Search results

Online collaboration in today's world is a topic of genuine interest to Internet researchers. The purpose of this paper is to explore the role of group knowledge heterogeneity (GKH) in open collaboration performance using the mediating mechanisms of group cognition (GC) and interaction to understand the determinants of the success of online open collaboration platforms.

Study findings are based on partial least squares structural equation modeling (PLS-SEM), the formal mediation test and moderating effect analysis from Wikipedia's 160 online open collaborative groups.

For online knowledge heterogeneous groups, open collaboration performance is mediated by both GC and collaborative interaction (COL). The mediating role of GC is weak, while the mediating role of COL is strengthened when knowledge complexity (KC) is higher. By dividing group interaction into COL and communicative interaction (COM), the authors also observed that COL is effective for online open collaboration, whereas COM is limited.

These findings suggest that for more heterogeneous large groups, group interaction would explain more variance in performance than GC, offering an in-depth understanding of the relationship between group heterogeneity and open collaboration performance, answering what determines the success of online open collaboration platforms as well as explaining the inconsistency in prior findings. In addition, this study expands the application of Interactive Team Cognition (ITC) theory to the online open collaboration context.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

This study investigates the effects of two types of self-disclosure by influencers (i.e. personal self-disclosure and professional self-disclosure) on followers’ parasocial relationships with them and online engagement with their content, which eventually affect followers’ purchase intentions.

This study collected data based on a cross-sectional survey of 823 social media users. Structural equation modeling analysis was used to test the overall structural model and the mediating roles of parasocial relationships and engagement.

This study reveals that influencers’ personal self-disclosure has a positive impact on followers’ parasocial relationships with them and online engagement with their content. Interestingly, the results indicate an inverted U-shaped relationship between influencers’ professional self-disclosure and followers’ parasocial relationships, as well as online engagement with the influencers’ content. Furthermore, followers’ parasocial relationships and engagement partially mediate the impact of influencers’ personal and professional self-disclosure on followers’ purchase intentions.

This study contributes to the literature by revealing the underlying mechanisms of the differential effects of influencers’ personal and professional self-disclosure on followers’ purchase intentions.

The findings will assist marketers in leveraging influencer-generated content to enhance influencer marketing effectiveness.

This research provides a better understanding of the potential linear and nonlinear effects of influencers’ self-disclosure on followers’ parasocial relationships and engagement in social media marketing.

Effectively engaging patients is critical for the sustainable development of online health communities (OHCs). Although physicians’ general knowledge-sharing, which is free to the public, represents essential resources of OHCs that have been shown to promote patient engagement, little is known about whether such knowledge-sharing can backfire when superfluous knowledge-sharing is perceived as overwhelming and anxiety-provoking. Thus, this study aims to gain a comprehensive understanding of the role of general knowledge-sharing in OHCs by exploring the spillover effects of the depth and breadth of general knowledge-sharing on patient engagement.

The research model is established based on a knowledge-based view and the literature on knowledge-sharing in OHCs. Then the authors test the research model and associated hypotheses with objective data from a leading OHC.

Although counterintuitive, the findings revealed an inverted U-shape relationship between general knowledge-sharing (depth and breadth of knowledge-sharing) and patient engagement that is positively associated with physicians’ number of patients. Specifically, the positive effects of depth and breadth of general knowledge-sharing increase and then decrease as the quantity of general knowledge-sharing grows. In addition, physicians’ offline and online professional status negatively moderated these curvilinear relationships.

This study further enriches the literature on knowledge-sharing and the operations of OHCs from a novel perspective while also offering significant specific implications for OHCs practitioners.

Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).

This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.

The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.

The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.

As the risk of uncertain quality of used products potentially hinders remanufacturing, this study aims to examine the impact of risk aversion under quality uncertainty of used products in a remanufacturing supply chain (RSC) consisting of a manufacturer and an independent remanufacturer.

We develop an RSC model where the manufacturer produces new products, outsources remanufacturing to the independent remanufacturer and sells both new and remanufactured products to end consumers. Using a manufacturer-led Stackelberg game framework, we derive the equilibrium solutions under risk-neutral and risk-averse scenarios. Additionally, we design a two-part tariff contract to achieve coordination.

We show that while risk aversion leads the manufacturer to raise the outsourcing fee, which in turn reduces both the remanufactured quantity and the collection rate of used products. Consequently, consumer surplus and social welfare decline, while environmental impacts rise. The proposed two-part tariff contract can improve the collection rate and social welfare. We also explore two extensions: an authorization remanufacturing scenario and a two-period scenario. We find that risk aversion has no impact on the selection of remanufacturing mode and the equilibria in the first period. Our findings provide timely managerial insights for RSC management.

One of the main risks deterring remanufacturing is the quality uncertainty of used products. However, the risk aversion arising from this uncertainty and its effects have rarely been studied within a game-theoretic framework. This paper fills this gap by analyzing the remanufacturer’s risk aversion under quality uncertainty and investigating its impacts.

The key purpose of this study is to systematically examine the landscape of education research funded by the National Plan of Educational Research Funding (NPERF) in China. The study aims to: (1) identify the thematic focus areas that reflect the national education agenda, (2) analyze the general funding patterns of education research projects and (3) gain insights into the distinctive nature of the research agenda in China. The study employs a rigorous data-driven approach to offer valuable insights into the dynamic discourses within the field of education research in China, which has received relatively little attention despite its potential significance.

In this study, we utilized word co-occurrence analysis and corpus-based frequency analysis to analyze the research projects funded by the National Plan of Educational Research Funding (NPERF) from 2011 to 2020.

The key characteristics of these projects highlight the focus on higher education research, addressing the interests of specific cohorts of students, teachers and disadvantaged populations. Furthermore, these projects demonstrate a remarkable responsiveness to the policy needs of the country and a robust inclination toward an international comparative framework.

The findings offer valuable insights into the landscape and features of funded education research in China, revealing a strong emphasis on addressing practical needs and enhancing the capacity of the education system in the country.

This paper presents a systematic examination of the topics covered in funded research under the National Plan of Educational Research Funding (NPERF) scheme from 2011 to 2020. It contributes to the advancement of understanding regarding knowledge traditions and practices in the Chinese context. Methodologically, this paper is the first in the literature to be prototyped with a word co-occurrence analysis approach to systematically investigate the funded education research in China. Additionally, it includes the development of a comprehensive corpus list to uncover the key characteristics of the funded projects. The analysis provides unique insights into the priorities and directions of education research supported by the Chinese government, which are of potential interest to international readers.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

The rapid development and widespread application of artificial intelligence tools have raised concerns about how designers are embracing these technologies. This study investigates the factors influencing designers' behavioral intention to use and disclose the use of generative artificial intelligence.

A quantitative research approach was employed, designing a structured questionnaire based on Self-Determination Theory to assess the impact of various psychological and social dimensions. The questionnaire included dimensions such as autonomy, competence, relatedness, social influence, value fit and social innovativeness. A Partial Least Squares Structural Equation Modeling analysis was conducted on 309 valid responses from diverse design fields.

Competence and relatedness are significant factors influencing designers' continuance intention to use generative artificial intelligence. Although autonomy does not significantly affect continuance intention, it plays a crucial role in the decision to disclose artificial intelligence participation. Social influence and value fit significantly shape autonomy, competence and relatedness, while the impact of social innovativeness is relatively limited.

This study clarifies the factors influencing designers' continuance intention and disclosure of generative artificial intelligence tools from both individual and social dimensions, enhancing the understanding of the relationship between designers and generative artificial intelligence tools. It provides valuable insights for the development of artificial intelligence technology and the future trends in the design industry, offering significant theoretical and practical value.

This chapter explores how the development of football fandom for the Chinese national team and local football clubs is strongly associated with societal changes. Although the performances of Chinese football teams, especially the national team, have failed to impress the world, football remains the most popular because of millions of supporters with loyalty and passion. Most studies related to fans mainly focus on the economic and political implications of spectatorship along with the rise of China. Nevertheless, few articles are available to answer the fundamental questions, such as ‘When did these supporters come out?’ and ‘What were the factors of the development of fandom?’. By going through archival records and published documents over the last decades, this chapter offers a comprehensive and historical analysis of the development of football fandom in the People's Republic of China (PRC) and deals with these unanswered questions. As such, this chapter does not intend to be the most authoritative one but is one of the rare sources to lay down the foundation for research on Chinese football fandom. Furthermore, this chapter also proves that studies on football fandom can be a useful window for observing Chinese society.