The impact of digital traceability on sustainability performance: investigating the roles of sustainability-oriented innovation and supply chain learning

Abstract

Purpose

Food sustainability is a world-acknowledged issue that requires urgent integrated solutions at multi-levels. This study aims to explore how food firms can improve their sustainability performance through digital traceability practices, considering the mediating effect of sustainability-oriented innovation (SOI) and the moderating effect of supply chain learning (SCL) for the food supply chain therein.

Design/methodology/approach

Hierarchical regression with a moderated mediation model is used to test the proposed hypotheses with a sample of 359 food firms from four provinces in China.

Findings

Digital traceability has a significant positive impact on the three pillars of sustainability performances among food firms. SOI (product innovation, process innovation and organisational innovation) mediates the relationship between digital traceability and sustainability performance. SCL plays moderating roles in the linkage between digital traceability and both product and process innovation, respectively.

Originality/value

This paper contributes as one of the first studies to develop digital traceability practices and their sustainability-related improvements for Chinese food firms; it extends studies on supply chain traceability to a typical emerging market. This finding can support food sustainability practice in terms of where and how to invest in sustainability innovation and how to improve economic, environmental and social performance.

Citation

Zhou, X., Lu, H. and Kumar Mangla, S. (2024), "The impact of digital traceability on sustainability performance: investigating the roles of sustainability-oriented innovation and supply chain learning", Supply Chain Management, Vol. 29 No. 3, pp. 497-522. https://doi.org/10.1108/SCM-01-2023-0047

1. Introduction

The current global food system is facing major challenges that hinder sustainable development. Food security has been a concerning issue for decades, with around 800 million people now facing hunger (FAO, 2022). The lack of food security is devastating when local and global food systems are disrupted by natural hazards, political conflicts or global outbreaks in the public health system. A vicious circle is in operation when the food system is heavily reliant on natural conditions and resources, such as weather, temperature, water and soil, while a series of environmental problems arise due to systems in farming, production and distribution. These are responsible for one-third of global greenhouse gas emissions (United Nations, 2021) and environmental-related pollutants (European Commission, 2022). In addition, the food sector is on one hand suffering from food insecurity and an energy crisis, on the other hand, causing a huge amount of food loss and waste (Wang et al., 2021; Zhao et al., 2021). Our food system is becoming more globally integrated and energy intensive in all activities; the current energy crisis has caused difficulties in the food sector to some extent where 40%–50% of total variable costs of cropping are direct and non-direct energy costs in advanced economies (IEA, 2022). On the other hand, approximately one-third of the total global food output is wasted, accounting for 38% of total energy usage in the global food system (United Nations, 2022). Drawing on the current issues in the food sector, the necessity of implementing sustainable development practices in the food chain is widely acknowledged (Chauhan et al., 2022; Lu et al., 2021; Zhou et al., 2022a).

Digital traceability is a key aspect of innovation that enhances sustainability practices in food supply chain management (SCM) (Pougnet et al., 2022; Gloet and Samson, 2022). When dealing with food safety and security issues, digital traceability allows supply chain tracking and tracing to better manage and monitor sustainability-related practices; these include food quality, operational efficiency, eco-friendly practice and reducing food waste (Hew et al., 2020; Epelbaum and Martinez, 2014; Saurabh and Dey, 2021; Zhou and Xu, 2022; Lu et al., 2021). For example, digital traceability can support on-farm productivity and efficiency, enhancing food production and processing, by improving supply chain performance and sustainability while addressing economic, social and environmental performance (Bahn et al., 2021). Managing food chain sustainability successfully requires extending beyond the firm level and integrating resources, information and capabilities in a multi-tier supply chain (Lu et al., 2021). A digital traceability system leverages technology and communication innovation (Epelbaum and Martinez, 2014), increasing transparency across different tiers of the supply chain to improve production efficiency and reduce food waste. A practical example is to better identify the cause of contamination during an outbreak of food safety issues (Astill et al., 2019).

The debate on companies adjusting business activities towards sustainability via innovation was initially highlighted through eco and environmental innovation; a more holistic view on sustainability covering the “triple-bottom line” should be addressed (Klewitz and Hansen, 2014). As such, scholars deliberate on the context of sustainability-oriented innovation (SOI) that has addressed changes in both incremental and radical innovation, aiming to create environmental and social sustainability while generating economic value (Neutzling et al., 2018). SOI consists of product, process and organisational-level innovation to achieve higher sustainability performance (Adams et al., 2016). The rapidly growing field of SOI emphasises product innovation to increase the triple bottom line practice of a product during every stage of its lifecycle (Hansen et al., 2009). This starts at the product design, viewing this as a way of reducing the environmental footprint, then implementing an environmental management system, such as international organization for standardization 14000, in the initial stage (Dey et al., 2020). As such, SOI not only encompasses technical innovation in the product-life cycle but also requires an organisational system to embrace wide-ranging social changes (Testa et al., 2022) that fundamentally engage with innovation partners in the supply network and external stakeholders in the social system (Inigo et al., 2020).

In current research on sustainable food chains, the overarching view of how SOI-related initiatives can make a substantial difference in sustainable food systems remains unclear, necessitating further investigation (Testa et al., 2022). Existing studies show that SOI can promote responsiveness to customers and enhance lean manufacturing to increase operational efficiency towards sustainability practice (Jum’a et al., 2022). Using SOI is claimed to improve food waste management (Martin-Rios et al., 2020) and global food chain sustainability (Friedman and Ormiston, 2022). Yet, there is a call for exploring solutions that contribute to sustainability development (Hansen et al., 2022). A theoretical gap needs to be further addressed to examine the impact of SOI on food chain sustainability, incorporating the holistic view of the three pillars in systemic changes. In addition, digital innovation is one of the key aspects of SOI that enables technology innovation for sustainability development. For example, blockchain drives technology innovation and organisational innovation in SOI that enhances the implementation of environmental and social sustainability in the food chain (Friedman and Ormiston, 2022). As such, this study aims to investigate the relationship between SOI and sustainable food chains to address the current research gap that could be influenced by the adoption of digital traceability.

Supply chain learning (SCL) is a form of synergy that involves experience sharing and practice transformation at various stages of inter-firm interaction (Bessant et al., 2003). The learning process is seen as a type of dynamic capability that influences the transformation of companies’ resources into sustainability-oriented routines and continuous improvement in SOI (Inigo and Albareda, 2019). This includes organisational learning processes with external stakeholders to build system resilience (Roome and Wijen, 2006). Current research shows the importance of SCL in influencing sustainable SCM, involving the adjustment of supply chain structure with multi-tier actors (Gong et al., 2018). Yet, there is a lack of comprehensive insights regarding the role of SCL in SOI and the influence of digital traceability. As such, this study aims to fill this research gap by investigating the influence of digital traceability on SOI, its impact on sustainability performance and the role of SCL in the relationship.

This study makes three important theoretical contributions. Firstly, it is among the first to investigate sustainability performance in the food sector from the SOI perspective; secondly, SOI extends product and process innovation to organisational and systemic changes along the three dimensions of breadth, depth and lifecycle for sustainability practice; finally, this study develops the insights needed to understand SCL in the field of SOI and of sustainable development in the food chain. This provides a means to tackle the barriers of undertaking SOI through a broad sustainable development programme (Testa et al., 2022).

The rest of the paper is structured as follows. Section 2 provides a literature background and addresses the hypothesis development. Section 3 presents the methodology followed by data analysis in Section 4. Section 5 discusses the study findings against the existing literature and summarises the theoretical and practical contributions. Finally, Section 6 concludes the paper, acknowledging identified limitations of the study.

2. Literature review and hypothesis development

2.1 Literature review

2.1.1 Digital traceability in supply chain management

Digital technologies have been a key to helping businesses reshape supply chains, resulting in better resource management and greater operational efficiency in recent years (Alexander et al., 2022). Digital technologies incorporate the dynamic capabilities for sensing, seizing and reconfiguring firm and supply chain capabilities for improving resiliency (Hallikas et al., 2021; Irfan et al., 2022). In the use of digital technologies, such as internet of things, blockchain and big data analytics, the global supply chain system is reconfigured with the capabilities of proven transparency, traceability and decentralised emergence (Bhandal et al., 2022). Digital traceability, recognised as one of the capabilities of using digital technologies, refers to the use of innovative technology to connect physical products for digital identification and recording, providing better ability of tracing and tracking the processes, location and function of SCM and that impacts on food operations effectiveness (Zhou et al., 2023; Casino et al., 2021). Digital traceability reflects the expectation of manufacturers and consumers to digitally record and document changes in all conditions and locations to visualise all features of a product (Zhou et al., 2023). Facilitating the track-and-trace system is one of the major drivers to adopt digital applications, with the aim of improving supply chain traceability and performance (Büyüközkan and Göçer, 2018; Zhou et al., 2023). A number of recent studies reveal the positive outcomes of integrating digital traceability in SCM (e.g. Bienhaus and Haddud, 2018; Nakandala et al., 2023; Paolucci et al., 2021). Annosi et al. (2021) triangulate the emerging findings from empirical studies, showing that digitalisation affects supply chain collaborative practices and food waste management. Digital traceability requires the generation and organisation of supply chain data; this enables increased supply chain visibility (Anastasiadis et al., 2022). Data analytics capabilities that are yielded in digital traceability significantly improve operations and supply chain processes, e.g. to visualise the processes in the procurement system and improve supply chain integration (Hallikas et al., 2021).

Digital traceability essentially falls within the scope of the supply chain. In a digital traceable supply chain, farmers and producers can document the source of their food and confirm adherence to a set of standards. Manufacturers and distributors can digitise data on secure, unalterable ledgers. Retailers can gauge the freshness of products, communicate authentically with consumers, make business operations more efficient and reduce food waste. Consumers can scan food products and quickly access relevant information online (Ringsberg, 2014). Existing research on digital traceability primarily examines the drivers and barriers to its implementation (Centobelli et al., 2021; Hew et al., 2020; Malik et al., 2021), as well as the technological realisation process (Casino et al., 2021; Sunny et al., 2020). Less attention is paid to performance improvement, particularly at the supply chain level. In practice, the implementation of digital traceability can provide unique advantages for food SCM (Zhou et al., 2022b). Carefully designed and managed traceability systems, regardless of the platform used, can facilitate market access for producers and enhance food safety for consumers (Aung and Chang, 2014). Digital traceability offers companies an opportunity to provide greater transparency to customers and ensure the reliability of sustainability claims in areas such as human rights, labour (including health and safety), the environment and anti-corruption (Hastig and Sodhi, 2020).

2.1.2 Sustainability-oriented innovation

SOI refers to the synergetic management of integrating economic, social and ecological aspects into the design of new products, processes and organisational structures (Adams et al., 2016). SOI strategies, applied in the context of SCM, are receiving increasing attention from practitioners and researchers (Neutzling et al., 2018). If some innovative technologies of food firms have unique value, these technologies will play a vital role in maintaining a competitive advantage throughout the food supply chain.

SOI is comprised of three perspectives – production innovation, process innovation and organisational innovation (Adams et al., 2016). Yet, in existing literature, firms’ SOI is often viewed as a single dimension (Le and Ikram, 2022; De et al., 2020). This requires further development in theory because, in actual performance, SOI is not only reflected in the development of new products or services but also in processes and organisational innovations that may result from management practices (Adams et al., 2016; Demirel and Kesidou, 2019; Jum’a et al., 2022). Therefore, this study takes an overarching view to investigate SOI by involving product innovation, process innovation and organisational innovation. Product innovation means the introduction of new or substantially improved products (Klewitz and Hansen, 2014). Process innovation is the substantial improvements made to production processes or logistical support (Adams et al., 2016). Organisational innovation refers to the reorganisation of practices and structures within the company and the adoption of new forms of management, including cooperation with supply chain members and better stakeholder engagement (Neutzling et al., 2018; Adams et al., 2016).

Research on SOI in SCM primarily focuses on how to enhance company performance by reconfiguring business models to prioritise sustainability implementation throughout the entire supply chain (Friedman and Ormiston, 2022). Companies are expected to comply with sustainability-related regulations; this may require adopting integrated approaches into the processes, practices culture and strategy innovation in socio-technological systems (Adams et al., 2016). In food SCM, adoption of SOI is claimed to assure equality in SCM, enhanced by digital traceability, to ultimately drive sustainability in SCM (Klewitz and Hansen, 2014; Friedman and Ormiston, 2022). Transformation of sustainability in practice is a challenge. While SOI stimulates an adaptive approach in product innovation processes (Keskin et al., 2020), companies with higher capabilities in SCM react in a more radical fashion in SOI with innovation processes to make changes (Klewitz and Hansen, 2014). Food companies need to adopt different innovative technologies in their product, process and organisational aspects to enable sustainable practices (Dey et al., 2020; Jum’a et al., 2022). In addition, research shows that SOI does not only influence innovative product and processes but also reshapes supply chain relationships; this impacts on resource investment, collaboration and supply chain governance structure for sustainability practices (Neutzling et al., 2018).

2.1.3 Supply chain learning

SCL refers to building the capacity to create new knowledge or new insights together through a process where participants can collectively learn how to rethink and renew their supply chain frame (Lambrechts et al., 2012). Research shows that there are two inclusive components of learning. These are the “core competence” which differentiates one company from the other and that can offer potential competitive advantages; the other is the long-term development of a capacity for continued learning and improvements (Bessant et al., 2003, p. 168). Most discussions of learning in current literature address learning not only as intra-firm processes but also in inter-firm applications to enhance the dynamic capabilities in managing the supply chain (Yang et al., 2008). Derived from inter-organisational learning, SCL addresses how members of an organisation act together to create collective knowledge (Gosling et al., 2016). Successful firms can acquire, assimilate and leverage knowledge within their internal functions as well as key suppliers and customers (Huo et al., 2021).

SCL shows a significant impact on logistics and supply chain performance (e.g. Manuj et al., 2014; Parast, 2020). However, there is a lack of investigation on their influence on digital traceability in SCM research. Exploring the adoption of digitalisation in sustainable SCM is currently a prominent area of research (Beske et al., 2014; Gruchmann et al., 2019; Lu et al., 2021). Embedded within the dynamic capability framework, the concept of knowledge management plays a pivotal role by encompassing knowledge acquisition and evaluation within SCM. This, in turn, has a significant impact on the development and implementation of environmentally friendly and sustainable practices (Agyabeng-Mensah et al., 2022). Through the lens of SCL, this study acknowledges the critical significance of knowledge management and learning as fundamental capabilities that organisations must cultivate to promote sustainability practices, extending these practices throughout their supply chains (Secundo et al., 2020). SCL fosters leadership transformation that can help to address uncertainty and supply chain ambidexterity, as a result of better adapting changes in the business environment (Ojha et al., 2018), as a result of yielding to better operations performance (Khan and Wisner, 2019; Yang et al., 2023). Besides, SCL positively influences knowledge on supply chains and green innovation (Agyabeng-Mensah et al., 2022). The assimilation of existing and new information into valuable knowledge for food companies is essential for effective innovation activities and for gaining an innovative edge (Gong et al., 2018). SCL is important in inspiring dialogues, the legitimacy they endow, the opportunities for new knowledge acquisition and the creative and responsive solutions they stimulate (Flint et al., 2008; Loke et al., 2012). With a high level of SCL capability, the food sector can use digital technology to accelerate the acquisition, digestion, integration and utilisation of the vast amount of traceability information dispersed among supply chain members to generate new knowledge and experience that can be useful for product development, process improvement and structural optimisation (Garcia-Torres et al., 2019).

2.1.4 Sustainability performance

Sustainability performance is the alignment of environmental, social and financial objectives in the delivery of core business activities to maximise value (Wang and Dai, 2018). Such performance involves three aspects of sustainability, namely, people – social sustainability, planet – environment sustainability and profit – economic sustainability (Elkington, 1998; Park and Li, 2021; Seuring and Müller, 2008). Specifically, economic sustainability focuses on achieving economic objectives through considerations related to cash flow and market share, as highlighted by Wang and Dai (2018). Environmental sustainability relates to pollution and resources, water security, climate change, energy conservation and biodiversity (Büyüközkan and Karabulut, 2018). Social sustainability addresses human rights and community, labour standards, health and safety, customer responsibility and animal welfare (Dey et al., 2020).

Sustainable SCM has been a growing area of research for a couple of decades. With evolving discussions on the complexity of sustainability practice, research has looked at stakeholder views (Hussain et al., 2018), socio-eco system perspectives (Büyüközkan and Karabulut, 2018), technology and traceability (Zhou et al., 2022a) to understand the internal capabilities (Bag and Rahman, 2023; Gruchmann et al., 2019), external motives (Kitsis and Chen, 2019) and multi-tier interactions (Oyedijo et al., 2023). Given that digitalisation is well-acknowledged in business and operations, scholars further develop the insights of adopting digital technologies (Belhadi et al., 2022; Garcia-Torres et al., 2019) in sustainable supply chain performance. For example, a recent study visualises the overarching view of data analytics at different levels to overcome challenges in food production for better performance in societal, environmental and economic responsibilities (Kamble et al., 2020). Future research on sustainable SCM can address the existing gap by investigating the interactions between different actors and the complexity of supply chain structure (Centobelli et al., 2022). As such, this study endeavours to investigate a holistic framework that integrates both digitalisation and innovation with SCL to identify the impacts on sustainability performance in SCM.

2.2 Hypotheses development

2.2.1 The direct effect of digital traceability on sustainability performance

To improve sustainability performance, food companies wish to be accountable for the provenance of their goods and the environmental-and-social sustainability of the supply chain in which they operate (Garcia-Torres et al., 2019). They seek opportunities to benefit from traceability across the entire supply chain by trimming costs, improving the carbon footprint or increasing the resilience of inputs or conversion (Zhou et al., 2022a).

Malik et al. (2021) applied the resource orchestration theory and the causal complexity perspective to conceptualise and validate supply chain traceability and supply chain transparency as interrelated organisational capabilities that may mutually enhance or compensate each other for competitive advantage. Gallo et al. (2021) stated that digital traceability can enhance supply chain operations in the food process, storing, shipping and monitoring throughout the product life cycle. Logistic and qualitative traceability of food production allows greater resource savings (e.g. reducing costs and the waste of inputs) through the use of real-time data for decision-making (Lezoche et al., 2020). Food traceability is becoming a must-have in the industry to mitigate and manage risks around food safety recalls (Aung and Chang, 2014). Aligning digital traceability with business objectives can enable quick and prompt recall of ineffective, unsafe or sub-standard products which may cause harm to consumers or negatively impact the brand’s reputation and market value (Stranieri et al., 2017). Adopting blockchain traceability in food companies not only facilitates quick insights into evolving market trends but also allows for the exploration of profit opportunities to meet the demands of new products or services, thereby enhancing the potential for economic growth (Hastig and Sodhi, 2020).

The adoption of food traceability systems is seen by many firms as one of the most certain approaches to ensure environmental sustainability (Ringsberg, 2014), especially in terms of waste control, pollution, environmental impact, emission monitoring or reduction. As energy systems target decarbonisation, promoting transparency will be a key tactic in ensuring that entities across the global supply chain meet new emissions standards; data-driven traceability may have a role to play (Hastig and Sodhi, 2020). Tracking carbon emissions with end-to-end traceability solutions can bring a competitive edge to organisations. A whole chain traceability system would allow sources of contamination (e.g. food loss and waste) in the supply chain to be identified and unsafe food recalled, thereby reducing the environmental damage caused by any accident (Zhou et al., 2022a). In addition to optimising available resources, facilitating reuse of materials and identifying pollutants, traceability allows a business to take control of their products’ carbon footprints (Hastig and Sodhi, 2020). Capturing carbon measurement across all supply chain participants into an immutable ledger facilitates audit trails, thus assuring traceability, security and accountability (Cousins et al., 2019).

Responsible food companies aim to have traceability systems that can assure social sustainability in their supply chains on pressing social problems (Hastig and Sodhi, 2020). Digital traceability allows a company to reduce food safety risks to the general public through adherence to legal and ethical procurement and production practices (Aung and Chang, 2014). Digital traceability can also help provide mechanisms to combat human rights violations and improve the occupational health and safety of employees. For example, food companies can strictly monitor supply and production conditions through traceability systems to avoid socially irresponsible practices such as poor conditions in meat processing or modern slavery (Zhou et al., 2022a). In certain markets where sustainability is becoming increasingly important to consumers, traceability offers companies a huge opportunity to comply with regulations and demonstrate the truth of claims such as “emissions neutral”, “organic” and “free of child labor” (Hastig and Sodhi, 2020). A product cannot be labelled as sustainable if there is no traceability system collecting and validating the data to prove this claim (Garcia-Torres et al., 2019). Besides, traceability can be seen as a tool to maintain trust within a supply chain and build a reputation for producing high-quality products (Saak, 2016). Digital traceability technologies can certify products to ensure only fair and sustainable goods make it to market and that the authenticity of these products is communicated to the consumer (Barling et al., 2009). Hence, a hypothesis is proposed:

H1.

Digital traceability implementation is positively related to improved sustainability performance in regard to (a) economic, (b) environmental and (c) social performance.

2.2.2 The direct effect of digital traceability on sustainability-oriented innovation

SOI encompasses technical, organisational and wide-ranging social changes (Testa et al., 2022). Digital traceability technologies, such as blockchain-enabled traceability, hold promise to enhance the responsiveness of supply chains to trends and movements (Hastig and Sodhi, 2020). These technologies enable the food sector to identify innovation opportunities for new product development, process optimisation and organisational structures. For instance, Walmart’s adoption of a blockchain traceability solution in its food supply chain provides a granular understanding of each event. This heightened visibility inspires food firms to innovate in their products, processes and organisations, empowering them to take well-informed action (Kamath, 2018).

More specifically, by using digital traceability systems such as blockchain, food companies can seek to implement renewables, innovation, rapid new product development, new business models/markets and rapid technological development in the longer term (Hastig and Sodhi, 2020). Food firms first of all may foster product innovation in terms of eco-design, life-cycle analysis and sustainable materials through digital traceability. On the one hand, as blockchain connects all partners, companies can access valuable intelligence related to new product development, such as eco-design and green packaging, through a uniform digital traceability platform (Anastasiadis et al., 2022). On the other hand, end-to-end traceability is the key to product life-cycle analysis. Through digital traceability technologies, firms can assess a product’s impact throughout all the stages of its life cycle, from the extraction of raw materials (cradle) through to disposal (grave) or recycling process (cradle) (Corallo et al., 2020). In addition, digital traceability systems provide an online platform for consumers to communicate, interact and exchange innovative suggestions with the food sector, helping firms to understand the latest feedback on consumer demand for sustainable products and materials. This provides fresh ideas and market positioning for new product design and development (Zhou et al., 2022a), such as optimising available resources and facilitating the reuse of materials.

Secondly, the implementation of digital traceability practices by food firms has the potential to act as a catalyst for process innovation, particularly in the advancement of cleaner production and the enhancement of both eco-efficiency and logistical efficiency. Organisations are likely to be more incentivised to optimise their processes if they can easily identify and track their products, and if greater transparency is provided on how they are produced (Malik et al., 2021). Upadhyay et al. (2021) argued that blockchain technology, with its ability to facilitate secure communication among stakeholders, can be a powerful instrument for promoting cleaner production of goods and services and addressing ethical considerations in business development. Moreover, digital traceability can enable the effective monitoring and management of non-product outputs such as waste, hazardous substances and effluent, thereby driving eco-efficient production activities and processes (Sunny et al., 2020). Additionally, logistics represents a unique domain of process innovation. A comprehensive, accurate and transparent traceability system can significantly improve visibility in logistics and help foster a more secure and sustainable supply chain (Pournader et al., 2020).

Finally, digital traceability can also facilitate innovation in organisational structure, stakeholder management and SCM. More specifically, by acquiring, processing and updating relevant traceability data in a timely manner, food firms can ensure that consumers have access to comprehensive quality information as required (Zhou et al., 2022b). By effectively using the opportunities provided by quality information to identify non-value-added processes, food companies help to continuously improve production processes and optimise organisational structures (Hastig and Sodhi, 2020). The possibilities of blockchain-based traceability systems may lead to new organisational systems by configuring the whole supply chain structure and partner interaction rules (Agrawal et al., 2021). Meanwhile, companies have stakeholders who should be engaged with and managed; these firms need to provide transparency and traceability for them (Sodhi and Tang, 2019). Blockchain-based digital traceability systems can facilitate supply chain partner connectivity (Wohlrab et al., 2020) and provide an interactive platform for stakeholders to carry out dialogues and means of communication; this helps to attract more stakeholders to participate in the drive towards sustainable practice (Zhou et al., 2022a). A blockchain-based traceability system can be helpful to achieve better supply chain coordination (Shou et al., 2021). Hence, the following hypothesis is proposed:

H2.

Digital traceability implementation is positively related to (a) product innovation, (b) process innovation and (c) organisational innovation.

2.2.3 The direct effect of sustainability-oriented innovation on sustainability performance

SOI can be defined as the introduction of novel or improved processes, organisational structures, products or technologies that confer economic, environmental and social benefits by mitigating the persistence of unsustainable practices (Adams et al., 2016; Klewitz and Hansen, 2014).

Product innovation refers to the improvement or completely new development of products or services. Eco-design, for example, can improve products that are made of more environmentally friendly materials (e.g. organic, recycled materials), are highly durable, and have low energy consumption designs, thus increasing the competitiveness of products and market share for food companies (Dey et al., 2020). Sustainable product innovation can effectively enhance and refine the design methodologies and approaches, resulting in reductions in material consumption and production costs, shorter product development cycles and ultimately, increased economic benefits (Chen, 2008). Digital traceability platforms can be adopted by food companies to trace the life cycle of specific products so that they can understand and control the environmental and social impact of any type of product by verifying its origin and measuring how the carbon footprint is incremental at each stage of the supply chain (Corallo et al., 2020). In addition, the use of safe processes and green materials across the entire supply chain helps food firms to ensure the ultimate reconciliation of environmental and economic concerns by reducing the emission of pollutants and hazardous substances (Anastasiadis et al., 2022). As societal concern for environmental preservation and sustainable development continues to grow, companies are proactively adopting sustainable materials, including high-performance, low-carbon new materials, to improve and develop products that align with environmental friendliness and meet the needs of society. Such an approach elevates a company’s sense of social responsibility, enhances its brand reputation and expands its market opportunities, resulting in heightened consumer recognition (Melander, 2017).

With regard to process innovation, those firms engaged in cleaner production can change the way they use resources to manage non-product outputs through closed-loop production or industrial symbiosis solutions, thereby improving the overall performance of their operations (Giannetti et al., 2020). Eco-efficiency differs from cleaner production with its stronger focus on the economic benefits of sustainability. Within this context, food companies may potentially derive short-term advantages by selectively targeting readily achievable goals with minimal resource outlay (Côté et al., 2006). Food firms may introduce process changes in terms of transportation modes to maximise the environmental efficiency of product transportation and delivery (Hussain, 2022), enhance energy and transport savings or develop new distribution channels with effects on product recognition. The implementation of cleaner production can prevent health hazards in the workplace, thereby improving occupational health and worker safety (Severo et al., 2018).

In terms of organisational innovation, businesses can manage risk by continuously innovating and improving their organisational structure to ensure customer responsiveness and loyalty to safeguard their market share (León-Bravo et al., 2021). The principal purpose of corporate stakeholder engagement is to establish and maintain relationships with stakeholders with the objective of understanding their viewpoints and concerns regarding significant issues, including those that pertain to environmental and social matters, then integrating those perspectives and concerns into the company’s corporate strategy (Klewitz and Hansen, 2014). Stakeholder engagement is a crucial component of corporate social responsibility and achieving the triple bottom line. Through the mechanism of dialogue, companies can elicit and understand the social and environmental issues that are most salient to stakeholders and engage them in the decision-making process, as emphasised by Hillman and Keim (2001). Innovating the way food firms manage their supply chains can help facilitate supply chain coordination and thus stabilise relationships with distributors and vendors (Dey et al., 2020). SCM represents a pivotal element that underpins economic growth, given its function in enabling the seamless exchange of goods between businesses and consumers. In particular, the adoption of sustainable SCM practices can lead to enhancements in living standards by providing consumers with greater access to essential products at lower costs (Klewitz and Hansen, 2014). Hence, the hypothesis is posited:

H3.

SOI (product, process, organisational) is positively related to improved sustainability performance (economic, environmental, social).

2.2.4 The mediating effect of sustainability-oriented innovation on the digital traceability-sustainability performance link

Implementing efficient SOI is essential if the goal of digital traceability in food firms is to attain sustainability performance improvements. Without effective product innovation, the implementation of digital traceability alone may not be sufficient to improve firm sustainability performance (Zhou et al., 2022a). Digital traceability provides an important source of information for advancing product eco-design (Shou et al., 2021). Food firms may benefit from the eco-innovation of their products as they reduce energy consumption and waste output (García‐Sánchez et al., 2021). The implementation of blockchain traceability by food firms can also promote transparency in product lifecycles, the circular economy and thus better management and control of their environmental footprint (Centobelli et al., 2021). In addition, digital traceability can optimise the use and reuse of materials or resources, thus enhancing the sustainability and cost-effectiveness of the food supply chain (Epelbaum and Martinez, 2014).

Effective process innovation is also necessary for food firms to reap sustainability performance through the implementation of digital traceability. Firms can engage in cleaner production to speed up production processes and raise productivity through cleaner technologies. Blockchain can be combined with more innovative and clean technology solutions to better address environmental sustainability issues (Parmentola et al., 2022). In particular, digital traceability can be used for monitoring, while end-point solutions and cleaner production technologies play a governance role, both of which can be configured to minimise environmental impact and improve economic gains (Sunny et al., 2020). Firms aim to move towards cleaner and more eco-efficient production, which may yield innovative potential in terms of redesigning, for instance, their packaging systems (Wong et al., 2020). In addition, there is a need to make the logistics process more efficient, connected and agile through digital traceability technology for food firms. This improves logistics efficiency (Bosona and Gebresenbet, 2013). The ability of firms to track and trace their products and furnish end consumers with the logistics impact of the products sold to them improves transparency; this results in improved social performance (Saak, 2016).

Organisational innovation is required if food firms want to improve sustainability performance through digital traceability. Relying on the robust digital traceability platform, food firms can evaluate and realign their organisational structures to minimise the environmental and social impact of their operations (Dey et al., 2020). By involving stakeholders in traceability practices, digital traceability can play a greater role in meeting stakeholders’ needs to show responsibility in society (Hastig and Sodhi, 2020). Businesses that value communication between supply chain members and create more synergistic partnerships can better lead to more sustainable products and address ethical and transparency issues with digital traceability platforms (Zhou et al., 2022b). Hence, the following hypothesis is presented:

H4.

SOI (product, process, organisational) mediates the relationship between digital traceability implementation and sustainability performance (economic, environmental, social).

2.2.5 The moderating effect of supply chain learning on the digital traceability-sustainability orentied innovation link

To achieve sustainable innovation, food firms need to integrate various knowledge resources relating to economic, social and environmental factors (Adams et al., 2016). Such knowledge management may not exist within a company, especially concerning sustainability tools. Instead, external expert knowledge may be required to help with navigation and implementation. Suppliers and customers are important sources of external knowledge that companies can access from their supply chains (Bessant et al., 2003). By identifying and digesting information or knowledge from the supply chain and applying it to sustainable practices, SCL can stimulate more innovative thinking and approaches (Loke et al., 2012; Adams et al., 2016).

Digital traceability is a digital investment based on knowledge and technological resources (Hastig and Sodhi, 2020) that provides a two-way mechanism for the exchange of information and knowledge between the focal firm and their supply chain members, encourages firms to develop daily routines shaped by best practices and can be used to build learning mechanisms and support innovation activities. SCL can facilitate knowledge transfer and allow firms to extend the value of their traceability practices (Engelseth, 2009). Food firms using digital traceability platforms to transform these knowledge resources into new sustainability-oriented capabilities can further promote SOI practices and processes (Chang et al., 2013).

In a traceable supply chain, when traceability information and heterogeneous knowledge (e.g. food safety, traceability, provenance and maintaining strict biosecurity conditions) is effectively shared and exchanged among supply chain members, it can be internalised into the organisation’s existing knowledge base through collective learning, thereby enriching the organisation’s knowledge base and capital (Zhou et al., 2022b). Relying on digital traceability platforms, food firms can share information related to product sources, expertise and applied research with their supply chain members (Huo et al., 2021). In this case, information dispersed within and outside the organisation will be absorbed, integrated and transformed into new knowledge and experience (Bessant et al., 2003); digital traceability may then help bring about sustainable innovation in products, processes and organisations. The stronger the SCL capability of food firms, the more the wealth of knowledge and information related to environmental management and social responsibility is gained. Accessing data externally through digital traceability may help facilitate new product development, process optimisation and organisational innovation (Chang et al., 2013). Furthermore, by integrating existing and new knowledge, digital traceability practices can further spawn new product eco-design, cleaner production processes and business management models. Klewitz and Hansen (2014) argued that food firms with strong SCL capabilities have the sustainable potential for innovation in digital traceability practices. Hence, the following hypothesis is developed:

H5.

SCL positively moderates the relationship between digital traceability implementation and (a) product innovation, (b) process innovation and (c) organisational innovation.

Considering the aforementioned arguments, based on the SOI perspective, this study develops a theoretical model in Figure 1 to examine the links among digital traceability implementation, SOI, SCL and sustainability performance.

3. Methodology

3.1 Survey instruments

This study uses a questionnaire survey to collect empirical data. To test the above hypotheses, a survey questionnaire is initially developed based on previous studies. It is then adjusted according to the suggestions of industrial managers considering the practical situation and characteristics of the Chinese food industry. To ensure content validity, the study designs measurement instruments using the following four steps.

The study is initiated by conducting semi-structured interviews with three academic experts and three industrial managers from the food supply chain sector. During these interviews, the research questions, methodology and rationale of the study are presented to the participants, who are then invited to evaluate the theoretical model. The industrial managers are also requested to provide information regarding digital traceability, SOI, SCL and sustainability performance within their respective companies, to refine the theoretical model to meet practical requirements.

Subsequently, a preliminary pool of measurement items is developed by consolidating the outcomes of the initial interviews and literature review. Notably, existing measures of related research constructs are reported in well-known academic journals (Adams et al., 2016; Epelbaum and Martinez, 2014; Huo et al., 2021; Klewitz and Hansen, 2014; Loke et al., 2012; Wang and Dai, 2018; Zhou et al., 2023).

The third stage of the study entails pre-testing the draft questionnaire with experts in relevant fields to assess its clarity, utility and relevance with respect to the research context and objectives. The expert panel consists of five senior scholars in the supply chain traceability domain, three government officials responsible for developing food traceability policies and six practitioners with extensive experience in digital traceability operations, whose companies received traceability system certification from the China Quality Certification Center in 2017. Each pre-test interview lasts approximately 2 h. Through three rounds of face-to-face discussions, the measurement scale of sustainability performance is refined by incorporating feedback from the experts, including combining or rephrasing scale items and eliminating irrelevant ones.

The fourth phase involves a pilot test with senior managers from the food industry, conducted during two workshops on digital traceability in Shanghai. A total of 25 valid questionnaires are collected during the pilot test. The managers are requested to provide feedback on the suitability of the questionnaire items and their understandability to the target respondents in the food sector. Based on the feedback received from respondents in the workshops, minor modifications are made to the questionnaire, particularly to enhance legibility and avoid potential misunderstandings. For example, in the initial questionnaire, the third and fifth questions which were derived from Loke et al. (2012) were “Our firm conducts systematic internal checks to ensure that the knowledge from stakeholders is utilized” and “Our firm has the ability to draw on the knowledge, experience, and capabilities of stakeholders to absorb and interpret new knowledge”, respectively. After receiving input from respondents and much discussion, we further clarified the stakeholders in these two items to be supply chain members, allowing respondents to more clearly understand that the questions are asking how they can assimilate knowledge from their supply chain members. The survey questionnaire was originally in English and is translated into Chinese by a team of native English and Chinese speakers, with meanings verified through a standard inter-translation procedure (Brislin, 1980).

This study encompasses four research constructs, that is, digital traceability, SOI, SCL and sustainability performance.

Digital traceability items are derived from prior empirical studies (Zhou et al., 2023), as reflected by the seven items selected. Zhou et al. (2023) did seminal work on the measurement of digital traceability and the items they developed are widely used in follow-up studies. Respondents are asked to assess the implementation level of each digital traceability practice for each firm regarding its complete supply chain. Among items asked are “To what level of implementation does your firm know the processes involved in producing food products across the complete supply chains?” Responses to digital traceability items use a five-point scale ranging from 1 = not considering it to 5 = successfully implemented.

Learning from two previous studies (Adams et al., 2016; Klewitz and Hansen, 2014), SOI comprises three dimensions, including product innovation, process innovation and organisational innovation. All items are evaluated by respondents considering the implementation level of each SOI in their firms. Responses to SOI items use a five-point scale ranging from 1 = not considered yet to 5 = implemented successfully.

SCL is measured with five items adapted from two earlier studies (Loke et al., 2012; Huo et al., 2021). Loke et al. (2012) devised a preliminary measuring instrument for SCL, which has since been extensively acknowledged and implemented by subsequent scholars. In line with the recommendations of the managers interviewed, as well as the current literature, specifically Loke et al. (2012) and Huo et al. (2021), the SCL measurement scale is modified to enhance its appropriateness to the specific setting of this study and to explicate the research inquiries. All items are assessed by respondents for each knowledge integration perceived in their firms. Items asked include “To what extent does your firm access and benefit from the basic, key business knowledge and technologies held by supply chain members?” Responses to knowledge integration items use a five-point scale ranging from 1 = strongly disagree to 5 = strongly agree.

The 15 measurement items on sustainability performance are adapted from previous empirical studies (Wang and Dai, 2018; Epelbaum and Martinez, 2014), and include three dimensions: economic performance, environmental performance and social performance. Wang and Dai (2018) proposed a measurement scale for evaluating sustainability performance that encompasses the aforementioned three dimensions. This scale is used by successive scholars to assess the sustainability performance of sustainable SCM practices. To better align the scale with the specific research context, field investigations of Chinese food companies are conducted; refinement of the measurement instrument developed by Wang and Dai (2018) is then made. Specifically, an additional item from Epelbaum and Martinez (2014) to evaluate social performance is included. Respondents are asked to evaluate the significant level of each sustainability performance improvement after implementing digital traceability/SOI practices. Items on each type of performance are rated using a five-point scale ranging from 1 = not at all to 5 = highly significant.

The control variables are primarily developed based on discussions during interviews with industrial managers when the questionnaire is developed. The control variables include size (i.e. number of employees), ownership (nature of firm), traceability year (i.e. length of time in traceability implementation) and industry sub-sector (i.e. manufacturers, distributors and retailers). The selection of these four variables refers to the control variables selected by Zhou et al. (2022b) which investigated the impact of supply chain traceability on performance improvement of food firms. Sub-sector and ownership are both represented by three classification dummy variables.

3.2 Data collection

China is chosen for data collection because it is one of the largest and fastest-growing food and beverage consumer markets in the world (Zhou et al., 2022b). In the global market, Chinese food firms have experienced rapid market changes and yet face severe challenges in achieving sustainable performance (Zhou et al., 2022b). A digital traceability programme is an important technical application and practice of digital transformation in terms of end-to-end supply chain visibility and sustainability (Gillani et al., 2020). Relying on well-functioning digital traceability to promote a high balance between economic development and social needs is an important pathway for developing a sustainable food supply chain in China.

Supported by both the Ministry of Commerce and the Ministry of Finance of China since 2016, four provinces, namely, Shandong, Shanghai, Ningxia and Fujian, have been selected as the pioneers in implementing digital technology-enabled traceability systems via exploratory projects, which is documented in the statistic report in the Ministry of Commerce People’s Republic of China (MOFCOM, 2016). After several years of sustained traceability efforts, these regions have made substantial investments in information technology infrastructure technical support and services, including cutting-edge technologies such as big data and blockchain. They also actively promote the development of traceability systems for important products, spanning platform construction, standardisation, management assessment mechanisms and integration with market regulations. This concerted effort has yielded multiple positive outcomes, and the experiences gained are both replicable and scalable (MOFCOM, 2019). As such, this study chose these four provinces as the sample of the study and collected the survey accordingly.

In detail, food firms that have implemented or participated in digital traceability systems in the above regions are selected as the targets of the survey. A list of 2,125 food firms is initially drawn up with the support of relevant government departments and industry associations, as well as through self-searching (including online resources, public reports, etc.). Taking into account the different positions of traceability dominators in the food supply chain, the list of food firms is divided into three groups according to sub-sectors; these include 1,167 food manufacturers, 542 food distributors and 416 food retailers. Using a stratified sampling technique, a randomly selected total of 500 food firms is made, divided into three groups, as targeted samples. Through the connections and coordination of the local bureau of commerce, the contact information of these firms is obtained.

Questionnaires are distributed to potential respondents from May to November 2019, using a mixed survey method including site visits, mail, email and posted online. The target respondents are managers, supervisors and management personnel in charge of purchasing, operations, production, logistics or information technology of food firms. Respondents have to be familiar with the general situations of their firms, especially in digital traceability system implementation and overall performance. Respondents are asked to answer questions with regard to the overall situation of their respective companies. For example, irrespective of whether a senior manager or department director completes the questionnaire, participants are requested to assess the general organisational situation while answering each related question. To improve the authenticity of the questionnaires completed by respondents, the academic purpose and value of the research are clearly expressed during the survey process. A promise to process the questionnaire anonymously is made to ensure that respondents are voluntary and free from any related concerns.

From May to July 2019, the first survey is conducted, with 600 questionnaires distributed to potential respondent firms in four regions; 231 questionnaires are eventually returned. Because some firms did not respond to the first survey, a second supplementary survey is conducted from August to October 2019, when questionnaires are sent to non-respondents; a further 163 questionnaires are returned. Eliminating questionnaires that are not complete or have more than six blanks, a total of 359 usable questionnaire returns are collated. This represents an effective response rate of 59.83%, including 127 from Shanghai, 98 from Shandong, 74 from Ningxia and 60 from Fujian. The characteristics of survey firms in the sample are shown in Table 1, including firm size, ownership, industry sub-sector, participation in traceability (years) and respondent profiles.

3.3 Non-response bias and common method bias

To examine the non-response bias, the t-test is used to determine whether a significant difference exists between the questionnaires collected from early (208) and late (151) responses; this follows the method recommended by a previous study (Armstrong and Overton, 1977). The t-test results of mean values for all constructs and items demonstrate that no significant difference exists between the groups (p > 0.05). Thus, non-response bias should not be a problem in this study.

To examine the common method bias (CMB), this study uses Harman’s one-factor test provided by Podsakoff et al. (2003). The results show that the variance interpretation rate of the largest factor is 31.847%; this is less than 50%, indicating that there is no single factor in the sample data that can explain most of the variation. To further check if CMB exists, Harman’s one-factor test using confirmatory factor analysis is conducted. Results of this one-factor model are χ²(434) = 5,069.539, CFI = 0.420, TLI = 0.378, IFI = 0.422, RMSEA = 0.173, suggesting a poor model fit. Thus, CMB should not be a problem in this study.

3.4 Factor analysis

An exploratory factor analysis (EFA) with maximum likelihood and a varimax rotation is applied to explore dimensions (factors) for all constructs from the survey data. Both the scree test and the initial eigenvalue test indicate the factor for digital traceability, explaining 75.978% of the inherent variation. Table 2 shows the loadings for digital traceability items. The reliability coefficient alpha value for digital traceability is high at 0.946.

The same method is used to indicate three factors for SOI, explaining 78.249% of the inherent variation. Table 3 shows the loadings for SOI items. Each item has a high loading (over 0.60) for one factor and low loadings (less than 0.30) for the other factors. Based on the item characteristics, the three factors labelled product innovation, process innovation and organisational innovation are high with 0.866, 0.845 and 0.862 of reliability coefficient alpha values, respectively.

EFA also presents the potential factor of SCL which explains 69.600% of the inherent variation. Table 4 presents the loadings for SCL items. The reliability coefficient alpha value for SCL is high at 0.890.

For sustainability performance, EFA reveals three factors, with the loadings of all items shown in Table 5. These three factors are labelled as economic performance, environmental performance and social performance which explain 74.539% of the inherent total. Each item has a high loading (over 0.60) for one factor and low loadings (less than 0.30) for the other factors. The reliability coefficient alpha values for the three performance factors are high at 0.903, 0.897 and 0.937, respectively.

According to the results of the reliability and validity assessment shown in Table 6, all composite reliability (CR) values are greater than 0.7, indicating that the inherent quality of the model is ideal. In terms of the results of the average variance extracted (AVE), the AVE value of each construct is greater than 0.5; this means that the quality of the convergent validity is high. Furthermore, Table 7 shows that the square root of the AVE of each construct is greater than the correlation coefficient of any two variables, thus supporting the discriminant validity.

3.5 Correlation analysis

Table 7 summarises the mean, standard deviation and correlation coefficient of each variable. The correlation of key constructs is consistent with the research hypothesis, providing preliminary evidence for verification of the hypotheses.

4. Statistical analysis and results

4.1 The main effect test

The causal steps approach is used to test the mediating effect of SOI in the relationship between digital traceability and sustainability performance (see Tables 8 and 9).

For the impact of control variables, as revealed, traceability year has significant positive effects on product innovation (M1, β = 0.136, p < 0.001), process innovation (M4, β = 0.143, p < 0.001) and economic performance (M10, β = 0.114, p < 0.01). State-owned has significant positive effects on product innovation (M1, β = 0.110, p < 0.05), process innovation (M4, β = 0.159, p < 0.05), organisational innovation (M7, β = 0.122, p < 0.05), economic performance (M10, β = 0.204, p < 0.001) and environmental performance (M18, β = 0.158, p < 0.01). Private firms has significant positive effects on product innovation (M1, β = 0.134, p < 0.01), economic performance (M10, β = 0.190, p < 0.001) and environmental performance (M18, β = 0.136, p < 0.01). Size has significant negative effects on process innovation (M4, β = −0.097, p < 0.05) and environmental performance (M18, β = −0.075, p < 0.05). Distribution (M4, β = −0.107, p < 0.05) has significant negative effects on process innovation.

When entering control variables into the model, as shown in Table 9, digital traceability is significantly and positively associated with economic performance (M11, β = 0.256, p < 0.001), environmental performance (M19, β = 0.145, p < 0.001) and social performance (M27, β = 0.177, p < 0.001), respectively. Thus, H1a approximately H1c is supported.

4.2 The mediating effect test

Product innovation is positively influenced by digital traceability (M2, β = 0.434, p < 0.001) after entering the mediator. Therefore, H2a approximately H2c is supported. Product innovation is positively linked with economic performance (M12, β = 0.263, p < 0.001), environmental performance (M20, β = 0.116, p < 0.001) and social performance (M28, β = 0.151, p < 0.001) as well. Therefore, H3 is supported. However, the direct effects of digital traceability on economic performance (M13, β = 0.170, p < 0.001), environmental performance (M21, β = 0.110, p < 0.01) and social performance (M29, β = 0.133, p < 0.01) are lessened but still significant. This implies that the effects of digital traceability on the three types of sustainability performance are partially mediated by product innovation. Thus, H4 is partially supported.

Process innovation is positively influenced by digital traceability (M5, β = 0.311, p < 0.001) after entering the mediator. Therefore, H2 receives support. Process innovation is positively linked with economic performance (M14, β = 0.282, p < 0.01), environmental performance (M22, β = 0.302, p < 0.001) and social performance (M30, β = 0.142, p < 0.001) as well. Therefore, H3 is supported. However, the direct effect of digital traceability on environmental performance (M23, β = 0.056, n.s) becomes non-significant; this implies that process innovation fully mediates the relationship between digital traceability and environmental performance. Meanwhile, the direct effects of digital traceability practices on economic performance (M15, β = 0.184, p < 0.001) and social performance (M31, β = 0.145, p < 0.001) are lessened but still significant; this implies that the effects of digital traceability on both economic and social performance are partially mediated by process innovation. Thus, H4 is partially supported.

Organisational innovation is positively influenced by digital traceability (M8, β = 0.305, p < 0.001) after entering the mediator. Therefore, H2 receives support. Organisational innovation is positively linked with economic performance (M16, β = 0.161, p < 0.001), environmental performance (M24, β = 0.119, p < 0.001) and social performance (M32, β = 0.344, p < 0.001) as well. Therefore, H3 is supported. However, the direct effect of digital traceability on social performance (M33, β = 0.078, n.s) becomes non-significant; this implies that organisational innovation fully mediates the relationship between digital traceability and social performance. Meanwhile, the direct effects of digital traceability practices on economic performance (M17, β = 0.224, p < 0.001) and environmental performance (M25, β = 0.118, p < 0.01) are lessened but still significant; this implies that the effects of digital traceability on both economic and environmental performance are partially mediated by organisational innovation. Thus, H4 is partially supported.

The bootstrapping method is further adopted to confirm the mediation results of SOI in the digital traceability-sustainability performance link. As depicted in Table 10, the direct impacts of digital traceability on three types of sustainability performance are significant (excluding zero) in the 95% confidence interval; the indirect effects via product innovation are also significant (excluding zero) therein. The direct impacts of digital traceability on both economic and social performance are significant (excluding zero) in the 95% confidence interval; the indirect effects via process innovation are also significant (excluding zero). Meanwhile, the direct impact of digital traceability on environmental performance is not significant (including zero) in the 95% confidence interval whereas the indirect effect via process innovation is significant (excluding zero). The direct impacts of digital traceability on both economic and environmental performance are significant (excluding zero) in the 95% confidence interval and the indirect effects via organisational innovation are also significant (excluding zero). Meanwhile, the direct impact of digital traceability on social performance is not significant (including zero) in the 95% confidence interval whereas the indirect effect via organisational innovation is significant (excluding zero). Therefore, H4 is partially supported.

4.3 The moderated mediating effect test

As revealed in Table 8, the moderating effect on the first half indirect path (digital traceability-SOI) is examined. The interaction term (digital traceability * SCL) has a significant and positive association with product innovation (M2, β = 0.175, p < 0.001), suggesting a significant and positive moderating effect on the relationship between digital traceability and product innovation. The interaction term (digital traceability * SCL) has a significant and positive association with process innovation (M4, β = 0.142, p < 0.01), suggesting a significant and positive moderating effect on the relationship between digital traceability and process innovation. The interaction term (digital traceability * SCL) has a significant and positive association with organisational innovation (M6, β = 0.105, n.s.), suggesting a significant and positive moderating effect on the relationship between digital traceability and organisational innovation. To sum up, SCL plays a moderating role in the first half-indirect path (digital traceability-product innovation, digital traceability-process innovation) of the mediation effects, thus partially confirming H5a approximately H5b.

This study further plots a moderating effect diagram using the simple slope diagram method proposed by Aiken et al. (1991). As shown in Figures 2 apparoximately 3, low SCL is represented by one standard deviation below the mean, and high SCL is represented by one standard deviation above the mean. As depicted in Figure 2, the moderating effect on the first half indirect path (digital traceability-product innovation) is examined. Digital traceability is found to be insignificantly related to product innovation when SCL is low (β = 0.192, p < 0.05), and more positively related to product innovation when SCL is high (β = 0.542, p < 0.001).

As revealed in Figure 3, the moderating effect on the first half indirect path (digital traceability-process innovation) is examined. Digital traceability is insignificantly related to process innovation when SCL is low (β = 0.049, n.s.), and more positively related to process innovation when SCL is high (β = 0.334, p < 0.001).

Table 11 further demonstrates the significance level of the mediating effect with the inclusion of the moderating variables. The mediating effect of both product and process innovation in the connection between digital traceability and sustainability performance is significant at all three levels of SCL, with an increasing trend. The results imply that as the level of SCL increases, digital traceability is more likely to promote improvements in sustainability performances via product and process innovation.

To present the results clearly and logically, we summarise the final results into a model with hypotheses and corresponding values, as shown in Figure 4.

4.4 The endogeneity test

To address potential endogeneity concerns arising from the potential influence of better sustainability performance on firms demonstrating higher levels of digital traceability, a two-stage least squares (2SLS) regression analysis with instrumental variables is used; a Durbin–Wu–Hausman post-estimation test of endogeneity is conducted following the recommended methodology of Lu et al. (2018). Within the extensive survey data set, coercive isomorphism is identified as a relevant sub-variable of institutional isomorphism (DiMaggio and Powell, 1983) to serve as the instrumental variable for the endogeneity tests. The institutional theory explores how external pressures encompassing social, political and economic factors influence organisational strategies, decisions and actions. It recognises three mechanisms through which institutional isomorphic changes occur – coercive isomorphism, mimetic isomorphism and normative isomorphism. Specifically in the Chinese context, coercive isomorphism assumes a significant role in the implementation of digital traceability, primarily through the imposition of strict laws and regulations by the Chinese Government. To validate the appropriateness of coercive isomorphism as an instrumental variable, a correlation analysis is conducted. The results confirm a statistically significant positive correlation between coercive isomorphism and digital traceability (R = 0.31, p < 0.01) while indicating no significant correlation between coercive isomorphism and sustainability performance. These findings reinforce the selection of coercive isomorphism as an instrumental variable in the above analysis, mitigating endogeneity concerns.

Subsequently, the 2SLS estimation procedure is used to mitigate potential endogeneity concerns. In the first stage, a linear regression analysis is performed, using coercive isomorphism as the independent variable and digital traceability as the dependent variable. The results, presented in Table 12, reveal a significant effect of coercive isomorphism on digital traceability (β = 0.236, p < 0.001). Moving to the second stage, the predicted values from the first stage are included as independent variables to estimate their respective effects on the three dimensions of sustainability performance. Table 12 (M35–M37) displays the results of the second-stage regression analysis, indicating statistically insignificant findings (p > 0.10). To comprehensively address endogeneity concerns, a Durbin–Wu–Hausman test is conducted incorporating the error terms from the first-stage regression model; this assesses the correlation between these error terms and those in the original model. The Durbin–Wu–Hausman test (p = 0.99) also suggests that the study results have no serious endogeneity problem.

5. Discussion and implication

5.1 Main findings

In the era of Industry 4.0, the linkage between digital technology-enabled food traceability and sustainability performance is a heating topic. Our empirical results reveal a positive correlation between digital traceability and sustainability performance. This finding broadly supports the view (Zhou et al., 2022a; Epelbaum and Martinez, 2014) that food traceability can positively improve sustainability performance. More specifically, the empirical results show that digital traceability practices have the greatest positive impact on economic performance, followed by social and finally environmental performances. In existing literature, Zhou et al. (2022a) previously verify the relationship between traceability practices and sustainability performance. Nevertheless, they have yet to thoroughly examine and compare the relationship between traceability and different dimensions of sustainability performance (economic, environmental and social). This study further extends their findings and helps scholars and practitioners to gain a more explicit understanding of how digital traceability affects economic, environmental and social sustainability.

This research shows that digital traceability positively affects sustainable product innovation, process innovation and also organisational innovation. Of these, digital traceability has the most significant impact on product innovation. This may be due to the fact that managers of food companies are very interested in product innovation and, therefore, pay more attention to feedback from external consumers on product quality or price, for example. The implementation of digital traceability provides as much information as possible about product updates, which can bring many new ideas to product development. These research findings differ from those of Shou et al. (2021), who suggest that substantial levels of traceability and supply chain coordination configurations could lead to significant organisational changes through operational innovation, resulting in better performance. However, their study does not consider traceability as a direct contributor to innovation and fails to investigate the direct relationship between traceability and innovation. In contrast, this study shows that digital traceability has a direct impact on SOI, highlighting the importance of food companies using digital traceability platforms and technologies as key drivers for product, process and organisational innovation.

A positive relationship between SOI and sustainability performance is also identified. Moreover, all three dimensions of SOI have a strong positive association with economic, environmental and social sustainability performance. As highlighted by Adams et al. (2016), SOI entails deliberate alterations to an organisation’s philosophy, values, products, processes or practices with the explicit goal of creating and achieving social, environmental and economic value. These study findings align with the main conclusions made by Dey et al. (2020), De et al. (2020) and Wu (2017), all of whom assert that SOI can drive improvements in sustainability performance. In this study, their ideas in a new research context with a large sample are validated, thus reinforcing their findings and providing new empirical evidence to understand the link between SOI and sustainability performance.

This study further examines the mediating effect of SOI on the causal relationship between digital traceability and sustainability performance to fill a current research gap. When considering digital traceability practices, few studies have investigated the impact of SOI on environmental and social sustainability in the food supply chain. Indeed, innovation provides an important mediating role between sustainable practices and performance improvement. This study fills this research gap by revealing that product innovation partially mediates the impact of digital traceability practices on economic, environmental and social performance. These findings extend the understanding of Klewitz and Hansen (2014), suggesting that product innovation is needed to some extent for digital traceability to improve sustainable performance. This is because sustainability-oriented product innovation aims at eco-design, life-cycle analysis and sustainable materials, all of which can contribute to improving the sustainable performance of food firms.

The statistical results also confirm that process innovation partially mediates the relationship between digital traceability and both economic and social performance, respectively. This means that process innovation is sometimes necessary if digital traceability practices by food companies aim to improve economic and social performance. Echoing the reflections of Dey et al. (2020), who demonstrate that sustainable process innovation is required for firms to gain sustainability performance through lean management practices, this study suggests that food firms who implement digital traceability practices may struggle to create economic benefits and may lose their social reputations without innovative practices in cleaner production, eco-efficiency and logistics.

The empirical results provide evidence of the full mediation effect of process innovation on the relationship between digital traceability and environmental performance. This means that the impact of digital traceability on environmental performances can be achieved only when process innovation is implemented; therefore, it is necessary and critical for food firms to promote process innovation during their trackback investigations. In contrast with the view of Chang (2011) who indicates that process innovation does not have a mediating role in corporate environmental ethics and competitive advantage, these findings highlight that food firms actively involved in digital traceability can reap environmental benefits if they value and implement process innovations.

The empirical results also represent the partial mediation effects of organisational innovation on the connections between digital traceability and both economic and environmental performances. This implies that associated economic and environmental performance with digital traceability can be partly achieved through effective organisational innovation activities; therefore, improving organisational structure and cooperation with supply chain members seems to be necessary. Our findings echo the observations of Zhu et al. (2019) who claim that corporate social responsibility practices can help improve financial and environmental returns through organisational restructuring, stakeholder management and SCM. Digital traceability is a quality management and risk prevention practice of high public value. Such findings support and broaden the view of Zhu et al. (2019) in the context of digital traceability.

The findings of this paper reveal that the link between digital traceability and social performance is fully mediated by organisational innovation. This implies that digital traceability practices may allow food firms to improve social performance only with the support of organisational innovation activities. These findings are inconsistent with Epelbaum and Martinez (2014), who assert that improvements in firm efficiency, including firm reputation, brand protection, product quality and communication with stakeholders, can result from the direct impact of technological innovation in traceability. This study highlights the pivotal role of organisational innovation in the food supply chain to enable the food sector to reap social benefits through the implementation of digital traceability practices. Accordingly, it is proposed that food companies can attain organisational innovation by enhancing their organisational structures, engaging with external stakeholders and embracing sustainable SCM practices.

Statistical results show that the linkage between digital traceability and product innovation is positively moderated by SCL. Such a finding supports the view of Tsai et al. (2015); their study posited an inverted U-shaped relationship between product innovation and knowledge integration mechanism. This study extends the results of the aforementioned work, revealing how SCL exerts its moderating role to enhance the behaviour of product innovation in the context of digital traceability. These results further suggest that the impact of digital traceability on product innovation in the food sector can be somewhat of an externalising effect, being more susceptible to changes in the external environment than process and organisational innovation.

The empirical results further suggest that SCL can positively moderate the relationship between digital traceability and process innovation. Such a result implies that digital traceability can reinforce the strength of process innovation in a close learning environment. This finding reinforces the work of Flint et al. (2008) who show that multiple supply chain partners engaging in interactions, learning and focusing on supply chain problems and solutions can enhance the effect of traceability practices on process innovation.

This study also reveals an unexpected result. SCL does not moderate the association between digital traceability and organisational innovation. This indicates that SCL does not significantly alter the impact of digital traceability on organisational innovation, neither strengthening nor weakening it. These findings contrast with those of Wohlrab et al. (2020) who observe that the use of traceability technologies facilitates large-scale organisational innovation in a collective learning environment. This research emphasises that digital traceability practices primarily facilitate product and process innovations in food firms. They are more likely to benefit from inter-organisational knowledge exchange and learning as opposed to organisational innovations that involve the reorganisation of practices and structures, as well as the adoption of new forms of management within firms.

5.2 Theoretical implications

Food sustainability is a major issue in global affairs for academia and practitioners alike. Being aligned with current sustainable SCM research, sustainable development in the food sector ought to consider economic, environmental and social performance from a holistic perspective (Jum’a et al., 2022). However, the current research in innovation shows a producer-centred model that mainly addresses product and process innovation for optimal profit incentives (Trischler et al., 2020). This study takes an overarching view to consider production and process innovation with company organisational culture in SOI, showing that production and process innovation can enhance economic and environmental performance. This is consistent with current literature (Martin-Rios et al., 2020; Friedman and Ormiston, 2022). This research further tests the causal relationship between organisational culture and social sustainability in the food supply chain. As such, this study contributes to filling the current research gap by providing further sustainability solutions in sustainable food systems (Testa et al., 2022).

Secondly, this study fills the theoretical gap to extend discussion of sustainability-related improvements in the food sector (Chauhan et al., 2021). Current research is broadly in agreement on the importance of digitalisation and its impact on sustainable practices (Saurabh and Dey, 2021; Zhou et al., 2023). This study examines the relationship between digital traceability and SOI, showing different levels and aspects of causality for economic, environmental and social sustainability for food chain management. This study revealed that digital traceability can substantially increase marketing and operational performance, yielding positive economic growth. These findings align with prior research (e.g. Joshi et al., 2023) that economic sustainability as a primary motivator for investments in digital technologies and innovation. However, it is crucial to acknowledge that the ecosystem and socio-economic factors play a substantial role in determining long-term economic sustainability within the food sector. Therefore, there is a need to consider the environmental and social dimensions alongside economic considerations.

Finally, SCL is taken into account as the study contributes to building the theoretical link to SOI and the impact on sustainability performance in the food chain. The study confirms the influence of SCL on supply chain practice, aligned with current literature (Manuj et al., 2014; Parast, 2020). Furthermore, this research tests the role of SCL in SOI and sustainability performance, thus developing the understanding of the dynamic capabilities of SOI (Inigo and Albareda, 2019). Despite the significance of digital traceability and innovation in fostering sustainable SCM, organisations and their extended networks face considerable challenges in the transformation of digitalisation and SOI, as highlighted by previous studies (Nambisan et al., 2019; Li, 2020). This study has unveiled how SCL plays a pivotal role in harnessing the results of SOI to amplify the influence of digital traceability on product, process and organisational innovation. These findings can be further examined and applied in the context of emerging countries to explore the factors that affect digital transformation in supply chains.

5.3 Managerial implications

Nowadays, companies are aiming to make the journey of adopting innovation in sustainability practice, implying profound sustainability transformation and making systemic changes (Klewitz and Hansen, 2014). This study provides instrumental insights for companies to invest on improving SOI and sustainability practices in digitalisation. Even though current literature shows the relationship between digitalisation and SOI (Friedman and Ormiston, 2022) and sustainable food supply chain (Chauhan et al., 2021), this study shows different levels of impact. This can help companies to make better decisions about where to invest in digitalisation to improve sustainability practice. Investing in product innovation is fundamentally important to improve food chain sustainability (Khurana et al., 2019). Companies can use digitalisation to enhance product innovation, for example, to consider product life cycle and green eco-system in product design and material usage. When designing and using digital traceability across the supply chain, companies and their upstream and downstream actors are recommended to restructure their operational processes to improve operations and logistics efficiency and optimise the use of energy and resources. By doing so, a company and its supply chain can potentially improve economic and environmental sustainability in practice. Proactive companies who embark on the strategy of making systemic changes, might shape their organisational culture and inter-firm arrangements to redefine, reconceptualise and restructure supply chain innovation to make the systematic transformation for sustainability practice.

6. Conclusion

This study reports on an empirical study as to what extent SOI can influence sustainability performance, considering digital traceability and SCL for the food supply chain. From the study and recommendations, it is shown how digital traceability and SOI can leverage sustainability performance and make substantial theoretical and implementation contributions in the food chain.

Similar to other empirical studies, this research has its limitations. Data is collected only from China’s major cities and that could be limiting in terms of geographic implications. Besides, given that the findings and conclusion are justified based on the survey research in the food sector, it may not be generalisable due to industrial features, similar to published research in specific sectors of the supply chain (Ingenbleek and Krampe, 2023).

Drawing on the limitations, this study provides future research opportunities in sustainable food SCM. Further investigation could be elaborated to a broader sampling scale in China and outside China, to test the consistency and generalisation of the module. Besides, the research could further test and develop the framework built in this study to address the study’s implication in different industries and sectors, drawing on the generalisability of survey research. Finally, the discussions and recommendations in this study are concluded based on survey and statistical findings. In-depth case studies could be developed to enrich the insights of the three aspects of innovation in SOI and the transforming process to sustainability performance.

Figures

Figure 1

Theoretical framework

Figure 2

Moderating effect of SCL on the digital traceability-product innovation link

Figure 3

Moderating effect of SCL on the digital traceability-process innovation link

Figure 4

Model with the hypotheses and corresponding values

Table 1

Sample characteristics

Category	Sample	%
Location
Shandong	98	27.30
Shanghai	127	35.38
Ningxia	74	20.61
Fujian	60	16.71
Size (number of employees)
Less than 200	72	20.06
201–500	122	33.98
501–1,200	73	20.33
More than 1,201	92	25.63
Ownership
State-owned	109	30.36
Private	214	59.61
Foreign or joint ventures	36	7.24
Industry sub-sector
Manufacturing	208	57.94
Distribution	99	27.58
Retailing	52	14.48
Participating in traceability (years)
Less than three	87	24.24
4–9	148	41.23
10–16	93	25.91
More than 17	31	8.64
Position of respondents
Senior executive	38	10.58
Senior managers	75	20.89
Middle managers	153	42.62
First-line managers	82	22.84
Others	11	3.06
Department of respondents
Purchasing	47	13.09
Production	54	15.04
Information technology	89	24.79
Operations	98	27.30
Logistics	50	13.93
Others	21	5.85

Source:

Authors’ own work

Table 2

Rotated component matrixa on digital traceability

Item description	Factor
Know the processes involved in producing food products across the complete supply chains	0.862***
Trace the source of our purchases throughout entire supply chains	0.856***
Track food product distribution channels and sales process	0.864***
Quickly exchange and transmit traceability information	0.846***
Implement quick and precise recalls of contaminated food products	0.903***
Improve digital records and storage of traceability information	0.900***
Optimize whole-process data management and digital analysis	0.869***

Notes:

Extraction method: principal component analysis; ^aOne component extracted

Source: Authors’ own work

Table 3

Rotated component matrix^a on SOI

Item description	Factors
	1	2	3
Eco-design/design for the environment	0.864***	0.127	0.085
Life-cycle-analysis	0.884***	0.127	0.167
Materials (reduce, replace, sustainable; e.g. recycled resources, biodegradables)	0.864***	0.065	0.182
Cleaner production	0.088	0.071	0.877***
Eco-efficiency	0.183	0.052	0.862***
Logistics (e.g. efficient transportation)	0.149	0.097	0.838***
Organisational structures (e.g. environmental team, health and safety, employee engagement in sustainability/CSR activities)	0.135	0.875***	0.018
Stakeholder management (e.g. interaction with external actors, dialog)	0.089	0.866***	0.087
Supply chain management	0.084	0.893***	0.117

Notes:

Extraction method: principal component analysis; Rotation method: varimax with Kaiser normalisation; ^aRotation converged in four iterations. CSR = Corporation social responsibility

Source: Authors’ own work

Table 4

Rotated component matrix^a on SCL

Item description	Factor
Accessing and benefiting from the basic, key business knowledge and technologies held by supply chain members	0.829***
Combining and integrating new knowledge with existing knowledge	0.823***
Systematically checking to ensure that the knowledge from supply chain members is used	0.829***
Capacity to assimilate new technologies and innovations that are useful or have proven potential	0.845***
Ability to draw on the knowledge, experience and capabilities of supply chain members to absorb and interpret new knowledge	0.845***

Notes:

Extraction method: principal component analysis; ^aOne component extracted

Source: Authors’ own work

Table 5

Rotated component matrix^a on sustainability performance

Item description	Factors
	1	2	3
Increase market share	0.195	0.793***	0.156
Increase profit	0.151	0.813***	0.204
Reduce the direct operational cost	0.100	0.842***	0.214
Improve the return on sales	0.114	0.831***	0.217
Improve the return on investment	0.164	0.801***	0.137
Decrease consumption of hazardous/harmful/toxic materials	0.120	0.132	0.818***
Decrease the frequency of environmental accidents	0.060	0.153	0.826***
Improve compliance with environmental standards	0.091	0.228	0.815***
Reduce the environmental damage caused by the accident	0.059	0.156	0.828***
Improve the firm’s environmental situation	0.076	0.225	0.806***
Reduce food safety risks to the general public	0.830***	0.184	0.073
Improve occupational health and safety of employees	0.902***	0.152	0.092
Ensure compliance with the laws and regulations	0.896***	0.072	0.122
Ensure the reliability of sustainability claims	0.907***	0.116	0.088
Improve the social reputation	0.851***	0.197	0.047

Notes:

Extraction method: principal component analysis; Rotation method: varimax with Kaiser normalisation; ^aRotation converged in five iterations

Source: Authors’ own work

Table 6

Reliability and validity assessment

Constructs	Reliability	AVE (>0.5)	CR (>0.6)
Digital traceability	0.946	0.760	0.957
Product innovation	0.866	0.758	0.904
Process innovation	0.845	0.738	0.894
Organisational innovation	0.862	0.771	0.910
SCL	0.890	0.696	0.920
Economic performance	0.903	0.666	0.909
Environmental performance	0.897	0.670	0.910
Social performance	0.937	0.770	0.944

Source:

Authors’ own work

Table 7

Descriptive statistics and correlations

Variable	Mean	SD	1	2	3	4	5	6	7	8
1. Digital traceability	3.738	0.623	(0.872)a
2. Product innovation	3.799	0.555	0.463**	(0.871)
3. Process innovation	3.772	0.650	0.356**	0.327**	(0.859)
4. Organisational innovation	3.666	0.670	0.283**	0.244**	0.185**	(0.878)
5. SCL	3.854	0.543	0.409**	0.297**	0.392**	0.213**	(0.834)
6. Economic performance	3.848	0.579	0.459**	0.495**	0.510**	0.303**	0.359**	(0.816)
7. Environmental performance	3.641	0.594	0.237**	0.216**	0.520**	0.228**	0.263**	0.432**	(0.819)
8. Social performance	3.683	0.675	0.230**	0.218**	0.213**	0.508**	0.195**	0.341**	0.221**	(0.877)

Notes:

Pearson correlations; n = 359; **p < 0.01; ^aSquare root of AVE reported along diagonal in italic

Source: Authors’ own work

Table 8

Regression analysis results (n = 359)

Variable	Product innovation			Process innovation			Organisational innovation
	M1	M2	M3	M4	M5	M6	M7	M8	M9
Constant	3.799	0	−0.072	3.772	0	−0.058	3.666	0	−0.043
Traceability year	0.136***	0.062	0.037	0.143***	0.089	0.060	0.050	−0.054	−0.07
Size	−0.035	−0.052	−0.044	−0.097*	−0.141	−0.118	−0.080	−0.112	−0.104
State-owned	0.110*	0.133	0.128	0.159**	0.198*	0.164*	0.122*	0.137	0.129
Private	0.134**	0.184*	0.169*	0.105	0.121	0.081	0.052	0.037	0.023
Manufacturing	0.010	0.027	0.045	−0.021	−0.027	0.007	0.017	0.030	0.045
Distribution	0.006	0.033	0.050	−0.107*	−0.150	−0.108	−0.033	−0.034	−0.019
Digital traceability		0.434***	0.367***		0.311***	0.192***		0.305***	0.253***
SCL			0.132*			0.276***			0.109
Digital traceability * SCL			0.175***			0.142**			0.105
R2	0.079	0.231	0.268	0.078	0.172	0.245	0.029	0.104	0.122
F	5.018***	15.029***	14.170***	6.057***	10.381***	12.580***	1.754	5.826***	5.366***

Notes:

***p < 0.001; **p < 0.01 and *p < 0.05

Source: Authors’ own work

Table 9

Regression analysis results (n = 359)

Variable	Economic performance
	M10	M11	M12	M13	M14	M15	M16	M17	M18	M19	M20	M21	M22	M23	M24	M25	M26	M27	M28	M29	M30	M31	M32	M33
Constant	3.848	3.848	3.848	3.848	3.848	3.848	3.848	3.848	3.641	3.641	3.641	3.641	3.641	3.641	3.641	3.641	3.683	3.683	3.683	3.683	3.683	3.683	3.683	3.683
Traceability year	0.114**	0.006	0.049	−0.006	0.052	−0.015	0.102**	0.012	0.031	−0.030	0.003	−0.03	−0.035	−0.056	0.022	−0.026	0.020	−0.055	−0.017	−0.061	−0.011	−0.064	−0.006	−0.037
Size	−0.040	−0.033	−0.024	−0.023	0.002	−0.001	−0.021	−0.022	−0.075*	−0.071	−0.068	−0.068	−0.030	−0.031	−0.061	−0.062	−0.025	−0.021	−0.016	−0.015	−0.004	−0.006	0.016	0.016
State-owned	0.204***	0.165***	0.152***	0.139**	0.135**	0.120**	0.174***	0.151**	0.158**	0.137**	0.136*	0.127*	0.085	0.08	0.137**	0.124*	0.068	0.042	0.039	0.029	0.034	0.022	0.006	−0.002
Private	0.190***	0.157***	0.127**	0.120**	0.145***	0.129**	0.178***	0.153***	0.136**	0.117*	0.108*	0.103*	0.087	0.082	0.127*	0.114*	0.030	0.006	−0.007	−0.012	0.007	−0.006	0.003	−0.006
Manufacturing	0.006	0.011	0.001	0.006	0.015	0.017	0.002	0.008	0.000	0.003	−0.002	0.001	0.010	0.011	−0.003	0.000	−0.019	−0.016	−0.022	−0.019	−0.015	−0.013	−0.028	−0.026
Distribution	0.016	0.029	0.013	0.022	0.063	0.064	0.024	0.033	−0.079	−0.072	−0.080	−0.07	−0.029	−0.029	−0.073	−0.069	−0.039	−0.030	−0.040	−0.033	−0.015	−0.015	−0.022	−0.019
Digital traceability		0.256***		0.170***		0.184***		0.224***		0.145***		0.11**		0.056		0.118***		0.177***		0.133**		0.145***		0.078
Product			0.263***	0.199***							0.116***	0.073*							0.151***	0.101*
Process					0.282**	0.232***							0.302***	0.287***							0.142***	0.102**
Organisational							0.161***	0.105***							0.119***	0.089**							0.344***	0.324***
R2	0.086	0.243	0.276	0.334	0.302	0.376	0.161	0.273	0.052	0.100	0.087	0.114	0.287	0.294	0.091	0.121	0.007	0.063	0.054	0.080	0.048	0.082	0.259	0.269
F	5.519***	16.127***	19.35***	21.937***	21.672***	26.363***	9.653***	16.399***	3.223**	5.599***	4.787***	5.706***	20.200***	18.200***	5.019***	6.000***	0.433	3.344***	2.840**	3.789***	2.503*	3.881***	17.62***	16.119***

Notes:

***p < 0.001; **p < 0.01 and *p < 0.05

Source: Authors’ own work

Table 10

Bootstrapping result of the mediation of SOI

Path	Effect		BootSE	Boot LLCI	Boot ULCI
Digital traceability → Product → Economic	Total	0.256	0.030	0.197	0.315
	Indirect	0.086	0.020	0.050	0.129
	Direct	0.170	0.031	0.109	0.230
Digital traceability → Product → Environmental	Total	0.145	0.034	0.080	0.211
	Indirect	0.032	0.018	−0.000	0.069
	Direct	0.114	0.036	0.042	0.186
Digital traceability → Product → Social	Total	0.177	0.039	0.100	0.253
	Indirect	0.044	0.020	0.006	0.083
	Direct	0.133	0.042	0.050	0.216
Digital traceability → Process → Economic	Total	0.256	0.030	0.197	0.315
	Indirect	0.072	0.018	0.041	0.111
	Direct	0.184	0.029	0.128	0.240
Digital traceability → Process → Environmental	Total	0.145	0.034	0.080	0.211
	Indirect	0.089	0.019	0.053	0.129
	Direct	0.056	0.031	−0.005	0.117
Digital traceability → Process → Social	Total	0.177	0.039	0.100	0.253
	Indirect	0.032	0.017	0.003	0.070
	Direct	0.145	0.040	0.066	0.224
Digital traceability → Organisational → Economic	Total	0.256	0.030	0.197	0.315
	Indirect	0.032	0.013	0.010	0.062
	Direct	0.224	0.031	0.164	0.284
Digital traceability → Organisational → Environmental	Total	0.145	0.034	0.080	0.211
	Indirect	0.027	0.011	0.009	0.050
	Direct	0.118	0.035	0.050	0.186
Digital traceability → Organisational → Social	Total	0.177	0.039	0.100	0.253
	Indirect	0.099	0.024	0.053	0.148
	Direct	0.078	0.036	−0.007	0.148

Notes:

Number of bootstrap samples = 5,000; **p < 0.01 and ***p < 0.001

Source: Authors’ own work

Table 11

Moderated mediating effect

		Effect	BootSE	Boot LLCI	Boot ULCI
Digital traceability → Product → Economic	M – 1 SD	0.038	0.018	0.005	0.077
	M	0.073	0.018	0.040	0.112
	M + 1 SD	0.108	0.025	0.064	0.162
	INDEX	0.035	0.012	0.014	0.061
Digital traceability → Product → Environmental	M – 1 SD	0.014	0.010	−0.002	0.037
	M	0.027	0.015	−0.001	0.058
	M + 1 SD	0.039	0.022	−0.001	0.085
	INDEX	0.013	0.008	−0.000	0.031
Digital traceability → Product → Social	M – 1 SD	0.019	0.012	0.000	0.045
	M	0.037	0.017	0.005	0.073
	M + 1 SD	0.055	0.025	0.006	0.108
	INDEX	0.018	0.010	0.002	0.041
Digital traceability → Process → Economic	M − 1 SD	0.011	0.020	−0.026	0.051
	M	0.044	0.017	0.014	0.081
	M + 1 SD	0.077	0.024	0.036	0.130
	INDEX	0.033	0.014	0.008	0.063
Digital traceability → Process → Environmental	M – 1 SD	0.014	0.024	−0.033	0.061
	M	0.055	0.019	0.019	0.093
	M + 1 SD	0.096	0.027	0.046	0.152
	INDEX	0.041	0.017	0.009	0.077
Digital traceability → Process → Social	M − 1 SD	0.005	0.010	−0.010	0.031
	M	0.020	0.013	0.002	0.051
	M + 1 SD	0.034	0.020	0.004	0.081
	INDEX	0.015	0.009	0.001	0.037
Digital traceability → Organisational → Economic	M – 1 SD	0.016	0.013	−0.006	0.043
	M	0.027	0.012	0.007	0.054
	M + 1 SD	0.038	0.017	0.010	0.074
	INDEX	0.011	0.008	−0.002	0.030
Digital traceability → Organisational → Environmental	M − 1 SD	0.013	0.010	−0.006	0.036
	M	0.023	0.009	0.007	0.043
	M + 1SD	0.032	0.013	0.010	0.060
	INDEX	0.009	0.007	−0.001	0.025
Digital traceability → Organisational → Social	M − 1 SD	0.048	0.033	−0.018	0.115
	M	0.082	0.025	0.036	0.136
	M + 1SD	0.116	0.033	0.056	0.183
	INDEX	0.034	0.021	−0.006	0.077

Source: Authors’ own work

Table 12

2SLS Regression model for endogeneity test

Variable	Digital traceability	Economic performance	Environmental performance	Social performance
	M34	M35	M36	M37
Constant	3.738	3.848	3.641	3.683
Traceability year	0.210***	−0.010	−0.030	−0.052
Size	−0.001	−0.038	−0.072	−0.024
State-owned	0.087	0.162***	0.136**	0.040
Private	0.087	0.150***	0.115*	0.002
Manufacturing	−0.020	0.015	0.004	−0.013
Distribution	0.008	0.017	−0.074	−0.039
Digital traceability	–	0.115	0.135	0.054
Coercive isomorphism	0.236***	–	–	–
R2	0.324	0.009	0.011	0.001
F	24.029***	3.360	4.003	0.479

Source:

Authors’ own work