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This study's purpose is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether gamification and social interaction as environmental stimulus antecedents to learners' perceptions of presence can affect their continuance intention of massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 353 usable questionnaires were analyzed using structural equation modeling (SEM).

This study demonstrated that learners' perceived gamification, learner–instructor interaction and learner–learner interaction in MOOCs positively affected their cognitive presence, teaching presence and social presence elicited by MOOCs, which jointly expounded their continuance intention of MOOCs. The results support all proposed hypotheses, and the research model accounts for 72.6% of the variance in learners' MOOCs continuance intention.

This study employs the S-O-R model as a theoretical umbrella to frame learners' MOOCs continuance intention as a series of the internal process, which is influenced by gamification and social interaction. Noteworthily, three psychological constructs of the community of inquiry model (i.e. cognitive presence, teaching presence and social presence) are adopted to represent learners' internal experiences of MOOCs usage. To date, hedonic/utilitarian concepts are more commonly adopted as organisms in prior studies based on the S-O-R model, whereas psychological constructs have received lesser attention. Hence, this study enriches the S-O-R model into an invaluable context.

The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.

Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.

The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.

The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.

Social media stage online patterns of social interaction that differ remarkably from ordinary forms of acting, talking and relating. To unravel these differences, we review the literature on micro-sociology and social psychology and derive a shorthand version of socially-embedded forms of interaction. We use that version as a yardstick for reconstructing and assessing the patterns of sociality social media promote. Our analysis shows that social media platforms stage highly stylized forms of social interaction such as liking, following, tagging, etc. that essentially serve the purpose of generating a calculable and machine-readable data footprint out of user platform participation. This online stylization of social interaction and the data it procures are, however, only the first steps of what we call the infrastructuring of social media. Social media use the data footprint that results from the stylization of social interaction to derive larger (and commercially relevant) social entities such as audiences, networks and groups that are constantly fed back to individuals and groups of users as personalized recommendations of one form or another. Social media infrastructure sociality as they provide the backstage operations and technological facilities out of which new habits and modes of social relatedness emerge and diffuse across the social fabric.

This study draws upon the theory of eudaimonic motivation to develop a model that explains job performance in high-tech industry. This study aims to clarify through what mediating mechanism employees' social interaction and self-efficacy can substantially influence their job performance. At the same time, competence enhancement is examined as a moderator that influences the effects of social interaction and self-efficacy.

The hypotheses developed in this study were empirically tested by collecting three-source data from a leading international business company in Taiwan's high-tech industry. The survey data of this study were first analyzed using confirmatory factor analysis and hierarchical regression analysis for testing the hypotheses of the study. Post hoc tests were then performed using structural equation modeling and bootstrapping analysis for the purpose of double verifications.

This study finds that social interaction and self-efficacy relate to job performance via the full mediation of occupational commitment and achievement striving. Besides, the relationship between social interaction and occupational commitment is positively moderated by competence enhancement, while the relationship between self-efficacy and occupational commitment is negatively moderated by competence enhancement.

This work shows important findings that complement previous research on personnel performance and competence. First, this work confirms that social interaction and self-efficacy play critical roles for indirectly influencing job performance through the full mediation of occupational commitment and achievement striving among engineers in high-tech industry. Second, the moderating effects of competence enhancement on the relationships between social interaction and occupational commitment and between self-efficacy and occupational commitment are confirmed by this study.

Our econometric research allows for a possible response of a person's hours worked to hours typically worked by members of a multidimensional labor market reference group that considers demographics and geographic location. Instrumental variables estimates of the canonical labor supply model expanded to permit social interactions pass a battery of specification checks and indicate positive and economically important spillovers for adult men. Ignoring or incorrectly considering social interactions in male labor supply can misestimate the response to tax reform by as much as 60%.

In their authoritative literature review, Breen and Jonsson (2005) claim that ‘one of the most significant trends in the study of inequalities in educational attainment in the past decade has been the resurgence of rational-choice models focusing on educational decision making’. The starting point of the present contribution is that these models have largely ignored the explanatory relevance of social interactions. To remedy this shortcoming, this paper introduces a micro-founded formal model of the macro-level structure of educational inequality, which frames educational choices as the result of both subjective ability/benefit evaluations and peer-group pressures. As acknowledged by Durlauf (2002, 2006) and Akerlof (1997), however, while the social psychology and ethnographic literature provides abundant empirical evidence of the explanatory relevance of social interactions, statistical evidence on their causal effect is still flawed by identification and selection bias problems. To assess the relative explanatory contribution of the micro-level and network-based mechanisms hypothesised, the paper opts for agent-based computational simulations. In particular, the technique is used to deduce the macro-level consequences of each mechanism (sequentially introduced) and to test these consequences against French aggregate individual-level survey data. The paper's main result is that ability and subjective perceptions of education benefits, no matter how intensely differentiated across agent groups, are not sufficient on their own to generate the actual stratification of educational choices across educational backgrounds existing in France at the beginning of the twenty-first century. By computational counterfactual manipulations, the paper proves that network-based interdependencies among educational choices are instead necessary, and that they contribute, over and above the differentiation of ability and of benefit perceptions, to the genesis of educational stratification by amplifying the segregation of the educational choices that agents make on the basis of purely private ability/benefit calculations.

This paper aims to describe an integration of the media naturalness theory, the continuum model of impression formation and the social identity model of deindividuation effects. The goal is to determine the compatibility of the central tenets and propositions of the two theories and reconcile their effects under a unified model that can be used to explain and predict changes in perceptions, attitudes and behaviors arising in computer-mediated interaction.

A review of the literature was used to determine whether the two theories were compatible. The reconciliation comes by way of a third theory, the motivated tactician theory, which focuses on the effects of cognitive effort on schema use, particularly on those schemas used in social categorization processes.

It was determined that the two models of focus could be combined via the tenets of the third. The combined model is expected to provide explanatory and predictive capabilities that exceed those of the individual theories and should prove to be relevant in the study of computed-supported collaboration, in the design of collaborative environments and in the analysis of individual and group behaviors in computer-mediated communication.

The current effort describes the main effects derived from the integration and offers four propositions that describe moderating factors that are derived from each of the three theories. The main effects must be tested and validated and, given support, must be extended to determine the validity of the moderating effects predicted by the propositions. Additionally, media naturalness theory is a relatively recent addition to theories of technology and so needs further empirical support for its propositions. As to the behavioral implications, the social identity model of deindividuation effects has yet to be tested with the specific intention of discovering how media characteristics affect self-concept.

The model can be used to inform information system designs that favor desirable behavioral outcomes or to prevent undesirable effects from occurring. For example, emphasis can place on media attributes and system features that individuate decision-makers within group decision support environments when consensus is a primary goal as a means to avoid group thinks and polarization. Conversely, attributes and features that are supportive of social categorization processes and deindividuation effects might be used to emphasize group membership, shared effort and to minimize social loafing or the frequency and intensity of inappropriate disparagement of ideas and contributions.

The combined model is principally useful in explaining and predicting human behavior in relation to computer-supported collaborative work such as distributed workgroups and online learning environments. For example, the explanatory elements of the combined theory can be used by managers as a diagnostic tool in problem situations within virtual teams. A specific instance would be to determine why a change to existing systems created a change in work habits. In a more proactive move, managers might use the predicted social categorization effects and subsequent depersonalization, to instill a group identity in an otherwise diverse workgroup.

The combined model is expected to provide explanatory and predictive capabilities that exceed those of the individual theories and should prove to be relevant in the study of computed-supported collaboration, in the design of collaborative environments and in the analysis of individual and group behaviors in computer-mediated communication.

An important concept for social marketing, role modelling has rarely been considered in detail. Instead the focus often lies with celebrity endorsement and has tended to be about product sales rather than behaviour change. The purpose of this paper is to suggest that the role model is complex and offers a typology to identify variations that need consideration by social marketers in selecting the most appropriate role model. Gen Y provides a context for this discussion because they are a significant target market for social marketers.

The article evaluates four theoretical perspectives of role modelling. Following this, an assessment is made of the types of interaction that can be instigated by role modelling variations.

Social marketers often use role models in behaviour change interventions. The typology of role model interactions offers a starting point for research and a more nuanced view of role modelling.

The design of role modelling programs is under‐researched. Through the provision of a typology of role model interaction the article begins to address this gap.

The purpose of this paper is to test empirically whether there exist spillover externalities in job satisfaction, i.e., to test whether individual-level job satisfaction is affected by the aggregate job satisfaction level in a certain labor market environment.

The authors use a linear-in-means model of social interactions in the empirical analysis. The authors develop an original strategy, motivated by the hierarchical models of social processes, to identify the parameters of interest. BHPS and WERS datasets are used to perform the estimations both at the establishment and local labor market levels.

The authors find that one standard deviation increase in aggregate job satisfaction leads to a 0.42 standard deviation increase in individual-level job satisfaction at the workplace level and a 0.15 standard deviation increase in individual-level job satisfaction at the local labor market level. In other words, the authors report that statistically significant job satisfaction spillovers exist both at the establishment level and local labor market level; and, the former being approximately three times larger than the latter.

First, this is the first paper in the literature estimating spillover effects in job satisfaction. Second, the authors show that the degree of these spillover externalities may change at different aggregation levels. Finally, motivated by the hierarchical models of social processes, the author develop an original econometric identification strategy.

Consumers are inclined to join longer queues due to social interactions in service consumptions. This purchase behavior brings in operational challenges in terms of capacity planning, which affects consumers’ demand, leading to an unstable and fluctuated arrival process. This paper aims to investigate the dynamic characteristics of the arrival process of a service system with boundedly rational consumers whose purchase decisions are influenced by the queue length under social interactions.

Consumers’ bounded rationality is modeled based on the random utility theory. Due to social interactions, the equilibrium queue length and its interaction with the expected waiting time affect consumers’ value perception. The authors first analyze the optimal service capacity decision with or without considering the influence of social interactions in a static setting. They then focus on the dynamic characteristics of the arrival process by a one-dimensional dynamical model in terms of the arrival rate.

This paper finds that the service system can behave chaotic in terms of arrival rate dynamics under social interactions. The results highlight the dynamical complexity of a simple service system due to consumers’ behavioral factors and the influence of social interactions, which may be the critical drivers leading to fluctuated and uneven demand.

The findings demonstrate that due to consumers’ limited cognitive ability and the influence of social interactions, the demand to a service system can be stable, periodic or even chaotic in terms of the arrival process. This study provides an alternative explanation to the observed demand fluctuations in various service processes under the influence of social interactions, which is important for service providers to effectively manage service capacity to achieve a stable service process and improve operational efficiency.