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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.

The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.

We adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.

The results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.

This study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

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.

Generative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.

Based on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.

The results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.

First, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.

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.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

Using surveys of Amazon and Tmall Global users, this paper aims to empirically investigate the issue of platform technological selection. We explore the impact of switching costs on users’ intentions to use an app-enabled cross-border e-commerce (CBEC) platform based on an extended technology acceptance model (TAM). The results suggest that the higher the switching cost of a platform is, the greater the users’ satisfaction and intention to use this platform. Therefore, for the platform, a moderate switching cost will be beneficial for retaining users.

Based on the TAM, this paper takes the switching costs as the starting point and focuses on exploring the relationships among switching costs, perceived usefulness, perceived ease of use, perceived reliability, satisfaction and intention to use. Online surveys of users of Amazon and Tmall Global are adopted as the main instruments of this research. We collected a total of 408 valid responses from Amazon users and 490 from Tmall Global users. For the data analysis, this study conducts frequency analysis, a test analysis of the reliability and validity of the measures, correlation analysis, and path analysis using a structural equation model.

The results show that switching costs positively affect the users’ satisfaction and intentions to use a CBEC platform through perceived usefulness, perceived ease of use and perceived reliability.

The questionnaire respondents were predominantly Chinese due to the constraints of the survey conditions. In fact, China has a high penetration rate in CBEC, and Chinese users have rich experience using the Amazon and Tmall Global platforms.

The development of CBEC has ups and downs, and users frequently switch platforms. Considering how platforms can stand out from the crowd and retain users, we believe that a moderate increase in the switching cost of the platform is helpful for companies to address these problems, and the implications of the results are particularly valid for decision-makers of CBEC platforms and companies.

Amazon and Tmall Global are the two largest CBEC platforms in the world. Using these two companies as examples for comparison can effectively identify the differences between the platforms and the conclusions are representative. We suggest that platforms can improve user satisfaction and willingness to use by establishing VIP communities, issuing coupons, providing shipping services as well as convenient after-sale complaint channels, and improving the platform’s easy-to-use interface, as ways to further enable the platform to retain more users and stand out in fierce competition.

This paper addresses an interesting and practical issue related to the effects of introducing switching costs in an extended TAM applied to CBEC platforms.