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Assessing the Activeness of Online Economic Activity of Taiwan’s Internet Users: An Application of the Super-Efficiency Data Envelopment Analysis Model

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Abstract

Discussions on the second-level digital divide focus on the skills of Internet users as well as their online activity. Thus, this study applies a super-efficiency data envelopment analysis (DEA) framework to investigate Internet users’ activeness of users’ online economic activity at an individual level. Here, we consider users’ access to information and communication technologies (ICT), their Internet literacy, and their level of usage. In addition, the framework offers a basis for measuring and comparing ICT users. First, we construct a super-efficiency DEA model, which we use to create an index measuring the extent of users’ online economic activity. Second, we apply an OLS regression to explore the factors that influence such activity. Our results can be used to analyze the issues surrounding the second-level digital divide in terms of Internet users’ online economic activities. Our regression results indicate that those Internet users who are female, more educated, employed, earn a higher monthly income, and live in eastern Taiwan demonstrate greater online economic activity than other users. Finally, the positive significance of the difference between using broadband and 3G to access the Internet emphasizes the importance of Internet connection speed to such activities.

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Notes

  1. Tseng and You (2013) studied the second-level DD using aggregated data for Taiwan, Hong Kong, and China.

  2. Henceforth, we focus our discussion of the second-level DD on users’ skills and behavior when using the Internet.

  3. We are thankful to an anonymous referee for pointing out the difference between active users and addicts. An Internet user becomes an “addict user” rather than an “active user” after a certain level of activity. Therefore, it is not adequate to refer to such an Internet user as an “active user.” In addition, the scope of this study is only concerned with a linear form of online activity, therefore we leave this problem for future studies.

  4. For further information regarding the difference between the DEA and SFA, please refer to Coelli et al. (2005).

  5. There are two kinds of mathematical expression for this linear optimization problem of output-oriented CCR model. The form expressed here is the dual form, while the other is the primal form. According to Cooper et al. (2011, p. 8–13), a DEA model in the primal form is called as multiplier model, and its corresponding dual model is called as envelopment model (p. 9). In addition, after the “Charnes–Cooper” transformation (Charnes and Cooper, 1962), a primal problem which maximizes its weighted output level of the objective function is transformed into an “input-oriented” DEA model (p. 9, model 1.3 and 1.4). Similarly, a primal problem which minimizes its weighted input level of the objective function is transformed into an “output-oriented” DEA model (p. 11, model 1.8 and 1.9). In this work, we apply the “output-oriented” DEA model directly for the brevity.

  6. The super-efficiency DEA model in Andersen and Petersen (1993) is in the slackness-based form. To be consistent with model (1) in this work, we adopted the model formulation in model (2) of Sadjadi et al. (2011). However, their model is an input-oriented CCR one.

  7. The original data use in this study was offered by Center for Survey Research, Research Center for Humanities and Social Sciences, Academia Sinica, Taiwan.

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Authors and Affiliations

  1. Graduate Program of Social Informatics and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, No. 135, Yuan-Tung Road, Chung-Li City, Tauyuan County, Taiwan

    Chih-Cheng Chen

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  1. Chih-Cheng Chen
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Correspondence to Chih-Cheng Chen.

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The author would like to thank the two anonymous reviewers for their helpful comments.

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Chen, CC. Assessing the Activeness of Online Economic Activity of Taiwan’s Internet Users: An Application of the Super-Efficiency Data Envelopment Analysis Model. Soc Indic Res 122, 433–451 (2015). https://doi.org/10.1007/s11205-014-0690-y

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