Dorsaf Zouari

Laurence Viale

Does digitalising the supply chain contribute to its resilience?

Keywords: Digital Supply Chain, Supply Chain Resilience, Digital Maturity, Digital Tool Adoption

Purpose: Supply chain resilience (SCR) is a key concept for managers who wish to develop the capacity to enhance the ability of their supply chain (SC) to cope with unexpected turbulence. Digital SC tools are often seen as a solution that provides greater visibility, anticipation and collaboration (SCR capability factors). The purpose of this paper is to investigate the link between SCR and SC digitalisation. Design/methodology/approach: The results were analysed using an ordinary least squares (OLS) regression model based on a sample of 300 managers in the field of SC management. Findings: SC digitalisation is characterised by the degree of digital maturity and the adoption of digital SC tools. Both the degree of digital maturity and the adoption of digital tools have a positive impact on SCR but the degree of digital maturity is stronger, especially among the smaller companies. Research limitations/implications: The findings do not indicate which tools most contribute to SCR. Practical implications: Managers should reflect on the need to continue digitalising their SCs and focus primarily on digital maturity degree, rather than digital SC tool adoption, if they want greater SCR in the current uncertain environment. Originality/value: This is the first study to focus on assessing the impact of digitalisation on SCR by considering several digital SC tools at the same time. Validation of the hypotheses model confirms the positive impact of SC digitalisation on SCR for researchers and managers.

Introduction

SC digitalisation emerges in an era in which real-time communication and coordination is pursued while many risks arise (e.g. cybersecurity, [START_REF] Tang | The strategic role of logistics in the Industry 4.0 era[END_REF][START_REF] Corallo | Cybersecurity in the context of Industry 4.0: a structured classification of critical assets and business impacts[END_REF] which may hinder SC resilience (SCR; [START_REF] Colicchia | Managing cyber and information risks in supply chains: insights from an exploratory analysis[END_REF]. It implies increasingly mature processes, organisation or even competences [START_REF] Schumacher | A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises[END_REF]Schuh et al., 2017;[START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF] along with digital tools adoption to support data-driven SCM [START_REF] Büyüközkan | Digital supply chain: literature review and a proposed framework for future research[END_REF]. According to [START_REF] Frank | Industry 4.0 technologies: Implementation patterns in manufacturing companies[END_REF], digital tools refer to technologies that provide intelligence and connectivity. SC digitalisation is attracting significant attention and the 'digital SC' (DSC) is the hottest buzzword in the industry [START_REF] Büyüközkan | Digital supply chain: literature review and a proposed framework for future research[END_REF]. The stakes are now focused on SC digitalisation in a business environment that is now more turbulent than ever (Ivanov et al., 2019a) with financial, economic, ecological and social risks. Of course, digitalisation can be viewed as a solution to managing SC risks (Ivanov and Dolgui, 2019a;[START_REF] Ivanov | New disruption risk management perspectives in supply chains: digital twins, the ripple effect, and resileanness[END_REF] but the ongoing 'wave' of digitalisation also creates new dynamics that are often difficult to follow in companies (McKinsey & Company, 2015;Deloitte, 2017), leading to new challenges for businesses and society. In such an environment, resilience is desirable in companies. To the best of our knowledge, only a few studies have provided new insights into the impact of digital tools on SCR [START_REF] Papadopoulos | The role of Big Data in explaining disaster resilience in supply chains for sustainability[END_REF][START_REF] Ivanov | Structural Dynamics and Resilience in Supply Chain Risk Management[END_REF][START_REF] Dubey | Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience[END_REF]. Given the paucity of research on the topic [START_REF] Garay-Rondero | Digital supply chain model in Industry 4.0[END_REF], this study aims to answer the question: What is the relationship between supply chain digitalisation and supply chain resilience? Following [START_REF] Sheffi | Preparing for disruptions through early detection[END_REF] and Sheffi and Rice's (2015) seminal study, [START_REF] Yao | Can you measure resilience if you are unable to define it? The analysis of supply network resilience (SNRES)[END_REF] characterised SCR as a dynamic SC capability that is necessary when dealing with an uncertain business environment. More precisely, Yao and Fabbe-Costes (2018, p. 260) define SCR as follows: "Resilience is a complex, collective, adaptive capability of organisations in the supply network to maintain a dynamic equilibrium, react to and recover from a disruptive event, and to regain performance by absorbing negative impacts, responding to unexpected changes, and capitalising on the knowledge of success or failure".

One key issue in operationalising the concept of SCR is the way data are collected and analysed [START_REF] Samuel | Systèmes d'information et résilience des chaînes logistiques globales / Information systems and resilience of global supply chains[END_REF]. Analysed data can help SC managers to make the right decisions and build the capabilities included in the SCR concept: for example, visibility, anticipation and collaboration [START_REF] Pettit | Ensuring supply chain resilience: development and implementation of an assessment tool[END_REF]. To this end, digital tools can be useful: the Internet of Things, big data analytics, artificial intelligence (AI), radio-frequency identification (RFID), smart sensor technology, etc. [START_REF] Schoenherr | Data science, predictive analytics, and big data in supply chain management: current state and future potential[END_REF][START_REF] Büyüközkan | Digital supply chain: literature review and a proposed framework for future research[END_REF][START_REF] Ivanov | A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0[END_REF]. Despite the growing adoption of digital tools, research on their possible impacts on SCR is still in its infancy, and most of the research on this topic has focused on the impact of a single digital tool on SCR (e.g., [START_REF] Papadopoulos | The role of Big Data in explaining disaster resilience in supply chains for sustainability[END_REF][START_REF] Dubey | Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience[END_REF]. However, when companies start to digitalise their SC, they may implement several digital tools at the same time [START_REF] Frank | Industry 4.0 technologies: Implementation patterns in manufacturing companies[END_REF].

Despite some encouraging first results on the interplay of SCR with some digital tools (see the example of Procter & Gamble during COVID-19 outbreak era 1 and the study from [START_REF] Ivanov | Viable supply chain model: integrating agility, resilience and sustainability perspectives-lessons from and thinking beyond the COVID-19 pandemic[END_REF], [START_REF] Pettit | The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience[END_REF] recommend further research. Addressing this gap in the literature is of utmost importance, as the emergence of new digital tools alters traditional ways of working and creates disruption across SC processes. This managerial reality needs to be explored by researchers to provide better understanding of the phenomenon. This article contributes to theory by exploring the impact of the adoption of several DSC tools at the same time on SCR, in line with the complex reality of digitalisation projects in the business world [START_REF] Issa | Industrie 4.0 roadmap: framework for digital transformation based on the concepts of capability maturity and alignment[END_REF]. The contribution of the degree of digital maturity to SCR has also never been measured before, whereas this study demonstrates its predominance.

Theoretical background

Digital supply chain management and digital tools adoption

Towards a digital supply chain management mechanism

The SCM has gradually become a strategic function that creates competitive advantages for companies and their network of SC entities. One key to its success lies in improving management of the SC information flow [START_REF] Christopher | Logistics and Supply Chain Management[END_REF]. Many companies invest in digitalising their SCM in order to respond to the growing demand for products that are better adapted to consumer needs (customisable and of good quality). SC processes must adapt to these trends and be increasingly interconnected [START_REF] Issa | Industrie 4.0 roadmap: framework for digital transformation based on the concepts of capability maturity and alignment[END_REF]. Digitalisation has an impact on overall SC processes [START_REF] Richey | A global exploration of big data in the supply chain[END_REF]: manual tracking systems and paper-dominated order processing systems are increasingly considered obsolete. [START_REF] Stank | Toward a digitally dominant paradigm for twenty-first century supply chain scholarship[END_REF] introduced a theoretically grounded digitally dominant paradigm framework to help guide future SCM research. They argue that "seeing" (enhanced visibility), "thinking" (improved analytics) and "acting" (heightened operational flexibility and reduced cycle time) are essential elements of SC digitalisation. Büyüközkan and Göçer (2018, p. 165) define a DSC as "an intelligent best-fit technological system that is based on the capability of massive data disposal and excellent cooperation and communication for digital hardware, software, and networks to support and synchronize interaction between organizations by making services more valuable, accessible and affordable 1 https://www.supplychainbrain.com/blogs/1-think-tank/post/31166-how-digital-solutions-are-creating-moreresilient-supply-chains (6th July 2020) with consistent, agile and effective outcomes". SCs have thus become increasingly data-driven.

Since digital tools disrupt traditional supply chains, some distinct characteristics are potentially associated with every DSC. [START_REF] Büyüközkan | Digital supply chain: literature review and a proposed framework for future research[END_REF] list these factors as Speed, Flexibility, Global connectivity, Real-time inventory, Intelligence, Transparency, Scalability, Innovativeness, Proactiveness and Eco-friendliness. These characteristics are traditionally the objectives of mature SCM 2 .

The digital tools support companies in transforming their traditional SC operations in order to better anticipate and respond to stakeholders' demands in real time. However, the companies implementing these tools require a degree of digital maturity.

Degree of digital maturity

Companies facing digitalisation challenges need methods and assessment tools that support the operationalisation of the process. Maturity models provide an effective way of measuring the quality of the processes [START_REF] Wendler | The maturity of maturity model research: a systematic mapping study[END_REF]. The main aim of existing digital maturity models is to assess the digital maturity stage of companies and guide them to the next stage through a set of activities [START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF].

Capability Maturity Model Integration (CMMI) [START_REF] Chrissis | CMMI: Guidelines for Process Integration and Product Improvement[END_REF] has frequently been used by the information technology (IT) industry to support its evolution and produce a set of best practices [START_REF] Wendler | The maturity of maturity model research: a systematic mapping study[END_REF]. Researchers have also broadened the applicability of the model to promote development in other industrial fields. [START_REF] Issa | Industrie 4.0 roadmap: framework for digital transformation based on the concepts of capability maturity and alignment[END_REF] considered the CMMI, which links the level of maturity to company performance, to be the most widely recognised model and both used and then improved the model. They define four levels of maturity, where Level 1 is "No Industry 4.0" and Level 4 is "Inter-organisational level (cross value chain/SC partners)".

It is important to translate a maturity model into a tangible model for companies wishing to make an accurate assessment of their digital approach. To this end, [START_REF] Schumacher | A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises[END_REF] defined nine dimensions for assessing Industry 4.0 maturity and they classify them in two groups. The first group concerns the basic enablers (products, customers, operations and technology) and the second is about organisational aspects (strategy, leadership, governance, culture and people). The definitions of dimensions as proposed by the authors are quite unclear and can easily be confused (example: strategy, governance and culture).

2 A table presents the main digital tools listed in the literature as contributing to improving SCM is available upon request Schuh et al. (2017) identified four maturity dimensions: Organisational structure, Resources, Information systems and Culture. However, their proposal lacks a dimension relating to the "capture of valuable information from data" Colli et al. (2019, p. 6), which is needed to gain useful business insights and make appropriate decisions. To address this gap, Colli et al. (2019, p. 5) used Schuh et al. (2017) as a basis for putting forward a new digital maturity assessment approach that defines five dimensions for measuring digital maturity in an SCM context: Governance, Technology, Connectivity, Value creation and Competences. Each dimension is assessed on six stages, from none to integrated.

Many authors [START_REF] Schumacher | A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises[END_REF]Schuh et al., 2017;[START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF] highlighted the need of these dimensions for a company to adopt digital tools. Low maturity degree is one of the biggest challenges to be faced in a DSC tools adoption project [START_REF] Raj | Barriers to the adoption of Industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective[END_REF]. More precisely, the governance dimension which represents the digital awareness, and engagement on different hierarchical levels in the company is perceived as a prerequisite element which, as such, enables the acquisition and the adoption of digital tools.

Digital competences are a key to success and thus training is necessary [START_REF] Petrillo | Fourth industrial revolution: current practices, challenges, and opportunities, digital transformation[END_REF].

Being digitally competent means having the ability to search for information, to understand it, to be critical about what is retrieved and to be able to communicate with others using a variety of digital tools [START_REF] Stank | Toward a digitally dominant paradigm for twenty-first century supply chain scholarship[END_REF]. It also appears to be the case that enhanced connectivity, along with sufficient IT security and elements needed for data transmission inside and outside the organisation, supports DSC tools adoption [START_REF] Lee | The Internet of Things (IoT): applications, investments, and challenges for enterprises[END_REF][START_REF] Gunasekaran | Information technology governance in Internet of Things supply chain networks[END_REF].

Companies need models to generate and capture value from data. Digital tools adoption reflects the extent to which digital technologies are available and adopted and used by all key stakeholders so they add value to their activities [START_REF] Schumacher | A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises[END_REF][START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF].

Therefore, the following hypothesis is presented: H1: The digital maturity degree (DMD) contributes positively to digital tools adoption (DTA) in an SC.

Supply chain resilience and digitalisation

In this subsection, we review the literature relative to different frameworks describing SCR capabilities and we build a model with a set of three hypotheses that can be used to better understand the link between SC digitalisation and SCR. [START_REF] Fiksel | Sustainability and resilience: toward a systems approach[END_REF] defined resilience as a company's ability to survive, adapt and grow in the face of turbulent change. The SC environment contains many uncertainties that create vulnerability. One solution is to mobilise the SCR concept. The notion of resilience applied to SCs appears particularly useful in analysing the potential for adaptation of interconnected physical and informational flows that are continually subject to hazards. [START_REF] Christopher | Logistics and Supply Chain Management[END_REF] believes that resilience refers to the ability of an SC to cope with unexpected turbulence. Thus, he explains that a resilient SC must be able to identify the following characteristics in order to adapt to an uncertain business environment:

Supply chain resilience capabilities

• Network-wide recognition of the most vulnerable parts of the SC; and

• Recognition of the need to maintain a stock of strategic resources or excess capacity in order to be able to respond to sudden events.

Whereas [START_REF] Christopher | Logistics and Supply Chain Management[END_REF] presents two key features of a resilient SC, Pettit et al.'s (2010Pettit et al.'s ( , 2013) ) research is more specific. The authors drew up a taxonomy of the different capability factors a SC should build in order to be resilient. The 14 main capabilities are as follows: flexibility in sourcing, flexibility in order fulfilment, capacity, efficiency, visibility, adaptability, anticipation, recovery, dispersion, collaboration, organisation, market position, security, and financial strength. Pettit et al.'s (2010Pettit et al.'s ( , 2013) ) taxonomy provides a basis for understanding the capabilities that need to be developed for a company to become resilient. Other classifications exist, such as those by [START_REF] Yao | Can you measure resilience if you are unable to define it? The analysis of supply network resilience (SNRES)[END_REF], which lists five capacities: absorption, response, capitalisation, anticipation and improvisation, and [START_REF] Ponomarov | Understanding the concept of supply chain resilience[END_REF], which includes elasticity, amplitude, hysteresis, malleability and damping. This classification of capabilities and subcapabilities offers an exhaustive assessment of SCR in empirical studies.

The advantages digitalization on supply chain resilience

Some researchers have recently analysed the relationship between digital tools and SCR [START_REF] Ivanov | New disruption risk management perspectives in supply chains: digital twins, the ripple effect, and resileanness[END_REF][START_REF] Pettit | The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience[END_REF][START_REF] Lezoche | Agrifood 4.0: a survey of the supply chains and technologies for the future agriculture[END_REF]. [START_REF] Ivanov | New disruption risk management perspectives in supply chains: digital twins, the ripple effect, and resileanness[END_REF] consider the impact of digitalisation on the resilience of operations and SCs as a complex issue. They highlight the value of the descriptive and predictive use of data analysis in gaining visibility and better forecast accuracy, as well as improving the activation of contingency plans. In the same vein, [START_REF] Zhang | The impact of Big data on supply chain resilience: the moderating effect of supply chain complexity[END_REF] show that big data enhances SCR by improving visibility. Cloud computing and blockchain technology improve visibility, anticipation and adaptability, which can foster SCR [START_REF] Pettit | The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience[END_REF].

A survey of the above studies showed that there is a need to mobilise all SCR capabilities (as defined by [START_REF] Pettit | Ensuring supply chain resilience: development of a conceptual framework[END_REF][START_REF] Pettit | Ensuring supply chain resilience: development and implementation of an assessment tool[END_REF] using several digital tools at the same time. Despite their first encouraging results on the interplay of SCR with some digital tools, [START_REF] Pettit | The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience[END_REF] recommend further research on the topic. In short, the lack of a clear understanding of these elements has contributed to ambiguity in clarifying the interplay between DSCs and SCR.

We also contribute to the literature by assessing the DMD with regard to digital tools adoption.

To the best of our knowledge, no research to date has attempted to link the degree of digital maturity with SCR, or the link between a bundle of digital tools and SCR. [START_REF] Amann | Designing robustness and resilience in digital investigation laboratories[END_REF] suggest that more investigations should be pursued in order to better understand the relationships between resilience and digital maturity. 'Value creation' appears to be one of the characteristics for measuring SC digital maturity [START_REF] Schumacher | A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises[END_REF][START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF], and this is essential to increase delivery competence, knowledge management and customer service to quickly recover from a disruption [START_REF] Tukamuhabwa | Supply chain resilience: definition, review and theoretical foundations for further study[END_REF].'Governance' is also a key construct for SC digital maturity [START_REF] Büyüközkan | Digital supply chain: literature review and a proposed framework for future research[END_REF][START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF], and it supports the effective information sharing which facilitates the rapid access to the appropriate resources necessary for recovery [START_REF] Tukamuhabwa | Supply chain resilience: definition, review and theoretical foundations for further study[END_REF]. In addition, 'connectivity' is a key point in assessing SC digital maturity (Akdil et al., 2018 Büyüközkan and[START_REF] Büyüközkan | Digital supply chain: literature review and a proposed framework for future research[END_REF][START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF], and it supports resilience capabilities as visibility and collaboration, which can spot potential disruptions [START_REF] Tukamuhabwa | Supply chain resilience: definition, review and theoretical foundations for further study[END_REF] and SC collaboration [START_REF] Pettit | Ensuring supply chain resilience: development of a conceptual framework[END_REF][START_REF] Pettit | Ensuring supply chain resilience: development and implementation of an assessment tool[END_REF].

Finally, digital 'competences' are recognised as key assets for measuring SC digital maturity as it allows a critical and creative use of information collected from different sources. (Akdil et al., 2018 Büyüközkan and[START_REF] Büyüközkan | Digital supply chain: literature review and a proposed framework for future research[END_REF][START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF]. These competences foster a SCR and better anticipate unexpected disruptive events.

H2. Digital maturity degree (DMD) contributes positively to supply chain resilience (SCR).

As academics analyse the positive impact of specific digital tools on some aspects of SCR components, we present Table 1 as a summary of those impacts linked to the classification of SCR capabilities proposed by [START_REF] Pettit | Ensuring supply chain resilience: development of a conceptual framework[END_REF][START_REF] Pettit | Ensuring supply chain resilience: development and implementation of an assessment tool[END_REF]. VR has an impact on SCR as it simplifies the user experience and collaboration between stakeholders.

Enterprises require the assistance of virtual and physical technologies to provide rapid adaptation for their businesses and operations. VR enables the evaluation of autonomous planning rules in accordance with system robustness. As a consequence of advancements in technologies, such as decreasing reaction times, manufacturing data will increasingly be practised in the cloud systems that provide more data-driven decisions. Cloud service platforms are based on real-time data analytics. Crowdsourcing help multiple sources.

Smart sensors (SS) Efficiency

The arrays of sensors enable stock levels to be monitored continuously and make warehouse management more efficient.

Büyüközkan and Göçer (2018)

Self-driving vehicles (SDV)

Efficiency Collaboration

SDV is capable of sensing its environment and navigating without human input. Seamless humanmachine interactions increase operational efficiency. [START_REF] Büyüközkan | Digital supply chain: literature review and a proposed framework for future research[END_REF] Table 1. Impacts of the main digital tools that contribute to the SC on SCR As shown in Table 3, digital tools play an important role in SCR and, therefore, the following hypothesis is presented: H3. Digital tools adoption (DTA) contributes positively to supply chain resilience (SCR).

The conceptual model is presented in Figure 1.

Research design and methodology

Measurement instruments

As shown in Figure 1, the model contains two second-order constructs: digital maturity degree and SCR.

Digital maturity degree. To measure DMD, items were developed based on the literature review and then refined through face and content validity as recommended by DeVellis (2016).

Since no survey-based literature was found to measure this construct, we've looked at several frameworks (e.g. [START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF]Schuh et al., 2017;[START_REF] Schumacher | A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises[END_REF]. We have retained the framework proposed by [START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF] with its five dimensions to measure digital maturity degree in the SCM context: governance, technology, connectivity, value creation and competences. We consider that this framework is the most relevant one to our study. Then, we explored further in the literature to make a better understanding of dimensions and created items.

In this way, for the DMD construct, we proposed five first order constructs with 33 items.

According to [START_REF] Devellis | Scale development: theory and applications[END_REF], when items are reviewed by experts in the domain of interest, it helps to validate the scale content and maximise item appropriateness. In order to evaluate the content validity of the DMD scale, we conducted a test with a researcher specialised in questionnaire design, together with two SC researchers and five professionals in SCM. This pre- test was carried out face to face in order to discuss the relevance of the scale. The experts helped us to categorise the different dimensions of DMD: variables such as "governance" and "competences" concern practices needed to prepare the implementation of digital tools the "value creation" and "connectivity" variables are inputs to manage data and to make more efficient use of the data provided by digital tools. With regard to the implementation and adoption of digital tools, only the "technology" variable concerns assets or, in this case, digital tools. The experts we consulted noted that this categorisation would help us to see that governance, connectivity, value creation and competence might be seen as essential factors that enable digital tools adoption. The majority of the experts also suggested separating the "technology" dimension, since it can be measured according to the level of adoption, ranging from 1 to 5: (1) not started, (2) basic, (3) developing, (4) intermediate, and (5) advanced. The "digital tools adoption" construct, which represents the dimension technology, is evaluated using a 15-item scale of digital tools that might have an impact on one or more of the capabilities of SCR (Table 2). The four other dimensions (governance, connectivity, value creation and competences) are measured by evaluating the level of agreement with statements based on a fivepoint Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). In order to guarantee better homogeneity in the measurement of the digital maturity variable, we decided to measure it based on the four dimensions detailed above and to separate "technology" (Table 3).

SC resilience. The measurement of this construct was based on [START_REF] Pettit | Ensuring supply chain resilience: development of a conceptual framework[END_REF][START_REF] Pettit | Ensuring supply chain resilience: development and implementation of an assessment tool[END_REF], who measured SCR with 14 first-order constructs: flexibility in sourcing, flexibility in order fulfilment, capacity, efficiency, visibility, adaptability, anticipation, recovery, dispersion, collaboration, organisation, market position, security, and financial strength. Each construct was measured with at least four items. In total, the SCR scale contains 71 items (Table 4). All items are measured by evaluating the level of agreement with statements based on a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). 4. Operational measurement scale for the second-order construct "SCR" [START_REF] Gray | Management development: key differences between small and large businesses in Europe[END_REF] show that the size of a company influences the decision-making processes and the strategic choices the organisation may adopt. Organisational size can be measured in terms of the number of employees, turnover or even market share. From a resilience point of view, most large firms have specialised teams working on day-to-day issues regarding risk and SC challenges. However, smaller firms could be exposed to the same challenges and difficulties but experience more constraints, such as in their ability to access certain resources, especially financial and intangible (human resources, lack of knowledge, etc.) [START_REF] Bhagwat | Information system architecture: a framework for a cluster of small and medium-sized enterprises (SMEs)[END_REF]. To test if the size of the company has any influence on our model, a factor that potentially exerts a key moderating effect is the turnover of the firm, since the financial aspect is important to consider in technology adoption and ensuring better SCR capacity.

Moderating effects

Data analysis and measurement model

The measurement model was designed as a composite factor model that follows a reflective approach [START_REF] Van Riel | Estimating hierarchical constructs using consistent partial least squares: the case of second-order composites of common factors[END_REF]. In a reflective model, the block of manifest variables (items) related to a latent variable (construct) is assumed to measure a unique underlying concept [START_REF] Vinzi | PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement[END_REF].

To test the research model, we used the ordinary least squares (OLS) regression model approach.

The reason for selecting OLS is that the research model represents relationships between multiple variables that are supposed to be linear. OLS also allows us to investigate the relationships between multiple independent constructs and the dependent construct at the same time [START_REF] Stouthuysen | Controls, service type and perceived supplier performance in interfirm service exchanges[END_REF]. Moreover, OLS allows analysis of the moderating effects in a single research model. For the estimation of our structural model, we used the OLS regression option for path coefficients implemented in the PLSPM module of the XLSTAT software [START_REF] Vinzi | PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement[END_REF].

Sampling method and data collection

We carried out a quantitative study to measure a new phenomenon in SCM: to measure the potential impacts of SC digitalisation on SCR. This quantitative study was based on an online survey and employed a questionnaire placed on a website, i.e., a web survey (Google Forms). The survey was conducted from 30 th November 2019 until 7 th January 2020.

A validity process for the questionnaire seemed particularly important in order to benefit from the changes or improvements recommended. Ten people were selected, either for their academic qualities or for their professional experience: eight are academic researchers in SCM and two are senior SC managers.

The target sampling frame of this study was that the respondent participates actively in the management of an SC without any restriction with regard to industry. Each person was contacted by email explaining the aim of the study and the link to our survey. In total, 2,320 surveys were sent to SC managers in different firms, 349 of whom responded. After eliminating several surveys because of recurrent values and inconsistent or incomplete responses, the sample contained 300 observations. The response rate is 12.93%; according to [START_REF] Dillman | Mail and Internet Surveys: The Tailored Design Method[END_REF], a range from 6% to 16% is considered acceptable.

Our sample mainly contains respondents with major responsibilities in SCM and considerable experience in their respective positions. 54% of respondents come from very large manufacturing companies (annual turnover of more than €500 million). Descriptive information and sample statistics are included in Table 5.

Respondent title Industry role

SC

Response and common method bias

Potential biases were considered during the survey and in the analysis. To achieve a high response rate and avoid non-response bias, personalised emails can be sent with the assurance of sharing the results [START_REF] Frohlich | Techniques for improving response rates in OM survey research[END_REF]. We assessed non-response bias using the procedure proposed by [START_REF] Rogelberg | Introduction: understanding and dealing with organizational survey nonresponse[END_REF]. The first 25% of respondents to reply were compared with the last 25%, to detect if any difference existed between their responses. A t-test comparison between these two groups regarding two descriptive variablesthe number of employees and the company turnoverindicated no significant difference in the responses of early and late participants (p<0.05). These results indicate that common method bias is of no concern.

Analyses and results

We used a theory-driven approach as a benchmark. The theoretical framework was initially evaluated through exploratory factor analysis (EFA) with varimax rotation [START_REF] Hair | A Primer on Partial Least Squares Structural Equation Modeling[END_REF] and confirmatory factor analysis (CFA) in OLS to assess the consistency between the scale items [START_REF] Stouthuysen | Controls, service type and perceived supplier performance in interfirm service exchanges[END_REF].

Validity of the constructs

Content validity: The framework includes two hierarchical constructs involving more than one dimension: the DMD degree scale contains 18 items with 4 variables (Table 3) and the SCR scale contains 71 items organised into 14 variables (Table 4). The DTA construct includes 15 items (15 tools) and a single variable (Table 2).

We used the EFA method as it allowed us to identify the underlying dimensions that explain the results regarding the unidimensionality of the variables. This analysis is recommended in the case of exploratory research, i.e., in the absence of theoretical knowledge of the relationships between the measurement indicators of the constructs. This step allowed us to eliminate three variables from the SCR scale (C2. Flexibility in Order Fulfilment, C3. Capacity, and C11. Organisation).

We eliminated 39 items during this phase.

The aim of CFA approach is to check whether the items are consistent and share sufficient variance. In order to verify sampling adequacy, we used the Kaiser-Meyer-Olkin (KMO) test, the value of which must be greater than 0.50 to be acceptable (Tables 4, 6 and7).

Convergent validity: This is defined as the extent to which blocks of items strongly agree in their representation of the underlying construct they were created to measure [START_REF] Chin | How to write up and report PLS analyses[END_REF]. We tested the validity of the latent variables and their items in our study and the results are shown in Tables 4, 6 and7. These tables show the weighting factors of the items. The role of factor weighting is to explain the contribution of an item to its construct. Thus, the objective is to show the degree of importance of each item and to establish if the weights are significant by having a value greater than 0.5.

Table 7 allows us to confirm that all the latent variables that make up the DMD scale are constructs that have significant convergent validities with factor weightings greater than 0.5 However, the DTA and SCR constructs contain some weight factors with non-significant values.

If we estimate that dropping these items will have an impact on the meaning of the variable we are investigating, we can keep them and investigate the loadings estimates. We can thus safeguard the construct content validity. As shown in Tables 4 and6, the loading estimates of all items are greater than 0.5 [START_REF] Hair | A Primer on Partial Least Squares Structural Equation Modeling[END_REF].

Construct reliability: Cronbach's alpha is an indicator that qualifies how well a set of indicators measures the unidimensionality of a construct [START_REF] Chin | How to write up and report PLS analyses[END_REF]. The Cronbach's alphas of the items that make up the DMD, DTA and SCR scales are between 0.6 and 0.8. This confirms the internal consistency of the scale.

Dillon-Goldstein's rho is an indicator that evaluates the composite reliability of latent variables [START_REF] Wertz | Intraclass reliability estimates: testing structural assumptions[END_REF]. Dillon-Goldstein's rho is above the 0.7 threshold for all the variables of the model (Tables 6 and7), which confirms the homogeneity of the constructs. Since reliability deals with the internal reliability of a measurement (evaluating the common variance of items), a reliability of 0.7 states that the item shares 50% variance when measuring the construct.

Convergent validity: This is the extent to which the indicators belonging to one latent variable actually measure the same construct [START_REF] Chin | How to write up and report PLS analyses[END_REF]. We used average variance extracted (AVE) to demonstrate convergent validity of the construct. This indicates how much of the indicators' variance can be explained by the latent variable. In our model, all AVE values are greater than 0.5 (Table 6 andTable 7), indicating the convergent validity of our variables.

Discriminant validity: Evaluating discriminant validity refers to the extent to which factors are different and uncorrelated statistically [START_REF] Chin | How to write up and report PLS analyses[END_REF]. To check the evidence of this difference, the square root of each construct's AVE should have a greater value than the correlations with other latent constructs. Our results (Tables 4, 6 and7) show that all constructs in the model are different (square root < AVE) (Table 8).

Nomological validity:

In reflective hierarchical constructs, the path coefficient of each first-order variable represents its contribution to the second-order variable [START_REF] Wetzels | Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration[END_REF]. Our results

show that the second-order variable (DMD) is well reflected in its four first-order variables (competences, governance, value creation, and connectivity) with significant path coefficient values between 0.6 and 0.9. The governance construct is the most representative (β=0.899) (Table 7). The reflection of the other second-order construct (SCR) in its eleven first-order variables is supported with an intensity of path coefficients that remains homogeneous for all constructs of the scale except for the flexibility of sourcing construct, which represents a low one (β=0.247) (Table 6).

Within-scale factor analysis -SCR scale Validation of the SCR scale

Evaluation of the structural model

The structural model exists to evaluate the relationships between hypothetical constructs. The coefficient of determination R² is used to evaluate the goodness of fit (GoF) in regression analysis [START_REF] Tenenhaus | PLS path modeling[END_REF]. In this study, we are using the OLS regression model where R² value gives the share of variance explained in a dependent construct [START_REF] Benitez | How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research[END_REF].

Table 8 shows that the structural model explains nearly 47% of the DMD variable (R²=0.468).

Table 9 shows that the structural model explains almost 42% (R²=0.423) of the SCR scale.

These two values are very interesting for the phenomenon being investigated, particularly with regard to the exploratory character of this research. Indeed, the R² value can be very low when a phenomenon is not very widely explored [START_REF] Sharma | PLS-based model selection: the role of alternative explanations in information systems research[END_REF]. We also note that the contribution of digital maturity degree to SCR R² is greater than for digital tools adoption (66.81% and 33.19%, respectively).

The contribution of DMD to DTA is confirmed with considerable intensity (β=0.679). It also appears that DMD has a significant impact on SCR (β=0.494) but the intensity remains at a medium level. The impact of DTA on SCR is real but less important (β=0.274). This leads us to conclude that the contribution of DTA to SCR is quite low.

These results point to DMD contributing to the SCR of firms and facilitating their DTA. To be statistically significant, the path coefficient estimate should be different from zero at a 5% significance level when its p-value is < 0.05. As shown in Tables 9 and10, the path coefficient estimates for the hypothesised relationships are significant at the 5% significance level.

The t-value result relating to the assumptions linked to the impact of DMD and DTA is significant (>1.96) (Table 9) and once again confirms the validity of our assumptions.

Quality of fit of the global model

The absolute GoF of the model (0.511) is very close to the bootstrap estimate (0.510) , which confirms the stability of the model. The relative GoFs for the internal and external models (respectively 0.997 and 0.727) are high, which reflects, on the one hand, the good quality of the links between the measurement variables and the latent variables and, on the other, the good quality of the structural relationships.

Multi-group analysis

The multi-group analysis (MGA) approach allows researchers to determine whether some parameters could have an impact on the measurement model or if the structural model remains invariant irrespective of the groups or sub-samples. To analyse the moderating effect of the "size of the company" variable on the relationship between the variables in the framework, we conducted MGA.

For the MGA, the responses were divided into three groups, depending on the turnover of the company (group 1 = €500 million and above group 2 = between €250 million and less than €500 million and group 3 = less than €250 million) (Table 5). Test analyses were done for each of these groups in order to estimate their path coefficients. To detect any moderating effect, the differences between the path coefficients of the groups were analysed. Thus, to determine the significance of the differences between the estimated parameters for each of the groups, we conducted a permutation test [START_REF] Chin | An introduction to a permutation based procedure for multi-group PLS analysis: results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA[END_REF].

The MGA was interpreted in two steps. We first evaluated the path coefficient of the structural model for each sub-sample, followed by a global test of invariance of some of the parameters, such as structural paths that are constrained to be stable across the groups [START_REF] Chin | An introduction to a permutation based procedure for multi-group PLS analysis: results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA[END_REF].

As Table 11 shows, with regard to the path coefficient of the model for each sub-sample (G1, G2 and G3), the first results concern G1 and G2 and indicate that there is no significant effect of the moderating test compared with the total sample, even if the path coefficients were to vary slightly. Some significant effects appear with G3. Namely, H3 (DTA => SCR) is not supported, since the path coefficient is close to zero (β=0.059). This could be explained by ( 1) financial resources and (2) the operational need for digital tools. First, DSC tools are expensive and the smaller firms may not be able to afford them. Second, the low number of tools adopted and the low adoption level of those tools by small firms. Even if the larger SC entities find it useful to adopt DSC tools because their need for internal and external collaboration is strong, the smaller companies do not face the same issues and may not need the same level of adoption of DSC tools. Another result concerns H2 (DMD => SCR) and how different it would be if G3 were compared with G1 and G2. H2 is supported for all groups, but with different intensity. We note that G3 has the highest path compared to the other groups (G1 β=0.461; G2 β=0.379; G3 β=0.571). We can, therefore, state that G3 may be relatively more advanced in terms of DMD than SC DTA, which would explain SCR only being positively and significantly impacted by DMD. Taking into account that H1 (DMD ⇒ DTA) is validated for G3 with a very high path coefficient (β=0.873), this suggests that those in G3 are well prepared from the point of view of competences, governance, value creation and connectivity in order to guarantee the right adoption of the few SC digital tools they choose.

The second step was to evaluate the invariance using permutation tests. The permutation test showed a significant difference with several p-values of < 0.05. This reinforces the interpretations of the results above. For instance, it appears that G1 vs. G3 is significantly different regarding H2 (DMD ⇒ DTA) (p-value=0.04) and H3 (DTA ⇒ SCR) (p-value=0.02).

We also note concerning H2 (DMD ⇒ SCR) that there are no significant differences throughout the three different groups. This makes it difficult to interpret the result concerning the significant difference (p-value=0.04) between G2 vs. G3 regarding H1 (DMD ⇒ DTA), since sub-sample G2 is based on only 26 responses which is quite low to generalise this result.

Therefore, we can state that the "turnover of the company" variable has a moderating impact on the G2 and G3 samples.

Results discussion

The results of this study enabled us to validate all three hypotheses previously presented.

While some earlier studies highlighted the potential impact of the use of certain DSC tools on SC risk mitigation [START_REF] Baryannis | Supply chain risk management and artificial intelligence: state of the art and future research directions[END_REF], SC traceability and transparency [START_REF] Somapa | Characterizing supply chain visibilitya literature review[END_REF][START_REF] Junge | Digital transformation technologies as an enabler for sustainable logistics and supply chain processes -an exploratory framework[END_REF], SC visibility and information sharing [START_REF] Koonce | The wild, distributed world: get ready for radical infrastructure changes, from blockchains to the interplanetary file system to the Internet of Things[END_REF][START_REF] Kache | Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management[END_REF][START_REF] Lezoche | Agrifood 4.0: a survey of the supply chains and technologies for the future agriculture[END_REF], the efficiency of SC processes [START_REF] Junge | Digital transformation technologies as an enabler for sustainable logistics and supply chain processes -an exploratory framework[END_REF][START_REF] Zhang | The impact of Big data on supply chain resilience: the moderating effect of supply chain complexity[END_REF] and even strategic decision making in the SC [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF][START_REF] Lezoche | Agrifood 4.0: a survey of the supply chains and technologies for the future agriculture[END_REF], none has ever statistically confirmed the relationship between SC digitalisation and SCR.

Our findings indicate that 'SC digitalisation' is based on two concepts: the adoption of DSC tools and the degree of digital maturity [START_REF] Colli | A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era[END_REF]. Previous studies have tended to focus on DSC tools, ignoring the degree of the digital maturity variable. However, our findings show the importance of the digital maturity variable: not only does it significantly influence the adoption of DSC tools, but it also positively influences SCR capabilities. In addition, when discussing the impact of SC digitalisation on SCR, it appears that recent studies have focused on one SC digital tool at a time (e.g., [START_REF] Papadopoulos | The role of Big Data in explaining disaster resilience in supply chains for sustainability[END_REF][START_REF] Dubey | Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience[END_REF]Ivanov and Dolgui, 2019a;[START_REF] Zhang | The impact of Big data on supply chain resilience: the moderating effect of supply chain complexity[END_REF]. In practice, many companies may introduce several DSC tools simultaneously, with different levels of adoption for each.

15 DSC tools and their adoption levels were considered at the same time. The findings show that, overall, the level of adoption of the different DSC tools has a positive impact on the SCR capabilities to a moderate degree.

The size of the company is a commonly used variable for determining whether results are valid, regardless of differences in the financial status between companies. When it comes to investments such as DSC tools, the turnover of the company can be expected to influence this type of strategic choice [START_REF] Gray | Management development: key differences between small and large businesses in Europe[END_REF][START_REF] Bhagwat | Information system architecture: a framework for a cluster of small and medium-sized enterprises (SMEs)[END_REF]. Indeed, our results show that the degree to which digital maturity facilitates the adoption of digital tools in the SC is significantly different between companies with the highest turnover (G1) compared with the lowest (G3). More precisely, the positive impact of the digital maturity degree on the adoption of digital tools in the SC is significantly stronger for G1 than for G3 companies. In the same vein, the results show that there is a significant difference between G1 and G3 with regard to the positive impact of the adoption of digital tools on SCR: if the strength of the impact of digital tools adoption on SCR is much less important than that of digital maturity degree for G1 (highest turnover) companies, it is important to point out that the hypothesis is not supported for G3 (lowest turnover) companies. Other than the link between digital maturity degree and SCR, where all groups show a strong positive impact, it appears that companies with the lowest turnover show a weaker contribution of digital maturity degree to SC digital tools adoption and in the contribution of those tools to SCR. This may be explained by the low capacity for investing in SC digital tools for those companies.

Conclusion

Theoretical implications

First, this study synthesises the existing literature to develop and apply a research model for examining the relationship between SCR and 'SC digitalisation'. We proposed evaluating SC digitalisation based on two concepts: DSC tools adoption and digital maturity degree.

Therefore, a higher-order variable was generated to measure the digital maturity degree of the firm. Second, this study is the first to assess statistically the impact of the degree of digital maturity and the adoption of several DSC tools at the same time on SCR. This contributes to theory since other studies have examined digital tools separately, as if companies do not use several digital tools at the same time with different adoption levels. The predominance of the degree of digital maturity as a positive contribution to SCR compared with the weaker (but significant) contribution of the adoption of DSC tools to SCR helps researchers grasp the key importance of the degree of digital maturity when examining this topic. Third, since all the hypotheses are supported, this confirms that SC digitalisation has a positive impact on SCR;

previous studies were either qualitative or focused on a single DSC tool, restricting their ability to draw formal conclusions regarding the impact of SC digitalisation on SCR. Here, the impact is clear, quantified and confirmed on a large scale (300 valid surveys concerning 15 DSC tools at the same). Fourth, the study used Pettit et al.'s (2010Pettit et al.'s ( , 2013) ) measurement scale, which includes 71 items spread over 14 variables. This is the most complete, and complex, SCR measurement scale that exists in the literature and is thus not often mobilised.

The data collection and analysis enabled us to improve and simplify this complex scale by analysing Cronbach's alphas and removing three variables and several other items. This study is one of the few to contribute to theory by studying the link between SC digitalisation and its resilience for companies of different sizes. Our MGA results show that, based on size, the link between digital maturity degree and SCR is supported in large, medium and small companies to different levels of intensity. This model could also be used in future research to explain this difference by testing the effect of other segmentation variables, such as 'company sector'.

Managerial implications

The findings are based on 300 valid surveys collected from qualified and highly qualified SCM professionals. This means that they are well grounded in the reality of businesses and their SCs, bringing clearer insights to the decisions, actions and practices of SC managers in relation to the research question. The results should encourage SC managers to consider continuing to digitalise their SCs (thus improving digital maturity degree and the level of adoption of digital tools) in order to obtain greater SCR in the current volatile and uncertain business environment. SC managers need to be aware that adopting digital tools in an SC is not enough in itself to improve SCR capabilities. The findings clearly show that there is a strong need to develop digital maturity, which will, of course, help to enhance the adoption of DSC tools but has an even stronger positive impact on SCR capabilities than the digital tools themselves. Improving the digital maturity degree implies, for instance, formalising processes, engaging all hierarchical levels in digital projects, improving data architectures and information sharing, and even training employees towards a digital mindset. All these elements can be linked to SCR capabilities, such as organisation, visibility, collaboration, and security. Thus, by focusing on and investing in digital maturity degree elements, any company, even one with a low turnover, could significantly improve its SCR. Today, many companies might launch SC digitalisation projects that encompass the adoption of new DSC tools without taking into account how to develop their digital maturity degree.

Finally, this research allows SC managers to prioritise their digitalisation projects and to be aware of the importance of the four variables that make up digital maturity. The engagement and support of the management team for the adoption of new digital tools are essential.

Limitations and future research directions

One limitation pertains to the measurement of the second-order construct "digital maturity degree", since this has never been measured. In further studies, researchers are encouraged to investigate this construct by exploring other potential variables. Another limitation is that the study does not identify exactly which tool(s) contribute the most to SCR capabilities. Future research could identify the more specific contributions of each digital tool to overall SCR capabilities, or even to each of the SCR variables. A third limitation is linked to the size of G2. Having more responses from middle-range companies would reinforce the MGA and contribute to the conceptual model generalisation. Furthermore, the study does not assess whether the companies in our sample have a low or advanced degree of digital maturity, a low or advanced level of digital tool adoption, or low or high SCR. This was not our aim, but future research could investigate these points.

There are other potential opportunities for future research. As the development of digital tools is a recent phenomenon, many professional reports have highlighted the difficulty of companies and their SCs in adopting these tools. It would be interesting to assess the challenges to the adoption of digital tools for SCM. Another research avenue, aligned with the MGA results, would be to study more precisely the case of small companies. It would be useful for them to better understand how to allocate their scarce resources (their financial ones in particular) between the several aspects of digital maturity degree and between existing SC digital tools in order to identify the optimum investment to enhance SCR.

To conclude, there are extensive research perspectives on the topic of SC digitalisation and SCR. This study encourages researchers to go further in detailing its various aspects and to contribute to empirical verification of the comprehensive measurement scales for DMD, SCR and DTA.

  . can receive, transmit process related data in advance and allow users to address issues as they cope with in real time decision making. Using mobile technologies, issues can now be recognized and dealt with faster as information moves with a higher velocity in the right position."Salkin et al. or the ways of doing work. IoTs are not just about new smart devices, but, more significantly, they mean productivity improvements and uninterrupted connectivity of economies all over the world. IoTs have an impact on efficiency, market position and collaboration.Salkin et al.

  Figure 1. Hypotheses model

Digital tool SCR capabilities (Pettit et al., 2010, 2013) Impact on SCR References

	Big data (BD)	Visibility	Enhances SCR by improving visibility and agility.	Papadopoulos et al.
		Security	BD help to trace the roots of disruptions and	(2017); Ivanov et
			observe disruption propagation in order to select	al. (2019); Zhang
			short-term stabilisation actions based on a clear	and Zhao (2019)
			understanding of what capacities and inventories are
			available. BD can improve SC risk management and
			be used to determine how best to achieve SCR in
			the face of disaster.
	Artificial	Anticipation	Data analytics, AI and additive manufacturing play	Salkin et al. (2018)
	intelligence (AI)		an important role in the adaptation to changing
			requirements to satisfy system stability and agility.
	Machine learning	Anticipation	ML have been applied to detect bottlenecks, high-	Dolgui et al. (2018)
	(ML)	Security	risk tasks and events in order to achieve adequate
			production rescheduling. ML contributes to supplier
			selection as a risk mitigation strategy that could
			assist optimisation and resilience management
			models.
	Augmented and	Collaboration
	virtual reality (VR)	Adaptability

Table 2 .

 2 Operational measurement scale for the construct "digital tools adoption"

	Construct	Digital tool/ Item	References		Weight	Loadings
			code
	Digital	tools	TECH_BD	Papadopoulos	et	al.	0.451	0.671
	adoption			(2017); Ivanov, Dolgui and
				Sokolov (2019); Zhang
	KMO = 0.935		and Zhao (2019)
			TECH_AI	Salki et al. (2018)			0.544	0.738
	Cronbach's alpha	TECH_ML	Dolgui et al. (2018)		0.524	0.724
	= 0.922		TECH_VR	Ganzarain and Errasti	0.443	0.666
				(2016)
			TECH_MDW	Salkin et al. (2018)		0.500	0.707
			TECH_ROBOT	Hofmann et al. (2020)		0.500	0.707
			TECH_IoT	Salkin et al. (2018)		0.523	0.723
			TECH_CLOUD	Schumacher et al. (2016);	0.538	0.734
				Salkin et al. (2018)
			THEC_BLOCKCH	Koonce (2016); Somapa et	0.460	0.678
			AIN	al. (2018); Min (2019);
				Dolgui et al. (2020)
			THEC_AHTI	Oyekan et al. (2017)		0.556	0.745
			TECH_SMT	Salkin et al. (2018)		0.380	0.616
			TECH_LDT	Ivanov et al. (2014)		0.494	0.703
			TECH_COLLAB	Sheffi and Rice (2005);	0.395	0.628
				Schumacher et al. (2016)
			TECH_SS	Büyüközkan and Göçer	0.533	0.730
				(2018)
			TECH_SDV	Büyüközkan and Göçer	0.357	0.598
				(2018)

Schumacher et al. (2016); Schuh et al. (2017); Büyüközkan and Göçer (2018); Issa et al. (2018); Colli et al.

Table 3 .

 3 Operational measurement scale for the second-order construct "digital maturity degree"

	First-order construct	Definition		Items
	C1. Flexibility in		C1_FLEX_SOUR_1	1.1 Our supplies are used in multiple finished goods.
	Sourcing	Ability to rapidly change input or the mode of	C1_FLEX_SOUR_2	1.2 Our finished goods use modular designs.

Table 5 .

 5 The Sample description

	Manager	116 Manufacturing activities	190

Table 8 .

 8 Square root < AVE

	D-G rho = 0.941

Table 9 .

 9 Structural coefficient and contribution to R² of the DTA construct

		Path coefficient	t-value	p-value	Hypothesis testing result
	(H1) DMD -> DTA	0.679	15.981	0.00	H1 supported
			R² (Bootstrap) = 0.468
	Note: ***p < 0.001

Table 10 .

 10 Structural coefficient and contribution to R² of the SCR construct

	Note: ***p < 0.001

Table 11 .

 11 Results of the structural models and permutation tests

	MGA: G1 vs G2
		Path coef. G1	Path coef. G2	Diff.	p-value	Sig.
	(H1) DMD -> DTA	0.722	0.762	0.040	0.713	No
	(H2) DMD -> SCR	0.461	0.379	0.082	0.693	No
	(H3) DTA -> SCR	0.330	0.356	0.027	0.921	No
	MGA: G1 vs G3
		Path coef. G1	Path coef. G3	Diff.	p-value	Sig.
	(H1) DMD -> DTA	0.772	0.562	0.159	0.040	Yes
	(H2) DMD -> SCR	0.461	0.572	0.111	0.406	No
	(H3) DTA -> SCR	0.330	0.059	0.271	0.020	Yes
	MGA : G2 vs G3
		Path coef. G2	Path coef. G3	Diff.	p-value	Sig.
	(H1) DMD -> DTA	0.762	0.562	0.200	0.040	Yes
	(H2) DMD -> SCR	0.379	0.572	0.193	0.228	No
	(H3) DTA -> SCR	0.356	0.059	0.298	0.079	No