Green supply chain collaborative innovation, absorptive capacity and innovation performance: Evidence from China

Journal of Cleaner Production 241 (2019) 118377

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Green supply chain collaborative innovation, absorptive capacity and innovation performance: Evidence from China Jiangtao Hong a, *, Ruyin Zheng a, Hepu Deng b, Yinglei Zhou c a International Business School, Shanghai University of International Business and Economics, No. 1900 Wenxiang Road, Songjiang District, Shanghai, 201620, China b School of Business Information Technology and Logistics, RMIT University, Melbourne, VIC, 3000, Australia c School of Management, Xiamen University, Xiamen, 361005, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 January 2018 Received in revised form 5 September 2019 Accepted 9 September 2019 Available online 13 September 2019

Green supply chain collaborative innovation (GSCCI) is about the utilization of specific advantages of individual organizations in a holistic manner along the supply chain through collaboration for jointly solving green management problems. This paper presents a conceptual model for better understanding the relationship between GSCCI, absorptive capacity and innovation performance in organizations. Taking 206 foreign trade organizations in China as the survey object, this study empirically tests and validates the conceptual model using multiple regression analysis. The study shows that organizationorganization collaborative innovation, organization-government collaborative innovation, and organization-institution collaborative innovation have a significant positive effect on innovation performance. It reveals that absorptive capacity has a significant positive effect on innovation performance. Furthermore, the study finds out that absorptive capacity plays a partially mediating role in the relationship between organization-organization collaborative innovation and innovation performance and between organization-institution collaborative innovation and innovation performance. This study illustrates the mechanism of GSCCI and its impact on innovation performance which provides guideline for foreign trade organizations in China to better compete in the international market through collaborative innovation. © 2019 Elsevier Ltd. All rights reserved.

Handling editor: Yutao Wang Keywords: Green supply chain Collaborative innovation Absorptive capacity Innovation performance

1. Introduction With the rapid development of information and communication technologies and the increasing globalization of the world economy, green supply chain collaborative innovation (GSCCI) is becoming increasingly popular in organizations for adequately addressing the emerging challenge of protecting the deteriorating environment while satisfying the changing demand of consumers along the supply chain (Gualandris and Kalchschmidt, 2013; Deng et al., 2018a,b). In such a context, GSCCI is about the integration of supply chain collaborative innovation and green innovation in green supply chain management (GSCM) in organizations. It is critical for the development of the economy and the society and the protection of the environment through collaboration and innovation (Lin and Tseng, 2016).

* Corresponding author. E-mail address: [email protected] (J. Hong). https://doi.org/10.1016/j.jclepro.2019.118377 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

Much research has been done for understanding what GSCCI is about, the impact of GSCCI on innovation performance, and the mechanism for the development of GSCCI in organizations. Wu (2013), for example, explores the relationship between green supply chain integration and green innovation in organizations. Lin and Tseng (2016) present a hierarchical model for investigating the impact of competing priorities including green innovation on the performance of green supply chains in organizations. There are, however, few studies about GSCCI with respect to specific industries or types of organizations. In particular, there are no studies of this kind in Chinese foreign trade organizations on empirically investigating the relationship between GSCCI and innovation performance. In the pursuit of GSCCI, upstream and downstream organizations along supply chains usually offer their resources to others for achieving win-win outcomes (An et al., 2014). These organizations learn from each other with respect to talents, information, equipment and technology. Dependent on the nature of organizations, there are differences between organizations with respect to their

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knowledge acquisition, assimilation, transformation and exploitation. Such differences in absorptive capacity often affect the performance of innovation in these organizations (Flor et al., 2018). This posts a question on how absorptive capacity affects the relationship between GSCCI and innovation performance in organizations. With the occurrence of anti-globalization waves across the world, Chinese foreign trade organizations have been facing numerous challenges including increasing trade frictions, the homogeneity of export products and the reduction of profit margins. This makes the investment in green innovation critical in these organizations for improving their competitiveness. It leads to an active pursuit of collaborative innovation as a trans-disciplinary approach for developing the wholeness synergy through holistic, competitive and complementary interactions between and among innovation participants in a specific environment along the supply chain (An et al., 2014). A review of the related literature shows that collaborative innovation is increasingly becoming the key to the development of sustainable organizations (Bai et al., 2015). The purpose of this study is to investigate the relationship between GSCCI, absorptive capacity and innovation performance by answering the following research questions: (a) Is GSCCI positively related to innovation performance? and (2) Does the absorptive capacity have a mediating effect between GSCCI and innovation performance? To adequately answer these two questions, this study develops a conceptual model for exploring the relationship between GSCCI, absorptive capacity and innovation performance based on a comprehensive review of the related literature. Such a conceptual model is then empirically tested and validated using the survey data on 206 Chinese foreign trade organizations. The rest of this paper is organized as follows. Section 2 presents a review of the related literature in GSCM, GSCCI, absorptive capacity and innovation performance. This facilitates the development of specific hypothesis in Section 3. Section 4 describe the research methodology that this study adopts. Section 5 presents the data analysis result. Section 6 discuss about the research findings. Finally, Section 7 elaborates on the implications of the research findings, limitations, and future research. 2. Related literature 2.1. Green supply chain management and collaborative innovation GSCM is about the consideration of sustainability development along the whole supply chain in organizations (Deng, 2015; Deng et al., 2018a,b). It focuses on minimizing the wastage in the operations of organizations along the supply chain through specific activities such as product design, material resourcing, product delivery, and management of the end-of-life of products. GSCM involves coordinating activities along the supply chain for improving the sustainability development of organizations (Chen, 2008). Effective GSCM helps organizations improve their competitiveness through (a) reducing pollution and waste, (b) working with environmental-friendly suppliers, (c) developing green products and services, and (d) reducing emissions related to the transportation and delivery of products and services (Schiederig et al., 2012; Rao and Holt, 2005; Azzone et al., 1997; Welford, 1995). Effective GSCM requires the integration of environmental thinking and the maximization of resource utilization at every stage of the supply chain in organizations (Deng et al., 2018). It highlights the use of green design, green manufacturing, and green logistics in the pursuit of sustainable development. With the deterioration of the world environment and the increasing awareness of the importance of the society, GSCM is becoming increasingly important for individual organizations across the

world (Deng, 2015). There are various perspectives for approaching the concept of GSCM in the literature. Sarkis (1998), for example, proposes that GSCM is related to internal logistics and procurement, material management, external logistics, packaging and reverse logistics. Beamon (1999) argues that the aim of GSCM is to reduce the negative impact on the environment throughout the product life cycle, including green design, conserving resources, reduction and recycling of harmful raw materials. Srivastava (2007) and de Oliveira et al. (2018) state that GSCM is related to integrating environmental thinking into every stage of the supply chain including manufacturing processes, material sourcing and selection, product design and the delivery of the final product to the consumers as well as end-of-life management of the product. As environmental protection laws and regulations are becoming increasingly strict and green products are becoming more mainstream, GSCM has gradually moved from traditional manufacturing industries to medicine, aerospace and construction industries (Xie and Breen, 2012; Ruiz-Benitez et al., 2017; Dadhich et al., 2015). Effective GSCM in organizations calls for collaborative innovations along the supply chain (Deng et al., 2018a,b). This is because collaborative innovation in organizations can produce numerous benefits including obtaining more resources when facing competition for limited resources, gaining better recognition, and improving the competitiveness of organizations (Chen, 2012; An et al., 2014). Collaborative innovation is a “coupling process” (Enkel et al., 2009) that allows individual organizations and other organizations with complementary resources to jointly undertake innovative activities in knowledge creation and resource inflow and outflow for pursuing competitive advantages. It is the interaction of knowledge, technology, information and resources among subjects and the integration of resources, decision-making and innovation (Serrano and Fischer, 2007). Collaborative innovation includes communication, coordination, cooperation and collaboration (An et al., 2014). Collaborative innovation is established on mutual trust, openness, risk and benefit sharing (Chesbrough and Appleyard, 2007; Fawcett et al., 2012; An et al., 2014). Organizations establish longterm and close cooperation relationships with suppliers and customers. Each organization shares information with other organizations in its supply chain to optimize resource allocation and reduce overall supply chain costs, consequently gaining competitive advantages (Hu et al., 2018). Market diversification, price wars and reduced product life cycles have led more organizations to strive to adopt supply chain collaborative innovation. Supply chain collaborative innovation focuses more on customer demandoriented joint innovation activities. It allows individual organizations along the supply chain to take full advantages of information technology for improving the overall efficiency of the supply chain and gaining sustainable competitive advantages (Fawcett et al., 2011; Liao et al., 2017). 2.2. Absorptive capacity Absorptive capacity is an organization's “ability to recognize the value of new information, assimilate it, and apply it to commercial ends” (Cohen and Levinthal (1989). It depends greatly on priorrelated knowledge and diversity of the background in organizations. Absorptive capacity is cumulative, meaning that it is easier for an organization to invest on a constant basis in its absorptive capacity than investing punctually. Efforts put to develop absorptive capacity in one period make it easier to accumulate it in the next one. Absorptive capacity can be approached from different perspectives. Cohen and Levinthal (1990) argue that absorptive capacity is

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the ability of an organization to recognize the value of information, assimilate it, and apply it to commercial ends. Zahra and George (2002) think that absorptive capacity is a set of organizational routines and processes by which organizations acquire, assimilate, transform and exploit knowledge to produce a dynamic organizational capability. Zhang et al. (2018) state that absorptive capacity is an important source of superior organization performance from the perspective of supply chains. In considering absorptive capacity, the concept of potential absorptive capacity is proposed in which the focus is more on knowledge acquisition and assimilation capability. It argues that the absorptive ability of organizations is more concerned with their knowledge transformation and exploitation capability (Liu et al., 2013; S aenz et al., 2014; Tzokas et al., 2015). 2.3. Innovation performance Innovation performance is a comprehensive evaluation of organizational innovation activities (Bai et al., 2015). It can be divided into broad-sense innovation performance and narrowsense innovation performance (Hagedoorn and Cloodt, 2004). Narrow-sense innovation performance focuses on innovation efficiency and the value generated by innovation including the research and development speed of new products, new technologies and new equipment (Freeman and Soete, 1997). Broad-sense innovation performance concerns about the evaluation of innovation results and the whole innovation process in organizations. There are various studies in evaluating innovation performance from different perspectives. Nonaka and Takeuchi (1995), for example, argue that innovation performance is reflected from product innovation, process innovation and organizational innovation, and strategic innovation. Meeus and Hage (2006) find out that innovation performance is about product and management innovation consisting of tangible and intangible product innovation and technological and management process innovation. Szabo and Csontos (2016) state that innovation performance is related to technological innovation and management innovation. The discussion above shows the multi-dimensional nature of innovation performance in organizations. Green innovation performance in GSCM includes green product innovation and green process innovation (Chen et al., 2006). Green product innovation is about the application of innovative ideas to the design, manufacturing and marketing of new products to significantly promote the environmental favourability of those products (Kam-Sing, 2012). Green process innovation is related to the utilization of innovative ways to diminish negative environmental impacts caused by production processes, which involve activities reducing hazardous emissions for re-use and decreasing energy and raw material consumption (Chen et al., 2006; Tseng et al., 2013). 3. Hypothesis developments This study aims to investigate the influence of collaborative innovation on innovation performance with respect to GSCM along the supply chain. It explores whether absorptive capacity has mediating effects between GSCCI and innovation performance. With this in consideration, GSCCI is divided into three kinds including GSCCI between organizations (suppliers, customers, competitors), GSCCI between organizations and government, GSCCI between organizations and institutions (industry associations, venture capital institutions, technical intermediaries). Absorptive capacity is related to knowledge acquisition, knowledge assimilation, knowledge transformation and knowledge exploitation. Innovation performance is about green product innovation performance and green management innovation performance. Based

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on a comprehensive review of the related literature, a conceptual model for exploring the relationship between GSCCI, absorptive capacity and innovation performance is depicted in Fig. 1. 3.1. GSCCI and innovation performance 3.1.1. Organization-organization collaborative innovation and innovation performance GSCCI between foreign trade organizations and other organizations is about collaborative innovation with suppliers, customers, and competitors in the supply chain. Supplier collaborative innovation (SCI) comes from related research about “Early Supplier Involvement” and “Supplier Involvement in New Product Development”. In this regard, Jean et al. (2012) emphasize the role of upstream suppliers in the supply chain collaborative innovation and argue that taking SCI into the development strategy would promote innovation performance of the whole supply chain. Calvi (2012) point out that the innovation level of suppliers plays a crucial role in the supply chain collaborative innovation, which determines innovation efficiency. Based on an empirical study of 285 companies, Li et al. (2013) find that collaborative innovation with upstream suppliers in the product design process can improve innovation performance of organizations. Customer collaborative innovation (CCI) is related to collaborative innovation between organizations and their customers. Through exploring the asymmetry on knowledge structure and innovation skills between customers and organizations and using customer knowledge and creativity as innovative resources, CCI can enhance the overall creativity of the supply chain by complementing the innovative advantages between organizations and their customers (Ojanen and Hallikas, 2009). CCI is developed based on the lead user theory (Schreier and Prügl, 2010) and the creative organization theory (Morgan, 1989). Lettl (2007) find that specific customer participation in breakthrough technology innovation could improve innovation performance. Liao et al. (2017) argue that collaborative innovation has become the key to the development of supply chain products. This discussion shows that customer knowledge is critical in the innovation of supply chain technologies. CCI can not only improve customer satisfaction, but also reduce business operations risks and innovation costs (Fernandes and Remelhe, 2016). Competitor collaborative innovation is concerned about collaborative innovation between organizations that produce similar products or provide similar services by sharing knowledge, information, and technologies. It is conducive to improving the technical level of the whole industry. Green collaborative innovation between organizations and their competitors can promote the integration of resources and capabilities and reduce research and development costs and innovation risks by sharing technologies. Guo et al. (2012) argue that the relationship intensity and quality between organizations and their competitors influence innovation performance of the organization. Competitor collaborative innovation not only facilitates the sharing and optimization of innovative resources on waste reduction, but also promotes the healthy development of the industry (Estrada et al., 2016). Since the reform and opening policy in 1978, Chinese foreign trade organizations have enjoyed a rapid growth in surrounding areas of the Yangtze River Delta and Pearl River Delta. Foreign trade organizations are becoming one of the engines of China's economic growth. The advantage of low labor costs in Chinese foreign trade organizations, however, is diminishing. They must reduce the gap between their knowledge and technology base and that of their counterparts in developed countries for maintaining their competitiveness (Zeng et al., 2010). This discussion leads to the development of the hypothesis as follows.

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Fig. 1. Conceptual framework.

H1. GSCCI between Chinese foreign trade organizations and other organizations is positively associated with organizational innovation performance.

3.1.2. Organization-government collaborative innovation and innovation performance Government provides an innovation environment or directly gives institutional and financial support for facilitating collaborative innovation in organizations. Specific strategies and policies can be implemented by government to facilitate the introduction of innovative talents and other resources. Government plays a macrocontrol and coordinating role in collaborative innovation through tax policies, financial supports, industrial policies, talent policies and special programs (Hartley and Torfing, 2013). Government support plays a critical role in facilitating collaborative innovation in organizations. Ranga and Etzkowitz (2013), for example, show that there is a systemic interaction between universities, organizations, and government for improving innovation performance. Abhyankar (2014) reveal that government has a positive effect on innovation performance through the formulation and implementation of specific strategies and policies on collaborative innovation. Patanakul and Pinto (2014) indicates that government policies could change the will, ability and opportunity of an organization, therefore promoting sustainable innovation. Such discussion above shows that GSCCI between Chinese foreign trade organizations and government influences innovation performance of organizations. This leads to the development of the following hypothesis. H2. GSCCI between Chinese foreign trade organizations and government is positively associated with organizational innovation performance.

3.1.3. Organization-institution collaborative innovation and innovation performance Collaborative innovation between foreign trade organizations and institutions is about collaborative innovation with industry associations, technology intermediaries, venture investment enterprises,

and technology markets using talent, capital, knowledge, and technology innovation resources in a comprehensive manner. It is common that many organizations lack funds and run high risks when investing in innovation. Intermediary agencies, which involve legal affairs, consultation, financing, and human resources, can provide professional services for organizational innovation (Howells, 2006). Intermediary service institutions play a role as a bridge in organizational innovation and improve their innovation performance (Billington and Davidson, 2013). Technical intermediary organizations can effectively solve the information asymmetry problem in a market economy by breaking information blockade and information monopolies. They can reduce technology transaction costs and information search costs (Zhang and Li, 2010). Regular meetings by industry associations may reveal innovation directions. This helps individual organizations obtain empirical knowledge from leading organizations. Venture investment organizations not only provide capital and value-added services for organization innovation. Their risk control mechanisms can contribute to the socialization of organization innovation risks and promote the application of collaborative innovation (Dushnitsky and Lenox, 2006). The presence of technology markets can help organizations cope with opportunities and threats associated with innovative activities in a highly competitive environment (Christensen et al., 2007). Organizations can obtain required technologies from technology markets and fully absorb and utilize external knowledge to make up for their own knowledge gaps, hence reducing the uncertainty of technological innovation and speeding up innovation (Zhou and Li, 2012). The discussion above reveals that collaborative innovation with institutions affects innovation performance. This leads to the following hypothesis: H3. GSCCI between Chinese foreign trade organizations and institutions is positively associated with organizational innovation performance.

3.2. Absorptive capacity and innovation performance Absorptive capacity can directly or indirectly affect innovation

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performance. It has significant impacts on innovation speed, innovation frequency and innovation degree. Absorptive capacity can help organizations better apply external knowledge into collaborative innovation (Leiponen and Helfat, 2010). Organizations with strong absorptive capacity have strong learning ability, therefore being able to integrate and translate external knowledge into their own knowledge effectively. This enables them to innovate successfully (Chen et al., 2009; Marcin, 2008). Huang and Rice (2009) find that absorption capacity has a significant impact on the technological innovation ability of organizations. Kostopoulos et al. (2011) suggest that external knowledge inflow is directly related to absorptive capacity and indirectly related to innovation performance. This discussion shows that absorptive capacity affects innovation performance, therefore leading to the development of the following hypothesis. H4. Absorptive capacity is positively associated with Chinese foreign trade organizational innovation performance.

3.3. GSCCI and absorptive capacity The source of external knowledge has a significant influence on knowledge acquisition, assimilation, transformation, and application ability of organizations (An et al., 2014). In GSCCI, organizations collaborate with suppliers, customers, competitors, governments, industry associations and other organizations. This enlarges their knowledge sources and promotes knowledge acquisition and knowledge transfer between organizations (Xiong and Deng, 2008). If other conditions hold constant, the more external knowledge sources belong to an organization, the stronger an organization's absorptive capacity is about. External relationship intensity significantly affects the absorptive capacity of organizations. There is a strong connection and trust basis between organizations and their partners in the supply chain. Communication and trust between these organizations can enhance the flow of knowledge between organizations. The assimilation ability and the transformation ability of organizations can effectively improve the integration and application of external knowledge. Absorptive capacity can be divided into demand-pulling capacity and technology-driving capacity (Murovec and Prodan, 2009). There are various critical factors that affect absorption capacity of organizations including internal research and development, personnel training, innovative cooperation and initiative attitude. Collaborative innovation can not only accelerate the flow of information and knowledge between organizations, but also enhance organizations’ knowledge accumulation and form effective learning and communication mechanisms, therefore promoting organizational absorptive capacity (Lin et al., 2012). There is a well-recognized relationship between GSCCI and absorptive capacity in organizations. Liao et al. (2007), for example, note that knowledge sharing across organizations is beneficial for acquiring comprehensive knowledge that can enhance the absorptive capacity of the organization for facilitating GSCCI. Chen et al. (2011) find that absorptive capacity of an organization that is affected by the channel of obtaining external knowledge, the degree of relationships between organizations and institutions, the corporate social capital and the external knowledge property directly influences GSCCI. Through collaborative innovation with upstream and downstream members of the supply chain, organizations can increase their knowledge acquisition channels and enhance their knowledge acquisition ability. This shows that GSCCI influences absorptive capacity of organizations. As a result, several hypotheses have been developed as follows.

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H5. GSCCI between Chinese foreign trade organizations and other organizations is positively associated with their absorptive capacity. H6. GSCCI between Chinese foreign trade organizations and government agencies is positively associated with their absorptive capacity. H7. GSCCI between Chinese foreign trade organizations and institutions is positively associated with their absorptive capacity.

3.4. The mediating effect of absorptive capacity Absorbing external knowledge into innovation can reduce internal research costs and innovation risks (Nicotra et al., 2014). Liao et al. (2007) argue that the knowledge acquisition ability of organizations can improve their knowledge reserves while the assimilation and transformation ability can affect their knowledge learning. Both the knowledge acquisition ability the assimilation and transformation ability directly influence the business performance of organizations. Knowledge spillover has no direct impact on innovation performance. Through the mediating effect of absorptive capacity, knowledge spillover can promote collaborative innovation. Organizations can absorb external knowledge for promoting innovation and improving innovation performance (Zubielqui et al., 2016). Existing studies have explored the mediating effect of absorptive capacity between knowledge spillover and innovation performance and investigated the mediating effect of absorption capacity between organizations and innovation performance. This shows that absorptive capacity has been incorporated into GSCCI. Organizations with stronger absorptive capacity can better integrate shared knowledge, information and innovation resources, therefore improving their innovation performance. This discussion shows that absorptive capacity has a mediating effect between GSCCI and innovation performance. As a result, the following hypotheses can be developed. H8. Absorptive capacity mediates the relationship between organization-organization collaborative innovation and innovation performance. H9. Absorptive capacity mediates the relationship between organization-government collaborative innovation and innovation performance. H10. Absorptive capacity mediates the relationship between organization-institution collaborative innovation and innovation performance.

4. Research methodology This study aims to investigate the relationship between GSCCI, absorptive capacity and innovation performance in organizations. To achieve this aim, a close-ended questionnaire is developed for collecting data due to the confirmatory nature of the research question (Cui et al., 2019; Duan et al., 2019). The following discussion focuses on the identification of the relevant variables and their measurements, the design and development of the questionnaire, and the implementation of the survey based on the selection of an adequate sample in the study. 4.1. Variable measurement Three dimensions (9 items) for measuring GSCCI are adopted from the studies of Singh (2005), Xie (2010), Patanakul and Pinto

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(2014), Zeng et al. (2010), and Gramkow and Angerkraavi (2017). The three dimensions include green supply chain innovation, supply chain collaborative innovation and collaborative innovation networks. There are three items for measuring the level of collaborative innovation between Chinese foreign trade organizations and other organizations, three items for measuring the level of collaborative innovation between Chinese foreign trade organizations and government, and the remaining three items for measuring the level of collaborative innovation between Chinese foreign trade organizations and institutions. Three items for measuring absorptive capacity are obtained from the studies of Lewin et al. (2011) and Petti (2014), including knowledge acquisition, knowledge assimilation, knowledge transformation and knowledge exploitation. Innovation performance is a comprehensive evaluation of innovation process efficiency and outcomes including product innovation and management innovation (Meeus and Hage, 2006; Chen et al., 2006). It is related to green product innovation and green management innovation. In this study, nine measurement items presented as in Appendix 1 have been used for measuring the green innovation performance (Hung and Chen, 2014; Dangelico et al., 2017). 4.2. Questionnaire design and data collection The questionnaire design is based on a 5-point Likert type scale based on existing literature. The items of constructs are assessed with a 5-point Likert scale, ranging from “1: strongly disagree”, “2: disagree”, “3: uncertain”, “4: agree”, “5: strongly agree”. Before conducting a thorough investigation, eight senior managers from Chinese foreign trade organizations and four experts who have research experience in supply chain management of foreign trade

organizations are selected for a pilot study. The content of the questionnaire has been revised based on the feedback from the pilot study. Several methods have been used in this study including field investigation, e-mail investigation and network investigation for selecting the sample population. In network investigation, electronic questionnaires are sent to the member of Shanghai Logistics Association-reverse Logistics Branch through Wechat groups. This study collected data from Chinese foreign trade organizations in different regions. A total of 639 questionnaires have been sent out. 218 questionnaires have been returned, including 206 valid questionnaires with an effective response rate of 32.2%. A non-response bias test is conducted by comparing the difference in the number of employees among the responses that are returned early and responses that are returned later. A t-test of differences is conducted. No statistically significant differences have been identified. This shows that no non-response bias has been detected. When the questionnaire is filled in by the same enterprise, there may be a common variation of the data (Duan et al., 2019). To check the existence of a common variation, the Harman's one factor test is conducted. The result shows that the factors with a characteristic value over 1 can explain 85.9% of the variance and the single factor can explain 32.3% of the variance. This means that the common method bias does not affect the data. 5. Data analysis 5.1. Descriptive statistical analysis An analysis of Table 1 shows that 69.9% of respondents (144 out of 206) are from the Yangtze River Delta and the Pearl River Delta

Table 1 Statistical characteristics of the sample data. Profiles

Category

Frequency Proportion Category (%)

Profiles

Frequency Proportion (%)

Location

Shanghai

57

27.67

26

12.62

Zhejiang Guangdong Anhui Jiangsu Fujian

31 22 13 13 8

15.05 10.68 6.31 6.31 3.88

9 14 11 9 17

4.37 6.80 5.34 4.37 8.25

Liaonin Chongqing Others

7 7 48

3.40 3.40 23.30

25 27 27

12.14 13.11 13.11

Senior executive Senior managers Managers First-line managers Others Less than 1 years 1e3 years 3e5 years More than 5 years Manufacturingoriented Circulation firms Service-oriented Technologyoriented Less than 100 100e300 301e500 501e1000 More than 1000

24 46 44 66 26 23 61 40 82 72

11.65 22.33 21.36 32.04 12.62 11.17 29.61 19.42 39.81 34.95

Food and beverage, alcohol and cigarettes Wood and furniture Pharmacy Building material Rubber and plastics Chemical products and petrochemical industry Metal machinery and Engineering Textiles and apparel Electronic products and electrical appliances Publishing and printing Others Less than 3 years 4e5 years 6e10 years 11e20 years More than 20 years State-owned enterprise Collective enterprise Private enterprise

10 31 9 18 71 61 47 25 21 84

4.85 15.05 4.37 8.74 34.47 29.61 22.82 12.14 10.19 40.78

50 67 17

24.27 32.52 8.25

Joint venture Foreign-funded enterprise Others

28 46 2

13.59 22.33 0.97

54 55 39 30 28

26.21 26.70 18.93 14.56 13.59

Less than 1 million 1-10 million 10-50 million 50-100 million More than 100 million

7 39 62 27 71

3.40 18.93 30.10 13.11 34.47

Position of respondents

Working experience in current firm (years)

Type of foreign trade firm

Size (Employees)

Industry

Firm history (years)

Ownership

Sales volume (Yuan Renminbi, ¥)

J. Hong et al. / Journal of Cleaner Production 241 (2019) 118377

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Table 2 Reliability and validity analysis. Variables

Sub-variables

Items

Loading

Cronbach's a

KMO

Variance Explained(%)

Green supply chain collaborative innovation

Organization-Organization

CC1 CC2 CC3 CG1 CG2 CG3 CO1 CO2 CO3 AC1 AC2 AC3 AC4 IP1 IP2 IP3 IP4 IP5 IP6

0.844 0.902 0.861 0.907 0.885 0.884 0.905 0.903 0.875 0.892 0.872 0.857 0.839 0.851 0.785 0.869 0.783 0.856 0.834

0.838

0.706

75.605

0.869

0.737

79.605

0.873

0.737

80.021

0.885

0.793

74.863

0.909

0.885

68.944

Organization-Government

Organization -Institution

Absorptive capacity

Innovation performance

Knowledge acquisition Knowledge digestion Knowledge transformation Knowledge application Product innovation Management innovation

region where the most economically dynamic and wealthy population is concentrated. Among the 206 respondents, 114 (55.34%) are Middle and upper level management personnel. Staff with more than three years of working experience in current organizations is the largest representative group in the sample that accounts for 59.22%. Since the managers know more about the organization, they can better understand the questionnaire. This can ensure the accuracy of the research findings. The type of industry that the surveyed organization is examined. The result reveals that various industries have been involved in this study including food and beverage, building materials, pharmaceutical, petrochemical, printing, and textile industries. In terms of the type of foreign trade organizations, manufacturing-oriented organizations and service-oriented organizations are the majority ones. Technology-oriented organizations are rare which only account for 8.25%. An analysis of the nature of organizations shows that private foreign organizations account for 63.11%. 86.89% of the surveyed organizations have been set up for more than 5 years. From the perspective of the organization size, there are 52.91% of organizations with less than 300 employees. 96.6% of the surveyed organizations have annual sales of at least CNY ¥10 million (CNY ¥1 ¼ USD $0.15), among which organizations with sales of more than CNY ¥100 million are the largest category.

5.2. Reliability and validity analysis The Cronbach's a coefficient is used for measuring the stability and the internal consistency of the questionnaire (Deng et al., 2018a,b). Factor analysis is adopted for testing the validity and accuracy of the questionnaire. The Kaiser-Meyer-Olkin (KMO) statistic and Bartlett's spherical check are conducted for determining whether the sample is suitable for factor analysis. In this study, SPSS software is utilized for conducting the statistical analysis. The internal concordance coefficient of the questionnaire is at 0.933. The Cronbach's a coefficient of each variable is ranged from 0.838 to 0.909. This shows that the questionnaire has high reliability and stability. There are several ways to estimate the validity of the questionnaire including the content validity and the construct validity (Karunasena and Deng, 2012). The questionnaire uses a 5-point Likert type scale derived from existing literature. It is pilot tested through a thorough discussion with the managers of foreign trade

organizations and academic experts. This can ensure that the questionnaire has content validity. In assessing the construct validity, KMO statistic and Bartlett's sphere test are performed. The result shows that the KMO value of each variable is greater than 0.7 and the P values of the Bartlett's sphere test are less than 0.001. This means that the factor correlation coefficient matrix of each variable is not a unit matrix. It shows that the factor analysis can be conducted in the study. Principal component analysis is adopted to obtain the factor loading of each dimension under the principle that the feature value is greater than 1. At the same time, the cumulative interpretation of variables in each dimension is more than 68%. All these principles ensure the questionnaire has construct validity. Table 2 presents the result.

5.3. Correlation analysis This study uses Pearson correlation coefficients to analyze the correlation between organization-organization collaborative innovation, organization-government collaborative innovation, organization-institution collaborative innovation, absorptive capacity and innovation performance. Table 3 shows that organization-organization collaborative innovation, organization-government collaborative innovation and organization-institution collaborative innovation all have a significant positive correlation with innovation performance and absorptive capacity respectively (p < 0.01). There is a weak correlation between organization-government collaborative innovation and absorptive capacity (r ¼ 0.284), and a strong correlation between absorptive capacity and innovation performance (r ¼ 0.706). As shown in Table 3, the correlation coefficients between the explanatory variables are less than 0.6. This indicates that multicollinearity is not an issue in this study.

Table 3 Correlation coefficient matrix. Variables

1

2

3

4

5

1. 2. 3. 4. 5.

1 .189** .570** .455** .556**

1 .408** .284** .409**

1 .524** .680**

1 .706**

1

Organization-Organization Organization-Government Company-Institution Absorptive capacity Innovation performance

Note:

**

Correlation is significant at the 0.01 level (2-tailed); N ¼ 206.

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5.4. Hypothesis testing In this subsection, multiple regression analysis is used to analyze the relationship between GSCCI and innovation performance and to verify whether absorptive capacity has a mediating effect on this relationship. The study sets the annual sales and the type of foreign trade organizations as control variables, and foreign trade organizations as the reference group with which manufacturing-oriented foreign trade organizations, serviceoriented foreign trade organizations and technology-oriented foreign trade organizations are compared. The influence of GSCCI and absorptive capacity on innovation performance. To verify the relationship between GSCCI and innovation performance, three dimensions of GSCCI are taken as independent variables, and innovation performance as the dependent variable, the annual sales and the type of foreign trade organizations as control variables for regression analysis. Table 4 shows the regression analysis result. The R2 value of the regression model is at 0.546. The adjusted R2 value is at 0.530. This shows that GSCCI can explain about 53% of the change in innovation performance. The F statistic is at 33.986 (p < 0.001). This means that the regression equation is significant. An analysis of Table 4 finds out that the annual sales of organizations have a weak influence on innovation performance (b ¼ 0.078, p < 0.01). It reveals that the influence of the type of foreign trade organizations on innovation performance is not statistically significant. The influence of organization-institution collaborative innovation on innovation performance is very significant (b ¼ 0.382, p < 0.001). The same is true on the influence of organization-organization collaborative innovation (b ¼ 0.226, p < 0.001). Organization-government collaborative innovation has the weakest influence on innovation performance (b ¼ 0.130, p < 0.01). With the improved GSCCI, collaborative innovation between different organizations, organizations and government, organizations and institutions can all promote innovation performance. This means that hypotheses H1, H2 and H3 are all supported. The study shows that organization-government collaborative innovation has the weakest influence on innovation performance. This means that current governmental policies supporting green innovation in China is not enough. There exist some problems in the process of implementing governmental policies. It may be due to the fact that the implementation of innovation-related polices is in isolation and there is lack of a coherent strategy. The government needs to play a more prominent role in promoting collaborative

innovation. The study examines the relationship between absorptive capacity and innovation performance using regression analysis. Table 4 presents the result. The R2 value of the regression model is 0.519, the adjusted R2 value is 0.507 and the F statistic is at 43.175 (p < 0.001). This means that absorptive capacity has a significant positive correlation with innovation performance (b ¼ 0.130, p < 0.01). It shows that hypothesis H4 is accepted. Organizations can improve their innovation performance by enhancing their abilities of knowledge acquisition, knowledge digestion, knowledge transformation and knowledge application. In both regression models above, the tolerances for the variables are greater than 0.1. The VIF values are less than 2. This means that there is no serious multicollinearity between these variables in the study.

5.4.1. The influence of GSCCI on absorptive capacity This study explores the relationship between GSCCI and absorptive capacity with the annual sales and the type of foreign trade organizations as control variables using regression analysis. Table 5 presents the regression analysis result. The R2 value of the regression model is 0.384. The adjusted R2 value is 0.362. The F statistic is at 17.630 (p < 0.001). This means that there is a significant relationship between GSCCI and absorptive capacity. The tolerances for the variables in this model are greater than 0.1. The VIF values are less than 2. This shows that there exists no serious multicollinearity between these variables. The study reveals that there is a significant positive correlation

Table 5 Multiple regression analysis on the influence of GSCCI on absorptive capacity. Variables

Control variable Annual sales Manufacturing-oriented Service-oriented Technology-oriented Independent variable Organization-Organization Organization-Government Organization -Institution F R2 △R2

Absorptive capacity

b

T value

.163 -.008 -.118 .010

3.891*** -.064 -.906 .051

.211 .057 .317 17.630*** 0.384 0.362

3.205** 1.049 5.111***

Note: P < 0.001***; P < 0.01**; P<0.05*; P>0.05 ¼ N.S.

Table 4 Multiple regression analysis on the influence of GSCCI and absorptive capacity on innovation performance. Variables

Innovation performance

b Control variable Annual sales Manufacturing-oriented Service-oriented Technology-oriented

.078 .116 .143 .059

2.255** 1.098 1.340 0.359

.226 .130 .382 33.986*** 0.546 0.530

4.167*** 2.906** 7.485***

Innovation performance

b

T value

-.023 .109 .287 .165

-.645 1.010 2.646 .985

Absorptive capacity

0.688

14.069***

F R2 △R2

43.175*** 0.519 0.507

T value

Independent variable Organization-Organization Organization -Government Organization -Institution F R2 △R2

Variables

Control variable Annual sales Manufacturing-oriented Service-oriented Technology-oriented Independent variable

Note: P < 0.001***; P < 0.01**; P<0.05*; P>0.05 ¼ N.S.

J. Hong et al. / Journal of Cleaner Production 241 (2019) 118377

between the annual sales of organizations and absorptive capacity (b ¼ 0.211,p < 0.01). No significant correlation, however, have been identified between the type of foreign trade organizations and absorptive capacity. Organization-organization collaborative innovation has a significant positive correlation with absorptive capacity (b ¼ 0.211, p < 0.01). This shows that hypothesis H5 is accepted. Organization-institutions collaborative innovation has a significant positive correlation with absorptive capacity (b ¼ 0.317, p < 0.001). This proves that hypothesis H7 is accepted. The influence of organization-government collaborative innovation on absorptive capacity is not statistically significant (b ¼ 0.057, p ¼ 0.295). This states that hypothesis H6 is rejected. In summary, organization-organization collaborative innovation and organization-institutions collaborative innovation both have a positive impact on absorptive capacity in terms of knowledge acquisition, knowledge assimilation, knowledge transformation, and knowledge application.

5.4.2. Mediating effect of absorptive capacity This study has proved the first three preconditions for the mediating effect of absorptive capacity. First, GSCCI has a significant influence on innovation performance of organizations. Second, GSCCI does have a significant positive correlation with absorptive capacity. Lastly, absorptive capacity has a significant positive correlation with innovation performance of organizations. Adding the mediating variable (absorptive capacity) to the model above, this study tests the influence of GSCCI on innovation performance. Table 6 presents the results. The R2 value of the regression model is 0.666. The adjusted R2 value is 0.653. The F statistic is 49.131 (p < 0.001). This shows that there is a significant correlation between the testing variables. The tolerances for variables in this model are greater than 0.1. The VIF values are less than 2. As a result, no serious multicollinearity is existent between these variables. The study shows that the correlation between the annual sales and innovation performance is not significant (b ¼ 0.009, p ¼ 0.778). Compared to circulation oriented foreign trade organizations, service-oriented foreign trade organizations have a more significant influence on innovation performance (b ¼ 0.193, p < 0.05). Table 6 shows that absorptive capacity has a significant positive effect on innovation performance (b ¼ 0.423, p < 0.001). The three dimensions of GSCCI, i.e., organization-organization collaborative Innovation (b ¼ 0.136, p < 0.01), organizationgovernment collaborative innovation (b ¼ 0.106, p < 0.05), and organization-institutions collaborative innovation (b ¼ 0.248,

9

p < 0.001), have a significant positive influence on innovation performance of organizations. It is interesting to note that the regression coefficients of collaborative innovation between different organizations, between organizations and government, between organizations and institutions have decreased compared to their counterparts in the previous model before adding the mediating variable. The t values of organization-organization collaborative innovation, organization-government collaborative innovation, and organization-institutions collaborative innovation have been decreased from 4.167 to 2.856, from 2.906 to 2.476, and from 7.485 to 5.308, respectively. This shows that absorptive capacity plays a partial mediating effect between GSCCI and innovation performance of organizations. To further evaluate the mediating effect between GSCCI and innovation performance, a nonparametric percentile bootstrap method (Hayes, 2013) is applied. Table 7 presents the results. The Bootstrap test result reveals that the indirect effect of absorptive capacity between organization-organization collaborative innovation and innovation performance is 0.0894, with the interval of [0.0208, 0.1952] which exclude 0. This confirms the existence of the mediating effect of absorptive capacity between organization-organization collaborative innovation and innovation performance. The mediating effect of absorptive capacity between organization-government collaborative innovation and innovation performance is 0.0241, with the interval of [-0.0224, 0.0971] which includes 0. This shows that the mediating effect of absorptive capacity between organization-government collaborative innovation and innovation performance is insignificant. The mediating effect of absorptive capacity between organization-institution collaborative innovation and innovation performance is 0.1341, with the interval of [0.0587, 0.2336] which excludes 0. This proves that the mediating effect of absorptive capacity between organization-institution collaborative innovation and innovation performance is present. The test above demonstrates that hypotheses H8 and H10 are acceptable while H9 has to be rejected.

Table 7 Bootstrap test. Variables

Indirect effect

Boot SE

BootLLCI

Boot ULCI

Organization-Organization Organization-Government Organization -Institution

0.0894 0.0241 0.1341

0.0419 0.028 0.0426

0.0208 0.0224 0.0587

0.1952 0.0971 0.2336

Table 6 Mediating effect of absorptive capacity. Variables

Control variable Annual sales Manufacturing-oriented Service-oriented Technology-oriented Independent variable Organization-Organization Organization-Government Organization -Institution Mediator variable Absorptive capacity F R2 △R2

Innovation performance

Test results

b

T value

.009 .120 .193 .055

.283 1.316 2.097* .387

.136 .106 .248

2.856** 2.746** 5.308***

.423 49.131*** 0.666 0.653

8.427***

Note: P < 0.001***; P < 0.01**; P<0.05*; P>0.05 ¼ N.S.

Significant,4.167 > 2.856,Partial mediation Significant,2.906 > 2.476,Partial mediation Significant,7.485 > 5.308,Partial mediation

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6. Results and discussions 6.1. The relationship between GSCCI and innovation performance This study demonstrates that the innovation performance of organizations can be enhanced when collaborative innovation between China's foreign trade organizations and the participants in the green supply chain increases. Such a finding is consistent with that of Wu (2013). In line with the study of Pierrakis and Saridakis (2017), this study finds that collaborative innovation between organizations and institutions reduces the cost for seeking innovation resources and improves innovation efficiency. The findings of this study are consistent with those of Zailani et al. (2015) who shows that environmental regulations, market demands and internal motivations are the critical factors for green innovation. The government can bring impetus for green innovation by providing preferential policies or financial support. From the practical point of view, this study explains the status of foreign trade organizations in China that are facing the pressure from increasingly strict environmental regulations, rising production cost and customer demand for green products. Chinese foreign trade organizations are keen to pursue collaborative innovation with participants in the green supply chain for seeking sustainable competitive advantages. This can result in more effective utilization of innovative resources, reduced complexity and uncertainty of innovation in green supply chains, and ultimately improved performance of innovation in organizations. This study shows that the type of foreign trade organizations and the annual sales of organizations have different impacts on the relationship between GSCCI and innovation performance. It reveals that the type of foreign trade organizations has no significant influence on the relationship between GSCCI and innovation performance. The annual sales of organizations has a week impact on innovation performance. 6.2. The relationship between GSCCI and absorptive capacity Increased GSCCI between different organizations and between organizations and institutions has greater impact on the absorptive capacity of organizations. This indicates that collaborative innovation between different organizations, and between organizations and institutions can provide external resources of knowledge that expand the channel for knowledge seeking. It further enhances the ability of organizations for knowledge acquisition, promotes the flow and transformation of knowledge, and comprehensively enhances the absorptive capacity of organizations. This finding is consistent with the that of Savin and Egbetokun (2016) which states organizations should enrich their knowledge through actively communication with suppliers, customers, competitors, industry associations and other participants for translating external knowledge into their own and applying what they have learned to improve collaborative innovation. This study shows that the influence of collaborative innovation between organizations and government on absorptive capacity is not significant. This may be due to the fact that government supports organizations in innovation mainly by providing capital and industrial policies for creating a suitable environment to innovation in collaborative innovation between organizations and government. This means that government provides less sources of knowledge in the process of collaborative innovation, therefore having an insignificant influence on absorptive capacity. 6.3. The relationship between absorptive capacity and innovation performance Strong absorptive capacity of foreign trade organizations

improves their innovation performance. This indicates that strong absorptive capacity can enable organizations to obtain more sources and channels for acquiring knowledge, therefore increasing their innovative knowledge accumulation. Strong absorptive capacity can enable organizations to better digest and absorb knowledge and apply them to research and development through innovation. This inevitably enhances innovation efficiency. This finding of the study is in agreement with the view that organizations with strong absorptive capacity can utilize external sources of knowledge to improve their innovative performance (Flor et al., 2017). Similar to the sample sources in this study, Wang and Li (2016) have used the data from 297 organizations in the Yangtze River Delta in China participating the global supply chains to confirm that absorptive capacity can enhance innovation performance of organizations. Their findings are consistent with the results of this study. Furthermore, the findings of this study are also consistent with those of Kotabe et al. (2017) who have found that the complementarity of political network ability and absorptive capacity can better enhance the innovation performance of organizations through the investigation of 108 executives in China. 6.4. Mediating effect of absorptive capacity This study shows that GSCCI affects innovation performance of organizations through mediating absorptive capacity. It means that absorptive capacity plays a partial mediating role in the relationship between organization-organization collaborative innovation and innovation performance and between organizationinstitutions collaborative innovation and innovation performance. This study, however, does not find the mediating role of absorptive capacity in the relationship between organization-government collaborative innovation and innovation performance. This indicates that collaborative innovation between Chinese foreign trade organizations and government has no significant impact on knowledge acquisition, digestion, transformation and application of organizations. The influence is more focused on providing a suitable external innovation environment. In line with the findings of this study, Liao et al. (2017) show that collaborative innovation in the supply chain can influence the innovation effect and competitive advantage of organizations through the mediating variable of supply chain capability including absorptive capacity by studying Taiwan's network communications enz et al. (2014) reveal that absorptive capacity plays a industry. Sa mediating role between supply chain collaboration and innovation performance by examining European multinational retail chains and American multinational spare parts distributors. Zubielqui et al. (2016) find out that external knowledge collaboration can affect innovation performance through absorptive capacity based on a survey of 838 Australian SMEs. Yang and Lin (2012) conclude that absorptive capacity plays an important mediating role in the relationship between trade openness and cooperation level and knowledge innovation of organizations by analyzing Chinese industrial data at the provincial level. From a practical point of view, the findings of this study show that Chinese foreign trade organizations are more inclined to collaborate with supply chain partners and related organizations in GSCCI. These organizations can further strengthen the status quo of collaborative innovation through improving their own absorptive capacities. 7. Conclusion The theoretical contributions of this study are three-fold. First, past research mainly focuses on the critical factors of green supply chains based on the stakeholder theory or the institutional theory. There are few studies about the driving mechanism of green supply

J. Hong et al. / Journal of Cleaner Production 241 (2019) 118377

chains from the perspective of collaboration innovation. This study fills that gap by combining green supply chains and collaborative innovation. This shows that this study is conducive to deepening and enriching the theoretical connotation and research scope of green supply chains. Second, this study adds absorptive capacity as a mediating variable into the research framework of the relationship between GSCCI and innovation performance. The study of the interrelationship and action path among the three variables reveals the internal mechanism that promotes innovation performance through collaborative innovation and absorptive capacity enhancement. Finally, in recent years, sustainable supply chain issues in developing countries have been put on the agenda with few studies (Hong et al., 2018). The issue of how to drive economic transformation in those countries through collaborative innovation in green supply chains has become urgent. This study takes China as the research object and uses empirical research methods to analyze multi-dimensional factors in exploring the synergistic effect of GSCCI in Chinese foreign trade organizations. The study provides a theoretical basis for promoting innovation and application of green supply chains in developing countries. The research findings of this study have at least two important managerial implications. First, the perspective of this study corresponds to the realistic background that the Chinese government is advocating innovation-driven and collaborative innovation strategies at the macro level. This study can provide a direction and focus for the government to develop relevant policies for green innovation and create a better external environment for GSCCI that aims to optimize the transformation of Chinese foreign trade organizations. Second, the findings of this study enable organizations to better understand the impact and different degrees of collaborative innovation among different participants in green supply chains, and to coordinate, integrate and utilize innovation resources for improving innovation performance in realizing the unification of economic and environmental benefits at the micro level. Especially in the current context of “Internet plus”, Chinese foreign trade organizations ought to make full use of the dividend policies proposed by the Chinese government for cross-border e-commerce to transform the traditional concept of trade, and accelerate the transformation of the industry to form a new development model of “Internet plus foreign trade”. Chinese foreign trade organizations should pursue purposeful green innovation. Through a variety of cross-border e-commerce platforms, foreign trade organizations can understand diverse consumer preferences for green products in different countries and develop tailored collaborative innovation between suppliers and customers. This will not only result in more efficient energy utilization and reduced costs in production, but also improve the product competitiveness and economic performance of foreign trade organizations, which ultimately leads to the sustainable development of organizations. As with any research, this study has some limitations. First, the sample data is limited. The samples mainly concentrate upon the Yangtze River Delta, Pearl River Delta and other developed regions in China. This makes the research findings less generalizable. As a result, the applicability of the findings needs to be further confirmed. Second, the main research objects of this study are foreign trade organizations. Future research can extend this to other types of organizations. Third, this study does not measure how GSCCI, absorptive capacity and innovation performance change over time. This shows that future research can study absorptive capacity and innovation performance at different stages of GSCCI. Finally, there are different modes of GSCCI including strategic alliance, patent cooperation, and R&D outsourcing which may have different impacts on innovation performance. This study has not specifically considered the influence of GSCCI modes. This means that future research can focus on the impact of different

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J. Hong et al. / Journal of Cleaner Production 241 (2019) 118377 science-based actors, absorptive capacity, innovation and performance: a study of SMEs. Int. J. Innov. Manag. 20 (06), 1650055.

Dr Jiangtao Hong*(Corresponding author) is an Associate Professor of Management Science in the International Business School at Shanghai University of International Business and Economics. He holds a PhD in Operation Management from School of Management at Shanghai Jiaotong University (SJTU). He did post-doctoral research at Fudan University in 2009e2011. His main research interests include supply chain management and innovation management. His research has been published in journals including International Journal of Production Economics, Journal of Cleaning production, Annals of Operations Research and International Journal of Logistics Research and Applications.(E-mail: [email protected]).

Ruyin Zheng is a researcher of Shanghai University of International Business and Economics. Her main research interests include supply chain management and innovation management.

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Dr Hepu Deng is a Professor in Information Systems at the School of Business Information Technology and Logistics, RMIT University, Australia. He holds a Bachelor degree in Mathematics, a Postgraduate Diploma in Management Engineering and a Master's degree and a PhD in Information Systems. His research interests include decision analysis, digital business, electronic government, knowledge management and their applications in business. He has published more than two-hundred refereed articles in various international journals and conferences, including European Journal of Operational research, Computers and Operational Research, International Journal of Approximate Reasoning, International Journal of Computer and Information Sciences, International Journal of the Operational Research Society, Information Technology and People, Government Information Quarterly and Journal of Knowledge Management.

Yinglei Zhou is a researcher of Xiamen University. Her main research interests include sustainable supply chain management and logistics management.