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

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

The Effect of Perceived Service Quality, Perceived Sacrifice and Perceived Service Outcome on Online Customer Loyalty

Customer loyalty is a key driver of financial performance in service organizations. We investigate whether or not online customer loyalty can be increased through enhancing the perceived service quality, reducing perceived sacrifice and improving the perceived service outcome in the online service context with the possible availability of live help service technology. We also investigate the moderating role of customer product knowledge on these relationships. The empirical results indicate that 1) online customer loyalty increases with higher perceived service quality, lower perceived sacrifice and better perceived service outcome, 2) perceived service quality positively influences perceived service outcome while perceived sacrifice negatively influences perceived service outcome, 3) customer product knowledge negatively moderates the relationship between perceived service quality and online customer loyalty such that greater product knowledge weakens that relationship, 4) customer product knowledge positively moderates the relationship between perceived sacrifice and online customer loyalty. Theoretical and practical implications are discussed

Exploring the Role of AI Explanations in Delivering Rejection Messages: A Comparative Analysis of Organizational Justice Perceptions between HR and AI

The increasing use of AI decision systems in recruitment processes has created challenges, including potential resistance from job applicants. To address this issue, drawing on organizational justice theory, we identify dimensions of AI explanations in the employment context and examine their impact on job applicants\u27 perceptions of organizational justice. We conducted an experiment to understand applicants\u27 reactions to AI versus HR managers without explanations and examined the impact of AI explanations on organizational justice perceptions and acceptance intention. Our findings show that without explanation, AI is perceived as lower organizational just and acceptance intention compared to HR managers. Organizational justice mediates the effects between outcome/process explanations of AI on acceptance intention. However, outcome explanations have a stronger impact compared to process explanations. Our study contributes to understanding explanation structures for AI-based recruitment and offers practical implications for developing explanations that improve the perceived justice of AI recruitment systems

Evaluation on a Personalized Mobile Advertising System: a Comparative Approach

Along with the high proliferation of mobile phones and other mobile devices, research on the use of short messaging service&#;SMS&#; to access customers through their handheld devices has gained much attention, which is termed as mobile advertising. In order to make the best use of mobile advertising to benefit companies and customers becomes more emergent. One of the most important and successful factor that will bring more positive attitudes towards mobile advertising and induce customers to behave positively is personalization, which has been confirmed in many prior studies. Therefore, it’s necessary and essential for researchers to design an effective system capable of recommending personalized mobile advertising to mobile users. The purpose of this paper is to fulfill this task. We present such a kind of personalized mobile advertising system based on Bayesian Network. Then, we brought out a thorough evaluation of our system in a laboratory environment. Experimental results showed better performance of our system in furnishing personalized mobile advertising than conventional method (random advertising)