Developing green innovation performance by fostering of organizational knowledge and coopetitive relations

Depósito de investigación de la Universidad de Sevilla https://idus.us.es/ Esta es la versión aceptada del artículo publicado en: This is a accepted manuscript of a paper published in: Review of Managerial Science (2018) DOI: https://doi.org/10.1007/s11846-017-0270-z Copyright: Springer-Verlag GmbH Germany, part of Springer Nature 2017 El acceso a la versión publicada del artículo puede requerir la suscripción de la revista. Access to the published version may require subscription. “This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11846-017-0270-z” 1 3 Developing green innovation performance by fostering of organizational knowledge and coopetitive relations Gema Albort‑Morant1,2 · Antonio Leal‑Millán1 · Gabriel Cepeda‑Carrion1 · Jörg Henseler3,4 Abstract This study explores the links between knowledge base, relationship learning, and green innovation performance within a coopetitive framework. We posit that green innovation is directly influenced by a broad and deep knowledge base. We also hypothesize that the knowledge base–green innovation performance link is positively mediated by relationship learning (indirect effect). These hypotheses were empirically tested using consistent partial least squares path modeling. A sample of 112 firms from the Spanish automotive components manufacturing sector was used. The mediating effect of relationship learning on the knowledge base–green innovation performance link was observed to be positive and significant. Therefore, managers should build strong relations with stakeholders to assimilate, transfer, and adapt new knowledge and thus enhance green innovation performance.  Gema Albort-Morant [email protected] Antonio Leal-Millán [email protected] Gabriel Cepeda-Carrion [email protected] Jörg Henseler [email protected] 1 Department of Business Management and Marketing, Universidad de Sevilla, Av. Ramón y Cajal, 1, 41018 Seville, Spain 2 Centro Universitario San Isidoro, C/ Leonardo Da Vinci, 17B, 41092 Seville, Spain 3 Faculty of Engineering Technology, University of Twente, Postbus 217, 7500 AE Enschede, The Netherlands 4 Nova Information Management School, Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal 1 3 Keywords Knowledge base · Relationship learning · Green innovation performance · Coopetition · Partial least squares JEL Classification M1 business administration · Q01 sustainable development 1 Introduction In recent decades, heightened concern for environmental issues, stricter regulations, the proliferation of international environmental protection conventions, and greater consumer environmentalism have led companies to develop strategies and policies that are linked to the environment. Green innovation performance measures firms’ efforts in terms of environmental management and sustainable practices to assess how well these firms comply with their environmental protection obligations (Chen et al. 2006). Green innovation allows firms to meet stakeholders’ demands for maximizing production without harming the ecosystem (Albort-Morant et al. 2016). Thus, environmental issues have become vital to organizations’ corporate strategies because of stricter environmental regulations and greater stakeholder demands. Green innovation arises as a corporate reaction to internal changes (cultural shifts, managerial approach, etc.) and external changes (i.e., market trends, stakeholder preferences, normative frameworks, and social demands). Organizations must continually improve their products and processes with agility and speed to attain sustainable competitive advantages. In light of economic and social globalization, it is hard to imagine how green innovation strategies can effectively occur if they are not at the core of coopetitive (cooperative and competitive) relationships among firms. Ritala et al. (2016, p. 1) report that “in particular, research has shown that coopetition can facilitate innovation among firms, networks, and ecosystems.” A paradigmatic example of a coopetitive relationship is the agreement that Toyota and General Motors signed in the 1980s. As reflected in Gast et al. (2015, p. 493), “although being two of the largest competitors on the automobile market, they decided to enter a coopetitive agreement and set up a jointly-owned plant in the US.” Numerous scholars have defined coopetition. Kraus et al. (2017, p. 2) offer a clear, concise definition of coopetition as “the simultaneous competition and collaboration between rival firms operating in the same markets.” According to Levy et al. (2003), coopetition combines competitive and collaborative elements. This mixture differentiates coopetition from other kinds of interfirm collaboration. Whatever the purposes and motivations that underlie coopetitive tendencies in partner selection (Kraus et al. 2017), coopetition entails not only risks, but also positive externalities through the development of certain capabilities. This benefit of coopetition includes the reinforcement and development of knowledge-based capabilities such as knowledge creation, knowledge transfer, and knowledge absorption (Loebecke et al. 1999). In addition, the extent to which companies rely on joint knowledge management activities because of their immersion in coopetitive contexts enhances firms’ knowledge base and relationship learning capability (Hamel 1991; Levy et al. 2003). In fact, strategic alliances are a pillar of coopetitive strategy because such alliances entail collaborative links with competitors and other stakeholders (Gast et al. 2015). Hamel (1991) affirms that coopetition in the form of strategic alliances with external partners might offer firms the chance to absorb external knowledge and competencies as well as efficiency in terms of knowledge accumulation. According to the knowledge-based view, knowledge is an essential strategic resource to generate new value creation and competitive advantage (Grant 1996). Their knowledge base gives companies the possibility and ability to understand and use new knowledge to resolve problems, make decisions, and innovate (Ahuja and Katila 2001). Therefore, companies must develop the competencies to build a deep and broad knowledge base that nurtures itself from internal and external knowledge sources. This knowledge base can then reinforce and support the innovation process and the launch of green, innovative products and services. This sort of innovation may involve a shift in the attitudes of companies that seek to develop close relations with stakeholders and partners (Lettice et al. 2010) and access external knowledge. Therefore, companies must work closely with stakeholders to share knowledge, skills, and a mutual goal to become greener (Chiou et al. 2011). Such efforts are particularly important for SMEs, where knowledge about suppliers, customers, partners, and competitors results from personal contact between organizational members and stakeholders. For example, in companies that operate through supply chains or projects, developing strong client–supplier relationships is essential for effective, efficient management. To attain and sustain green relationship learning, ensuring cooperation among parties is critical. Therefore, strengthening relationship learning is vital to the firm’s knowledge base and the effective attainment of green innovations. Numerous studies have highlighted the need to reinforce networks and cooperative ties, which are fundamental drivers of organizational success and green innovation performance (Cainelli et al. 2015; De Marchi 2012; Lin and Chang 2009). For instance, Lin and Chang (2009) report the positive impact of green relationship learning on green innovation performance for a sample of Taiwanese manufacturing firms. According to De Marchi’s (2012) study of Spanish manufacturing firms, formal cooperation with external partners is more important for green innovation than for other types of innovation. Leal-Millán et al. (2016) showed the positive effects of developing information technology capability and fostering relationship learning on green innovation performance and customer capital. Similarly, Albort-Morant et al. (2016) describe the ties between dynamic capabilities and green innovation performance, introducing relationship learning as a mediating variable. Building on Albort-Morant et al.’s (2016) study, our goal is to prove that relationship learning mediates the relationship between knowledge base and green innovation performance. We believe that companies with a strong knowledge base need to relate and learn from stakeholders to update their environmental knowledge base and thereby drive their green innovation performance. Therefore, when companies share knowledge with suppliers and customers in supply chain management activities, they improve their knowledge base and capabilities through relationship learning. Recently, the cooperation literature has grown, expanding its focus on the progress of core firm competencies and capabilities (Leal-Millán et al. 2017). In SMEs, collaboration with competitors, suppliers, and customers is vital to create effective green innovations because organizations must extend the knowledge creation process beyond the company. Nevertheless, the mediating role of relationship learning in the link between knowledge base and green innovation performance has been neglected. To the best of our knowledge, no empirical research has been conducted on this topic. This paper responds to these concerns and targets the aforementioned gaps in the literature on knowledge base, green innovation performance, and relationship learning. The green perspective that underlies our analysis constitutes an innovative contribution to the coopetition literature. According to Devece et al. (2017, p. 3), “the number of published articles on coopetition is increasing rapidly, with a point of inflection around the year 2000. Despite this growth, however, the coopetition literature remains relatively scarce.” A major research gap relates to the organizational consequences of coopetition (Gast et al. 2015). This paper also covers this gap by analyzing an issue that has yet remained unexamined in the coopetition literature, namely the role of coopetition in strengthening knowledgebased capabilities and green innovation performance. Given that knowledge base and green innovation performance are positively related and that there is a need to know which other coopetitive organizational mechanisms/ capabilities managers must activate to improve the success of green innovations, this study addresses the following question: How does the presence of relationship learning (as an ordinary capability) affect the link between knowledge base and green innovation performance? This paper theorizes and examines this central question, evaluating whether relationship learning mediates the knowledge base–green innovation performance link. Thus, we analyze a model that describes the links between knowledge base, relationship learning, and green innovation performance. We shed light on whether and how the deployment of a strong relationship learning capability (in coopetition with a range of stakeholders and competitors) accentuates the effect of a broad and deep knowledge base on industrial firms’ green innovation performance. The study is organized as follows: Sect. 2 presents the primary theoretical foundations, the research model, and the hypothesis. Section 3 describes the empirical analysis of a dataset consisting of data on 112 companies in the Spanish automotive components manufacturing sector (ACMS). Section 4 presents the results of the analysis using consistent partial least squares (PLSc) path modeling, a variancebased structural equation modeling technique. Finally, Sect. 5 discusses the results, implications for research and practice, and future lines of research. 2 Literature review 2.1 Coopetition The term coopetition was coined to describe cooperative competition. The essential beliefs of coopetitive organizations have been explained in game theory, a scientific field that emerged in 1944 with the publication of John von Neumann and Oskar Morgenstern’s book Theory of Games and Economic Behavior. Game theory was subsequently developed through John Forbes Nash’s work on non-cooperative games. Coopetition can occur in interorganizational or intraorganizational spheres. Intrafirm coopetition occurs between individuals or operative divisions or units inside the same company. Drawing upon game theory and social interdependence theory, several studies have explored simultaneous cooperation and competition between departments or operational units—the antecedents of coopetition—and the influence on knowledge sharing behaviors (Loebecke et al. 1999). The concept of “coopetitive knowledge sharing” was developed to explain mechanisms whereby coopetition influences effective knowledge sharing practices in cross-functional teams (Ghobadi and D’Ambra 2012). The core argument is that while organizational teams need to cooperate, they are likely to experience strain due to diverse professional values and conflicting objectives from different cross-functional departments. At the interfirm level, coopetition happens when companies with certain overlapping interests work together. They collaborate with each other to boost value creation yet compete to attain competitive advantage. Coopetition routinely occurs when firms in the same market work together to explore knowledge and new products while competing to gain market share and exploit knowledge that has been created at different stages of the value chain (Fernandes and Ferreira 2017). For example, the PSA group (Peugeot-Citroën) and the Japanese company Toyota share components for a small car, which is marketed by each company as the Peugeot 107, the Toyota Aygo, and the Citroën C1. This agreement has numerous benefits such as cost cutting, resource complementarity, and knowledge or high-tech transfer. Coopetition may also involve different companies in a supply chain, as in the ACMS. Despite difficulties and risks such as lack of confidence, a major advantage is the knowledge that results from relationship learning. In this paper, we assume that coopetition provides the ideal scenario for developing organizational capabilities that are linked to both knowledge base enhancement and relationship learning and that may improve firms’ green innovation performance. The theoretical linkages between these three constructs are discussed in the research framework and hypothesis section. 2.2 Knowledge base According to the knowledge-based view of the firm, knowledge is a key strategic resource for companies that aim to generate and maintain sustainable competitive advantages (Grant 1996). Although knowledge is produced and spread across the company, it potentially enhances organizational value by boosting proficiency while enabling better reactions to new, unexpected, or unusual situations. The increasing relevance of knowledge as a fundamental organizational resource has led managers to pay more attention to developing knowledge management strategies. Thus, the knowledge-based view recommends firms to develop, absorb, and apply managerial knowledge to attain superior organizational performance (Nonaka 1994) because knowledge is arguably a firm’s most important resource (Spender 1996). An organization’s current knowledge base establishes its prospects and capabilities to recognize the value of new knowledge and apply it to decision-making, problem solving, or innovation (Ahuja and Katila 2001). Zhou and Wu (2010) report that a firm’s present knowledge base (i.e., its knowledge breadth and depth) represents the firm’s main source of organizational innovativeness. Knowledge breadth and depth are the two components that shape an organization’s knowledge base. They reflect the firm’s primary knowledge structures and contents. Knowledge depth refers to the degree of intricacy and sophistication of a firm’s knowledge base (Bierly and Chakrabarti 1996). Knowledge depth corresponds to the vertical dimension of knowledge—exclusivity, complexity, and specificity. Knowledge breadth refers to the horizontal dimension of knowledge—the heterogeneity of the firm’s knowledge base (Zhou and Li 2012). Knowledge can be divided into several dimensions: the systemic dimension (data, information, and knowledge), the ontological dimension (individual–social), and the epistemological dimension (explicit–tacit). A firm’s knowledge base nurtures itself from a variety of internal and external knowledge sources. Some knowledge is more difficult to manage or articulate. Retaining some knowledge within the company is difficult. Therefore, effectively managing the firm’s knowledge base is a valuable way to create and sustain new sources of competitive advantage (Grant and Baden- Fuller 2004). 2.3 Relationship learning In stakeholder theory and the resource-based view of the firm, interorganizational connections are based on mutual individual stakeholders’ contributions to shared value creation (Haslam 2004). Drawing upon these theoretical perspectives, this study examines the concept of relationship learning. In the current socioeconomic context, organizations deliberately share information and knowledge with suppliers, customers, and other partners to mutually enhance their knowledge bases and competencies and thereby support the innovation process. Our theoretical background primarily builds on the approach of Selnes and Sallis (2003, p. 86), who coined the concept of relationship learning, defining it as “a joint activity in which two parties strive to create more value together than they would create individually or with other partners.” Therefore, companies strive to collaborate with specific partners that enable the improvement of future behaviors and increase the benefits that are associated with such relationships. Cheung et al. (2011) denote relationship learning as a joint activity between buyers and suppliers in which two parties share information. According to Selnes and Sallis (2003), relationship learning has three dimensions: information sharing, joint sensemaking, and knowledge integration. We focus on the joint sensemaking dimension of relationship learning. We consider that this dimension most accurately fits the behavior of ACMS firms within supply chain relationships. It is crucial for ACMS firms to build common understanding and joint sensemaking with their primary customers (large automakers). This is consistent with the views of Leal-Millán et al. (2016, p. 448), who state that “organisations in a customer–supplier relationship introduce management meetings, face-to-face communications in visit programmes, informal interpersonal networks, task-forces teams and cross-functional teams as instruments to solving operational problems in the relationship, and creating joint learning arenas.” Thus, only through these face-to-face meetings and personal relationships between both firms’ employees can tacit knowledge (the richest form of knowledge) be effectively shared and assimilated (Nonaka 1994). Accordingly, joint sensemaking is the most critical dimension of relationship learning (Selnes and Sallis 2003). Normally, the exchange of knowledge between parties in supply chain cooperation represents a relationship-specific component of understanding and cohesion. Nevertheless, the groups might differ in the way they grasp and perceive the same information (i.e., sensemaking). Alternatively, they might lack the right knowledge to make sense of this information. Thus, companies may use an array of mechanisms to foster joint sensemaking (i.e., face-to-face communication during visit programs, management meetings, informal or personal networks, and project-based and crossfunctional teams). Such instruments guide firms along their cooperative path, creating joint learning areas and solving the operational problems that are inherent to relationships. 2.4 Green innovation performance Green innovation performance measures the extent to which organizations develop innovations that reduce or minimize environmental damages, impact, and deterioration while optimizing the use of natural resources (Albort-Morant et al. 2016, 2017; Chang and Chen 2013; Chen et al. 2006). As a strategy, green innovation offers great opportunities to meet buyers’ requirements while preserving the ecosystem (Albort- Morant et al. 2016, 2017). Customers around the world increasingly seek to buy products and services that are ecological, environmentally conscious, eco-friendly, or green. The “green” tag offers a real incentive for firms to develop nonstop innovation, craft new market opportunities, and comply with new consumer requests, thereby building and enhancing customer capital (Leal-Millán et al. 2016). To carry out environmental procedures, companies develop new processes, products, technologies, and/or management strategies that are designed to raise effectiveness. We adopt Chen et al.’s (2006, p. 332) definition of green innovation as “hardware or software innovation that is related to green products or processes, including the innovation in technologies that are involved in energy-saving, pollution-prevention, waste recycling, green product designs or corporate environmental management.” Chang (2011) conceptualized green innovation as a particular type of innovation that enables a company to improve its corporate image, develop new markets, and extend its competitive advantage while satisfying stakeholders’ environmental protection requests. Likewise, Leenders and Chandra (2013) affirm that green innovation entails product or process innovations that deal with technological development for pollution prevention, recycling, waste reprocessing, energy saving, and ecoefficient design. Hashim et al. (2015) argue that this sort of innovation reduces organizations’ environmental footprint by embracing significant shifts in corporate strategy, product design methods, productive processes, resource use, and waste treatment procedures. In our research model, we focus on green innovation performance to capture the outcomes of firms’ green innovation efforts. 3 Research framework and hypothesis 3.1 The mediating effect of relationship learning on the knowledge base–green innovation performance link in a coopetitive context Innovation involves the invention and application of new or novel ideas about products, services, or processes. Nowadays, many firms are pressed to embrace active strategies that address the growing importance of environmental issues. According to Chesbrough (2003), companies should use external and internal ideas to generate successful green innovation processes and products. Accordingly, the starting point of numerous green innovations might be a partner’s ideas and proposals (Koc and Ceylan 2007). Such a starting point might apply to most ACMS companies that are involved in coopetitive networks. Consistent with Dagnino and Padula (2002), simple network coopetition is shaped by coopetition among multiple companies at one level of the value chain. In car manufacturing, buyer–supplier relationships are often under the parallel sourcing, as opposed to sole sourcing, framework (Richardson 1993). Often, parallel buyers (e.g., Renault) choose two or three suppliers, at least one of which is from the internal market (Dagnino and Padula 2002). This relationship structure serves to keep extensive transmission of materials and knowledge about process techniques among the distinct members throughout the supply chain. Commitment to long-term cooperation does not imply the abandonment of competition between suppliers. In fact, Japanese car manufacturers have traditionally trusted multiple suppliers that are entrenched in a multi-tier system more than their American counterparts have. The key to blending cooperation and competition lies in the firms’ inclination to work with suppliers to resolve technical or financial issues, rather than replacing them immediately with alternative sources (i.e., non-switching). ACMS companies may also supply their products (i.e., automotive equipment and components) to different end customers (i.e., large automakers). The tire industry exemplifies network coopetition. An agreement was signed by several key actors in the tire industry. This agreement involved a particular automotive component. Despite remaining fierce rivals, four competitors—Michelin, Goodyear, Pirelli, and Dunlop—jointly designed and commercialized the PAX system, which has been made available in some Renault and Audi automobiles. A firm’s resources and capabilities are among the main sources of competitive advantage and business innovation. Firms’ knowledge management and organizational learning strategies are greatly influenced by these firms’ knowledge-related resources and capabilities such as technical competencies, technological updates, knowledge bases, and the management and storage of organizational knowhow. Arguably, the main driver of knowledge management is the firm’s stock of Table 6 Structural model results Original coefficient Model 95% confidence intervals 2 RL 2 GIP = 0.037 = 0.465 a: KB → RL 0.214 0.214* (2.110) [0.047; 0.432] Sig. c’: KB → GIP 0.329 0.197*** (3.787) [0.191; 0.528] Sig. b: RL → GIP 0.619 0.620*** (7.786) [0.455; 0.763] Sig. H1: KB → RL → GIP 0.133 0.1329* (2.027) [0.0279; 0.277] Sig. (Indir. effect) t-values in parentheses; bootstrapping 95% confidence intervals bias corrected in square brackets (based on n = 5000 subsamples) ns not significant ***p < .001, **p < .01, *p < .05 (based on t(4999), one-tailed test); t(0.05, 4999) = 1.645, t(0.01, 4999) = 2.327, t(0.001, 4999) = 3.092 sufficient condition for the indirect effect of knowledge base on green innovation performance through relationship learning (Nitzl et al. 2016). Consequently, we tested whether a × b was also significant. This model also showed that the indirect effect (H1 = 0 .1329*; 2.027) was significant. To estimate the indirect effect of knowledge base on green innovation performance, PLSc analysis provided 95% bootstrap confidence intervals for the indirect effect. If 0 was absent from the interval for an indirect effect, the indirect relationship differed significantly from zero with a 95% confidence level. Our study showed partial (complementary) mediation because the indirect and direct effects were significant. This result implies that relationship learning partially mediates the effect of knowledge base on green innovation performance. The results in Table 6 confirm that the structural model was satisfactory. 6 Discussion and conclusions Recent studies have explored the existence of a direct link between knowledge base and green innovation performance (Albort-Morant et al. 2016; Leal-Rodríguez et al. 2013) and between relationship learning and green innovation performance (Cainelli et al. 2015; De Marchi and Grandinetti 2013). The mediating effect of relationship learning on the link between knowledge base and green innovation performance, however, has scarcely been explored. Building on prior studies, this paper presents a model that explores this mediating effect. The empirical analysis shows that the total and indirect effects of knowledge base on green innovation performance are positive and significant. The structural model supports the hypothesis that a firm’s knowledge base positively affects green innova- R R tion performance through reconfiguration and enhancement of relationship learning capability. The analysis provides evidence of this indirect effect of knowledge base on green innovation performance via relationship learning. According to these findings, a company’s knowledge base affects green innovation performance, but this effect is greater if the company shares and compares information with stakeholders through relationship learning. This study shows that a strong organizational knowledge base and knowledge acquisition and creation through coopetitive relationship learning are vital for firms to achieve high green innovation performance. This study makes some noteworthy contributions for academics and practitioners. First, it sheds light on the conceptualization of knowledge base, relationship learning, and green innovation performance. Green innovation might become a key variable for researchers and executives because it potentially acts as a catalyst for achieving high organizational performance and attaining competitive advantages. Second, drawing on the knowledge base, relationship learning, and green innovation performance literatures, we propose a research model that explores the mediating link between these constructs. Third, we present the results of empirical hypothesis testing for a sample of 112 Spanish ACMS firms. In addition, this study contributes to the resources and capabilities literature by demonstrating that the existence of competitive advantage (here, effective green innovations) requires not only a set of powerful resources (e.g., a broad and deep knowledge base), but also certain capabilities (e.g., relationship learning) to develop these resources through interorganizational cooperative and competitive relations. This study has major practical implications for strategic managers of manufacturing firms who seek a green knowledge base to improve green innovation performance. The green innovation performance in these companies is often highly conditioned by prior accumulation of associated knowledge in the knowledge base. We provide the theoretical and empirical foundations for further analysis of green innovation performance of firms in the ACMS. In a context of close cooperation and competition, ACMS companies can gather interesting information on the latest environmental changes within the sector. Therefore, these companies should establish and strengthen ties with stakeholders, thereby forming a partnership rather than a traditional customer–supplier relationship. Accordingly, knowledge base and relationship learning should be among the key resources and capabilities that are encouraged at the managerial level to achieve better green innovation performance. Our results reveal that fostering a strong relationship learning capability (in coopetition with a broad set of stakeholders and competitors) enhances the effect of a broad and deep knowledge on the green innovation performance of industrial firms. This study nonetheless has certain limitations. First, although this study provides evidence of causality, the analysis did not test causality itself. Second, the analysis was based on the subjective perceptions of the respondents, and to elicit insights from respondents, a single method was used. Finally, this study was conducted for a single geographic context (Spain) in a single sector (ACMS). Therefore, researchers must be cautious when generalizing these results and conclusions to other settings. Based on the implications and limitations that can be derived from this study, the following lines of future research would prove interesting. First, scholars should examine the moderating effect of certain managerial variables that we expect to influence green innovation performance. Second, scholars should also adopt a longitudinal approach to collect data at different times. Doing so would enable verification of the hypotheses that are captured by our research model. Third, replicating this study in a different geographic context or sector would be helpful to generalize our insights and conclusions. 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