Enabling environmental innovation via green supply chain integration: A perspective of information processing theory

Abstract

Although green supply chain integration (GSCI) is important, its influence on environmental innovation remains inconclusive. Based on information processing theory, we explore how the two dimensions of GSCI (i.e. green supplier and customer integration) affect two types of environmental innovation (i.e. incremental or radical environmental innovation) via information sharing and information redundancy, as well as the moderating role of knowledge combination. We test hypotheses using two-waved survey data from 206 Chinese manufacturers and structural equation model analysis. The results indicate that information sharing with customer mediates the impact of green customer integration on radical environmental innovation, while information redundancy mediates the effects of green supplier integration on environmental incremental and radical innovation. In addition, information sharing with supplier negatively affects information redundancy, while information sharing with customer positively affects information redundancy. We also find that knowledge combination strengthens the positive effects of information sharing with supplier and customer on incremental environmental innovation and the negative impact of information redundancy on radical environmental innovation. Our findings contribute to GSCI and environmental innovation literature.

Keywords

Environmental innovation green supply chain integration information redundancy information sharing knowledge combination

1. Introduction

Conducting environmental innovation to improve the production process or produce new products and ultimately realize the coordinated development of the economy, society, and environment have been considered an effective strategy to solve environmental problems (Jiao et al., 2020; Soewarno et al., 2019). To facilitate environmental innovation, many firms, such as Huawei, Samsung, and General Electric, have integrated supply chain partners into green management practices to jointly achieve environmental and economic goals (Yu and Huo, 2019). Existing research suggested that green supply chain integration (GSCI) contributes to realizing environmental innovation by providing key information and specific capabilities (Ardito et al., 2019). However, not all firms adopting GSCI have achieved their expected goals. Firms are willing to continue to invest in GSCI only if their performance can be enhanced. Therefore, how firms can benefit from GSCI should be explored.

Although GSCI is conducive to firms, some previous studies suggest that the availability of resources is not sufficient to improve environmental innovation (Arena et al., 2018; Luo et al., 2010), sometimes may even bring potential negative effects, such as increased coordinating cost (Feng and Wang, 2016), transitioning costs (Zhao et al., 2015), and excessive reliance on supply or customer experience results in lack of know-how (Gassmann et al., 2010). These inconsistent findings may be because these studies attribute the environmental innovation brought by GSCI to the expansion of critical resources while ignoring the changes of firm capability in this process. In this study, we attempt to dialectically explain how GSCI improves firm environmental innovation from information processing capability.

Information processing theory (IPT) provides a theoretical basis for understanding the impact paths of GSCI on environmental innovation. IPT suggests that organizations need to improve their information processing capabilities to cope with environmental uncertainties (Galbraith, 1974; Zhou and Benton, 2007). As a result, GSCI enhances environmental innovation by increasing information processing capability. Specifically, GSCI puts firms in a complex task environment related to the change of customer needs, the instability of suppliers, the unpredictable behavior of competitors, and the complexity of inter-organizational activity (Wong et al., 2015). Information sharing, as one of the crucial information processing capabilities, facilitates inter-organizational information networks (Wong et al., 2020; Zhou and Benton, 2007). Our past research supports that firms can timely obtain talents, equipment, and technical information from supply chain partners (Wong et al., 2011; Zhao et al., 2020), which effectively alleviates the uncertainty caused by GCSI and improves access to environmental innovation resources (An et al., 2014; Hong et al., 2019). Thus, we suggest that information sharing builds a bridge between GSCI and environmental innovation. Furthermore, the complexity of the environment also increases information processing needs from the perspective of IPT (Galbraith, 1974). GSCI can expand resource scope, including desirable and undesirable resources (Du et al., 2018). This redundant information of duplication, conflict, and uncertainty hinders the transmission efficiency of environmental innovation information among supply partners (Hussain et al., 2019). In this process, redundant information adds additional information processing needs and occupies the firm’s limited resources and capabilities to process helpful information, resulting in the negative impact of environmental innovation. Therefore, we propose that information redundancy mediates the GSCI-environmental innovation link.

Furthermore, there is a dark side in the process of information sharing; that is, it contains redundant information (Grover et al., 2006). Based on the perspective of IPT, most decision-makers are bound by rationality (Tushman and Nadler, 1978). When the information received by a firm exceeds its information processing capability, it will increase the cognitive burden of decision-makers, resulting in redundant information (Grover et al., 2006; Villena et al., 2011). Therefore, we reflect on the dark side of information sharing by studying whether information sharing affects information redundancy.

IPT emphasizes that the information processing capability to meet information processing needs is crucial in improving firm performance (Galbraith, 1974). Knowledge combination is an information processing capability that is absorbed and reorganized from customers and suppliers (Collins and Smith, 2006), which breaks the barriers of internal and external information integration, improves the efficiency of information sharing to meet more complex environmental needs to achieve innovation goals (Graham, 2018). However, as knowledge combination improves, redundant information is also processed more efficiently. It means that the higher the capability of knowledge combination, the higher the capability to identify and process redundant information, which further aggravates the negative effect of redundant information on environmental innovation. Therefore, we propose that knowledge combination might strengthen the link between information sharing and environmental innovation while weakening the negative effect of redundant information on environmental innovation.

This research has the following three main contributions. First, this study examines two different underlying micro-mechanisms of how GSCI affects environmental innovation from the perspective of IPT, which expands the theoretical framework of the relationship between GSCI and environmental innovation. Second, this study reveals that information sharing with suppliers can improve environmental innovation than a customer from the perspective of information redundancy, which enriches the understanding of the existing environmental innovation research. Third, our research provides empirical evidence for matching the views in IPT by verifying that knowledge combination magnifies the positive effect of information sharing on environmental innovation.

2. Theoretical background and hypotheses development

2.1. Information processing theory

IPT emphasizes that firms face different sources of uncertainties in their daily operation, which stimulates firms to improve information processing capability to gain a competitive advantage (Zhou and Benton, 2007). Galbraith (1974) stressed that firms need to fit information processing needs and information processing capabilities to deal with uncertainty and ultimately achieve better performance. This theory explains that firms improve their competitive advantage by transforming the uncontrollable exogenous uncertainty into controllable endogenous uncertainty. IPT regards the organization as an information processing system, the effective flow of information should be fully considered when designing the organization. The firm describes the information processing requirements of the organization by analyzing the uncertainty of the environment and tasks, determines the information processing capability by designing the structure and process of the organization, and improves the organizational performance by seeking the matching between the ability and the requirements (Galbraith, 1974).

According to the IPT, information processing capabilities to meet information processing needs are crucial to improving firm performance (Galbraith, 1974). With the uncertain environment created by GSCI, expanding the scope of information is not enough to improve firm performance, which needs higher information processing capability to deal with it. Knowledge combination strengthens the capability of information transformation, absorption, and utilization (Linderman et al., 2004). Grant (1996) suggests that in the process of knowledge combination, the more relevant knowledge a firm has, the more knowledge can be expressed in a common language, thus facilitating the application of knowledge. In other words, under a high level of knowledge combination, the firm’s information processing capabilities enable firms to handle more complex information needs and ensure effective meet of the firm’s external information with its own information processing system. Knowledge combination improves the utilization efficiency of information based on information sharing, which directly improves the information processing ability and meets the more complex environment. Therefore, we consider the knowledge combination when implementing information sharing to enhance environmental innovation.

2.2. The effects of GSCI on information sharing and information redundancy

2.2.1. GSCI and information sharing

Our past research results indicate that GSCI aims to coordinate and manage the information of suppliers and customers to the greatest extent, reducing firms’ negative effect on the environment, thereby promoting sustainable development (Zhou et al., 2020). It is a strategic partnership to promote sustainable environmental goals and practices (Shi et al., 2012). In the existing literature, GSCI is divided into green supplier integration (GSI), green customer integration (GCI), and internal integration (Cao et al., 2015). This study mainly considers the critical role of upstream GSI and downstream GCI in improving firm environmental innovation.

New knowledge and technologies brought by GSI and GCI have stimulated the willingness of firms to learn from supply chain partners. Firms are gradually aware of the limitations of their resources, technology, and capabilities, and it is difficult to rely solely on the firms to solve environmental problems (Feng et al., 2018). GSI and GCI help firms break the boundaries of resources and expand the scope of the firm’s environmental information resources (Du et al., 2018). Firms learn new technologies and knowledge through GSI and GCI to solve environmental problems and achieve environmental goals (Zahra and George, 2002).

GSI and GCI form effective communication mechanisms among supply chain partners (Lin et al., 2012), facilitating information sharing between customers and suppliers (Lau et al., 2010). GSI and GCI collaboratively manage inter-organizational processes to reduce negative environmental impacts (Song and Wang, 2018) based on long-term contracts, common goals, and close supply chain partnerships (Atuahene-Gima, 2003). As our past research shows, GSI and GCI nurture mutual trust and recognition among supply chain partners to frequently share timely information (Zhao et al., 2020). Furthermore, as integration increases, the willingness of firms to share information with customers or suppliers grows, and the efficiency of information sharing also increases (Chen and Paulraj, 2004; Hong et al., 2019).

GSI and GCI help firms establish a reciprocal interdependent social environment, thus creating an information and knowledge exchange network (Vachon and Klassen, 2008). For firms, GSI and GCI mean that supply chain partners often provide resources, joint planning, and coordination to solve environmental problems to achieve win-win solutions (An et al., 2014). Many cross-organizational collaborative activities in the supply chain are formed to achieve a common environmental vision (Du et al., 2018). These practices create a reciprocal interdependent network among supply chain partners and create information sharing (Vachon and Klassen, 2008). Hence, GSI and GCI strengthen sharing information with supplier and customer (Philipp et al., 2014). We hypothesize the following:

H1a. GSI is positively related to information sharing with supplier.

H1b. GCI is positively related to information sharing with customer.

2.2.2. GSCI and information redundancy

In cross-organizational cooperation, the common cognitive may make supply chain partners’ thinking homogeneous (Bendoly and Swink, 2007), thus becoming information redundancy (Noordhoff et al., 2011). GSI and GCI are strategic cooperation among supply chain partners, and the collaborative management process intra- and inter-organizational, which aim to jointly improve environmental performance (Shi and Liao, 2015), and based on supply chain partners’ common environmental objective and relies on close cooperation between firms with supply chain partners (Du et al., 2018). Common cognition may result in homogeneous thinking of supply chain partners (Bstieler and Hemmert, 2010), making it easy for supply chain partners to reach a consensus while generating similar information. Noordhoff et al. (2011) found that information redundancy is the degree of similarity in partner information. Therefore, GSI and GCI enable supply chain partners to gain insight into each other’s environmental goals. Firms may have the same cognition and viewpoint in solving environmental problems, thus causing redundant information.

GSI and GCI form reciprocal networks among supply chain partners (Du et al., 2018) and require each participant to consciously strengthen the reciprocity criterion to maintain the smooth implementation of GSCI. Strengthening reciprocity norms may create “unnecessary” obligations for supply chain participants, which forces them to provide resources that are not optimal or beyond their commitment (Bendoly and Swink, 2007). To inform partners about fulfilling obligations, participants need a positive attitude in integration. When information exceeds the firm’s information processing capabilities, it will cause redundant information. Thus, we propose:

H2. (a) GSI and (b) GCI are positively related to information redundancy.

2.2.3. Information sharing and information redundancy

IPT emphasizes that employees and decision-makers are bounded rational (Galbraith, 1974). If the information received by the firm exceeds its information processing capability, the cognitive burden for decision-makers will increase (Grover et al., 2006; Villena et al., 2011). Information beyond the information processing capability is easy to make firms face redundant information. The more frequently information sharing with suppliers or customers, the more information the firm needs to process. When the amount of information obtained exceeds the firm’s own information processing level, it will cause decision-makers cognitive burden, resulting in information redundancy.

Cross-organizational information sharing can benefit participants, and the main characteristics are negotiation cooperation and game sense. Resource sharing is not unconditional, and both sides must commit (Woo et al., 2016). In the process of information sharing, the absolute equality of the two sides cannot be guaranteed. Therefore, a party that pursues maximization of its interests will deliberately distort or provide meaningless information received by supply chain partners (Mason-Jones and Towill, 1997), thus causing information redundancy. For example, Grocery Manufacturers Association (GMA, 2009) indicated that to avoid suppliers gaining an advantage in future transactions, 40% of retailers are not active in the process of information sharing, even though they may repeat or share meaningless information to perform routine information sharing. In practice, customers may not be willing to share the information because they are worried that manufacturers will abuse the information to gain an advantage in future price negotiations (Ha and Tong, 2008). Furthermore, firms tend to assume they know what customers or suppliers can share with them and think that the ideas of supply chain partners are old or similar (Noordhoff et al., 2011). Thus, we form our hypothesis as follows:

H3. (a) Information sharing with supplier and (b) information sharing with customer positively relates to information redundancy.

2.3. The effect of information sharing and information redundancy on environmental innovation

According to existing studies, environmental innovation is generally divided into radical and incremental environmental innovations. Radical environmental innovation means development a new production pathway using environmental protection technology to get rid of existing knowledge about the environment (Subramaniam and Youndt, 2005). By utilizing environmental protection technology, incremental environmental innovation seeks to improve the performance of existing products, services, and management performance while enhancing environmental protection (Liao and Zhang, 2020; Subramaniam and Youndt, 2005).

Information sharing improves radical environmental innovation and incremental environmental innovation. By providing a stable communication environment, information sharing with suppliers and customers provide an avenue for firms to obtain participants’ concerns and needs (Nyaga et al., 2010), which helps firms timely supplement resources and optimize resource allocation (Hu et al., 2018). This expands the search scope of radical environmental and incremental innovation and enables participants to have a broad understanding of environmental protection measures implemented by firms. It is beneficial to improve the efficiency of information and communication among supply chain participants, thus improving two dimensions of environmental innovation (Feng and Wang, 2016; Wu et al., 2012).

In addition, information sharing provides reciprocity benefits ties among supply chain partners (Pai and Tsai, 2016), which helps the firms achieve environmental innovation goals. With the severe environmental pollution and the public’s attention to environmental protection, the industry faces strict environmental regulation pressure stringent. Environmental regulations make supply chain partners more willing to share resources and achieve their environmental goals (Feng et al., 2018). Firms can acquire new knowledge, technology, and information through information sharing (Feng et al., 2010). Our previous research indicates that this information includes the needs and preferences of suppliers and customers for the environment and new knowledge and technology to improve the ability of environmental innovation (Graham, 2018; Zhao et al., 2020). Therefore, based on mutual understanding of environmental needs, supply chain partners can better carry out cooperation and coordination activities to improve the firm’s environmental innovation capabilities.

However, information redundancy hinders the transmission of information across hierarchical boundaries (Hussain et al., 2019). Studies found that information redundancy reduces employees’ willingness to exchange information and hinders the exchange of information across organizations (Hussain et al., 2019), which disperses the firm’s information processing capability, weakens the firm’s processing of useful information, reduces the efficiency of information flow and hinders firms from acquiring new knowledge and technology, thus reducing the firm’s environmental innovation.

Moreover, information redundancy makes it more difficult for firms to make timely decisions (Villena et al., 2009). Firms need to commit finances, resources, and efforts to process existing information needs (Grover et al., 2006). Under the limited firm resources, too much redundant information will take up the energy, time, and cost invested in environmental innovation information. Thus, we propose the following hypotheses:

H4a. Information sharing with supplier is positively related to (a) incremental environmental innovation and (b) radical environmental innovation. Information sharing with customer is positively related to (c) incremental environmental innovation and (d) radical environmental innovation.

H5. Information redundancy is negatively related to (a) incremental environmental innovation and (b) radical environmental innovation.

2.4. The mediating role of information sharing and information redundancy

IPT suggests that the dynamism supply chain prompts firms to increase information processing capabilities to perform better. GSI and GCI are inter-organizational collaborative environmental practices, which integrates the critical knowledge of customers and suppliers and new technologies related to the environment, creating more information needs for firms than ever before (Song and Wang, 2018). As one of the crucial information processing capabilities, information sharing establishes an inter-organizational information-sharing network among supply chain partners to share talents, equipment, and technology (Hong et al., 2019; Zhou and Benton, 2007). Firms can better understand the needs and concerns of suppliers and customers and improve the efficiency of information communication and decision-making (Feng et al., 2019; Vachon and Klassen, 2008). Our past research has confirmed that by information sharing with customers and suppliers, firms can improve their information processing capabilities (Zhou and Benton, 2007), which assists firms to utilize customer and supplier resources to facilitate environmental innovation (Du et al., 2018; Wong et al., 2020; Zhao et al., 2020). Therefore, firms that adopt GSI and GCI are more willing to improve sharing information with supply chain partners to respond to environmental needs, thereby obtaining more significant competitive advantages (Busse et al., 2017).

GSI and GCI improve incremental environmental innovation by information sharing, attributed to the instant supplement of information and technology needed by firm innovation. To successfully implement environmental innovation, customers and suppliers need to be integrated into the innovation process (Du et al., 2018; Melander, 2018), thereby obtaining support and understanding the information and resources from key customers or suppliers (Mueller et al., 2013). GSI and GCI establish an interdependent social environment for customers and suppliers to enable firms to obtain external information (Vachon and Klassen, 2008). Our previous research has indicated that such information help firms constantly improve their existing technology combination and ultimately benefit their incremental environmental innovation (Huang et al., 2014; Zhao et al., 2020). Similarly, GSI and GCI also enhance radical environmental innovation by information sharing. Radical environmental innovation requires firms to store large amounts of new technology and knowledge (Alexander and Van Knippenberg, 2014), while cooperation with customers and suppliers maybe fill these gaps (Zhao et al., 2018). This is because GSI and GCI’s long-term cooperative relationship provides a prerequisite for firms to share real-time information, enabling them to obtain new knowledge or capabilities to overcome the limitations of innovation capabilities by information sharing (Mueller et al., 2013), which further facilitates radical environmental innovation.

However, GSI and GCI bring challenges to the firm’s information processing capability. Our past research findings support GSI and GCI’s expanded access to information, including desirable and undesirable information that affects company performance (Feng et al., 2020). This includes redundant information that increases the firm to process additional information processing needs. Due to limited resources, firms’ processing of redundant information occupies resource investment in environmental innovation, which hinders the advancement of environmental innovation.

In practice, it is also found that GSI and GCI reduce environmental innovation by increasing redundant information. GSI and GCI are based on supply chain participants’ common environmental awareness and long-term cooperation relationship (Cucciella et al., 2012; Du et al., 2018). The stable partnerships tend to form homogeneous thinking for participants and “unnecessary” obligations (Bendoly and Swink, 2007), resulting in unnecessary duplication of information and redundant labor, ultimately affecting firm environmental innovation. From the above logic, we propose:

H6. Information sharing with suppliers mediates the impacts of GSI on (a) incremental environmental innovation and (b) radical environmental innovation. Information sharing with customers mediates the impacts of GCI on (c) incremental environmental innovation and (d) radical environmental innovation.

H7. Information redundancy mediates the impacts of GSI on (a) incremental environmental innovation and (b) radical environmental innovation. Information redundancy mediates the impacts of GCI on (c) incremental environmental innovation and (d) radical environmental innovation.

2.5. The moderating role of knowledge combination

IPT suggests that the information processing capability to meet information processing needs is the key to improving firm performance. Linderman et al. (2004) demonstrated that knowledge combination mainly depends on whether the organization effectively collects and diffuses information within or between organizations. Knowledge combination helps firms eliminate functional barriers and facilitates cooperation and communication between inter-organizations, which improves the efficiency of external information collection and dissemination (Cui et al., 2020; Graham, 2018; Johnsen, 2009). From this logic, the higher the ability of knowledge combination, the higher the firm’s information sharing efficiency to meet the complex environmental innovation needs. Therefore, knowledge combination magnifies the impact of information sharing on environmental innovation.

However, knowledge combination magnifies the negative effect of redundant information on environmental innovation. Knowledge combination increases the capability of firms to absorb and process information (Graham, 2018), which also includes redundant information. It means that the higher the ability of knowledge combination, the higher the ability to identify and process redundant information. Therefore, under given resources, processing redundant information aggravates the extra expenditure, which hinders the capability for environmental innovation. Consequently, we hypothesize:

H8. Knowledge combination positively moderates the impacts of information sharing with suppliers on (a) incremental environmental innovation and (b) radical environmental innovation. Knowledge combination positively moderates the impacts of information sharing with customers on (c) incremental environmental innovation and (d) radical environmental innovation.

H9. Knowledge combination negatively moderates the negative impacts of information redundancy on (a) incremental environmental innovation and (b) radical environmental innovation.

Figure 1 presents the theoretical model.

Figure 1.

Conceptual model.

3. Research methods

3.1. Sampling and data collection

The research uses data from Chinese manufacturing enterprises in five provinces with varying levels of economic growth. Shaanxi and Henan are typical of early industrialization in China, Shandong represents China’s average economic development, and Jiangsu and Guangdong reflect high levels of economic growth in China. The five provinces of China have different geographic bases, that we have seen in our past research can represent different market conditions and levels of economic development (Feng et al., 2020; Jiang et al., 2018). We obtained a list of more than 12,000 manufacturing firms based on the recommendations of administrative divisions of the economic development zones. Then, we randomly selected 120 firms from each province to participate in the survey (600 in total).

We followed three steps to collect the data. First, we developed a survey instrument to measure GSI, GCI, knowledge combination, and two dimensions of environmental innovation. Since this survey used a Chinese questionnaire, and the questionnaire items were derived from the English version, two researchers first translated the questionnaire items into Chinese. Two other researchers then translated Chinese into English. Finally, the differences from the original text were verified and clarified. Before conducting the formal survey, we conducted a pilot test to ensure the scale items were rational and simple. Ten firms were randomly selected as pilot test subjects and helped modify the questionnaire. We conducted face-to-face interviews with executives of these firms to clarify the meaning of these projects and discussed them after they filled out the questionnaire. Following their feedback, the question was revised. Their responses were excluded from the subsequent analysis (Appendix 1).

Second, further seeking feedback from selected firms, 284 firms agreed to participate. Among the firms that accepted the invitation, those that did not include supply chain partners in the corporate green plan were deleted. Every firm selects an informant familiar with relationships in the supply chain and GSCI adoption. Firms sent a reminder every other week to facilitate questionnaire responses.

Third, we collected two rounds of data to reduce the possible impact of common method bias (CMB; Podsakoff et al., 2003). In the first round, independent variables, moderators, mediators, and control variables were included. There were 261 valid questionnaires with a response rate of 43.5% (261/600). After 6 months, questionnaire including dependent variables were distributed. There were 206 valid questionnaires with a response rate of 34.3% (206/600). The sample included firms with various industries, sizes, and ownership structures. The detailed descriptions are reported in Table 1.

Table 1.

Profile of sampled firms.

Characteristics of firms	Number of firms	Percentage
Positions of respondents
President/CEO/vice president	20	9.7
Senior managers	57	27.7
Middle-level managers	125	60.7
Others	4	1.9
Years stayed in the firm
1–3	11	5.3
3–5	42	20.4
5–7	97	47.1
7–10	50	24.3
> 10	6	2.9
Industry
Textile	6	2.9
Machinery	18	8.7
Metal products	14	6.8
Food and beverage	8	3.9
Rubber and plastics	14	6.8
Transport equipment	22	10.7
Pharmaceutical and medical	10	4.9
Chemical and related products	11	5.3
Non-metallic mineral products	13	6.3
Instruments and related products	8	3.9
Electrical machinery and equipment	30	14.6
Communication and computer-related equipment	45	21.8
Others	7	3.4
Type of ownership
Private firms	76	36.9
Foreign-invested firms	47	22.8
State-owned and collective firms	83	40.3
Number of employees
1–49	21	10.2
50–99	9	4.4
100–299	34	16.5
300–900	34	16.5
1000–1999	27	13.1
2000–4999	37	18.0

3.2. Measures

To ensure reliability and validity of the measurement scale, each indicator was measured with a 7-point Likert-type scale (Appendix 1). Based on the works of Vachon and Klassen (2008) and Wu (2013), the GSI scale (six items) and GCI scale (six items) were developed. For the different information sharing partners, this study used three items measuring information sharing with suppliers and three items measuring information sharing with customers adapted from Nyaga et al. (2010). Information redundancy was measured using three items based on the works of Noordhoff et al. (2011), Grover et al. (2006), and Nyaga et al. (2010). Five items were used to assess knowledge combination adapted from Shu et al. (2012). Incremental environmental innovation and radical environmental innovation, respectively, used five items based on the work of Dai et al. (2015).

3.3. Common method bias

As each questionnaire was completed by one informant, a potential CMB should be examined (Podsakoff et al., 2003). We used the following method to check CMB. First, we evaluated CMB by Harman’s single-factor test (Podsakoff et al., 2003; Podsakoff and Organ 1986). Our results of exploratory factor analysis showed that the first factor explained 30.9% of the variance, and there were seven factors with eigenvalues greater than 1, indicating CMB is not a serious concern. Second, we added common method factors to the model and conducted confirmatory factor analysis (CFA) (Flynn et al., 2010). The method factor model was tested by CFA, which yielded χ² = 1031.821, RMSEA = 0.068, NNFI = 0.876, CFI = 0.935, SRMR = 0.044. The SRMR change is not more than 0.05, the CFA change is not more than 0.1, and the fitting index changes little compared with the original model, proving there is no serious CMB.

4. Analysis and results

4.1. Reliability and validity

The research evaluated scale reliability using Cronbach’s alpha and composite reliability (CR). Cronbach’s alpha value is generally required to be greater than 0.70, and the combined reliability of the scale needs to be 0.70. The reliability test results of this study are presented in Table 2. Cronbach’s alpha for each construct is between 0.919 and 0.961, and the CR value is between 0.927 and 8.969, which is all above the standard of 0.70, showing that the scale used in the study is internally consistent.

Table 2.

Results of CFA.

Constructs	Items	Standardized factor loading	Cronbach’s α	Composite reliability	AVE
Green supplier integration	GSI1	0.921	0.961	0.969	0.838
	GSI2	0.914
	GSI3	0.868
	GSI4	0.940
	GSI5	0.927
	GSI6	0.921
Green customer integration	GCI1	0.798	0.927	0.943	0.733
	GCI2	0.882
	GCI3	0.883
	GCI4	0.914
	GCI5	0.840
	GCI6	0.813
Information sharing with supplier	ISS1	0.935	0.927	0.953	0.872
	ISS2	0.922
	ISS3	0.944
Information sharing with customer	ISC1	0.949	0.931	0.956	0.879
	ISC2	0.929
	ISC3	0.934
Information redundancy	IR1	0.924	0.926	0.954	0.872
	IR2	0.942
	IR 3	0.936
Knowledge combination	KC1	0.831	0.919	0.920	0.759
	KC2	0.889
	KC4	0.867
	KC4	0.868
	KC5	0.897
Incremental environmental innovation	IEI1	0.922	0.955	0.965	0.847
	IEI2	0.933
	IEI3	0.881
	IEI4	0.946
	IEI5	0.921
Radical environmental innovation	REI1	0.894	0.958	0.968	0.857
	REI2	0.944
	REI3	0.937
	REI4	0.936
	REI5	0.918

χ²(206) = 1232.682, RMSEA = 0.076, NNFI = 0.865, CFI = 0.914, and SRMR = 0.049.

This study measures validity from three aspects: content validity, convergent validity, and discriminant validity. Content validity was validated through extensive literature reviews, interviews, and the pilot test. The measurement model indices were χ² = 1232.682, RMSEA = 0.076, NNFI = 0.865, CFI = 0.914, and SRMR = 0.049, which indicates that the model was acceptable. All constructs had an average variance extraction (AVE) value greater than 0.5. Moreover, each factor loading was greater than 0.6 and significant, indicating adequate convergent validity.

Tests of discriminant validity are conducted by comparing the roots of AVE with shared variance between different constructs. Table 3 shows that the square roots of each construct’s AVE values in this study were larger than the correlation coefficients between the construct and the other constructs, indicating that the scale has good discrimination validity.

Table 3.

Means, standard deviations, and correlations.

Variables	1	2	3	4	5	6	7	8	9	10	11
1. Firm size	−
2. Firm age	0.435**	−
3. Industry	−0.081	−0.110	−
4. Green supplier integration	−0.102	−0.024	0.087	0.915
5. Green customer integration	−0.046	−0.029	0.076	0.462***	0.856
6. Information sharing with supplier	−0.129	−0.022	0.048	0.493**	0.300***	0.934
7. Information sharing with customer	−0.116	0.002	−0.066	0.339**	0.273***	0.490***	0.938
8. Information redundancy	0.015	0.054	−0.017	0.302**	0.100	0.073	0.202**	0.934
9. Knowledge combination	−0.181	0.021	−0.104	0.301**	0.164*	0.255***	0.219**	0.104	0.871
10. environmental innovation	−0.095	−0.129	0.117	0.378**	0.335**	0.243***	0.142*	−0.296***	0.178**	0.889
11. Radical environmental innovation	−0.158	−0.116	0.009	0.271	0.401***	0.090	−0.076	−0.088	0.210**	0.399***	–
Mean	6.704	2.604	0.368	4.872	5.242	5.432	5.331	4.417	5.155	4.802	4.791
SD	1.723	0.769	0.483	1.180	1.173	1.025	1.300	2.034	1.086	1.581	1.761

p < 0.05; **p < 0.01; ***p < 0.001 (two-tailed); bold values on the diagonal are the square root of AVE values.

4.2. Structural equation modeling analysis

The hypothesized direct-effect model was tested by structural equation modeling (SEM). As shown in Figure 2, GSI has positive effects on information sharing with suppliers (β = 0.526, p < 0.001) and information redundancy (β = 0.358, p < 0.001). GCI has positive impact on information sharing with customers (β = 0.279, p < 0.001), while has insignificant effect on information redundancy (β = –0.084, p > 0.1). Therefore, H1a, H1b, and H2a are supported, while H2b is not. Information sharing with suppliers has negative direct impact on information redundancy (β = –0.183, p < 0.05), while information sharing with customers has positive effect on information redundancy (β = 0.211, p < 0.05). Therefore, H3b is supported, but H3a is not. Information sharing with suppliers has positive effects on incremental environmental innovation (β = 0.254, p < 0.001), radical environmental innovation (β = 0.187, p < 0.05). Information redundancy has negative impact on incremental environmental innovation (β = –0.344, p < 0.001), but has insignificant effect on radical environmental innovation (β = –0.055, p > 0.1). Information sharing with customers has insignificant effects on incremental environmental innovation (β = 0.095, p > 0.1), radical environmental innovation (β = –0.128, p > 0.1). Therefore, H4a, H4b, and H5a are supported, but H5b, H4c, and H4d are not.

Figure 2

SEM results.

H6 and H7 hypothesize that information sharing and information redundancy mediate the impacts of GSI and GCI on two dimensions of environmental innovation. We tested the mediating effect using bootstrapping (Preacher and Hayes, 2008). As shown in Table 4, the 95% bias correction confidence interval does not include zero as follows: the indirect effects of information redundancy between GSI and incremental environmental innovation ([–0.235, –0.067]) and between GSI and radical environmental innovation ([–0.140, –0.016]). These findings support H7a and H7b. In addition, our research found that information sharing with customers has an indirect effect between GCI and radical environmental innovation (β = –0.062, [–0.148, –0.013]). Therefore, H6d is supported. The rest of the mediators assume that the 95% bias-corrected confidence interval includes zero. Therefore, H7c, H7d, H6a, H6b, and H6c are not supported.

Table 4.

Mediating effects.

Path	Indirect effect	SE	95% confidence interval		Direct effect
Path	Indirect effect	SE	Lower	Upper	Direct effect
GSI-ISS-IEI	0.039	0.056	−0.063	0.157	0.364***
GSI-ISS-REI	−0.032	0.513	−0.131	0.072	0.336***
GCI-ISC-IEI	0.016	0.024	−0.022	0.077	0.343***
GCI-ISC-REI	−0.062	0.034	−0.148	−0.013	0.515***
GSI-IR-IEI	−0.145	0.043	−0.235	−0.067	0.548***
GSI-IR-REI	−0.063	0.031	−0.140	−0.016	0.368***
GCI-IR-IEI	−0.035	0.028	−0.095	0.019	0.394***
GCI-IR-REI	−0.015	0.017	−0.068	0.004	0.468***

Two thousand bootstrap samples, GSI, green supplier integration; GCI, green customer integration; ISS, information sharing with supplier; ISC, information sharing with customer; IEI, incremental environmental innovation; REI, radical environmental innovation.

***

p < 0.001.

H8 hypothesize that knowledge combination positively moderates the relationship between information sharing with suppliers/information sharing with customers and two dimensions of environmental innovation. H9 hypothesize that knowledge combination negatively moderates the relationship between information redundancy and two dimensions of environmental innovation. The interaction effect of information sharing with suppliers and knowledge combination on incremental environmental innovation is positively significant (β = 0.260, p < 0.05), the interaction effect of information redundancy and knowledge combination on radical environmental innovation is negatively significant (β = –0.234, p < 0.01), the interaction impact of information sharing with customers and knowledge combination on incremental environmental innovation is positively significant (β = 0.174, p < 0.05). The other interactions have insignificant effects on the two dimensions of environmental innovation. These findings suggest that H8a, H8c, and H9b are supported (Figure 2), but H8b, H8d, and H9a are not.

Figure 3 indicates that the higher level of knowledge combination, the stronger the positive impact of information sharing with suppliers on incremental environmental innovation. Figure 4 suggests that the higher the level of knowledge combination, the stronger the negative effects of information redundancy on radical environmental innovation. Figure 5 depicts that the higher the level of knowledge combination, the stronger the positive impact of information sharing with customers on incremental environmental innovation. These results provide further support for H6a, H6c, and H6f.

Figure 3.

Interactive effects of information sharing with supplier and knowledge combination on incremental environmental innovation.

Figure 4.

Interactive effects of information redundancy and knowledge combination on radical environmental innovation.

Figure 5.

Interactive effects of information sharing with customer and knowledge combination on radical environmental innovation.

4.3. Robustness check

We used hierarchical regression to re-examine the hypotheses. Table 5 outlines the findings, which are consistent with the main analysis approaches of SEM (Figure 2). Therefore, our results are robust.

Table 5.

Results of regression analysis.

Variables	Information sharing with supplier		Information sharing with customer		Information redundancy				Incremental environmental innovation				Radical environmental innovation
	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12	M13	M14	M15	M16
Firm size	−0.086	−0.056	−0.102	−0.095	−0.009	0.019	0.015	0.004	−0.032	0.000	0.024	0.021	−0.103	−0.104	−0.072	−0.099
Firm age	0.055	0.032	0.102	0.105	0.107	0.083	0.081	−0.125	−0.162	−0.157	−0.183	−0.152	−0.102	−0.092	−0.127	−0.085
Industry	0.091	0.010	−0.174	−0.221	−0.034	−0.102	0.018	−0.185	0.266	0.256	0.305	0.312	−0.022	−0.072	−0.007	0.014
Green supplier integration (GSI)		0.413***				0.402***
Green customer integration (GCI)				0.267***		−0.06
Information sharing with supplier (ISS)							−0.046			0.253**	0.213*	0.190*		0.201*	0.147	0.134
Information sharing with customer (ISC)							0.276***			0.127	0.112	0.112		−0.181*	−0.200*	−0.185*
Information redundancy (IR)										−0.288***	−0.298***	−0.299***		−0.059	−0.072	−0.067
Knowledge combination (KC)											0.203*	0.180*			0.269**	0.233*
ISS × KC												0.136				0.122
ISC × KC												0.105				−0.073
IR × KC												−0.140 +				−0.306**
R ²	0.020	0.248	0.024	0.098	0.003	0.100		0.013	0.029	0.186	0.211	0.248	0.028	0.058	0.097	0.148
∆R ²		0.228		0.074		0.097		0.013		0.157	0.025	0.037		0.030	0.039	0.051
∆F		60.964***		16.606***		10.725***		0.874		12.837***	6.214*	3.209*		2.142 +	8.575**	3.870**

p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001 (two-tailed).

5. Discussions

GSI and GCI are universally recognized by academia and industry in solving environmental problems and obtaining sustainable competitive advantages for firms (Feng et al., 2020; Zhu and Sarkis, 2004). Based on IPT, this study integrates information sharing and information redundancy into the theoretical framework of GSCI- environmental innovation to clarify that GSCI brings both challenges and opportunities to environmental innovation. Furthermore, knowledge combination is integrated into the research model to clarify the influence mechanism of knowledge combination on the relationship between information sharing and environmental innovation. Thus, our study introduces GSCI, information sharing, information redundancy, knowledge combination, and environmental innovation to construct a process mechanism model of GSCI’s effect on environmental innovation.

5.1. Relationships among GSCI, information sharing, and environmental innovation

First, our results confirm that GSI and GCI promote information sharing with customers and suppliers. This finding follows IPT, which holds that the external needs of firms facilitate their information processing ability to improve innovation. Therefore, this study explains how to improve the information processing capability of firms through the information sharing of customer or supplier to deal with the complex supply chain environment. These findings support our past research and the existing literature that GSCI positively affects information sharing (Feng et al., 2020; Huo et al., 2021).

Second, our results find that GSI increases information redundancy, but GCI has no significant effect. On one hand, this is consistent with Noordhoff et al.’s (2011) view that stable relationships are more likely to cause redundancy. Integrating with suppliers is more biased toward energy saving and emission reduction in raw material logistics, the information is more defined, routinized, and familiar to both parties (Huo et al., 2021), which may make the thinking of suppliers and firms homogeneous (Bendoly and Swink, 2007), thus resulting in information redundancy. On the other hand, firms can obtain more green market knowledge, customers’ green preference, and new technologies in GCI. This is less overlapped with its existing customer business, so it is not easy to cause redundant information.

Third, information sharing with suppliers can reduce redundant information and positively impact incremental and radical environmental innovation. However, information sharing with customers increases redundant information and does not affect environmental innovation. This inconsistency may be the difference in the content of information shared between customer and supplier. This finding supports Holmberg’s (2000) view that information sharing with different entities has different impacts on environmental innovation in the supply chain. Information sharing with suppliers is more about raw materials. This information is more transparent and more procedural (Huo et al., 2021), while information sharing with customers is more inclined to customer needs, preferences, and information about commodity prices (Peterson, 2002; Swink et al., 2007), the customer is not willing to actively disclose this information in practice.

Fourth, our results also find that information redundancy negatively impacts incremental environmental innovation but has no impact on radical environmental innovation. Compared with the two kinds of environmental innovation, radical environmental innovation has higher requirements on the ability and resources of the firm than incremental environmental innovation (Wang et al., 2020), and the ability of firms to process redundant information is more likely to occupy the incremental environmental innovation rather than the radical environmental innovation.

Fifth, our results indicate that information sharing has no mediating role between GCI and environmental innovation. This may be environmental innovation that emphasizes green technology innovation (Long et al., 2017). Under the influence of GSCI, information sharing focuses more on whether raw materials and transportation are green or customers’ preferences and needs on green products, which is inconsistent with the technical contribution required by environmental innovation. Therefore, GSCI and environmental innovation may not be related through information sharing. Interestingly, we conclude that information sharing with customers suppresses the effect of GCI and radical environmental innovation. This suggests that there is another intense mediation between the relationships.

5.2. Relationships among information sharing, knowledge combination, and environmental innovation

Regarding the moderating role of knowledge combination in the model, we found that knowledge combination magnifies the positive relationship between information sharing with suppliers/information sharing with customers and incremental environmental innovation. However, information sharing cannot be improved radical environmental innovation when potent firm knowledge combination. It may be that radical environmental innovation has higher requirement on the firm’s capability (Wang et al., 2020). The acquisition of external resources is not the primary factor in improving radical environmental innovation. Therefore, even with specific knowledge combination ability and the information resources of suppliers and customers, it is not enough to meet the requirements of resources and capabilities of radical environmental innovation.

Furthermore, it is found that knowledge combination enlarges the negative relationship between information redundancy and radical environmental innovation but effect not between information redundancy and incremental environmental innovation. This confirms our previous finding that external resources are not the key to radical environmental innovation in the firm (Wang and Feng, 2022). These findings further advance our rational understanding of knowledge combination capabilities.

5.3. Theoretical implications

Three aspects of this study’s theoretical contribution are outlined below. First, our research examines two different underlying micro-mechanisms of how GSCI affects environmental innovation based on IPT, which expands the theoretical framework of the relation between GSCI and environmental innovation. The relation between GSCI and environmental innovation is neither positive or negative, and the two effects are not considered simultaneously, our previous study suggested that both effects should be considered in the future (Zhao et al., 2020). By adopting the IPT in the relationship between GSCI and environment innovation, this study considers that GSCI generates both information needs and information processing capability, thus introducing information sharing and information redundancy into the research framework, which clarifies the differential mechanism of GSCI on environmental innovation, and provides a new theoretical perspective and empirical evidence for the controversial issue of how GSCI affects environment innovation. Interestingly, this study counter-intuitively concludes that information sharing with customers suppresses green customer integration and environmental innovation. This suggests another powerful mediation between the relationships, further providing empirical evidence to study the mediating relationship between GSCI and environmental innovation.

Second, this study indicates that information sharing with suppliers has a stronger impact on environmental innovation than customers, which enriches our understanding of information sharing. Information sharing with suppliers may inhibit redundant information, while information sharing with customers may increase redundant information, revealing that information sharing with suppliers has a stronger effect on environmental innovation than information sharing with customers. The finding is in accordance with the study by Huo et al. (2021), namely, that information sharing is more suitable for suppliers. It further provides scientific support for information sharing and environmental innovation.

Third, our research enriched the scope of the application of IPT by verifying the moderating effect of knowledge integration. According to existing research, information sharing is an important information processing capability to improve environmental innovation (Liao, 2018). In this study, we extend the boundary conditions of this relationship by using knowledge combination. We reveal that knowledge combination enhances the transformation efficiency of information sharing to meet a more complex environment innovation need. Therefore, this research provides novel insights into the information sharing-environmental innovation relationship.

5.4. Managerial implications

With consumers’ increasing awareness of environmental protection and stricter government environmental regulations, the conclusions of this study have significant implications for business managers. First, our study indicates that information sharing is more suitable for communication with suppliers than customers. Therefore, managers should be careful to enhance environmental innovation by sharing information with customers, and they should take the initiative to strengthen communication with customers. For example, firms can improve the efficiency of customer information acquisition through machine learning and big data analysis technology. Firms should invest more resources in the supplier information sharing support system, conduct necessary training on how to use these information systems, encourage internal functional departments to implement these systems fully, and further improve the efficiency of information sharing with suppliers.

Second, our study found that GSI is more easily generates redundant information than GCI. The long-term stability of the GSI strategy will lead to the cognitive convergence of participants and increase redundant information in firms (Bendoly and Swink, 2007). Therefore, supplier managers should use advanced digital technology to improve the efficiency of GSI and communication modes to tap new suppliers, or actively find new ways of integration with suppliers, develop further business cooperation and operation mode, thus reducing habitual communication mode avoid the convergence of thinking.

Third, we found that knowledge combination expands information sharing influence on incremental environmental innovation, and at the same time, increases the negative effect of information redundancy on radical environmental innovation. This emphasizes the double-edged sword effect of knowledge combination. Therefore, in practice, managers should not blindly integrate a firm’s external information, knowledge, and resources but should have a specific recognition capability in the early stage of practice and immediately avoid and delete redundant knowledge.

5.5. Research limitations

There are several limitations providing opportunities for future research. First, although we use a time lag approach to measure variables, future research could examine whether the mediating effect of information sharing and information redundancy and the moderating effect of knowledge combination is valid over a more extended period. This exploration could bring interesting results to guide firms better. Second, there may be other contingency factors (i.e. commitment, environmental dynamism, and dependence) that influence the correlation between GSCI and environmental innovation (Markey et al., 2021). It would be worthwhile to examine the moderating effect of these contingency factors in future research, such as CEO characteristics, resource orchestration capability, and environmental public opinion pressure (Williams et al., 2021). Third, we study the influence mechanism of GSI and GCI affect two dimensions of environmental innovation. GSCI also includes internal integration (Wu, 2013). Therefore, future research should examine how different dimensions of GSCI interact with each other to affect environmental innovation, which may provide more insightful results.

Footnotes

Appendix 1 Acknowledgements

The authors would like to sincerely thank the editor and anonymous reviewer for their insightful and constructive comments. This work was partially supported by the National Natural Science Foundation of China (72172040 and 71702148), Taishan Scholar Project of Shandong Province (tsqn201909154), Science and Technology Program for Innovation of Shandong Universities (2020RWG003), and Soft Science Research Project in Shaanxi Province (2020KRM159).

Final transcript accepted 18 March 2022 by Yunting Feng (AE, Special Issue).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:This work was partially supported by the National Natural Science Foundation of China (72172040 and 71702148), Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University (CX2021092), Taishan Scholar Project of Shandong Province (tsqn201909154), Science and Technology Program for Innovation of Shandong Universities (2020RWG003), and Soft Science Research Project in Shaanxi Province (2020KRM159).

ORCID iD

Cong Zhou

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