Information & Management最新文献

The rise of blockchain technology has sparked interest in decentralized identity management (DIdM). However, DIdM's interdisciplinary nature has led to a fragmented understanding. We propose a “Task Structure-Technological Properties-Fit” framework to clarify the application of DIdM across tasks and contexts. We conducted a comprehensive review of 149 DIdM papers to define task structure (task goals and stakeholder values), task goals (identity value creation and value maintenance), stakeholders (users, service providers, identity issuers, regulators, and infrastructure builders), and DIdM properties (interoperability and self-sovereignty). We explored how these elements could align and then highlight research gaps and present a DIdM research agenda.

Online physician recommender systems alleviate information overload by automatically recommending the best-fit physicians to patients. In contrast to general recommendations, physicians with greater uncertainty (i.e., greater variance in patients’ feedback) may not be preferred as this could affect patients’ treatment. However, most existing recommender systems don't consider uncertainty, reducing systems’ reliability and patients’ readiness to trust. To address this concern, this study leverages Bayesian theory and develops an uncertainty-aware online physician recommender system, including a Bayesian deep collaborative filtering (BDCF) model and a novel uncertainty-aware ranking algorithm. Experiments on real-world data demonstrate the superiority of BDCF and the ranking algorithm.

Time scarcity deals, which offer time-limited discounts, are prevalent in online shopping. These deals have two temporal aspects: deal time and lead time. Deal time, the remaining duration before the discount expires, signals scarcity and enhances the deal's appeal. In contrast, lead time, the delay between purchase and receipt/consumption, postpones gratification and diminishes the deal's attractiveness. This study proposes that as deal time and lead time fluctuate, they progressively bring consumers psychologically closer to the act of purchase/consumption. Drawing on construal level theory, we contend that firms can use image zooming as a digital tool to generate product images at various zoom levels and align with the psychological distances induced by deal time or lead time throughout the deal's availability. This strategy amplifies the positive effects of deal time and mitigates the negative impacts of lead time. Our research model is validated by data from three experiments. We discovered that (1) product image zooming affects consumers’ psychological proximity to an online deal; (2) when a deal with a short (long) deal/lead time is paired with a zoom-in (zoom-out) product image, deal evaluation and purchase intention increase; and (3) these effects are mediated by consumers’ “feeling right” experience during deal evaluation, which arises from a match between the construal levels prompted by the temporal feature and the product image. Theoretical contributions and managerial implications are discussed.

Short video commerce has become increasingly prevalent; however, the factors affecting customer engagement have not been adequately examined. Drawing on social learning theory, attitude transfer theory and relationship norms theory, we explored how social learning behavior from three dimensions of atmospheric cues is associated with customers’ attitudes, how such attitudes mediate these engagement relationships, and also how the relationship norms moderate the mediating effect of attitude. The empirical findings obtained through the PLS-SEM largely support the hypotheses. The fsQCA results illustrate how combinations of antecedents can lead to high customer engagement. This study contributes to the literature on short video commerce, customer attitudes, and relationship norms, and provides influencers and managers with practical strategies to improve customer engagement.

Increasingly, large organisations are turning to cybersecurity leaders to protect their information resources against attack. However, because cybersecurity leadership roles are new, educational literature and practice targeting this role are nascent. In this systematic review, we assess the value of case-based learning (CBL) in educating cybersecurity leaders. We also aim to discover what gaps, if any, exist in this body of research. We find that cybersecurity leaders’ attitudes and metacognitive abilities are important but overlooked elements of their competence, and that CBL has potential to develop these competencies. The article concludes with a competency matrix and agenda for further research.

Leveraging online networks like Twitter and Foursquare, our study explores the dynamics of self-disclosure and impression management through location sharing. Grounded in social and hyperpersonal information processing theories, we investigate how peer influence, geographical proximity, and exposure to promotional content impact self-disclosure behaviors. Through an integrated dataset, we determine that friends’ check-ins and their physical proximities significantly sway individual self-disclosure. Particularly, in the presence of health-conscious peers, users tend to avoid check-ins that create a negative health impression. Furthermore, exposure to health-related promotional content significantly influences these behaviors. Our findings provide crucial perspectives on impression management in the digital age.

Ethical consumerism has led manufacturers and retailers to develop new initiatives to align their products and services with related requirements, such as the management of supply chains for ethically produced products. To this end, recent years have witnessed the growing reliance on technological innovations to corroborate claims on ethically produced products. RFID (Radio-Frequency Identification)-based systems have found a natural fit for such applications due to their ability to uniquely identify individual items and locally store their characteristics and history. While the benefits of such a setup are clear, there is a paucity of research on associated unethical risks. What is often unclear is the possibility of deception when RFID-generated information is used to certify ethical production. We draw on stockholder theory, stakeholder theory, social contract theory, and Mason’s information ethics theory to study the underlying dynamics. We show that enhancing RFID information accessibility and accuracy in supply chains is key to mitigating the unethical risk of deception and ensuring that RFID-based systems can truly certify ethically produced products. We develop a portfolio of such risks, identify means to mitigate identified risks, and then use a case study to examine the impact of varying degrees of RFID-generated information accessibility and accuracy on the detection and prevention of associated deceptions.

Although analytics have become a widespread practice, we still have minimal knowledge about how dashboards influence decision-makers and through what mechanisms they enhance decision making. In this study, we built on an experiment-based approach with mock-up visualizations and recruited 524 participants, who were divided into two groups (A and B) with variations in their visualizations. We found that the format, currency, and completeness of information indirectly affect decision making quality by reducing the perceived task complexity and enhancing information satisfaction. Our results contribute to a better understanding of the role of visual representation of information quality on dashboard visualizations.

Short video platforms have become increasingly popular in recent years. Their success relies heavily on users’ continuous watching and advertising revenues. However, the factors that drive users’ continuous watching behavior are still unclear, and the potential influence of advertising on users’ continuous watching is largely neglected. Drawing upon the theory of consumption values (TCV) and the cognitive–affective–behavior (CAB) model, this study explores the factors influencing consumers’ continuous watching intentions from both cognitive and affective perspectives. We employed an exploratory mixed-methods study approach. Focus group discussions among 25 short video users were first conducted to identify five short video consumption values and develop the questionnaire. Then the survey was distributed to 584 short video users in China to obtain quantitative data for analysis using structural equation modeling. The results show that continuous watching intention is determined by affective factors of satisfaction and dependence, and affective factors are influenced by cognitive consumption values. Moreover, advertising irritation weakened the influence of satisfaction on continuous watching intention. Our study established and empirically verified a new cognitive and affective model to explain short video consumers’ behavior. The findings could help short video creators and platforms cultivate customers’ continuous watching by concentrating on the specific cognitive and affective factors, and provide guidance on balancing between users’ continuous watching and advertising revenue.

Social media platforms such as Instagram and Twitter have emerged as critical channels for marketing and selling illegal drugs. Detecting and labeling online illicit drug trafficking activities have become an important measure to combat online drug trafficking. Recently, machine learning has been applied to drug trafficking detection. However, the effectiveness of conventional supervised learning methods in detecting drug trafficking heavily relies on access to substantial amounts of labeled data, while data annotation is time-consuming and resource-intensive. Furthermore, these models often face challenges in accurately identifying trafficking activities when drug dealers use deceptive language and euphemisms to avoid detection. To overcome this limitation, we conduct the first systematic study on leveraging large language models (LLMs), such as ChatGPT, to detect illicit drug trafficking activities on social media. We propose an analytical framework to compose knowledge-informed prompts, which serve as the interface that humans can interact with and use LLMs to perform the detection task. Additionally, we designed a Monte Carlo dropout-based prompt optimization method to further improve performance and interpretability. Our experimental findings demonstrate that the proposed framework outperforms other baseline language models in terms of drug trafficking detection accuracy, showing a remarkable improvement of nearly 12%. By integrating prior knowledge and the proposed prompts, ChatGPT can effectively identify and label drug trafficking activities on social networks, even in the presence of deceptive language and euphemisms used by drug dealers to evade detection. The implications of our research extend to social networks, emphasizing the importance of incorporating prior knowledge and scenario-based prompts into analytical tools to improve online security and public safety.