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- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
ISSN : 1738-3110
Study of Virtual Goods Purchase Model Applying Dynamic Social Network Structure Variables
Bae, Jungho
Abstract
Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.
- keywords
- Virtual Good Purchase, Social Media, Time-varying Network Structure Variables, Random Effects Panel Tobit Model, Social Network Analysis
Reference
Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: measuring hedonic and utilitarian shopping value. Journal of consumer research, 20(4), 644-656.
Beauchamp, M. A. (1965). An improved index of centrality. Behavioral science, 10(2), 161-163.
Becker, M. H. (1970). Sociometric location and innovativeness: Reformulation and extension of the diffusion model. American sociological review, 35(2), 267-282.
Braha, D., & Bar‐Yam, Y. (2006). From centrality to temporary fame: Dynamic centrality in complex networks. Complexity, 12(2), 59-63.
Burt, R. S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press, Cambridge, MA.
Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349-399.
Cancian, F. (1979). The innovator's situation:Upper-middle-class conservatism in agricultural communities. University Press, Palo Alto, CA: Stanford.
Chen, X., Chen, Y., & Xiao, P. (2013). The impact of sampling and network topology on the estimation of social intercorrelations. Journal of Marketing Research, 50(1), 95-110.
Choi, H., Kim, S. H., & Lee, J. (2010). Role of network structure and network effects in diffusion of innovations. Industrial Marketing Management, 39(1), 170-177.
Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, 95-120.
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers'product evaluations. Journal of Marketing Research, 28(3), 307-319.
Frenzen, J. K., & Davis, H. L. (1990). Purchasing behavior in embedded markets. Journal of Consumer Research, 17(1), 1-12.
Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821-7826.
Goffman, E. (1959). The presentation of self in everyday life. Garden City, NY.
Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009). The role of hubs in the adoption process. Journal of Marketing, 73(2), 1-13.
Goodman, L. A. (1961). Snowball sampling. The Annals of Mathematical Statistics, 32(1), 148-170.
Granovetter, M. (1973). The strength of weak ties. The American Journal of Sociology, 78(6), 1360-1380.
Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 201-233.
Hamari, J., Alha, K., Järvelä, S., Kivikangas, J. M., Koivisto, J., & Paavilainen, J. (2017). Why do players buy in-game content? An empirical study on concrete purchase motivations. Computers in Human Behavior, 68, 538-546.
Hamari, J., & Keronen, L. (2016). Why do people buy virtual goods? A literature review. In 2016 49th Hawaii International Conference on System Sciences (HICSS)IEEE, 1358-1367.
Henry, P. C. (2005). Social class, market situation, and consumers' metaphors of (dis) empowerment. Journal of Consumer Research, 31(4), 766-778.
Hinz, O., Skiera, B., Barrot, C., & Becker, J. U. (2011). Seeding strategies for viral marketing: An empirical comparison. Journal of Marketing, 75(6), 55-71.
Iyengar, R., Van den Bulte, C., & Valente, T. W. (2011). Opinion leadership and social contagion in new product diffusion. Marketing Science, 30(2), 195-212.
Jensen Schau, H., & Gilly, M. C. (2003). We are what we post? Self-presentation in personal web space. Journal of Consumer Research, 30(3), 385-404.
Katona, Z., Zubcsek, P. P., & Sarvary, M. (2011). Network effects and personal influences: The diffusion of an online social network. Journal of Marketing Research, 48(3), 425-443.
Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111-126.
Kim, H. W., Chan, H. C., & Kankanhalli, A. (2012). What motivates people to purchase digital items on virtual community websites? The desire for online self-presentation. Information Systems Research, 23(4), 1232-1245.
Kim, H. W., Gupta, S., & Koh, J. (2011). Investigating the intention to purchase digital items in social networking communities: A customer value perspective. Information & Management, 48(6), 228-234.
Koh, J., Kim, Y. G., & Kim, Y. G. (2003). Sense of virtual community: A conceptual framework and empirical validation. International Journal of Electronic Commerce, 8(2), 75-94.
Leary, M. R. (1995). Self-presentation: Impression management and interpersonal behavior. Brown &Benchmark Publishers.
Lee, H. T., & Kim, B. D. (2013). The role of brokers in social network on the product purchase. Journal of Korean Marketing Association, 28(6), 1-22.
Lehdonvirta, V. (2009). Virtual item sales as a revenue model: Identifying attributes that drive purchase decisions. Electronic Commerce Research, 9(1-2), 97-113.
Mäntymäki, M., & Salo, J. (2013). Purchasing behavior in social virtual worlds: An examination of Habbo Hotel. International Journal of Information Management, 33(2), 282-290.
Nieborg, D. B. (2015). Crushing candy: The free-to-play game in its connective commodity form. Social Media+Society, 1(2), 1-12.
Oestreicher-Singer, G., & Sundararajan, A. (2012). Recommendation networks and the long tail of electronic commerce. Mis Quarterly, 36(1), 65-83.
Oestreicher-Singer, G., & Sundararajan, A. (2012). The visible hand? Demand effects of recommendation networks in electronic markets. Management Science, 58(11), 1963-1981.
Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata. STATA press.
Salganik, M. J., & Heckathorn, D. D. (2004). Sampling and estimation in hidden populations using respondent‐driven sampling. Sociological Methodology, 34(1), 193-240.
Schlenker, B. R. (2003). Self-presentation. M. R. Leary, J. P. Tangney,eds. Handbook of Self and Identity. Guilford Press, New York, 492–518.
Schwämmlein, E., & Wodzicki, K. (2012). What to tell about me? Self-presentation in online communities. Journal of Computer-Mediated Communication, 17(4), 387-407.
Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159-170.
Stephen, A. T., & Toubia, O. (2010). Deriving value from social commerce networks. Journal of Marketing Research, 47(2), 215-228.
Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203-220.
Tepper, K. (1994). The role of labeling processes in elderly consumers' responses to age segmentation cues. Journal of Consumer Research, 20(4), 503-519.
Trusov, M., Bodapati, A. V., & Bucklin, R. E. (2010). Determining influential users in internet social networks. Journal of Marketing Research, 47(4), 643-658.
Valente, T. W., & Fujimoto, K. (2010). Bridging: locating critical connectors in a network. Social Networks, 32(3), 212-220.
Walker, K. (2000). “It's difficult to hide it”: The presentation of self on Internet home pages. Qualitative Sociology, 23(1), 99-120.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge University Press.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440-442.
Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441-458.
Wohn, D. Y. (2014). Spending real money: purchasing patterns of virtual goods in an online social game. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems (CHI'14), ACM(2014), 3359-68.
Yoganarasimhan, H. (2012). Impact of social network structure on content propagation: A study using YouTube data. Quantitative Marketing and Economics, 10(1), 111-150.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. The Journal of Marketing, 52, 2-22.
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