Industrial changes in corporate sustainability performance – an empirical overview using data envelopment analysis

Journal of Cleaner Production 56 (2013) 147e155

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Industrial changes in corporate sustainability performance e an empirical overview using data envelopment analysis Dong-Shang Chang a, *, Li-chin Regina Kuo b,1, Yi-tui Chen c, 2 a

National Central University, Department of Business Administration, No. 300, Jhongda Road, Jhongli City, Taoyuan County 32001, Taiwan 16F, 106, Sec. 1, Hsin-tai 5th Road, His-chih, Taipei County, Taiwan c National Taipei University of Nursing and Health Sciences, Department of Health Care Management, 89, Nei-Chiang St., Wan-Hua Dist., Taipei, Taiwan b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 June 2010 Received in revised form 13 September 2011 Accepted 15 September 2011 Available online 7 October 2011

In this paper, we report the results of an empirical study of corporate sustainability3 conducted at the industry-level. The aim of the study was to determine the change in corporate sustainability performance over time. A composite index of corporate sustainability performance was created using DEA relative efficiency scores in sixteen industries, and the changes in efficiency were measured using the Malmquist index for three consecutive years. The findings indicate that sustainability performance varies across industries and reflect a trend of on-going improvement in corporate sustainability performance in most industries. Of the 16 industries, 7 industries have improved their sustainability performance consistently during the three consecutive years. The natural resources sector tends to have more consistent and stable sustainability performance than the other sectors. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: DEA Sustainability performance Technical efficiency Malmquist index

1. Introduction The corporate world is experiencing a trend toward sustainable management and reporting (Panchak, 2002), and an increasing number of firms have begun to move beyond general sustainability management to focus on industry-specific sustainable management in recent decades. The influence of the characteristics of specific industries on corporate sustainability or social responsibility have long been the focus of research in this field. The specific industrial context tends to shape the policies and strategies that exist at the firm-level due to its unique institutional regulations and the influence of its particular stakeholders. In studies exploring the relationship between corporate sustainability and financial performance, it tends to be difficult to measure cross-industry

* Corresponding author. Tel.: þ886 3 426 7242; fax: þ886 3 422 9609. E-mail addresses: [email protected] (D.-S. Chang), [email protected], [email protected] (L.-c. Kuo), [email protected] (Y.-t. Chen). 1 Tel.: þ886 2 2696 2665x121; fax: þ886 2 2696 2667. 2 Tel.: þ886 2 23885111; fax: þ886 2 23758291. 3 The sustainability scores were supplied by the SAM (Sustainable Asset Management) group. The views expressed in this paper are those of the authors and do not necessarily represent those of the SAM Group. All of the corporate sustainability indicators used in the present study were developed independently from the original data provided by the SAM Group by the authors of this article. Any possible errors in the interpretation of the data remain the sole responsibility of the authors. 0959-6526/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2011.09.015

performance due to the specialized social interests that exist in different industries (Holmes, 1977). Henriques and Sadorsky (1996) examined the determinants of environmental responsibility at the firm-level and argued that firms in the natural resource sector are more likely to develop environmental plans, whereas firms in the service sector are less likely to develop such plans. Industry type has been used as a boundary condition, a control variable or a moderating variable to overcome the challenges of studying cross-industry differences in corporate social or sustainability performance (Cochran and Wood, 1984; Fineman and Clarke, 1996; Johnson and Greening, 1999; Christmann, 2000; Chand, 2006). Nevertheless, most studies have compared differences between the corporate sustainability performance in two industries rather than across multiple industries. This study proposes an overview of industry-level changes in corporate sustainability performance across industries using data envelopment analysis (DEA). A scatter plot of the relative performance of 16 industries is presented to illustrate the changes in corporate sustainability over time and to identify the industry with the best sustainability performance in a group of multiple industries. The DEA approach in industry-level research has been widely used to assess relative efficiency in both public and private organizations, including banking institutions, steel and iron companies, telecommunications companies and universities (Sarkis and Weinrach, 2001). This type of analysis can eliminate the effect of firm-level inefficiency within each industry and determine

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corporate sustainability performance based on a single indicator in multiple industries (Cooper et al., 2000). This study delineates the progress of corporate sustainable development at the industrylevel and provides insight into the appropriate sustainability performance milestones in various industries. The paper begins with a review of the literature on changes in industrial corporate sustainability and the intrinsic effects on industries. Next, the methods used in the empirical study are presented. Finally, we propose a pathway for increasing corporate sustainability based on a macro overview of industrial sustainability performance over time. 2. Theoretical overview During two centuries of industrialization, concern for the environment has increased throughout the world due to aggravated ecological degradation stemming from industrial development. Sociologists have investigated the impact of industrialization on societies (Fisher and Freudenburg, 2004). Firms may play a crucial role in achieving ecological sustainability (Shrivastava, 1995). Sustainability is generally defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” (Dunphy et al., 2003). Until now, the tools used to assess sustainability have varied across different research fields. The debate continues regarding how far the sustainability policy of firms should extend. Several approaches to measuring the sustainability performance of organizations and regions have been proposed. These include the Pressure-StateResponse model, the Ecological Footprint and Barometer of Sustainability approaches, the Environmental Sustainability Index, and others. Phillis et al. (2010) have provided an overview of these approaches. Vollenbroek discussed sustainable development from a process-oriented point of view and suggested that innovation would be an effective tool for sustainable development in the future (Vollenbroek, 2002). Tsolas (2008) proposed the use of DEA to derive composite environmental sustainability indicators. Singh et al. (2007) developed a method of creating a composite sustainability performance index (CSPI) for steel industries. The CSPI consists of five dimensions: the economic, environmental, societal, and technological dimensions and that of organizational governance. The weight of each indicator at various levels is determined using the analytic hierarchy process. Azapagic (2004) presented a framework for identifying sustainability indicators for the mining and minerals industry. Krajnc and Glavic (2005) quantitatively compared the economic, environmental, and social indicators, constructing an integrated assessment of sustainable development among countries. Most researchers have used the environmental performance of firms as the measurement of corporate sustainability performance (Möller and Schaltegger, 2005; Figge and Hahn, 2005; Wagner, 2005). Some authors have partially focused on corporate social responsibility within a specific industry such as banking and financial services (Weber, 2005) or chemical manufacturing (Parthasarathy et al., 2005). Several studies have discussed the relationship between a firm’s economic performance and its environmental performance (Stanwick and Stanwick, 1998; Russo and Fouts, 1997; Fogler and Nutt, 1975) or the influence of stakeholders as part of the social dimension (Litz, 1996; Sturdivant and Ginter, 1977). Putzhuber and Hasenauer (2010) identified 15 impact indicators covering economic, social and environmental impacts using public data provided by the Austrian government. The authors suggested that the available data indicate that particular impact indicators exist within specific regions. Olsthoorn et al. (2001) proposed that environmental performance can be measured based on the interaction between business and

the environment. They also suggested that the comparability of environmental data may be increased through standardization. Phillis et al. (2011) developed a model for measuring sustainability called SAFE that includes 75 indicators. Using this model, 128 countries were assessed and ranked. The results revealed that Switzerland and Sweden scored the highest and Mauritania and Sudan the lowest. Nevertheless, few studies have presented empirical profiles of industry-level corporate sustainability or assessed industry-level changes in sustainability over time. Some researchers have suggested developing sustainability accounting systems from the stakeholder point of view (Perrini and Tencati, 2006), arguing that firms should focus on their relationships with their stakeholders rather than with society as a whole (Clarkson, 1995). It is suggested that stakeholder management may improve shareholder value, whereas firm involvement in social issues is negatively associated with shareholder value (Hillman and Keim, 2001). In contrast, another study reported that social rewards can compensate firms for disadvantages in terms of harvest profits (Osés-Eraso and Viladrich-Grau, 2007). However, the impacts of stakeholders on firms may differ based on the character of the industry and the environment. Firms may adopt different types of sustainability practices and focus more on the intermediate phase of sustainability, recirculating materials and redesigning processes (Sharma and Henriques, 2005). An empirical overview of how industries approach corporate sustainability performance may “bring the market-oriented nature and the social nature of business corporations into harmony on a higher plane, within the context of the times.” (Lazonic, W., 1999). Therefore, in this paper, we provide an industry-level evaluation of corporate sustainability performance. Previous studies suggest that the process of identifying individual indicators varies across research fields even when the process is clearly robust based on the theory, the empirical analysis and practical considerations. This research explores corporate sustainability performance at the industry-level across multiple industries to better characterize the institutional effects of fundamental firm policy. Given that the aim of sustainable development is to achieve a compromise between economic progress and the protection of the environment, this study employs a composite indicator for corporate sustainability that consists of three pillars: the economic, environmental and social dimensions of performance. This composite indicator was constructed within a coherent framework by the Dow Jones Sustainability Group Index (DJSGI), and each dimension includes multiple factors. The DJSGI identifies and tracks firms’ sustainability performance, particularly in the technology sector. The DJSGI has been validated by the interest of investors and particular stakeholders in recent years, and it is agreed upon that this index is not plagued by the problems that emerge from a lack of information regarding certain individual indicators (Knoepfel, 2001). 3. Research methods 3.1. Data envelopment analysis DEA (Data Envelopment Analysis) has long been used to evaluate the efficiency of decision-making units (DMUs) since Charnes et al. (1978) introduced it three decades ago. It has been widely used to assess the performance of organizations, such as banks, hospitals, schools, factories and economies. DEA is a mathematical linear programming methodology that measures the relative efficiency of a homogeneous set of decision-making units’ resource input and production output. As a nonparametric approach, DEA can be used to measure relative efficiency and gauge productivity without requiring the production function to take a specific mathematical form. It can also be used to analyze

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non-standard types of data, such as categorical and imprecise data, to measure eco-efficiency (Dyckhoff and Allen, 2001). Kuosmanen and Kortelainen (2005) use DEA to aggregate multiple factors affecting sustainability performance into a composite indicator, reviewing performance at the industrylevel rather than at the level of the individual firm. Hence, they reveal industry-level changes in corporate sustainability performance over time. The DEA Malmquist productivity index has been widely used to measure changes in industry performance over time and can provide a justification for changes in strategy (Chen and Ali, 2003). The Malmquest index can evaluate the productivity change of a decision-making unit between two periods of time. The projected sustainability scores of all 311 DMUs were used in a distribution test prior to the analysis of the Malmquest index, which made it possible to observe the changes in corporate sustainability productivity in each industry over time. A general multi-attribute decision-making model was used in this paper to construct an aggregated indicator for ranking DMUs, establishing an input value of one for every DMU (Bernroider and Stix, 2005). The DEA Excel Solver program (Zhu, 2003) was used with the basic DEA model proposed by Charnes et al. (1978) for measuring each DMU’s sustainability based on output. 3.2. Data and variables This paper uses the same dataset detailing the sustainability performance of 311 firms worldwide that was used to verify the effects of corporate sustainability management on financial performance (Chang and Kuo, 2008). However, this paper does not discuss financial considerations. Our main goal is to compare various industries in terms of their sustainability performance. A composite sustainability index for each industry is generated that indicates relative efficiency by applying DEA to the original scores for each firm’s sustainability performance as evaluated by SAM. The validity of this approach was verified in our previous study. This study divides the 311 firms into 16 industrial sectors according to the North America Industrial Code Standard 2002 (NAICS 2002). Table 1 shows the 16 industrial sectors; the firm is seen as the decision-making unit (DMU) in each industrial sector. The dataset covers three consecutive years of sustainability scores (scores from 2003 to 2005) for all 311 DMUs supplied by SAM. The data indicate sustainability performance in terms of economic, environmental and social dimensions. A non-disclosure agreement limited the available data to those from the period from 2003 to 2005. In the original framework of sustainability performance designed by SAM, the sustainability performance indicator contains the three dimensions of economic, environmental, and social responsibility, and each dimension is comprised of three factors. Table 2 identifies the 9 factors as the output variables for this study. Code of conduct (CC), corporate governance (CG) and investor relations (IR) represent the economic dimension of corporate sustainability; environmental performance (EE), environmental policy (EP), and environmental reporting (ER) represent the environmental dimension; and the social dimension consists of labor practice indicators (LPI), human capital development (HC) and talent attraction and retention (TA). Industry-specific codes of conduct (CC) are believed to improve stakeholder integrity and eliminate inefficacy. The objective of a code of conduct is to help ensure that economic activities support fair trade and improve trust between producers, buyers, and sellers in the market. Thus, codes of conduct have been selected as a measure of corporate sustainability. Given the important role of corporate governance in achieving continual business growth,

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Table 1 Industrial category of the 311 DMUs. Industrial category of NAICS 2002

Industrial code

DMU quantity

Agriculture, forestry, fishing and hunting Mining Utilities Construction Manufacturing (1) includes food, textile, apparel, leather Manufacturing (2) includes wood, paper, printing, petroleum and coal, chemical, plastic and rubber, nonmetallic mineral Manufacturing (3) includes metal, machinery, computer and electronic, electrical equipments appliance, components, transportation equipments, furniture related, miscellaneous Retail trade Transportation and warehousing Information Finance and insurance Real estate and rental and leasing Professional, scientific and technical services Health care and social assistance Arts, entertainment, and recreation Accommodation and food services Total DMU quantity:

11 21 22 23 31

6 11 23 5 16

32

32

33

89

44 48 51 52 53 54 62 71 72

18 13 22 37 9 14 5 4 4 311

many researchers incorporate corporate governance into their models for assessing sustainability (e.g., Singh et al., 2007). In practice, the mode of corporate governance (CG) has shifted from top-down decision models to participative management systems, as governments have become increasingly democratic. Thus, firms must obtain access and legitimate their business operations within their society and their community of stakeholders. Firm interaction with stakeholders varies significantly across industrial contexts. Investor relationships (IRs) are defined as long-term interactive relationships between companies and their private and institutional investors in capital markets (Tuominen, 1999). As an external resource, an IR may create value by lowering the cost of capital (Botosan, 2006) or increasing stock liquidity (Healy et al., 1999) by integrating financing, communications, and marketing. A successful IR may help to increase economic gains and assure sustainability. Environmental performance (EE) is the interaction between the firm and the environment, which determines the environmental effects of production and consumption. For example, resource consumption, pollution emissions, and health impacts are facets of environmental performance. Many researchers argue that a positive relationship exists between environmental policy (EP) and

Table 2 Output variables of corporate sustainability. Dimension

Assessment indicator

Abbreviation code

Economic

Code of conduct/Compliance/Corruption Corporate Governance Investor Relations Environmental performance (Eco-Efficiency) Environmental Policy/Management Environmental Reporting Labor practice indicators Human capital development Talent attraction and retention

CC CG IR EE

Environmental

Social

EP ER LPI HC TA

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Table 3 Descriptive statistics of industrial targeted scores. Industrial code

Year

11

2003 2004 2005

21

2003 2004 2005

22

2003 2004 2005

23

2003 2004 2005

31

2003 2004 2005

32

2003 2004 2005

33

2003 2004 2005

44

2003 2004 2005

48

2003 2004 2005

51

2003 2004 2005

52

2003 2004 2005

53

2003 2004 2005

Descriptive statistics

Economic CC

CG

IR

Environment EE

EP

ER

Social LPI

HC

TA

mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd.

60.60 15.54 61.38 8.20 57.82 11.83 55.63 15.04 67.05 9.88 71.48 12.52 62.87 11.75 62.35 8.54 69.57 10.06 46.73 9.41 48.02 12.11 57.04 12.10 58.96 14.46 62.94 14.35 65.93 11.46 56.53 12.50 64.41 9.15 71.44 9.03 61.38 12.57 63.68 9.89 70.74 7.19 57.47 15.91 59.14 19.64 70.00 5.35 57.21 15.61 61.06 8.99 57.15 10.45 59.27 16.35 67.02 11.72 65.08 12.35 59.64 12.92 62.82 14.62 65.63 12.85 58.80 17.67 68.25 6.56 64.55 11.41

64.73 7.99 67.52 9.70 70.23 15.23 68.87 13.74 75.32 9.47 78.78 7.10 71.21 14.16 65.95 12.78 77.03 13.09 51.79 18.25 41.14 25.35 73.48 9.96 70.11 9.40 65.10 6.13 76.18 6.40 68.27 9.21 75.50 7.51 72.78 10.18 67.52 11.15 65.34 12.00 72.12 11.04 64.56 8.09 68.70 9.04 76.23 9.99 62.65 10.68 58.57 11.90 67.32 13.69 64.90 10.24 75.76 7.58 75.05 17.14 67.70 9.06 75.94 8.47 75.75 8.26 58.42 20.62 57.38 14.17 69.46 11.99

46.93 28.13 56.08 21.63 39.48 32.78 59.16 31.63 62.12 29.47 64.57 24.51 51.64 21.76 58.54 21.89 59.94 28.05 22.53 28.00 50.50 29.50 26.80 27.76 31.88 21.19 38.77 18.21 58.59 16.60 59.76 22.92 64.81 20.28 69.15 19.34 37.51 20.91 63.25 14.45 54.70 20.95 25.90 17.42 41.87 16.35 65.15 23.56 57.72 19.54 72.69 16.18 34.61 22.83 52.16 21.57 74.68 14.84 51.28 29.10 43.16 19.59 68.18 17.50 62.95 17.65 44.43 13.72 56.49 14.34 39.63 18.36

49.37 28.94 61.63 36.12 53.43 36.84 59.13 37.69 72.08 31.16 56.89 35.97 59.20 28.13 48.29 31.97 60.64 32.82 11.01 24.59 16.00 22.54 8.96 14.19 39.99 37.95 44.12 32.10 65.67 25.95 59.51 31.80 77.22 22.28 66.47 16.24 53.92 29.61 62.91 21.39 57.22 21.26 27.94 28.45 41.82 28.63 65.13 23.70 51.29 26.96 45.63 19.05 33.51 33.73 56.93 36.29 58.51 33.49 40.81 34.18 29.30 21.56 44.86 20.19 44.17 30.91 20.56 25.42 29.66 27.07 53.10 38.85

75.10 18.79 68.00 16.28 69.58 24.89 79.73 12.39 79.59 13.22 81.04 22.90 81.27 15.10 78.10 12.24 80.46 13.68 56.96 30.15 51.29 40.11 55.42 25.91 73.54 14.82 72.91 14.10 78.95 9.91 80.00 14.01 78.35 17.08 87.77 7.04 67.26 18.07 88.04 8.37 89.67 8.71 60.32 15.68 65.05 14.61 87.97 7.05 78.68 16.43 81.69 16.31 73.66 22.87 73.01 18.11 74.32 20.89 75.11 18.05 66.55 18.86 71.92 17.50 70.91 19.68 76.54 10.87 74.22 9.02 74.00 12.86

41.76 37.66 70.00 24.73 62.58 39.63 72.60 34.26 81.26 29.11 63.59 36.21 66.86 31.23 69.35 28.67 85.74 17.60 32.76 42.79 33.60 46.00 37.70 35.41 63.61 21.93 65.39 33.22 85.99 12.41 70.64 26.40 79.98 24.56 79.38 21.30 50.58 27.28 84.67 12.57 85.03 19.62 37.15 32.57 58.91 26.84 94.54 16.58 53.85 32.02 79.94 20.98 65.11 37.38 79.22 22.28 75.23 21.01 69.08 35.96 60.14 34.90 64.55 29.35 64.65 31.22 50.00 25.00 57.78 20.57 76.82 24.82

59.14 16.10 68.78 14.82 62.57 12.14 67.74 17.61 72.41 14.86 71.69 15.74 65.52 14.77 61.50 12.33 72.16 12.32 38.73 11.12 36.21 20.55 55.36 13.89 55.25 15.92 57.13 13.30 74.85 9.53 60.91 16.50 72.86 9.66 74.85 11.06 57.67 15.83 63.01 7.51 76.86 8.62 55.90 11.40 57.68 15.47 76.33 11.60 65.23 15.53 64.70 8.16 61.37 14.66 58.16 18.63 66.26 11.39 70.85 16.53 57.63 13.90 67.96 14.57 68.69 14.90 58.67 11.11 53.34 9.93 60.52 13.17

54.40 32.12 60.75 22.73 31.78 21.01 66.21 22.41 73.06 22.61 41.34 15.11 59.89 13.64 60.10 14.30 44.67 22.31 36.23 21.60 46.50 32.84 18.20 4.54 46.10 29.23 57.31 23.93 41.96 15.44 69.81 15.42 68.49 23.04 66.09 18.42 60.54 27.51 81.84 21.90 50.55 17.46 46.25 25.37 58.11 26.62 56.43 16.39 63.60 18.47 72.68 12.78 31.95 30.66 56.21 23.77 65.64 21.74 48.90 29.61 55.23 26.57 63.00 20.99 45.48 26.03 56.75 17.85 67.86 12.78 37.07 17.04

48.22 13.87 47.60 10.72 39.82 16.06 54.40 8.42 55.88 8.91 62.79 15.11 51.94 9.13 49.78 11.24 55.61 15.84 36.69 13.62 36.52 24.83 29.28 3.22 43.07 11.78 47.98 11.25 57.16 14.29 46.55 12.20 54.91 6.99 58.51 9.67 46.06 11.59 55.67 8.82 58.71 10.71 38.68 8.82 43.04 9.00 59.81 8.55 45.48 12.44 50.84 4.68 42.87 16.24 44.21 13.55 57.95 9.73 50.43 18.38 52.97 16.16 58.04 13.49 51.94 13.88 43.79 10.87 49.53 7.80 42.36 14.91

D.-S. Chang et al. / Journal of Cleaner Production 56 (2013) 147e155

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Table 3 (continued ) Industrial code

Year

54

2003 2004 2005

62

2003 2004 2005

71

2003 2004 2005

72

2003 2004 2005

Descriptive statistics

Economic CC

CG

IR

Environment EE

EP

ER

LPI

HC

TA

mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd. mean sd.

48.33 20.71 58.52 11.89 65.52 10.99 53.49 12.46 41.14 23.71 65.12 12.04 50.15 23.64 58.45 4.36 59.75 15.49 55.13 12.34 56.76 15.69 59.29 9.75

62.25 11.21 70.32 12.11 78.69 8.68 65.60 10.88 58.72 7.47 78.94 10.51 52.56 16.14 59.09 9.31 72.33 9.16 58.93 4.20 67.60 5.52 74.93 10.59

22.75 17.73 57.31 12.56 43.40 28.87 32.95 28.84 31.10 23.03 35.00 35.17 53.55 23.76 70.38 10.63 53.50 42.81 35.85 11.98 61.00 15.60 20.86 35.51

23.83 28.90 31.54 31.90 48.85 27.17 8.01 17.88 14.00 31.30 16.00 35.77 53.81 29.16 53.70 36.35 38.75 45.16 0.00 0.00 10.00 26.45 26.04 39.89

48.64 20.08 52.11 18.65 69.99 14.93 59.32 27.01 44.97 28.94 52.30 31.15 73.24 17.10 83.89 8.64 73.29 30.24 53.91 17.26 67.60 17.40 54.87 28.53

23.15 32.41 24.68 31.03 65.34 31.80 10.01 22.35 16.80 37.56 31.00 45.33 75.00 28.86 73.38 10.43 70.00 47.60 14.29 24.39 58.21 31.04 32.86 44.98

49.52 10.34 52.36 9.62 68.82 11.41 51.88 7.09 51.89 4.93 58.23 12.45 50.43 14.89 55.44 5.46 55.08 10.27 50.00 9.83 53.26 10.79 61.95 12.24

47.85 29.05 51.13 29.82 28.70 15.63 34.00 14.47 45.20 14.25 27.00 29.27 59.50 25.89 62.10 24.73 23.50 27.14 53.79 15.18 57.57 14.40 14.14 18.36

36.22 10.28 43.64 9.36 47.49 19.49 41.86 9.17 44.57 5.86 35.81 17.44 43.31 16.30 47.04 12.77 46.91 19.86 38.71 8.03 48.48 9.23 39.65 20.61

innovation in general (see Jaffe et al., 2002 for an overview). Environmental policy instruments (e.g., emissions charges, permits, and standards) may motivate firms to increase their innovation (Rennings et al., 2004). The diffusion of innovative green technology can increase both economic and environmental gains. Environmental reporting (ER) helps firms to enhance their social responsibility. Firms are required to provide environmental accounting and financial statements through which they report and disclose their environmental data. Such environmental reports need to disclose information about environmental performance as assessed by the firm for public reference. Increasing numbers of stakeholders from various groups in society are urging firms to reveal information about their environmental performance and environmental practices because this information can provide help for them to decide whether to invest or lend funds and whether to purchase an organization’s products. Developed countries have adopted extensive labor practices by developing labor policies intended to protect labor, including workers’ compensation insurance, pensions, disability insurance, health insurance, unemployment insurance, on-the-job training, and bargaining. The labor practice indicator (LPI) is used to measure the level of labor protection. The LPI may allow the public to assess a firm’s social accountability with respect to the environment. Human capital (HC) can be created through productive consumption via investments in education and health, which may increase productivity and contribute positively to economic growth (Steger, 2002). Education helps to create human capital and disseminate common norms and regulations that increase social cohesion and enhance the formation of social capital, which will eventually contribute to economic growth (Gradstein and Justman, 2000). Many researchers argue that human capital in practice is helpful to aggregate production (Lucas, 1988) and thus that human capital development is an element of sustainability performance. Talent plays a vital role in supporting global operations (Scullion et al., 2008), and thus, an appropriate policy is required to retain talented workers and to recover the difficulty of talent labor mobilization. The best practices for attracting and retaining talent (TA) have been examined by many researchers (e.g., Tymon et al.,

Social

2010; Bhatnagar, 2007), as they are critical to firm success and sustainable development. The DEA approach aims to measure relative efficiency across DMUs. In this paper, the desired goals are the input variables, whereas the actual performance required to achieve the desired goals is the output variable (Cooper et al., 2000). Because corporate sustainability performance is a goal of all firms, the value of the input variable is identical for all firms. Furthermore, because all DMUs have the same input value, the actual sustainability scores are completely determined by the output variables. First, a set of target sustainability scores for all firms within each industrial sector is selected. All inefficient DMUs are eliminated. The second step is to assess the efficiency of all 311 firms and then determine the average efficiency score for each industry during the three-year interval. Table 3 presents the descriptive statistics based on the projected target scores and original scores for the 9 factors for all 311 DMUs across the 16 industry sectors over the three consecutive years from 2003 to 2005. The changes in corporate sustainability performance at the industry-level are also examined over time using the outputoriented DEA-based Malmquist productivity index (MPI). The DEA-based Malmquist productivity index (MPI) was developed by Färe et al. (1992) to measure cross-interval efficiency. In creating this index, the researchers extended the Malmquist productivity index proposed by Cave et al. (1982a, b). In this study, the Malmquist productivity index is used to measure changes in efficiency and changes in the technology frontier (Färe et al., 1992). The authors specify that an index value greater than one indicates a productivity gain, an index value smaller than one represents a productivity loss, and an index value equal to one indicates no change in productivity over time. To estimate the MPI index of each industry, the technical and scale efficiency (TSE), catch-up efficiency (CIE), and Malmquist index (MI) must be estimated using the DEA Excel Solver program. The formula for the catch-up effect from one period to a later period and the Malmquist index are defined by Cooper et al. (2007), and the Malmquist productivity index (MPI) is defined by Coelli et al. (1998). An MPI score of unity indicates that there has been no change in productivity over time. If the MPI score

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is greater than unity, a productivity gain has occurred. Otherwise, a productivity loss has occurred. 4. Results and discussion 4.1. Testing the effect of industry on sustainability performance The projected target scores are derived from the original scores for all 311 DMUs and assigned to categories representing 16 industries. It is necessary to verify whether the 16 independent groups are significantly different or whether random samples from the same population are simply different. To test the effect of the projected scores for all 311 firms, we used a KruskaleWallis oneway analysis of variance by rank, a nonparametric statistical method that does not assume a normal population. This method was used to examine the differences between the performance scores of the different industries (Siegel and Castellan, 1988). We conducted the KruskaleWallis-Test with the projected efficiency scores of the 311 DMUs for all variables for the years 2003, 2004 and 2005 using the Statistica Release 6 program. The results show significant variance between the different groups for the year 2003 (H (15, N ¼ 311) ¼ 28.1215. p ¼ 0.0208), and the median test also indicates significant variance (c2 ¼ 31.9871, p ¼ 0.0065) for that year and for 2004 (H (15, N ¼ 311) ¼ 92.4193, p ¼ 0.000, c2 ¼ 103.404, p ¼ 0.000) The 2005 projected scores display significant variance between the groups (H (15, N ¼ 311) ¼ 38.7953, p ¼ 0.0012), but the median test displays insignificant variance among the overall median scores (c2 ¼ 24.3239, p ¼ 0.0827). These results may indicate that the efficiency frontier shifted with time so that approximately 50% of all cases in each group fell above the median. 4.2. Comparison among industrial sectors The results of our analysis demonstrate that 7 industries constantly improved their sustainability performance over the 3year period in terms of technical and scale efficiency (TSE). These industries are the construction (Code 23), manufacturing 2 (Code 32), retail trade (Code 44), information (Code 51), finance and insurance (Code 52), professional scientific and technical service (Code 54) and accommodation and food service (Code 72) industries. The average sustainability efficiency for each industry for the three consecutive years is indicated in Fig. 1, which provides an overview of these 16 industries.

Table 4 lists the technical and scale efficiency figures for 2003 (TSE1), 2004 (TSE2) and 2005 (TSE3), the catch-up efficiency figures (CIE), the Malmquist index (MI), and the Malmquist productivity index. These figures help to indicate the changes in productivity improvement. The figures for catch-up efficiency (CIE) reveal the degree of improvement in technical efficiency from period one to period two (CIE1 / 2) and from period one to period three (CIE1 / 3). The Malmquist index (MI) reflects the degree of change in production technology from one period to the next or from period one to period three, and the Malmquist productivity index (MPI) shows the overall change in the degree of productivity from one period to the next or from period one to period three. The technical and scale efficiency figures for 2005 (TSE3) and the Malmquist productivity index from 2003 to 2005 (MPI31) have been plotted to produce a relative position chart representing the changes that took place in the 16 industries in Fig. 2. The results of the Malmquist estimation reveal that the accommodation and food services (Code 72), retail trade (Code 44) and construction (Code 23) industries rank the highest for productivity improvement over the three-year period, whereas the industries with the least improvement in productivity are transportation and warehousing (Code 48), real estate, rental and leasing (Code 53) and arts, entertainment and recreation (Code 71). The top three industries dramatically improved their sustainability performance, with the accommodation and food services industry exhibiting the most drastic change since 2003 (MPI31 ¼ 1.22047); the construction industry is in third place in terms of the degree of change. According to Jones et al. (2006), the intrinsic business environment of the construction industry makes certain key performance indicators less helpful, and that industry exhibits low participation rates in general benchmarking exercises in the UK construction industry. Therefore, there are fewer mechanisms for assessing the corporate social performance of the construction industry. This may explain why the construction industry performed the worst in 2003 but made significant improvements in 2005. Although all of the top three industries, including the accommodation and food services industry (code 72, TSE ¼ 0.767), the construction industry (code 23, TSE ¼ 0.756) and the retail trade (code 44, TSE ¼ 0.857) industry, exhibited relatively lower sustainability efficiency performance in 2003, they made significant progress in this regard within three years (code 72 MPI31 ¼ 1.2204, code 23 MPI31 ¼ 1.1738, code 44 MPI31 ¼ 1.2014). Sustainability policies may be new to these industries, which would

Three Years' Industrial Average Sustainability Performance

Relative Efficiency

1.0000 0.9500 0.9000 0.8500 0.8000 0.7500 11

21 22 23 2

31

32 33 4 48 3 444

51 52

53

Industry Year Y Ye a 03 ar

Y Ye Year a 04 ar

Year 05

Fig. 1. Three Year’s industrial average sustainability performance.

54

62 71

72

D.-S. Chang et al. / Journal of Cleaner Production 56 (2013) 147e155

153

Table 4 Industrial-level of corporate sustainability performance and change over three years of period (2003e2005). / 2

CODE

TSE1

TSE2

TSE3

CIE1

11 21 22 23 31 32 33 44 48 51 52 53 54 62 71 72

0.9362 0.9490 0.9181 0.7563 0.9027 0.9208 0.9113 0.8579 0.9477 0.8884 0.8796 0.8748 0.8146 0.8431 0.9133 0.7677

0.91992 0.95954 0.89850 0.79848 0.89386 0.95009 0.98046 0.86859 0.94341 0.92749 0.90448 0.86621 0.86580 0.78147 0.89830 0.87377

0.94108 0.94186 0.94185 0.85314 0.94931 0.95868 0.96149 0.99304 0.93517 0.93718 0.91756 0.86835 0.91227 0.90531 0.90107 0.90395

0.9826 1.0111 0.9787 1.0558 0.9902 1.0318 1.0759 1.0124 0.9955 1.0441 1.0283 0.9902 1.0628 0.9269 0.9836 1.1381

explain why they did not perform as well earlier but showed improvements later; learning effects would have been a factor in this regard. The Malmquist figures show that all 16 industries made significant progress from 2003 to 2005. Of these 16 industries, 7 industries increased their TSE constantly from 2003 to 2005; the manufacturing (2) industry (code 32) performed the best, with TSE scores falling between 0.90 and 0.95. Furthermore, the manufacturing (2) industry (code 32) constantly improved its sustainability performance, whereas the other industries made less steady progress during the observed interval. Because the manufacturing (2) industry (code 32) relies on natural resources such as wood, paper, plastic and rubber to support its production process, it is associated with the risk of environmental pollution. This industry has faced strict institutional regulations and the environmental concerns of stakeholders. Hence, it may be more aggressive in adapting a value-oriented corporate environmental

CIE1

/ 3

1.0052 0.9925 1.0259 1.1280 1.0516 1.0411 1.0551 1.1575 0.9868 1.0550 1.0431 0.9926 1.1199 1.0738 0.9866 1.1774

MI21

MI31

MPI21

MPI31

0.97836 0.98007 0.98792 0.98287 0.99067 0.97618 0.97737 0.98165 0.98829 0.96957 0.96678 0.97193 0.98010 0.96979 0.97713 0.97826

1.02827 1.04046 1.02338 1.04062 1.03139 1.03243 1.03218 1.03797 1.02131 1.03138 1.03538 1.02851 1.03915 1.05866 1.03550 1.03657

0.96134 0.99094 0.96684 1.03768 0.98092 1.00723 1.05159 0.99384 0.98385 1.01229 0.99411 0.96239 1.04168 0.89891 0.96108 1.11336

1.03362 1.03262 1.04986 1.17386 1.08459 1.07491 1.08908 1.20141 1.00784 1.08807 1.08004 1.02093 1.16372 1.13678 1.02163 1.22047

strategy and more likely to achieve positive environmental and economic performance (Wagner and Schaltegger, 2004). Stead et al. (1998) surveyed the institutional environmental performance of US industries and found a similar pattern: highly polluting industries in general must strive to improve firm-level environmental performance. In contrast, the transportation and warehousing industry (code 48) made the least progress of the 16 industries with MPI index values of MPI21 ¼ 0.9838 and MPI31 ¼ 1.0078. Because the production process in the transportation and warehousing industry (code 48) involves a lower risk of environmental pollution than is the case in industries involving the consumption of natural resources, the former industry may focus less attention on corporate sustainability management. The low sustainability performance achieved by the transportation and warehousing industry indicates that it may be the slowest to pursue sustainability management strategies.

Fig. 2. Positioning of industrial sustainability performance and change over time.

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5. Conclusion This study examines corporate sustainability performance at the industry-level and ranks the performance of 16 industries with respect to corporate sustainability during a particular three-year period. Sustainability performance reporting has been emphasized by firms during recent decades because stakeholders have increasingly demanded improvements in corporate sustainability performance. However, the argument has been made that simply providing sustainability information is not sufficient for firms and thus that firm-level concerns regarding sustainability may be more rhetorical than real (Aras and Crowther, 2009). An external auditing mechanism or regulation policy for each industry should play a leading role in motivating firms to be innovative (Porter and van der Linde, 1995). Innovation not only ameliorates the effects of industrial regulations but also transforms the competitive landscape. Furthermore, innovation may prompt firms to change the way in which they conduct their business, pushing them to become more environmentally friendly (Nidumolu et al., 2009). Based on the industrial improvements in corporate sustainability performance, we propose that industrial regulations and policy initiatives may promote sustainable development in the supply chain and that external guidelines and auditing schemes should be designed for each industry based on its particular context. This study describes industrial-level corporate sustainability performance and provides a macro overview of the current situation. The changes observed in these industries provide abundant information for policymakers seeking to design regulations and policies that may prompt firms to be innovative in pursuing sustainable development by changing their business practices and increasing environmental conservation and social responsibility. This paper has refined the arguments in support of corporate sustainability and may affect stakeholder theory, institutional theory and hierarchical demand theory. This paper provides information that was not previously available regarding the significant differences between different industries with regard to their sustainability management practices. We report that the sustainability performance of firms in the natural resource sector is greater than that of other industries in terms of its stability and ecological benefits. However, the industry has most likely achieved these results by borrowing energy resources from outside the system (Voinov, 2008). Therefore, the firms that rely on natural resources must build sustainable business plans to achieve real sustainability. The stringent institutional regulations and supervision enforced by stakeholders provides an external motivation for firms in the natural resource sector to develop their sustainability. These empirical findings address deficits in the relevant theory (Alvesson and Kärreman, 2007) that has been developed during the past four decades of social movement and industrial change. One shortcoming of this study is that a three-year interval is not sufficiently long to fully observe sustainable development; this interval should be extended if the necessary datasets are available. This particular paper did not use a longer interval of observation because the use of the dataset was limited by a non-disclosure agreement. Therefore, the value of such observations will be strengthened by the use of datasets that will be accessible in the future. Follow-up studies should thereby be able to further advance our understanding of corporate sustainability performance at the industry-level. References Alvesson, M., Kärreman, D., 2007. Constructing mystery: empirical matters in theory development. Academy of Management Review 32 (4), 1265e1281. Aras, G., Crowther, D., 2009. Corporate sustainability reporting: a study in disingenuity? Journal of Business Ethics 87, 279e288. Azapagic, A., 2004. Developing a framework for sustainable development indicators for the mining and minerals industry. Journal of Cleaner Production 12, 639e662.

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