Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model (2024)

RQ1

What are the dimensions of SCR?

RQ2

How does SCR play a role in supply chain management and operation process?

Urban lockdowns, logistical disruptions, and changes in workplace and market environment have accelerated the process of supply chain digitalization (SCD), attracted widespread attention from companies, and become one of the hottest topics in operations management (Ardolino et al., 2022; Sawik, 2022; Holmström et al., 2019; Frank et al., 2019). Emergent technologies, such as big data analytics, blockchain, and artificial intelligence, have accelerated the digitalization of supply chain (Dubey et al., 2020b; Gawankar et al., 2019; Saberi et al., 2019; Song et al., 2021). Digitalization is a concept that emphasizes systematic use of data to empower production and operation activities, realize supply chain transformation and upgrading, and improve overall operational efficiency and quality development. SCD has changed the traditional supply chain operation mode and brought new products and business models (Eller et al., 2020).

Considering the advantages of digitalization, scholars have started to investigate how SCD can help companies improve SCR in crises, enabling them to recover quickly from disruptions to their original performance levels (Büyüközkan and Göçer, 2018; Stank et al., 2019; Hennelly et al., 2020). Most relevant studies have focused on discussing the role of digital technology application in resilience enhancement (Yang et al., 2021; Zouari et al., 2020; Belhadi et al., 2021c; Weking et al., 2020). However, digitally driven SCR enhancement is not only about adopting certain specific digital technologies to reduce risk but also integrating digital technologies into the whole SCR system and establishing a digital resilience management framework. Belhadi et al. (2022) state that a digitalized supply chain can promote supply chain visibility and enable flexible adjustment of structure, organization, and capabilities, improving product quality and enhancing supply chain efficiency while helping the supply chain achieve resilience. Ivanov et al. (2022) also points out that, in a digitally driven supply chain, structure and processes are dynamically changing and inherently self-adaptive. This dynamic structure can respond to the uncertainty of internal and external systems and provide higher resilience when disruption occurs. However, this digitally driven process of SCR enhancement needs to be validated by empirical studies to produce quantifiable results that can help guide enterprise resilience management practices (Hennelly et al., 2020; Ageron et al., 2020; Li et al., 2022; Seyedghorban et al., 2020; Belhadi et al., 2022).

Furthermore, some literature state that digitalization and SCR can contribute to higher performance levels. Again, empirical exploration of how supply chain performance (SCP) changes when SCD and SCR are applied to the supply chain simultaneously is needed. As such, this study raises the third and the fourth research questions as follows.

RQ3

How does digitalization contribute to SCR and SCP?

RQ4

What are the mechanisms involved?

To answer the research questions, this study firstly reviews and integrates the literature on the stages and the dimensions of SCR. Three significant dimensions of SCR, namely absorptive capability, response capability, and recovery capability, are conceptualized by combining the management practices of responding to risk at different supply chain stages. Secondly, recognizing the dynamic adaptation of supply chain digitalization and the resource requirements in developing SCR capability, an influence mechanism model depicted as “SCD→SCR→SCP” is constructed based on dynamic capability theory. Relevant hypotheses on the mechanism are proposed and a structural equation model is built to empirically analyze the data collected in a survey involving 210 Chinese manufacturing companies.

We believe that findings of this study can contribute to the existing field of research on SCR. First, by comprehensively reviewing and integrating the literature, this study establishes a system of indicators to measure the multiple dimensions of SCR. It reveals the stages of SCR and argues that SCR includes three dimensions, namely absorptive capability, response capability, and recovery capability. The study points out that the three SCR capabilities have a certain degree of independence, and as such companies can choose a flexible resilience strategy to suit their purposes. Through empirical research, the direct influence of SCD on the three SCR capabilities and SCP and the mediating role of the three SCR capabilities between SCD and SCP are thoroughly investigated. The results show that SCD during a crisis can improve absorptive capability, response capability, and recovery capability, thus achieving a better SCP. The findings demonstrate the structural changes and performance fluctuations that result from digitalization and resilience objectives taking effect at the same time. Furthermore, the findings highlight the effectiveness of response and recovery capabilities. These results extend the application of dynamic capability theory from within a firm to the entire supply chain. This study also provides practical suggestions for business managers to build digital and resilient supply chains strategically to cope with highly uncertain market conditions.

The rest of the article is arranged as follows. First, the definitions of relevant constructs are introduced. Then, the theoretical foundation and the research hypotheses are proposed. Next, the research methodology and how the data were analyzed to get results are described. Next, the findings and their theoretical and managerial implications are presented. Finally, the limitations of this paper and suggestions for future research are discussed.

2.1. SCR

COVID-19 was one of the most significant disruptions in human history, severely disrupting the normal operational state of supply chains across the globe and causing catastrophic effects on companies (Ivanov and Dolgui, 2020; Queiroz et al., 2020). Global complex supply chain networks are intertwined and interdependent, making them more vulnerable to risk and uncertainty. Disruption at a supply chain node involving only a few companies can generate a ripple effect and result in the paralysis of the entire supply chain (Nikookar and Yanadori, 2022). In the current business environment, where the outbreak of the COVID-19 epidemic exacerbates uncertainty, and most businesses are unprepared, companies no longer limit their operational objectives to cost efficiency but also focus on improving their ability to cope with supply chain disruptions (Cappelli and Cini, 2020; Ivanov and Dolgui, 2020). SCR has long been regarded as a core supply chain capability to cope with disruptive events (Vanany et al., 2021). It can reduce vulnerability and help companies cope with disruptions to return to their previous operational state, if not better (Pettit et al., 2013; Sheffi and Rice, 2005). The COVID-19 epidemic has made SCR a top priority of companies and generated extensive discussions in both the academic and management circles (Gölgeci and Kuivalainen, 2020; Ketchen and Craighead, 2020; Raj et al., 2022).

SCR is a multidisciplinary concept involving psychology, engineering, ecology, and economics (Holling, 1973; Torabi et al., 2015). The definition of SCR is usually developed based on risk stages, including before, during, and after the disruptions (Sawik, 2017). Contemporary research has taken various theoretical perspectives to analyze SCR, the most popular of which is the capability perspective. From this point of view, SCR is seen as the ability of a supply chain to absorb the shock of a disruptive event, respond to the disruption, and recover quickly to its original state or to achieve a more optimal operational state (Ponomarov and Holcomb, 2009; Yu et al., 2019; Sheffi and Rice, 2005; Ali et al., 2017a). Enhanced SCR can help companies better cope with disruptions, reduce vulnerability and maintain business continuity (Vanany et al., 2021; Vali-Siar and Roghanian, 2022; Pettit et al., 2013; Sheffi and Rice, 2005). Unlike supply chain robustness, the concept of SCR highlights the response capability and recovery capability of supply chains to unknown risks (Brandon-Jones et al., 2014; Scholten et al., 2019; Ponomarov and Holcomb, 2009). Some factors, such as supply chain flexibility, redundancy, agility, visibility, collaboration, and supply chain learning, are increasingly recognized by scholars as capability elements that can enhance SCR. (Jüttner and Maklan, 2011; Hohenstein et al., 2015; Christopher and Peck., 2004; Dubey et al., 2014; Afraz et al., 2021).

Most of the previous studies treat SCR as a one-dimensional dynamic capability. However, as Parast (2022) illustrates, SCR is a multidimensional capability. The different dimensions require the development of different types of organizational resources and capabilities. Owing to the great variety of SCR capability elements, scholars are gradually conceiving a multidimensional SCR capability framework. Most of the current research regard SCR as a two-dimensional capability structure that includes proactive and reactive capabilities (Cheng and Lu, 2017; Ji et al., 2020; Wieland and Wallenburg, 2013; Llaguno et al., 2022). Proactive capability reflects the preparedness of an organization in activities before a supply chain disruption risk occurs. Reactive capability refers to the response and recovery activities after the occurrence of the risk. (Wieland and Wallenburg, 2013). Chowdhury and Quaddus (2017) develop a three-dimensional SCR framework based on dynamic capability theory comprising supply chain design quality, proactive and reactive capabilities. They analyze the role of SCR in influencing supply chain operational vulnerability and performance. Ali et al. (2017a) advocate a well-established division of SCR capabilities based on three phases: before, during, and after disruptions. Similarly, Hosseini et al. (2019) use the time attribute to identify three dimensions of SCR: absorptive capability (before the disruption), adaptive capability (during the disruption), and recovery capability (after the disruption). Furthermore, the competency elements included in the corresponding dimensions are identified. Ali et al. (2022) argue that SCR consists of three dimensions of capabilities: readiness capabilities, response capabilities, and recovery capabilities. Amalgamating the views of various scholars, this study puts forward a capability framework for SCR underpinned by dynamic capability theory and the stage characteristics of the supply chain in coping with disruption risk. Under this framework, SCR comprises three dimensions, namely absorptive capability (before disruption), response capability (during disruption), and recovery capability (after disruption). These three capabilities are independent in responding to disruptions. Because of such independency, firms need to integrate different resources to form the corresponding capabilities to respond to different types of disruptions (Parast, 2022). Absorptive and response capabilities highlight the endogenous capabilities of the supply chain while recovery capabilities reflect the exogenous capabilities (Hosseini et al., 2019). Absorptive capability emphasizes the ability of a supply chain system to absorb and withstand supply chain disruptions by using its original redundant resources and other risk preparation activities (Essuman et al., 2022; Hosseini et al., 2019). It is considered the first line of defense for supply chains to deal with disruption events (Hosseini et al., 2019).

A supply chain with absorptive capability means firms involved fully understand the state of supply chain operations and prepare accordingly before disruptive events occur (Parast, 2022). Therefore, absorptive capability reflects three SCR elements: supply chain situational awareness, redundancy, and visibility (Ivanov, 2021a, Ivanov, 2021b; Mubarik et al., 2021; Ye et al., 2022). Visibility ensures that actors in the supply chain have access to timely and accurate demand and supply information (Kalaiarasan et al., 2022). Pettit et al. (2010) also mention that a supply chain should enhance visibility and perceive early risk signals to reduce supply chain disruptions. Chowdhury and Quaddus (2017) state that a supply chain should predict imminent risk before a disruptive event occurs. Early disaster prediction allows for the preparation of mitigating resources in advance, such as redundant inventory and multiple suppliers, which helps the supply chain use its resources and absorb some of the disruptions when the risk occurs (Ali et al., 2022; Knemeyer et al., 2009; Dabhilkar et al., 2016; Ivanov and Dolgui, 2018; Sheffi and Rice, 2005).

Response capability is a supply chain's ability to respond correctly to risk on time by adjusting the flow of activities and resource allocation in the face of a disruption event (Sheffi and Rice, 2005; Hosseini and Barker, 2016). Response capability is considered an essential determinant of SCR and is the second line of defense against supply chain disruptions (Hosseini et al., 2019). Response capability means that firms know what to do and can adjust their normal functions to respond to disruptions on time when faced with a highly uncertain market (Furstenau et al., 2022). Companies that respond quickly to market changes have a better chance of capturing market share and improving their position in the industry (Sheffi and Rice, 2005). Thus, response capability embodies three SCR elements of risk management decisions, agility, and collaboration (Jüttner and Maklan, 2011). A supply chain with a high level of response capability means that it can review the operational status of the supply chain promptly when the risk of disruption occurs, make rapid risk response strategies, and implement non-standardized practices to reduce disruptions (Christopher and Peck, 2004; Jüttner and Maklan, 2011).

Recovery capability refers to the ability of a firm to quickly reach its original operating or a better state by using optimal methods to resolve risk shocks quickly and cost-effectively in the later stages of risk occurrence (Chowdhury and Quaddus, 2017). Recovery capability is the last line of defense for supply chain systems against the risk of disruption. When absorptive and response capability cannot maintain the initial operational state, recovery capability needs to be urgently enhanced (Hosseini et al., 2019). Recovery capability emphasizes the reconfiguration of internal and external resources and capabilities to help the supply chain recover quickly to its initial state (Ali et al., 2022; Birkie et al., 2017; Zsidisin and Wagner, 2010). Thus, recovery capability embodies three elements of supply chain recovery efficiency, contingency planning, and knowledge management (Adobor and McMullen, 2018; Han et al., 2020; Raj et al., 2015). By integrating knowledge and information on disruption events and learning from feedback, companies can develop better plans and solutions for future supply chain operations and achieve a better operational state (Ponomarov and Holcomb 2009; Sheffi and Rice, 2005).

Companies must reconfigure their production systems, operational processes, and supply chain activities to withstand risk impact. In this regard, what factors are effective in improving SCR and how SCR can help companies improve their performance levels are areas that need to be explored in depth. (Munir et al., 2022; Queiroz et al., 2022; Ivanov and Das, 2020; Ali et al., 2017a). Scholars have begun to use empirical research to explore the antecedents and outcomes of SCR capabilities. For example, El Baz and Ruel (2021) demonstrate the role of risk management practices in influencing SCR based on a resource-based view and organizational information processing theory. Through the development and validation of theoretical model based on information process theory, Wong et al. (2020) report that SCR can positively affect risk management, market, and financial performance. Although resilience is regarded as an essential capability in a firm, it is more often considered a single-dimensional capability in previous studies. In fact, SCR is a multi-dimensional capability and the role of each dimension in the supply chain needs to be better understood. In addition, the mediating role of SCR needs further investigation to understand how SCR connects with the drivers and the outcomes (Wieland and Wallenburg, 2013; Chowdhury and Quaddus, 2017; Asamoah et al., 2020). With these objectives in mind, this study empirically investigates a whole framework of SCR capabilities to analyze the direct impact of individual SCR capabilities on SCP and their roles in mediating the impact of digitalization and performance (Ali et al., 2017b; Ivanov and Dolgui, 2020).

2.2. SCD

With the advent of the digital era, digital services for the supply chain and the analytical algorithms behind the supply chain have become the core competitive factors in the new era (Dolgui and Ivanov, 2021a, Ivanov, 2021b). Especially since the outbreak of COVID-19, which caused city blockades and logistics disruptions, brought about the need for remote working, paperless operation, and supply chain structure reconstruction, which accelerated the pace of digital supply chain construction and helped companies to quickly cope with the risk of disruption (Ardolino et al., 2022). For example, the healthcare industry has pioneered the adoption of digital platform technologies and created digital operational solutions to facilitate the development of digital healthcare service processes (Chakraborty et al., 2021). Blockchain technology has also been gradually applied to the food supply chain to help core companies and other stakeholders to monitor and trace the food production process (Rogerson and Parry, 2020). To achieve the digitalization goal, companies introduce digital technologies and components, as well as other digital preparations such as digital strategy, digital organizational structure, digital culture, and digital talent (Gürdür et al., 2019; Li et al., 2022; Eller et al., 2020). The SCD driven by new technologies has also attracted more attention and research from academia and industry, observing the adjustments in business and supply chain activities brought about by SCD (Büyüközkan and Göçer, 2018; Stank et al., 2019; Hennelly et al., 2020).

SCD refers to the integration of digital technologies (such as big data, cloud computing, blockchain, Internet of Things, and artificial intelligence) into supply chain activities to form an operational process of “data-driven decision-making” (Colombari et al., 2022; Büyüközkan and Göçer, 2018; Hartley and Sawaya, 2019; Holmström et al., 2019; Caputo et al., 2021; Ageron et al., 2020). Integrating digital technologies into traditional supply chain activities generates a large amount of data and information. Leveraging the data analysis results to enhance the efficiency of specific business processes can add significant value to the supply chain. For example, Zhou et al. (2023) propose that the use of digital technologies such as smart contracts, digital storage, and intelligent labels enables traceability throughout the entire product lifecycle from raw material creation to final product delivery. This provides a digital traceability service and significantly improves the transparency and integrity of the supply chain. Unlike other related concepts such as digital transformation and digital technology adoption, SCD emphasizes the specific transformation of supply chain business processes and decision-making processes brought about by digital technology applications (Ageron et al., 2020; Richey et al., 2016). Battistoni et al. (2023) analyze the steps to achieve digitalization in enterprises and conclude that companies can extract valuable knowledge from data analysis results and improve existing operational processes through the four stages of collecting, integrating, processing, and analyzing data (Lu and Weng, 2018). Zhou et al. (2021) argue that digitalization can be categorized as either internal or external digitalization. Internal digitalization aims to reduce costs and improve the efficiency of internal operational processes. Internal digitalization includes digital information and communication technology solutions, such as video conferencing and email, as well as digital training and support for work tasks. External digitalization emphasizes reliance on digital technology to enhance interactions with stakeholders. Not only that external digitalization can reduce inter-company communication costs and facilitate partnerships with suppliers and other partners, but it can also accurately anticipate customer needs and increase customer loyalty. Colombari et al. (2022) analyze the digitalization process in digital technology application and data integration and conclude that digital technology applications can help companies optimize their operational processes and increase operational efficiency. The high level of data integration required for digitalization makes data more credible and available throughout the company, thus creating new structured processes and functions.

Proksch et al. (2021) argue that digitalization can be reflected in two main areas, namely digital products/services and digital processes. Non-digital companies offer non-digital products and services and rely mainly on manual processes. A highly digital enterprise provides complete digital products and services and operates with mature digital processes. Similarly, Truant et al. (2021) suggest that digitalization includes two aspects: (1) adding digital components to physical products to form digital products/services, and (2) empowering digital capabilities in operational processes to bring value-added products and services to the company. Kamalaldin et al. (2020) opine that digitalization contributes to the servitization (where customers pay for a service instead of buying the equipment) of manufacturing companies. Digital servitization creates a new digital business model and value-creation opportunities (Kohtamäki et al., 2019; Rachinger et al., 2018).

Existing research reveals the role of digitalization in enhancing performance levels. SCD can enhance supply chain visibility, connectivity, innovation, real-time, transparency, speed, etc., thus enabling nodal companies to better plan resources and build capabilities to meet the diverse needs of consumers (Culot et al., 2020; Frank et al., 2019). Digitalization in manufacturing can accelerate business innovation, improve resource efficiency and cost savings, and promote sustainable supply chains (Rossit et al., 2019; Haseeb et al., 2019). Ha (2022) measures digitalization from a macro perspective taking into consideration digital connectivity, internet use, e-business, e-commerce, and e-government. The findings demonstrate a positive impact of digitalization on the development of financial markets and financial institutions. Zhang et al. (2022) show that, by enhancing digital capabilities, buyer companies can improve the level of information sharing and relationship transparency between themselves and supplier firms. This can help reduce opportunistic and unethical behavior of suppliers and improve the buyer-supplier partnership.

Scholars have started to study the role of digitalization in a supply chain during crises. Studies have found that SCD can enhance SCR. Hald and Coslugeanu (2022) identify four critical resilience capabilities that can be enhanced by applying digital technologies, They include flexibility, visibility, risk management, and collaboration. Tseng et al. (2022) suggest that digital platforms can help companies gain timely insight into consumer demand, adjust sales processes and channels flexibly, and ensure business continuity during disruptions. Papanagnou et al. (2022) point out that predictive analytics, an emerging digital technology, can help supply chains quickly predict and respond to disruption risk issues and make timely risk management decisions, thereby enabling the supply chain to shift towards a complete and robust structure. Bianco et al. (2022) study the achievement of resilience in enterprises through digitalization. They conclude that application of digital technologies, such as big data, cloud computing, and the Internet of Things, in a supply chain can lead to intelligent management practices, such as improving production system autonomy and energy efficiency. Smart management practices enable the development of digital capabilities, such as digital culture and innovation capabilities, which can positively impact on SCR. Cui et al. (2022) state that digital technology adoption can impact on resilience by facilitating supply chain integration, including internal, customer, and supplier integration. Internal integration emphasizes the ability to enhance cross-functional collaboration through digital technology, which enhances internal process efficiency. External integration (including customer and supplier integration) can lead to collaboration between upstream and downstream partners, enhancing the response capability of the firm to the market. Naghshineh and Carvalho (2021) develop a detailed “AM-SCR” framework in which the disruptive digital technology of additive manufacturing (AM) will bring about a dynamic change in the state and the structure of the supply chain which can enhance SCR and mitigate supply chain vulnerability. Moreover, AM technology can facilitate the formation of duality dynamic capabilities (sensing capability, seizing capability, and reconfiguration capability) to reconcile the dilemma between SCR and supply chain efficiency (Belhadi et al., 2022).

Furstenau et al. (2022) contend that existing studies should have paid more attention to illustrating the role of digitalization in enhancing resilience capabilities in different dimensions. As such, they develop a SCR framework that identifies different dimensions of SCR, including anticipating capabilities, monitoring capabilities, responding capabilities, and learning capabilities. The aim is to analyze the differential impact of different digital technologies on these dimensions. The authors call for future research to validate these effects with large data samples. Furthermore, there also calls for further exploration of how digitalization can lead to SCP improvement in crisis scenarios (Pettit et al., 2019; Zouari et al., 2020). Achieving digitalization requires companies to gain insight into the market, remain open to new technologies and management styles, understand the opportunities presented by digital technologies, completely transform their core business, and find ways to leverage them to create value (Arias-Pérez et al., 2021). Digitalization is not restricted to adopting certain specific digital technologies to reduce risks. It also integrates digital technologies into the whole SCR process and establishes a digital resilience management framework. Therefore, the feasibility of and the extent to which SCD can contribute to improving the different SCR capabilities and SCP in various dimensions in a crisis scenario needs to be further examined empirically.

3.1. Theoretical background

3.2. Hypothesis development

3.2.4. Mediating role of SCR

SCR, as a special supply chain dynamic capability, plays a mediating role between SCD and SCP (Belhadi et al., 2021c). The integration of digital technologies with the existing supply chain processes of companies improves data visibility, enables digital business processes such as digital product design and manufacturing, improves operational efficiency, and reduces production costs, which can have a positive impact on SCP (Hald and Coslugeanu, 2021; Holmström et al., 2019; Ivanov, 2021). At the same time, the increased visibility brought about by digitalization facilitates risk perception and resource preparation and enhances absorptive capability (Ivanov and Dolgui, 2020). Absorptive capability is a guarantee of business continuity, allowing companies to operate robustly in the face of disruptive events and helping to translate their operational strategies into performance results (Hosseini et al., 2019). At the same time, the quantity and quality of acquired data are enhanced by digital technologies such as big data and cloud computing. Based on the powerful data analysis capability, digital supply chains can improve the response capability of companies to changes in corporate markets when disruptions come (Balakrishnan and Ramanathan, 2021). The increased response capability allows companies to maintain connections and collaboration with supply chain partners, quickly coordinate upstream and downstream supply chain resources, and ensure effective integration of SCD with supply chain business processes in the face of volatile competitive environments and high market demand uncertainty, thereby improving SCP levels (Ye et al., 2022; Munir et al., 2022). Moreover, recovery capability is the ability to quickly reconfigure supply chain resources to recover from supply chain disruptions after they occur (Hosseini et al., 2019). Digital supply chains can enhance the learning of supply chain knowledge and information to improve resilience during disruptions. Companies that recover quickly to their original state of operation or better than their competitors are able to maintain a leading competitive advantage in a volatile market, leading to long-term sustainable performance. In summary, SCD can improve absorptive capability, response capability, and recovery capability, which in turn can improve SCP. Based on these arguments, the following hypotheses are proposed:

H4

Absorptive capability mediates the relationship between SCD and SCP.

H5

Response capability mediates the relationship between SCD and SCP.

H6

Recovery capability mediates the relationship between SCD and SCP.

The digitalization of the supply chain can have a direct impact on SCP through process efficiency improvement, and in a turbulent environment, digitalization can enhance the dynamic capability of SCR, enabling companies to achieve better performance and a competitive position in the market. Therefore, based on the dynamic capability theory and the above hypotheses, this paper establishes the analytical framework and theoretical model of “SCD→SCR→SCP” to analyze the direct impact of SCD on SCR and SCP, and the mediating role of SCR on SCP. Fig. 1highlights the research model synthesizing the hypotheses and their relationships.

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Fig. 1

Research model.

4.1. Questionnaire design

The design of the survey questionnaire for data collection is as follows. Firstly, the research framework and the measurement indicators were derived according to the relevant theories and previous studies. Secondly, three groups of professionals in the field were invited to define the concept and adjust the semantics. The method of direct narration was adopted to avoid ambiguity in the questions of the questionnaire. Finally, 30 enterprises were selected to conduct a pre-test which was anonymous. Based on the feedback of the respondents, difficult and semantic questions with the unclear definition of constructs in the questionnaire were revised to produce the final version for use in the main survey. Table 1shows the structure and measurement items used in this study. A 5-point Likert reflective scale is used to measure the response to the questions, where “1” stands for “strongly disagree” and “5” stands for “strongly agree”.

SCD not only means the adoption of digital technology but also the application of digital technology in supply chain activities. The application of digital technology is not only reflected in the development and innovation of digital products and services, and the digitalization of supply chain processes, but also in the creation of digital business models (Weking et al., 2020). Therefore, our study established a three-item scale to measure the level of digitalization in the supply chain. Respondents were asked to indicate the extent to which their companies and supply chains adopted digital products and services, digital operation processes, and digital business models.

SCR is a comprehensive capability. Based on the viewpoints of Han et al. (2020) and Singh et al. (2019), according to different stages of SCR, this study divided the SCR into three sub-variables: absorptive capability, response capability, and recovery capability. In this study, absorptive capability was measured by three dimensions: supply chain situational awareness, supply chain redundancy, and supply chain visibility. Respondents were asked to evaluate the situational awareness, redundancy, and visibility of their company and supply chain. Response capability was measured by three dimensions: supply chain risk management decisions, agility, and collaboration. Respondents were asked to evaluate the accuracy of risk management decisions, agility, and collaboration of their company and supply chain. Recovery capability was measured by three dimensions: supply chain recovery efficiency, contingency planning, and knowledge management. Respondents were asked to evaluate the recovery efficiency, contingency planning, and knowledge management skill of their company and supply chain.

SCP can measure the overall efficiency of the supply chain (Gunasekaran et al., 2001; Beamon, 1998). SCP is not only concerned with the performance of a single enterprise but also reflects the operation status of the entire supply chain. Performance measurement can help enterprises to review achievements, set development goals, and determine the direction of future action (Gunasekaran et al., 2004). Gunasekaran et al. (2001) believed that SCP measurement should not only include financial performance related to cost but also comprehensively cover non-financial indicators related to output. Beamon (1999) proposed that SCP should be measured from three aspects: resource, output, and flexibility. Based on the viewpoints of Beamon (1999), SCP was measured by four dimensions: operating cost, return on investment, lead time, and customer satisfaction. Respondents were asked to evaluate the extent to which their company and supply chain fit these four items.

4.2. Sampling and data collection

The survey objects of this study are manufacturing enterprises with a certain level of digitalization located in the Yangtze River Delta region of China which are greatly affected by supply chain disruptions. Compared with other regions of China, the Yangtze River Delta region is more developed in the economy and the manufacturing industry is more concentrated. Enterprises in this region are more deeply involved in supply chain management, As the region is rapidly developing, companies therein are more inclined to adopt digital management to gain their competitive advantages. In this study, the questionnaire design, sampling process, and distribution of the questionnaire all followed strict empirical analysis steps. The questionnaires were distributed to the middle and senior management of enterprises to ensure that the collected data reflect accurately the operating conditions of the surveyed enterprises. The questionnaires were distributed through email, online survey, and on-site survey. Telephone and e-mail reminders were used to improve the response rate. In total, 976 questionnaires were distributed and 235 of which were returned. Invalid questionnaires with many missing values were removed and finally, 210 valid questionnaires were retained, with a response rate of 21.5%.

Table 2 gives a profile of the respondents. In this study, respondents included senior executives (8.1%), senior managers (22.4%), managers (18.1%), first-line managers (33.8%), and others (17.6%), which means that respondents were better equipped to solve complex supply chain system problems of their companies and had a better understanding of supply chain operations and digital technology applications (Gu et al., 2021). The survey covered a wide range of manufacturing industries, including publishing and printing, electronic products and appliances, chemicals and petrochemicals, textiles, and apparel, etc. There were different types of manufacturing enterprises, including state-owned enterprises, private enterprises, foreign enterprises joint venture enterprises, and others. In terms of company size, 82.4% of the sampled enterprises had annual operating revenue of more than CNY 10 million, and more than half of the enterprises had more than 500 employees. The sample chosen was considered representative of the population.

4.3. Common method bias and non-response bias

To ensure the reliability of the results, the sampled data were tested for any common method or non-response bias.

First, the common method bias problem was examined. Following the suggestions of Podsakoff et al. (2003), common method bias was tested before and after the questionnaire collection process. After data collection, both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) methods were used to conduct a single-factor test to check for common method deviations. First, the EFA method was used to conduct the Harman single-factor test (Podsakoff and Organ, 1986). The results showed that the first factor accounted for 44.6% of the total variance, less than the threshold of 50%, indicating that our research model was not significantly affected by common method bias. Then, CFA was applied to Harman's one-factor test and all items of the constructs were placed in a single factor. The results showed that the model fit indices of the single-factor model (CMIN/DF=9.504, CFI=0.584, IFI=0.587, RMSEA=0.202, and SRMR=0.123) were obviously unacceptable and considerably worse than those of the original measurement model (CMIN/DF=2.778, CFI=0.919, IFI=0.920, RMSEA=0.092 and SRMR=0.1142), indicating that all measurement items should not belong to a single factor. Therefore, based on CFA and EFA results, it was concluded that common method bias was not an issue in this study.

Second, the problem of non-response bias was examined. Referring to the method of Armstrong and Overton (1977), a comparative analysis between early and late respondents was conducted. T-test results showed that there was no significant statistical difference in the number of employees and sales (p>0.05). This indicated that the problem of non-response bias did not significantly affect the data results in this study.

5.1. Reliability and validity

EFA and CFA were used to evaluate the reliability and validity of the focal constructs. In this study, the internal reliability of the scale was tested using EFA to find out whether there was a high internal consistency between items. SPSS26.0 software was used to test the reliability of the scale. The results are shown in Table 3. Cronbach's α values are all greater than 0.75 and composite reliability is greater than 0.8, indicating that the scale has good internal consistency (Bagozzi et al., 1981; Hair et al., 2017).

Structural validity was verified using CFA. The analysis results of convergent validity and discriminant validity are shown in Table 3. All factor loadings are greater than 0.7 and all the average variance extracted values (AVE) are greater than 0.5, and the composite reliability is greater than 0.7. Therefore, this scale has high convergent validity (Bagozzi et al., 1981). Discriminant validity was confirmed by comparing the square root of the AVE value for each construct with the correlations between this construct and other constructs. When the square root of AVE on the diagonal is greater than the correlation between this construct and other constructs, it indicates that the constructs have good discriminant validity (Bagozzi et al., 1981). It can be seen from the results in Table 4that our results meet the requirements of acceptable discriminant validity.

A structural equation model, a method based on covariance analysis, was used to test the relationships between constructs and explore our hypothesis testing results. AMOS26.0 software was used to analyze the measurement model and the structural model. Firstly, we tested the fitness of our model, and the results showed that the measurement model provided a good fit to the sample data (CMIN/DF=2.778, CFI=0.919, IFI=0.920, TFI=0.900, RMSEA=0.092) (Hu and Bentler, 1999). Meanwhile, SPSS26.0 software was used to conduct multiple regression analyses on the structure of our prediction model. The test results showed that the range of variance inflation factor (VIF) values was between 1.442 and 1.967, all below 2 (Hair et al., 2017). It showed that multi-collinearity was not a problem in our study. Given the complexity of our models, we examined the explanatory power of the research model based on explained variance (R²) and predictive power based on Stone-Geisser's Q². The explained variance (R²) of the endogenous variables was ASC (0.25), RSC (0.29), RCC (0.35), and SCP (0.41). It can be seen that in our model, all the values of R² are in the range of 0.25–0.50, which meets the empirical requirements (Hair et al., 2017). We conducted the classic Stone-Geisser's Q²test to measure the predictive relevance (power) (Wamba et al., 2020; Stone, 1974). This test is conducted by a blindfolding algorithm, which in turn performs a determined number of resamples (Chin, 1995). Values of Q²greater than zero are considered to have good prediction power of the research model (Hair et al., 2019). Stone-Geisser's Q²for endogenous constructs are ASC (0.128), RSC (0.113), RCC (0.193), and SCP (0.241). All the values of Q²are greater than zero, indicating there is an acceptable predictive relevance in our model (Peng and Lai, 2012; Hair et al., 2019).

5.2. Hypothesis testing

As shown in Fig. 2, the correlation and the significance of the structural model relationships were first evaluated using AMOS26.0 software to verify our hypotheses (H1a, H1b, H1c, H2a, H2b, H2c, H3). Fig. 2 shows the path analysis results, including standardized path coefficients, significance and the value of variance explained. Table 5summarizes the specific statistical results of the hypothesis tests.

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Fig. 2

Structural modeling results. + p<0.1, *p<0.05, **p<0.01, ***p<0.001.

H1a, H1b, and H1c respectively studied the influence of SCD on absorptive capability, response capability, and recovery capability. As shown in Table 5, SCD had positive and significant effects on absorptive capability (β=0.408; p<0.001), response capability (β=0.397; p<0.001), and recovery ability (β=0.428; p<0.001). Therefore, hypotheses H1a, H1b, and H1c were all supported. At the same time, hypotheses H2a, H2b, and H2c respectively studied the effects of absorptive capability, response capability, and recovery capability on SCP. The results showed that response capability (β=0.279; p<0.01) and recovery ability (β=0.338; p<0.001) had a significant positive impact on SCP, while absorptive capability (β=−0.088; p=0.224) had a negative and insignificant impact on SCP. So, hypotheses H2b and H2c were supported, while H2a was not. Meanwhile, H3 studied the influence of SCD on SCP, and the results showed that SCD (β=0.202; p<0.05.) had a significant positive impact on SCP.

H4, H5, and H6 respectively studied the mediation effect of absorptive capability, response capability, and recovery capability on the relationship between SCD and SCP. Bootstrapping application was used to test the mediating effects (Baron and Kenny, 1986). As can be seen from the above statements, the paths from SCD to resilience in three stages were all positive and significant, and the paths from the response and recovery capability to SCP were also positive and significant. Furthermore, the bootstrapping with 5000 resamples was executed to test the mediating effects of absorptive capability, response capability, and recovery capability (Preacher and Hayes, 2008). The results are shown in Table 6. The indirect impact of SCD on SCP was positive and significant through response capability (indirect effect=0.111, SE=0.065) and recovery capability (indirect effect=0.145, SE=0.069). However, the indirect effect of SCD on SCP through absorptive capability (indirect effect=−0.036, SE=0.044) was negative and insignificant. Meanwhile, the bias-corrected 95th percentile confidence interval (CI) for the indirect influence on SCP was [0.073, 0.431], which did not contain zero. Therefore, considering the direct impact of SCD on SCP was significant and positive, it could be seen that response capability and recovery capability had a partial mediation effect, while absorptive capability did not have the mediation effect. Therefore, this study supported H4 and H5 but did not support H3.

5.3. Tests for endogeneity

We discussed possible endogeneity issues below. SCD may be endogenously affected by SCP, and such reverse causality may lead to inconsistency and bias of our results (Li et al., 2020). In order to solve this problem, this study adopted the Hausman test proposed by Davidson and MacKinnon (1993). We used firm sales as a potential instrumental variable because this variable had no significant direct effect on SCP (Yu et al., 2019). In addition, Frank et al. (2019) pointed out that larger companies were more willing than smaller ones to digitally transform their supply chains. We established the first-stage regression model with SCD as the dependent variable. The results showed that there was a significant correlation between firm sales and SCD (β=0.150, t=3.73, P<0.05), and the prediction of the first-stage model was obtained. Then, we included this residual in the second-stage regression model with SCP as the dependent variable and found that there was no significant relationship between the residual and SCP (β=−0.187, P=0.423>0.05), which indicated that reverse causality was not a serious problem in our model setting. Therefore, it can be concluded that endogeneity is not an issue affecting the results of our study.

6.1. Discussion of study findings

This study investigates the quantitative relationship between SCD, SCR and SCP. By distributing questionnaires to manufacturing industries in the Yangtze River Delta region of China and using structural equations to analyze the collected data in a normative manner, the study reveals the paths and effects of SCD on SCR and SCP in a crisis situation. The specific research results will be presented in the following four aspects.

First, SCD has a positive impact on SCP. This is consistent with the results obtained by many scholars (AlMulhim, 2021; Frank et al., 2019). In the context of the digital economy, the integration of digitalization with supply chain processes helps companies to form digital operational processes, improve the efficiency of supply chain operations, and save operational costs (Ivanov, 2020). Embedding digital technologies and components into products and services can be more conducive to collecting information and data from suppliers and from consumers, and the analysis results can guide companies to conduct better product development and promotion, meet the diversified and customized needs of consumers, and improve SCP (Ivanov et al., 2022). At the same time, through the digital business model, companies implement online and offline delivery channels, which can help them create multiple revenue opportunities. At the same time, the digital supply chain maintains open interfaces to facilitate end-to-end connectivity with supply chain partners, creating an open ecosystem and improving the long-term profitability of the company.

Second, SCD has a significant positive effect on SCR. SCD had a significant positive effect on absorptive capability. This result is consistent with Liu et al. (2013). Supply chain absorptive capability emphasizes the use of a firm's own resources and capabilities to absorb a portion of shocks when disruptive events occur. By establishing a digital business development model that shifts product and supply chain operations from offline to online, supply chain visibility can be greatly enhanced, thus maintaining information sharing between upstream and downstream of the supply chain and achieving good risk preparedness (Ivanov, 2021a, Ivanov, 2021b; Li et al., 2022). At the same time, digital supply chains can enable the capture of risk signals, maintain a high degree of situational awareness, reduce the likelihood of supply chain disruptions, and improve absorptive capability (Kache and Seuring, 2017; Wagner et al., 2009). With powerful data analysis capabilities, digital supply chains can develop sound inventory management strategies that can fully mobilize inventory and enhance the risk-absorptive capability of the supply chain when risks come. (Cavalcante et al., 2019). Meanwhile, existing digital technologies such as blockchain can bring smart contracts into the transaction process (Manupati et al., 2022). The speed, transparency, and high reliability of smart contracts also facilitate the establishment of cooperative relationships among supply chain partners, improve the robustness of supply chain networks, and enhance resistance to risk (Lohmer et al., 2020; Saberi et al., 2019). Therefore, this study provides empirical support for the facilitative effect of SCDs on absorptive capability.

SCD has a significant positive effect on response capability. This result is consistent with Dolgui an＀＀Ivanov, 2021a, Ivanov, 2021b. Response capability emphasizes the ability to make quick risk response decisions when risks occur, mobilize resources flexibly, communicate with partners in a timely manner, and optimize overall supply chain resource allocation (Cabral et al., 2012). Digital platforms can help internal departments and supply chain partners to communicate online, even in the case of urban lockdown (Frank et al., 2019). Through digital technologies such as big data analytics and artificial intelligence, the supply chain can integrate operational and environmental data to quickly generate effective, visual decisions in response to disruptions (Ivanov et al., 2019). This can be more objective and accurate than traditional risk response plans based on managers' personal experience and judgment (Singh and Singh, 2019). At the same time, a digital supply chain has the advantages of streamlined operational processes and efficient collaboration, which greatly increases the efficiency of employees and managers, thus improving the efficiency of resource integration when risks occur (Ganbold et al., 2020). At the same time, companies upstream and downstream of the supply chain can effectively share data on resources, operating conditions, environmental conditions, and risk indices after digitalization (Ivanov et al., 2022). This allows companies in the supply chain to make joint decisions about new technologies, new production lines, and new products when they face risks, ensuring supply chain flexibility (Shukor et al., 2020). Therefore, this study provides theoretical support for the positive impact of supply chain sustainability on supply chain response capability.

SCD would also have a significant positive impact on recovery capability. This assertion is in line with Chen et al. (2019). Recovery capability emphasizes the ability of a supply chain to quickly return to its original level of operation, or even better after the interrupt event has occurred (Chowdhury and Quaddus, 2017). By adding digital technology into existing management, supply chains can quickly and effectively develop operational strategies and integrate resources, thus minimizing the shortcomings of traditional supply chains that are costly and inefficient (Carlucci et al., 2004). At the same time, the digital supply chain advocates win-win cooperation and benefit sharing, which will effectively promote supply chain partners to agree on common risk preparation and cooperative planning. The integration of existing risk data into the original decision database through digital technology can guide managers to better make more rational management decisions and business plans (Belhadi et al., 2021a). In addition, supply chain information and knowledge sharing bring an increased level of learning to the supply chain. Upstream and downstream companies in the digital supply chain can extract knowledge, exchange knowledge, and learn knowledge from disruptions, thus promoting innovation in the supply chain and achieving better business status (Rui et al., 2008). Therefore, this study also provides an empirical explanation for the positive impact of SCD on recovery capability.

Third, SCR has a significant positive effect on SCP. Absorptive capability does not promote the improvement of SCP, and may even have a negative impact, while response and recovery capability have a significant positive impact on SCP. Response and recovery capability reflect the ability of the supply chain to respond in time, allocate resources reasonably, recover quickly and achieve better business conditions when the risk occurs (Chowdhury and Quaddus, 2017; Sheffi and Rice, 2005). Obviously, the more responsive and resilient the supply chain is, the less likely the supply chain will suffer from disruption loss, which will have a positive impact on SCP (Tukamuhabwa et al., 2015). However, absorptive capability does not have a significant positive effect on SCP, which is inconsistent with previous scholars' views. For this result, this paper argues that absorptive capability emphasizes that companies use their existing resources and capabilities to resist risk shocks. However, supply chains with higher absorptive capability tend to store more redundant inventory and cash flow, arrange diversified procurement rather than single procurement, and invest more resources in risk perception and visibility, which are contrary to the business objectives of lean production and cost minimization (Govindan et al., 2013; Pettit et al., 2013). Excessive SCR preparation will erode profits to a certain extent, and thus have a certain negative impact on the supply chain level, which is consistent with the view of Fraccascia et al. (2020). Therefore, the impact results of the three resilience capabilities on SCP in this study also provide empirical support for the impact of SCR on SCP.

Fourth, different SCR capabilities have different mediating roles. This study supports the mediation effect of response and recovery capability. However, it does not support the mediation effect of absorptive capability, which particularly explains the mechanism among SCD, SCR, and SCP. It concludes that the establishment of SCD will not only have a direct impact on SCP but also enhance risk response efficiency and risk adaptability of a supply chain by improving response and recovery capability, thus bringing a stronger improvement to SCP (Chowdhury and Quaddus，2017; Chen et al., 2019), 2016. As for the mediation path of absorptive capability, this study believes that the improvement of SCD can indeed enable the supply chain to fully mobilize redundant inventory and improve visibility and situational awareness accuracy. However, the elements building of the absorptive capability, such as visibility and redundancy, also requires additional infrastructure support, such as digital development costs, inventory management costs, and supply chain visual network construction costs (Govindan et al., 2013). The increase in these costs can erode SCP and therefore this intermediary path is not supported.

6.2. Theoretical implications

This study takes 210 manufacturing companies as the research objects, builds an inner influence mechanism of “SCD→SCR→SCP” based on the dynamic capability perspective, and analyzes the specific digitalization process to enhance SCR and SCP as well as the mediating role of SCR through structural equation modelling. The findings of this study contribute to the extant SCR literature.

First, many of the SCR elements, such as visibility, agility, and recovery efficiency, are complex and widely used in research on complete risk cycle. As such, there is a strong need to develop a comprehensive SCR framework linking them together with solid theoretical support. This study analyzes the various SCR dimensions in detail. It develops a comprehensive SCR framework based on a dynamic capability perspective and validates it empirically, thereby extending the current quantitative research of SCR. The study also points out that improvement of SCR depends on the combined effect of the three identified capabilities, namely absorptive, response and recovery capabilities. Compared with other studies that consider SCR as a unidimensional capability, this study comprehensively explores the building process and the performance impacts of different SCR capabilities. It considers SCR a complex dynamic capability to cope with risks and emphasizes the integration process of resources and capabilities throughout the internal and external parts of the supply chain. As such, it provides a reference for future quantitative research in this regard that can help companies examine the weaknesses of their SCR according to their unique circ*mstances.

Next, this study explores digitalization as an antecedent helps the formation of SCR. It argues that realization of SCR requires companies to adjust their infrastructure, break down the information barriers between departments and partners, and achieve risk prediction and planning, agile response, and rapid recovery through process optimization and resource reorganization. As a driving factor, SCD includes the application of digital technology and the overall innovation of business processes, products and services, and business models brought by the application of technology. Digitalization enables enterprises to reconstruct their value-creation logic and achieve flexibility, agility, and efficiency. Therefore, digitalization of the supply chain has a certain degree of consistency with the strategic objectives and realization process of SCR. Digitalization can effectively help build different dimensions of SCR capabilities. This study extends the SCD research focus from a single digital technology to the whole digital management process. The research perspective focuses on digital supply chain activities with digital technology adoption as the underlying logic and integrated use of digital technologies as the approach. This study extends the scope of research in digitalization from conventional to crisis scenarios, enriching the research context in the field of digitalization.

Finally, this study enriches the research on the mediating role of supply chain resilience in SCD and SCP. Previous studies in this regard mainly focus on the relationship between SCD and SCP, as well as SCR and SCP. Research on how SCR and SCD jointly affect SCP in a turbulent environment is limited. This study integrates these three variables into a theoretical framework of “SCD→SCR→SCP” based on dynamic capability theory. The results highlight that new resources, capabilities, and models brought by SCD have helped shape SCR, a complex capability required to maintain business continuity in a highly uncertain market. This dynamic process helps the supply chain achieve better performance outcomes. In short, this study has successfully responded to the call for research to build dynamic supply chain capabilities in crisis scenarios (Ambulkar et al., 2015).

6.3. Managerial implications

In addition to theoretical contributions, the findings of this study provide some valuable managerial insights for companies. The results show that digitalization enables the reorganization of existing products and services, business processes, and business models to improve SCP through the dynamic capability of SCR to fully unleash the digital driving effect in times of crisis. Specific management implications for managers and practitioners are as follows.

First, our study highlights the combined effect of absorptive capability, response capability, and recovery capability in resisting supply chain disruption. Changes in the external environment, such as natural disasters, political interventions, and the complexity of supply networks, can lead to unexpected supply chain disruptions. Our research shows that improving SCP in highly volatile market environments relies on the building of resilience capabilities. It requires firms to enhance their abilities in forecasting the internal and external environment and achieving dynamic management of redundant resources (Namdar et al., 2018; Pettit et al., 2013). The findings of this study echo the view of Ivanov et al. (2022) that redundant assets are resources “waiting” to be used in crisis scenarios. Using them only to respond to emergencies may be inefficient and will not significantly improve performance levels. Especially for SMEs with limited resources and capability, the costs of stocking redundant inventory and arranging diversified purchases are also high. As SCR has become a decisive factor for enterprises to ensure business continuity, enterprises should strengthen the collaboration with different partners to expedite development of joint risk mitigation plans and quickly respond to changes in the market environment. At the same time, enterprises should accelerate the construction of a diversified supply chain network. Advocating win-win cooperation and benefit sharing can extend the advantages of resources and capabilities of individual enterprises to the entire supply chain to achieve more sustainable development.

Next, our results show the differential roles of supply chain digitalization in building SCR at different stages. SCD can achieve absorptive capability by increasing the effectiveness of supply chain risk preparedness. It can improve the response capability and recovery capability through the reconstruction of internal and external resources by intelligent operational processes and digital business models. Differences such as the industry sector, the risk stage, and the current state of resilience building can determine the priorities for developing different SCR capabilities (Cohen et al., 2022). Managers can use our SCR framework to examine their current supply chain weaknesses and build the required resilience. They need to re-examine their resilience and digital resource base to align their digital strategies with their resilience capabilities at different stages. Using the digital tools flexibly to re-plan enterprise structure, process, and business model is important. Starting with resilience capability that needs to be improved most, managers can improve the SCR levels of their companies from point to point. Considering the mediating role of SCR, managers should further stimulate the potential of digitalization by combining digital management with the dynamic capability of the enterprise. Companies need to recognize the inherent mechanisms of SCD, develop a digital strategy aligned with their development in times of crisis, and undertake a more profound digital transformation. They need to advance their SCD process purposefully and directionally. At the same time, companies need to recruit digital talents, develop digital skills of their employees, and actively adjust the digital supply chain structure to ensure the success of supply chain digitalization.

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Miss Nanyang Zhao is a researcher of Shanghai University of International Business and Economics. Her main research interests include sustainable supply chain management and logistics management.

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Dr. Jiangtao Hong*(Corresponding author) is Professor of Management Science in the International Business School at Shanghai University of International Business and Economics. He holds a PhD in Operation Management from School of Management at Shanghai Jiaotong University (SJTU). He did his post-doctoral research at Fudan University in 2009–2011. His main research interests include sustainable supply chain management and logistics management. His research has been published in journals including International Journal of Production Economics, Annals of Operations Research, Transportation Research Part E, Journal of Cleaner Production, International Journal of Logistics Management, International Journal of Logistics Research and Applications and Emerging Markets Finance and Trade.

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Dr. Kwok Hung Lau is Associate Professor and Deputy Head of the Department of Supply Chain and Logistics in the School of Accounting, Information Systems and Supply Chain at RMIT University in Australia. He has more than 30 years of working experiences in the private sector, the government, and the academia. His research focuses on supply chain optimization, collaboration, simulation and design which has been published in journals including International Journal of Production Economics, Supply Chain Management: An International Journal. International of Physical Distribution and Logistics Management, and International Journal of Logistics Management.

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