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

A Finite Element Method for the Multiterm Time-Space Riesz Fractional Advection-Diffusion Equations in Finite Domain

We present an effective finite element method (FEM) for the multiterm time-space Riesz fractional advection-diffusion equations (MT-TS-RFADEs). We obtain the weak formulation of MT-TS-RFADEs and prove the existence and uniqueness of weak solution by the Lax-Milgram theorem. For multiterm time discretization, we use the Diethelm fractional backward finite difference method based on quadrature. For spatial discretization, we show the details of an FEM for such MT-TS-RFADEs. Then, stability and convergence of such numerical method are proved, and some numerical examples are given to match well with the main conclusions

Plasmonic Tamm states: second enhancement of light inside the plasmonic waveguide

A type of Tamm states inside metal-insulator-metal (MIM) waveguides is proposed. An impedance based transfer matrix method is adopted to study and optimize it. With the participation of the plasmonic Tamm states, fields could be enhanced twice: the ffirst is due to the coupling between a normal waveguide and a nanoscaled plasmonic waveguide and the second is due to the strong localization and field enhancement of Tamm states. As shown in our 2D coupling configuration, |E|^2 is enhanced up to 1050 times when 1550 nm light is coupled from an 300 nm Si slab waveguide into an 40 nm MIM waveguide.Comment: 3 pages, 4 figure

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

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

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

Carrier Dynamics in Submonolayer InGaAs/GaAs Quantum Dots

Carrier dynamics of submonolayer (SML) InGaAs/GaAs quantum dots (QDs) were studied by micro-photoluminecence (MPL), selectively excited photoluminescence (SEPL), and time-resolved photoluminescence (TRPL). MPL and SEPL show the coexistence of localized and delocalized states, and different local phonon modes. TRPL reveal shorter recombination lifetimes and longer capture times for the QDs with higher emission energy. This suggests that the smallest SML QDs are formed by perfectly vertically correlated 2D InAs islands, having the highest In content and the lowest emission energy, while a slight deviation from the perfectly vertical correlation produces larger QDs with lower In content and higher emission energy.Comment: 12 pages, 5 figure