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An architectural framework for developing a recommendation system to enhance vendors’ capability in C2C social commerce

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Abstract

The rapid growth of social networking usage has initiated a new business model called C2C s-commerce which has opened a novel opportunity for SNS users to conduct commercial activities among members. Novice vendors (low-maturity merchants) perform business using a trial-and-error method. Through learning by doing, capability enhancement in online business arrangements to become a mature vendor is difficult and takes time. Having a recommendation system can effectively and systematically support inexperienced vendors to conduct online business and enable vendors to become high-maturity merchants. This study proposes architecture of a C2C s-commerce recommendation system presented in conjunction with its input, process, and output. The architecture was devised to infer from information collected from case study interviews, observation, and secondary research methods. Artificial intelligence technologies and big data were considered to design the proposed framework in order to generate efficient recommendations for capability enhancement in online business arrangements.

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Acknowledgements

The authors gratefully acknowledge the very helpful comments and suggestions of the two anonymous reviewers of the study that make it becomes more meaningful for the development of recommendation system to support social commerce in emerging economies.

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Authors and Affiliations

  1. Prince of Songkla University, Trang Campus, Trang, Thailand

    Supattana Sukrat

  2. King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Borworn Papasratorn

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  1. Supattana Sukrat
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  2. Borworn Papasratorn
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Correspondence to Supattana Sukrat.

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Sukrat, S., Papasratorn, B. An architectural framework for developing a recommendation system to enhance vendors’ capability in C2C social commerce. Soc. Netw. Anal. Min. 8, 22 (2018). https://doi.org/10.1007/s13278-018-0500-7

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  • DOI: https://doi.org/10.1007/s13278-018-0500-7

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