The Influence of Digital Transformation and Supply Chain Integration on Overall Sustainable Supply Chain Performance: An Empirical Analysis from Manufacturing Companies in Morocco

Abstract

This study examined the association between digital transformation (DT), supply chain integration (SCI), and overall sustainable supply chain performance (OSSCP). The current literature has preliminarily explored the concepts of DT and SCI and their relationship with sustainable supply chain performance. However, real empirical evidence of the direct impact of DT and SCI on OSSCP has been missing so far. To fill this gap, data were collected from 134 professionals working in international manufacturing companies operating in Morocco through a questionnaire-based survey from August 2022 to November 2022. A conceptual framework was developed based on DT, SCI, and OSSCP and analyzed by partial least squares structural equation modeling (PLS-SEM) with the assistance of SmartPLS 4.0 software. The findings revealed that DT has a significant positive influence on SCI and OSSCP. Furthermore, SCI directly and positively impacts OSSCP with a partial mediation effect on the relationship between DT and OSSCP. Further, this research provides insights for practitioners into enhancing sustainable supply chain performance by adopting digital technologies and integrating SC functions. In particular, this study revealed that DT adoption drives a higher ethical supply chain level from the perspective of sustainability and efficiency in operations. This study is the first to analyze the influence of digital transformation and supply chain integration on sustainable supply chain performance in a manufacturing context.

1. Introduction

Amid technological advances in many industries, supply chain management (SCM) has received substantial attention from organizations seeking supply chain efficiency [1]. Focusing on supply chains is a move toward the broader adoption and development of sustainability, as the supply chain considers products from procuring raw materials to delivery to customers. Supply chain sustainability has arisen from increasing public awareness of social and green aspects and strengthening environmental legislation and regulations in developed countries [2]. Moreover, with global climate change, both manufacturers and customers are increasingly aware of the pillar of sustainability, which is driving manufacturing activities throughout supply chains to be more eco-friendly and has shifted the transition from classic supply chains to sustainable ones [3,4]. Companies have pushed their suppliers to implement International Organization for Standardization (ISO) standards, such as ISO 14.0 0/14.0 01. Therefore, sustainable supply chain management (SSCM) seeks to integrate the issue of sustainability within overall supply chain management, focusing on economic, social, and environmental goals [5]. Both academia and practitioners have argued that improved sustainable supply chain performance comes from achieving sustainability in supply chains [6]. Sustainable supply chain performance (SSCP) can be seen as a measure of how well, e.g., a company’s supply chain practices align with environmental, social, and economic sustainability goals. These practices include reducing waste and pollution, supporting local communities (societal sustainability), guaranteeing fair labor practices, aiming to reduce energy usage, looking for long-term operational and environmental feasibility, and so on. In other words, a sustainable supply chain benefits the environment and society and can improve a company’s long-term financial performance by reducing costs and increasing efficiency. Generally, to achieve well-balanced sustainable supply chain performance levels, one has to follow the current technological trends, be aware of what is going on in the digital transformation front, and at the same time, focus appropriately on operational excellence [7]. This makes assessing and understanding overall sustainable supply chain performance critical from both the practical and the theoretical standpoints. By adopting sustainability practices into supply chains, businesses could focus on their customers, innovate, and better use their resources, capabilities, and productivity while maintaining certain environmental aspects [8]. The overarching objective of SSCM is to enhance supply chain performance (SCP), which is linked to sustainable development (SD) goals [9]. However, achieving such objectives is challenging given the complexity of decision-making processes and the incidence of trade-offs between indicators involved in decision-makers’ choices.

Recently, two terms have widely been discussed in the context of SCs: digital transformation (DT) and supply chain integration (SCI) [8,10,11,12,13,14]. DT technologies support supply chains by increasing the accessibility of information, collecting real-time data, optimizing SCM practices, reducing production and transaction costs, improving the timely delivery of products to customers, and increasing both the efficiency and effectiveness of SC functions [15,16]. It is also highly likely that digital technologies will continue to play a central role in shaping the future of business and social activities through the exponential development speed of current technologies [17]. Therefore, companies could reach operational efficiency and minimize their bullwhip effect by making internal processes smarter using DT technologies [18]. DT refers to how businesses use digital competencies to transform their business development models and ecosystems. It includes the internet of things (IoT), cyber-physical systems (CPS), big data analytics (BDA), machine learning (ML), radio frequency identification (RFID), and business-to-business (B2B) networks [19,20]. On the other hand, SCI has received much attention from both industry and academia in the last two decades due to the increasing level of competition in today’s marketplace that has made decision-makers look for partnership strategies to enhance SC performance and reduce costs and lead time [8,14,21,22]. From a supply chain perspective, integration is how a company can structure its organizational practices, activities, and strategies into collaborative, synchronized, and manageable processes to meet customer needs. Therefore, SCI is essential for an efficient SC [23,24,25].

Although the previous literature has explored the concepts of DT and SCI and their relationship with supply chain performance [8,10,15,24,26,27,28], empirical evidence and research models targeting the evaluation of the impact of DT and SCI on sustainable supply chain performance have not been investigated yet. More particularly, the role of DT in enhancing SCI and further sustainable supply chain performance has not been explored yet. Otherwise, Morocco is one of the emerging economies fully committed to sustainable development and has recently undergone a digital transformation in different sectors, particularly the manufacturing context [29,30]. This has motivated authors to examine whether a direct relationship exists between digital transformation, supply chain integration, and overall sustainable supply chain performance in international manufacturing companies located in Morocco. The proposed research realizes the importance of an empirical study to examine to what extent manufacturing firms could involve digital transformation technologies to improve their supply chain integration and further their sustainable supply chain performance. Thus, the main objective of this work is to empirically examine the relationship between DT, SCI, and OSSCP in a manufacturing context by suggesting a research model based on the literature review to address the above-mentioned knowledge gap. To the best of our knowledge, the proposed study is the first empirical study that includes and assesses all these constructs together in the context of the manufacturing sector.

The remainder of the proposed study is structured as follows. Section 2 goes into the literature review and theoretical background for digital transformation adoption, supply chain integration, and sustainable supply chain performance. Section 3 describes the conceptual model, including the hypotheses’ development. Section 4 details the empirical research methodology and the data analysis approach. Findings, discussion, and study implications are presented in Section 5, Section 6 and Section 7, respectively. Finally, the paper concludes with a summary of the main conclusions, the research limitations, and recommendations for future research.

2. Literature Review and Theoretical Background

2.1. Digital Transformation

DT has emerged as an essential aspect that drives several academic fields and affects practice, yielding independent research streams [31]. DT includes digital platforms, infrastructures, high-level asset management, political and lexical will, as well as supporting and needed technologies [32,33]. DT-related technologies, such as sensors and tracking devices, can provide real-time data and insights to help companies to optimize their SC operations. For example, sensors on shipping containers can provide information on their location, temperature, and other factors, allowing companies to manage their inventory better and reduce the risk of spoilage or damage. On the other hand, anyone utilizing sensor technologies must be knowledgeable about their strengths and limitations [34]. Additionally, IoT technologies can automate and streamline operations such as scheduling and routing, improving efficiency and reducing costs. Overall, the integration of digitalization into current operations and the digital transformation of old ways to modern models leads to more efficient and sustainable operations. Here, DT uses digital technologies to generate new business processes and widens the available business landscape. Thus, with the utilization of DT, companies are undergoing digital transitions to enhance system integrations [31]. Supply chain digitalization enables firms to fulfill customers’ dynamic needs on time and address the challenge of SCM and the expectations in the quest for a competitive edge [35,36]. This shows the need to shift from a classic supply chain to a digitally enhanced supply chain [37,38]. DT enables organizations to generate innovation and increase productivity, transparency, and flexibility. These disruptive technologies under DT, such as IoT, CPS, BDA, ML, RFID, and B2B networks are used to design robust, transparent, and secure supply chain management systems [39]. Moreover, they have made smart manufacturing possible and sped up innovation at a dizzying pace. For example, Industry 4.0 is a clear tool for sustainability improvement, and it can significantly impact SC behavior toward reaching intelligent and more flexible processes, helps it to be more circular, promotes resource efficiency, automation, and optimization measures, and increases employees’ welfare [40,41,42,43]. Likewise, blockchain technology (BCT) has a tremendous potential to transform SC functions from procuring raw materials to delivery to customers. It also ensures the transparency, reliability, and authenticity of information, as well as smart contractual relationships for a secure environment [44,45,46]. Therefore, SCs are affected by DT with no exception.

Several studies have dealt with DT in the context of SCM. For example, Tavana et al. [31] have reviewed the most-used technologies in the context of DT and SCM. They have found that BDA and BCT are the widespread clusters that have been considered in recent years. Büyüközkan and Göçer [38] have proposed a framework for digital supply chains (DSCs), including advantages, drawbacks, and limits regarding the existing DSC literature, and have identified features, components, and technologies that enable the development of a DSC framework. Furthermore, Stroumpoulis and Kopanaki [47] have analyzed the impact of DT technologies, including BDA, BCT, and IoT on SSCM. Findings revealed that DT plays a vital role in enhancing business performance and supporting sustainable strategies.

2.2. Supply Chain Integration

Over the last two decades, the focus from academia and practitioners on integration practices between SC partners has mainly grown due to the highest level of global competition and the increased customer demand patterns [48,49,50]. Consequently, SCI has been recognized as a pivotal issue in the SCM literature. SCI refers to the extent to which all processes within an organization, along with the operations of its suppliers, customers, and other SC partners, are incorporated [48,51]. Thus, companies have recognized that creating strategies and incorporating internal functions, suppliers, and customers is a suitable model for achieving competitive advantage. This has made SCI a useful practice widely adopted by firms focusing on improving their performance by establishing closer relationships between SC functions [52]. SCI is a multidimensional concept. Previous works have highlighted that considering SCI dimensions is crucial to understand how the individual dimensions affect performance and also how they interact [23,28,48,49].

SCI is devised into external and internal integration. Despite being directly linked, they both play separate roles in the context of SCI and thus are crucial SCI measurement concepts. External integration (EI) means linking a company’s logistics operations with its customers and suppliers across boundaries [23,24,53]. EI involves the strategic alignment with suppliers and customers, where the business creates strategic cooperation with its suppliers and customers and jointly establishes strategies to address potential markets. Information sharing and synchronized planning are the most critical issues in external integration. Furthermore, EI enables organizations to develop collaborative relationships with trading partners and capitalize on their core capabilities while minimizing transaction costs [14,54]. Meanwhile, internal integration (II) is the coordination, collaboration, and integration of SC functions and logistics with other functional areas [48,49]. II mainly entails data and information systems integration using enterprise resources planning (ERP), real-time inventory and operating data searching, and activity integration across multiple functional areas. It also includes cross-functional cooperation and collaboration over multiple functions to improve processes or develop new products [55]. Generally, II refers to information sharing between internal SC functions, strategic cross-functional cooperation, and collaboration. Therefore, EI and II are essential for an efficient SC. The main objective of SCI is to make supply chains seamless with fully integrated information and material flows [50,52]. SCI is only achieved by proceeding across various phases, as II usually comes before EI. In practice, it is not sufficient to merely align the activities of various SC functions and internal structures within firms to enhance their performance. By connecting internal operations to external suppliers and customers along the SC network, tremendous outcomes can be reached [21,56]. Due to this, both upstream and downstream supplier and customer integration have become critical in business strategy.

Over the last couple of decades, many studies on SCI have been carried out. For instance, Bagchi et al. [57] have conducted a quantitative study to obtain an overall perspective of the supply chain integration state in European companies. The findings showed that SCI positively impacts operational performance, and efficiency and cost are influenced by the degree of integration. Wong et al. [58] have proved how both internal and external integrations separately and collectively improve product innovation. They have shown that external integration independently influences product innovation positively. In contrast, the relationship between internal integration and product innovation is not supported. However, they have found that the complementary integration of II and EI (not the balance between them) improves their capability to develop product innovation. Likewise, Zhao et al. [55] have studied the relationship between II, EI, and relationship commitment. Their study has clearly illustrated based on the literature and empirical evidence that II and relationship commitment positively impact EI. Particularly, II enables EI, as companies should firstly improve internal integration capabilities using data system integration before they can commit to significant external integration. Furthermore, organizations should be aware of incorporating trading partners, which is evidenced by their relationship commitment, before implementing external integration.

2.3. Sustainable Supply Chain Performance

Sustainable supply chain performance is indeed a concept that measures and assesses both the efficiency and effectiveness of SC processes in a dynamic environment by integrating financial, social, and environmental measures in SC operations [15,25,43,59]. It is considered a multidimensional criterion that should be conceptualized and understood consistently. Sustainability in SC operations has been considered a highly emerging issue for companies, as the lack of resources requires adopting sustainable development strategies (SDS) to achieve sustainable performance [60]. This has increased the focus on sustainable strategy from stakeholders that desire to protect the environment from pollution and maintain a balanced ecosystem [15,61,62,63]. Therefore, SSCP is crucial for companies to mitigate any negative effects on employees, suppliers, customers, the environment, and society.

Developing SSCP as a higher-order multidimensional construct has not only enhanced sustainable performance, it also provides an execution guideline that leads to competitive advantages. This concept enables organizations to integrate it into vision, strategy, and operations to achieve overall sustainable outcomes [64]. Every SCM strategy that desires to maintain a competitive advantage must incorporate all dimensions of SSCP (financial, social, and environmental aspects) into the businesses’ strategy, with a powerful and more visible orientation [47,64].

Based on the triple bottom line (TBL) sustainability approach, three basic dimensions are considered to measure SSCP (economic, social, and environmental performance). They have been recognized as the triple-bottom-line pillar of sustainability [65,66]. Firstly, economic performance indicators include return on investment (ROI), SC overall cost (i.e., manufacturing cost, distribution cost, and transaction cost), along with environmental costs such as energy cost and profitability or sales revenue [6,67,68]. The economic pillar focuses on the financial performance and profitability of the organization. Social performance indicators include employees’ wellness, work conditions, satisfaction, safety, health, and truthfulness [68,69,70]. The social pillar considers the organization’s impact on people and communities, including issues such as human rights, labor practices, and social welfare. At the same time, environmental performance indicators involve energy and water consumption, asset utilization, and waste disposal [65,68,71]. The environmental pillar evaluates the organization’s impact on the natural world, including issues such as greenhouse gas emissions, waste, and resource use [72]. Hence, organizations must align financial objectives with social and ecological objectives to conduct a comprehensive review of their sustainability performance and to be able to improve their overall business performance.

A rich body of literature has dealt with sustainable supply chain performance. For example, Shibin et al. [73] have developed a framework that explains SSCP and its enablers. They identified nine enablers for SSCP (i.e., coercive pressure, mimetic pressure, normative pressure, top management belief, logistics capability, top management participation, supply chain information-sharing, supply chain connectivity, and supply chain talent). In another work, Kumar and Goswami [64] attempted to develop a conceptual model of SSCP. They argued that SSCP is designed based on three dimensions (supply chain performance, supply chain environmental performance, and supply chain social performance). The findings revealed that social capital attracts more to achieve sustainable performance, while environmental issues are given priority.

3. Conceptual Framework and Hypothesis Development

3.1. Linking Digital Transformation Adoption with Overall Sustainable Supply Chain Performance

Adopting DT technologies across supply chains is a key prerequisite for organizations to fulfill the existing market’s needs in the quest for a competitive edge [36]. This is reflected in the necessity to shift from a traditional supply chain to a sustainable, digitally enhanced supply chain [37]. This transformation is driven by shorter product life cycles, changing market dynamics, limited resources, and global competition challenges. DT tools offer crucial benefits for supply chains, such as the improved availability of information, real-time data collection, the optimization of SCM practices, reduced production and transaction costs, the timely delivery of products to the customers, and the increased efficiency and effectiveness of SC functions [16,25,74]. However, with the companies’ increased carbon emissions and energy consumption, organizations must focus on a low-carbon and energy consumption strategy and follow green metrics [75]. To be able to reduce emissions, there is a need to develop digital SC networks to achieve sustainable and operational outcomes [76], including minimizing asset utilization and reducing emissions from manufacturing activities. Using digital technologies, companies could enhance operational efficiency and reduce their bullwhip effect by making internal processes smarter [18,25]. Moreover, DT adoption in the SC network significantly influences product development efficiency and adds long-term value for customers [10]. Therefore, DT provides a sustainable competitive advantage, enhancing overall SC performance.

In the existing literature review, various studies have shown the crucial role of DT on sustainable supply chain performance. For example, Gupta et al. [27] found that big data analytics (BDA) and Industry 4.0 are the most suitable tools for enhancing SC performance among other DT tools, such as the internet of things (IoT) and blockchain technology. In another work, Lee et al. [77] investigated the relationship between digital supply chains (DSCs), SC performance, and organizational performance in the Malaysian manufacturing industry. They found a direct impact of digitalization on SC performance. However, findings revealed a mediating role of SCP in the relationship between digitalization and organizational performance. Similarly, Dudukalov et al. [10] found statistical evidence of a relationship between digital transformation and supply chain performance. Nayal et al. [78] investigated the relationship between flexibility, AI–IoT adoption, and SC firm performance under the circular economy (CE) environment. In addition, they pointed out the direct impact of AI–IoT adoption on SC firms’ performance. However, Nayal et al. [15,45] have conducted two other research studies. Firstly, they investigated the impact of supply chain collaboration and coordination (SCC), collaborative advantages (COA), DT, and sustainable development strategy (SDS) on sustainable supply chain firm performance (SSCFP). The findings revealed that DT positively impacts SSCFP and fully mediates the link between SCC and COA. Thus, in their second research work, they studied the mediating role of BCT to enhance SSCP. The outcomes revealed that BCT adoption is influenced by numerous factors such as green and lean practices, supply chain integration, supply chain risk, performance expectancy, top management support, internal and external environmental conditions, regulatory support, costs, and innovation capability. Moreover, BCT adoption positively impacts sustainable supply chain performance in the agriculture sector. Furthermore, according to Kim and Lee [79], digitalization positively affects the formation of social capital, which in turn has a positive effect on supply chain performance. This means that the direct effect of healthcare digitalization on supply chain performance is small, and relatively large parts are mediated and influenced by social capital. Thus, Sharma et al. [76] examined whether digital supply chain networks can shape sustainability performance in the manufacturing sector. Additionally, through a quantitative study, Raut et al. [60] have shown that BDA directly impacts sustainable supply chain business performance and fully mediates the relationship between environmental practices and sustainable supply chain business performance. Their conceptual model includes numerous variables such as BDA, lean practices, environmental practices, social practices, financial practices, organizational practices, supply chain practices, and total quality management and sustainable supply chain business performance. Based on their outcomes, Kamble et al. [80] have supported the hypothesis by arguing that BCT technologies positively influence SSCP. However, this relationship is fully mediated by SCI.

From the above literature review, it is argued that digital transformation benefits organizations and supply chain performance. However, the current literature fails to deal with sustainability performance across supply chains and its relationship with DT technologies. In other words, there is a shortage of academic research on the direct relationship between digital transformation and sustainable supply chain performance [81,82]. As a result, the following hypothesis is proposed:

Hypothesis 1 (H1).

There is a positive and significant relationship between digital transformation and overall sustainable supply chain performance.

3.2. Linking Digital Transformation Adoption with Supply Chain Integration

Although the role of DT technologies is to improve both the efficiency and effectiveness of organizations’ processes, they also have the potential to achieve information sharing, coordination, and organizational linkages, which are the main focus of SCI [45,77]. DT includes inter-organizational and intra-organizational transactions driven by digital technologies. Valuable data-driven decisions could only be reached with accurate information provided by digital technologies and involved SC partners along the overall SC network. For example, blockchain technology has the potential to make information stable and unchangeable, so it cannot be edited without the authorization of approved stakeholders, thereby preventing corruption [47]. This leads to enhancing both internal and external integration, which are the two key types of SC integration. Internal integration involves integrating data and information systems using ERP, retrieving real-time inventory and operational data, and integrating activities across multiple functional areas using blockchain technology. At the same time, external integration means strategic alignment with suppliers and customers to create strategies that address potential markets. So, information sharing and synchronized planning are the most critical issues in external integration. However, they can only be strengthened by utilizing DT technologies [26,36,58,83].

With the emergence of disruptive technologies such as Industry 4.0, DT has streamlined complex structures and boosted supply chain integration through the use of various advanced technologies, including cloud computing, big data, and blockchain [20,38,84]. Thus, various studies have covered that digital transformation technologies could be a key factor in developing integration within organizations [26,28,44]. For example, Nayal et al. [78] have argued that the deployment of digital technologies such as artificial intelligence (AI) and the internet of things (IoT) would enable accurate and real-time information sharing, production, planning, and functional alignment, and therefore improve SC performance. Wang et al. [85] have explored a conceptual framework that reflects the significant role of BCT in improving SC integration capabilities, which are supply chain visibility, supply chain flexibility, and supply chain agility. Thus, supply chain visibility refers to collaboration, trust, and information sharing between SC partners without boundaries along the supply chain network. Supply chain agility is defined by an organization’s capability to react to internal and external changes and disruptions quickly. Otherwise, it is needed to handle issues in the strategic decision-making process and enhance companies’ responsiveness against environmental challenges. Additionally, supply chain flexibility is a firm’s ability to allow flexible operations management to fulfill customer needs and economic targets, reconfigure assets to reduce cost and time further, and mitigate supply chain risks. Furthermore, Li et al. [86] have shown through their empirical study that IT implementation cannot directly impact SCP without the mediating role of SCI. However, the hypothesis that links IT implementation and SCI is supported. In another work, Shi et al. [87] highlighted digitalization’s positive impact on improving SCI. However, their study was limited to the Chinese manufacturing industry.

Following the above discussion, this study expects that DT technologies’ adoption enhances internal and external integration, which are the two types of SCI. However, there is no empirical evidence of the direct link between DT and SCI [80]. Therefore, the following hypothesis is suggested:

Hypothesis 2 (H2).

There is a positive and significant relationship between digital transformation and supply chain integration.

3.3. Linking Supply Chain Integration with Overall Sustainable Supply Chain Performance

As market demand has become more volatile, supply chain uncertainty management is crucial [2,47]. Therefore, organizations have recognized that creating strategies along with incorporating internal functions, suppliers, and customers is a suitable alternative for achieving competitive advantage. This made SC integration a useful practice widely adopted by firms focusing on improving their sustainable performance by establishing closer relationships between SC functions [48,56]. SC integration is an SCM approach in which contribution from SC partners is needed. By coordinating intra- and inter-organizational processes, it has been considered a strategic collaboration and partnership between supply chain firms to manage the environmental and operational impacts [28,48]. Thus, SCI can potentially have a positive influence on cooperative environmental activities [56,77]. This explains the SCI practices’ capability to minimize environmental externalities [48]. At the same time, SCI is a practical approach that simplifies both internal and external business operations while improving the performance of an organization’s customers and suppliers [14]. Internal operations and external suppliers and customers must be aligned along with the SC network to reach sustainable performance outcomes [21,56].

Previous papers have tried to report a positive relationship between SCI and SSCP. For example, Ahmed Khamis al Naqbi et al. [88] have suggested a conceptual model that includes internal integration, supplier integration, customer integration, and SSCP. They found that internal integration is a strong predictor to achieve firms’ sustainable performance. Prajogo and Olhager [89] have investigated the impact of logistics integration on operational performance and the relationship between information and material flow integration and SCI using data from Australian firms. The findings showed that SCI significantly affects performance and that long-term supplier relationships have both direct and indirect positive effects on performance. Abdallah et al. [90] have explored the effect of SCI in terms of suppliers, customers, and internal integration on SCP and export performance. Their results showed that internal and customer integration positively affect SCP, while supplier integration does not. However, both customer integration and internal integration indirectly influence export performance through SCP. In another work, Mackelprang et al. [91] studied the relationship between strategic SCI and performance by conducting a meta-analysis. They found that the relationship between integration and performance is complicated and nuanced, such that integration should not be universally considered as improving performance. Kumar et al. [49] investigated the association between SCI metrics and SC performance in the UK food sector. The findings showed that SCI provides advantages and benefits such as reductions in total logistics costs, which would ultimately lead to enhanced profitability for companies and improve the flexibility of organizations to meet the external changes in the market. Therefore, it positively impacts SC performance. Furthermore, Junaid et al. [8] have proposed a framework relating sustainable SCI, green innovation, and firm performance. The results showed that sustainable SCI fosters firm green innovations, which enhance a firm’s financial performance. However, they have not highlighted a direct relationship between SCI and SC performance. Subsequently, Han and Huo [92] have explored the impact of green SCI on sustainable performance, particularly on environmental, social, and economic performance. They have argued that Green internal integration plays a major role in green customer integration (GCI) and green supplier integration (GSI), and is linked to both environmental and social performance. GSI positively impacts economic performance. However, GCI positively influences social performance. Mofokeng and Chinomona [93] examined the impact of collaboration, partnership, and integration on supply chain performance, particularly within the small and medium enterprise (SME) sector. The findings revealed that SCI strongly influenced SME supply chain performance, but supply chain partnership and supply chain collaboration have a weaker relationship with supply chain performance. Additionally, Erboz et al. [94] examined the influence of Industry 4.0 on supply chain integration (SCI) and supply chain performance (SCP). The outcomes showed that Industry 4.0 positively influences SCI and SCP. However, SCP is positively impacted by SCI. In addition, SCI partially mediates the association between Industry 4.0 and SCP.

Further to the previous discussion, there is a shortage of research focusing on the direct link between SCI and SSCP. However, they have only dealt with the relationship between dimensions of SCI and SCP or firms’ performance, neglecting the concept of sustainability [23,26,90,94]. Therefore, the following hypothesis is suggested:

Hypothesis 3 (H3).

There is a positive and significant relationship between supply chain integration and overall sustainable supply chain performance.

3.4. Conceptual Framework

The proposed conceptual framework is based on an in-depth literature review and experts’ opinions. The model in Figure 1 clearly reveals the expected links between DT, SCI, and OSSCP. The proposed model hypothesizes that adopting DT in manufacturing companies in Morocco will increase the level of integration of SCs and enhance overall sustainable supply chain performance. Thus, SCI is expected to influence OSSCP.

4. Empirical Research Methodology and Data Analysis

4.1. Survey Design and Data Collection

This study uses a questionnaire survey to evaluate the interrelationships among variables [71,78,90]. The questionnaire was constructed using a five-point Likert scale, where 1 denotes strongly disagree, and 5 signifies strongly agree. This scale achieves an appropriate balance between the responses’ complexity and the data analysis’s simplicity [5]. Firstly, the questionnaire was pre-tested by a panel of experts from academia and industry for the relevance of the items and variables. The panel included fifteen experts: three full professors in the field of supply chain management, two associate professors, two postdoctoral researchers, four Ph.D. students in the area of sustainability, two supply chain managers with more than six years of experience, and two IT managers with an excess of five years’ experience in supporting firms in their digital transformation. Based on their opinions, the instrument was modified to ensure that the participants understood the questions clearly. The questionnaire includes four sections: (a) general information about the participants and their companies, (b) DT, (c) SCI, and (d) OSSCP. The constructs and items were developed based on the academic literature (see Appendix A).

After adjusting the required corrections, the questionnaire survey was sent to 580 professionals working in international manufacturing companies located in Morocco from August 2022 to November 2022. A total of 134 professionals answered the survey, corresponding to a response rate of 23.10%, which is considered suitable for the partial least squares structural equation modeling (PLS-SEM) analysis, with a minimum sample size of 50 respondents [95,96]. In addition, the response rate can be considered acceptable relative to previous works (e.g., Delic and Eyers [95], Lee et al. [77], and Marodin et al. [97]).

The sample characterization of the participants is shown in

Table 1. Sample characterization of the respondents.

Characteristics	Sub-Characteristics	Frequency	Percentage
Gender	Male	78	58.2%
	Female	56	41.8%
Job position	Top-level/executive manager	23	17.2%
	Middle manager	39	29.1%
	Assistant manager	8	6%
	Team leader	6	4.5%
	Engineer/operational employee	51	38.1%
	Others	7	5.2%
Academic degree	High school	8	6%
	Bachelor	10	7.5%
	Master	98	73.1%
	P.h.D.	9	6.7%
	Executive/MBA	9	6.7%
Experience	<5 years	77	57.5%
	5–10 years	27	20.1%
	10–20 years	22	16.4%
	>20 years	8	6%
Number of employees in the firm	<100	24	17.9%
	101–500	34	25.4%
	501–1000	19	14.2%
	>1000	57	42.5%
Sector activity	Operations/manufacturing	78	58.2%
	Financial services	2	1.5%
	Logistics or other service provider	40	29.9%
	Others	14	10.4%

. As seen in Table 1, out of 134 participants, 51 participants are engineers/operational employees (38.1%), 39 participants are middle managers (29.1%), 23 participants hold top management positions (17.2%), 8 participants are assistant managers (6%), and 6 participants are team leaders (4.5%). Males represent 58.2% of the total respondents and respondents with working experience of more than 5 years were the most common (57.5%). Moreover, 73.1% of participants have a master’s degree. In this work, Excel was used to analyze records from the respondents. The data entry of participants’ demographics was performed with Excel.

4.2. Data Analysis Approach

For data analysis, the partial least square structural equation modeling (PLS-SEM) has been used with the assistance of SmartPLS 4.0 software. However, PLS-SEM has been widely applied in the quantitative research approach. This tool differs from the SPSS technique as it is considered a variance-based approach and not covariance-based utilized in SPSS [48]. In addition, prediction rather than confirmation is more the purpose of PLS-SEM models [98]. Indeed, it achieves a high level of complex model prediction with small sample sizes and develops the relationship between constructs [99,100]. According to Hair et al. [98] and Iqbal et al. [101], SEM has been considered a suitable technique to measure direct and indirect paths, as it analyzes unobservable latent constructs that are difficult to evaluate. Furthermore, it includes inner and outer model analyses, assessing the links between independent and dependent variables and between latent variables and their estimated items. Therefore, the estimation of our model was performed in two stages: first, the measurement model, where validity and reliability statistics were detected, and second, the analysis of the structural model, where the proposed hypotheses were identified [48,98]. Thus, using SmartPLS 4.0 software, both the measurement and structural models were tested, estimating parameters with the bootstrap procedure [77,98].

4.3. Measurement Model

The reliability and validity of the collected data were evaluated by Confirmatory Factor Analysis (CFA). According to the CFA, all items with factor loading (FL) less than 0.5 were dropped [48,77,102,103]. In this study, the lowest FL value among items was 0.61. Therefore, all items were retained. The measurement model aims to measure the reliability of each item along with the internal consistency of variables’ reliability, convergent validity, and discriminant validity (see Figure 2) [98,103]. Cronbach’s alpha (CA) and composite reliability (CR) were used to evaluate the internal consistency of the reliability. According to Hair et al. [98,103], the threshold values of both CA and CR are 0.7. Therefore, the proposed study revealed CA values ranging between 0.948 and 0.973, whereas the CR values ranged between 0.95 and 0.974. According to Lee et al. [77], CR values above 0.80 are considered suitable for confirmatory research, whereas values greater than 0.90 represent high reliability. Furthermore, the average variance extracted (AVE) was calculated to check all constructs’ convergent validity [48,104]. AVE values should exceed 0.50 so that a satisfactory model has been reached [104,105]. In this study, the AVE values varied between 0.525 and 0.69, all of which are higher than the recommended cut-off value of 0.50. In aggregate, all variables have met the requirement of reaching the satisfaction level of convergent validity.

Table 2. Construct reliability and validity.

Construct	Total Number of Items	Item Code	FL	CA	CR	AVE
Digital Transformation (DT)	14	DT1	0.668	0.948	0.950	0.569
		DT2	0.710
		DT3	0.724
		DT4	0.718
		DT5	0.691
		DT6	0.831
		DT7	0.828
		DT8	0.769
		DT9	0.786
		DT10	0.862
		DT11	0.760
		DT12	0.697
		DT13	0.738
		DT14	0.752
Supply Chain Integration (SCI)	17	SI1	0.736	0.949	0.951	0.525
		SI2	0.776
		SI3	0.659
		SI4	0.618
		SI5	0.788
		CI1	0.715
		CI2	0.764
		CI3	0.656
		CI4	0.663
		CI5	0.685
		CI6	0.707
		II1	0.785
		II2	0.727
		II3	0.799
		II4	0.769
		II5	0.753
		II6	0.682
Overall Sustainable Supply Chain Performance (OSSCP)	16	OSSCP1	0.804	0.973	0.974	0.690
		OSSCP2	0.723
		OSSCP3	0.812
		OSSCP4	0.901
		OSSCP5	0.883
		OSSCP6	0.905
		OSSCP7	0.870
		OSSCP8	0.752
		OSSCP9	0.830
		OSSCP10	0.796
		OSSCP11	0.859
		OSSCP12	0.820
		OSSCP13	0.833
		OSSCP14	0.883
		OSSCP15	0.825
		OSSCP16	0.768

Note: SI, CI, and II refer to supplier integration, customer integration, and internal integration, respectively.

provides the results of the constructs’ reliability and validity for the main study.

Once convergent validity was established, the discriminant validity was tested to measure the mean correlation of items throughout the model. In other words, it reveals the ability of items to clearly identify different variables. The discriminant validity was evaluated using the Fornell and Larcker criterion [104]. The square root of the AVE was calculated and compared against all variable correlations. Indeed, if the square root of the AVE of any variable is higher than the correlation between that variable and all other variables, discriminant validity is ascertained [104].

Table 3. Fornell–Larcker criterion.

Construct	DT	OSSCP	SCI
DT	0.755
OSSCP	0.719	0.831
SCI	0.823	0.749	0.725

shows Fornell and Larcker’s tests which reveal no discriminant validity issues in the proposed study. However, the heterotrait–monotrait (HTMT) ratio has emerged as a new method to detect discriminant validity, which was developed by Henseler et al. [105]. This method has currently gained preference over Fornell and Larcker as it estimates the real correlation between two constructs if they were measured perfectly [5,100,106,107]. According to Henseley et al. [105], the HTMT value should be at most 0.9. In this study, the HTMT ratio ranges between 0.715 and 0.823.

Table 4. Heterotrait–monotrait (HTMT) discriminant validity of the main study.

Construct	DT	OSSCP	SCI
DT
OSSCP	0.715
SCI	0.823	0.749

reveals the existence of discriminant validity since all the obtained values are less than 0.9.

5. Findings

Structural Model and Hypothesis Testing

The structural model was designed to analyze the interrelationships between the latent variables of the main study. Due to the model criteria and the sample, PLS-SEM is suitable for analysis [98]. This study qualified for the consistent PLS algorithm, as all the research constructs were reflective [107,108]. Hence, the significance of the constructs path analysis was checked through consistent bootstrapping in SmartPLS 4.0. The R² value was used in the dependent constructs to examine the model’s predictive power. It indicates the degree of variance in the dependent variables that the independent variables can explain. Chin [109] suggested a cut-off value of 0.4 for R². For SCI and OSSCP, the determination coefficient R² values were 0.594 and 0.677, respectively, confirming the prediction validity. Furthermore, the effect size f² was analyzed to evaluate the impact of independent variables on the dependent ones. According to Henseler et al. [105], cut-off values of 0.02, 0.15, and 0.35 were proposed to categorize the effect sizes into small, medium, and large. The findings show that DT has a substantial impact on the endogenous variable SCI (f² = 2.093). In addition, the predictive relevance Q² of the corresponding independent variable (DIT) for the dependent variables, SCI (Q² = 0.601) and OSSCP (Q² = 0.469), are higher than zero [107]. This means that the proposed model has predictive capacity. Moreover, the variance inflation factor (VIF) was analyzed to evaluate the collinearity between independent and dependent variables. The VIF values of the inner model are less than 3.3 [110]. This depicts no issue of multi-collinearity in the model. Therefore, the proposed research variables have met the pre-determined requirements, allowing the hypotheses to be evaluated.

Table 5. Structural model results.

Construct	R2	Q2	f2 in Relation to		VIF
			OSSCP	SCI
DT	-	-	0.080	2.093	1.000
OSSCP	0.594	0.469			3.093
SCI	0.677	0.601	0.190		3.093

summarizes the outcomes from the structural model.

The proposed study analyzed the causal relationship between the variables by the path coefficient β, t-statistic value, and p-value. In this study, the directional hypotheses were tested by a one-tailed test with a significance level of 0.05 (5%) [77,111]. Thus, a hypothesis is supported when the t-value of the path coefficient is greater than 1.96. At the same time, the p-value must be less than 0.05. The bootstrapping results for the structural model analysis are presented in

Table 6. Hypothesis testing.

Hypothesis	Path	β	Standard Deviation (SD)	T-Statistics	p-Values	Decision
H1	DT -> OSSCP	0.318	0.136	2.339	0.010	Supported
H2	DT -> SCI	0.823	0.048	17.015	0.000	Supported
H3	SCI -> OSSCP	0.488	0.136	3.581	0.000	Supported

. The findings confirm that DT has a statistically significant positive influence on OSSCP (β = 0.318, t-statistics = 2.339, p = <0.05), and so H1 is supported. H2, which predicts DT positively impacts SCI, is also found to be statistically significant (β = 0.823, t-statistics = 17.015, p = <0.05). Furthermore, H3, between SCI and OSSCP, is supported (β = 0.488, t-statistics = 3.581, p = <0.05), which hints that SCI positively impacts OSSCP. Therefore, the evidence shows that all the research hypotheses are supported.

Regarding the mediating effect of SCI, the findings show strong associations between DT and OSSCP with SCI (β = 0.402, t-statistics = 3.462, p = <0.05). Moreover, Table 5 shows that the coefficient β of H1 (β = 0.318) is both significant and less important than others. This reflected the mediation effect of SCI. According to Lowry and Gaskin [112], there are three mediation effects: no mediation, partial mediation, and full mediation. No indirect effect between variables means no mediation effect. Partial mediation reflects the significance of both direct and indirect effects between variables. However, full mediation signifies only indirect effects. This strengthens the partial mediation effect of SCI on the relationship between DT and OSSCP since both the direct and indirect effects of DT on OSSCP are significant.

Table 7. Mediating effects.

Relationship	β	Standard Deviation (SD)	T-Statistics	p-Values	Decision
DT -> SCI -> OSSCP	0.402	0.116	3.462	0.000	Partial mediation

summarizes the results of the mediating effect of SCI.

6. Discussion

This study has made it possible to empirically explain the impact of digital transformation (DT) and supply chain integration (SCI) on overall sustainable supply chain performance (OSSCP). The findings unveiled a significant effect of DT on OSSCP (H1). Previous works are missing the ability to highlight the importance of digital transformation as a construct in enhancing sustainable supply chain performance. What has been previously present in the academic literature are explorations of the association between sustainable supply chain performance and digital transformation technologies such as blockchain technology [80,113], RFID [114], warehouse automation [115], big data analytics [27,60], the internet of things [78], and management practices [116]. Economically, DT in connection to new technology adoption [117], can favor companies and their sustainability performance from the operational to the strategic level, positively impacting their economic performance. For example, by reducing the amount of generated waste and emissions from operational manufacturing activities, a company can enhance its sustainability performance outcomes such as reduced raw material and water consumption and energy efficiency [118]. Subsequently, digital technologies can help to reduce and limit human errors and reduce delays and the time needed for transactions while improving employees’ wellness, work conditions, and job satisfaction. In addition, the hypothesis testing results also showed the mediation effect of SCI on the relationship between DT and OSSCP. This means that DT adoption enhances OSSCP under the partial mediation effect of SCI. Hence the three dimensions of SCI, namely, supplier integration, customer integration, and internal integration, promote the successful adoption of DT intending to enhance OSSCP.

Furthermore, DT positively affects SCI in manufacturing companies (H2). Companies conducted internal integration to share information between internal supply chain functions, cooperating and collaborating strategically and cross-functionally using digital transformation technologies such as enterprise resources planning (ERP) and the internet of things. At the same time, external integration enables companies to develop collaborative relationships with suppliers and customers. Hence, information sharing and synchronized planning using digital technologies are the most critical issues in external integration, not just from a purely technical point of view, but also as they provide changes for better open-book practices, visibility to collaborators’ actions, and add to the level of shared trust, which is a crucial element in successful collaboration relationships [119,120]. Moreover, despite no previous empirical studies investigating the direct impact of DT on SCI statistically, this empirical study was found to be in line with the current DT-related literature that states that the three dimensions of supply chain integration are highly impacted by digital transformation adoption [36,58,83,94].

SCI positively impacts OSSCP in the manufacturing company context (H3). Companies have recognized that integrating internal functions, suppliers, and customers is a suitable alternative for the better coordination of activities to improve supply chain performance and sustainability. In addition, integrating internal and external activities is practically helpful for corporations to reach their internally set or externally forced sustainability performance outcomes, especially when aligned with the SC network operations [21,56]. Furthermore, strategic collaboration and partnership between supply chain firms enable the management of environmental and operational impacts, so SCI practices can potentially have a high effect on cooperative environmental activities and further reduce environmental externalities [48,56]. Still, academic studies have yet to pinpoint a direct relationship between SCI dimensions (internal, supplier, and customer integration) and overall sustainable supply chain performance. In other words, previous studies have been missing a positive link between supplier integration and sustainable supply chain performance [88,90], and they found a weak association between supply chain partnership and collaboration with supply chain performance [93]. The proposed study filled this gap compared to earlier studies by examining the direct relationship between SCI and OSSCP.

In this way, the main findings have many significant theoretical and managerial implications, as reported below.

7. Study Implications

7.1. Theoretical Implications

The outcomes of this study provide several valuable insights into the current literature on DT, SCI, and OSSCP. The proposed research has confirmed that DT has a significant positive influence on SCI and OSSCP. On the other hand, prior research could not highlight the importance of DT adoption in improving SCI and further enhancing OSSCP. Therefore, this study is the first to design a conceptual framework that links these constructs. The findings showed that the application of digital technologies such as enterprise resources planning and the internet of things improves supply chain integration, as it strategically and cross-functionally supports shared information between internal supply chain functions, cooperation, and collaboration. Additionally, the analysis results have shown that DT adoption can support manufacturing companies in Morocco and their sustainability performance from various levels impacting their economic performance.

Furthermore, the results have revealed that sustainability performance outcomes such as reduced raw material and water consumption and energy efficiency could be enhanced by reducing waste production and emissions from manufacturing activities. Subsequently, the mediating effect of SCI on the association between DT and OSSCP reveals that DT adoption enhances OSSCP under the partial mediation effect of SCI. As a result, the three dimensions of SCI, namely, supplier integration, customer integration, and internal integration, promote the successful adoption of DT intending to enhance OSSCP. Otherwise, the empirical results showed that SCI positively impacts OSSCP. This shows that SCI is a valuable practice adopted by manufacturing firms in Morocco to improve sustainability performance by establishing closer associations between SC functions. In addition, strategic collaboration and partnership among manufacturing companies and their suppliers and customers help to manage environmental and social impacts, so SCI practices potentially have a high influence on enhancing sustainability performance outcomes.

Finally, this research is valuable as it focuses on the association between digital transformation adoption, supply chain integration, and overall sustainable supply chain performance in the Moroccan manufacturing sector, which is not considered in the literature. In such a sector, the process is heavily digitalized; consequently, manufacturing companies in Morocco could gain the most from applying digital technologies.

7.2. Practical Implications

This research contributed to the theory by providing empirical evidence on the impact of digital transformation adoption and supply chain integration practices on overall sustainable supply chain performance for practitioners in manufacturing companies located in Morocco. The analysis results provide new factual support for decision-makers about the need to embrace digital technologies to efficiently improve the achievable level of integration in supply chain networks. This is imperative for practitioners, as their companies’ capabilities to achieve trustable information sharing, coordination, and organizational linkages among SC partners are directly related to the level of integration, the trust that the SC partners share, and the quality of data and information they share between each other. Indeed, DT adoption in manufacturing companies can strengthen the reliability, trust, and collaboration between trading partners, including suppliers and customers [26,80]. Therefore, DT is found to have a direct and positive influence on SCI dimensions (internal, supplier, and customer integration). This shows that digital technologies have a dynamic capability to improve both internal and external integration. So, SC managers in manufacturing companies can utilize this knowledge to improve SC efficiency by integrating digital technologies.

Furthermore, manufacturing companies in Morocco still need to continue implementing front-line digital technologies in their production, operations, and value-adding services to keep enhancing sustainability performance outcomes, as quickly developing DT is constantly offering new ways to influence overall sustainable supply chain performance positively. DT adoption provides crucial advantages for supply chain networks, as it improves the availability of information, collects real-time data, optimizes SCM practices, reduces production and transaction costs, and further increases both the efficiency and effectiveness of SC functions. In addition, DT enables companies to enhance SC flexibility and visibility and reduce SC risks [27,121]. However, manufacturing companies in Morocco must focus on a low-carbon and energy consumption strategy due to increasing carbon emissions and high energy consumption. So, digital technologies need to be integrated into SC networks to achieve sustainable and operational outcomes [76], including reducing carbon emissions from manufacturing activities and minimizing asset utilization. In addition, manufacturing companies should be more aware of the strategies developed for the integration of internal functions, suppliers, and customers, as, currently, it does not seem to be embraced as much as it should be, given the gains it offers on the competitive advantage front. This reflects the role of SCI practices in manufacturing firms to improve their sustainable performance by building deeper associations between SC functions [48,56]. Indeed, supply chain manufacturing firms must collaborate and cooperate with each other to manage the environmental and social impacts, as SCI practices can potentially have a high effect on cooperative environmental activities and further reduce environmental externalities [48].

8. Conclusions

The study extends the current knowledge space on the association between digital transformation, supply chain integration, and overall sustainable supply chain performance. First, this study developed a conceptual framework with three hypotheses to empirically assess the impact of DT and SCI on OSSCP in manufacturing companies located in Morocco. Using sample data from manufacturing companies’ senior executives and managers, the analysis results reported that the proposed conceptual framework is valid since all three hypotheses are supported in this study. The supported hypotheses are:

• Digital transformation directly and positively influences overall sustainable supply chain performance (H1).
• Digital transformation directly and significantly impacts supply chain integration (H2).
• Supply chain integration positively affects overall sustainable supply chain performance (H3).

Furthermore, the hypothesis testing findings also showed the partial mediation effect of SCI on the association between DT and OSSCP.

The proposed study and its findings provided new targeted theoretical development and empirical research on what manufacturing companies in Morocco need to adopt digital technologies and integrate their SC functions to enhance sustainability performance and successfully address the changing business and market requirements. This study is the first empirical study that includes and assesses all these constructs together in the context of the manufacturing sector.

However, this research contained limitations that pave the way for future research directions. Firstly, the study is based on a sample size of 134 respondents from a specific sector (the manufacturing sector), as the authors faced challenges in obtaining manufacturing-based companies’ direct contacts. Hence, it can be challenging to generalize the findings. Secondly, this study is cross-sectional research in one specific country, with a sample size of 134 respondents. So, future empirical studies can duplicate this research by analyzing the hypotheses in other countries and sectors and designing longitudinal research based on mediation models to better capture the association between the constructs in the long term. Furthermore, the findings are likely to be different for under-developed countries whose organizational capacity and availability of information and communications technology resources are limited.

Further, the study measured the overall sustainable supply chain performance items from the respondents’ perceptions. Thus, there is a need to conduct a suitable follow-up for this research to gather objective measures for the endogenous variable from the same representative firms and compare it with the perceptual responses to ensure that both measures are meaningfully interrelated. Indeed, the financial, social, and environmental components of OSSCP were considered in the main study. However, regarding the environmental pillar, future studies are recommended to consider energy consumption related to costs, as it has numerous effects on the overall cost of the supply chain [122]. Moreover, energy efficiency evaluation is also recommended, as it improves competitiveness, profitability, and quality, providing significant cost savings and improving the working environment [123]. Moreover, future studies need to examine how DT adoption can drive other sectors besides the manufacturing sector by improving and redefining their products and services through digital content in order to develop new revenue streams to sustain their survival.