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Tsinghua Science and Technology 2019, 24(1): 18-29 https://doi.org/10.26599/TST.2018.9010067
Open Access | Issue | Published: 08 November 2018

Architecture of Next-Generation E-Commerce Platform

Show Author's Information Yadong Huang,  Yueting Chai( ),  Yi Liu,  Jianping Shen
National Engineering Laboratory for E-Commerce Technology, Department of Automation, Tsinghua University, Beijing 100084, China.
Keywords:
e-commerce, personalized portal, accurate demand, reliable supply, smart cyberspace
Cite this article:
Huang Y, Chai Y, Liu Y, et al. Architecture of Next-Generation E-Commerce Platform. Tsinghua Science and Technology, 2019, 24(1): 18-29. https://doi.org/10.26599/TST.2018.9010067
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Abstract Full text About this article

E-commerce, driven by computer and internet technology, has experienced a significant growth in almost all fields during the past two decades. E-commerce has significantly changed the rules of business. Numerous research institutions and enterprises have made e-commerce more intelligent and convenient. Here, we propose a novel prototype of next-generation e-commerce platform with an architecture framework and theoretical models. Each subject, including the individual, enterprise, and administrative department, has his/her personalized portal to complete the subject information synchronization, supply release, demand satisfaction, and social contact. By using the personalized portal, instead of the traditional trading platform, the consumers and suppliers can complete intelligent matching transactions without intermediate traders. Moreover, the overall transaction process can be reviewed, making the transaction safer, more transparent, and more interesting. Moreover, the interconnected personalized portals solve the isolated islands of information, and the counterparts support parallel processing. Thus, this may improve the operating efficiency of the entire society.


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About this article

Architecture of Next-Generation E-Commerce Platform

Show Author's information Yadong Huang, Yueting Chai( ), Yi Liu, Jianping Shen
National Engineering Laboratory for E-Commerce Technology, Department of Automation, Tsinghua University, Beijing 100084, China.

Abstract

E-commerce, driven by computer and internet technology, has experienced a significant growth in almost all fields during the past two decades. E-commerce has significantly changed the rules of business. Numerous research institutions and enterprises have made e-commerce more intelligent and convenient. Here, we propose a novel prototype of next-generation e-commerce platform with an architecture framework and theoretical models. Each subject, including the individual, enterprise, and administrative department, has his/her personalized portal to complete the subject information synchronization, supply release, demand satisfaction, and social contact. By using the personalized portal, instead of the traditional trading platform, the consumers and suppliers can complete intelligent matching transactions without intermediate traders. Moreover, the overall transaction process can be reviewed, making the transaction safer, more transparent, and more interesting. Moreover, the interconnected personalized portals solve the isolated islands of information, and the counterparts support parallel processing. Thus, this may improve the operating efficiency of the entire society.

Keywords: e-commerce, personalized portal, accurate demand, reliable supply, smart cyberspace

References(27)

[1]
Lee J. K., Business-to-business electronic commerce, Encycl. Inf. Syst., vol. 15, no. 1, pp. 81-97, 2003.
DOI Google Scholar
[2]
Eliott S., Electronic commerce: B2C strategies and models, Eur. J. Inf. Syst., vol. 12, no. 1, p. 72, 2003.
DOI Google Scholar
[3]
Chu J. H. and Manchanda P., Quantifying Cross and Direct Network Effects in Online C2C Platforms, http://bizfaculty.nus.edu/media_rp/publications/HE2jL1452440116.pdf, 2015.
DOI
[4]
Tsai T. M., Yang P. C., and Wang W. N., Pilot study toward realizing social effect in O2O commerce services, in Proc. 5th Int. Conf. Social Informatics, Kyoto, Japan, 2013.
DOI
[5]
Dong X. Y., Xiong L., and Han S. T., How adoption is G2B model E-government? Evidence from Xi’an, in Proc. 2010 Int. Conf. Management and Service Science, Wuhan, China, 2010.
DOI
[6]
Robinson L., The cyberself: The self-ing project goes online, symbolic interaction in the digital age, New Media Soc., vol. 9, no. 1, pp. 93-110, 2007.
DOI Google Scholar
[7]
Wen J., Ming K., Wang F. R., Huang B. X., and Ma J. H., Cyber-I: Vision of the individual’s counterpart on cyberspace, in Proc. 8th IEEE Int. Conf. Dependable, Autonomic and Secure Computing, Chengdu, China, 2009.
DOI
[8]
Shen J., A study on cyber-anima model and its application in intelligent recommendation, PhD dissertation, Fudan University, Shanghai, China, 2015.
[9]
Slywotzky A. J., The age of choiceboard, Harv. Bus. Rev., vol. 78, no. 1, pp. 40-41, 2000.
Google Scholar
[10]
Yue M., Houé R., and Grabot B., MRP-based negotiation in customer-supplier relationship, in Proc. 14th IFAC Symposium on Information Control Problems in Manufacturing Bucharest, Bucharest, Romania, 2012.
[11]
Popescu A. M. and Etzioni O., Extracting product features and opinions from reviews, in Natural Language Processing and Text Mining, Kao A. and Poteet S. R., eds. Springer, 2007, pp. 9-28.
[12]
Chen Y. H., Wang Y. Z., and Wong M. H., A web-based fuzzy mass customization system, J. Manuf. Syst., vol. 20, no. 4, pp. 280-287, 2001.
DOI Google Scholar
[13]
Hansen T., Scheer C., and Loos P., Product configurators in electronic commerce-extension of the configurator concept towards customer recommendation, in Proc. 2nd Interdisciplinary World Congress on Mass Customization and Personalization, Munich, Germany, 2003.
[14]
Thomas C. C., Cooper P. J., and Blake C. A., An advisory system for customers objective needs elicitation in mass customization, Hong Kong Journal of Occupational Therapy, vol. 21, no. 2, pp. 64-71, 2004.
Google Scholar
[15]
Stormer H., Improving product configurators by means of a collaborative recommender system, Int. J. Mass Customisation, vol. 3, no. 2, pp. 165-178, 2009.
DOI Google Scholar
[16]
Piller F., Schubert P., Koch M., and Möslein K., Overcoming mass confusion: Collaborative customer co-design in online communities, J. Comput.-Med. Commun., vol. 10, no. 4, pp. 1-28, 2005.
DOI Google Scholar
[17]
Miceli G., Ricotta F., and Costabile M., Customizing customization: A conceptual framework for interactive personalization, J. Interact. Mark., vol. 21, no. 2, pp. 6-25, 2007.
DOI Google Scholar
[18]
Chen Z. X. and Wang L. Y., Personalized product configuration rules with dual formulations: A method to proactively leverage mass confusion, Exp. Syst. Appl., vol. 37, no. 1, pp. 383-392, 2010.
DOI Google Scholar
[19]
Bahill A. T. and Chapman W. L., A tutorial on quality function deployment, Eng. Manage. J., vol. 5, no. 3, pp. 24-35, 1993.
DOI Google Scholar
[20]
Yang Y. Z., Zhang Q., and Wu L. Y., Technique for order preference by similarity to ideal solution based on entropy weight for supplier selection, (in Chinese), Transactions of Beijing Institute of Technology, vol. 26, no. 1, pp. 31-35, 2006.
Google Scholar
[21]
Fang H., Zhang J., Bao Y., and Zhu Q. H., Towards effective online review systems in the Chinese context: A cross-cultural empirical study, Electron. Commer. Res. Appl., vol. 12, no. 3, pp. 208-220, 2013.
DOI Google Scholar
[22]
Zhao Y., Yang S., Narayan V., and Zhao Y., Modeling consumer learning from online product reviews, Mark. Sci., vol. 32, no. 1, pp. 153-169, 2013.
DOI Google Scholar
[23]
Schlosser A. E., Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments, J. Consum. Psychol., vol. 21, no. 3, pp. 226-239, 2011.
DOI Google Scholar
[24]
Robson K., Farshid M., Bredican J., and Humphrey S., Making sense of online consumer reviews: A methodology, Int. J. Mark. Res., vol. 55, no. 4, pp. 521-537, 2013.
DOI Google Scholar
[25]
Wang X. P., Grey prediction with rolling mechanism for electricity demand forecasting of Shanghai, in Proc. 5th Int. Conf. Grey Systems and Intelligent Services, Nanjing, China, 2007.
[26]
Sun X., Chai Y. T., Liu Y., Shen J. P., and Huang Y. D., Evolution of specialization with reachable transaction scope based on a simple and symmetric firm resource allocation model, Tsinghua Sci. Technol., vol. 22, no. 1, pp. 10-28, 2017.
DOI Google Scholar
[27]
Xu Z. S., Multiple attribute decision making based on different types of linguistic information, J. Southeast Univ., vol. 22, no. 1, pp. 134-136, 2006.
Google Scholar
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Publication history

Received: 31 March 2017
Accepted: 16 May 2017
Published: 08 November 2018
Issue date: February 2019

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© The author(s) 2019

Acknowledgements

This work was supported by the National Key Research and Development Plan of China: Research on Fundamental Theories and Methods of Crowd Science (No. 2017YFB1400100).

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