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Live streaming commerce has emerged as an essential strategy for vendors to effectively promote their products due to its unique content presentation and real-time interaction. This study aims to investigate the influence of viewer-streamer interaction and viewer-viewer interaction on consumer trust and the subsequent impact of trust on consumers' purchase intention within the live streaming commerce context.

A survey questionnaire was conducted to collect data, and 403 experienced live streaming users in China were recruited. Covariance-based structural equation modeling (CB-SEM) was used for data analysis.

The results indicated that viewer-streamer interaction factors (i.e., personalization and responsiveness) and viewer-viewer interaction factors (i.e., co-viewer involvement and bullet-screen mutuality) significantly influence trust in streamers and co-viewers. Additionally, drawing on trust transfer theory, trust in streamers and co-viewers positively influences trust in products, while trust in co-viewers also positively influences both trust in streamers and products. Furthermore, all three forms of trust positively impact consumers' purchase intentions.

This study enriches the extant literature by investigating interaction-based trust-building mechanisms and uncovering the transfer relationships among three trust targets (streamers, co-viewers and products). Furthermore, this study provides some practical guidelines to the streamers and practitioners for promoting consumers’ trust and purchase intention in live streaming commerce.

A rash of security incidents in ride-sharing have made discovering the mechanisms to repair consumers' trust essential for the information technology (IT)-enabled ride-sharing platforms. The purpose of this paper is to explore how the two response strategies (i.e. security policies [SPs] and apologies) of platforms repair passengers' trust and whether the two implementation approaches of SPs (i.e. pull and push) lead to different results in repairing passengers' trust in the platforms.

A field survey based on a real scenario (n = 238) and an experiment (n = 245) were conducted to test the hypotheses empirically. Structural equation modeling and one-way analysis of variance (ANOVA) are employed in the data analyses.

This study finds that (1) both SPs and apologies aid in repairing trust; (2) repaired trust fully mediates the influence of SPs on continuance usage and partially mediates the influence of apologies on continuance usage; (3) security polices and the three dimensions of apologies play different roles in repairing trust and retaining passengers and (4) both pull-based and push-based SPs can repair the violated trust; however, the effect of the pull approach is greater than that of the push approach.

The findings provide guidelines for ride-sharing platforms in taking appropriate actions to repair users' trust after security incidents.

The findings reveal the mechanism of trust repairing in the fields of ride-sharing and extend the contents of the trust theory and pull–push theory.

Live-streaming e-commerce is becoming a new way for many consumers to shop. During the live broadcast process, the interaction between anchors and customers plays a decisive role on consumers' purchasing decisions. This study aims to explore how two types of interaction between the anchor and the customers (i.e. task-oriented interaction and relationship-oriented interaction) affect customers' purchase decisions.

The study establishes a model based on online trust theory and multi-sensor interaction theory. To validate the model, we carried out five simulated live-streaming events and collected data through a scenario-based survey of the viewers participating in the live-streaming (N = 244). Structural equation modeling was employed to test the hypotheses.

Both task-oriented interaction and relationship-oriented interaction have a positive impact on users' purchase decisions through the mediation of virtual touch, emotional trust and cognitive trust. Sense of power has opposite moderating effects on the impacts of relationship-oriented interaction on emotional trust and cognitive trust.

This study enriches the theory of live-streaming e-commerce by demonstrating the decisive roles of two types of anchor–customer interaction, the mediation roles of virtual touch, cognitive trust, and emotional trust in customer purchase decisions, as well as the moderating effect of sense of power on customer decision-making processes. The findings provide practical insights for anchors and live-streaming platforms about how they should arrange live-streaming content to enhance consumer purchasing decisions.

Most studies have focused on the impact of the application of AI on management attributes, management decisions and management ethics. However, how job demand and job control in the context of AI collaboration determine employees' learning process and learning behaviors, as well as how AI collaboration moderates employees' learning process and learning behaviors, remains unknown. To answer these questions, the authors adopted a Job Demand-Control (JDC) model to explore the influencing factors of employee's individual learning behavior.

This study used questionnaire survey in organizations using AI to collect data. Partial least squares (PLS) predict algorithm and SPSS were used to test the hypotheses.

Job demand and job control positively influence self-efficacy, self-efficacy positively influences learning goal orientation and learning goal orientation positively influences learning behavior. Learning goal orientation plays a mediating role between self-efficacy and learning behavior. Meanwhile, collaboration with AI positively moderates the impact of employees' job demand on self-efficacy and the impact of self-efficacy on learning behavior.

This study introduces self-efficacy as the outcome of JDC model, demonstrates the mediating role of learning goal orientation and introduces collaborative factors related to artificial intelligence. This study further enriches the theoretical system of human–AI interaction and expands the content of organizational learning theory.

As an emerging technology, medical artificial intelligence (AI) plays an important role in the healthcare system. However, the service failure of medical AI causes severe violations to user trust. Different from other services that do not involve vital health, customers' trust toward the service of medical AI are difficult to repair after service failure. This study explores the links among different types of attributions (external and internal), service recovery strategies (firm, customer, and co-creation), and service recovery outcomes (trust).

Empirical analysis was carried out using data (N = 338) collected from a 2 × 3 scenario-based experiment. The scenario-based experiment has three stages: service delivery, service failure, and service recovery. The attribution of service failure was divided into two parts (customer vs. firm), while the recovery of service failure was divided into three parts (customer vs. firm vs. co-creation), making the design full factorial.

The results show that (1) internal attribution of the service failure can easily repair both affective-based trust (AFTR) and cognitive-based trust (CGTR), (2) co-creation recovery has a greater positive effect on AFTR while firm recovery is more effective on cognitive-based trust, (3) a series of interesting conclusions are found in the interaction between customers' attribution and service recovery strategy.

The authors' findings are of great significance to the strategy of service recovery after service failure in the medical AI system. According to the attribution type of service failure, medical organizations can choose a strategy to more accurately improve service recovery effect.

The peer-to-peer (P2P) accommodation-sharing market has developed rapidly on the strength of information technology in recent years. Matching providers and customers in an information technology (IT)-enabled platform is a key determinant of both parties' experiences and the healthy development of the platform. However, previous research has not sufficiently explained the mechanism of provider–customer matching in accommodation sharing, especially at the psychological level. Based on field cognitive style theory, this study examines how the match and mismatch affect customers' online and offline satisfaction and whether a significant difference exists between online and offline satisfaction under different matching patterns.

The authors test the proposed theoretical model using 122 provider–customer dyad data collected through a field study.

The results suggest that customers' online and offline satisfaction under match is significantly higher than that under mismatch. In addition, customers' online satisfaction is significantly higher than their offline satisfaction under mismatch, but there is no significant difference between the two under match. The perceived price fairness also plays a moderating role in the case of mismatch.

In summary, these findings provide a novel understanding about the matching patterns and their outcomes in the accommodation-sharing context and expand the contents and applications of field cognitive style theory and matching theory. This study will help these IT-enabled platforms to provide personalized matching services at the psychological level, thereby enhancing user experience and corporate competitiveness. 10; 10;

As human–AI hybrid teams become more common, it is essential for team members to interact effectively with artificial intelligence (AI) to complete tasks successfully. The integration of AI into the team environment alters the cooperative dynamics, prompting inquiry into how the design characteristics of AI impact the working mode and individual performance. Despite the significance of this issue, the effects of AI design on team dynamics and individual performance have yet to be fully explored.

Drawing upon coping theory, this study presents a research model aimed at elucidating how the characteristics of AI in human–AI interaction influence human members’ adaptive behavior, subsequently impacting individual performance. Through the creation of experiments that require human–AI collaboration to solve problems, we observe and measure various aspects of AI performance and human adaptation.

We observe that the explainability of AI enhances the behavioral adaptation of human team members, whereas the usability and intellectuality of AI improve their cognitive adaptation. Additionally, we find that human team members’ affective adaptation is negatively affected by the likability of AI. Our findings demonstrate that both behavioral and cognitive adaptations positively impact individual performance, whereas affective adaptation negatively impacts it.

Our research findings provide recommendations for building efficient human–AI hybrid teams and insights for the design and optimization of AI.

Overall, these results offer insights into the adaptive behavior of humans in human–AI interaction and provide recommendations for the establishment of effective human–AI hybrid teams. These findings pioneer an understanding of how design characteristics of AI impact team dynamics and individual performance, establishing a connection between AI attributes and human adaptive behavior.

The purpose of this paper was to investigate the relationships among the intellectual ability of artificial intelligence (AI), cognitive emotional processes and the positive and negative reactions of human members. The authors also examined the moderating role of AI status in teams.

The authors designed an experiment and recruited 120 subjects who were randomly distributed into one of three groups classified by the upper, middle and lower organization levels of AI in the team. The findings in this study were derived from subjects’ self-reports and their performance in the experiment.

Regardless of the position held by AI, human members believed that its intelligence level is positively correlated with dependence behavior. However, when the AI and human members are at the same level, the higher the intelligence of AI, the more likely it is that its direct interaction with team members will lead to conflicts.

This paper only focuses on human–AI harmony in transactional work in hybrid teams in enterprises. As AI applications permeate, it should be considered whether the findings can be extended to a broader range of AI usage scenarios.

These results are helpful for understanding how to improve team performance in light of the fact that team members have introduced AI into their enterprises in large quantities.

This study contributes to the literature on how the intelligence level of AI affects the positive and negative behaviors of human members in hybrid teams. The study also innovatively introduces “status” into hybrid organizations.

Residing on social networking platforms, social games have unique characteristics distinguishing them from other digital games or online games. The purpose of this paper is to explore both social and gaming factors of social games and investigate their roles on enhancing perceived enjoyment. The authors also examine the relationships between perceived enjoyment, subject norm, perceived critical mass, intention to play, and actual behavior.

This paper develops a research model including nine hypotheses. Using a survey questionnaire, empirical data were collected from 169 actual social game players. Structured equation modeling was used to test the proposed research models.

Social identification, social interaction, and diversion significantly influence perceived enjoyment. Perceived enjoyment significantly influences the intention to play, which in turn significantly influences the actual behavior. Moreover, subject norm and perceived critical mass play different roles in determining the intention to play and the actual behavior.

The results of this study provide social game practitioners with a set of rich insights into guidelines on designing specific social and gaming characteristics to improve users’ perceived enjoyment and actual playing behavior.

Through analyzing characteristics of social games, The authors emphasize the difference between social games and other online games or computer games and recognize the enhancing role of social and gaming factors on perceived enjoyment. Findings of this study contribute to the literature on social games.

The purpose of this paper is to investigate the developmental trajectory and patterns of physical aggression and relational aggression over time, and also to examine the gender differences of the three-year developmental process as well as the impact of the developmental trajectory on mental health.

Participants: the participants of this study were newly enrolled junior school students. The study spanned three years with continuous tracking performed once every other year. Measures: class play questionnaire. Aggressive behaviors were measured by an adaptive Chinese version of the revised class play assessment. Statistical analysis: to address the questions of the present study, the latent class growth model (LCGM) was used to analyze the three-year longitudinal data by Mplus 6.1.

The initial level of physical aggression in boys was higher than that in girls. There were three types of developmental trajectory for boys, corresponding to a lower initial level-increasing group, a middle initial level-increasing group and a higher initial level-stable group. However, girls demonstrated different patterns, corresponding to a lower initial level-increasing group, a middle initial level-increasing group and a higher initial level-decreasing group. In contrast to the physical aggression, the initial level of relational aggression in boys was lower than that in girls. There were four types of developmental trajectory for boys, corresponding to a lower initial level-increasing group, a middle initial level-increasing group, a middle initial level-declining group and a higher initial level-declining group. Girls illustrated different patterns, corresponding to a lower initial level-stable group, a middle initial level-increasing group and a higher initial level-declining group. Different developmental trajectory of physical and relational aggression would influence the interpersonal relationship.

This paper used a person-centered latent variable approach instead of the variable-centered approach to investigate the developmental trajectory and patterns of physical aggression and relational aggression over three year by employing the LCGM. The initial level of physical aggression in boys was higher than that in girls. In contrast, the initial level of relational aggression in boys was lower than that in girls. There were gender differences in the pattern of physical and relational aggression development trajectory. Different developmental trajectory of physical and relational aggression would influence the interpersonal relationship.