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The purpose of this study is to draw on the updated information system success model, perceived value and new product novelty to identify the factors that may influence user satisfaction and loyalty with paid knowledge platforms.

The authors used the survey method to collect 540 valid sample data. The structural equation modelling (SEM) technique was employed to test the proposed research model and hypotheses.

The findings suggested that system, information and service quality significantly affected the perceived utilitarian value, while service quality and new product novelty had significant impact on perceived hedonic value. Besides, both the perceived utilitarian value and the perceived hedonic value had a significant effect on user satisfaction and further significantly impacted user loyalty. The authors also found user differences, including gender, education level and use frequency, which had a significant influence on perceived utilitarian value, perceived hedonic value and user loyalty.

The results can help researchers and practitioners better understanding the factors that influence user satisfaction and loyalty with paid knowledge platforms.

The authors applied the theories of perceived value, new product novelty and user loyalty to the domain of paid knowledge platforms and explored the factors influencing the user satisfaction and loyalty to paid knowledge platforms.

Knowledge sharing has entered the stage of knowledge payment with the typical models of paid Q&A, live session, paid subscription, course column and community service. Numerous knowledge suppliers have begun to pour into the knowledge payment market, and users' willingness to pay for premium content has increased. However, the academic research on knowledge payment has just begun.

In this paper, the authors searched several bibliographic databases using keywords such as “knowledge payment”, “paid Q&A”, “pay for answer”, “social Q&A”, “paywall” and “online health consultation” and selected papers from aspects of research scenes, research topics, etc. Finally, a total of 116 articles were identified for combing studies.

This study found that in the early research, scholars paid attention to the definition of knowledge payment concept and the discrimination of typical models. With the continuous enrichment of research literature, the research direction has gradually been refined into three main branches from the perspective of research objects, i.e. knowledge provider, knowledge demander and knowledge payment platform.

This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, the authors found out conflicting and contradictory research results and research gaps in the existing research and then put forward the urgent research topics.

In the hybrid knowledge-sharing platform where paid and nonpaid (“free”) knowledge activities coexist, users’ free knowledge contribution may be influenced by financial factors. From the perspective of opportunity cost, this study investigates the direct effect of how the amount of monetary income from users’ contribution to paid knowledge activities influences their free knowledge contribution behavior in the future. Further, this study aims to verify the interaction effect of financial and nonfinancial factors (i.e. the experience of free knowledge contribution and social recognition) on free knowledge contribution.

Objective data was collected from a hybrid knowledge-sharing platform in China and then analyzed by using zero-inflated negative binomial regression model.

Results show that the amount of monetary income that knowledge suppliers gain from paid knowledge contribution negatively influences their free knowledge contribution. Experience of free knowledge contribution strengthens the negatively main effect, while social recognition has the weakening moderating role.

Although some studies have explored and verified the positive spillover effect of financial incentives on free knowledge contribution, the quantity dimension is ignored. This study examines the hindering influence of the quantity of monetary income from the perspective of opportunity cost. By taking the characteristic of knowledge suppliers and platforms as moderators, this study deepens the understanding of the influence of monetary income on free knowledge contribution in the hybrid knowledge-sharing platform.

The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.

This study explores identity signaling used by an emerging class of knowledge celebrities in China – Knowledge Wanghong – who sell knowledge products on online platforms. Because identity signaling may involve constructing unique online identities and controlling over product-related and seller-related characteristics, the purpose of this study is two-fold: (1) to uncover different online identities of knowledge celebrities; and (2) to examine the extent to which the online identity type is associated with their product-related characteristics, seller-related characteristics and sales performance.

A unique data set was collected from a Chinese leading pay-for-knowledge platform – Zhihu – which featured the online profiles of tens of thousands of knowledge celebrities. Online identity types were derived from their self-edited content using Latent Dirichlet Allocation (LDA) topic modeling. Thereafter, their product-related characteristics, seller-related characteristics and respective sales performance were analyzed across different identity types using analysis of variance (ANOVA) and multiple-group linear regression.

Knowledge celebrities are clustered into four distinctive online identities: Mentor, Broker, Storyteller and Geek. Product-related characteristics, sell-related characteristics and sales performance varied across four different identities. Additionally, the online identity type moderated the relationships among their product-related characteristics, sell-related characteristics and sales performance.

As emerging-phenomenon-based research, this study extends related literature by using the notion of identity signaling to analyze a peculiar group of online celebrities who are setting an important trend in the pay-for-knowledge model in China.

The purpose of this study is to investigate the influencing mechanism of consumers' continuous purchase intention toward the subscriber-based knowledge payment platforms (SBKPPs).

This study obtained 226 valid samples through questionnaire surveys and used partial least square structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) methods to elucidate the complex causal patterns of consumers' continuous purchase intention toward the SBKPPs.

The findings revealed that perceived utilitarian value, perceived hedonic value and perceived social value directly affected consumers' continuous purchase intention, while content quality and service quality indirectly affected consumers' continuous purchase intention. In addition, this study also demonstrated that all factors must be combined to play a role, and there exist four configurations resulting in consumers' continuous purchase intention toward the SBKPPs.

The results can help researchers and practitioners better understand the causal patterns of consumers' continuous purchase intention toward the SBKPPs.

This study contributes to the knowledge payment literature by investigating consumers' continuous purchase intention toward the SBKPPs. This study also provides practical enlightenment for the SBKPPs' marketing.

This conceptual paper explores the implications for the sociology of the professions of robo-advice (RA) provided by robo-advisors (RAs) as an early example of successfully programmed algorithmic knowledge managed by artificial intelligence (AI).

The authors examine the drivers of RAs, their success, characteristics, and establish RA as an early precursor of commercialized, programmed professional advice with implications for developments in the sociology of the professions.

Within the lens of the sociology of the professions, the success of RAs suggests that the diffusion of this innovation depends on three factors: the programmed flows of automated professional knowledge are minimally disruptive, they are less costly, and attract attention because of the “on-trend” nature of algorithmic authority guided by AI. The on-trend nature of algorithmic governance and its increasing public acceptance points toward an algorithmic paradox. The contradictions arise in the gap between RA marketed to the public and as a set of professional practices.

The incursion of RA-like disembodied advice into other professions is predicted given the emergence of tech-savvy clients, the tie between RA and updatable flows of big data, and an increasing shift to the “maker” or “do-it-yourself” movements.

Using the success of RAs in the financial industry, the authors predict that an AI-managed platform, despite the algorithmic paradox, is an avenue for growth with implications for researchers in the sociology of the professions.

In the context of multi-sided platforms (MSPs), the authors address the evaluation of search- and experience-based information and the effect on different components of user satisfaction.

The instrument was developed by either modifying previous measures or developing new scales. The authors collected the sample of experienced 300 TripAdvisor users via online questionnaire survey of a customer panel. The structural equation modeling (SEM) package (AMOS) with the maximum likelihood estimation method was used to test the sample data.

Attitudes toward search-based information can foster user satisfaction with information interaction rather than user satisfaction with social interaction. Attitudes toward experience-based information can foster user satisfaction with information interaction and user satisfaction with social interaction. The motivation for information interaction is stronger than the motivation for social interaction to enhance user satisfaction with information quality.

The distinction between search- and experience-based information provides different route messages to develop the attitude-driven framework of platform-enabled interactions.

The support for platform-enabled interactions to enhance the motivation for information and social interactions should be aligned with the evaluation of information quality.

The satisfaction-driven framework has been widely used to examine the post-adoption of information technologies (IT). In contrast, the attitude-driven framework was less examined in the literature. The authors develop a research model based on the attitude-driven framework to examine the platform-enabled interactions that can foster repeated intention.

Online food delivery has been prevalent in recent years worldwide, especially during the COVID-19 pandemic, and people's consumption behaviors have changed significantly. This study aims to investigate the consumption behavior of young adults using online food delivery platforms during the COVID-19 pandemic and focuses on the dominant factors influencing their decision to use online food delivery platforms.

Semi-structured interviews including 14 young adults aged 18–25 living in Hong Kong were conducted to collect data about their perspectives on online food delivery platforms in five areas. This research adopted the stimulus-organism-response model (S-O-R model) to analyze how the factors influence young adult users' loyalty and satisfaction with online food delivery platforms.

Thematic analyses revealed that young adults were attracted to online food delivery platforms for their numerous benefits. They had a high frequency of usage and significant spending. Usability, usefulness, satisfaction and loyalty influenced young adults' behaviors on online food delivery platforms. Participants were overall satisfied with their experiences, but platforms still had room for improvement.

Few prior studies investigated the factors affecting the consumer experience and behavioral intention of online food delivery for young adults in Asia. This study contributes to understanding young adults' experiences and problems with online food delivery platforms. It provides practical insights for system engineers and designers to improve the current services and for the governments to enhance the existing regulatory loopholes.

WeChat official account (WCOA) is an emerging and important platform for academic library services, which greatly accelerates the development of this field. This article aims to identify key evaluation indicators for users' satisfaction of the Academic Library WeChat Official Account (ALWCOA) as a reference for future improvements.

Based on the updated DeLone and McLean (D&M)’s model and Delphi method, an evaluation system of ALWCOA satisfaction was constructed. Then 212 university students were recruited to fill out a questionnaire on evaluation indicators. The grey relational analysis (GRA) and Pareto's principle were employed to analyze the questionnaire and select key evaluation indicators.

An ALWCOA service satisfaction evaluation system with three evaluation dimensions and 15 evaluation indicators was constructed, and three key evaluation indicators were identified, including service responsiveness, information timeliness and system security.

This article provides a strategy for assessing ALWCOA service satisfaction, as well as insights for improving of ALWCOA service. Specifically, academic libraries should pay more attention to improving service responsiveness, information timeliness and system security.

This article innovatively applied the updated D&M model in academic library service. Additionally, it facilitates the development of research fields, such as academic library services, microservices and user service evaluation, and provides a case study to better understand the WCOA.