Strategic corporate sustainability performance of Chinese state-owned listed firms: A meta-frontier generalized directional distance function approach

Abstract

Although sustainability recently became a key factor in social science, little progress has been made toward improving the measurement of sustainability performance. This paper proposes composite corporate sustainability performance indicators using a meta-frontier generalized directional distance function. This advanced approach can measure the efficiency of corporate social responsibility activities by benchmarking, while simultaneously considering industrial heterogeneities, using the meta-frontier approach. First, we propose the concept of a meta-frontier generalized directional distance function. Second, several standardized composite indicators related to corporate sustainability performance are developed. The meta-frontier directional distance function is estimated by solving a series of data envelopment analysis models. Chinese state-owned listed enterprises are then empirically examined using the proposed model. We find significant group heterogeneities in terms of corporate sustainability performance. We also derive some policy implications using the empirical results.

Keywords:

• Corporate sustainability performance
• Generalized directional distance function
• Chinese state-owned listed firms
• Data envelopment analysis
• Meta-frontier approach

Acknowledgements

The authors are very grateful to the Editor Prof. Scott Alan Carson and four anonymous reviewers for their constructive and helpful comments on the earlier version of this research. We are also grateful to the financial support provided by the Inha University and the major project of the National Social Science Foundation of China (No. 12&ZD213).

Notes

1 Strong disposability means that the producer is free to dispose of good output; the firm can abandon its good product at will. The opposite concept is the weak disposability assumption, which implies that, in the terms of the proportional reduction of good and bad outputs, the abatement of bad outputs, such asemissions, are not free but costly. See Färe and Grosskopf (2005) for more details.

2 Another reason for choosing g = (x, y) is that we can obtain the standardized efficiency measure when we define the sustainability performance indicator in Eqs. ⁽³⁾(3) $CSPT=\frac{T/S}{(T+{\gamma }_t^{*}T)/(S-{\beta }_s^{*}S)}=\frac{1-{\beta }_s^{*}}{1+{\gamma }_t^{*}}$(3) ⁽⁴⁾(4) $CSPV=\frac{V/S}{(T+{\gamma }_v^{*}T)/(S-{\beta }_s^{*}S)}=\frac{1-{\beta }_s^{*}}{1+{\gamma }_v^{*}}\text{.}$(4) . We also provide a comparison between the results for both settings [i.e., g = (x, y) and g = (1, 1)] and present the results in Section 3.

3 We treat sustainability activities as inputs for the following reasons. According to Porter and Kramer (2006a, 2006b), sustainability activities can become a source of innovation and competitive advantage for a firm, thus increasing its profit. Therefore, from the viewpoint of resources, sustainability activities have the characteristics of inputs like capital, labor, and energy. The positive relationship between implementing sustainability, environmental, practices and improved firm performance is empirically confirmed by previous studies (López-Gamero, Molina-Azorín, & Claver-Cortés, 2009;Jacobs, Singhal, & Subramanian, 2010). The aim of this paper is to measure how a firm can perform sustainable activities more efficiently. Note that increasing the firm's sustainability activities is not our focus; rather, similar to the concept of labor efficiency, we wish to measure how a firm can earn better profits while implementing the same number of sustainability activities. Undertaking sustainability activities is costly, and therefore, their implementation is of the utmost importance to firms. While calculating sustainability performance, Lee and Saen (2012) also treat the cost of sustainability activities as an input variable. Most studies usually treat emissions as undesirable outputs while measuring environmental performance, which differs from sustainability performance.

4 Enterprises from the manufacturing sector make up the largest proportion in the sample, because the manufacturing industry is the largest industry in China. For a given number of variables, the discriminatory capacity of DEA decreases as the number of DMUs decreases. To counter this, Golany and Roll (1989) suggests that the number of DMUs should be at least twice the number of inputs and outputs being considered. The sample size of each group in our study meets these criteria.

5 As data on the cost of sustainability practices for each firm are not available, we use the score of the sustainability activities as the proxy variable.

6 Picazo-Tadeo et al. (2005) use the data transformation of all variables for individual firms as a fraction of the maximum input/output in the sample. See this literature for more details.