology-supported consumption experiences and the related distracting mechanisms triggered by this interaction in varied contexts. Specifically, the actual collection of papers in this special issue covers three main themes: (1) conceptualizing a Customer Smartphone Distraction (CSD) organizing framework, (2) drivers (including musical atmosphere, the context of the application, parasocial interaction and anthropomorphisms of virtual agents), and (3) consequences (cognitive, affective and behavioral responses, including sensory overload and discomfort).