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Based on the social interaction theory and trust theory, this study investigates the switching of users on social question and answer (Q&A) platforms from knowledge seekers to knowledge contributors.

We used Python to gather data from Zhihu, performed hypothesis testing on the models using Poisson regression and finally conducted a mediation effect analysis.

The findings reveal that knowledge seeking impacts users' motivation for information interaction, emotional interaction and trust. Notably, information interaction and trust exhibit a chained mediation effect that subsequently influences knowledge contribution.

Current studies on user knowledge behavior typically examine individual actions, rarely connecting knowledge seeking and knowledge contribution. However, the balance of knowledge inflow and outflow is crucial for social Q&A platforms. To cover this gap, this paper empirically investigates the switching between knowledge seeking and knowledge contribution based on the social interaction theory and trust theory.

The purpose of this study is to examine the effect of trust on user adoption of artificial intelligence-generated content (AIGC) based on the stimulus–organism–response.

The authors conducted an online survey in China, which is a highly competitive AI market, and obtained 504 valid responses. Both structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) were used to conduct data analysis.

The results indicated that perceived intelligence, perceived transparency and knowledge hallucination influence cognitive trust in platform, whereas perceived empathy influences affective trust in platform. Both cognitive trust and affective trust in platform lead to trust in AIGC. Algorithm bias negatively moderates the effect of cognitive trust in platform on trust in AIGC. The fsQCA identified three configurations leading to adoption intention.

The main limitation is that more factors such as culture need to be included to examine their possible effects on trust. The implication is that generative AI platforms need to improve the intelligence, transparency and empathy, and mitigate knowledge hallucination to engender users’ trust in AIGC and facilitate their adoption.

Existing research has mainly used technology adoption theories such as unified theory of acceptance and use of technology to examine AIGC user behaviour and has seldom examined user trust development in the AIGC context. This research tries to fill the gap by disclosing the mechanism underlying AIGC user trust formation.

The purpose of this paper is to draw on the social cognitive theory to identify the determinants of online knowledge community user continuance, which reflects a user’s continued use.

Based on the 271 valid responses collected from a survey, structural equation modelling was employed to examine the research model.

The results indicated that the cognitive factors of outcome expectation and the environmental factors of system quality and knowledge quality significantly affect a user’s continuance intention, which, in turn, affects continuance usage.

The results imply that service providers need to enhance community platforms and improve knowledge quality in order to retain users and facilitate their continuance.

Although previous research has examined online knowledge community user behaviour from multiple perspectives such as the social exchange theory and the motivational theory, it has seldom explored the relative effects of personal cognitions and environmental factors on user behaviour. This research fills the gap.

The purpose of this paper is to examine the effect of flow experience on users’ social commerce intention.

Based on the 287 valid responses collected from a survey, structural equation modeling was used to examine the research model.

The results indicated that social interaction, which includes human–computer interaction and human–human interaction, has a significant effect on the flow experience, which in turn affects social purchase and social sharing intention.

The results imply that companies need to facilitate social interaction to improve users’ experience and promote their social commerce intention.

Although prior research has examined social commerce user behaviour from multiple perspectives such as trust, perceived value and technological perceptions, it has focused on the effect of cognitive beliefs and neglected the effect of affective beliefs such as flow experience. This research tries to fill the gap.

The purpose of this paper is to draw on the elaboration likelihood model to examine location-based services (LBS) users’ privacy concern.

Based on the 266 valid responses collected from a survey, structural equation modeling was employed to examine the research model.

The results indicated that privacy concern receives a dual influence from both central cues and peripheral cues. Central cues include privacy policy and information quality, whereas peripheral cues include reputation and privacy seals. Privacy control moderates the effects of privacy policy and privacy seals on privacy concern.

The results imply that service providers need to consider both central and peripheral cues in order to mitigate users’ privacy concern associated with using LBS.

Although previous research has found the effect of privacy concern on user adoption of LBS, it has seldom examined the influence processes of external factors on privacy concern. This research tries to fill the gap.

The purpose of this research is to examine the effect of information privacy concern on users' social shopping intention.

Based on the 340 valid responses collected from a survey, structural equation modeling (SEM) was employed to examine the research model.

The results indicated that while disposition to privacy positively affects privacy concern, both reputation and laws negatively affect privacy concern, which in turn decreases social shopping intention. In addition, trust partially mediates the effect of privacy concern on social shopping intention.

The results imply that social commerce companies need to mitigate users' privacy concern in order to facilitate their shopping behavior.

This research disclosed that privacy concern receives a tripartite influence from users (disposition to privacy), platforms (reputation) and governments (laws). The results help us gain a complete understanding of information privacy concern mitigation in social shopping.

The purpose of this research is to draw on the elaboration likelihood model (ELM) to examine users' information adoption intention in online health communities (OHC).

The authors collected 350 valid responses using a survey and conducted the moderated regression analysis to examine the research model.

The results indicated that users' information adoption intention is influenced by both central cues (argument quality) and peripheral cues (source credibility and emotional support). In addition, self-efficacy moderates the effect of both central cues and peripheral cues on information adoption intention.

Previous research has focused on the effect of individual motivations such as reciprocity and benefits on user behavior, and has seldom disclosed the influencing process of external factors on OHC users' behavioral decision. This research tries to fill the gap by adopting ELM to uncover the mechanism underlying OHC users' information adoption.

The purpose of this paper is to draw on the stimulus-organism-response (SOR) framework to examine users’ knowledge sharing in online health communities (OHC).

Based on the 326 valid responses collected from a survey, structural equation modelling was employed to examine the research model.

The results indicated that both information quality and service quality affect trust in community, whereas both informational support and emotional support affect trust in other members. Both types of trust and privacy risk determine users’ sharing intention, which in turn affects sharing behaviour.

The results imply that service providers need to improve their community platforms and create a supportive climate in order to facilitate users’ trust and their knowledge sharing behaviour.

Previous studies have examined a few determinants of OHC user behaviour such as privacy concern, trust and motivations. However, they have seldom disclosed the internal decision process underlying users’ knowledge sharing. This research tries to fill the gap.

Based on the C-A-C framework, this article examined users' information avoidance intention in social media platforms.

The authors conducted data analysis using a mixed method of the SEM and fsQCA.

The results indicated that information overload, functional overload and social overload influence fatigue and dissatisfaction, both of which further determine users' information avoidance intention. The results of the fsQCA identified two paths that trigger users' information avoidance intention.

Extant studies have examined the information avoidance in the contexts of healthcare, academics and e-commerce, but have seldom explored the mechanism underlying users' information avoidance in social media. To fill this gap, this article will empirically investigate users' information avoidance in social media platforms based on the C-A-C framework.

As users often lack the motivation to contribute their ideas and knowledge in open innovation communities, it is necessary to identify the determinants of users’ contribution. This paper aims to examine users’ contribution in open innovation communities based on the social capital theory.

The authors collected 474 valid responses from a survey and adopted structural equation modeling (SEM) to conduct data analysis.

The results indicated that social interaction, which includes informational and emotional interaction, has a significant effect on social capital, which in turn affects users’ contribution.

The results imply that companies need to facilitate users’ interactions and develop social capital to promote their contribution in open innovation communities.

Although previous research has found the effect of individual motivations such as perceived benefits and behavioural control on innovation community users’ behaviour, it has seldom considered the effect of social capital embedded within the social relationship networking. This research tries to fill the gap and the results disclosed the mechanism underlying open innovation community users’ contribution.