Involving purchasing and supply management in open ecological innovation: the moderating role of digital technologies

ABSTRACT

Open Eco-innovation (OEI) is an emerging approach for achieving sustainable development in manufacturing industries. However, studies on internal organisational factors and employee involvement in the development of eco-innovations are lacking. Here, we developed a model to examine whether supply management innovativeness (SMI) and environmental collaboration (EC) with suppliers affect firms’ eco-product and eco-process innovations. The proposed model was tested using data from 328 manufacturers in China. We found that SMI has a direct and positive impact on both eco-product and eco-process innovation. Mediation analysis suggests EC partially mediating this relationship. Additionally, EC had an inverted, U-shaped relationship with eco-products innovation (EPDI)and a positive effect on eco-process innovation (EPCI). While, digital technologies (DT) moderate the relationship between EC and EPDI, they do not affect EPCI. This study presents an opportunity for supply management managers to understand these challenges more comprehensively and add value to the eco-innovation process.

KEYWORDS:

• Supply management innovativeness
• environmental collaboration
• eco-product innovation
• eco-process innovation
• digital technologies

Introduction

Peak carbon dioxide emissions are expected in China to occur in 2030, and the country aims to attain carbon neutrality by 2060. The Chinese government announced a number of energy-saving and emission-reduction promises to limit the nation's energy usage in order to accomplish these goals. In addition, being major consumers of energy and natural resources and thereby one of the main polluters, manufacturing firms are required to continuously develop innovations to mitigate environmental risks (Chang et al. 2021; Li, Dai, and Cui 2020). Eco-innovation (EI) is a strategic green solution for achieving sustainable development in manufacturing industries in response to increasing economic and environmental pressures (Bag et al. 2020a). Coca-Cola, Nestle and Pepsi, for instance, promised to achieve carbon neutrality and 100% renewable resource packaging by 2030, respectively (Yu et al. 2022). EI in processes and products has been seen as essential to reducing the negative consequences of the ecologically harmful aspects of manufacturing (Awan et al. 2021; Woo et al. 2016). Traditional impediments to EI, such as a lack of resources and expertise, may be overcome by collaboration and access to outside knowledge (Ben Amara and Chen 2020; Del Río et al. 2016). OEI is a term that is widely used to describe this strategy (Chistov, Aramburu, and Carrillo-Hermosilla 2021; Ghisetti, Marzucchi, and Montresor 2015).

Owing to the growing importance of OEI, many studies have explored the factors that affect OEI (Cai and Li 2018; Hojnik and Ruzzier 2016; Munodawafa and Johl 2019; Peng and Liu 2016; Tumelero, Sbragia, and Evans 2018). Some of these studies have explored the external and contextual factors that affect an organisation's capability for OEI (Cai and Li 2018; Hojnik and Ruzzier 2016). For example, Cai and Li (2018) discovered that market-based instruments, competitive pressures, environmental organisational capacities, technology skills and green customer demand all contribute to the development of OEI. On the contrary, regulations and market pull factors are the most important OEI drivers, according to Hojnik and Ruzzier's (2016) analysis of the growing body of literature on the subject. Other researchers have explored the internal factors that expand an organisation's OEI capability. For example, Peng and Liu (2016) demonstrated how corporate OEI operations are favourably impacted by managerial environmental awareness and the acquisition of external resources. According to Tumelero, Sbragia, and Evans (2018), collaboration in research and development with heterogeneous agents is beneficial for the introduction of multidimensional eco-innovations that are both technological (i.e. products and processes) and organisational. In Munodawafa and Johl (2019), they explored how big data analytics can improve process efficiency. Xu, He, and Ji (2022) explored how digital transformation (i.e. digital strategy and capability) affected eco-products, eco-processes and eco-management innovation. Besides, some internal capabilities of an organisation, also known as organisational factors, such as organisational culture, management of knowledge (Chaithanapat et al. 2022), human capital, infrastructure and resources, etc. (Habib, Abbas, and Noman 2019), impact the organisational capabilities to innovate green (Fan et al. 2023). While there are more studies on external factors affecting OEI than on internal organisational factors (Chiou et al. 2011; Díaz-García, González-Moreno, and Sáez-Martínez 2015), especially, there are rare studies conducted on the effect of employee involvement in the development of eco-innovations (Buhl, Blazejewski, and Dittmer 2016), such as purchasing and supply management function.

The strategic significance of both purchasing and supply activities in attaining a company's long-term success and tackling sustainability concerns is acknowledged (Touboulic and Walker 2015). When value chain participants like customers and suppliers are taken into account, innovation is sparked (Araújo and Franco 2021). As purchasing concerns the acquisition of materials, components, or services, it can play a crucial role in promoting innovation within different members of the value chain (Castaldi, Ten Kate, and Den Braber 2011). According to the research conducted by Melander and Pazirandeh (2019), it has been demonstrated that organisations collaborate with their suppliers and clients to exchange knowledge regarding these matters and concepts related to EI. In their study conducted in Kobarg et al. (2020) examined how companies’ collaboration in innovation with various partners influences the development of diverse forms of ecological innovation, with a particular focus on process- and product-ecological innovation. They found that collaboration with suppliers is only positively associated with process-ecological innovation. Overall, collaborations between actors improve environmental sustainability. Nevertheless, studies centred around this issue have concentrated mainly on inter-organisational collaborations facilitated by purchasing and supply management departments to achieve environmental and economic performance targets (Ahmed et al. 2020; Grekova et al. 2016; León-Bravo et al. 2017; Xu, He, and Ji 2022), and researches on the innovation power of the supply management function remains limited (Lintukangas, Kähkönen, and Hallikas 2019). To increase the understanding of the significant role of the supply management function and its capability to improve eco-innovation, in general, a study that clarifies the relationships of supply management innovativeness (SMI) and environmental collaboration (EC) with suppliers and their impact on eco-innovation performance in one research setting is needed.

Moreover, an emerging body of literature has suggested the significance of collaboration to attain environmental and economic goals (Ahmed et al. 2020; Grekova et al. 2016; León-Bravo et al. 2017; Xu, He, and Ji 2022). Furthermore, sustainable development initiatives and financial success have inconsistent results, despite the majority of studies reporting positive links. However, other investigations (Ahmed et al. 2020; Nishitani and Kokubu 2020) have found negative, U-shaped and inverted U-shaped relationships. We need more thorough and detailed research to improve our understanding of various facets of the current problem. With the announced introduction of Industry 4.0 in supply chains, digitisation is projected to restructure how buying organisations and suppliers function, particularly in the context of sustainability challenges (Yang, Luo, and Pan 2024). On issues relating to sustainability, it is projected that digital technology would promote information sharing and corporate cooperation. The relevance of Industry 4.0 in the context of sustainable supply chain management is, however, only partially supported by actual data (Kunkel et al. 2022).

In light of these arguments and knowledge gaps, this study is the first study to explore the extent to which the supply management function and its capabilities contribute to eco-innovation within firms. Innovativeness in supply management is considered an internal resource of a firm, and collaboration with suppliers is considered a firm's ability to exploit external resources. First, we examined whether and how SMI and EC are associated with eco-innovation. Next, we examined whether digital technologies (DT) moderate the relationships between SMI, EC and eco-innovation, including eco-product and eco-process innovation.

The rest of this paper is structured as follows: we go over the theoretical background in the following part and build our hypotheses. After that, the research design and results of the empirical analysis are presented, and finally, we discuss our findings.

Literature review and hypotheses

Dynamic capability view (DCV)

Dynamic capabilities include sensing, seising and reconfiguring capacities (Teece 2007). The ability to scan, discover, recognise and understand new opportunities and hazards is referred to as sensing. It also entails comprehending latent demand, market structural evolution, and supplier and rival responses. Sensing supply markets as a component of the business environment can assist organisations identify new opportunities for innovation as well as modifications that are necessary to the resource base due to the environment's dynamic (Teece 2007). The ability of supply management to identify potential for innovation present in supplier marketplaces is implied by the term ‘innovative supply management.’ On the other hand, seising capability describes how a business may seise chances by developing structures and processes for decision-making (Teece 2007; 2012). The ability of supply management to seise chances for innovation at the point where the supplier base and product development converge is essential. In particular, this intersection is meant to encourage cooperation along the entire supply chain (Reuter et al. 2010). Taking advantage of supply base opportunities can have a big impact on resource reconfiguration. To enable renewal and keep the resource base consistent with observed changes and possibilities, configuration entails the alignment and realignment of individual assets (Teece 2007; 2012). In order to deal with the dynamic of the business environment, one must be able to innovate as well as develop and implement sustainable company plans.

Open eco-innovation (OEI)

Open innovation scholars have recently started to investigate this idea in the context of sustainability and its potential to address significant societal problems (Bogers, Chesbrough, and Strand 2020; Chistov, Aramburu, and Carrillo-Hermosilla 2021). A shift in innovation toward sustainable economic activities has been brought about by investments in the creation and adoption of eco-friendly technologies and sustainable consumption patterns, which have resulted in eco-innovations (Foxon 2011; Jakobsen and Clausen 2016). In this study, based on Garcia, Wigger, and Hermann (2019), we defined an OEI as follows: ‘The development of innovations utilizing inflows and outflows of knowledge to accelerate internal innovation and expand the market for innovations created with partners outside the firm, with one of the goals of achieving a positive impact on society, the environment, or both (Garcia, Wigger, and Hermann 2019).’ Recent studies on the subject underline how systematic and naturally cooperative the idea is.

Eco-product innovation (EPDI) entails the release of items that are either entirely new or greatly improved; for instance, these products might be enhanced in terms of their technical elements or the materials utilised to make them (Pujari 2006). Advanced eco-technologies, shortened product life cycles and increased competitiveness are frequently the driving forces behind eco-product innovation (Carrillo-Hermosilla, Del Río, and Könnölä 2010). The environmental impact of eco-product developments comes from their disposal (e.g. heavy metals in batteries) and use (e.g. fuel use and CO₂ emissions of cars). Product life cycle analysis, according to Pujari, Peattie, and Wright (2004), includes all facets of a product, from its development and use to its disposal. The innovation of eco-friendly products can also use this theory. Wind energy is used to produce electricity as an example of the utilisation of creation. The compact fluorescent bulb is another example of energy-saving in the context of a product, whereas a chlorofluorocarbon-free air conditioner is considered as green mostly due to its reduced environmental impact in the context of its disposal. Environmental consequences are minimised throughout an eco-product's life cycle, in essence, by eco-product innovations (Christensen 2011).

In contrast, eco-process innovation (EPCI) is the addition of new components to a company's manufacturing process in order to create eco-products (Negny et al. 2012). In general, eco-process innovation refers to improving current industrial processes or developing new ones to have a smaller negative impact on the environment. According to Rennings (2000), innovation can be incorporated into industrial processes by substituting inputs, increasing production efficiency and recovering outputs. Additive solutions (such smokestack scrubbers) are also possible. In order to develop new or considerably better eco-products and lessen the organisation's influence on the environment, eco-process innovation alters the organisation's operational processes and systems (Negny et al. 2012).

Digital technologies (DT)

The fourth industrial revolution in manufacturing is known as Industry 4.0, and it was first introduced in Germany. This idea has altered the way organisations are organised by bringing intelligent and disruptive technologies for efficient production, digitisation and cyber-physical system interconnectivity that enable the integration of the real world, automation and data networks to develop new production models (Dubey et al. 2019; Gupta et al. 2020; Kumar and Bhatia 2021). The term ‘Digital Technologies’ (DT) refers to a collection of various intelligent technologies, such as the Internet of Things (IoT), cloud computing (CC), big data analysis (BDA) and digital platforms, that enable connectivity, digitisation and automation (Gupta et al. 2020; Li, Dai, and Cui 2020; Nayal et al. 2022; Yang, Fu, and Zhang 2021). According to Li, Dai, and Cui (2020) and Sarkis, Kouhizadeh, and Zhu (2021), the Internet of Things (IoT) is an information technology infrastructure that creates connectivity between real-world and virtual things. According to De Sousa Jabbour, de Oliveira Frascareli, and Jabbour (2015) and Kumar and Bhatia (2021), bar codes, wireless sensors and Radio Frequency Identification (RFID) are a few examples of IoT. Big data analytics (BDA) on the other hand, refers to the gathering and analysis of enormous amounts of data (Kunkel et al. 2022; Singh and El-Kassar 2019) in order to capture, manage, store and analyse data (Bag et al. 2020b, 2023; Bag, Gupta, and Luo 2020; Raut et al. 2021). To enhance decision-making processes, the data are put through these processes for modelling and prediction (Li, Dai, and Cui 2020). Without the requirement for installation, cloud computing offers online storage services for all applications, programmes and data on a virtual server (Zheng et al. 2021). According to Li, Dai, and Cui (2020) and Mittal et al. (2019), Digital platforms are digitally driven infrastructures built up by a focal company for the continuous and swift transit of information throughout the supply chain. The term ‘dynamic’ is used by Teece, Pisano, and Shuen (1997) to describe the capacity to update abilities in order to react to quick environmental changes. It has also been demonstrated that an industrial activity's potential scope and rate of advancement are related, in part, to the technical prospects that it can take advantage of (Danneels 2008; Li, Dai, and Cui 2020). According to studies, DT adoption can help businesses respond more quickly to changing environments by capturing, transforming, sharing and analysing data, all of which increase the effectiveness of decision-making (Bag, Gupta, and Luo 2020; Chan, Denford, and Jin 2016; Yang, Fu, and Zhang 2021). In many areas of operations and supply chain management, including studies of supply chain risks (Ivanov, Dolgui, and Sokolov 2019), social and environmental sustainability (Dubey et al. 2019), supply chain and organisational performance (Gunasekaran et al. 2017), and servitisation (Opresnik and Taisch 2015), big data and analytical technologies play a significant role in the implementation of Industry 4.0.

Supply management innovativeness and eco-product and eco-process innovation

In order to meet environmental criteria and minimise negative externalities, new or significantly improved goods using cutting-edge eco-technologies or materials are referred to as eco-product innovations (Wu, Zhou, and Zhu 2023). Eco-process innovation refers to the application of novel or enhanced techniques to modify current processes in order to lower costs, energy use and pollutant emissions. Technology-focused innovations concentrate on integrating cleaner production methods within the product manufacturing process (Hojnik and Ruzzier 2016). In order to improve process restrictions and lessen the harmful environmental effects of manufacturing processes, integrated clean production technology entails the adoption of new green energy sources, clean production and other technologies or approaches (Demirel and Kesidou 2011). According to Lintukangas, Kähkönen, and Hallikas (2019), the ability of purchasing professionals to innovate collectively and their receptivity to fresh ideas with the intention of influencing supplier markets can be characterised as innovativeness in supply management. However, an innovative culture motivates supply management managers and employees to adopt innovative behaviours (Escrig-Tena et al. 2018) and focuses on developing and testing new ways of thinking and working, such as external market scanning (Kilpi et al. 2018). According to Schmelzle and Tate (2022), market scanning entails searching, keeping an eye on and scanning a supplier market for pertinent (technical) advancements. Innovativeness, however, can upend established technologies and industrial processes, enabling a move toward renewable energy and ethical sourcing and standards, a replacement for dangerous compounds, and a removal of pointless packaging and trash disposal (Porter and van der Linde 1995). Ecotechnology can only be adopted and applied fully by the most creative businesses (Gabler, Richey, and Rapp 2015). Better eco-product innovation may result from supply management's crucial role in identifying and perceiving opportunities for innovation in the supplier market (Azadegan and Dooley 2010; Teece 2007; 2012). Therefore, in accordance with the DCV's presumptions, supply management's capacity for innovation and its ability to dynamically adjust to changing requirements can be viewed as a strategic organisational resource and source of competitive advantage.

H1: Supply management innovativeness positively impacts eco-product innovation.

H2: Supply management innovativeness positively impacts eco-process innovation.

Supply management innovativeness, environmental collaboration and eco-innovation

According to Gölgeci et al. (2019), environmental collaboration refers to collaborative environmental planning, joint environmental goal-setting and corporate partners to minimise environmental impacts. It can engage partners from various societal backgrounds and is a relational activity that transcends organisational boundaries. A firm's environmentally sustainable strategy and operations must include collaboration that benefits the environment (Gölgeci et al. 2019). According to Hult (1998), an organisation's ability to innovate is influenced by its culture, which has an impact on how it manages its suppliers. By showing that strong learning and future orientation pervade an organisation, Hult, Ketchen, and Nichols (2003) expanded to this approach by pointing out that partnerships thrive in such environments. Innovativeness fosters a culture that values chain integration and success and facilitates the growth of supply chain management (Kalyar, Shafique, and Ahmad 2020; Seo, Dinwoodie, and Kwak 2014). Similar to this, innovativeness is anticipated to drive businesses toward supplier and customer integration, information sharing, strategic decision-making collaboration and partnerships to obtain a competitive edge. The tendency to reinvent and create encourages a firm's orientation toward cooperative connections and dissuades hostile interactions. As a result, Kalyar, Shafique, and Ahmad (2020) predict that increasing levels of innovativeness will improve the chance of supplier and customer integration. The purchasing function, according to Yu, Chavez, and Feng (2017), can assist firms in working with suppliers who use sustainable innovations in their business practices. Additionally, research has demonstrated that innovativeness improves the use of sustainable supply management methods (Gualandris and Kalchschmidt 2014) and that innovativeness is a requirement for the adoption of sustainable supply chain management techniques (Pagell and Wu 2009). Thus, the following hypothesis is proposed:

H3: Supply management innovativeness is positively related to environmental collaboration.

Given the challenges of collecting essential knowledge in today's cutthroat marketplaces, businesses struggle to achieve green innovation within a single firm, further emphasising the importance of collaborations in creation of valuable green products and services (Melander and Pazirandeh 2019). External knowledge acquisition may help to promote green innovation, according to Guo et al. (2018). First, suppliers’ specialised, heterogeneous knowledge is a unique, inimitable resource for innovation, especially tacit knowledge. External knowledge sharing helps firms’ design teams better understand the product and technology, and enables design improvements, improving design innovativeness. Second, the proprietary technical knowledge from suppliers integrated with the firm’s knowledge contributes to forming the ‘cross-pollination’ effect. Gao, Xie, and Zhou (2015) demonstrated that heterogeneous knowledge shared by suppliers brings more opportunities for knowledge complementation, ultimately enhancing a firm’s product innovation. For the success of eco-innovation, Cicconi (2020) considered the significance of partnerships between a buying firm and its suppliers throughout manufacturing and the value chain. In particular, it has been demonstrated that in order to create environmentally friendly products, input resources outside of the company's current purview must be sourced, and the input materials’ eco-viability must be guaranteed (Melander and Pazirandeh 2019). Through cooperative relationships with suppliers, firms may be better able to ensure the environmental sustainability of input materials and, in particular, to study and apply technologies that were previously out of their purview. Similar studies have shown that eco-innovators collaborate more frequently with suppliers than do traditional innovators (De Marchi 2012), and bilateral learning with raw material suppliers is essential for achieving environmental goals (Theyel 2006). We contend that new or modified machinery and input materials are necessary for significant alterations in the production processes targeted at ecological sustainability. Therefore, it is very likely that working with suppliers will help develop innovative processes that aim to lessen manufacturing's impact on the environment by finding or developing production tools or input materials that use less energy or emit fewer emissions. In some cases, buying firms collaborate with suppliers to (re)design green products or manufacturing processes, or to develop inter-organisational routines for joint pollution control (Tachizawa, Gimenez, and Sierra 2015). Overall, we believe that effective collaborative innovation with suppliers can create innovative outputs, specifically in terms of resultant products and processes underlying their production.

However, there is a detrimental side to excessive collaboration in eco-innovation. There are three reasons for this. First, interaction with external suppliers has both cognitively stimulating and disturbing effects. As EC increases, design team members’ attention may be diverted for handling large amounts of technical information from suppliers (Sun et al. 2021; Tang and Marinova 2020), which constrains their ability to generate creative ideas. Social cognitive theory suggests that ‘attention is a scarce resource, and effective strategic decisions require the scarce managerial attention should be allocated to the most related issues’. There exists a marginal diminishing effect of valuable information, and firms may lose their creativity if they rely too heavily on external technology sourcing. Second, a high level of collaborative innovation can produce the problem of ‘information overload,’ excessive diverse knowledge may impede knowledge absorption and transformation. Excessive gaps in ecological knowledge between suppliers and firms can cause interpretation and communication barriers, preventing the effective translation of suppliers’ ecological knowledge into eco-product innovation (Wu et al. 2022). Additionally, the likelihood of supplier opportunism is higher when manufacturers show a larger reliance on cooperative ties with suppliers to meet environmental goals. The end goods’ green image is ruined by pollution and waste from raw materials and processing, which undermines manufacturing companies’ attempts to safeguard the environment and exposes them to situations that could damage their reputations (Lee et al. 2015). The negative effects of a collaborative partnership brought on by the supplier's potential for opportunistic behaviour have also been highlighted, especially in light of the rise in social capital in EC (Villena, Revilla, and Choi 2011). Manufacturers may lose specific investments and significant downstream payments owing to risk transfer if suppliers lie or break an agreement. In summary, when firms overcommit to collaborative innovation, the extent to which they benefit from such collaborations appears to decrease over time.

We suggest an inverted U-shaped relationship between environmental collaboration and eco-product/process innovation to describe how eco-product/process innovation changes as environmental cooperation rises while taking into account the advantages and disadvantages of collaborative innovation.

H4: An inverted U-shaped relationship exists between environmental collaboration and firms’ eco-product innovation, such that the impact is initially positive but becomes more negative as the level of environmental collaboration increases.

H5: An inverted U-shaped relationship exists between environmental collaboration and firms’ eco-process innovation, such that the impact is initially positive but becomes more negative as the level of environmental collaboration increases.

Furthermore, we presume that supply management innovativeness has not only a direct influence but also an indirect effect on eco-product and process innovation. That is,

H6: Environmental collaboration mediates the positive impact of supply management innovativeness on eco-product and process innovation.

Moderating role of digital technologies

In the era of Industry 4.0, manufacturing organisations are investing more and more in digital technology in order to enhance their internal information processing and decision-making capabilities. According to Wamba et al. (2015), big data and predictive analytics allow for the use of cutting-edge information technology tools and frameworks to acquire, store, extract and analyse substantial volumes of data in order to affect decision-making processes. In purchasing and supply management, big data analytics enables supply managers to obtain real-time knowledge of supplier markets, external supplier environments, knowledge of their internal business partners, and technical skills (Schütz et al. 2020). As a result, this method catalyses the enterprises’ ability to embrace servitisation through the creative pairing of products and services (product-service systems) and enables them to produce eco-innovations by providing them with insights. Additionally, the use of digital technology enhances employee digital literacy and empowers them to accurately identify and evaluate green information using digital tools, which is helpful for enhancing resource utilisation and the effectiveness of R&D processes meant to slow down environmental degradation (Zhuge et al. 2023). Thus, we hypothesise that,

H7: Digital technology positively moderates the effects of supply management innovativeness on eco-product innovation.

H8: Digital technology positively moderates the effects of supply management innovativeness on eco-process innovation.

According to Sedera et al. (2016), a digital platform can be thought of as a technical framework that connects companies to the platform and allows them to gather, integrate and calculate information on the platform. It possesses the qualities of hierarchical modularisation, self-growth and network effects, which are efficient ways for organisations to achieve quick information interaction, minimise information asymmetry and uncertainty, and reduce resource search and transaction costs (Jiang, Yang, and Gai 2023).

On the other hand, according to Tachizawa, Gimenez, and Sierra (2015), monitoring is a crucial risk management strategy for reducing information asymmetry. Traceability is a component of one monitoring strategy (Wowak, Craighead, and Ketchen 2016). The importance of supply chain traceability (SCT), which is facilitated and expedited by cutting-edge digital technologies like IIoT sensors, smart labelling, blockchain and big data, has gradually grown. To track product quality throughout the entire process and in real time, many top food companies, like Nestlé and Danone, have used traceability systems (Ringsberg 2014). The impact of organisationally focused green supply chain management and environmental performance is positively moderated by the ability to track and trace products and activities because it reduces information asymmetry among supply chain participants and suppliers’ potential to act opportunistically (Cousins et al. 2019). The increased level of monitoring that traceability allows increases suppliers’ initial and ongoing investments in environmental activities, boosting the influence of ECs on eco-innovation (Klassen and Vachon 2003; Lee and Klassen 2008). Without supply chain traceability, businesses’ efforts to boost performance may be hindered and might even cause their suppliers to receive the wrong signals (Wowak, Craighead, and Ketchen 2016). For instance, managers can record product data and streamline distribution by using radio frequency identification devices. Thus, we hypothesise that,

H9: Digital technologies moderate the inverted U-shaped relationship between environmental collaboration and eco-product innovation, such that the inverted U-shaped relationship is flatter for firms that most frequently use digital technologies.

H10: Digital technologies moderate the inverted U-shaped relationship between environmental collaboration and eco-process innovation, such that the inverted U-shaped relationship is flatter for firms that most frequently use digital technologies.

We propose the research model shown in Figure 1.

Figure 1. Conceptual framework.

Research methodology

Sampling and data collection

The targeted respondents were middle- and senior-level managers, and the units of study were distinct Chinese manufacturing companies. Owing to their high energy consumption and pollution, manufacturing enterprises, which occupy the main position in the national economy, are increasingly concerned with all sectors of society and face greater pressure to mitigate their impact on the environment. Manufacturing enterprises must improve their resource utilisation efficiency and environmental quality through eco-innovation. The survey included areas for target sampling in Beijing, Tianjin, Hebei and Shanxi in the north, Guangdong, Guangxi and Fujian in the south, Shanghai, Zhejiang, Jiangsu, Shandong and Anhui in the east, and Sichuan, Yunnan, Chongqing and Shaanxi in the west. Mail questionnaires were used to gather data. First, we randomly selected manufacturers using the China Telecom Yellow Pages (Jacobs, Yu, and Chavez 2016) and the China Manufacturing Company Directory. We chose this sampling procedure because it allowed for equal representation of the population and increased generalisability (Ghauri, Grønhaug, and Strange 2020). Second, we determined the companies’ willingness to participate in the study through telephone and e-mail. We also chose a worker from the organisation who consented to facilitate follow-up communication. The questionnaire was then mailed to every company. We included a cover letter with each questionnaire, noted the motivation for the study, and guaranteed the respondents’ privacy. Every two weeks, we called the respondents to provide answers to their questions and to remind them to complete the survey in an effort to increase the response rate. A total of 477 questionnaires were given out, and 328 of those were collected that were usable, yielding a response rate of 68.763%. Table 1 shows that the four industries of chemicals and petrochemicals, electronics and electrical, metal, mechanical and engineering, as well as textiles and apparel, are where the majority of businesses are located. With over 300 employees, middle-sized businesses make up more than 90% of the sample, and 50.92% of them have been operating for more than ten years.

Table 1. Sample descriptive.

Characteristics	Frequency	Proportion (%)
Type of enterprise
State-owned company	91	27.74
Private company	147	44.82
Foreign owned, and joint venture	68	20.73
Others	22	6.71
Operation years
‘5–10 years’	161	49.08
‘11–15 years’	145	44.21
‘more than 15 years’	22	6.71
Number of Employees
‘less than 300’	12	3.66
‘300-2000’	228	69.51
‘more than 2000’	88	26.83
Industry type
‘Chemicals and petrochemicals’	128	39.02
‘Electronics and electrical’	28	8.53
‘Metal, mechanical and engineering’	81	24.70
‘Textiles and apparel’	75	22.87
‘Others’	16	4.88
Position
‘R&D manager’	97	29.56
‘Purchasing manager’	103	31.41
‘Product manager’	103	31.41
‘Others’	25	7.62
Working years
‘1–5 years’	34	10.37
‘6–10 years’	137	41.77
‘11–15 years’	79	24.08
‘More than 15 years’	78	23.78

Questionnaire design

To increase content validity and dependability, a variety of strategies have been used (Churchill 1979). First, a thorough literature review was conducted to determine the measurement scales’ content validity. In order to ensure that the final scales were consistent with the original ones, a strict translation/back-translation procedure was used, in which the scales were first formulated in English, translated into Chinese by professional translators, and then translated back into English by different translators (Brislin 1970). A pilot survey of senior managers at 10 local companies was undertaken prior to the official survey. These comments were taken into account, and superfluous or unclear items were removed or changed.

Measurement

All constructs involved in the focal study have been validated in previous studies (Gölgeci et al. 2019; Lintukangas, Kähkönen, and Hallikas 2019; Trujillo-Gallego, Sarache, and de Sousa Jabbour 2022; Xu, He, and Ji 2022). A five-point Likert scale was used for all variables to measure the constructs, with 1 and 5 representing ‘strongly disagree’ and ‘strongly agree,’ respectively. The measurement instruments for the constructs were adopted from previous studies and adapted to suit the context of this study. Table 2 presents the details of all variables.

Table 2. Measurement scales.

Constructs and items	Loading	CR	AVE
Supply management innovativeness (SMI) α = 0.875(Lintukangas, Kähkönen, and Hallikas 2019)
The purchasing process supports finding innovative solutions.	0.800	0.840	0.529
Supply management participates in the innovation processes of a company.	0.788
The capability to innovate in supply management is systematically developed.	0.752
Goals and measures related to innovations are set for supply management.	0.751
The collaboration between supply management and research and development is seamless.	0.749
Supply management coordinates and facilitates new ideas in the organisation.	0.728
Environmental Collaboration(EC) α = 0.731 (Gölgeci et al. 2019)
Our firm cooperates with its suppliers to achieve environmental objectives.	0.771	0.825	0.519
Our firm provides its suppliers with design specification that include environmental requirements for purchased items.	0.818
Our firm encourages its suppliers to develop new source reduction strategies.	0.822
Our firm cooperates with its suppliers to improve their waste reduction initiatives.	0.812
Our firm works with its suppliers for cleaner production.	0.773
Our firm collaborates with its suppliers to acquire materials, parts and/or services that support its environmental goals.	0.801
Eco-product innovation(EPDI) α = 0.887, (Xu, He, and Ji 2022)
We often emphasise developing new eco-products through new technologies to simplify their packaging.	0.881	0.923	0.751
We often emphasise developing new eco-products through new technologies to simplify their construction.	0.867
We often emphasise developing new eco-products through new technologies to reduce waste as much as possible.	0.864
We often emphasise developing new eco-products through new technologies to reduce damage by waste as much as possible.	0.853
Eco-process innovation(EPCI) α = 0.891 (Xu, He, and Ji 2022)
We often update manufacturing processes to protect against contamination.	0.801	0.846	0.578
We often update manufacturing processes to meet standards of environmental law.	0.819
We often employ new manufacturing processes so as not to contaminate the environment.	0.795
We often introduce new technologies into manufacturing processes to save energy.	0.755
Digital technologies(DT) α = 0.876 (Trujillo-Gallego, Sarache, and de Sousa Jabbour 2022)
The extent to which our firm use Internet of Things.	0.852	0.807	0.511
The extent to which our firm use cloud computing.	0.840
The extent to which our firm use big data analytics.	0.803
The extent to which our firm use digital platforms.	0.801

Innovation in eco-products and processes are dependent variables. We followed other studies and separated firm-level eco-innovation activities into product and process innovations based on the characteristics of various innovation activities (Kobarg et al. 2020). The two eco-innovation dimensions were modified from (Xu, He, and Ji 2022). The four components of eco-process innovation capture the extent to which businesses enhance their manufacturing procedures, technology, and tools to lessen their adverse effects on the environment. Four factors make up the measuring of eco-product innovation: businesses’ ongoing development, optimisation and upgrading of green products.

Independent variables: Lintukangas, Kähkönen, and Hallikas (2019) served as the foundation for the measurement of supply management innovativeness. In order to gather information on supplier networks, involvement in businesses’ innovation processes, and the coordination/facilitation of new ideas, six statements were provided on innovation capabilities in supply management, procedures and operational models. The Gölgeci et al. (2019) scale was developed to evaluate supplier environmental collaboration.

Digital technologies: The DT measures were adapted from Trujillo-Gallego, Sarache, and de Sousa Jabbour (2022). They include the frequency of the application of Internet of Things, big data analytics, cloud computing and digital platforms.

Control variables: We use industry, firm size and environmental uncertainty as control variables. The degree to which various industries are willing to engage in eco-innovation activities varies greatly. For instance, businesses in sectors with high levels of pollution are encouraged to use eco-innovation to enhance their environmental performance. Large companies are also more inclined to allocate funding to new projects. The degree of instability and uncertainty in consumer preferences and competitor behaviour requires changes in the firm's product and process architecture, which may have some influence on the level of eco-innovation. As a result, companies will be more motivated to pursue eco-innovation as environmental uncertainty rises.

Analysis and results

Descriptive statistics analysis

The mean, standard deviation and correlation coefficients between the variables are displayed using descriptive statistics and correlations (see Table 3). According to the findings, SMI, EC, ECPD and ECPC all exhibit significant correlations, while SIM and EC are strongly and favourably connected (γ = 0.281, p < 0.01), while SMI (γ =  286, p < 0.01; γ = 0.309, p < 0.01), EC (γ =  0.267, p < 0.01; γ =  0.284, p < 0.01), and ECPD and ECPC show a significant correlation. These results provide the foundation for further analyses.

Table 3. Correlation matrix and the analysis of discriminant validity.

Correlation	AVE	1	2	3	4	5	Mean	Std.
SMI	.529	.727					3.948	.757
EC	.519	.281**	.720				3.908	.735
ECPD	.751	.286**	.267**	.867			3.955	.620
ECPC	.578	.309**	.284**	.195*	.760		3.443	1.043
DT	.511	.016	.078	.153	.100	.715	3.608	.782

Note: Square root of AVE on the diagonal in bold.

***P < 0.001; **P < 0.01; *p < 0.05.

Common method bias and non-response bias

In order to control the common technique bias, this study used both statistical and procedural methods. Regarding the procedure corrections, we kept the questionnaire short and to the point, separated the measurement items for each construct into separate sections, chose experienced upper- and middle-level managers as respondents (nearly 90% of them had more than five years of work experience), and ensured complete anonymity protection (Podsakoff et al. 2003). These methods made sure that responders could provide thoughtful and truthful answers. As a result, we discovered trustworthy sources of information. Additionally, we used CFA to run the Harman single factor test. Less than 40% of the total variable, or 22.78%, was explained by the first extracted factor, which did not explain the majority of the variance. After that, CFA was also applied by tying every construct item to a single method factor. The fit indices were manifestly unacceptable and significantly worse than those of the measurement model (χ2/df  = 1.182, CFI = 0.944, IFI = 0.946, GFI  = 0.900, RMSEA  = 0.042). They were as follows: (χ2/df = 2.744, CFI = 0.445, IFI = 0.456, GFI  = 0.463, RMSEA  = 0.129). We came to the conclusion that common technique bias was not a significant threat in this study based on the aforementioned statistical analysis.

By analysing the differences in responses from early and late respondents, the non-response bias was evaluated (Armstrong and Overton 1977). The number of employees and industry were compared between the early and late responses in this study using a t-test. The findings revealed no appreciable variations in the industry or personnel count.

Reliability and validity

According to the reliability analysis, which is displayed in Table 3, the Cronbach's alpha varies for all variables from 0.731 to 0.891. Without comparing it to the number of items in the scale, the majority of research that have employed alpha consider values thereof equal to or exceeding 0.70 to be sufficient (Cortina 1993), indicating excellent reliability of the scale. The scales were developed in accordance with the literature, pretested and changed based on the comments and recommendations from the key informants, therefore content validity is presumed to exist. With the CFA, this study evaluates the construct validity of the scale (Sumi 2018). When the absolute values of factor loading are more than 0.4, construct validity is acknowledged (Crick and Crick 2019). The findings indicate that each item's factor loading is greater than 0.6 and that more than 60% (63.746%) of the variance is explained overall. As a result, it demonstrates strong fit between the scale constructions and the data. Average variance extracted (AVE) was calculated using a fully standardised CFA solution in order to determine convergent validity. Table 3 shows that all AVEs are over or close to 0.5, proving the validity of convergent analysis. Last but not least, discriminant validity was utilised to compare all inter-construct correlations and the square roots of the AVEs in order to assess how distinctively different latent variable measurements are. In accordance with Fornell and Larcker's criterion for discriminant validity (Fornell and Larcker 1981), Table 3 demonstrates that the square root of the variance shared between a construct and its items was greater than the correlations between the construct and any other construct in the model. These findings suggested that all constructs in this article had satisfactory discriminant validity.

Hypothesis tests

Hypothetical model path analysis

To test the hypotheses, hierarchical regression analysis was used. EC is the dependent variable in Models 1 and 2, eco-product innovation is the dependent variable in Models 3–5, and eco-process innovation is the dependent variable in Models 6–8. We examined the impact of the independent factors on the dependent variables using a tiered process that involved first including the control variables as a benchmark in a model. Table 4 displays the main-effect regression analysis’ findings.

Table 4. Results of regression analysis.

	Environmental collaboration		Eco-product innovation			Eco-process innovation
Control variables	Model1	Model2	Model3	Model4	Model5	Model6	Model7	Model8
Industry	0.035	0.032	0.061	0.022	0.055	0.039	0.048	0.029
Size	0.029	0.046	0.261**	0.235**	0.232**	0.101*	0.094*	0.079*
EU	0.045	0.012	0.116	0.015	0.064	0.046	0.054	0.046
Direct effects
SMI		0.286**		0.287***	0.277***		0.308***	0.315***
EC				0.166***	0.152***		0.151***	0.159***
EC^2				−0.185**	−0.193**		0.371	0.373
DT				0.113*	0.140*		0.084**	0.115**
Interaction effects
SMI*DT					0.187***			0.196*
EC*DT					0.227**			0.250
EC^2*DT					0.083**			0.089
R^2	0.004	0.082	0.085	0.310	0.473	0.014	0.360	0.484
ΔR^2		0.078		0.225	0.163		0.246	0.124
F-value	0.141	2.247**	3.133***	6.216***	7.278***	0.462	6.636***	7.186***

Note(s):***P < 0.001; **P < 0.01; *p < 0.05.

The positive and significant regression coefficients of SMI in Models 4 and 7 (β = 0.287, p < 0.001; β = 0.308, p < 0.001), support its beneficial influence on eco-product and process innovation. This result validates H1 and H2.

The outcomes of Models 1 and 2 demonstrate that SMI can have a favourable effect on EC (β = −0.185, p < 0.01). H3 is therefore supported.

To Models 4 and 7, we included the EC's linear and squared terms. The empirical findings for Model 4 support Hypothesis 4 by showing that the coefficient of the squared term of EC is significantly negative (β = −0.185, p < 0.01), indicating an inverted U-shaped curvilinear relationship between EC and eco-product innovation. In other words, EC initially has a favourable effect on the development of eco-products, but this effect quickly turns unfavourable. Companies with a low or excessive EC have less eco-product innovation than those with a moderate degree of EC. To confirm that the curve had turned, we conducted additional tests at the point of inflexion. However, in Model 7, the empirical results indicate that the coefficient of EC and the squared term of EC is positive (β = 151, p < 0.001; β = 0.371, p > 0.05), which shows the positive and significant impact of EC on eco-process innovation. Therefore, H5 is not supported. The fact that process innovation frequently takes place more internally within the firm and is also more evolutionary, systemic and tacit may be one explanation for why there isn't an inverted U-shaped effect. Un and Asakawa (2015) suggested that suppliers can suggest changes to certain production processes to improve manufacturability and increase efficiency while collaborating with a focal firm to generate new goods. However, the focal firm's internal choices will determine whether it follows the suggestion. This outcome is consistent with research by Kobarg et al. (2020), which shown a positive relationship between supplier collaboration and ecological process innovation.

Mediating role of EC

We used a bootstrap analysis to assess the mediating effect in order to strengthen the validity of EC's mediating effect. To evaluate whether there is an intermediary impact, the bootstrap approach is based on whether the confidence interval of the indirect effect contains zero. The outcomes of the bootstrap analysis are shown in Table 5. For the EPDI, neither the indirect effect's interval (LLCI = 0.009; ULCI = 0.178) nor the direct effect's interval (LLCI = 0.058; ULCI = 0.439) contained zero. This result shows that EC mediates SMI and EPDI in part. These findings supported the idea that EC partially mediates the connection between SMI and EPCI.

Table 5. Mediation analysis.

Path	Direct effect			Indirect effect
	Coef (β)	S.D	95% CI	Coef (β)	S.D	95% CI
SMI→EC→EPDI	0.248	0.096	(0.058,0.439)	0.060	0.023	(0.009,0.178)
SMI→EC→EPCI	0.229	0.097	(0.037,0.421)	0.057	0.021	(0.019,0.172)

Note(s):***P < 0.001; **P < 0.01; *p < 0.05.

Moderating role of DT

The four hypotheses in this study suggest that DT has a moderating effect on how SMI, EC and eco-innovation are related. To examine the moderating functions of the interaction terms produced by the centralised variables, we used a hierarchical regression. The findings are displayed in Table 4. In a nutshell, DT has a vital impact on how SMI and eco-innovation interact. The coefficients of the interaction between SMI and DT and between EC and DT in Model 5 are specifically positive significant for EPDI (β =  0.187, p < 0.001; β =  0.227, p < 0.01), which is consistent with H7 and H9, respectively. As a result, the direct effects of SMI and EC on EPDI were favourably mitigated by DT. Model 8 demonstrates that H8 is supported by the fact that the coefficient of the interaction between SMI and DT is both positive and significant (β = 196, p < 0.05). Model 8 does not support H10 since it does not demonstrate that DT mitigates (β = 0.250, p > 0.05) the direct impact of EC on EPCI. An interpretation of this finding lies in the attributes of process innovation typically more internal to the firm, as well as evolutionary, systemic and tacit (Un and Asakawa 2015). Specifically, information coming from suppliers could become excessive and less relevant, and thus complicate decision-making and the development or implementation of internal operations such as EPCI (Chavez et al. 2023). The last prediction made by H9 is that DT positively modifies the curvilinear relationship between EC and EPDI. This prediction is supported by Model 5's interaction coefficients, which are both significantly positive (β = 0.083, p < 0.01) for the squared term of EC and DT and considerably positive (β = 0.227, p < 0.01)for the linear term of EC and DT. This finding supports H9 since it shows that the inverted U-shaped curve of EC steepens with increasing DT. With the medium, high and low levels set at the mean, 1 and +1 standard deviations, respectively, we displayed the moderating influence of DT on the connection between EC and EPDI (Figure 2) in order to better understand the moderating pattern.

Figure 2. Moderating effect on EC – EPDI relationship.

It makes intuitive sense that when the DT is stronger, the inflection point of an inverted U-shaped curve rises uphill and the slope of the curve on either side of the inflection point is steeper. These findings support the notion that DT lowers EC's negative effects when EC is high but strengthens EC's favourable effects on EPDI. In particular, EC ultimately had a negative effect, indicating that DT could counteract the deleterious consequences of excessive EC. The moderating impact of DT causes the inflection point of the inverted U-shaped curve of EC to shift, as depicted in Figure 2. DT affects the ideal level of EC differently. Figure 3 depicts the research model and findings.

Figure 3. Research model with results. Note(s): ***P < 0.001; **P < 0.01; *p < 0.05.

Discussion and conclusion

Main findings

Identifying and capturing innovation in the supplier market by innovation-purchasing function is prevalent (Kim and Chai 2017; Schmelzle and Tate 2022; Servajean-Hilst and Calvi 2018). Nevertheless, there are rare studies conducted on the effect of purchasing and supply management on a company’s sustainable development and eco-innovation performance. Using a dynamic capability approach, this study focused on the relationship between SMI, EC with suppliers, and eco-innovation, namely, EPDI and EPCI. We explored the impact of SMI and EC on different types of eco-innovation and probed the inner mechanisms between them, including direct effects, mediating effect of EC and moderating effect of DT. The specific research conclusions are as follows.

First, our results confirmed the conclusions of earlier empirical study (Gabler, Richey, and Rapp 2015), demonstrating that innovativeness, particularly in supply management, considerably enhances the development of eco-friendly products and processes. An innovative culture motivates supply management managers and employees to adopt innovative behaviours (Escrig-Tena et al. 2018) and focuses on developing and testing new ways of thinking and working, such as external market scanning (Kilpi et al. 2018). Acting as boundary-spanning ‘innovation facilitator’, purchasing supports the internal customer-facing function(s) and the influence of culture and technological uncertainty on innovation projects. According to Schmelzle and Tate (2022), market scanning entails searching for new, unknown technologies, keeping an eye on and scanning a supplier market for pertinent (technical) advancements. This study also closes a research gap, because little research has focused on the important function of supply management and how it might enhance firm-level eco-innovation (Lintukangas, Kähkönen, and Hallikas 2019). Therefore, those in charge of purchasing and supply should support initiatives that foster novel approaches to problem-solving and supply market innovation. To discover creative solutions, businesses can embrace sustainability criteria in their acquisitions.

Second, we discovered that the relationship between SMI and eco-product and eco-process innovation is partially mediated by EC. In particular, effective collaboration between the focal firm and its supplier(s) in the innovation process is based on open communication, mutual trust between the two organisations, and a relatively long-term focus and partnering (instead of arm’s length) relationship characteristics (Schmelzle and Tate 2022). Purchasing department, as the supplier relationship facilitator, enhances supplier motivation and commitment (Mikkelsen and Johnsen 2019) and facilitates an open and rich knowledge exchange with the key suppliers (Tchokogué and Merminod 2021). This clarifies the debate between innovations developed internally by supply management and those that were obtained from the supply base. These findings support the claims made by Reuter et al. (2010) that proactive management of external resources, such as supplier collaboration, and accounting for the dynamism of the business environment, are valuable resources in a firm's value generation. These findings are consistent with those of DCV.

Third, there is an inverted U-shaped relationship between EC and EPDI whereas a positive impact on EPCI. This finding echoes the findings of the study by Wu et al. (2022), where knowledge sharing with suppliers had an inverted U-shaped effect on product performance. Although suppliers’ specialised, heterogeneous knowledge is a unique, inimitable resource for eco-innovation, excessive collaboration in eco-innovation may cause interpretation and communication barriers, which hampers firms’ creativity. And then supplier opportunism lead by excessive dependence may destroy the green image of the buying firms and make the collaboration advantage decline. For EPCI, contrary to our expectation, EC positively and significantly affects it. Un and Asakawa (2015) suggested that suppliers can suggest changes to certain production processes to improve manufacturability and increase efficiency while collaborating with a focal firm to generate new goods. However, the focal firm's internal choices will determine whether it follows the suggestion. Our findings are also consistent with research by Kobarg et al. (2020), which shown a positive relationship between supplier collaboration and EPCI.

Additionally, we investigated the moderating impact of DT and came to some intriguing conclusions. Because DT can enhance supply managers’ internal information processing and decision-making capacities, the interplay between SMI and DT has a favourable impact on eco-product and eco-process innovation. However, contrary to what we had anticipated, the interaction of EC and DT has a beneficial impact on EPDI but no impact on EPCI. Differences in invention mechanisms can account for this.

Theoretical implications

Three significant advances to previous research are made by this study: First, we reference a recent demand for more research on purchasing departments’ contributions to the growth of businesses’ eco-innovation made by Viale, Vacher, and Bessouat (2022). To the best of our knowledge, this is the first study to empirically test the hypothesis that innovations acquired from the supply base (i.e. environmental collaboration with suppliers) and the internal innovation capacity of supply management directly contribute to a firm's eco-innovation. Previous studies have mostly explored the consequence of EC facilitated by purchasing and supply management departments to achieve environmental and economic performance targets (Ahmed et al. 2020; Grekova et al. 2016; León-Bravo et al. 2017; Xu, He, and Ji 2022), and little attention has been paid to the innovative capacity of supply management functions (Lintukangas, Kähkönen, and Hallikas 2019). Moreover, EC also serves to partially mediate the relationship between the internal innovation capacity of supply management and eco-product and eco-process innovation. In order to better comprehend the purchasing department's involvement in firm's innovation (Patrucco, Luzzini, and Ronchi 2017; Schiele et al. 2021; Servajean-Hilst and Calvi 2018), we expand our knowledge to include open eco-innovation (Viale, Vacher, and Bessouat 2022).

Second, this study contributes to EC studies by shedding light on the inverted U-shaped linkage between EC and EPDI. Prior studies have mostly concluded that EC is always beneficial for firms, and can lead to effects such as relationship performance (Ritala et al. 2015) and innovation performance (Wang and Hu 2020). The underlying ‘dark side’ of supplier collaboration has received little attention. A few recent research, however, express scepticism and point out that supplier collaboration might potentially be harmful. For example, excessive EC may cause negative effects such as opportunism (Xie, Wu, and Devece 2022), and increased complexity of the innovation process (Laursen and Andersen 2016). This paper posits that this controversy results from the fact that existing research ignored the complex dynamic nature of EC. This study further confirms that EC has an inverted U-shaped effect on product innovation, reconciling the linear contradictory viewpoint.

Third, this study offers recommendations on how an inverted U-shaped relationship between supplier collaboration and EPDI might be modified through the interaction of DT with EC. In particular, the effectiveness of collaboration is further enhanced when DT is strong. However, when the level of DT exceeds a certain point, DT can also mitigate its negative impact. With the use of cutting-edge digital technology, supply chain traceability (SCT) is made possible and hastened, which lowers information asymmetry among supply chain participants and suppliers’ potential for opportunistic behaviour (Cousins et al. 2019). These findings add to the existing perspective of supply chain traceability and transparency on environmental performance and broaden our understanding of the moderating role of DT in terms of the impact of supplier collaboration on innovation performance (Cousins et al. 2019; Gligor et al. 2022).

Managerial implications

Additionally, our findings offer manufacturing companies managerial insights. Managers of businesses need to understand how important supply management is as an essential element of eco-innovation. Purchasing agents prefer to seek out, recognise and reward eco-innovation actions and initiatives because they encourage innovation among supply management staff. Our findings reasonably imply that the purchasing agent represents a key resource in an eco-innovation activity. As a result, top management of a company should encourage the management and staff of their purchasing and supply management to find and develop more creative and innovative ways to carry out purchasing and supply. Finding novel solutions might be aided by sensing supplier marketplaces and spotting and discovering new openings and possibilities.

The studies mentioned above also show that managers need to be aware that collaboration has two sides to it when it comes to a company's creativity. Our results specifically show that the best eco-product innovations result from an intermediate EC (as opposed to a low and a high one). On the one hand, businesses should actively develop a cordial and transparent working relationship with their major suppliers and motivate them to take part in eco-product innovation efforts. On the other hand, it also highlights the potential pitfalls of EC in co-creation – too much is just as bad as not enough – and firms that rely on EC excessively perform even worse than firms that rely on it to a reasonable level.

These findings give reason to believe that managers can improve environmental information collection, analysis and monitoring by using DTs like the Internet of Things, cloud computing, big data and analytics. They can also strengthen complex decision-making processes by reducing information asymmetry and uncertainty. Manufacturing companies need a digitally empowered technological infrastructure to succeed in both eco-product and eco-process developments.

Limitations and future research

There are still a number of limitations in this study, which also guide future research, despite the fact that the findings offer theoretical and managerial insights for academics and practitioners. We begin by concentrating on two aspects of eco-innovation: eco-product and eco-process innovation. Previous research suggests incorporating eco-organisation innovation (Wu, Zhou, and Zhu 2023). We advise incorporating the eco-organisation innovation, so that the function of the purchasing department in eco-innovation can be thoroughly explored. Second, because the current data is cross-sectional and the research circumstances are limited, we have refrained from making any conclusions about cause and effect. In the future, alternate data should be used in longitudinal research to examine alternative hypotheses. Finally, this study conducted a survey in China, which is regarded as a developing market, to examine the hypotheses that had been put out. The Chinese manufacturing sector has embraced and advanced Industry 4.0 technologies in recent years. In order to examine any potential differences, future study may test the model in developed markets.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the finding of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 71872148].