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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.

This study aims to explore how and when learning from others promotes creative performance over the contributor’s tenure in the context of open innovation communities.

The authors analyze a publicly available data set that includes 25,923 innovative items developed by 2,194 contributors from an open innovation community of an online game spanning eight years. Logistic regression model is used for analyzing the data.

The results show that multicultural experiences are negatively related to contributor’s creative performance, and this negative relationship weakens as contributor’s tenure increases. While diverse skills are positively related to contributor’s creative performance, and this positive relationship strengthens as contributor’s tenure increases.

This research highlights the importance of online team collaboration in knowledge transfer through learning from others in open innovation communities. By identifying two outcomes of learning from others through online team collaboration, the authors demonstrate the double-edged role of learning from others and advance the understanding on how the effect of learning from others varies over the contributor’s tenure. These results expand the understanding of online team collaboration and provide a new perspective for research on learning from others.

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.

Social shopping website (SSW) introduce the social side into the shopping process, thus making “window” shopping or browsing more interesting for customers. The purpose of this paper is to investigate customer online browsing experience and its antecedents (i.e. information quality and social interaction) and consequences (i.e. urge to buy impulsively and continuous browsing intention) in the context of SSW.

A survey questionnaire was distributed to visitors of online SSW to collect data, and partial least squares technology was used to test the research model.

The results of this study reveal that three types of web browsing, namely, utilitarian browsing, hedonic browsing and social browsing, take place in a SSW. The unique factors of SSW, namely, the quality of user generated contents and social interaction are critical for facilitating customers’ browsing experiences. Furthermore, the findings reveal that hedonic browsing experience is found to be the most salient factor influencing customers’ urge to buy impulsively and continuance intention.

The findings suggest that practitioners, such as designers and managers of SSW should give special attention to the benefits of browsing activity to convert web browsers into impulse purchasers and increase customers’ loyalty. Moreover, they should focus on improving the quality of user generated content and pay more attention to support and encourage social interaction to enhance browsing experiences on a SSW.

Existing studies about browsing behavior mostly focus on traditional online e-commerce website. This study represents the first step toward understanding browsing activity on SSW. Moreover, prior studies mainly focused on utilitarian and hedonic browsing experience; however, there is a lack of research on social browsing experience. The current study attempts to fill this research gap.

This study investigates whether and how the service quality of artificial intelligence (AI) chatbots affects customer loyalty to an organization.

Based on the sequential chain model of service quality loyalty, this study first classifies AI chatbot service quality into nine attributes and then develops a research model to explore the internal mechanism of how AI chatbot service quality affects customer loyalty. The analysis of survey data from 459 respondents provided insights into the interrelationships among AI chatbot service quality attributes, perceived value, cognitive and affective trust, satisfaction and customer loyalty.

The results show that AI chatbot service quality positively affects customer loyalty through perceived value, cognitive trust, affective trust and satisfaction.

This study captures the attributes of the service quality of AI chatbots and reveals the significant influence of service quality on customer loyalty. This study develops research on service quality in the information system (IS) field and extends the sequential chain model of quality loyalty to the context of AI services. The findings not only help an organization find a way to improve customers' perceived value, trust, satisfaction and loyalty but also provide guidance in the development, adoption, and post-adoption stages of AI chatbots.

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;

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.

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.

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.

In online user innovation communities (UICs), firms adopt external innovations beyond their internal resources and capabilities. However, little is known about the influences of organizational adoption or detailed adoption patterns on subsequent user innovation. This study aims to examine the influence of organizational adoption, including its level and timing, on users' subsequent innovation behavior and performance.

This research model was validated using a secondary dataset of 17,661 user–innovation pairs from an online UIC. The effect of organizational adoption on users' subsequent innovation likelihood was measured by conducting a panel logistic regression. Furthermore, the effects of organizational adoption on subsequent innovation’ quality and homogeneity and those of the adoption level and timing on subsequent innovation likelihood were tested using Heckman's two-step approach.

The authors found that organizational adoption negatively affects the likelihood of subsequent innovation and its homogeneity but positively affects its quality. Moreover, more timely and lower-level adoption can increase the likelihood of users' subsequent innovation.

This study comprehensively explores organizational adoption's effects on users' subsequent innovation behavior and performance, contributing to the literature on UICs and user innovation adoption. It also provides valuable practical implications for firms on how to optimize their adoption decisions to maintain the quantity, quality, and diversity of user innovations.