Search results

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

In this study, we drew on the theories of social exchange and social learning and hypothesized that the online social capital (SC) and offline SC of social networking affect the online self-disclosure (OSD) of individuals through social self-efficacy (SSE).

After retrieving 514 valid questionnaires, we used structural equation modeling to analyze the data.

The results indicated that the users’ SSE affected their OSD, and that both online and offline bridging and bonding SC increased their SSE. However, online bonding SC directly affected their OSD, whereas online bridging SC did not considerably affect their OSD. Given these findings, we presented both theoretical and practical implications to elucidate SSE and OSD behavior from the perspective of online and offline bridging and bonding SC.

In this study, we drew on theories of social exchange and social learning to examine the effects of online and offline bridging and bonding SC on users’ SSE and OSD on SNSs. Given the importance of SC and SSE in social relationships and the effects of OSD on SNSs, our goal was to provide SNS marketers with a thorough understanding of how to facilitate SSE and OSD from the perspective of online and offline bridging and bonding SC.

This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course.

The exploration and visualisation of the data were first carried out to gain a better understanding of the students, the course(s) each student was enrolled in and each course’s virtual learning resources. Following this, the construction of the social network graphs was performed to depict how each student behaved online before the degree centralities were computed for each of the nodes in a social network graph. Data pre-processing to assign labels based on the final result a student obtained in a course was then performed before we trained and tested models to predict which students did or did not graduate.

The study’s findings demonstrate that the constructed predictive model has good performance, as shown by the accuracy, precision, recall and f-measure metrics. The outcomes also showed that students’ use of online resources is a crucial element that influences how well they perform in their academics.

The similarity index is as low as 9%.

Motivating users to self-disclose online is significant to the long-term development of social media. Therefore, research on emotional disclosure, a significant form of self-disclosure, is required. By developing a stimulus-organism-response model, this study aims to investigate the mechanisms by which the social media environment affects users' online emotional disclosure.

This paper proposes two dimensions of social media environments, the online interpersonal environment (tie strength and network size) and the online information environment (self-reference). They are hypothesized to stimulate users' internal psychological needs (image management and emotional expression) which in turn will influence their emotional disclosure intentions. Using data from 489 users of WeChat Moments, the authors conduct partial least squares analysis to validate the research model.

The findings show that users' intrinsic psychological needs are stimulated by social media environments, but network size is not correlated with the need for emotional expression. The user's need for emotional expression promotes both positive and negative emotional disclosure intentions. The need for image management has a positive impact on positive emotion disclosure intentions but has a negative impact on negative emotion disclosure intentions.

This study contributes to the understanding of online emotional disclosure. It can also help social media managers create efficient plans to encourage users to create content.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2022-0245.

Due to the cross-network effect, two-sided users communicate with each other, producing a coupling network. To study the spread of platform self-operation in two-sided users' marketing and purchasing tactics, this paper considers the differences in reputation acquired by platform-owned and third-party operating channels.

This study proposes a two-layer network with cross-network links: one layer represents the social network of consumers, while the other layer represents the competitive network of buyers. A closed system of differential equations, based on the binary dynamics of the stochastic network, is developed to study the trend and stability points of the platform self-operation dissemination. Then the overall benefits of platform are analyzed to unify the platform diffusion and pricing strategies.

The degree of difference in social influence and cross-network effects affect diffusion synergistically. Cross-network effects hinder diffusion when there is a significant difference of social influence between consumers and sellers but promote diffusion when there is little difference of social influence between consumers and sellers. Additionally, the network weights and reputation gap exhibit a nonlinear correlation with diffusion. For pricing strategy of the platform, it can achieve maximum profit when the pricing of self-operated goods and third-party-operated goods is equal.

This study considers the complex network architecture created by bilateral markets and the dynamic influence of group interactions on product. Additionally, this study takes reputation into account when considering the price and dissemination tactics of various operating channels, offering guidelines for platforms to control merchants and mediate disputes between various operating channels.

The 2020 Coronavirus pandemic forced universities to hastily transition to eLearning on a mass scale, necessitating the identification of populations who are more challenged by the transition. This study aims to identify how students’ level of introversion/extraversion and digital literacy come to play in their satisfaction with the eLearning environment.

The analysis examined 272 Israeli students who moved from a face-to-face learning environment to a Zoom learning environment between March–July 2020, following the outbreak of the pandemic. All the participants completed two rounds of surveys, and 62 of the 272 participants were then interviewed, and their social network was mapped using a sociogram.

Findings indicated that, in accordance with the “poor get richer” hypothesis, introverts expressed more satisfaction from the transition to the video-conferencing Zoom platform than extraverts. In addition, for highly introverted people, high digital literacy was significantly associated with increased course satisfaction, whereas for highly extraverted people, a high number of social ties with peers from the course was associated with course satisfaction.

As expected, the study’s findings shows that there is no “one size fits all” approach for online learning. Learners with different personalities can benefit from learning environments that foster greater satisfaction with the learning experience. Online platforms can, and should, be designed in a way that offers this needed personalization, and this study provides initial principles that can inform such personalization.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2023-0028

Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.

This paper, through empirical evidence, presents a framework for exploring how entrepreneurs navigate the challenges of building legitimacy in a digital context. In so doing, this paper goes beyond the seemingly forgone conclusion that legitimacy is important for the entrepreneur's success by focusing on the contextualised mechanisms through which digital legitimacy is built.

Empirical findings are drawn from semi-structured interviews conducted with 21 digital entrepreneurs in Nigeria, a leading example of the West African context and analysed using a phenomenological approach.

The paper shows how digital entrepreneurs in a non-Western context draw on an aspect of legitimation in the digital space, and in particular, highlights three mechanisms via which this takes place, namely: digital shielding, digital curating and digital networking. Presented via an inductive approach, the three mechanisms described in the paper provide a scaffold for thinking about and understanding entrepreneurial legitimacy within a contextual framework, which incorporates institutional, cultural and digital dimensions.

This paper contributes to the literature on digital entrepreneurship by empirically identifying and theoretically elaborating themes that are important for understanding how entrepreneurs navigate the challenges of digital entrepreneurship and build entrepreneurial legitimacy in complex contexts.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.