Information & Management最新文献

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.

The advent of social media platforms has made cyberbullying a growing concern. Current studies primarily investigate singular individual-level or environmental-level factors, yet cyberbullying is a multifaceted phenomenon that cannot be fully understood through single-dimensional perspectives. In this research, we draw on situational action theory (SAT) to explore how cyberbullying develops on social media by examining the synergistic effect of individual propensity and criminogenic exposure. Our empirical results reveal that individuals’ cyberbullying behavior is predominantly influenced by previous habits and exposure to the cyberbullying behavior of important others. Furthermore, individuals tend to employ neutralization techniques to rationalize their cyberbullying behaviors.

Although technological developments have made AI chatbot teaching assistants a lived reality, limited insights exist indicating how students perceive and use these new technologies. Recognizing that technology usage research adopts a static view that overlooks the role of temporal distance, we draw on temporal construal theory to examine user intentions and actual usage behavior over time by re-approaching the same set of participants after developing and deploying an AI chatbot in the educational setting. Our results highlight the significance of temporal distance in shaping user perceptions of the technology, with the need for interaction level to play a moderating role.

This study examines the impact of emotional bias on entrepreneurial decisions in crowdfunding platforms, specifically considering the impact of mass shootings as exogenous emotional shocks. Using data from a leading crowdfunding platform, Kickstarter, paired with US mass shooting incidents, we apply a difference-in-differences approach. Our analysis shows that entrepreneurs in cities proximate to a mass shooting tend to adjust their fundraising targets downward. This adjustment subsequently increases the likelihood of campaign success, a trend that is even more pronounced for projects tied to the geographic location of the mass shooting incident. Our findings not only attest to the profound influence of emotions in crowdfunding decisions, but also unveil the nuanced, varying effects based on geographic relevance. This research underscores the importance of understanding and addressing such biases to bolster crowdfunding platform efficiency and to support the consistent success of crowdfunded enterprises.

While digital technologies have undoubtedly enhanced the quality of life for individuals with visual impairment, their influence on social justice remains underexplored. Drawing upon intergroup contact theory, this study employs a mixed-methods approach, utilizing both surveys and interviews, to examine the impact of real-time volunteer apps on social justice from the perspectives of both visually impaired people (VIP) and non-impaired people (NIP). Our results reveal unintended adverse effects associated with the use of real-time volunteer apps. Despite enhancing perceived social connection, these apps paradoxically increase VIP's prejudice vulnerability, which is potentially attributed to the social contrast effect. Meanwhile, app usage increases NIP's stereotyping and reduces their social acceptance of VIP groups, as these apps fail to create equal status contact between NIP and VIP. This research illuminates the complex dynamics of how information technology (IT) affects social justice for VIP and highlights the overlooked downside of IT. It advances our understanding of intergroup contact theory by providing empirical evidence on contact effects when equal status conditions are lacking and serves as a reminder to app designers and developers of the crucial role of social influence in app design, particularly in the development of apps intended for minority groups.

Natural disasters arouse threats of health and economic damage, altering consumer behavior in both the short term and the long term. By adapting coping theory to health and economic threats, this study examines the heterogeneous impacts of epidemic disasters, in the COVID-19 pandemic context, on online consumer demand by product type and their dynamics after stabilization. Our results show that the local epidemic outbreak initially increased (decreased) the online demand for essential (discretionary) goods, but the increased purchasing of essential goods diminished as the associated risks were alleviated, whereas the decreased demand for discretionary goods persisted. Additional analyses reveal that engaging more with online consumers during disastrous periods helps retain a specific type of consumer even after stabilization. Our findings highlight the role of threats as psychological drivers in changing online demand, providing managerial implications for online retailers on how to convert threat-driven consumers into regular customers afterward.