Digital Item Purchase Model in SNS Channel Applying Dynamic SNA and PVAR

LEE,, H. , & JUNG,, B. (2020). Digital Item Purchase Model in SNS Channel Applying Dynamic SNA and PVAR. The Journal of Distribution Science, 18(3), 25-36, https://doi.org/https://doi.org/10.15722/jds.18.3.202003.25

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Abstract

Purpose: Based on previous researches on social factors of digital item purchase in digital contents distribution platforms such as SNS, we aim to develop the integrated model that accounts for the dynamic and interactive relationship between social structure indicators and digital item purchase. Research design, data and methodology: A PVAR model was used to capture endogenous and dynamic relationships between digital item purchase and network indicators. Results: We find that there exist considerable endogenous and dynamic relationships between digital item purchase and network structure variables. Not only lagged in-degree and out-degree but also in-closeness and out-closeness centrality have significant and positive impacts on digital item purchase. Lagged clustering has a significant and negative effect on digital item purchase. Lagged purchase has a significant and positive impact just on the present in-closeness and out-closeness centrality; but there is no significant effect of lagged purchase on the other two degree variables and clustering coefficient. We also find that both closeness centralities have much higher carryover effect on digital item purchase and that the elasticity of both closeness centralities on the purchase of digital items is even higher than that of other network structure variables. Conclusions: In-closeness and out-closeness are the most influential factors among social structure variables of this study on digital item purchase.

keywords: Digital Items, Digital Contents Distribution Platforms, Social Network Service (SNS), Panel Vector Autoregression (PVAR), Impulse Response Function

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Article Contents

Vol.18 No.3

Digital Item Purchase Model in SNS Channel Applying Dynamic SNA and PVAR

Abstract

Reference

The Journal of Distribution Science