International Journal of Information Management最新文献

Although organizations are increasingly adopting extended reality (XR) technologies to transform their operations, their adoption has been slower than initially expected. Scholars and practitioners have pointed to user resistance as a potential hindrance to their adoption. This mixed-methods study examines how managerial perceptions of expected employee resistance and organizational value are related to organizational adoption intention. An organizational-level resistance-value adoption model was developed and tested using the structural equation modeling approach with cross-sectional data from 206 European industrial decision makers. The results show that trialability, internal support capabilities, and extant user skills are negatively associated with expected employee resistance. Moreover, mimetic pressures strongly influence value perceptions, whereas expected employee resistance is negatively related to organizational value perceptions and adoption intentions. Perceived value had the strongest positive relationship with adoption intention. A qualitative study consisting of 58 interviews further revealed 12 key conditions affecting the relationships between resistance, value, and adoption intention. As a theoretical contribution, we proposed and validated an integrated resistance-value adoption model that extends our understanding of factors affecting organizational XR adoption. As a practical contribution, practitioners can use the study’s results to help them focus on relevant factors for mitigating user resistance and promoting XR adoption.

AI applications hold great potential for improving healthcare. However, successfully operating AI is a complex endeavor requiring organizations to establish adequate management approaches. Managing AI applications requires functioning information exchange between a diverse set of stakeholders. Lacking information processing among stakeholders increases task uncertainty, hampering the operation of AI applications. Existing research lacks an understanding of holistic AI management approaches. To shed light on AI management in healthcare, we conducted a multi-perspective literature analysis followed by an interview study. Based on the organizational information processing theory, this paper investigates AI management in healthcare from an organizational perspective. As a result, we develop the AI application management model (AIAMA) that illustrates the managerial factors of AI management in healthcare and its interrelations. Furthermore, we provide managerial practices that improve information processing among stakeholders. We contribute to the academic discourse by providing a conceptual framework that increases the theoretical understanding of AI's management factors and understanding of management interrelations. Moreover, we contribute to practice by providing management practices that promote information processing and decrease task uncertainty when managing AI applications in healthcare.

Many firms use social media marketing and implement different message frames as a strategy to persuade consumers, enhance engagement, and to purchase their products. How firms frame social media content to be more believable, however, is not well understood. The main aim of this paper is to understand how social media content can be framed to enhance believability, and how this ultimately leads to improved social media outcomes. Drawing on Prospect Theory and Construal Level Theory and utilizing data from the field in the form of social media posts (n = 1756), and four experimental studies (n = 1141 total participants), and a critical incident study (n = 263 participants) the current research shows that self-gain frame combinations contribute to significantly higher levels of believability. Furthermore, believability was found to mediate the impact of social media message framing on purchase intentions and social media engagement. The practical implications of these findings and exciting new avenues for research are also discussed.

Online games are popular technology-enabled applications designed to satisfy a wide range of player needs. Self-determination theory (SDT) has been used in past online game studies to explain player satisfaction, but it is rarely used to examine game design as a trigger for player satisfaction, which reveals a research gap. This gap keeps game makers in the dark about the design of games that effectively satisfy players, throwing gamers’ ongoing game usage into doubt. Aiming to fill this gap, we proposed three new game design elements and examined their impacts on player satisfaction, continuance, and usage. We followed 546 participants who responded to our online survey and permitted us to collect their system-captured game usage data, generating two-wave and two-source data. We found that game achievability and game immersibility are game design elements that satisfy players. Competence satisfaction and autonomy satisfaction—but not relatedness satisfaction—are characteristics that secure players’ continuance and actual usage. Two replication studies were conducted to further verify these findings. Our study extends SDT backward to examine the game-contextualized triggers of satisfaction. These theorized triggers showcase game system design, theoretically clarifying the means of using SDT to design games, and providing practical insights to guide game makers in securing player continuance and actual usage.

This research examines vulnerabilities and potential attacks using Bitcoin payment systems at physical retail locations. It also lays a theoretical and practical foundation for effective countermeasures that ensure the integrity of decentralized transactions and bolster the confidence of both merchants and customers. The study focuses on potential double-spend attacks, particularly in the context of instant payments made via non-custodial crypto wallets. It also evaluates alternatives for instant crypto payments that use alternative coins and second-layer blockchain solutions. Research has indicated that Eclipse, Sybil, Majority, Race, and Finney attacks carry a relatively low risk, especially when payment amounts are limited. The paper identifies Replace-By-Fee attacks as the most significant threat and presents a set of countermeasures. These findings are of great importance to cryptocurrency experts, payment tech researchers, retail enterprises, and stakeholders using non-custodial wallets while exploring decentralized blockchain aspects.

The virtualization of cultural events in the metaverse creates opportunities to generate valuable and innovative experiences that replicate and extend in-person events; but the process faces associated challenges. In the absence of relevant empirical studies, the aim of this article is to analyze the positive and negative aspects of the user experience in a cultural event held in the metaverse. A mixed-methods approach is employed to test the proposed hypotheses. The results from three focus groups demonstrated the difficulty that users face in focusing their attention on the main elements of the metaverse, and the inability of this virtual sphere to convey the authenticity of a cultural event. Based on these findings, a metaverse-focused quantitative study was conducted to examine whether perceived gamification mitigate the negative effects of users failing to pay attention in their metaverse experiences. When users increased their attention levels, their ability to imagine the real experience and their perceptions of the authenticity of the cultural event increased, which produced positive behavioral intentions. This is one of the first studies to empirically analyze the tourist experience in the metaverse; managers and policymakers can benefit from the results to hold valuable virtual cultural events.

Virtual agents (VAs) are increasingly used by firms to establish relationships with customers. While this form of artificial intelligence offers numerous advantages, adverse outcomes can arise when customers’ expectations of VAs are not met. This paper examines how the disconfirmation of both parasocial interactions with the VA and its anthropomorphic characteristics affects users’ dissatisfaction with the VA. Such dissatisfaction might then influence subsequent behavioural outcomes, specifically spreading negative word-of-mouth about the VA (NWOMVA) and avoiding future use of VAs. Data were gathered from VA users in Egypt and the UAE via an online questionnaire. The findings from the two studies confirm that all direct hypotheses were supported and that dissatisfaction with VAs mediates the relationships between both negative parasocial interaction disconfirmation (NPID) and negative VA anthropomorphism disconfirmation and the behavioural outcomes of NWOMVA and VA avoidance. Thus, this paper offers managers guidelines on the effective characteristics of VAs, namely anthropomorphism and parasocial interaction, which allow them to identify the pitfalls of VAs and take preventive or remedial actions.

“Artificial Intelligence” in all its forms has emerged as a transformative technology that is in the process of reshaping many aspects of industry and wider society at a global level. It has evolved from a concept to a technology that is driving innovation, transforming productivity and disrupting existing business models across numerous sectors. The industrial and societal impact of AI is profound and multifaceted, offering opportunities for growth, efficiency, and improved healthcare, but also raising ethical and societal challenges as the method is integrated into many aspects of human life and work. This editorial is developed by contributors of the 4th Royal Society Yusef Hamied Workshop ( in 2023 devoted to Artificial Intelligence), designed to enhance collaboration between Indian and the UK scientists and to explore future research opportunities. The insights shared at the workshop are shared here.

Hackers follow a standard cyber kill chain process and install ransomware payloads using phishing emails on firms belonging to critical industry, resulting in huge losses in revenue, reputation, and customer churn. This study uses a mixed-method explanatory approach to mitigate ransomware attacks. We present the quantitative Ransomware Risk Management Model (R2M2) based on protection motivation theory (PMT). The Ransomware Risk Assessment module based on the threat appraisal component of PMT and the National Institute of Standards and Technology (NIST) guidelines can help the chief information security officer (CISO) to assess the risk using predictive analytics techniques. The Ransomware Risk Quantification module uses collective risk modeling to compute the severity of a ransomware attack on an organization. The Ransomware Risk Mitigation module helps the CISO to minimize the probability and impact of a ransomware attack on their organization by (i) investing in perimeter security technologies and training to ensure prepare, deter, detect, restrain, recover, and review mitigation strategies, (ii) adopting the National Institue of Standards and Technology (NIST) Cybersecurity Framework to ensure business resilience, and (iii) mitigating the residual risk by investing in cyber-risk insurance. We validated the proposed method using a qualitative study by interviewing participants form firms affected by ransomware, managed security service providers, security consultants, and cyber-risk insurers.

Metaverse platforms devoted to educational purposes are becoming increasingly popular among educators worldwide due to the benefits they offer in elevating students’ lives. This study explores how immersive platform features, blockchain features, a safe learning environment and new learning models contribute to the educational outcomes. Emerging literature on educational Metaverse platforms has been used to develop a genuine conceptual model. The model was tested using data collected from 100 educators located in different countries from worldwide. The data were analyzed using partial least squares structural equation modeling (PLS-SEM), Random Forest and other different feature importance methods, fuzzy-set qualitative comparative analysis (fsQCA), thematic content analysis and Latent Dirichlet Allocation (LDA). The application of PLS-SEM outlines that immersive platform features and safe learning environments positively affects educational outcomes. Random Forest and feature importance methods investigate the predictive power of items included in the model, fsQCA reveals the combinations of these factors that lead to high educational outcomes, while the qualitative insights reveal the key themes emerged from educators’ perspectives on the use of Metaverse platforms. This study contributes to Metaverse literature, which is relatively understudied in the educational sector.