How artificial intelligence-based supply chain analytics enable supply chain agility and innovation? An intellectual capital perspective Free

During the last two decades, the world has moved toward innovative economies based on the latest technologies and the information and communication revolution to avoid uncertainty and achieve high efficiency by allocating resources and using them optimally (Ben-Daya et al., 2019). Therefore, most firms’ supply chains try to integrate their various operational and logistical activities with modern technologies based on accurate information to increase the flow of capital to them and increase their rapid transformation into innovative supply chains (ISCs) (Fosso Wamba and Akter, 2019). By surveying the relevant literature, most of the ISCs are considered an operational model in their logistical operations. Which dispense in their work with traditional operations that depend on human forces. Such as the use of accurate technological techniques and high sensitivity in their performance based on big data, artificial intelligence, blockchains, automation and digital management to achieve effective cooperation in all different activities, internally and externally, to make innovative decisions and provide distinct and unique products or services to customers (Hopkins, 2021). Therefore, the “innovative” characteristics of supply chains are embodied in their precise, risk-free digital operations and smart solutions by adopting the latest technologies (Shamout, 2019; Ülkü and Engau, 2021).

A number of studies indicated that the innovation that is adopted in supply chains could be divided into two different strategies, namely, exploration and exploitation (Secundo et al., 2017; Švarc et al., 2021). The concept of exploration refers to the search for new technologies and resources, their deployment and inclusion within the various operational processes, while the concept of exploitation refers to the use of renewable and advanced knowledge in the establishment of modern and unique activities Dabić et al., 2021; Mubarik et al., 2022). Therefore, most smart and industrial control systems adopt a strategy of integrating the exploration and exploitation factors to carry out their various logistical operations and activities in a smart manner through the use and adoption of modern and emerging technologies such as big data, blockchain and artificial intelligence to improve their digital performance and so on (Shou et al., 2018a, 2018b; Pinto, 2020).

The originality of this study lies in its focus on intellectual capital (IC) and artificial intelligence (AI)-based supply chain analytics in Jordan’s manufacturing sector. While prior research has extensively examined these relationships in developed countries (e.g. Ülkü and Engau, 2021; Pinto, 2020), limited attention has been given to the unique challenges and opportunities within developing economies. This study emphasizes the interaction between IC components and AI-based analytics to enhance supply chain agility and innovation, offering insights applicable to Jordan and similar emerging markets facing technological adoption barriers (Mubarik et al., 2022; Shamout, 2019).

Nowadays, most firms are facing many increasing challenges because of the rapid shifts in the requests and needs of customers as well as the volatile dynamic environments, which has led to the difficulty of the flow and stability of operations and logistical activities related to the firms’ supply chains. Therefore, it has become necessary for ISCs to work smartly by relying on modern technologies to connect their members and increase cooperation among them to facilitate the smooth flow of goods by making decisions based on big data and spreading innovation in the supply chain network. Thus, Production complexity is also increasing under the pressures of the accelerated advancement in industrial technologies and the continued development and introduction of novel products to markets. There are a number of challenges, including uncertainty that prompted most firms to reconsider the restructuring, design and engineering of private supply chains in line with modern and ever-accelerating developments to adopt supply chain innovation (SCI) to enhance their survival in the markets and raise their competitiveness for all its members. From this point of view, Yuan et al. (2022) described SCI as complex operations to deal with uncertainty and rapid response to diverse and ever-changing demands by relying on modern technologies to improve the efficiency and performance of supply chains.

The role of SCI has become one of the important factors for obtaining sustainable competitive advantages. Most firms in the current working environments seek strongly to adopt and use modern analytical techniques based on intelligence to support their decisions to balance supply and demand, speed and flexibility in responding to changing demands, reduce costs, increase productivity and optimal use of resources (Kache and Seuring, 2017). All these factors made supply chains depend heavily on their strategies on supply chain analytics. Which have a pivotal role in raising their efficiency, especially with the digital era in the birth of big data applications and their role in predictive analytics (Kache and Seuring, 2017). It made their operations and activities Logistics is more able to adapt and deal in turbulent and volatile business environments through agility and rapid response to the challenges of dynamic business environments (Alzoubi and Yanamandra, 2020). Digital transformation has become driven by the continuous development of modern smart technologies, hence the question to what extent and what do firms need to support the power of their supply chain analytics to keep pace with this development. Therefore, it was necessary to adopt IC that relies in its work and methodology on renewable and inherent knowledge in the workforce, corporate structures and relationships between business partners. Knowledge, investing in innovative digital solutions and business intelligence technology is essential these days (Mubarik et al., 2022).

Many related literature confirmed the great value of supply chain analytics, but it was found that there is a significant discrepancy with regard to the extent of adoption of modern technologies and advanced techniques among manufacturing firms. Therefore, this study comes to fill the knowledge gap in the literature by empirically examining the relationship between supply chain analytics, IC, structural capital and social capital relaying on resource-based theory (RBT). This is in addition to the impact and ability of supply chain analytics on supply chain agility and supply chain innovation in the research model of this study, in addition to addressing the potential link between them. Furthermore, assess the impact of supply chain agility on supply chain innovation.

This study attempts to fill some research gaps in the field of AI-based supply chain analytics and innovation in addition to its agility in manufacturing firms in developing countries, as shown in the following points:

• With regard to the supply chain management (SCM) literature, the main factors addressed in this study were not addressed by previous studies to focus on supply chain analyzes as a strategic and competitive advantage and put them into a single model framework and their effects on supply chain agility and innovation in developing countries; and

• Most of the previous studies, the subject of the study, were conducted in developed countries, while a limited number were conducted in developing countries.

To address these gaps in the literature, this study formulates the following research questions:

To further strengthen the theoretical foundation, this study integrates seminal works on supply chain analytics and IC in emerging markets. For example, Pinto (2020) highlights the barriers to AI adoption in developing regions, including infrastructure and resource constraints. Furthermore, Ülkü and Engau (2021) emphasize the critical role of social capital in enabling technological innovation and collaboration among supply chain partners. These perspectives complement the study’s focus on IC as a driver for AI-based analytics adoption in Jordan’s manufacturing sector.

This study is organized as follows. Sections 2 and 3 will present a theoretical background and hypotheses testing; while Section 4 will explain the research design and methods used. Following that, the data analysis and discussion will be presented in Sections 5 and 6. A conclusion and study’s limitations will be addressed in the last section.

This theoretical framework draws on RBT, which emphasizes leveraging a firm’s valuable, rare, inimitable and non-substitutable (VRIN) resources and capabilities to achieve competitive advantage (Barney, 1991). IC, supply chain analytics and agility represent such resources and capabilities, particularly in the context of volatile markets and developing economies (Ülkü and Engau, 2021; Mubarik et al., 2022). RBT further highlights that these VRIN resources enable firms to withstand environmental uncertainties, capitalize on opportunities and sustain competitive advantages by building and maintaining innovative and agile operations (Barney, 1991; Wamba et al., 2020). RBT highlights that firms must focus on using these VRIN resources to create sustainable competitive advantages and improve their performance (Barney, 1991).

Many researchers have increasingly used technology adoption theories and models to apprehend and predict user’s behavior on adoption or acceptance of the technology (Fosso Wamba and Akter, 2019). Therefore, most managers resort to knowing the main factors that may affect the user’s decision to adopt modern innovative technologies that will speed up, facilitate work and raise their performance (Dubey et al., 2019; Ogbuke et al., 2022). Hence the importance of adopting business analytics in supply chains, which refers to the processes it uses in its various logistical activities by extracting value from big data that is related to supply and demand operations. As well as purchasing operations of various types in general, in addition to the optimal distribution of resource use in real time from warehouse and logistics transportation (Aamer et al., 2020). In addition, it is possible to study supplier plans and the possibility of predicting the availability of quantities of raw materials needed for production operations through the firms’ supply chain analytics, which enables them to develop a better understanding of dynamic processes as well as monitor vendor lead times, schedules and packing operations better to get rid of redundant orders. From this standpoint, four types of analyses can be included in supply chain activities through descriptive, analytical, predictive, diagnostic and cognitive analyses (Khan et al., 2023).

Supply chain analytics capability adoption is a translation of using information and analytical tools to improve decision-making, planning, sourcing, manufacturing and integrating operations of supply chains (Dubey et al., 2019). Further, it can be driven by the high potential of big data in terms of collecting, analyzing, using and interpreting supply chain data, which has enabled firms to create business value, gain actionable insights and establish competitive advantages in rapidly changing contexts (Ogbuke et al., 2022). Within the RBT framework, supply chain analytics can be seen as a technological resource that helps firms achieve strategic advantages by enabling real-time decision-making and predictive analysis (Wamba et al., 2020; Ülkü and Engau, 2021). This aligns with the growing need for data-driven decision-making to address supply chain complexities in both developed and developing economies. While these studies provide valuable insights, they predominantly focus on developed countries, leaving gaps in understanding how supply chain analytics can address the unique challenges faced by firms in emerging markets, such as resource constraints, technological barriers and skill gaps (Ülkü and Engau, 2021; Wamba et al., 2020). It has extracted from innovation diffusion theory and decomposed theory of planned behavior framework (Wamba et al., 2020). By reviewing the literature, most of the previous studies dealt with different factors and aspects of big data analytics in the activities and operations of supply chains, with a focus on adopting the latest technological technologies (Leung et al., 2020). The role of supply chain using big data analytics in improving demand forecasting has attracted many scholars (Zhao et al., 2018; Leung et al., 2020; Stekelorum et al., 2021). Another stream of research has focused on challenges and cyber threats (Arunachalam et al., 2018; Ogbuke et al., 2022). However, although scholars have confirmed the role of analytics capability in supply chain management, there is still a lack of theory and empirical research on the drivers and impact of supply chain analytics, particularly in developing countries (Mubarik et al., 2022).

IC has gained great importance in many previous studies as intangible assets of high importance in the development and advancement of companies and giving them a comparative advantage in many fields, especially in the recent years of the digital era, which is also considered an integral part of firms’ value-creating processes (Mubarik et al., 2022). Therefore, many researchers argued that most firms are looking for external communication of IC, which gives them several competitive strengths and thus the value of their firm on know-how, patents, skilled employees and such alike (Pinto, 2020). The evolution of IC theory has been associated with the revolution of knowledge-based economy. However, there is a general agreement that IC is an umbrella of three intangible capitals, including human, structural and social (Han and Li, 2015). Human capital represents the development and use of individuals’ knowledge in specific firms, which also elaborates on the firms’ thinking assets and collective capabilities and intelligence of employees through their qualifications, expertise, skills and variety of talents. In the end, it provides a firm with the power of knowledge and learning of innovation (Tu, 2020). The other type is social capital, which refers to the incorporated networks and relationships internally and externally. The focus has been on social capital in a number of studies that collect expertise and competencies to facilitate the work of firms to create collaborative knowledge among themselves (Han and Li, 2015). In the context of RBT, IC serves as a vital intangible resource that enhances firms’ dynamic capabilities, enabling them to respond to market uncertainties and innovate effectively (Chen and Chen, 2022; Al-Omoush et al., 2022).

Most previous studies emphasized that IC is considered as a renewable warehouse of innovation and learning that continually produces novel knowledge, enabling firms to deal with environment uncertainty (Mubarik et al., 2022). According to Al-Omoush et al. (2022), IC assets have an essential role in developing and exploiting dynamic organizational capabilities (Chen and Chen, 2022). The theory of IC has demonstrated valuable contributions to supply chain management, which is considered a fundamental enabler of ISC management. However, most studies have declared the significant adoption of IC and IT and their fundamental role in the tendency of firms to use novel digital solutions in innovative ways to achieve better outcomes (Mubarik et al., 2022). Recently, the impact of IC on different issues of big data has gained the attention of researchers (Švarc et al., 2021; Ahmed et al., 2022a, 2022b). Nevertheless, rarely empirical studies have examined the potential impact of IC on supply chain analytics in manufacturing firms context; thus, this study has been conducted.

SCI refers to collecting developments in the field of information technology and modern technologies and matching them with modern procedures in operations, functions and various logistical activities within the chain to improve operational efficiency and raise the effectiveness of its capabilities (Malacina and Teplov, 2022). Therefore, SCI is the result of new and unique innovations that are made, adopted and changed gradually or completely. So that, they work on the integration of the various functions in terms of production and marketing operations and the allocation and distribution of resources. Which are carried out through well-scheduled plans so that they are implemented in cooperation with managers in the supply chain, which includes all the various related functions to achieve common value and a unique competitive advantage for all members within the chain (Shafique et al., 2019; Sarkis, 2020). Thus, SCI, according to many previous studies, is considered the heart of maintaining supply chain sustainability (Malacina and Teplov, 2022).

Nowadays, most supply chains adopt strategies to be more adaptive and flexible in their organizational processes, which need to collaborate between their business partners and managing supply chains (Beltagui et al., 2020). Numerous studies indicated that SCI is represented in innovations and models of new operations and logistics support services in a unique pattern (Fan, 2022). However, recent studies confirm a lack of empirical research exploring the impact of intelligent technological capabilities on SCI that needs more investigation (Karaman Kabadurmus, 2020; Sarkis, 2020). While there is an increasing attention to studying the association between big data, cloud computing, blockchain capabilities and SCI in different contexts (e.g. Zhang et al., 2022; AL-Khatib, 2023), very little empirical research is available on the role of supply chain analytics in the context of manufacturing firms.

The agility factor has become important in most firms and their supply chains, which would interact and respond quickly to various variables and conditions in uncertain volatile markets (Naughton et al., 2020). Therefore, many previous studies confirmed that preventive action and behavior is one of the determinants of agile practices, especially in amoebic markets with an unknown trend in unexpected requests (Do et al., 2021). In addition, the agility of the supply chain gives it the critical dynamic ability that represents adaptive intelligence by analyzing signals coming from different markets and sensing indicators to react quickly to disruptions in business environments (Al Humdan et al., 2020). According to Cegarra-Navarro and Martelo-Landroguez (2020), agility in supply chains represents its ability to deal with and move quickly to adjust its paths and strategic plans in all its logistical activities to adapt to rapid changes in volatile business environments and respond to them with high efficiency.

Unprecedented risks and uncertainty in local and global business environments and disruptions in supply and demand have emphasized the importance of supply chain agility. Recently, a wide range of studies have attempted to understand and analyze the drivers and critical success determinants that make supply chains agile (Haq et al., 2015). Previous studies have examined the impact of information access, planning and control, operational capabilities, supply chain processes integration, top management support, forecasting and replenishment, the role of collaborative relationships between supply chain members, market sensitivity and continuous learning on supply chain agility (Irfan et al., 2019; Al Humdan et al., 2020). There is a great and increasing interest in the role of logistics operations and operational capabilities, such as speed in delivering orders to customers, in shaping the speed of response of agile supply chains to them (Al-Shboul, 2017). In addition, the role of digital solutions in supporting supply chain agility has attained substantial attention (Abourokbah et al., 2023). However, the literature review shows a clear lack of examination of the potential relationship between supply chain velocity and supply chain innovation.

Numerous past kinds of research are based on the resource-based view (RBV) theory in explaining the different relationships within the supply chain framework. In this paper, the theoretical basis and the framing lens that explain the relationships between the constructs is the RBV theory. This theory is one of the most common and widely used theories in understanding how to mobilize and exploit resources in a way that guarantees operational and organizational superiority (Barney et al., 2001), leading to acquiring competitive advantages (Barney, 2001) that guarantee firms superiority over their competitors. The RBV theory emphasizes that firms achieve sustainable competitive advantage by leveraging their VRIN resources to enhance their strategic capabilities. These VRIN resources include IC, supply chain analytics and supply chain agility, which enable firms to respond effectively to environmental uncertainties and drive innovation (Ülkü and Engau, 2021; Wamba et al., 2020). The theory serves as a foundation for understanding the mechanisms through which these resources interact to impact supply chain innovation and agility.

In the era of Industry 4.0, supply chains are increasingly dependent on emerging technologies, which are the cornerstones of enhancing digital supply chain capabilities (Al-Khatib and Ramayah, 2023; Mahmood and Mubarik, 2020), making them more responsive to dynamic changes in the business environment. IC provides the ability to adopt new technology in various organizations and firms (Kusi-Sarpong et al., 2022). IC is one of the necessary determinants to enhance firms’ innovation and competitiveness. According to the IC -based view, firms’ possession of tangible and intangible knowledge-generating assets can move firms to a distinct competitive position (Yaseen et al., 2016). IC can be seen as a set of skills, experiences and tacit and collective knowledge that individuals possess in an organization (Alkhatib and Valeri, 2024). The skills, experience and knowledge possessed by the members of the teamwork, which are used to accomplish the tasks entrusted to them, play a major role in enhancing the productivity of firms and achieving financial and nonfinancial returns.

Although empirical studies that review the relationship between IC and supply chain analytics are scarce, there is a body of studies that have investigated the importance of potential relationships between IC and Industry 4.0 technology (Cabrita et al., 2019; Gashenko et al., 2020; Wang et al., 2021) and big data analytics (De Santis and Presti, 2018).

Human capital is one of the most important components of IC, which expresses the intangible asset of firms (Mubarik et al., 2018), which raises firms’ ability to innovate (Gravili et al., 2021). In the context of Industry 4.0, supply chain analytics can reach new, unfamiliar links by analyzing a wide range of diverse and different data (Jeble et al., 2020) and thus find new solutions in the supply chain to enhance performance (Al-Khatib and Ramayah, 2023). Training and acquiring new skills, especially big data analytics skills, will help firms use big data to promote innovation (Kusi-Sarpong et al., 2022) and improve supply chain response to sudden and different changes in the business environment (Mubarik et al., 2022) by increasing control over the flow of materials and inventory (Rejeb et al., 2019) and controlling logistical activities such as transportation (Gashenko et al., 2020; Rejeb et al., 2022), enhancing firms’ capabilities to improve the chain supply performance.

Human capital plays an important and critical role in firms’ adoption of new technology and supply chain analytics (Kusi-Sarpong et al., 2022; Popkova and Sergi, 2020). Human capital within firms can contribute to developing algorithms and software related to supply chain analytics, enhancing the efficiency of the analysis process (Al-Khatib and Shuhaiber, 2022), and thus increasing these analytics level of use by supply chain managers.

Consequently, the following hypothesis can be assumed:

Structural capital is one of the components of IC that expresses the extent of the organizational capacity of an organization and helps it transform the innovations, ideas and creativity of its teamwork into tangible reality or assets (Shou et al., 2018a, 2018b). These assets are characterized by the ability to store and retrieve them at any possible time, and thus they differ from human capital by firms’ ability to keep them for a long time (Al-Khatib, 2022). Structural capital includes organizational policies, rules, procedures, databases, patents and trademarks (Kusi-Sarpong et al., 2022) to create new business value. Structural capital can transform the tacit knowledge within the minds of individuals into an organizational routine that can be shared with everyone (Mahmood and Mubarik, 2020). In the context of the supply chain, structural capital plays a role in increasing the integration between all units and parties in the supply chain (Al-Khatib and Shuhaiber, 2022), leading to an improvement in the response of the supply chain to sudden changes.

Structural capital can improve firms’ use of supply chain analytics through the ability of these firms to access knowledge through their multiple sources in the supply chain, such as suppliers, manufacturers, customers and stakeholders (Mubarik et al., 2022), leading to using reliable and more relevant data in the activities of the supply chain (Fosso Wamba and Akter, 2019), achieving greater efficiency during the analysis of supply chain data, which enhances the benefit of the supply chain from this data to reach wise and smart decisions that reflect positively on performance (Kusi-Sarpong et al., 2022). In addition, structural capital can provide transparency in using big data in the supply chain (Gul et al., 2021) and improve the quality of this data. According to the point of view of Mahmood and Mubarik (2020), the firms’ possession of a strong structural capital will set institutional standards that help adopt new technology, including big data analytics and supply chain analytics, contributing to developing operations efficiency.

Consequently, the following hypothesis can be assumed:

Social capital refers to the organizational resources organizations possess that are rooted in their social relationships through social networks between individuals, organizations and stakeholders (Al-Omoush et al., 2020). Social capital helps build long-term relationships internally and externally (Al-Khatib, 2022), helps build trust between all parties in the supply chain beginning with suppliers and ending with consumption and sales centers (Ye et al., 2023) and creates a large amount of data to be used in the long run (Al-Khatib and Valeri, 2022).

Firms’ ability to build social capital will help to benefit from the expertise of suppliers and cooperating firms (Al-Khatib and Shuhaiber, 2022) and thus provide new opportunities to access data through these sources (Kusi-Sarpong et al., 2022). Social capital helps firms access the most reliable and transparent data through positive cooperation between firms, stakeholders and suppliers to transfer data and information between all these parties easily and without any barriers or obstacles between them (Zhang et al., 2023). In addition, firms having solid social capital can transfer knowledge between suppliers and key stakeholders, thus improving cooperation and integration between them and facilitating the adoption of new technology, including Industry 4.0 and supply chain analytics (Shamout, 2019).

Social capital contributes to improving the supply chain performance by exchanging experiences related to big data analytics between the firm and cooperating firms such as suppliers, enhancing firms’ ability to exploit their analytical capabilities to reach the best predictive and statistical models by analyzing data issued from the supply chain (Gul et al., 2021; Al-Khatib and Shuhaiber, 2022), and improve operational performance and enhance research and innovation in the supply chain.

Consequently, the following hypothesis can be assumed:

One of the clear contributions of Industry 4.0 technology is to enhance organizations’ capabilities to introduce new products, processes or ways of working (Cannavacciuolo et al., 2023; Ghobakhloo et al., 2021) that enable new value creation within associated activities in the supply chain. Supply chain innovation is one of the issues that have gained great importance and popularity among scholars during the past periods (Al-Khatib, 2023). With the emergence of many disasters and crises, such as the Russian–Ukrainian crisis and the COVID-19 pandemic, viewing innovation in general and in the supply chain, in particular, has become more important than before (Khan et al., 2023).

Supply chain innovation refers to the strategies that managers use to enhance supply chain performance by introducing new products and organizational processes that help them build a supply chain capable of responding more quickly to changes in demand, supply and markets (Bahrami et al., 2022). Firms using their innovative capabilities will lead to providing unprecedented innovative models that can be used to promote innovation within the supply chain (Liu et al., 2022; Yuan et al., 2022).

A body of studies showed a positive relationship between big data analytics and supply chain innovation (Al-Khatib and Ramayah, 2023; Bahrami et al., 2022; Kalaitzi and Tsolakis, 2022; Ogbuke et al., 2022; Shamout, 2019). Big data analytics provides appropriate methods and methodologies to reach the best decisions based on facts through data (Shafiq et al., 2020). Supply chain analytics is used to enhance the organization’s efforts to achieve the highest benefits from research and development efforts (Al-Khatib and Ramayah, 2023) and to provide new and innovative solutions to the problems that the supply chain may be exposed to (Bahrami et al., 2022) and thus improve operational performance.

Supply chain analytics provides the ability to analyze inbound data and outbound data, which contributes to understanding the requirements and desires of suppliers, customers and stakeholders (Ahmed et al., 2022a, 2022b), which improves integration between them and enhances internal integration, creating great opportunities for knowledge transfer and exchange, and thus creating an innovative environment that can contribute to enhancing supply chain innovation (Ali et al., 2021; Wang and Hu, 2020).

Consequently, the following hypothesis can be assumed:

Supply chain agility refers to the organizational capabilities that a company acquires and helps it proactively modify and shape its operations and strategies in the supply chain to respond to dynamic changes in the supply chain (Gligor et al., 2013). The concept of supply chain agility is very important in the era of digital transformation (Centobelli et al., 2020) and in the era of various crises and disasters (Rahman et al., 2022), which poses a major challenge to the sustainability of global supply chains. Supply chain agility is one of the necessary determinants necessary to achieve organizational success and excellence. Supply chain agility strategies enable the exploitation of all the capabilities of individuals and work teams within the supply chain to reach new potential opportunities that enable these firms to enhance their performance.

It is recognized among scholars in the field of supply chain management that the new determinants in enabling supply chain agility require a massive flow of data related to markets, demand, supply, inventory and manufacturing processes (Dubey et al., 2019). Data is considered a strategic asset to enable companies to respond to sudden and unexpected changes in the supply chain (Wamba et al., 2020). Industry 4.0 has played an important role in facilitating data access and processing by using the Internet of Things and artificial intelligence (Agrawal et al., 2023) and big data analysis, which enhances the process of collecting and processing this data and using it to improve integration and cooperation between all parties involved in the supply chain (Mubarik et al., 2019).

The use of supply chain managers for supply chain analytics can provide many advantages that reflect positively on supply chain agility (Shamout, 2020). It is possible to use this data flowing from its various sources to seize potential opportunities by using sensing (Ogbuke et al., 2022). Furthermore, internal integration, customer integration and supplier integration can be enhanced by sharing this data with them (Shamout, 2020), which enhances response to changes in the supply chain (Cadden et al., 2022). Supply chain analytics can be used to understand and analyze consumer behavior and supplier behavior, making it easier for firms to choose reliable suppliers accurately based on empirical evidence (Kalaitzi and Tsolakis, 2022), taking into account consumer behavior, which saves these firms time and effort in conducting market research studies (Olabode et al., 2022) with an interest in reaching the most interested segments.

Supply chain analytics helps increase supply chain visibility and make the supply chain more flexible and more manageable to monitor the flow of materials and products, contributing to rapid decision-making in real-time (Al-Khatib, 2023b) and making it easier for managers in this field to make production, scheduling, maintenance and storage decisions smoothly and without errors or delays, which enhances firms’ response in an agile manner to any disruption or change in the supply chain.

Consequently, the following hypothesis can be assumed:

The relationship between supply chain agility and supply chain innovation has been rooted in the supply chain management literature. Russell and Swanson (2019) and Abdallah et al. (2021) argued that firms would not come up with innovations that improve the reliability and responsiveness of the supply chain without organizational agility that helps the specialized team in research and development and workers in other departments to reach innovations. In a business environment characterized by uncertainty and sudden rapid change, supply chain agility can contribute to the formation of dynamic organizational capabilities that focus on sensing, seizing and reconfiguring (Aslam et al., 2018), making it easier for the supply chain to access available resources very quickly and then use these resources in innovation activities and reformulate strategies and processes to comply with this innovation (Bhatti et al., 2024), allowing firms to support innovative activities in the supply chain.

Furthermore, numerous scholars view that supply chain agility has many contributions in improving the efficiency of activities across the supply chain by reducing lead time, which allows for responding to customer requirements more quickly (Abdallah et al., 2021). In addition, supply chain integration can support innovation in the supply chain, which the organizational agility strategy contributes to, facilitating work and collaboration between firms, suppliers and stakeholders, in particular innovative cooperation (Ayoub and Abdallah, 2019).

Therefore, firms that have a strategy and action plan that supports the supply chain agility will enhance the position and competitive landscape of these firms by supporting innovations that enhance and facilitate work across the supply chain.

Consequently, the following hypothesis can be assumed:

To test the theoretical model developed for this study, primary data was collected through a cross-sectional survey using snowball sampling. It was communicated through email and phone with managers and officials in manufacturing firms in Jordan to identify individuals working in administrative positions related to supply chain management, such as logistics, transportation, production, information systems and purchase, because these jobs are the most relevant and knowledgeable in supply chain analytics and innovative activities within the supply chain.

The manufacturing firms sector in Jordan includes numerous subsectors such as pharmaceuticals, food and beverages, textile and clothes and construction (Al-Khatib and Ramayah, 2023). Since this study used a cross-sectional survey, the data were obtained during two different periods: the first period was from the beginning of the fourth quarter of 2022 until the beginning of 2023, and the later period was from the beginning of 2023 until March of 2023.

In general, 275 respondents filled out the questionnaire through links distributed through firms’ representatives who were contacted. After conducting the initial sorting of the responses, 23 responses were deleted because the respondents were not related to the work of supply chain management to ensure accurate results. To collect data on the impact of IC on AI-based supply chain analytics, snowball sampling was used. This method was chosen due to its efficacy in reaching a networked community of professionals where direct contacts and referrals are crucial for engaging participants with relevant expertise (Biernacki and Waldorf, 1981). While snowball sampling is particularly adept at accessing hidden populations, it may introduce bias by overrepresenting individuals within particular networks (Heckathorn, 1997). To mitigate this, we used several strategies:

• Initial seeds were carefully selected to cover a broad range of industries within the supply chain sector, ensuring a diversity of backgrounds and perspectives;

• We set a limit on the number of referrals each participant could make, preventing dominance by any single network (Johnston and Sabin, 2010); and

• Demographic and professional diversity checks were conducted at various stages of data collection, allowing adjustments to the sampling process to enhance representativeness (Sadler et al., 2010).

After verifying that the data is free of outliers and is normally distributed, 252 valid responses were used to test the hypotheses. Table 1 presents the characteristics of the sample (n = 252).

According to the guidelines (Duan et al., 2021), pretested items and measures were used in the published studies. Premeasured items are more capable of achieving the empirical objectives of any study. The items were derived from previous work, modified and adapted according to the current context of this paper. Before starting data collection, the items were presented to a group of academic specialists and administrative practitioners to test the content validity of these items (Sekaran and Bougie, 2020). The questionnaire was translated into Arabic and English to ensure and encourage the respondents to fill out the questionnaire comfortably and smoothly.

After completing the content validity stage, a pilot study was conducted on 15 managers in manufacturing firms to ensure the reliability of the items. A version of the questionnaire containing 25 items used to measure research constructs was adopted (see Table 2).

Hypothesis testing and other statistical tests were implemented through the AMOS software which is based on the structural equation modeling (SEM) approach (Hair et al., 2017). This approach is widely used in business research and in particular when testing hypothesized paths simultaneously. This approach provides for using confirmatory factor analysis (CFA) to verify the measurement model, model fit indices and hypothesis testing using the structural model.

This research used a set of statistical procedures to determine the convergent validity, reliability and discriminant validity of all constructs and calculate model fit indices to ensure that the hypothesized model fits the data (Hair et al., 2017).

Referring to Table 3, it is clear that the data supported the convergent validity and reliability indicators. The average variance extracted (AVE) values and factor loadings were greater than (0.50) while the values of composite reliability (CR) and Cronbach’s alpha were greater than (0.70) for all constructs, and thus the results supported the convergent validity and reliability (Hair et al., 2019).

The discriminant validity was evaluated through two criteria; the first criterion is Fornell and Larcker (1981). The square root values of the AVE coefficients were calculated which were all greater than the correlation coefficient values across the constructs (see Table 4), indicating that the requirements of discriminant validity are fulfilled.

As another criterion for evaluating the discriminant validity, the Hetero Trait-Mono Trait ratio (HTMT) scores were extracted. All scores were less than (0.85), indicating that the discriminant validity values were achieved according to this criterion (Henseler et al., 2015) (see Table 5). Given the discriminant validity test, it can be said that there is strong empirical evidence for achieving statistical differences and differentiation between the constructs.

The research model fit was statistically acceptable by calculating the model fit indices, as this procedure is necessary and important before testing the hypotheses. The data fit with the hypothesized model is one of the assumptions of the SEM methodology when using CFA analysis (Hair et al., 2017). The model fit indices were acceptable (χ2 = 464.98, DF = 260.000, χ2/df = 1.788, CFI = 0.950, RMSEA = 0.056, SRMR = 0.049, PClose = 0.113).

This study aimed to explore the effects of the components of IC (human capital, structural capital and social capital) on supply chain analytics and identify the effect of supply chain analytics on supply chain agility and supply chain innovation, and determine the impact of supply chain agility on supply chain innovation. To reach these objectives, six hypotheses were developed to test these relationships, as shown in Table 6.

Usually, when implementing hypothesis testing through the SEM methodology, the statistical suitability of the model’s predictive power is confirmed by calculating the R² value for endogenous constructs. It was (0.648) for supply chain analytics, (0.124) for supply chain agility and (0.162) for supply chain innovation. Furthermore, variance inflation factor (VIF) values were calculated to ensure full collinearity to evaluate the correlations between the exogenous constructs because all VIF values were less than 5, confirming no problems related to full collinearity (Hair et al., 2019).

The results shown in Table 6 confirmed the support of all hypotheses. The empirical results confirmed the acceptance of all hypothesized nexus because the positive relationship between the three components of IC and AI-based supply chain analytics was confirmed. The effects of human capital, structural capital and social capital were positive and statistically significant (β = 0.190, p < 0.001; β = 0.217, p < 0.001; β = 0.630, p < 0.001). Therefore H1, H2 and H3 are accepted. Furthermore, H4 and H5 were acceptable as supply chain analytics had a positive impact on both supply chain innovation and supply chain agility (β = 0.229, p = 0.002; β = 0.352, p < 0.001). Finally, the empirical results provided support for H6, which confirmed a positive effect of supply chain agility on supply chain innovation (β = 0.260, p < 0.001).

This study contributes to the field of SCM in many areas. First, it is important to study the impact of each of the three capital effects (i.e. human, structural and social) on supply chain analytics adoption. In addition, the mediating role of supply chain analytics adoption was highlighted, as shown in the study model in its mediation between each of the human capital, structural and social factors, respectively, for the relationships on both supply chain agility and innovation.

The findings as shown in Table 6 indicate that there is a direct, significant and positive impact of human capital on the firm’s adoption of novel technologies and supply chain analytics as shown and mentioned by the effect of H1. This finding is consistent with some previous studies in the literature (e.g. Kusi-Sarpong et al., 2022; Popkova and Sergi, 2020). Therefore, most studies emphasized the role of human capital in developing innovative ideas, functions and processes at any firm via training, recruiting and acquiring new skills and talents in the innovative use of big data analytics (Kusi-Sarpong et al., 2022; Mubarik et al., 2022). Furthermore, the findings also confirmed the role of human capital in enhancing the effectiveness and efficiency of process analysis and developing algorithms and software related to supply chain analytics to increase control over the flow of materials, inventory and different logistical activities (Rejeb et al., 2019; Al-Khatib and Shuhaiber, 2022). While we did not find any study in the literature indicating that this factor has no significant effect on supply chain analytics adoption.

The findings reveal that structural capital also has a significant and positive effect on supply chain analytics, as shown and mentioned by the effect of H2. Structural capital includes one of the three necessary elements of IC and consists of the supportive infrastructure, processes and databases of the firm that enable human capital to perform its main functions. Therefore, this factor is owned by a firm and remains with it even when employees leave. This result aligns with other prior studies showing the impact of structural capital assets on achieving greater efficiency in using and analyzing reliable and more relevant data in supply chain activities. This factor has great value in structural networks, which bond similar people and bridge diverse people, with norms of reciprocity through analysis of the supply chain (Fosso Wamba and Akter, 2019; Mubarik et al., 2022). They also agreed with the Kusi-Sarpong et al. (2022) study, which also emphasized that excellent structural capital enhances the benefits of the supply chain data to reach wise and smart decisions through adopting and using up-to-date technological tools, equipment and software. According to Mahmood and Mubarik (2020) and Gul et al. (2021), most prior studies confirmed that structural capital is considered a key enabler of transparency and improving the quality of reliable big data and cloud computing capability analytics of supply chain activities.

The outcomes emphasize that social capital has a significant and positive impact on supply chain analytics, as shown and mentioned by the effect of H3. This outcome goes together with earlier studies that confirmed the role of firms’ social networks in transferring knowledge between supply chain members and partners that facilitate the adoption of innovative digital solutions for their logistical activities, processes and functions. Thus, it provides the network members to provide value to its members by allowing them access to the social resources that are embedded within the network (Shamout, 2019; Kusi-Sarpong et al., 2022). The literature confirmed the role of social capital in providing new opportunities to access reliable and transparent data through suppliers, partners and cooperating firms within the same supply chain network (Al-Khatib and Shuhaiber, 2022; Zhang et al., 2023). This outcome is consistent with other studies such as Gul et al. (2021) and Al-Khatib and Shuhaiber (2022), which emphasize the role of social capital in exchanging experiences and knowledge related to big data analytics between business partners, enhancing firms’ analytical capabilities of data issued from the supply chain. We did not find any study in the literature indicating that this social capital has not any significant effect on supply chain analytics adoption.

The statistical and empirical results in this study showed that there is a significant, direct and positive correlation in the relationship between adopting supply chain analytics and both the agility and innovation of the supply chain, as shown and mentioned by the effects of H4 and H5. These results support prior studies on the role of data in firms and their supply chains’ agility and enabling firms to respond to sudden and unexpected supply chain disruptions (Dubey et al., 2019; Wamba et al., 2020). Scholars emphasized the impact of data sharing in internal integration, customer integration and supplier integration, which enhances sense and response to changes in the supply chain environment (Shamout, 2020; Cadden et al., 2022). These findings also agree with studies confirming the role of supply chain analytics in increasing supply chain visibility, responsive and flexibility, contributing to decision-making in real-time, which enhances firms’ response in an agile manner to supply chain disruptions (Kalaitzi and Tsolakis, 2022; Al-Khatib, 2023b). As the main factors in adopting supply chain analytics are effective management, the readiness of corporate infrastructure and the capabilities and expertise of employees are important in the series.

The results also indicated that the adoption and ability of supply chain analytics have positively and directly also affected supply chain agility, which in turn also directly affects supply chain innovation as shown and mentioned by the effect of H6 (Kalaitzi and Tsolakis, 2022; Ogbuke et al., 2022). Scholars have linked innovations, improving responsiveness and organizational agility in uncertain environments (Russell and Swanson, 2019; Abdallah et al., 2021). In addition, these results agree with earlier findings confirming the relationship between supply chain collaboration, innovation and organizational agility (Ayoub and Abdallah, 2019; Al-Omoush et al., 2020). These findings are in line with prior studies that have emphasized the impact of supply chain agility on the development and improvement of dynamic capabilities of firms (Aslam et al., 2018; Bhatti et al., 2024). In addition, supply chain agility plays a mediating role in its positive and significant impact on adopting supply chain analytics and innovation supply chain, which serves to strengthen the relationship between the latter two variables. Findings related to the effects of supply chain analytics adoption capabilities, supply chain agility and supply chain innovation correspondingly showed agreement with previous literature and provided new insights into the contribution of all dimensions to supply chain innovation improvement. Scholars emphasized the role of big data analytics in reaching the best decisions and achieving innovative solutions to the problems that the supply chain operational performance may be exposed to (Shafiq et al., 2020; Bahrami et al., 2022). Furthermore, these results are in line with studies that have investigated the role of supply chain analytics in analyzing inbound and outbound data, which creates an innovative environment to enhance supply chain performance (Ahmed et al., 2022, 2022b; Ali et al., 2023; Wang and Hu, 2020).

These findings are particularly relevant for other developing economies facing similar challenges in AI adoption, such as limited infrastructure, resource constraints and skill gaps (Ülkü and Engau, 2021; Wamba et al., 2020). The insights gained from Jordan’s manufacturing sector can inform policies and practices in regions with comparable socioeconomic contexts. For instance, developing economies in the Middle East, Africa and Southeast Asia could leverage IC components – human, structural and social capital – to overcome barriers to AI implementation and enhance supply chain agility and innovation (Mubarik et al., 2022; Ülkü and Engau, 2021).

The current research sought to test the causal relationships shown in Figure 1 through the RBV lens, as the analyzed results confirmed the acceptance of all assumed hypotheses. This current study had a set of contributions and implications that helped enrich the literature related to IC, supply chain analytics, supply chain agility and supply chain innovation in manufacturing companies operating in Jordan.

First: Although previous studies in the literature recognize the importance of IC as a major determinant in the success of implementing and applying the adoption of supply chain analytics and how supply chain analytics affect the agility and innovation of the supply chain, there is no empirical study that explores the direct relationships that were envisioned in the research model. This study contributed to understanding the mechanisms of applying supply chain analytics through RBV lens and IC view, which leads to improving the level of innovation and agility in the supply chain.

Second: The current study contributed to understanding how the agility of the supply chain helps companies enhance their innovative capabilities. Although much of the current work in the literature (Russell and Swanson, 2019; Abdullah et al., 2021; Al-Omoush et al., 2020) has supported and proven this relationship, the current study has formed a new and unparalleled discussion about this relationship by understanding the antecedents of the agility of the supply chain through the digital transformation capabilities of supply chain analytics. It was necessary to study many of the determinants that make companies more capable of innovation, in particular the determinants related to responding to dynamic changes represented by agility and the determinants related to digital transformation, as integrating analysis capabilities and benefiting from data will enhance companies’ response to any emergency change in the business environment through Improve response to new innovations at the supply chain level.

Third: This paper also contributes to expanding the discussion about the importance of digital transformation in the supply chain and its impact on responding to dynamic changes in a context that has not been sufficiently studied, such as emerging contexts. This study is considered one of the first studies that explore these relationships in the context of the State of Jordan, which is from emerging countries; therefore, this study can contribute to expanding the debate on how to achieve adaptive performance in the supply chain through supply chain innovation.

The current research presents new conclusions that could serve as guidelines that help managers in the manufacturing companies sector in Jordan implement best practices on adopting supply chain analytics, which will positively affect the agility and innovation of the supply chain. First: The results of the study provided interesting conclusions regarding the impact of IC components (human capital, structural capital and social capital) on supply chain analytics, and therefore these results can be useful for managers with regard to working on investing in tangible and intangible assets. This paper also invites managers to work on attracting the most distinguished human resources and train them in training directed toward the use and analysis of data. The paper also recommends the importance of managers working on updating regulations and procedures with regard to building an organizational culture geared toward big data analytics. Furthermore, managers should work on using databases for data storage and use in the supply chain. Also, through the results that have been reached, managers in this sector can work to encourage workers to build formal and informal networks of relationships that work to take advantage of the available data to transfer knowledge in the supply chain.

The results also revealed the importance of supply chain analytics in enhancing the agility and innovation of the supply chain. Thus, this paper can guide managers in investing in collecting reliable data across the supply chain and using it to enhance collaboration and integration between all parties in the supply chain and thus increase the chain’s response to any emergency or unprecedented change. In addition, this data can be used in formulating corporate strategy based on organizational agility, and supply chain analytics can also be transformed into a unified organizational effort within the supply chain to help make decisions related to work, in addition to enriching the innovation strategy to reach new, unfamiliar innovations that help achieve corporate objectives efficiently and effectively.

Also, managers should work on formulating strategies and agile work systems within the supply chain to help the work teams in these companies work to enhance research and development efforts in a flexible organizational manner that allows for the implementation of innovation initiatives, which enhances innovation in the supply chain.

This study’s implications extend beyond managerial strategies, highlighting societal benefits such as workforce upskilling, enhanced policymaking for technology adoption and improved supply chain resilience. In emerging economies, such advancements could lead to job creation, reduced operational risks and increased competitiveness on a global scale. For example, upskilling employees in data analysis and supply chain analytics enables firms to reduce unemployment and bridge skill gaps in developing economies (Ülkü and Engau, 2021; Mubarik et al., 2022). Policymakers could leverage these findings to develop frameworks and incentives that support technology adoption in manufacturing sectors.

Future research could explore the long-term societal impacts of AI adoption, including its effects on employment patterns, socioeconomic development and bridging the digital divide in developing countries. These societal benefits reflect the broader potential of supply chain analytics to enhance not only firm-level performance but also contribute to regional and national economic growth.

The current paper reached a set of interesting results that put a lot of theoretical and practical contributions for academics or managers in the field of supply chain management and new insights for future studies for further research. First: The current paper used the cross-sectional survey method in capturing data. Despite the importance of this method in the field of business sciences, the current study neglected the use of other methods in capturing data, such as interviews or published reports. Second: The current research relied on manufacturing companies in Jordan without taking into account the different manufacturing differences; therefore, supply chain analytics practices may differ in different subsectors, so it would be useful to conduct more research on the subsectors in addition to studying other contexts. Third: It is useful to conduct prospective studies that take into account precedents other than IC, such as institutional pressures or the technology-organization-environment model, in understanding how manufacturing companies apply supply chain analytics.