The effect of network structure on the purchase of virtual goods on social networking services

Abstract

PurposeThis paper aims to identify the effect of social structure variables on the purchase of virtual goods. Using field data, it also tests whether their effects on a social networking service are dynamic.Design/methodology/approachTo achieve the research objectives, the authors have applied the random effects panel Tobit model with actual time-series corporate data to explain a link between network structure factors and actual behavior on social networking services.FindingsThe authors have found that various network structure variables such as in-degree, in-closeness centrality, out-closeness centrality and clustering coefficients are significant predictors of virtual item sales; while the constraint is marginally significant, out-degree is not significant. Furthermore, these variables are time-varying, and the dynamic model performs better in a model fit than the static one.Practical implicationsThe findings will help social networking service (SNS) operators realize the importance of understanding network structure variables and personal motivations or the behavior of consumers.Originality/valueThis study provides implications in that it uses various and dynamic network structure variables with panel data.

Application of Internet of Things and Sensors in Healthcare

“…After shopping, the money is debited from the user’s Amazon account, and the customer collects all the purchased items. Thus, the customer is able to pay without waiting in line for the counter [ 55 , 56 ] and, therefore, without compromising social distancing requirements.…”