主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
   中国科学院科技战略咨询研究院
Articles

Study on E-commerce Profitability Determinant Under Dynamic Competitive Market Environment

Expand
  • 1. Management of School, University of Chinese Academy Sciences, Beijing 100190, China;
    2. Research Center on Fictitious Economy & Data Science, Chinese Academy Sciences, Beijing 100190;
    3. Key Research Lab on Big Data Mining and Knowledge Management, Chinese Academy Sciences, Beijing 100190

Received date: 2015-05-04

  Revised date: 2015-07-09

  Online published: 2016-08-24

Abstract

A major current focus in e-commerce study is to explore why there are high inputs but low profits in e-commerce. Focusing on the excessive effect of inputs on outputs, market competition rate and mobile e-commerce development, input congestion amount is firstly calculated using data envelopment analysis models, and then e-commerce firm profitability determinants are explored by constructing the panel data regression model. Thirty five pure e-commerce websites from the stock markets of Shanghai, Shenzhen, Hong Kong and NASDAQ are considered, all of which utilize at least one of the two e-business models, namely business-to-business (B2B) and business-to-customer (B2C). The results of DEA models identify that e-commerce suffers from congestion, and the regression shows that asset input congestion has a negative effect on e-commerce firm profitability while market concentration rate and the development of mobile e-commerce can strengthen e-commerce firm profitability. These findings indicate that it is input congestion that leads to low profits. It also enlightens decision makers to strengthen their profitability by eliminating congestion resources and adding new marketing channels such as mobile e-commerce.

Cite this article

YANG Zhuo-fan, SHI Yong . Study on E-commerce Profitability Determinant Under Dynamic Competitive Market Environment[J]. Chinese Journal of Management Science, 2016 , 24(8) : 18 -27 . DOI: 10.16381/j.cnki.issn1003-207x.2016.08.003

References

[1] Bremser W G, Chung Q B. A framework for performance measurement in the e-business environment [J]. Electronic Commerce Research and Applications,2005,4(4):395-412.

[2] Ellickson P B, Market structure and performance [M]// Wright J D. International Encyclopedia of the Social & Behavioral Sciences (Second Edition), Oxford: Elsevier, 2015: 549-554.

[3] 王斌,田志龙, 动态竞争战略中的企业环境分析 [J]. 研究与发展管理,2005,17(3):39-58.

[4] Penrose E T. The theory of the growth of the firm [M]. New York: Oxford university press,1995.

[5] 黎传国,陈收,毛超,等, 资源配置视角下战略调整测度及其对绩效的影响 [J]. 中国管理科学,2014,22(11), 19-26.

[6] Schumpeter J A. Capitalism, socialism, and democracy [M]. New York: Harper Torchbooks, 1942,

[7] Kamien M I, Schwartz N L. Market structure and innovation [M]. Cambridge: Cambridge University Press,1982.

[8] Raider H J. Market structure and innovation [J]. Social Science Research,1998,27(1):1-21.

[9] Grifell-Tatjé E, Lovell C A K. Profits and productivity [J]. Management Science,1999,45(9):1177-1193.

[10] Garrigosa E G, Tatjé E G. Profits and total factor productivity: A comparative analysis [J]. Omega,1992,20(5-6):553-568.

[11] Gold B. Technology, productivity and economic analysis [J]. Omega,1973,1(1):5-24.

[12] Serrano-Cinca C, Fuertes-Callén Y, Mar-Molinero C. Measuring DEA efficiency in Internet companies [J]. Decision Support Systems,2005,38(4):557-573.

[13] Min S, Wolfinbarger M. Market share, profit margin, and marketing efficiency of early movers, bricks and clicks, and specialists in e-commerce [J]. Journal of Business Research,2005,58(8):1030-1039.

[14] Dehning B, Richardson V J. Returns on investments in information technology: A research synthesis [J]. Journal of Information Systems,2002,16(1):7-30.

[15] Porter M E. Competitive advantage: Creating and sustaining superior performance [M].Hong Kong:Free Press,1985.

[16] 戴维·贝赞可,戴维·德雷诺夫,马克·尚利,等. 战略经济学 [M]. 詹正茂,冯梅红等,译.3版,北京:中国人民大学出版社, 2006:346-351.

[17] Coase R H. The nature of the firm [J]. Economica,1937,4(16):386-405.

[18] Färe R, Svensson L. Congestion of production factors [J]. Econometrica,1980,48(7):1745-1753.

[19] Odeck J. Congestion, ownership, region of operation, and scale: Their impact on bus operator performance in Norway [J]. Socio-Economic Planning Sciences,2006,40(1):52-69.

[20] Khodabakhshi M. A one-model approach based on relaxed combinations of inputs for evaluating input congestion in DEA [J]. Journal of Computational and Applied Mathematics,2009,23(0):443-450.

[21] Khodabakhshi M, Lotfi F H, Aryavash K. Review of input congestion estimating methods in DEA [J]. Journal of Applied Mathematics,2014:1-9.

[22] Marques R C, Simões P. Measuring the influence of congestion on efficiency in worldwide airports [J]. Journal of Air Transport Management,2010,16(6):334-336.

[23] Chen Y. Congestion in commodity trading advisors [J]. INFOR,2011,49(1), 63-74.

[24] Flegg A T, Allen D O. Congestion in the Chinese automobile and textile industries revisited [J]. Socio-Economic Planning Sciences,2009,43(3):177-191.

[25] Brockett P L, Cooper W W, Wang Yuying,et al. Inefficiency and congestion in Chinese production before and after the 1978 economic reforms [J]. Socio-Economic Planning Sciences,1998,32(1):1-20.

[26] Zarate J M, Boksa P, Baptista T, et al. Using DEA to identify and manage congestion [J]. Journal of Productivity Analysis,2004,22(3):207-226.

[27] Cooper W W, Thompson R G, Thrall R M.Chapter 1 Introduction: Extensions and new developments in DEA [J]. Annals of Operations Research,1996,66(1):1-45.

[28] Banker R D, Charnes A, Cooper W W. Some models for estimating technical and scale inefficiencies in data envelopment analysis [J]. Management science,1984,30(9):1078-1092.

[29] Cooper W W, Seiford L M, Zhu J. A unified additive model approach for evaluating inefficiency and congestion with associated measures in DEA [J]. Socio-Economic Planning Sciences,2000,34(1):1-25.
Outlines

/