Alleviating Information Cocoons and Fatigue with Serendipity: Effect of Relevant Diversification and its Timing

With the rapid development of online media, in which personalized recommendations are provided, users are gaining increasingly narrow access to information, trapping them in so-called “information cocoons.” At the same time, the increase in homogenized content has brought boredom and fatigue, which are not conducive to the long-term interests of a platform. Grounded in the entertainment consumption context, as represented by the Tik Tok short video platform, this study focuses on the information cocoon reinforcement and browsing fatigue phenomena caused by the lack of proper diversification. Then, to mitigate these issues, this paper proposes relevant diversified content and diversification timing countermeasures to optimize the “what” and “when” technical designs. We explore the role of perceived serendipity as a key path toward user diversity acceptance and browsing duration, thus alleviating the phenomenon of information cocoons and browsing fatigue and facilitating the common development of platforms and users

Exploring Explanation Effects on the Usage of Artificial Intelligence in Recruitment: Human Resources Professionals\u27 Perspective

Artificial intelligence (AI) is increasingly used in recruitment for its data handling and decision consistency, but human resources professionals (HRPs) remain skeptical about predictive accuracy and potential biases (e.g., only hiring males), influencing the justice of AI’s decision. Meanwhile, such advanced capabilities of AI may make HRPs worry that AI could replace their roles and threaten their identity. To address such concerns and improve the acceptance of AI, it is essential to increase the explainability of the AI. Thus, we propose classifying AI explanations into input, process, and output. Our study will determine the effect of explanation on HRPs’ reliance of AI and will explore how organizational justice and threat to identity influence HRPs’ reliance on AI usage. This research aims to clarify the psychological mechanisms affecting AI acceptance in hiring, contributing to the human-machine interaction and HR management literature

Why Users Accept Discriminatory Pricing: The Roles of AI Agent\u27s Presence and Explanation

Discriminatory pricing practices have raised consumers’ negative reactions. This study investigates how AI agent’s presence and the use of explanations impact consumers\u27 acceptance of discriminatory pricing. A scenario-based experiment revealed that AI agent’s presence negatively moderates the negative relationship between offer unfavorability and offer acceptance, which is mediated by perceived justice and invasion of privacy. Moreover, this research indicated that for unfavored price, environment-based explanation is more effective than user-based explanation and the positive effect of AI agent’s presence on offer acceptance is more pronounced when providing user-based explanations. This study contributes to price management literature and AI decision literature by illustrating how the AI agent\u27s presence asymmetrically shapes consumers\u27 perceptions of offer outcomes, enriching our understanding of consumer responses to AI. The findings have implications for firms managing discriminatory pricing, offering insights into optimal AI agents and explanation utilization for enhancing customer experience and business performance

Will Humans be Free-Riders? The Effects of Expectations for AI on Human-AI Team Performance

The failure of human-AI augmentation is a common problem that is usually believed to be highly related to poor AI design and human’s inability to identify appropriate AI suggestions, but existing interventions like explainable AI were not effective to solve this problem. We propose that a crucial factor contributing to the failure of human-AI augmentation lies in the withholding of human effort. Moreover, high expectations for AI performance, which is generally positive for AI adoption, may undermine human-AI team performance by reducing human involvement in the task. Based on the Collective Effort Model (CEM), we explore how expectations for AI performance, perceive indispensability and task meaningfulness influence human effort and human-AI team performance. We plan to conduct laboratory experiments in image classification and idea generation to test our hypotheses. We expect to enhance the understanding of human-AI collaboration and the effects of social loafing effect in human-AI teams

The Effects of Service and Consumer Product Knowledge on Online Customer Loyalty

Customer loyalty is a key driver of financial performance for online firms. The effect of service quality on customer loyalty has been well established. Yet, there is a paucity of research that has studied the cost of obtaining service quality during the service process and the service outcome influenced by such cost. We extend previous research and propose the 3S Customer Loyalty Model by integrating sacrifice and service outcome as additional important service dimensions together with service quality when predicting online customer loyalty, and examining how their influences on loyalty vary across customers with different degrees of product knowledge. Further, we theorize that service quality and sacrifice -- as service process dimensions -- influence service outcome, and we theorize how “live help” technology improves customer perceptions of service quality and sacrifice. The empirical results indicate that 1) customer loyalty increases with higher perceived service quality, lower perceived sacrifice, and better perceived service outcome, 2) service quality and sacrifice influence service outcome, 3) customer product knowledge negatively moderates the relationship between service quality and online customer loyalty and positively moderates the relationship between sacrifice and customer loyalty, and 4) live help technology enhances service quality and reduces sacrifice. These findings support the theoretical importance of including sacrifice and service outcome (parallel with service quality) as antecedents of online customer loyalty. Our study also advances the theoretical understanding of what service process consists of and how the service process (i.e. service quality and sacrifice) influences service outcome

Algorithmic Pricing and Fairness: A Moderated Moderation Model of AI Disclosure and Typicality of AI Pricing

In the era of big data, the utilization of algorithms for dynamic pricing has become prevalent. However, concerns have been raised about the potential negative impact of these practices on consumers\u27 fairness perceptions. Using attribution theory as the underlying framework, we explore how AI disclosure moderates the relationship between AI pricing type (unified/personalized dynamic pricing) and fairness perceptions (procedural/distributive fairness) and how this moderation effect is further moderated by the perceived typicality of AI pricing. An online scenario-based experiment was carried out with 145 participants. The results reveal that personalized dynamic pricing elicits lower fairness perceptions than unified dynamic pricing. Furthermore, we observe a significant moderated moderation effect, indicating that the negative impact of personalized dynamic pricing can be mitigated by AI disclosure for consumers who perceive AI pricing as typical. These findings contribute to AI pricing literature and the development of fairer platform designs

A Bayesian Network-Based Framework for Personalization in Mobile Commerce Applications

Providing personalized services for mobile commerce (m-commerce) can improve user satisfaction and merchant profits, which are important to the success of m-commerce. This paper proposes a Bayesian network (BN)-based framework for personalization in m-commerce applications. The framework helps to identify the target mobile users and to deliver relevant information to them at the right time and in the right way. Under the framework, a personalization model is generated using a new method and the model is implemented in an m-commerce application for the food industry. The new method is based on function dependencies of a relational database and rough set operations. The framework can be applied to other industries such as movies, CDs, books, hotel booking, flight booking, and all manner of shopping settings

The Effect of Online Review Portal Design: The Moderating Role of Explanations for Review Filtering

The flood of non-constructive and fake online consumer reviews erects a considerable barrier to consumers making efficient decisions. Various review filtering algorithms have been developed to address this challenge, but the design of post-development review portals continues to lack a consensus. In review portals, disclosing more transparent reviews is efficient for enhancing users’ trust. However, it will cause users’ diminished focus on recommended reviews, leading to sub-optimal decisions. A research model is then developed to investigate users’ cognitive processes in their responses to three review exhibition designs (i.e., informed silent display design, filtered review display design, and composite display design) regarding trust in the review portal and perceived decision quality. We also suggest that explanations for review filtering play a moderating role in users’ perceptions, which appears to be a viable resolution to this dilemma. This paper provides significant theoretical and practical insights for the review portal design and implementation