Coevolution of environmental sustainability orientation and strategic alliance learning in green supply chain management

Chu, Kuo Ming Article Coevolution of environmental sustainability orientation and strategic alliance learning in green supply chain management International Journal of Management and Economics Provided in Cooperation with: SGH Warsaw School of Economics, Warsaw Suggested Citation: Chu, Kuo Ming (2019) : Coevolution of environmental sustainability orientation and strategic alliance learning in green supply chain management, International Journal of Management and Economics, ISSN 2543-5361, Sciendo, Warsaw, Vol. 55, Iss. 1, pp. 3-12, https://doi.org/10.2478/ijme-2019-0001 This Version is available at: https://hdl.handle.net/10419/309675 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ International Journal of Management and Economics 2019; 55(1): 3–12 Kuo-Ming Chu* Coevolution of environmental sustainability orientation and strategic alliance learning in green supply chain management https://doi.org/10.2478/ijme-2019-0001 Received September 28, 2018; accepted December 29, 2018 Abstract: The main aim of this paper is to show how green supply chain (SC) environmental sustainability orientation and strategic alliance learning coevolve over time. Our position is that the level of interfirm learning is a determinant of the formations and mastery mechanisms evolving in a green SC. Therefore, this study discusses the environmental sustainability of the learning processes of firms’ alliances during the life cycle of the alliances. This is done in order to encourage firms to follow green innovation and green SCs through enhancing their environmental performance and increasing their competitive advantage in the global market. In addition, this study develops a research structure and test hypotheses on the basis of survey data from 342 Taiwanese firms listed on the stock market. The results indicate that green knowledge acquisition plays a prominent role in the performance of firms’ alliances, especially when implemented in a green SC management (SCM) context. Moreover, according to one of the main findings, as companies evolve through the different phases of the alliance life cycle, their situation shows high potential for creating knowledge sharing through their exploration capabilities. Finally, when firms focus their internal organizations on learning and environmental requirements, they become better able to expand their learning capacity as well as to build and maintain a sustainable competitive advantage. Keywords: green supply chain management, alliance knowledge learning, coevolution, alliance performance 1 Introduction Economic growth that relies on the intensive depletion of natural resources in the long run cannot be thought to be sustainable from either an environmental or economic perspective. In recent years, there has been a global rise in the importance of environmental issues, and environmental consciousness has increased among consumers. Moreover, strict environmental regulations and the popularity of environmentalism have increasingly focused attention on the polluting behaviors of companies, encouraging them to manage their activities’ impacts on the environment in order to diminish their reputational risks and prevent extra costs [Pineiro-Chousa et al., 2017]. As a result, it is important for firms to find solutions that can help them reduce environmental burdens associated with their economic activities. In this regard, green innovation is a win–win strategy to overcome the conflict between environmental protection and economic development. Recently, many companies have come to recognize the concept of green supply chain management (SCM), which is also known as supply chain (SC) environmental management. However, although there is an abundance of green SCM literature, relatively little research has concentrated on the relationships among greening the SC, green innovation, environmental sustainability, and the competitive advantage Research Article *Corresponding author: Kuo-Ming Chu, Department of Business Administration, Cheng Shiu University, Kaohsiung City, Taiwan, ROC, E-mail: [email protected] Open Access. © 2019 Kuo-Ming Chu, published by Sciendo. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. 4 K.-M. Chu in Taiwanese industries [Evangelista et al., 2018]. Taiwan is a major global manufacturer of information products; Taiwanese industries’ expenditure of resources cannot be underestimated. The combination or collection of different business partners for the development of green innovation is often referred to as a technology collaboration portfolio [Calza et al., 2017]. Strategic alliances are voluntary, cooperative interfirm agreements that are intended to promote competitive advantage by allowing partners to share resources and risks, acquire knowledge, and access markets. With efficacious knowledge sharing, the strategic intention of interorganizational cooperation for sustainable competitive advantage is achievable by consolidating the capabilities and relevant organizational resources of all sections [Wang et al., 2018]. Thus, the interactive learning opportunities gained from such alliances help firms to raise their capabilities and develop their resource endowments [Teece, 2018]. Although coevolutionary theory has received increasing attention in the social sciences and in organizational theory, it has seen limited application in the study of green SCM. This raises the following questions regarding practitioners’ knowledge: 1) What is their level of awareness regarding the environmental implications of their activities? 2) Do they operationalize them in their current practices as well as in the planning of future activities? This study uses coevolutionary theory as a lens through which to explore the empirical alignment between business and green SCM strategies. The present study also identifies the mechanisms of coevolution that have continued to be latent in prior research and elucidates how its alliance portfolio coevolves as a consequence of strategic decisions (e.g., downsizing or diversification), which in turn limits the accomplishment of a firm’s strategy. This study’s scope is therefore extended by providing a contextual account of specific causal linkages between its alliance portfolio and a firm. 2 Literature review and hypotheses 2.1 Environmental sustainability orientation The resource-based view (RBV), in the context of environmental sustainability, recommends that firms recognize and utilize strategic resources and capacities to create unique and hard to imitate practices, as well as reducing their operations’ effect on the natural environment, while simultaneously creating value. In the study of environmentally friendly policies, it was shown that corporate social responsibility actions by firms are more likely to improve their efficiency, resulting in the evolution of a beneficial source of competitive advantage [Amankwah-Amoah et al., 2018], and thus sustainability orientation is a good predictor of entrepreneurial success [Sung and Park, 2018]. The firms have been led by customer expectations and environmental sustainable stresses to strive to offer attractive and better offers to customers continuously. In order to provide services and competitive products, sure strategies that may not be environmentally sustainable are sometimes adopted by firms. There is more to explore in the research on how firms organize their technology-related strategic alliances [Jiang et al., 2010]. Meanwhile, little attention has been paid to the trade-offs and management mechanisms among alliance firms with different partners from the portfolio-level perspective, especially in the case of emerging markets. In this paper, the main emphasis has therefore been on understanding the strategies and concept of alliance knowledge and environmental sustainability orientation learning in its influence and green SCM on sustainability of the society and environment. 2.2 Dynamics of alliance learning mechanisms in green SCM Alliance provides a context in which vicarious learning is facilitated and encouraged. In order to survive selection pressures and remain competitive, firms must find and exploit new external information. In this regard, firms scan the environment for new ideas and knowledge, and when a promising idea is found, they transfer the idea from the external environment to the internal one, so that it will be commercially exploited. In the alliance learning process, partners undertake intentional efforts to learn, cumulate, and leverage practical alliance management knowledge [Kale and Singh, 2007]. Alliances arguably provide a context in which vicarious learning is facilitated and boosted. Indeed, it has been proposed that it is their knowledge-sharing characteristics that enable their existence [Zosh et al., 2018]. All the aforementioned Coevolution of green SCM 5 researchers recognize that collaboration with partners enables learning by providing partners for access to new knowledge that resides outside a single firm’s boundaries and by collaboratively that leverages existing knowledge. This study thus aims at developing a better understanding of the different learning mechanisms that affect the management of collaborative green technology portfolios. This study suggests that green knowledge acquisition and sharing with customers in the green SC is the first step in understanding value, and green knowledge acquisition and sharing with suppliers in the SC offers insights and also boosts the efficiency and effectiveness of delivery and value creation. Therefore, alliance green knowledge acquisition and sharing is conceptualized as a joint activity in which two firms strive to create more value together than they would create individually or with other partners. It is treated as a multidimensional construct with multiple facets, including green relationship learning (GRL), joint sense making (JSM), and green knowledge integration (KI) [Cheung, 2005; Chen and Chang, 2013]. Thus, green knowledge acquisition and sharing in an alliance is conceptualized as a joint action in which interfirms strive to produce more value jointly than they would have created either individually or with other members in the green SCM. Furthermore, it is treated as a multidimensional construct with multiple structures, including GRL, JSM, and green KI [Tan, 2017]. 2.3 Coevolution theory The coevolutionary viewpoint is emerging as a considerable organizational framework for investigating outcomes and organizational behavior, especially as a joint function of selection pressures and managerial achievement imposed by the environment. Although researchers have studied the reasons behind the formation of such alliances, questions regarding whether they always move from one stage to the next and what factors can destroy the alliance process remained unanswered. Coevolutionary thinking considers organizations’ embeddedness in an historical and complex sociocultural situation, where the forces of change and interactions meet and reverberate. More specifically, in regard to coevolution, movement should be apparent in at least one of the three evolutionary procedures influenced by those mutual causal mechanisms between the two populations (i.e., fluctuation, selection, and retention). The evolution of a firm’s strategic alliances and networks is arguably a coevolutionary adaptation to the concurrent choice of individuals, dyads, and groups. More specifically, the evolution of alliances is examined by this study with an emphasis on the simultaneous and changing relationships between alliance learning and institutional processes. Diverse scenarios could therefore unfold, depending on how the strategic alliance shifts from the initial conditions to the evolved circumstances. Thus, focusing on evolutionary forces is the key to understanding how alliances evolve from one stage to another. The major rationale of the interorganizational knowledge governance perspective is the achievement of organizational learning, knowledge acquisition, and knowledge creation and accumulation through structural arrangement of the interorganizational knowledge flow. Drawing on the green SCM literature review, firms transfer knowledge through their supplier chain and support the notion that environmental learning occurs within a firm’s SC [Jackson, 2004]. Therefore, although researchers have studied the reasons behind the formation of alliances, whether or not an alliance always moves from one stage to the next or what factors destroy the alliance process are not yet clear because the alliance evolutionary force is the key to understanding how alliances evolve from one stage to another. 3 Methodology 3.1 Conceptual framework Figure 1 shows the entire conceptual framework and delineates the proposed relationships among green SCM strategic orientation and alliance institutional processes. As such, based on this research model, interorganizational knowledge governance’s major rationale is the achievement of organizational learning, knowledge acquisition, and knowledge creation and accumulation by interorganizational knowledge flow’s structural arrangements. 6 K.-M. Chu Figure 1. Research Framework 3.2 Data collection Data collection occurred over a 5-month period from January 2018 to May 2018. All of the potential respondents for the main test were selected from a database acquired from the Taiwan stock exchange (854 TSE companies) and the over-the-counter (969 OTC firms) market. For these selected companies, this research required the respondents to be engaged in SCM with Asian partners (domestic, Hong Kong, mainland China, and other Asian countries). More specifically, the focus was on logistics, manufacturing, and marketing managers at the vicepresidential or directorial levels. In total, three reminder e-mails were sent at evenly distributed time intervals. The result of this procedure was that there were 741 unique visitors to the survey website (39.5% of qualified potential respondents actually accessed the survey), after which 368 submitted their finished responses (49.7% of the 741 visitors accessing the website or 25.8% of the 1,814 aimed respondents). The remaining 47.6% of the potential respondents dropped out quickly or within the first few pages of the survey (Table 1). Table 1. Characteristics of the Firms Description Frequency % Description Frequency % Type of Industry Established Electronics 138 40.35 Less than 5years 2 0.58 Industrial products 96 28.07 6–10years 76 22.22 Chemicals/plastics 24 7.02 11–15years 115 33.63 Pharmaceutical 47 13.74 More than 15years 149 43.57 Others 37 10.82 Total Employees Annual Sales Less than 500 65 19.01 Less than 50 million 35 10.24 501–1,000 138 40.35 51–200 million 84 24.56 1,001–2,000 84 24.56 201–1 billion 89 26.02 More than 2,000 55 16.08 More than 1 billion 134 39.18 Times for Alliance with Current Partner Partners’ Country of Origin Newly established 5 1.46 Taiwan 193 56.44 1–3 times 128 37.43 Asia 96 28.07 4–6 times 96 28.07 American 31 9.06 More than 6 times 113 33.04 European 22 6.43 Information on the sampled firms in regard to their experience of managing green SC strategic alliances is summarized in Table 2. These results indicate that the majority of the survey participants had more than 5 years of experience managing green SC strategic alliances (more than 50%). Only 6.15% firms had newly established alliances with their current partners, while approximately 70% had engaged in alliances more than four times. As expected, the length of the alliances with the current partners varied: less than 1 year (15.79%); less than 3 years (14.33%); less than 6 years (21.93%); and more than 6 years (47.95%). Approximately 28.36% of the companies had a dedicated division to manage their green SC strategic alliances, while 26.02% included a mixed division. In addition, 15.50% of the firms perceived their Coevolution of green SCM 7 company’s green alliance to be in its initial stage, 27.19% viewed it as ongoing, 54.09% indicated that it was in the mature stage, and 3.22% viewed their green alliance as dissolved. Table 2. Characteristics of the Firms Managing Green SC Description Frequency % Description Frequency % Length of Alliance Frequency of Alliance Less than 1 year 54 15.79 Newly established 21 6.15 1–3years 49 14.33 1–3 times 81 23.68 4–6years 75 21.93 4–6 times 152 44.44 7–10years 84 24.56 More than 6 times 88 25.73 More than 10years 80 23.39 Benefit from the Alliance Form of Alliance Management Our company 75 21.93 A dedicated division 97 28.36 Our partner 64 18.71 A mixed division 89 26.02 Both 203 59.36 No special division 156 45.62 Who initiated this Green Alliance? Our company 107 31.29 Stage of the Green Alliance Our partner 57 16.67 Initial stage 53 15.50 Both 178 52.04 Ongoing stage 93 27.19 Importance of the Alliance Mature stage 185 54.09 R&D alliance 84 24.56 Dissolved stage 11 3.22 Production alliance 154 45.03 Initial stage 53 15.50 Distribution alliance 104 30.41 Abbreviations: R&D, research and development; SC, supply chain. 4 Empirical study 4.1 Hypotheses testing Thus, the second-order factor was employed by this study to test the full model using structural equation modeling (SEM). The structural model results, as shown in Figure 2, indicate that the chi-squared value of 157.73, with 95 degrees of freedom, is acceptable at a 0.05 significance level. Besides, the goodness-of-fit index (GFI) is 0.945, the adjusted GFI (AGFI) is 0.946, and the root mean square error of approximation (RMSEA) is 0.040. A good fit is indicated by these fit indices for this model. Since the goodness of fit suggests further implications, it is significant to further identify the significance and magnitude of the model’s structural path coefficients. The path from green SCM to alliance knowledge acquisition sharing was significant (p < 0.001) in the hypothesized direction and strong (0.243). Thus, the hypothesis that green SCM has a positive effect on alliance knowledge acquisition and sharing was supported. Moreover, as shown in Figure 2, alliance knowledge learning is positively influenced by GRL, JSM, and KI among green SC partners. In fact, the higher the levels of GRL, KI, and JSM in green SC partners, the higher the level of alliance knowledge learning. Figure 2. Structural Model Tested Notes: 1. χ2 (df) = 157.73 (95); p < .05; GFI (RMR) = .945 (.042); CFI (RMSEA) = .943 (.040). 2. EO = Environmental Orientation; SCO = Supply Chain Orientation; SO = Sustainability Orientation; GRL = Green Relationship Learning; JSM = Joint Sense-Making; and KI = Knowledge Integration. 8 K.-M. Chu 4.2 Model testing with moderators This section shows that the sampled firms have the experience to manage the strategic alliances of green SCM among the research variables and discusses the effects of the alliances’ learning processes on overall success. The regression was performed applying a hierarchical two-step approach. The regression models’ overall fit is summarized in Table 3. Table 3. Regression Model Fit Model R R2Adjusted R2SE Est. Change Statistics ∆R2F change df 1 df 2 Sig. F change 1 0.472 0.223 0.235 2.143 0.225 8.274 8 334 0.000 2 0.534 0.285 0.240 2.086 0.069 2.433 24 320 0.072 Abbreviation: SE, standard error. Overall, the research model explains 53.4% (adjusted R2 = 0.240) of the variance in green SCM, the dependent variable. The main effects of alliance knowledge learning and alliance performance account for 22.3% (adjusted R2 = 0.235) of the explained variance, while the moderating variables include: length of alliance, form of alliance management, the number of times alliances were formed, benefits from alliance, and the stage of green alliance. Regression analysis was used to evaluate the impact of independent variables on alliance performance, associated with the adoption of green SCM practices in China and Taiwan. A total of 342 responses were analyzed. Based on the analysis, Table 4 summarizes the emergence of a significant model (F (6,335) = 22.651, p < 0.001) with the adjusted R2 being 0.097. The significant variable includes alliance knowledge learning (β = 0.245, p = 0.000) on alliance performance. For the moderator variables, important beta path coefficients were shown statistically. Length of alliance, forms of alliance management, number of times alliances were formed, and benefits from alliance did not exhibit significant interactions with alliance knowledge learning. However, the stage of green alliance exhibited a positive interaction effect, along with alliance knowledge learning on alliance performance. 4.3 Coevolution of alliance knowledge learning This section presents the linear relationship results among the research variables, specifically the effects of the developmental stages of green alliance and alliance knowledge learning processes on alliance performance, and the effects of institutional processes on alliance success. Table 5 summarizes the multiple regression results and indicates that the following stages of green alliance have significant effects on green knowledge learning (M1 in the table): the initial stage (β = 0.420, p < 0.001); the ongoing stage (β = 0.275, p < 0.001); the mature stage (β = 0.296, p < 0.001); and the dissolution stage (β = 0.289, p < 0.001). JSM also indicates that the four developmental stages of green alliance have significant effects (M2 in the table): the initial stage (β = 0.449, p < 0.001); the ongoing stage (β = 0.354, p < 0.001); the mature stage (β = 0.320, p < 0.001); and the dissolution stage (β = 0.201, p < 0.001). Furthermore, there are significant effects of the developmental stages of green alliance on alliance KI (M3 in the table): the initial stage (β = 0.541, p < 0.001); the ongoing stage (β = 0.471, p < 0.001); the mature stage (β = 0.306, p < 0.001); and the dissolution stage (β = 0.253, p < 0.001). The regression results indicate that the development in the initial stage of green alliance (β = 0.406, p < 0.001), the ongoing stage (β = 0.363, p < 0.001), the mature stage (β = 0.273, p < 0.001), and the dissolution stage (β = 0.192, p < 0.001) has significant effects on relationship value (M4 in the table). Finally, perceived SC performance is significantly influenced by the initial stage (β = 0.583, p < 0.001), the ongoing stage (β = 0.439, p < 0.001), the mature stage (β = 0.290, p < 0.001), and the dissolution stage (β = 0.204, p < 0.001; M5 in the table). Coevolution of green SCM 9 Table 5. Regression Results of the Effects of Strategic Alliance Competence Independent Variables Dependent Variables Alliance Learning Process Alliance Performance GRL JSM KI RV PSCP M1 M2 M3 M4 M5 Control Variables LA 0.025 0.006 0.011 0.042 0.032 FMA −0.023 −0.010 −0.069 0.069 0.046 TA 0.028 0.031 0.004 0.041 0.020 BFA 0.075 0.038 0.129+ 0.047 0.038 Developmental Stages of Green Alliance Initial stage 0.420*** 0.449*** 0.541*** 0.406*** 0.583*** Ongoing stage 0.275*** 0.354*** 0.471*** 0.363*** 0.439*** Mature stage 0.296*** 0.320*** 0.306*** 0.273*** 0.290*** Dissolution stage 0.289*** 0.201*** 0.253*** 0.192*** 0.204*** R20.532 0.479 0.500 0.498 0.600 Adjusted R20.540 0.482 0.509 0.469 0.569 F50.978 37.707 45.575 43.732 69.509 p0.000 0.000 0.000 0.001 0.000 Notes: ***p < 0.001 level; ** p < 0.01 level; *p < 0.05 level. Abbreviations: BFA, benefit from alliance; FMA, form of manage alliance; GRL, green relationship learning; JSM, joint sense making; KI, knowledge integration; LA, length of alliance; TA, times alliance. Table 4. Model Testing with Moderators for the Treatment Regression Model Model R R2Adjusted R2Standard Error of the Estimate 1 0.314a0.099 0.097 0.778 ANOVA Model Sum of Squares df Mean Square F-value Significance 1Total 87.564 6 14.594 22.651 0.000a Factors effecting Green SCM Practices Alliance Performance Unstandardized Coefficients Standardized Coefficients T-statistics Significance BStandard error β (Constant) 3.012 0.138 12.435 0.000 AKL 0.353 0.047 0.245 7.043 0.000 LA 0.078 0.051 0.034 1.324 0.274 FMA 0.063 0.028 0.095 1.647 0.259 TA 0.093 0.042 0.102 1.951 0.122 BFA 0.021 0.017 0.050 0.061 0.961 SGA 0.121 0.069 0.142 5.121 0.000 Notes: aPredictors: (constant), AKL. bDependent variable: AP. Abbreviations: AKL, alliance knowledge learning; ANOVA, analysis of variance; AP, alliance performance; BFA, benefit from alliance; FMA, form of manage alliance; LA, length of alliance; SCM, supply chain management; SGA, stage of green alliance; TA, times alliance. 10 K.-M. Chu The abovementioned results (Table 5) indicate a statistically significant F-value in the developmental stages of green alliances affecting the alliance knowledge learning process and alliance performance. An investigation of the related coefficients in the four stages of developing green alliances appears to indicate that the factors were related to the knowledge learning process. Most respondents in the initial and ongoing stages exhibited significantly higher related coefficients than in the other stages. Remarkably, all of the groups displayed a relatively greater willingness to engage in alliance knowledge learning processes and to contribute to alliance performance. The interaction’s beginning is marked by an evolutionary phase between partners. The ongoing and initial stages involve extensive scanning of the potential alliance environment, including government policies, societal culture, competition and markets, and to some extent, the potential partner’s corporate culture. As the alliances mature, the initial partner motivations and conditions alter, making it imperative to reevaluate and readjust learning priorities. 5 Conclusion 5.1 Discussion of the findings Important theoretical contributions are provided by our findings. First, the main aim of this paper is to show that green SC environmental sustainability orientation and strategic alliance learning coevolve over time. Our stance is that the level of interfirm learning is a determinant of the structures and control mechanisms evolving in a green SC. Therefore, this study discusses the environmental sustainability of the learning processes of firms’ alliances during the life cycle of the alliances, in order to encourage firms to apply green innovation and green SCs, enhance their environmental performance, and increase their competitive advantage in the global market. However, because interfirm learning cannot be immediately generated or destroyed, partner firms should balance the inevitable trade-offs among GRL, JSM, and KI. As Kirchoff [2011] suggested, by guiding such products and implementing environmentally friendly policies, firms are more likely to enhance their efficiency, because there are several avenues firms could take with their supplier management. Furthermore, the results of testing the study hypothesis show that a green SC partner’s learning capability appears to play an important role in raising knowledge that it shares with its supporting firms. A green SC is considered to be a learning process, a corporate culture, and an organizational belief system incorporating environmental responsibility, apprehension, and management [Kirchoff, 2011]. Shared interorganizational knowledge enhances the SCs’ competitive advantage as a whole. Thus, the influence of alliance knowledge sharing and learning on environmental performance is significant because meeting the standards of green directives has already become the common consensus among firms in green SCM. This perspective is discussed herein as a multidimensional construct with multiple aspects, including information sharing, JSM, and KI. JSM refers to the extent and degree of information sharing and GRL as well as the integration of knowledge into a firm’s memory [Russo and Cesarani, 2017]. Hence, the development of knowledge sharing in green SC relationships will lead to beneficial performance outcomes, as identified in the present study. Therefore, the hypothesized relationship and the empirical findings in this study support interfirm knowledge sharing and green SCM as antecedents to higher alliance performance in the green SC. Lastly, the findings also suggest that alliance performance has a positive effect on perceived SC performance. The pursuit of collaborative values may be hindered by overemphasis on alliance performance since it prevents the partners from developing mutual commitment to the relationship. Furthermore, shared alliance knowledge elucidates how interfirm knowledge sharing improves relationship value and perceived SC performance as a whole. This strategic perspective regarding the coevolution of interorganizational relationships implies that firms can coevolve by leveraging such relationships and obtaining knowledge resources and assets. Moreover, reciprocal relationships allow alliance performances to increase, not only in regard to virtual profit but also to invisible value. This study provides a practical application of