An integrated approach for identifying the efficiency-oriented drivers of electronic markets in electronic business

Abstract

Purpose

Effectively evaluating the efficiency of individual e-markets for better understanding the efficiency-oriented critical drivers for individual e-markets is of great significance to the development of electronic business. The purpose of this paper is to develop an approach through adequately integrating data envelopment analysis (DEA) and bootstrapped Tobit regression analysis for identifying the efficiency-oriented critical drivers on the development of e-market in electronic business.

Design/methodology/approach

A review of the related literature is conducted for adequately formulating the e-market evaluation problem. DEA is appropriately used for assessing the efficiency of available e-markets, leading to the identification of the efficient e-market. Tobit regression analysis is then employed to examine the outcome of the DEA analysis for identifying the efficiency-oriented critical drivers in the development of e-markets in electronic business.

Findings

A better understanding of the operations of individual e-markets with respect to their overall efficiency in electronic business can be achieved with the use of the developed approach. Such understanding is built on the identification of the efficiency-oriented critical drivers on the development of e-market in electronic business.

Originality/value

This paper develops a novel approach for better understanding of the operations of individual e-markets with respect to their overall efficiency in electronic business. The adoption of this approach helps existing e-markets improve their efficiency by focussing on the efficiency-oriented critical drivers and provide new players in e-markets with guidelines for developing their efficient e-markets.

Citation

Duan, S.X., Deng, H. and Luo, F. (2019), "An integrated approach for identifying the efficiency-oriented drivers of electronic markets in electronic business", Journal of Enterprise Information Management, Vol. 32 No. 1, pp. 60-74. https://doi.org/10.1108/JEIM-05-2018-0090