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The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

This study aims to explore the overall relationship between a boundary spanner and a partner firm, i.e. boundary spanner closeness to partner firm. Drawing on consumer-service provider relationship literature and the tripartite model of affect-behavior-cognition, the authors identify three key dimensions of such closeness, namely, boundary spanners’ relational ties, customer-specific capabilities and accommodative behaviors, and examine their effects on exchange outcomes in turbulent versus stable environments.

The paper examines the effects of three dimensions of boundary spanner closeness on various exchange outcomes (i.e. retailers’ cooperation, satisfaction and willingness for investment) using two industries as exemplars, characterized by distinct levels of environmental turbulence – the retailing networks of a major cell phone company and a petroleum company in China.

The results indicate that the three dimensions individually and jointly affect exchange outcomes and the interplay of customer-specific capabilities and relational ties affect exchange outcomes differently across industry turbulence.

The existing literature lacks a comprehensive understanding of the function of boundary spanners, which serve as a key relational interorganizational governance component. By identifying three key dimensions of boundary spanner closeness and examining their effectiveness in promoting exchange outcomes, this study advances the understanding of the role of boundary spanners in interorganizational governance.

In an online shopping environment, individual reviews and aggregated ratings are important anchors for consumers’ purchasing decisions. However, few studies have considered the influence of aggregated ratings on consumer decision-making, especially at the neural level. This study aims to bridge this gap by investigating the consumer decision-making mechanism based on aggregated ratings to uncover the underlying neural basis and psychological processing.

An event-related potential experiment was designed to acquire consumers’ electrophysiological records and behavioral data during their decision-making processes based on aggregated ratings. The authors speculate that during this process, review valence categorization (RVC) processing occurs, which is indicated by late positive potential (LPP) components.

Results show that LPP components were elicited successfully, and perceptual review valence can modulate its amplitudes (one-star [negative] and five-star [positive] ratings evoke larger LPP amplitudes than three-star [neutral] ratings). The electroencephalogram data indicate that consumer decision-making processes based on aggregated ratings include an RVC process, and behavioral data show that easier review valence perception makes the purchase decision-making easier.

This study enriches the extant literature on the impact of aggregated ratings on consumer decision-making. It helps understand how aggregated ratings affect consumers’ online shopping decisions, having significant management implications. Moreover, it shows that LPP components can be potentially used by researchers and companies to evaluate and analyze consumer emotion and categorization processing, serving as an important objective physiological indicator of consumer behavior.