The Study On Measurement Of Users’ Active Behavior And Purchase Decision In Social Network Service

Social network services (SNSs) have blended into our daily life gradually in recent years. The abundant content and relationship resources in SNSs play more and more important roles in changing users’ social patterns and decision mechanism. The proliferation of social commerce makes SNSs combine with commercial applications or cooperate with e-commerce websites, which makes SNS users participate in commercial activities more conveniently. However, there are two issues faced by the SNSs in China. In one hand, the activity level of SNS users in China is relatively low, which limit the increase of marketing capital and profitability. In the other hand, the design principle of comercial applications in SNSs is unclear, which hinder the business model evolution from social service to social commerce. To address these two issues, this paper investigates users’ active behavior and purchase decision pattern in SNSs, which can provide guidelines for improving users’ activity level of social behavior and designing reasonable commercail appliactions. Meanwhile, these investigations can make theoretical contributions to social psychology, value co-creation, social commerce, design science and other related domain.This paper includes five studies. In the first three studies, focusing on measuring users’ active behavior in a SNS, we explore its conceptual framework, develop its measurement model and investigate its driving factors. In the following two studies, we explore the roles of users’social behavior in social commerce components of SNSs on the decision process and decision quality.First, we analyze the scope of users’ active behavior in a SNS, and structure a classification framework. Using Delphi approach and two panelists, we explore the specific active behaviors in SNSs, and then narrow them down, and finally25indicators are classified into four categories:content creation behavior, content transmission behavior, relationship building behavior and relationship maintaining behavior. This classification framework reflects the co-creation pattern between SNS users and the websites. Second, we develop a measurement model of users’ overall active behavior in a SNS. Based on477valid questionnaires of Chinese SNS users, both the first-order measurement model and second-order measurement model display satisfactory model fit, after conducting an exploratory factor analysis and a confirmatory factor analysis. All the indicators and four dimensions of users’ overall active behavior in a SNS demonstrate good reliability and validity. The measurement instruments can be used in future empirical studies.Third, through integrating commitment theory, social support theory, sunk cost theory and social influence theroy, we investigate the mind-sets driving users’ active behavior in a SNS and antecedents of these mind-sets. The empirical results of our survey of1242Renren users in China indicate that affective and continuance commitments are the main drivers of users’ active behavior in a SNS Further, informational support and emotional support, reputational capital and relational capital, subjective norm and perceived critical mass perform well as antecedents of affective, continuance, and normative commitment, respectively. These results enrich the literature of SNS and extend the application of related theory, and provide advices to enhance users’ active level for SNS operators.The commercial applications of SNSs have incorporated various social commerce components, which play important roles in changing users’decision process and decision quality. Based on social learning theory and cognitive and affective dimensions of attitude, we develop a research model to explore how customers’ learning behavior in three main social commerce components formulate users’ decision process. The empirical results suggest that learning behavior in three social commerce components can influence users’ attitude in both cognitive and affective dimension, which trigger users’ purchase intention in turn. Investigating three social commerce components (i.e., forums and communities, ratings and reviews, and recommendations) simultaneously, we can distinguish the different roles of diferent social commerce components in changing users’ decision process.At last, based on the social learning theory and uncertainty theory, we develop a research model to explore how customers’ learning behaviors in social commerce components influence their uncertainty in shopping experience, and thus improve their decision quality. The empirical results suggest that demand uncertainty is one of the most important factors that reduces decision quality, whereas product quality uncertainty has a significant positive influence on decision quality, and seller quality uncertainty has no influence on decision quality. Also, learning from forums and communities can only reduce demand uncertainty, learning from rating and reviews can reduce demand uncertainty and enhance product quality uncertainty, whereas learning from recommendations can only enhance seller quality uncertainty. In addition, product type can moderate most of the above associations. In summary, these findings increase our understanding of the customers’ decision pattern in social commerce context and extend the scope of social learning theory and uncertainty theory. The findings also provide insights for social commerce practitioners in developing strategies for implementation of social commerce as well as the design of social commerce sites.