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Sub-Network Structure and Information Diffusion Behaviors in a Sustainable Fashion Sharing Economy Platform

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  • Youn Kue Na

    (Department of Art & Culture Research Institute, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea)

  • Sungmin Kang

    (College of Business and Economics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea)

  • Hye Yeon Jeong

    (Department of Fashion Business Management, Fashion Institute of Technology (FIT), State University of New York (SUNY) Korea, 119 Songdo Moonhwa-ro, Yeonsu-gu, Incheon, Korea)

Abstract

It is important to understand the creative processes of social value networks in terms of the interdependent connections between fashion sharing economy businesses and consumers. In particular, when the similarity in the values of each member is shared in the sub-network, the closeness of the relationships can be further strengthened. In such value chains, the overall process is important because the content, which is originally provided through the distribution process, is reinterpreted from the consumer’s point of view and it is reproduced as new creative output with high added value. In this study, the characteristics of sub-network structure, the characteristics of social relations, and network externality are proposed and analyzed as influential variables of information diffusion behaviors that explain the diffusion of shared information in fashion sharing economy platforms. We examined the shared information diffusion performance of the sharing economy platform as a multidimensional influential factor including the network characteristics, and proposed a structural model that integrated network research and mobile information diffusion research. We surveyed 400 people with experience of fashion information activity on sharing economy platforms. Frequency, validity, reliability, measurement model and path analyses were conducted using SPSS and AMOS statistical packages. The results showed that trust value, profit/risk sharing, interdependence, and cultural/social similarity of the sub-network structure characteristics affected social relations, while trust value and cultural/social similarity also influenced relational embeddedness. Social relations and relational embeddedness, in turn, affected perceived complementarity and social interaction, both of which affected fashion information diffusion behaviors. Finally, social pressure, social ties, and social unity affected trust values. The results of this study can be applied not only to social connections among the members of the sub-network of a fashion sharing economy platform, but also as an effective means to explain the maintenance and reinforcement of mutual relations, thereby advancing the current academic research and practical applications.

Suggested Citation

  • Youn Kue Na & Sungmin Kang & Hye Yeon Jeong, 2019. "Sub-Network Structure and Information Diffusion Behaviors in a Sustainable Fashion Sharing Economy Platform," Sustainability, MDPI, vol. 11(12), pages 1-21, June.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3249-:d:239244
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    References listed on IDEAS

    as
    1. Craig E. Landry & Andreas Lange & John A. List & Michael K. Price & Nicholas G. Rupp, 2006. "Toward an Understanding of the Economics of Charity: Evidence from a Field Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(2), pages 747-782.
    2. Angela A. Hung & Charles R. Plott, 2001. "Information Cascades: Replication and an Extension to Majority Rule and Conformity-Rewarding Institutions," American Economic Review, American Economic Association, vol. 91(5), pages 1508-1520, December.
    3. Yang, Jun & Mai, Enping (Shirley), 2010. "Experiential goods with network externalities effects: An empirical study of online rating system," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1050-1057, September.
    4. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    5. Jacob Goldenberg & Oded Lowengart & Daniel Shapira, 2009. "Zooming In: Self-Emergence of Movements in New Product Growth," Marketing Science, INFORMS, vol. 28(2), pages 274-292, 03-04.
    6. Barnes, Stuart J. & Mattsson, Jan, 2016. "Understanding current and future issues in collaborative consumption: A four-stage Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 200-211.
    7. Akbar, Payam & Mai, Robert & Hoffmann, Stefan, 2016. "When do materialistic consumers join commercial sharing systems," Journal of Business Research, Elsevier, vol. 69(10), pages 4215-4224.
    8. Uphoff, Norman & Wijayaratna, C. M., 2000. "Demonstrated Benefits from Social Capital: The Productivity of Farmer Organizations in Gal Oya, Sri Lanka," World Development, Elsevier, vol. 28(11), pages 1875-1890, November.
    9. David Hirshleifer & Siew Hong Teoh, 2003. "Herd Behaviour and Cascading in Capital Markets: a Review and Synthesis," European Financial Management, European Financial Management Association, vol. 9(1), pages 25-66, March.
    10. Goldenberg, Jacob & Libai, Barak & Muller, Eitan, 2010. "The chilling effects of network externalities," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 4-15.
    11. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    12. Buchan, Nancy R. & Johnson, Eric J. & Croson, Rachel T.A., 2006. "Let's get personal: An international examination of the influence of communication, culture and social distance on other regarding preferences," Journal of Economic Behavior & Organization, Elsevier, vol. 60(3), pages 373-398, July.
    13. Fiedler, Marina & Sarstedt, Marko, 2014. "Influence of community design on user behaviors in online communities," Journal of Business Research, Elsevier, vol. 67(11), pages 2258-2268.
    14. James Moody & Douglas R. White, 2000. "Structural Cohesion and Embeddedness: A Hierarchical Conception of Social Groups," Working Papers 00-08-049, Santa Fe Institute.
    15. Han, Sangman & Kim, Beom Jun, 2008. "Network analysis of an online community," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(23), pages 5946-5951.
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