Pressures affecting green supply chain performance

Management Decision Pressures affecting green supply chain performance M. Lee Sang Sung Rha Jin Choi Donghyun Noh Yonghwi

Article information: To cite this document: M. Lee Sang Sung Rha Jin Choi Donghyun Noh Yonghwi , (2013),"Pressures affecting green supply chain performance", Management Decision, Vol. 51 Iss 8 pp. 1753 - 1768 Permanent link to this document: http://dx.doi.org/10.1108/MD-12-2012-0841 Downloaded on: 24 July 2015, At: 07:56 (PT) References: this document contains references to 75 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 1676 times since 2013*

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Users who downloaded this article also downloaded: Kenneth W. Green, Pamela J. Zelbst, Jeramy Meacham, Vikram S. Bhadauria, (2012),"Green supply chain management practices: impact on performance", Supply Chain Management: An International Journal, Vol. 17 Iss 3 pp. 290-305 http:// dx.doi.org/10.1108/13598541211227126 Tritos Laosirihongthong, Dotun Adebanjo, Keah Choon Tan, (2013),"Green supply chain management practices and performance", Industrial Management & Data Systems, Vol. 113 Iss 8 pp. 1088-1109 http://dx.doi.org/10.1108/ IMDS-04-2013-0164 Sang M. Lee, Sung Tae Kim, Donghyun Choi, (2012),"Green supply chain management and organizational performance", Industrial Management & Data Systems, Vol. 112 Iss 8 pp. 1148-1180 http://dx.doi.org/10.1108/02635571211264609

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Pressures affecting green supply chain performance

Pressures affecting GSC performance

Sang M. Lee and Jin Sung Rha Department of Management, University of Nebraska-Lincoln, Lincoln, Nebraska, USA

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Donghyun Choi Department of Management, Kansas State University, Manhattan, Kansas, USA, and

Yonghwi Noh Downloaded by UFPB At 07:56 24 July 2015 (PT)

College of Business Administration, Myongji University, Seoul, South Korea Abstract Purpose – This study aims to empirically investigate the relationship between the directions of pressures affecting green supply chain management (GSCM) and supply chain (SC) performance. Design/methodology/approach – This research is based on a survey because there is no archival database providing detailed information on GSC practices and performance. A survey questionnaire was developed to collect research data, based on the GSC literature and a pilot study in the field. The authors developed hypotheses based on two theories: institutional theory and the resource-based view (RBV). Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to test the hypotheses with SPSS (16.0) and AMOS (5.0). Findings – The results confirmed the importance of implementing environmental SC practices to sustain organizations’ competitive advantage and performance. Increased SC flexibility helped reduce resources, through decreased cost factors and improved output. Enterprises with environmental SC policies tended to increase SC flexibility and, hence, enhanced profits. Originality/value – This paper applies organizational theories to GSCM by extending the institutional-versus-internal-pressure construct to the way enterprises implement GSC strategies for sustainable SC. Keywords Green supply chain management, Supply chain flexibility, Supply chain performance, Supply chain management, Environmental management Paper type Research paper

1. Introduction Supply chain management (SCM) plays a central role in the firm’s global competitiveness. A supply chain (SC) is a network of buyers and suppliers (Choi and Hong, 2002) which focuses on how a firm coordinates its partner organizations’ processes, technology, and capabilities to improve its competitive advantage (Farley, 1997). Previous studies on SCM focused on diverse topics including inventory control, risk management, sustainable SCM, SC network and the like. Many researchers and practitioners have attempted to find the factors that affect SCM either positively or negatively. In particular, green supply chain management (GSCM) has emerged as an important topic within the domain of sustainable SCM, which includes environmental management and producing goods or service ethically and fairly along the SC (Walker and Jones, 2012).

Management Decision Vol. 51 No. 8, 2013 pp. 1753-1768 q Emerald Group Publishing Limited 0025-1747 DOI 10.1108/MD-12-2012-0841

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GSCM is usually a part of an organization’s innovation process (Pagell and Wu, 2009). For an organization to survive and prosper in the dynamic business environment, innovation is imperative (Gupta et al., 2006; Lee et al., 2012). According to Zhu and Sarkis (2004), GSCM refers to “integrating environmental thinking into SCM, including product design, material sourcing and selection, manufacturing processes, delivery of the final product to the consumers as well as end-of-life management of the product after its useful life.” GSCM concepts cover diverse areas, including SCM participants and practices, green purchasing, integrated SCs flowing from suppliers to manufacturers, to customers, and the reverse SC (Rao and Holt, 2005; Zhu and Sarkis, 2006). GSCM is affected by various pressures, which can be categorized as external and internal factors. External factors are mostly linked to stakeholder pressure, while internal factors are related to specific business-led strategic processes (Testa and Iraldo, 2010). The impact of SCM pressures on economic and environmental performance might be differentiated based on where these pressures come from (Zhu et al., 2007). The corporate commitment to environmental issues could improve the possibility of GSCM implementation (Drumwright, 1994; Green et al., 1998). However, Maloni and Brown (2006) state that the motivation for implementing GSCM practices may come entirely from outside, if the firm’s capabilities are insufficient for launching the GSC on its own. Despite the rise of concerns about green management, few studies have investigated GSCM performance in relation to various pressures imposed on the organization for GSC. Most previous studies simply tried to find the relationship between the firm’s GSCM and its economic or environmental performance. The purpose of this study is to empirically investigate the relationship between the source of pressures affecting GSCM and SC performance. We developed hypotheses based on two theories: institutional theory and the resource-based view (RBV). In explaining corporate behavior, institutional theory suggests that external factors require organizations to pursue strategic actions because of institutional pressure (Dacin et al., 2002). RBV states that even though external pressures strongly influence organizations’ strategies, they are insufficient to drive competitive advantage (Barney, 1986). This study is organized as follows. In section 2, we briefly review the relevant literature on GSCM. Section 3 presents theoretical framework for this research and a set of hypotheses to be tested. In section 4, the research methodology is presented. Section 5 provides the results of statistical analysis for hypotheses tests. Finally, we conclude the study with a summary of study findings, contributions, limitations, and future research directions. 2. Green supply chain performance Walley and Whitehead (1994) state that many managers consider green management simply as compliance with regulations while evaluating tradeoffs between environmental and economic performance. Zhu et al. (2007) indicate that enterprises implementing GSCM in China have only slightly improved environmental and operational performance, and that GSCM practices have not resulted in any significant economic performance improvement. Yu et al. (2009) also found that there is no significant relationship between environmental management and firm performance. However, most studies that investigated green management suggest that it significantly enhances organizational performance (Molina-Azorıń et al., 2009). Some anecdotal evidence has also shown that substantial environmental management leads to


lower manufacturing costs by eliminating waste (Schmidheiny, 1992). One study indicated that environmental performance positively affected the financial performance of the firms through both increasing the market share and decreasing costs (Klassen and McLaughlin, 1996). Rao and Holt (2005) pointed out that organizations adopting GSCM in Southeast Asia ultimately enhanced both competitiveness and economic performance. As social responsibility, GSCM plays an important role in the entire SC (Testa and Iraldo, 2010). Thus, the consensus of a substantial number of researchers seems to be that GSC allows organizations to create economic value (Mefford, 2011). It is possible that the differences in various studies’ results stem from the heterogeneity of green management practices that different organizations and industries adopt in different environments (Elsayed and Paton, 2005). Klassen and McLaughlin (1996) state that environmental management performance is derived from long-term decisions. Collaboration in green management with suppliers and customers enhance manufacturing and environmental outcomes, and organizations can attain better quality and flexibility performance through such partnerships (Vachon and Klassen, 2008). Among the several motivations, financial benefit is the strongest factor that encourages enterprises to implement green strategic plans (Zhu et al., 2010).

3. Theoretical framework and hypotheses development 3.1 Institutional theory This study employs both institutional theory and RBV to develop its conceptual framework and hypotheses. Institutional theory examines the influence of external pressures on the firm (Hirsch, 1975) and how enterprises adopt policies and implement strategies that are legitimate within their organizational fields (Scott and Christensen, 1995). Organizations consider industry norms, firm tradition, and management fads, among other concerns, to formulate their strategies (Meyer and Rowan, 1977; Zucker, 1987). There are three major sources of external pressure: functional, political, and social (Oliver, 1992) which force organizations to implement appropriate strategic actions (Dacin et al., 2002). Institutional theory offers a useful research framework for the study of GSCM in respect to how external factors force firms to implement certain GSCM practices (Sarkis et al., 2010). Most developed and developing countries have by now created regulations that require firms to make green policies for their SC. Thus, firms are required to implement green strategies because of increased external pressure for sustainability (Lewis and Gertsakis, 2001) in the form of compulsory environmental regulations that are directly related to GSCM (Kilbourne, 2006). Firms should look to their external pressures and prepare countermeasures with diverse GSC practices (Darnall et al., 2008; Delmas and Toffel, 2004; Zhu et al., 2007). Such practices can positively affect environmental and economic performance, and SC agility and flexibility (Testa and Iraldo, 2010; Zhu and Sarkis, 2004). SC flexibility, which involves demand control, allows firms to react and respond quickly and effectively to requirements changes of their partners and customers (Lin et al., 2006; Swafford et al., 2008; Weber, 2002). Based on these sources, we propose the following hypothesis: H1. GSC practices based on external pressure are positively related to SC flexibility.

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According to the standard economic perspective, implementing green management policies burdens firms with increased operating costs and decreased speed in productivity improvement because green practices require firms to invest in additional facilities and processes (Zhu et al., 2007). Thus, firms’ SC resource requirement would be increased to handle the additional total cost, distribution cost, manufacturing cost, inventory cost, and also return on investment (Beamon, 1999). Thus, the following hypothesis is suggested:


H2. GSC practices based on external pressure are positively related to SC resource requirement. 3.2 Resource-based view Institutional theory does not describe why enterprises undertake different strategies or practices under the same institutional pressures, in a given marketplace and business sector (Delmas and Toffel, 2004). RBV is a theory that investigates firms’ strategies given their internal resources and capabilities. RBV implies that sustainable competitive advantage is achieved through resources that are valuable, rare, inimitable, and non-substitutable (Barney, 1991). Enterprises strive to undertake environmental SC practices to gain competitive advantage (Sharfman et al., 2009). From the green perspective, RBV is useful to investigate how firms’ resources affect internal green practices and SC performance because firms’ strategies rely on their internal competencies and abilities to sustain them (Wernerfelt, 1984). GSCM can enhance enterprises’ competitive abilities to continuously innovate and develop new products and services (Dodgson et al., 2008; Dyer and Singh, 1998; Rao and Holt, 2005) since environmental practices help increase the cooperation between players in the SC (Testa and Iraldo, 2010). In addition, these abilities allow firms to react quickly to market forces. Hart (1995) suggests the natural resource-based view (NRBV) of firms by extending RBV to include natural resource concepts. NRBV suggests that enterprises can achieve internal competency through their own basic capabilities, such as pollution prevention, product stewardship, and sustainable development. Internal green practices have also been proven to be positively associated with firms’ environmental and economic performance (Zhu and Sarkis, 2004). In addition, enterprises with the ability to implement green practices can effectively handle environmental regulations and institutional pressure (Dean and Brown, 1995). Braunscheidel and Suresh (2009) indicate that organizations with high levels of internal integration processes tend to have high levels of external integration processes and thus they enhance SC agility. Swafford et al. (2006) proved that an organization’s internal SC effort is positively associated with the degree of flexibility in manufacturing and procurement/sourcing processes of the SC. Based on the above reviewed literature, we suggest the following hypotheses: H3. GSC practices based on internal pressure are positively related to GSC practices based on external pressure. H4. GSC practices based on internal pressure are positively related to SC flexibility. H5. GSC practices based on internal pressure are positively related to SC resource requirement.


3.3 Supply chain performance Beamon (1999) summarized three measures of SC performance: resources, output, and flexibility. First, resources are associated with SC efficiency, including total cost, distribution cost, manufacturing cost, inventory cost, and return on investment. Second, output stands for the level of customer service, sales, profit, on-time deliveries, backorder/stockout, customer response time, manufacturing lead time, shipping errors, and customer complaints. Third, flexibility is defined as the ability to respond to uncertainty, which is related to volume, distribution, responsiveness, product, and/or new product flexibility. Achieving SC flexibility leads to inventory reduction, counteracting market variations efficiently, responding to consumer demand quickly, and integrating with suppliers and partners effectively (Mason et al., 2002). Organizations with agile SC are very market oriented, since they are able to communicate effectively with their suppliers, enhancing organizational performance (Swafford et al., 2008). Flexibility encourages enterprises to react with little penalty in time, effort, cost, or performance (Gerwin, 1993; Kumar et al., 2006; Upton, 1995). Thus, the process of enhancing flexibility positively impacts on overall organizational performance and enhances the range of products (Prater et al., 2001; Sanchez, 2007). In addition, flexibility reduces the cost and time required to convert resources into products and services (Sanchez, 2007), and decreases inventory and SC total costs, which in turn improve operational outputs (Harrison and New, 2002). This study is concerned with how different SC practices, arising from diverse pressures, may be linked with SC flexibility and resources, and how the flexibility and resources affect SC outputs. Thus, H6, H7, and H8 are proposed:


H6. SC flexibility is negatively related to SC resource requirement. H7. SC resources requirement is negatively related to the SC output. H8. SC flexibility is positively related to the SC output. Figure 1 presents the proposed theoretical framework on how GSC practices would affect SC performance.

Figure 1. The proposed theoretical framework

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4. Methodology 4.1 Sample and data collection For this research, data were collected in South Korea. South Korea has emerged as a major economic and manufacturing power in terms of GDP and global presence. South Korean firms such as Samsung, Hyundai, and LG have become world leaders in technology and organizational innovations (Lee and Miller, 1996; Liu and White, 2001). Social and political concerns about the environment emerged in the early 1990s when the Korean government established new regulations for green management throughout the entire supply chain (Lee, 2008). This research is based on a survey because there is no archival database which provides detailed information on GSC practices and performance. The design process for the survey questionnaire consisted of two stages. In the first stage, an extensive literature review on GSC practices and performance was conducted to ensure the content validity of the questionnaire. Then, a pilot study was conducted with 15 managers to review the preliminary questionnaire, checking for ambiguity and the appropriateness of the items. The managers were asked to review whether the measurement items were appropriate in real-world business situations. Based on suggestions received, the questionnaire was refined for clarity. A double translation protocol was used for questionnaire development. The English version of the questionnaire was translated into Korean, and then the Korean version was translated back into English by a bilingual SCM faculty. No significant difference was found. The survey instrument was administered through the Korean Logistics and Distribution Association. A number of SC managers were also contacted by mail. The survey was conducted on Qualtrics, a web-based survey system. In total, a hypertext link of questionnaires was sent to 331 SC and logistics managers of South Korean firms by e-mail. We received 157 responses, a response rate of about 47 percent, but 29 of them were incomplete and were deleted. Thus, the final sample size was 128. The sample characteristics are given in Table I. Potential non-response bias was examined by comparing early and late responses for all items using t-test (Armstrong and Overton, 1977). Since no significant differences were found between two groups, a non-response bias is not problematic. 4.2 Measures 4.2.1 Green supply chain practices. This study empirically tested the impact of GSC practices on SC performance of organizations. Whereas the existing literature discusses the diverse operationalizations of GSC practices as general and broad concepts, this study follows the work of Zhu and Sarkis (2004). They developed four categories of GSC practices: internal environmental management, external practices, investment recovery, and eco design, based on Zsidisin and Hendrick (1998). Of these four categories, we used internal environmental management to measure practices through internal pressures, and external practices to measure activities by external pressures. For each latent variable, multiple measurement items were used since they offer a greater degree of reliability than singular items. Ten items on a seven-point Likert type scale (1 ¼ very bad, 7 ¼ very good) were used for measuring GSC practices, including internal and external environmental management. 4.2.2 Supply chain performance. Beamon (1999) developed a clear and refined SC measurement system, including resource measures, output measures, and flexibility


Frequency

%

Respondents’ characteristics job title type Supply chain manager Logistics manager Sales manager Product manager Manufacture manager Others

47 30 10 8 6 27

36.7 23.4 7.8 6.3 4.7 21.1

Organizations’ characteristics industry type Manufacturing Service Electronics Construction Others

74 19 17 10 8

57.8 14.8 13.3 7.8 6.3

26 17 15 16 54

20.3 13.3 11.7 12.5 42.2

Number of employees 1 , 299 300 , 499 500 , 699 700 , 899 900 , Total respondents ¼ 128

measures, in order to reflect the inherent complexity of the typical SC. Ten items relating to GSCM performance were developed for this study. The items of SC performance results from implementing GSC practices were based on a seven-point scale (1 ¼ strong disagreement, 7 ¼ strong agreement). 5. Results Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to test the hypotheses with SPSS (16.0) and AMOS (5.0). Many researchers have chosen SEM models since the models provide a statistical approach for explicitly considering measurement error in the observed variables including both dependent and independent variables in a given model (Kline, 2010). A two-stage procedure was used for data analysis, as suggested by Anderson and Gerbing (1988). In the first stage, CFA was conducted to test the measurement models representing internal and external GSC practices, and SC performance. The measurement model was subsequently examined through confirmatory techniques. CFA, with a covariance matrix as input, and maximum likelihood estimation were performed on the entire set of items simultaneously. In the second stage, structural equation modeling (SEM) was used to examine the hypothesized structural relationships associated with GSCM performance. 5.1 Measurement model CFA was used to measure reliability and validity of the measurement of each construct. There are various measures to determine goodness of fit, such as CMIN/DF, CFI, GFI, AGFI, and RMSEA (Bentler, 1990; Bentler and Bonett, 1980; Browne and


Table I. Respondents and organizations’ characteristics

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Table II. Measurement model fit indices

Cudeck, 1992; Hu and Bentler, 1995). Overall, the CFA results of the measurement model provided a fair model fit as shown in Table II. Using CFA requires checking for convergent and discriminant validity, as well as reliability. As shown in Table III, the unidimensionality for each construct was validated since the critical ratios (CR) of the regression weights of the items exceeded 1.96 (Anderson and Gerbing, 1988). In addition, the significant factor loadings were evidences of convergent validity (Anderson and Gerbing, 1988). The AMOS output represents the t-values as CR values. The convergent validity was supported because all the CR values in Table III were statistically significant.

CMIN/DF

CFI

GFI

AGFI

RMSEA

1.732 (,3)

0.937 (.0.90)

0.839 (. 0.95)

0.785 (. 0.80)

0.076 (,0.10)

Note: Recommended value in parentheses

Constructs and item measures

Mean SD

Factor loading

CR

p-value

Practices based on internal pressure (Cronbach’s alpha ¼ 0.895, Composite reliability ¼ 0.908) IN1: Commitment of GSCM from senior managers 5.16 1.31 0.83 (fixed) ,0.001 IN2: Support for GSCM from mid-level managers 5.02 1.25 0.88 12.16 ,0.001 IN3: Cross-functional cooperation for environmental improvements 5.07 1.36 0.89 12.38 ,0.001 IN4: Total quality environmental management 5.34 1.26 0.72 9.11 ,0.001 Practices based on external pressure (Cronbach’s alpha ¼ 0.911, Composite reliability ¼ 0.899) EX1: Consideration of suppliers’ ISO14000 certification 5.02 1.35 0.86 (fixed) ,0.001 EX2: Cooperation with customer for eco-design 5.12 1.33 0.72 9.37 ,0.001 EX3: Cooperation with customers for cleaner production 4.84 1.36 0.78 10.81 ,0.001 EX4: Cooperation with customers for green packaging 4.95 1.34 0.83 11.92 ,0.001 EX5: Investment recovery (sale) of excess inventories/ materials 5.04 1.24 0.72 9.48 ,0.001 EX6: Sale of scrap and used materials 4.80 1.36 0.80 11.25 ,0.001 SC flexibility (Cronbach’s alpha ¼ 0.829, Composite reliability ¼ 0.836) F1: Ability to introduce and produce new products 4.73 1.33 F2: Ability to change the variety of products produced 5.02 1.13 F3: Ability to change planned delivery dates 4.64 1.20 F4: Ability to change the output level of products produced 4.30 1.19

0.74 0.83 0.75

6.72 ,0.001 7.21 ,0.001 8.30 ,0.001

0.67

(fixed) ,0.001

SC resource requirement (Cronbach’s alpha ¼ 0.881, Composite reliability ¼ 0.884) R1: Total cost 3.92 1.34 0.88 (fixed) ,0.001 R2: Distribution cost 3.98 1.26 0.93 12.96 ,0.001 R3: Manufacturing cost 3.77 1.25 0.73 9.66 ,0.001 Table III. Measurement model results

SC output (Cronbach’s alpha ¼ 0.902, Composite reliability ¼ 0.902) O1: Sales (total revenue) 4.82 1.21 O2: Profit (total revenue less expenses) 4.57 1.20 O3: On-time deliveries 4.36 1.21

0.91 0.84 0.85

(fixed) ,0.001 12.79 ,0.001 13.00 ,0.001

To test the constructs’ internal consistency reliability, both Cronbach’s alpha and composite reliability were calculated, as shown in Table III. If all values exceed 0.7, reliability is assured (Fornell and Larcker, 1981). Thus, the measurement showed a high level of internal consistency reliability. Discriminant validity can be checked by comparing the average variance extracted (AVE) and squared correlation between constructs (Anderson and Gerbing, 1988). As shown in Table IV, the discriminant validity was established since AVE for a construct was higher than the squared correlation between constructs.



5.2 Structural model Based on the measurement model, the complete structural model was tested with SEM. As shown in Table V, the model had satisfactory model fit results. The statistical significance of the coefficients on the paths in the model was shown in Figure 2. For the first part of the model, we tested the relationship between GSC practices and SC performance, including flexibility and resources. The coefficient on the path

Constructs

AVE

Practices based on external pressure

Practices based on external pressure Practices based on internal pressure SC flexibility SC resource requirement SC Output

0.91 0.90 0.84 0.89 0.90

1 0.738 * 0.467 * 0.475 * 0.640 *

Practices based on internal SC SC resource SC pressure flexibility requirement output 1 0.470 * 0.413 * 0.600 *

1 0.126 0.479 *

1 0.380 *

1

Note: *p , 0.01 (two-tailed); n ¼ 128

CMIN/DF 1.846 (, 3)

CFI

GFI

AGFI

RMSEA

0.926 (. 0.90)

0.829 (. 0.95)

0.774 (. 0.80)

0.082 (, 0.10)

Note: Recommended value in parentheses

Table IV. AVE and correlation

Table V. Structural model fit indices,

Figure 2. Structural equation model: significant path coefficients in the model

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between GSC external practices and supply chain flexibility had a value of 0.157, which is not statistically significant. Therefore, H1 was not supported. The coefficient on the path between external practices and SC resource requirement had a value of 0.607, which is statistically significant at the 0.001 level. Thus, the results supported H2. In addition, H3 was supported since the path between internal and external practices had a coefficient value of 0.846 and is statistically significant at the 0.001 level. Next, the coefficient on the path between internal practice and SC flexibility had a value of 0.381, which is statistically significant at the 0.01 level. This supports H4. However, H5 was not supported because the path between internal practices and SC resource requirement had a coefficient value of 0.096 which is not statistically significant. In the second part, we investigated the relationship among SC performance parameters. SC flexibility significantly and negatively affected SC resource requirement, as the coefficient value was 2 0.306, which is statistically significant at the 0.05 level. This result supported H6. Since the coefficient had a value of 0.349, which is statistically significant at the 0.001 level, SC resource requirement positively and significantly affected SC outputs, whereas we hypothesized that resource requirement would be negatively associated with the output. Therefore, H7 was not supported. The coefficient on the path between SC flexibility and SC output had a value of 0.639, which is statistically significant at the 0.001 level. Thus, H8 was supported. Table VI summarizes results of significance test for paths of the model. 6. Conclusion 6.1 Summary of findings and contributions GSCM research has thus far focused on the impact of green practices on economic and environmental performance of firms. Pressures and flexibility in establishing GSC, however, have yet to be empirically investigated. This study considers the GSC pressures from the organizational theory aspect and flexibility from the pressure aspect. Hypothesized path

Table VI. Results of significance test for paths of the model

H1: GSC practices based on external pressure ! SC flexibility (not supported) H2: GSC practices based on external pressure ! SC resource requirement (supported) H3: GSC practices based on internal pressure ! GSC practices based on external pressure practices (supported) H4: GSC practices based on internal pressure ! SC flexibility (supported) H5: GSC practices based on internal pressure ! SC resource requirement (not supported) H6: SC flexibility ! SC resource requirement (supported) H7: SC resource requirement ! SC output (not supported) H8: SC flexibility ! SC output (supported) Notes: *p , 0.05; * *p , 0.01; * * *p , 0.001

Path coefficient

SE

p-value

0.157

0.131 0.228

0.607

0.168 0.000 * * *

0.846

0.096 0.000 * * *

0.381

0.143 0.008 * *

0.096 20.306 0.349 0.639

0.181 0.146 0.075 0.113

0.596 0.036 * 0.000 * * * 0.000 * * *


This study developed a path model to investigate the relationship between SC practices from diverse pressures and SC performance. We tested and found support for the impact of GSC practices on performance parameters by showing that external pressure increased the SC costs, and that internal pressure enhanced the ability to adapt or respond to change and uncertainty. Overall, the results confirm the importance of implementing environmental SC practices to sustain organizations’ competitive advantage and performance (Rao and Holt, 2005; Vachon and Klassen, 2008; Zhu and Sarkis, 2004). In addition, we conclude that firms with demonstrated experience in internal improvement have the ability to develop advanced environmental strategies, such as GSC practices to adapt to external pressures (Rugman and Verbeke, 1998; Russo and Fouts, 1997). Contrary to expectations, internal pressure did not show increased costs. Since enterprises with internal pressure proactively manage environmental regulations, they can reduce the costs and achieve first mover advantages that are difficult to imitate (Barrett, 1992). According to Sańchez and Pe´rez (2005), mutual understanding can be created by the players in a SC which positively improves SC flexibility. Since developing GSC increases the coordination and mutual understanding (Sańchez and Pe´rez, 2005; Vachon and Klassen, 2008), this study hypothesized that external pressure can improve an organization’s flexibility. However, the result suggests that environmental institutional pressure does not guarantee increased capabilities to adapt to change and uncertainty. As expected, increased flexibility reduced resources required for SCM, including cost factors, and improved output. Therefore, enterprises with environmental SC policies can achieve increased SC flexibility and, hence, enhanced profits. In addition, enhanced SC flexibility allows organizations to save costs from the resource aspect. This result reinforces other empirical studies about this issue (Fantazy et al., 2009; Gerwin, 1993; Kumar et al., 2006; Sańchez and Pe´rez, 2005). This study provides two major managerial insights to enterprises. First, the internal effort to develop GSC is imperative. Our study findings confirmed that internal GSC pressures such as commitment of the top management team and cross-functional cooperation are the driving force behind GSC external practices. Also, practices based on internal efforts make SC more flexible and improve the output performance. Once a commitment is made for GSC, the main force of internal practice, support for GSCM from senior managers, should be ensured first. Second, the management decision to enhance SC flexibility is also critical. Achieving supply chain agility leads to inventory reduction, counteracting market variations more efficiently, responding to consumer demand more quickly, and collaborating with suppliers and partners more effectively. In addition to implementing GSC practices, mangers should develop strategies to make their SC more resilient. This study makes three theoretical contributions to the GSCM literature. First, it provides a new empirical evidence of the effect of GSC practices on SC flexibility. The existing literature contains little empirical investigation of the relationship between green policies and SC flexibility. The results of this study indicate that internal practices significantly improve flexibility, while external practices have no relationship to flexibility. Second, this study sheds new understanding of GSCM by extending the institutional-versus-internal-pressure construct to characterize how enterprises implement GSC strategies for sustainable SC. To maximize the impact of GSC


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policies, enterprises should try to create their own capabilities and improve internal commitment for sustainable supply chains. While the beneficial effect of GSC practices has been hypothesized in the literature, few studies have distinguished between institutional pressure and internal pressure. This study also suggests that external GSC practices are influenced by prior internal capability. Finally, this study applied organizational theory to GSCM. Organizational theory has received a great deal of attention in the SCM field (Ketchen et al., 2007). However, only a few studies have investigated organizational theory’s influence on and relationship with environmental SCM (Sarkis et al., 2011). As suggested by other studies, two organizational theories, institutional theory and RBV, have been employed in this study to explain the relationship between the direction of environmental pressures and SC performance. 6.2 Limitations and future research This study has several limitations. These limitations present opportunities for future research. First, several problems are associated with the use of self-reports. The common method bias may be a problem because survey data was gathered from a single respondent per firm (Podsakoff et al., 2003). Second, the sample size was insufficient for testing additional hypotheses, and the industrial type of the respondents was restricted primarily to manufacturing. Because of the difficulties involved in collecting data from individual enterprises, help was solicited from the Korean Logistics and Distribution Association where members are mostly from the manufacturing sector. For future research, data should be collected from a more diverse sample across industries. Third, the research did not control for organization size. Because large firms typically have more available resources and well developed GSCM practices, researchers should control for organization size (Zhu and Sarkis, 2004). Finally, the existing studies suggest several other types of GSCM practices such as investment recovery, the closed-loop system, eco design, and so on. Future GSC research should include divers GSC practice dimensions. References Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review and recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-423. Armstrong, J.S. and Overton, T.S. (1977), “Estimating nonresponse bias in mail surveys”, Journal of Marketing Research, Vol. 14 No. 3, pp. 396-402. Barney, J. (1986), “Strategic factor markets: expectations, luck, and business strategy”, Management Science, Vol. 32 No. 10, pp. 1231-1241. Barney, J. (1991), “Firm resources and sustained competitive advantage”, Journal of Management, Vol. 17 No. 1, pp. 99-120. Barrett, S. (1992), “Strategy and the environment”, Columbia Journal of World Business, Vol. 27 Nos 3/4, pp. 202-208. Beamon, B.M. (1999), “Measuring supply chain performance”, International Journal of Operations & Production Management, Vol. 19 No. 3, pp. 275-292. Bentler, P.M. (1990), “Comparative fit indexes in structural models”, Psychological Bulletin, Vol. 107 No. 2, pp. 238-246. Bentler, P.M. and Bonett, D.G. (1980), “Significance tests and goodness of fit in the analysis of covariance structures”, Psychological Bulletin, Vol. 88 No. 3, pp. 588-606.


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