Open AccessArticle

Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance

Safinaz H. Abourokbah

^*,

Reem M. Mashat

and

Mohammad Asif Salam

Department of Business Administration, King Abdulaziz University, P.O. Box 80201, Jeddah 21589, Saudi Arabia

Author to whom correspondence should be addressed.

Sustainability 2023, 15(4), 3636; https://doi.org/10.3390/su15043636

Submission received: 14 January 2023 / Revised: 7 February 2023 / Accepted: 11 February 2023 / Published: 16 February 2023

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Abstract

Digitalization is essential for supply chain (SC) systems to thrive in the extremely dynamic and competitive business environment of the present day. The purpose of this study is to examine the role and the importance of absorptive capacity (ACAP) on supply chain innovation performance (SCIP), mediated by digital capability (DCAP), supply chain resilience (SCR), supply chain agility (SCA), and digital innovation (DI). The study has been designed to empirically investigate the hypothesized relationships on a sample of 116 firms across industries in Saudi Arabia, using a partial least-squares-based structural equation model (PLS-SEM). Based on the findings, all the hypothesized paths are supported, justifying that ACAP positively and significantly impacts DCAP, SCA, and SCR. Moreover, SCA and SCR partially mediated the relationship between ACAP and SCIP. This study contributes to the resource-based view (RBV) and a dynamic capability (DC) theories by examining how the innovation of digital technologies affects SCIP, providing empirical support to the ACAP and SCIP interaction through numerous mediators to develop SCIP, from which also many practical implications emerged. For instance, especially in the wake of the COVID-19 pandemic, businesses must improve their SC performance by building and integrating their ACAP to make the most of their digital-platform-based dynamic capabilities.

Keywords:

absorptive capacity; supply chain agility; supply chain resilience; digital capability; digital innovation; supply chain innovation performance; PLS-SEM

1. Introduction

Industries and businesses experiment with various strategies, such as becoming digital, to address the high levels of environmental unpredictability and to stay competitive in today’s market. Uncertainty in the supply chain (SC) context regards the inability to predict the future confidently or make accurate decisions because of information asymmetry or a lack of relevant data [1]. Notably, digitalization such as blockchain may be considered a solution for reducing SC risks [2]. In the ever more volatile and uncertain corporate climate, with financial, economic, ecological, and social risks, SC digitalization is key to increasing SC resilience (SCR), adaptability, and long-term sustainability [3]. Productivity improvements, decreased expenses, and creative innovations are some benefits of digitalization that impact digital transformation [4]. Given dynamic environments, changes in customer demand, increased product diversity, and decreased product life cycles [5], organizations are increasingly aware of possible fundamental changes regarding their SCs and acutely aware of the value digital SC solutions may provide for enterprises [6]. The increasing development and deployment of Industry 4.0 (I4.0) and its accompanying technologies can profoundly impact production in every enterprise and organization aspect, yielding significant enhancements to SC and logistics management [7]. However, effective I4.0 adoption and deployment require integration between employees, machines, and production processes within and across SCs to provide benefits for businesses and enhance competition with others via reduced manufacturing costs, shorter lead times, and high product quality [8].

In the current paradigm of globalized markets, manufacturing sectors are increasingly aligning their operations to changing customer requirements and adapting to shifting market demands via reduced lead time, increased diversity of goods, and flexible production processes [9]. Therefore, applying advanced digital technologies progressively empowers SCs to be more effective by increasing their sensitivity to market demands and reducing production costs and potential human errors [5]. Global competition is increasing, making it progressively more challenging for businesses to build long-term competitive advantages [10]. Supply networks have emerged as a major source of competition for businesses [11,12]. The integration of a company, suppliers, and consumers forms an SC network. Given the dynamic environment of the rapidly evolving world, inter-company competition hinges on SC effectiveness. Indeed, innovation and collecting more data can induce SC improvement, which is key to competitive advantage [13,14]. The absorptive capacity (ACAP) is an organization’s ability to absorb, integrate, convert, and apply external information for innovation, adaptability, and performance [15,16]. Much of the value created via information technology stems from its integration with other corporate sources and processes [17]. Thus, information technology competence fosters organizational agility via ACAP [15]. Firms can improve their performance by effectively implementing their ACAP to develop and improve new products, improve management practices, and enhance firm routines [10].

Amid the COVID-19 pandemic, SCs were redesigned and redefined, emphasizing the importance of SCR [18] for innovative solutions [19] and competitive advantages [20]. Firms that invest in I4.0 technologies exhibit greater adaptability and resilience than those that do not [21]. With an ever-growing volume and variety of environmental disturbances and in a dynamic environment, supply networks must develop new ways to react better to crises quickly and efficiently [22]. In other words, SCs must aim to respond quickly to unexpected events. Carvalho et al. [23] posit that agile and resilient techniques influence SC performance and competitiveness. However, no study examines the effect of the innovative of digital technologies in improving SC agility (SCA) and resilience for improved firm performance.

Given SC disruptions, countries aim to strengthen their local SC and adapt to the need for agility. A strong SC network is built on a strong, well-integrated database that allows stakeholders to make important business decisions [24]. Accordingly, it is important to lay the groundwork for digitalization to strengthen the local SC [24]. The Saudi annual report of the National Industrial Development and Logistic Program (NIDLP) 2020 notes that maintaining smooth operations even amid emergencies is the foundation of a strong SC. The pandemic has exposed many SC infrastructure weaknesses worldwide, highlighting the need for anticipating and preparing for potential disruptions in supply deliveries. Such challenges underline the significance of SCs and logistics systems, per the NIDLP. The program ensures I4.0 technology implementation such that SC disturbances will not affect production, thus establishing a digital database for the crucial sectors (food, medical, and military) by considering any risks and recovery plans. Additionally, per a sustainability development program issued by the Saudi government, Saudi Arabia aims to create an exceptionally sustainable and responsible SC via sophisticated planning systems to optimize products flow. Thus, it intends to use technology to complete inventory visibility and advanced infrastructure to increase transportation efficiency, build employee skills by providing training programs, and promote logistics modeling that employs tools to boost warehouse and terminal location, size, and transportation [25]. Thus, Saudi Arabia is keen on the important role of I4.0 and technologies in improving SCs. This study considers the effect of digitalization and the benefits of collecting more SC data to enhance SCA, resilience, and capability, thus improving supply chain innovation performance (SCIP). Specifically, it addressed the following research questions. How can ACAP enhance SCIP through digital innovation (DI), SCA, and SCR? Does the DI, SCA, and SCR mediate the relationship between ACAP and SCIP?

Different studies show the importance of the innovative of digital technologies in improving an SC. Acquiring more data internally and externally (customers, suppliers, or even competitors) yields up-to-date awareness of the dynamic environment. This study assumes that the role of ACAP is important in an SC, enhancing agility, resilience, and hence, innovation performance. High performance stems from highly developed technological capabilities, as firms master process innovations when they acquire innovative technologies for competitive advantage [26]. The value of technological skills increases with time as it is embedded in organizational routines, thereby comprising a major source of ACAP [27]. Firms need dynamic capabilities to quantify an organization’s ACAP or its ability to receive and digest new information to generate new goods and services [28]. Several factors to consider include an organization’s capacity to manage and use digital technology in the innovation process beyond the relevance of an organization’s attitude toward digital technology adoption [29]. Digital capabilities (DCAPs) describe a mix of capabilities that boost an organization’s ability to improve, mobilize, and successfully utilize its resources and to develop its processes by leveraging digital technology [30,31].

Given the lack of relevant studies on the mediation role of SCR, SCA, and DI, the current study examines the role of digital technologies and ACAP in enhancing SCIP via DI, SCR, and SCA in the context of Saudi Arabia during the COVID-19 pandemic. Moreover, given insufficient evidence, the authors highlight the role of DCAPs, DI, SCR, and SCA in contributing to SCIP improvement. This study contributes to the extant literature in several ways. First, it examines the role and effect of digital technologies of the innovation in enhancing SCR and SCA to improve SCIP. Second, it analyzes the importance of ACAP in enhancing SCA, SCR, and the DCAP through DI, which will finally improve the SCIP. Third, it adopts the RBV and DC as the research framework, and the role of digital capabilities as an antecedent of digital innovation is investigated, along with its capability to improve firms’ innovation performances. Fourth, it tests the effects of SCR, SCA, and DI as mediators between ACAP and SCIP. Finally, to the best of our knowledge, this study is the first in the context of Saudi Arabia to analyze the role of digital technologies in SC.

The remainder of the paper is organized as follows. Section 2 reviews the theoretical background and develops hypotheses. Furthermore, Section 2.4 presents the conceptual model, and Section 3 describes the research methodology. Subsequently, Section 4 presents the results, and Section 5 discusses these results. Finally, Section 6 concludes the study and highlights the implications and limitations of the study and future research directions.

2. Literature Review and Hypotheses Development

2.1. Absorptive Capacity

Cohen and Levinthal [32] posit that ACAP is a firm’s ability to “recognize the value of new information, assimilate it, and apply it to commercial ends”, which is crucial to the organization’s innovative activities. A firm’s ability to innovate depends on its ability to gather, analyze, and synthesize data from various sources [27]. Moreover, Zahra and George [33] define ACAP as “a collection of organizational routines and processes by which businesses acquire, absorb, transform, and use information in order to generate a dynamic organizational capability”. Firms can improve performance by effectively implementing the development and improvement of new products, improving management practices, and enhancing firm routines [10]. Companies with high ACAP are more likely to acquire external knowledge from sources, such as rivals, customers, channel partners, and suppliers, as per the dynamic capability perspective [34]. Organizations use such data to spot market opportunities, which may significantly increase their profit and market share by catering to consumer expectations, technological innovation, environmental uncertainty, and the cyclical nature of the marketplace [34].

2.1.1. Absorptive Capacity and Digital Capability

Innovation is the process of transforming possibilities into new ideas and realizing them, and businesses may control innovation processes [35]. The critical aspects of managing innovations are daily routines and processes that focus on intra-firm innovation development. Such routines and processes are internalized as continuing operations interlocked and interconnected in workflows [36]. Organizational routines and procedures at the forefront of daily operations comprise the organization’s capabilities, which differ per dynamic capability, indicating an organization’s capacity to learn [28]. Lim and Ok [37] argue that an organization’s innovation is strengthened by its knowledge absorption ability. Thus, dynamic capabilities define an organization’s ACAP or its ability to acquire and process new information to develop new products and services [33]; it is critical where new technologies and actors disrupt the operating environment [28]. Thus, dynamic capabilities and ACAP are two critical intra-organizational aspects of innovation management.

Developing technical capabilities requires the accumulation and storage of information [30]. The acquisition of technical knowledge boosts product innovation abilities and a firm’s capacity to participate in the transformation process by evaluating, applying, and implementing new technologies [26]. Therefore, technological capability refers to the organization’s ability to apply multiple technologies [38]. The value of technological skill increases as it is embedded in organizational routines, constituting a major source of ACAP [27]. DCAPs increase an organization’s ability to improve, mobilize, and successfully use internal resources and develop its processes using digital technologies [30]. They emerge from the need to be agile and rapidly adapt to technological evolution by improving new capabilities [39]. The DCAPs of companies confronted with digital transformation enhance their performance relative to competitors and provide high-quality products or services to clients. An adequate business strategy can help the company manage its relationships with stakeholders, such as suppliers and employees [40]. Vigren et al. [28] interview 32 real estate owners in Swedish and reveal that real estate owners develop their capabilities of innovation by digitalizing their activities and improving their ACAP, changing their routines and structures. Therefore, organizations should improve their SC by configuring digital resources to take advantage of opportunities and quickly react to market demands. Thus, we propose the first hypothesis:

Hypothesis 1 (H1).

ACAP is a predictor of DCAP.

2.1.2. Digital Capability and Digital Innovation

Nambisan et al. [41] described DI as the process of developing new products, business processes, or models by utilizing digital technology. This explanation encompasses various innovative goods, systems, services, and new experiences of the customer and value routes; the outcomes may stem from firms’ use of digital technology and automated processes [29]. Thus, firms must acquire numerous competencies in various areas for an effective digital revolution, which may vary per the industry and business demands [42]. While the impact of DCAPs on company performance varies, to increase business performance, they must generate DIs, which may ultimately improve business performance [43]. Digital competence, a technical skill in the digital environment, is necessary for DI given that the achievement of creating innovative products depends heavily on the company’s ability to deal with digital technology such that every stage of DI, from the purchase of digital technology to building new digital solutions, require highly skilled personnel [29]. DI uses information, computer, communication, and connectivity technologies to produce new goods, improve processes, reform organizational structures, and establish and alter business models [41]. Given the fast growth of digital technology, storing and sharing data for innovation increased communication, costs for research have decreased, and there is a shift in focus on innovation from inside the organization to distributed entities that cannot be defined in advance [44]. Few studies link DCAP with DI. Khin and Ho (2018) [29] find the role of DCAPs, DI, and digital orientation on financial and non-financial performance, where there is a significant impact of DCAPs on DI. In Indonesia, Hartono and Halim [45] examine an e-travel company and show that DCAPs significantly affect DI. Eidhoff et al. [46] qualitatively show that the technical and environmental elements are the most important determinants of a company’s choice to pursue digital product innovation. Nasiri et al. [47] posit that technical and innovation capabilities are the primary drivers of the market offering of DI. Hence, we propose the second hypothesis:

Hypothesis 2 (H2).

DCAPs have a positive effect on DI.

Some firms invest in new digital platforms to increase their innovation capabilities and digitize their services and processes [28]. Regarding digitalization expertise and innovation facilitation, Zahra and George [33] suggest that firms can improve their absorption ability and their routines for identifying new opportunities, adapting new ideas, and applying them for business performance enhancement [28]. DI differs per platform depending on how practices are stimulated, simplified, and managed [48]. For the last several years, researchers have shown that DCAPs indirectly impact performance [49]. Hartono and Halim [45] show that companies with strong DCAPs may create new offerings to please consumers, thus increasing sales and financial returns when mediating between DI and competitiveness and performance. Digital competence is crucial for achieving DI because the success of digital product creation is contingent on the organization’s capacity to handle digital technology [29]. Every DI stage necessitates the optimal degree of skills possessed by brilliant people to acquire digital technologies and create new digital solutions. Accordingly, we propose the third hypothesis.

Hypothesis 3 (H3).

DCAPs mediate the relationship between ACAPs and DI.

2.1.3. Digital Innovation and Supply Chain Innovation Performance

Hund et al. [50] define DI as value-added innovation (e.g., product, service, process, or business model). DI is the invention or adoption and exploitation of an essentially limitless novelty (e.g., digital technology). Research shows that technological proficiency is crucial to the success of innovative initiatives. As per Cohen and Levinthal [32], technology can help organizations learn new techniques to develop new products. Thus, the importance of DI must be recognized as providing new goods, enhancing manufacturing processes, improving organizational structures, and building and modifying models of the business [51]. The fast-growing digital technology has improved the speed of information storage and distribution, communication, and search costs and changed the emphasis of innovation from inside the company to dispersed entities that cannot be specified in advance [44]. By analyzing data from many different sources, businesses can learn more about their customers, cut costs, and get a better handle on SC risks [52]. For example, Salehan and Kim [53] note that companies use big data digital technology because of its range of advantages, such as decreasing costs, computer task efficiency, or innovation in products and services, allowing companies to compete in a rapidly changing business world [54]. Firms must use the valuable information they can get by harvesting diverse beneficial data for better customer service, efficient operations, organizational strategic direction reform, new markets and customers, and innovative services and products [55,56]. Sanders and Ganeshan [52] note that SC management improvement may stem from analyzing data for intelligent SCs. Thus, firms that use digital technologies with features like data homogenization and accessibility to copy advanced technologies and new ideas in their industry or other industries can save money by reducing investment resources and lowering the cost of starting a business [44]. Khin and Ho [29] investigate the influence of DI on firms’ financial and non-financial performance in different industries, revealing that DI impacts firm performance. Therefore, DI depends on the acquisition of digital technology for the development of new digital solutions by expanding their DCAP. The existing literature has mixed evidence on how DI contributes to the SCIP, especially in the context of emerging markets. Hence, based on the dynamic capability theory, this study proposes the fourth hypothesis:

Hypothesis 4 (H4).

DI positively affects SCIP.

Open or user innovation may help companies access a wide range of resources and expertise to improve their innovation performance [44]. Innovation in the digital economy no longer occurs in a single firm but is dispersed throughout a network of partners [57]. The capabilities at the firm level challenge the narrow, inward focus by convincing businesses to improve their internal knowledge by effectively integrating and gathering market information via digital collaboration with external business partners, such as customers and suppliers [58]. Therefore, the more the IT-enabled skills, the greater the ability to assist partners in participating in DI (i.e., providing feedback on new product issues, diverse expertise, and innovative ideas) [44]. As long as firms can digitize their process to engage customers and suppliers in sharing information, DI can emerge [58]. Accordingly, businesses employ digital technologies such as big data and artificial intelligence to gather customer knowledge and demands, modify original goods, and provide new goods via necessary modifications, thereby lowering the risk of corporate DI [59]. Khin and Ho [29] argue that the mediation effect of DI is based on the premise that a corporation with a strong digital competence is better positioned to supply new solutions that better attract consumers, improving sales and financial returns. Thus, we propose the fifth hypothesis:

Hypothesis 5 (H5).

DI mediates the relationship between DCAP and SCIP.

2.2. Supply Chain Agility

Modern SC management relies heavily on the notion of agility. SCA “is a firm’s ability to quickly adjust tactics and operations within its supply chain to respond or adapt to changes, opportunities, or threats in its environment” [60]. Teece et al. [61] define dynamic capabilities as “the firm’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments”. Studies using dynamic capability theories provide theoretical explanations for ACAP outcomes, such as invention, adaptability, and performance, and organizational agility as two dynamic capabilities [15,62]. SCA establishes shared networks between partners because only firms can react rapidly and effectively to market fluctuations if they have a superior level of collaboration [10,63]. An agile SC is critical to a business’ competitiveness in an unpredictable market because it allows for quick and effective reactions to operational changes, such as changes in procurement, production, delivery, and marketing [16]. SC managers can identify which areas of their business operations should be changed to improve SCA [60,64,65].

2.2.1. Absorptive Capacity and Supply Chain Agility

Businesses today operate in a highly competitive environment marked by continual changes in technical advancements and client expectations. Thus, businesses must stay up to date on such developments by improving their DCAP and acquiring new knowledge to improve operations [10]. ACAP can help organizations use and acquire new knowledge to enhance internal processes. The company’s agility is determined by the degree of access to knowledge and its absorptive capacity, which leads the company to achieve the competitive advantage by developing a unique operational capability [16]. Increased ACAP enables businesses to have more up-to-date information and control over their whole SC [66]. Organizations with higher ACAP are better equipped to react to changing client requirements and recognize market developments faster [10]. Consequently, SCA may measure how successfully a company works with partners to take advantage of resources and capabilities through information-sharing routines to manage market shifts collaboratively [16]. Inter-company collaboration is a key component of agility, as it furnishes knowledge about market trends [67]. Therefore, in dynamic markets, a greater ACAP will improve SCA. Hence, we propose the sixth hypothesis:

Hypothesis 6 (H6).

ACAP has a positive effect on SCA.

2.2.2. Supply Chain Agility and Supply Chain Innovative Performance

ACAP outcomes, such as creativity, adaptability, and performance, are the primary focus of dynamic capability theories [62]. Innovativeness regards a business’ ability to provide new goods and processes, produce new ideas, adapt swiftly to changes in client requirements, strengthen its ability to escape competition, and increase customer satisfaction [68]. Agility directly influences SC operations, such as sourcing, manufacturing, and delivery, and improves the total SC performance. Thus, for the required outcomes, agility requires significant effort on the part of decision-makers [69]. Chen [68] studies Taiwanese manufacturing managers to probe the effect of SCA and innovativeness in enhancing a company’s competitive advantage through IT integration and trust between SC members. Accordingly, IT integration and trust enhance SCA and innovation, improving competitive advantage [68]. Inman and Green [70] also argue for a strong relationship between SCA and SC operation performance by showing the impact of environmental uncertainty on organization performance and agility and the impact of SCA on performance. Thus, we propose the seventh hypothesis:

Hypothesis 7 (H7).

SCA has a positive effect on SCIP.

With agile manufacturing, a business may swiftly adapt to changing client needs and react to the dynamic demand using various unique characteristics and innovative features [71]. Manufacturing with agile characteristics can generate more innovative items quickly than rivals; a company’s capacity for product innovation and production elasticity is critical for agility development [72]. Some studies posit SCA as a mediator between ACAP and firm performance. Ref. [62] shows that an organization’s agility may improve by using information technology and knowledge skills. Kale et al. [67] also determine the mediating role of strategic agility in the impacts of ACAP on the performance of Turkish lodging facilities. Accordingly, ACAP positively influenced strategic agility. Martinez-Sanchez and Lahoz-Leo [10] study the trends in 231 Spanish companies and find that SCA mediates the relationship between ACAP and firm performance. Liu et al. [16] examine the effect of IT on firm performance when mediated by ACAP and SCA. They find that SCA partially mediates the relationship between ACAP and firm performance, and ACAP and SCA fully mediate the relationship between IT and firm performance. Technology has proven to be critical to the SC, especially during the COVID-19 pandemic. Therefore, any firm can become agile by improving the technological process. Hence, we propose the eighth hypothesis:

Hypothesis 8 (H8).

SCA mediates the relationship between ACAP and SCIP.

2.3. Supply Chain Resilience

SCR is an SC’s capacity to build the needed degree of preparation, reaction, and recovery capabilities to manage disruption risks and restore the SC to its original or even better condition after interruptions [73]. Agility is an organization’s capacity to adapt rapidly and efficiently to unforeseen changes in the competitive environment, and resilience is an organization’s ability to recover from significant SC interruptions or turbulence [65]. Although no risk can be eliminated, firms may protect their ability to meet consumer demand by building SCR, given potential disruptions [74,75]. SCR entails avoiding identifiable risks, attaining corporate goals amid interruptions, and reestablishing necessities to enhance performance [76]. SCR enables rapid adaptation to sudden events by reducing instabilities [77]. It is not limited to one situation; a firm can continuously expect and adapt to continual changes [64]. New paradigms, ideas, and models in SC management and SCR are being developed as a result of digital technology innovations. The COVID-19 pandemic has shown that companies that make substantial use of digital technology may function with greater resiliency because of the increased visibility and coordination these technologies provide [78,79]. Roque Júnior et al. [80] perform a systematic literature review to present a model that assists supply chain managers in developing resilience measures depending on the maturity of the chains they administrate, allowing them to handle crises such as the COVID-19 pandemic. Khalili et al. [81] also provide a unique two-stage scenario-based mixed stochastic–possibilistic programming model for integrated production and distribution planning in a two-echelon supply chain under risk over an intermediate range and suggest a new indicator for optimizing the chain’s resilience level based on capacity restoration. Ivanov and Dolgui [2] posit that the digitalization effect on the resilience of operations and SC is complicated. They emphasize the need for using data analysis descriptively and predictively to gain visibility, improve prediction accuracy, and enhance the activation of contingency plans. Hosseini et al. [82] identify quantitative determinants that contribute to SC resilience based on resilience ability, which was further classified as absorptive capacity, adaptive capacity, and restorative capacity by a systematic literature review on SCR, examining both the qualitative and quantitative determinants of SCR.

2.3.1. Absorptive Capacity and Supply Chain Resilience

Utilizing a resource-based lens enables us to better understand how and when partners contribute to SCR. RBV focuses on particular resources, which may be quantified in absorptive and operational capacities [83]. Per this view, SC participants may gain a competitive edge by pooling precious, scarce, unique, and nonreplaceable resources to develop capabilities [84]. Thus, resilience capabilities in SC allow for recovery and adaptation when supply networks are exposed to and impacted by environmental and operational changes [78]. Numerous studies on dynamic capabilities underline the critical nature of such skills in a rapidly changing business environment, emphasizing their ability to enable enterprises to adapt to disruptions by allocating their present resources per ecological parameters [85,86]. Thus, resilience is a critical capacity for a corporation, especially given changes to the business environment, and its importance can be explained using the dynamic capability theory [87]. Accordingly, organizations and SCs must work to strengthen their dynamic skills, particularly their resilience capabilities to mitigate the effects of any disruptions [88]. Studies show that ACAP affects SCR. Cheng and Lu [83] study 297 Taiwanese senior managers to investigate the influence of ACAP on practical and responsive dimensions of SCR. They show that firms may increase ACAP to increase SCR proactively and reactively. Roh et al. [89] study the impact of ACAP on low and high resilience by examining 205 managers. They find that ACAP highly impacts firm resilience. Goaill and Al-Hakimi [88] also examine the moderating role of ACAP between entrepreneurial orientation and SCR using a sample of 171 Yemeni manufacturing SMEs. They show that entrepreneurial orientation positively affects SCR, and ACAP moderates the link between entrepreneurial orientation and SCR. Additionally, Gölgeci et al. [90] confirm the relationship between ACAP and SCR. Therefore, the study posits the ninth hypothesis:

Hypothesis 9 (H9).

ACAP has a positive effect on SCR.

2.3.2. Supply Chain Resilience and Supply Chain Innovation Performance

Firms more adept at minimizing the length and challenge of SC interruptions than their rivals are more resilient and may utilize SCR as a strategic weapon for competitive advantage [91]. Dynamic recoverability is described as a firm’s capacity to adapt to disturbances via prompt and cost-effective recovery to achieve a better post-disaster condition than before the interruption [19]. Specifically, this study defines dynamic resilience as a firm’s capacity to harness its innovation potential to initiate the dynamic recovery process and remain resilient to disturbance. Businesses that use innovation often outperform their competition. Per prior research [92,93], the aspects of SC innovation include process and technological innovation. Technical innovation bolsters real-time tracking technology and new logistics equipment and acquires information systems throughout the SC, which is critical for distinguishing logistics services and providing value for consumers [94]. Process innovation describes the process of redesigning and reengineering the SC, which is critical because it enhances product innovation, is tied to the total output generated, and induces lower costs and increased service quality [93]. Beyond overcoming disruptions, SCR may impact an organization’s performance results directly [73]. Therefore, we posit the tenth hypothesis:

Hypothesis 10 (H10).

SCR positively affects SCIP.

Dubey et al. [95] claim that the analytics of data capabilities help businesses gain a competitive edge through SCR. Bahrami and Shokouhyar [96] show that digital capabilities enhance a company’s performance by boosting SCR. Baral et al. [97] show that organizational resilience mediates the substantial impact of four latent factors—flexibility, digitalization, risk management culture, and cooperation—on the performance of micro, small, and medium-scaled enterprises. Bahrami et al. [94] study 187 senior managers in Iranian companies and show that the impact of big data capability is significant in improving SC performance through SCR. Thus, SCR fully mediates the relationship between big data capabilities and firm performance. Asamoah et al. [98] find that building SCR amid disruptions stems from establishing business and non-business social network relationships because of its importance in shaping customer-oriented performance via producing reliable products and providing continuous services to customers. Accordingly, we propose the eleventh hypothesis:

Hypothesis 11 (H11).

SCR mediates the relationship between ACAP and SCIP.

2.4. Conceptual Model

This study explores the effect of the innovative of digital technologies on an SC by examining the effect of ACAP on SCIP. A DCAP that improves with more data can induce DI. Given a dynamic environment and changing customer demand and preferences, companies become more agile and resilient with more data. These factors may benefit the SC and induce SCIP (see Figure 1).

Theoretical Foundation

The RBV, pioneered by Wernerfelt [99], stems from the field of strategic management and shows how to develop specific resources. Anything an organization owns or obtains is a resource. However, capacity is the ability to use resources to enhance operations [100]. Therefore, valuable resources can help firms deploy and use value-creating strategies hard for competitors to copy [83]. Firms can gain a long-term advantage in the market by digitizing SC and accessing beneficial information.

2.5. Impact of COVID-19

The COVID-19 pandemic has been devastating for many SCs, with factory output stoppages, business closures, and global supply chain disruptions occurring first in China and then spreading to the rest of the world in the beginning of 2020 [18,22]. As a consequence of the pandemic’s several waves, numerous original equipment manufacturers in a variety of sectors and their associated supply chains are still experiencing unrelenting interruptions that are expected to last for some time. In that sense, the COVID-19 pandemic serves as a reminder to business leaders of the importance of expanding company performance metrics to include resilience, responsiveness, and reconfigurability [19]. The COVID-19 pandemic has shown that companies that make substantial use of digital technology may function with greater resiliency and agility because of the increased visibility and coordination these technologies provided [78,79]. In a corporate climate that is more volatile than ever before, as it is characterized by unprecedented financial, economic, ecological, and social risks, the innovation of digital supply chain (SC) technologies has emerged as the most important factor [3]. Obviously, digitalization may be considered as a solution for reducing SC risks [78], but the continuing wave of digital innovation also produces new dynamics that are frequently difficult for enterprises to follow, hence posing new issues for businesses and society [3]. Therefore, this study also shows the role of ACAP on SCIP and reduces the impact of COVID-19 on the SC.

3. Research Methodology

This study employs a quantitative approach to probe the relationship between constructs. Quantitative research is useful for empirically testing hypotheses by examining the connections between concepts when such concepts can be measured objectively with instruments; the resulting data can be analyzed numerically using statistical procedures [101]. With facts and data gathered and sorted into categories, conclusions may be drawn intelligently [102,103]. The positivism philosophy is used in this investigation to ensure that there is no bias (i.e., the phenomena are unique and far away from the researcher’s worldview). In other words, positivism is uncomplicated and efficient. Accuracy, ease of measurement, data gathering, factor identification, and extending and contributing to theory and practice are all made possible by the quantitative research approach from a positivist view [104].

This study follows a deductive methodology, which is mainly used in management research, where the concept is built upon a theoretical foundation gleaned from the existing literature and evaluated against a set of assumptions [87]. In this instance, the study employs a mono-quantitative methodology, which uses a single approach for data gathering by comparing predetermined slices [103]. The study conducted a questionnaire survey to collect data [101] and find correlations between variables [103]. A group of questions was then chosen from the literature to measure the study model’s components, thus creating the survey instrument. The study evaluated the instrument’s validity and reliability and tested the hypothesized relationships using data from a survey sample.

3.1. Measurement Model

Measures for the survey instrument were derived from the literature. Where applicable, the scale items were adapted to the context of SCs. ACAP was measured using the measures of Flatten et al. [105]. The study employed items from Zhou and Wu [27] to assess DCAP. Moreover, we adapted the Gölgeci and Ponomarov [106] measures for SCR. Furthermore, to measure SCA, the study adapted the measures of Gligor et al. [60]. Additionally, we measured DI based on the scale of Paladino [107]. Finally, we followed Li et al. [108] and Lee et al. [109] to build a scale for SCIP.

The noted prior studies show that the relevant instruments meet or exceed the necessary standards for loading, predictive power, reliability, and validity. The instruments were back-to-back translated to eliminate misunderstandings from cultural differences [110]. Online surveys were administered in Arabic and English. Focus group experts on the subject assessed the questionnaire to ensure that its components accurately measured the targeted variables and were easy to understand. This study used a seven-point Likert-type scale [111,112] to measure the variables, where “1” means strongly disagree and “7” means strongly agree (see Appendix A for a summary of the scale items and their sources).

3.2. Sampling Method and Data Collection

The study employed firms in Saudi Arabia engaged in processing activities (e.g., food and beverage, construction, mining and minerals, petroleum, chemicals, and pharmaceuticals) as the unit of analysis in this research for a comprehensive insight into the effects of SC decisions on performance [113]. The study distributed 350 questionnaires; approximately 35–40 questionnaires were sent to different companies in each industry in the first round. In the second round, after two weeks, a soft reminder was sent by email and LinkedIn. Following Flynn et al. [114], we conducted the survey with input from a single respondent at each organization, using convenience and snowball sampling. Convenience sampling was used to choose the participants; it was the quickest and easiest method to communicate with participants [115]. The respondents (executives, directors, senior managers, general managers, and functional managers of SC, quality control, and operations) were chosen because the authors believed they could provide the most insightful responses to the questions on SC. Given the diverse structure of Saudi Arabia’s primary economic sectors, the respondents come from various industry codes representing the country’s mining, construction, and manufacturing industries. Notably, there is no perfect method of sampling [103].

Web-based surveys were utilized for ease and speed, hence reducing costs and effort [116]. In the data collection process, the authors contacted the firms by sending messages via email and social media such as LinkedIn, WhatsApp, and Twitter. Furthermore, to ensure the quality of the sample selected, we reached SC experts through LinkedIn. The sample selection was good, given that potential participants’ profiles attested to their competence to answer the survey. Given the validity and reliability of prior measurement items, the assurance of non-response bias, and subsequent changes to correct various inaccuracies, the study developed a robust foundation for measurement.

4. Results

Out of 350 questionnaires, 116 were returned for analysis. The 33% were over the minimum required response rate for online surveys of 30%; thus, it was considered acceptable [117], and the sample size was adequate to test the hypotheses. As per Hair et al. [118], the sample size complies with the required ratio of 15 observations per variable and the chosen minimum sample size of 90 observations to perform the analysis successfully. Normality tests require that the data obtained be distributed proportionally [119]. Given that no outliers were found, all complete responses were included in the study. To determine whether the data were normally distributed, a preliminary check was performed. The validity of results is provided by skewness, and the kurtosis values, which are ±1 [120], met the cut-off. Univariate normality testing was undertaken using the “skewness–kurtosis technique,” with cut-off points of 3 and 8, respectively [120]. Overall, the researcher utilized the skewness–kurtosis technique, signifying that the statistical outputs were within the expected range. This study meets the criterion of data violation of normality by using Smart-PLS [121].

4.1. Descriptive Analysis

A total of 116 respondents participated in this study, and 58.93% work in a firm with 501 or more employees, followed by 25% in a firm with 51–500 employees and 16.07% in a firm with 50 or fewer employees. Half of the respondents were aged 49 years or more, followed by those aged 11–40 years (30.36%) and 10 years or less (9.64%). Half of the respondents had revenue of less than SAR 59 million, followed by those with 501 million or more (35.71%), 51–250 million (21.43%), and 251–500 million (17.86%). Furthermore, 41% were general managers, followed by 16.07% each for executive and senior managers and 10.71% for directors; the rest accounted for less than 10%. See Table 1 for the results.

4.2. Common Method Bias and Variance

A variance inflation factor (VIF) larger than 3.3 is an indicator of pathological collinearity, and a model may be exposed by common method bias (CMB). Thus, the model may be declared free of CMB if all the inner model VIF values from a thorough collinearity test are greater than or equal to 3.3 [122]. Moreover, to identify common method variance (CMV) problems in partial least-squares-based structural equation modeling (PLS-SEM) models, the measured latent marker variable method may be used [123]. A random variable was initiated to test the CMB in the model. Accordingly, the outer (inner) VIF model is <5 (less than 3.3). Both tests indicate that there are no CMB and CMV.

4.3. Measurement Model

4.3.1. Convergent Validity

This study employs SEM using Smart-PLS version 3.0 to verify the hypothesized connections between the variables presented in the study framework (Figure 1). SEM is important because multiple indicators may be included in the hypothesized and final model, and the link between independent and dependent variables can be tested concurrently [124].

After collecting the responses to the survey, we analyzed the data using SEM to clarify the significance of factor loading, weight, and path coefficients. First, to evaluate the validity, reliability, and fitness of the data, we used confirmatory factor analysis to achieve the best model fit. Subsequently, we employed bootstrapping (1000 resample) to determine the relationships between the variables. Per the theoretical background, paths, or hypotheses, they were presented using the value of β = coefficient path, T = T-statistic, and square = R2 [125,126].

Table 2 presents the hypothesized model. The factor loading (FL) values satisfy the recommended values (FL > 0.70) [126]; all items and indicators were over 0.70. Moreover, the values of VIF were less than the cut-off value of 5 [126]. Some items—ACAP6, DCAP1, DI2, and 5, SCA1, 4, 5, 7, 9, 10, 13, and 14, SCR2, SCIP1, 2, 3, and 4—were eliminated from the model because of high VIF values to reduce multicollinearity.

The standardized root mean squared residual (SRMR) value was below 1.00 (SRMR = 0.078), which is acceptable. Moreover, the average variance extracted (AVE) values met the recommended values of >0.50. Likewise, the composite reliability (CR) and Cronbach alpha (α) were significant (CR and α > 0.71). This study examined the significant value of path estimations (β) based on the t value (p < 0.05). R² is a function of the influence of the independent variable on the dependent variable. Thus, R² of the SCIP was predicted by 0.814—81% of the influence from independent variables affects the dependent variables. Moreover, the mean scores of the variables indicated a moderate level, ranging between (5.06 ± 1.640 moderate–high level) and (4.59 ± 1.558 moderate level). Furthermore, there are significant and positive correlations between the constructs, ranging between (r = 0.804, p < 0.01) and (r = 0.569, p < 0.01). The approach of predictive sample reuse (Q²) may be used for predictive relevance [127]. Q² demonstrates how well-collected data may be empirically rebuilt using the model and PLS parameters based on the blindfolding technique. Q² was determined utilizing cross-validated redundancy approaches, following Chin [124]. If Q² > 0, then the model has a predictive relevance [128]. Q² for SCIP is 0.569; for all other variables Q² > 0, indicating that all variables have acceptable predictive relevance. See Table 3 for the results.

4.3.2. Discriminant Validity

After establishing the reliability and convergent validity of reflectively assessed constructs, the following stage determines their discriminant validity. This analysis illustrates the amount to which a construct is empirically different from other constructs regarding its correlation with other constructs and the degree to which indicators uniquely reflect this single concept. Accordingly, the lowest square root of AVE (0.829) was higher than the highest correlation (r = 0.804). Thus, we can accept the model for testing the hypotheses (see Table 4). Moreover, discriminant validity is determined by examining the heterotrait–monotrait ratio (Table 5). All values are less than 0.9 [129].

4.4. Evaluating the Structural Model

This segment describes the influence of independent variables on the dependent variable (see Table 6 and Figure 2). Smart-PLS 4.0 was used to test the hypotheses and structural model, with a 5000-iteration bootstrapping approach to determine the statistical significance of the weights of sub-constructs and path coefficients [127].

The results confirm H1 (β = 0.749, t = 14.581, p > 0.000), demonstrating that ACAP significantly influences DCAP. Moreover, the results show a significant and positive structural path between DCAP and DI (β = 0.692, t = 13.821, p < 0.000), supporting H2. Furthermore, the results show that the structural path between DI and SCIP is significant (β = 0.309, t = 4.751, p < 0.000), supporting H4. The SEM results show a positive effect of ACAP on SCA (β = 0.698, t = 10.441, p < 0.000), supporting H6. H7 was ultimately supported (β = 0.336, t = 4.453, p < 0.000), demonstrating that SCA significantly influences SCIP. Regarding the effect of ACAP on SCR, the results (β = 0.649, t = 7.154, p < 0.000) support H9. Additionally, the results (β = 0.377, t = 5.151, p < 0.000) support H10.

4.5. Mediation Analysis

The study examined the mediating effect of the constructs through a bootstrapping procedure using 5000 resamples. It performed the mediation analysis to assess the mediating role of DCAP between ACAP and DI. The relationships between ACAP and DI (p < 0.000), ACAP and DCAP (p < 0.000), and DCAP and DI are significant. Thus, DCAP partially mediates the relationship between ACAP and DI (β = 0.519, t = 9.254, p < 0.000), supporting H3. Moreover, DI partially mediates the relation between DCAP and SCIP (β = 0.214, t = 4.268, p < 0.000), supporting H5. Moreover, for H8, the relationships between all paths are significant (β = 0.234, t = 3.348, p < 0.001), supporting the hypothesis. Additionally, for H11, the relationships between all paths were significant (β = 0.245, t = 4.123, p < 0.001), supporting the hypothesis.

5. Discussion

Prior studies indicate that digital technologies are crucial for supply chain efficiency. They demonstrated how implementing digital technology may improve an organization in general and SC in particular. This study examines how SC may improve their innovation performance based on their ACAP via DI, resilience, and agility. Organizational innovation is a critical dimension in enabling businesses to adapt to dynamic business environments, as it helps them produce new and distinctive services that serve as the foundation for an unmatched and long-term competitive edge [19]. Almost 89% of the companies participating in this study were somehow impacted by the COVID-19 pandemic. The findings of this study also confirm that supply chains can benefit from implementing digital solutions and innovations [130]. Thus, SCs that adapt technologies and are driven by data-based decisions via ACAP can survive and thrive. The model presented in this study shows that innovative SCs contribute to dynamic resilience, agility, and SCIP.

By testing the impact of ACAP on three variables—DCAP, SCIP, and SCA—via H1, H6, and H9, respectively, this study (as can be seen in Figure 2) finds that ACAP positively impacts DCAP. The significance and improvement of ACAP and digitalized activities can develop DCAPs, according to Vigren et al. [28]. Moreover, ACAP, as predicted, positively impacts SCR. Following prior research [89,131,132], this study examined the relationship between ACAP and SCR. An SC can achieve SCR by having strong relationships with its key suppliers and customers, running a well-integrated business process and increasing employees’ skills and abilities for improved performance, with reduced costs and improved quality. Furthermore, there is a positive impact of ACAP on SCA [10]. Better responsiveness and agility in the SC are achieved by constantly upgrading technology and integration with partners [133] to provide quick and agile responses [134]. Organizations rely on innovation to remain competitive in the market [135]. Firms adapt by using technology to improve processes, create new products, and provide flexible shipping in response to market needs [136]. Increased ACAP enables businesses to have up-to-date information and control over their SC [66]. Organizations with a higher ACAP are better equipped to react to changing client requirements and recognize market developments faster [10].

In the wake of the COVID-19 pandemic, digital technology has proven to be crucial in the SC. The capacity to manage digital technologies is critical to the success of digital product creation, which is heavily reliant on how successfully a company manages digital technology. DCAP is a positive predictor of DI [29,46,47]. Innovation and data collection induce SC development for competitive advantage [14]. Businesses require DCAPs to update, activate, and build processes using digital technologies [31]. To swiftly create new processes and products in response to the ever-changing market orientation, organizations must adapt and align their rules and procedures and create innovation to tailor goods to market demand [134].

Various studies examine SC performance from different aspects [68,94,137,138,139,140]. This paper examined the direct effect of DI, SCR, and SCA on the SCIP. It demonstrates that DI significantly impacts the SCIP, as per Khin and Ho [29]. The fast growth of digital technology has improved the speed of information storage, helping organizations learn and develop new products. It is necessary to acknowledge the role of DI in creating new products, manufacturing process improvement, organizational structure reformation, and business model development and modification. Improvements in SC performance may be achieved via process innovation, which is a subset of systems innovation that increases SC efficacy and efficiency [140]. Moreover, product innovation may increase SC efficiency, given that they manufacture new items continuously [141]. Reducing product delivery delays and customer attrition are two of the main goals of an innovation system, implemented by streamlining operations, establishing operational standards, and using technology [134]. Furthermore, this study examined the relationship between SCR on SCIP. It confirmed that SCR determines innovation performance. As per Chowdhury et al. [73] and Bahrami et al. [94], there is a positive relationship between SCR and innovation performance. Similarly, SCA positively affects performance, as per the finding that IT integration and trust enhance SCA and innovation, improving the competitive advantage [68,138,142]. Some studies relate agility to firm performance and show a positive relationship between SCA and organization performance [10,16,70,142,143].

Furthermore, the study investigated the mediating role of DI between DCAPs and SCIP, finding that DI partially mediates the relationships. Given the discovery that DI mediates the influence of SCIP, companies devoted to adopting digital technologies and improving their abilities to better manage digital technology are more likely to generate new digital solutions. Therefore, if a firm improves its DCAPs to acquire more data and implements contemporary digital technology to address business challenges via process optimization, customer experience enhancement, and the introduction of novel business models, SCIP can improve. Indeed, prior studies confirm that SCA partially mediates the relation between ACAP and SCIP [10,16,62,67]. Adapting agility can help organizations adapt to changes based on customer needs and respond to dynamic demand using several innovative features [71]. Furthermore, SCR mediates the relationship between ACAP and SCIP, as per Bahrami et al. [94], where the impact of the innovative of digital technologies significantly improves SC performance through SCR.

6. Conclusions

In the wake of the COVID-19 pandemic, technology has proven to be critical to SC success. Studies show the importance of SC innovation and its gradually increased recognition of the difference between day-to-day interruptions and catastrophic occurrences. This study primarily addresses the factors that induce SCIP. It shows the importance of ACAP in enhancing SCIP with more data for resilience and agility. Dynamic SC resilience describes a company’s ability to use its innovation potential to start the dynamic recovery process and stay strong amid disturbances. Similarly, SCA shows the ability of companies to respond swiftly and effectively using their capabilities and innovation to recover quickly. Accordingly, a questionnaire was distributed and collected from companies operating in Saudi Arabia. Smart-PLS was used to analyze the data and demonstrate the findings. ACAP demonstrated its importance and impact on DCAP, agility, and resilience. Therefore, ACAP enhances the SCIP when this relationship is mediated by DCAP and DI. Moreover, ACAP improves SCR and SCA, improving SCIP. Notably, there is no systematic theory-driven empirical examination of the unique performance implications of SCA and SCR in the context of SC innovation. SC theory on the implications of agility and resilience in the SC context remains fragmented and without a foundation in established theoretical frameworks. As long as organizations improve the innovation of digital SC technologies, SCIP will improve. Finally, few studies examine the impact of ACAP and companies’ capabilities to enhance DI, agility, and resilience, thereby improving the SCIP.

6.1. Theoretical Implications

Given the moderate to severe impacts of the COVID-19 pandemic on companies, ACAP can improve SCIP. The greater the level of ACAP, the greater the SCIP. ACAP may be a crucial factor that favorably impacts SC connections. By assimilating, transforming, and exploiting the latest and most relevant externally acquired knowledge, businesses can better understand their customers’ needs or suppliers’ capabilities, allowing them to synchronize with suppliers, align and coordinate resources along the SC, and improve the overall SC’s responsiveness and efficiency more precisely.

Organizations may leverage existing information and communication technology to communicate data with customers, suppliers, and other businesses in the SC; therefore, ACAP may also help with data integration. For instance, organizations can integrate more knowledge and information, enhance their ability to spot market shifts quickly, respond to capture the need of new customers, and predict competitors’ movements. Thus, DCAPs, SCR, and SCA allow ACAP to increase firm performance.

Although the management literature gives some insight into how ACAP may impact DCAPs, thus impacting DI, this study implies that the incentive for disaster relief activities after the pandemic stems from SC dynamic capacities. Using DCAPs to boost a company’s performance is one of the primary goals of this study. Although DCAPs are not required, they will help companies move toward innovativeness. The DI (mediating variable) exerts a beneficial influence on SCIP based on SC DCAP.

The empirical findings present an interesting picture of the interrelationships and complementarities between ACAP and SCA, SCR, and DCAPs for companies affected by the pandemic. This research examines the concept of ACAP and how it benefits SC. The capability to absorb data can enhance SCA and SCR for an increased sustainable advantage in the long term. SCA helps an organization respond quickly to changes in consumer demand volume, product variety, and delivery schedule. Meanwhile, SCR permits the SC to execute innovatively despite any limits caused by disruptions in supply or during a pandemic. Once the ACAP-oriented innovative digital technologies process is implemented, it can enhance a firm’s ability to respond to changes in client demand at any given moment, hence increasing a firm’s long-term competitive edge.

This discussion hinges on the premise that SCA and SCR are two dynamic SC characteristics. Initially, they were often considered alternatives, given the shortage of resources and the constraints of the management scope. As per the conventional perspective, firms benefit from refining their SCA capabilities or expanding their SCR capability.

6.2. Managerial Implications

This study offers several insights for managers and practices to enhance SC innovation. First, keeping the company’s information flow and knowledge base up to date is essential. The company’s ability to react to the dynamic environment hinges on the acquisition, assimilation, and transformation of such knowledge, which may stem from staff, suppliers, consumers, or even rivals. Thus, ACAP is a valuable dynamic capacity; managers should focus their efforts on fostering this capacity and allocating sufficient resources to boost their company’s success. Therefore, by embracing digital technology, companies can boost their performance. The considerable impact of DI is crucial in transforming DCAP into improved performance. Firms’ digital strengths must be utilized for DI.

Second, the findings may provide practitioners involved in disaster relief operations with some intriguing recommendations. During the post-pandemic phase, an organization’s flexibility can help prioritize its reactive skills, such as speed and recovery. SC partners must collaborate to survive amid disruptions. Hence, improving the information system by investing in ACAP may be an effective way to collect quality data that can help organizations to increase SCA and SCR and to be better equipped to manage SC interruptions. These factors may help firms continue to deliver services, resulting in continuous customer satisfaction and long-time success. In uncertain environment, managers can find ways to improve the company’s productivity and performance and its competitive advantage by improving SC capability and resilience. Successful deployment of dynamic capabilities like ACAP is necessary to grow, maintain, and apply SCA and SCR capacity to react rapidly to market disturbances. The findings accord with best practices for creating robust and flexible SCs. They may be put to use by supply chain managers who choose to apply ACAP to attain SCIP via the enhancement of DCAP, DI, SCA, and SCR. ACAP may help managers develop fundamental capabilities in response to the ever-changing environment, ultimately giving them a distinct edge. Therefore, this study reveals the importance of ACAP in creating and attaining SCR and SCA. Additionally, it shows that ACAP has an indirect effect on the SCIP of the organizations.

6.3. Limitations and Future Research Directions

Despite the implications, it is important to be aware of the study’s possible limitations. This study was conducted in the context of the COVID-19 pandemic; thus, the findings of this study are limited. First, the sample study was small, which can affect generalizability. Second, this study was conducted in Saudi Arabia, which has a different experience of the pandemic relative to other countries. It is unclear whether the study can be applied to other crises or specific sectors, but it is worth investigating in the future. Future studies must collect more data and compare two countries to see the impact of a digitalized SC on performance during the pandemic. Third, future studies can probe the impact of digital orientation and technological culture on innovation. Others could implement longitudinal or case-based studies to further understand and explain how organizations learn from and enhance their SC operations. Nevertheless, the findings of this study are adequately robust to offer empirical support for the explanation of SCIP by showing the impact of ACAP on DCAP, DI, agility, and resilience.

Author Contributions

Conceptualization, S.H.A.; formal analysis, S.H.A.; methodology, S.H.A.; supervision, M.A.S.; validation, S.H.A., R.M.M. and M.A.S.; writing—original draft, S.H.A.; writing—review and editing, S.H.A., R.M.M. and M.A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Scale items.

Absorptive Capacity
In our firm, ideas, concepts, and information are communicated smoothly across departments	[105]
2. In our firm, there is a quick information flow (e.g., if a business unit obtains important information, it communicates this information promptly to all other business units or departments)
3. Our employees can structure and use collected market knowledge
4. Our employees are used to absorbing new market knowledge, preparing it for further purposes, and making it available
5. Our management supports the implementation of marketing strategies based on acquired market knowledge
6. Our firm regularly reconsiders technologies and routines and adapts them according to new market knowledge
Digital Capability
Acquiring important digital technologies	[27]
2. Identifying new digital opportunities
3. Responding to digital transformation
4. Mastering the state-of-the-art digital technologies
5. Developing innovative products/services/processes using digital technology
Supply Chain Resilience
Our firm’s supply chain can adequately respond to unexpected disruptions by quickly restoring its product flow	[106]
2. Our firm’s supply chain can quickly return to its original state after being disrupted
3. Our firm’s supply chain can move to a new, more desirable state after being disrupted
4. Our firm’s supply chain is well-prepared to deal with the financial outcomes of potential supply chain disruptions
5. Our firm’s supply chain can maintain the desired level of control over structure and function at the time of disruption
Supply Chain Agility
We can quickly detect changes in our environment	[60]
2. Our firm can promptly identify opportunities in its environment
3. My organization can rapidly sense threats in its environment
4. We always receive the information we demand from our suppliers
5. We always obtain the information we request from our customers
6. My company can make resolute decisions to deal with changes in its environment
7. We can make definite decisions to address opportunities in our environment
8. My organization can make firm decisions to respond to threats in its environment
9. My firm can quickly respond to changes in the business environment
10. We can rapidly address opportunities in our environment
11. We can swiftly deal with threats in our environment
12. When needed, we can adjust our supply chain operations to the extent necessary to execute our decisions
13. My firm can increase its short-term capacity as needed
14. We can adjust the specification of orders as requested by our customers
Digital Innovation
The quality of our digital solutions is superior to that of our competitors	[107]
2. The features of our digital solutions are superior to those of our competitors
3. The applications of our digital solutions are totally different from those of our competitors
4. Our digital solutions are different from those of our competitors regarding product platform
5. Our new digital solutions are minor improvements to existing products
6. Some of our digital solutions are new to the market at the time of launching
Supply Chain Innovation Performance
Pursue continuous innovation in core processes	[108,109]
2. Pursue new technological innovation
3. Focus on process innovation
4. Just-in-time
5. Inventory turnover and cash-to-cash cycle time
6. Customer-led time and load efficiency
7. Delivery performance and quality
8. Supply chain inventory visibility and opportunity costs
9. Total logistics cost

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Figure 1. Study framework.

Figure 2. R² and T values.

Table 1. Work characteristics (N = 116).

Variable		N	%
Full-time employees (size)	50 or fewer	19	16.07
	51–500	29	25.00
	501 or more	68	58.93
Company is in the business (age)	10 or fewer	23	19.64
	11–40	35	30.36
	41 or more	58	50.00
Company’s sales revenue (in riyals)	<SAR 50 million	29	25.00
	SAR 51–250 million	25	21.43
	SAR 251–500 million	21	17.86
	SAR 501 million or more	41	35.71
Job level	General manager	48	41.07
	Executive	19	16.07
	Senior manager	19	16.07
	Director	12	10.71
	Logistics supervisor	4	3.57
	Others	14	12.06
Impact of the COVID-19 pandemic on the firm’s overall performance	Severe	33	28.57
	Moderate	71	60.71
	Not at all	12	10.71

Table 2. Factor loading and VIF (N = 116).

Dimension	Items	Factor Loading	VIF
Absorptive Capacity (ACAP)	ACAP1	0.836	2.582
	ACAP2	0.792	2.155
	ACAP3	0.837	2.594
	ACAP4	0.863	2.969
	ACAP5	0.892	3.037
Digital Capability (DCAP)	DCAP2	0.88	2.977
	DCAP3	0.914	3.913
	DCAP4	0.909	3.679
	DCAP5	0.937	4.606
Digital Innovation (DI)	DI1	0.884	2.219
	DI3	0.915	2.914
	DI4	0.9	2.549
Supply Chain Agility (SCA)	SCA2	0.854	2.809
	SCA3	0.744	1.961
	SCA6	0.855	2.936
	SCA8	0.825	2.666
	SCA11	0.874	3.024
Supply Chain Innovation Performance (SCIP)	SCIP5	0.872	2.927
	SCIP6	0.89	2.796
	SCIP7	0.777	2.015
	SCIP8	0.846	3.108
Supply Chain Resilience (SCR)	SCR1	0.912	3.284
	SCR3	0.893	2.822
	SCR4	0.895	3.072
	SCR5	0.88	2.669

Note: VIF—variance inflation factor.

Table 3. Average variance extracted, composite reliability, Cronbach’s alpha, R square.

Variable	AVE	Composite Reliability	Cronbach’s Alpha	R Square	Q² (=1 − SSE/SSO)
Absorptive capacity	0.713	0.907	0.899	NA
Digital capability	0.828	0.939	0.931	0.562	0.552
Digital innovation	0.81	0.883	0.882	0.479	0.379
Supply chain agility	0.69	0.912	0.909	0.46	0.473
Supply chain innovative performance	0.718	0.871	0.868	0.754	0.569
Supply chain resilience	0.802	0.92	0.918	0.422	0.394

Note: AVE—average variance extracted; SSE—sum of squares error; SSO—sum of squares of observation.

Table 4. Fornell–Larcker criterion.

	ACAP	DCAP	DI	SCA	SCIP	SCR
ACAP	0.845
DCAP	0.750	0.910
DI	0.635	0.692	0.900
SCA	0.678	0.777	0.659	0.831
SCIP	0.781	0.778	0.754	0.778	0.847
SCR	0.650	0.595	0.642	0.711	0.768	0.895

Note: ACAP—absorptive capacity; DCAP—digital capability; DI—digital innovation; SCA—supply chain agility; SCIP—supply chain innovation performance; SCR—supply chain resilience.

Table 5. Heterotrait–monotrait ratio.

	ACAP	DCAP	DI	SCA	SCIP
ACAP
DCAP	0.811
DI	0.707	0.757
SCA	0.74	0.839	0.735
SCIP	0.881	0.865	0.859	0.875
SCR	0.708	0.642	0.711	0.78	0.859

Note: ACAP—absorptive capacity; DCAP—digital capability; DI—digital innovation; SCA—supply chain agility; SCIP—supply chain innovation performance; SCR—supply chain resilience.

Table 6. Relationship between variables (N = 116).

H	Path	β	T	Remarks
H1	ACAP → DCAP	0.75 ***	14.594	0
H2	DCAP → DI	0.692 ***	13.838	0
H4	DI → SCIP	0.329 ***	4.672	0
H6	ACAP → SCA	0.678 ***	9.918	0
H7	SCA → SCIP	0.334 **	2.955	0.003
H9	ACAP → SCR	0.65 ***	7.173	0
H10	SCR → SCIP	0.319 **	2.888	0.004
H3	ACAP → DCAP → DI	0.519 ***	9.261	0
H5	DCAP → DI → SCIP	0.214 ***	4.268	0
H8	ACAP → SCA → SCIP	0.227 *	2.347	0.019
H11	ACAP → SCR → SCIP	0.207 *	2.389	0.017

Keys: β = coefficient path; T = T-statistic. * p < 0.05, ** p < 0.01, *** p < 0.001.

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Abourokbah, S.H.; Mashat, R.M.; Salam, M.A. Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance. Sustainability 2023, 15, 3636. https://doi.org/10.3390/su15043636

AMA Style

Abourokbah SH, Mashat RM, Salam MA. Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance. Sustainability. 2023; 15(4):3636. https://doi.org/10.3390/su15043636

Chicago/Turabian Style

Abourokbah, Safinaz H., Reem M. Mashat, and Mohammad Asif Salam. 2023. "Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance" Sustainability 15, no. 4: 3636. https://doi.org/10.3390/su15043636

APA Style

Abourokbah, S. H., Mashat, R. M., & Salam, M. A. (2023). Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance. Sustainability, 15(4), 3636. https://doi.org/10.3390/su15043636

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Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance

Abstract

1. Introduction

2. Literature Review and Hypotheses Development

2.1. Absorptive Capacity

2.1.1. Absorptive Capacity and Digital Capability

2.1.2. Digital Capability and Digital Innovation

2.1.3. Digital Innovation and Supply Chain Innovation Performance

2.2. Supply Chain Agility

2.2.1. Absorptive Capacity and Supply Chain Agility

2.2.2. Supply Chain Agility and Supply Chain Innovative Performance

2.3. Supply Chain Resilience

2.3.1. Absorptive Capacity and Supply Chain Resilience

2.3.2. Supply Chain Resilience and Supply Chain Innovation Performance

2.4. Conceptual Model

Theoretical Foundation

2.5. Impact of COVID-19

3. Research Methodology

3.1. Measurement Model

3.2. Sampling Method and Data Collection

4. Results

4.1. Descriptive Analysis

4.2. Common Method Bias and Variance

4.3. Measurement Model

4.3.1. Convergent Validity

4.3.2. Discriminant Validity

4.4. Evaluating the Structural Model

4.5. Mediation Analysis

5. Discussion

6. Conclusions

6.1. Theoretical Implications

6.2. Managerial Implications

6.3. Limitations and Future Research Directions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Appendix A

References

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