Classifying, Measuring, and Predicting Users' Overall Active Behavior on Social Networking Sites

Although understanding the role of users' overall active behavior on a social networking site (SNS) is of significant importance for both theory and practice, the complexity and difficulty involved in measuring such behavior has inhibited research attention. To understand users' active behaviors on an SNS, it is important that we identify and classify various types of online behaviors before measuring them. In this paper we holistically examine users' active behaviors on an SNS. Toward this end, we conduct three studies. First, we classify active behaviors on an SNS into four categories using the Delphi method. Then, we develop a measurement model and validate it using the data collected from an online survey of 477 SNS users. The measures of the developed instrument exhibit satisfactory reliability and validity and are used as indicators of the latent constructs. This instrument is then used in a predictive model based on commitment theory and tested using data from 1,242 responses. The results of data analysis suggest that affective commitment and continuance commitment are good predictors of overall active behavior on an SNS. This study complements the existing research on social media, cocreation, and social commerce. Most important, this study provides a theoretically sound measurement instrument that addresses the complex characteristic of overall active behavior on an SNS and which should be useful for future research. The findings of this study have important implications for practice as they highlight managing and stimulating users' active behaviors on an SNS.