Exploring IT-driven supply chain capabilities and resilience: the roles of supply chain risk management and complexity Open Access

Since the post-World War II era, globalisation has incentivised businesses ( Vanham, 2019 ) to offshore their manufacturing activities ( Liu et al. , 2018 ). This shift has led to the formation of global supply chains and provided substantial profitability advantages for businesses. However, it has also increased the complexity and uncertainty of supply chains, making them more susceptible to disruptions – unplanned events that interrupt the flow of materials and goods ( Chowdhury and Quaddus, 2016 ; Revilla and Saenz, 2017 ).

The emergence of supply chain disruption risks has led to extensive exploration of the conventional risk management playbook, which encompasses risk identification, risk assessment and implementation of mitigation strategies ( Braunscheidel and Suresh, 2009 ; Tummala and Schoenherr, 2011 ; Sodhi et al. , 2012 ). However, this traditional approach has proven to be inadequately scalable for addressing the complexities and dynamics within (global) supply chains ( Wieland and Durach, 2021 ), as evidenced by the impact of recent disruptions.

For example, the COVID-19 pandemic in 2020, has significantly impacted the global economy ( Herold et al. , 2021 ), shrinking the EU’s real GDP by 6.1% ( Verwey and Monk, 2021 ) and disrupting supply chains for 94% of Fortune 1000 companies ( Sherman, 2020 ). Moreover, a McKinsey survey found that only 40% of corporate directors are adequately prepared for future disruptions ( McLaughlin, 2022 ), and only 2% of companies have visibility beyond their second-tier suppliers ( Alicke et al. , 2021 ).

In addition, the rise of the BRICS ^[1] nations towards a multipolar world order is expected to increase geopolitical instability and tensions, potentially leading to further geopolitical risks for global supply chains ( Reivan-Ortiz et al. , 2023 ). Faced with a future marked by economic turbulence and the limitations of the conventional risk management playbook, supply chain partners must strengthen their Supply Chain Resilience (SCR) to manage future disruptions effectively.

Resilience enables supply chain partners to adapt to and overcome disruptions ( Wieland and Durach, 2021 ). In this context, IT resources play a crucial role in creating higher-level dynamic IT capabilities. These dynamic IT capabilities drive superior performance over time ( Winter, 2003 ) and are particularly needed in today’s dynamic and turbulent business environment ( Qaiyum and Wang, 2018 ).

Building on this understanding, SCM IT Integration (SCMITI) catalyses the creation of IT-driven supply chain capabilities (SCCs) by integrating IT platforms that store and transform various forms of information across the supply chain. This integration fosters resilience by facilitating the development of critical IT-driven SCCs, including flexibility ( Chowdhury and Quaddus, 2017 ), visibility ( Jüttner and Maklan, 2011 ; Mandal et al. , 2016 ), responsiveness ( Braunscheidel and Suresh, 2009 ), collaboration ( Mandal et al. , 2016 ) and integration ( Rai et al. , 2006 ; Huo, 2012 ). These SCCs, in turn, significantly contribute to the enhancement of SCR, thereby enhancing the supply chain’s ability to recover from disruptions.

Despite significant growth in the literature on SCR, research on how firms develop resilience to supply chain disruptions remains insufficient ( Blackhurst et al. , 2011 ; Ambulkar et al. , 2015 ). In particular, there is limited knowledge about the underlying mechanisms by which IT enhances SCR. As Sodhi et al. (2012) emphasise, addressing these gaps through empirical research will enhance practitioners’ understanding ( van Hoek, 2020 ). This, in turn, enables managers to invest in appropriate IT tools and capabilities strategically, safeguarding business continuity and profitability ( Sawyerr and Harrison, 2020 ).

This study integrates the resource-based view (RBV) and dynamic capability view (DCV) as dual complementary theoretical lenses ( Wang et al. , 2024 ; Xia et al. , 2024 ) to conceptualise the constructs. We investigate the interplay mechanism between resources (i.e. IT resources), dynamic capabilities (i.e. IT-driven SCCs) and competitive advantage (i.e. SCR), as outlined by Huang et al. (2023) . Therefore, we formulate the following Research Question (RQ):

Besides the advantages that IT can provide in the form of dynamic IT capabilities, it also introduces vulnerabilities and risks such as cybersecurity threats, system failures and data breaches that can harm business continuity. Lloyd’s of London, a central hub in cyber insurance, warns that a major cyberattack on a global payment system could cause $3.5tn in losses ( Smith, 2023 ). To mitigate these risks, supply chain risk management (SCRM) must adopt robust IT risk management strategies and enhance cybersecurity ( Cheung et al. , 2021 ).

Additionally, the attainment of benefits through IT may be hindered by supply chain structural complexity (SCSC), which can present challenges in monitoring and managing interactions among supply chain partners ( Craighead et al. , 2007 ). Given the inconclusiveness of the empirical evidence on the role of SCSC in SCR ( Akın Ateş et al. , 2022 ) and acknowledging the existing knowledge gap regarding the relationships between resilience, risk and SCM ( Blackhurst et al. , 2005 ; Ponomarov and Holcomb, 2009 ), we formulate RQ2 as follows:

Our study makes substantial theoretical contributions by integrating RBV and DCV perspectives to elucidate how SCMITI enhances SCR, focusing on IT-driven SCCs, SCRM and SCSC. Practically, this study provides valuable insights for supply chain managers. It emphasises the importance of strategically investing in SCMITI to enhance SCR, highlighting that mere technology acquisition is insufficient. Managers must leverage IT investments to create unique capabilities that enhance resilience. The study guides IT platform selection to boost key SCCs and underscores the need for robust risk management practices, including cybersecurity measures. It advises a holistic approach to SCM, considering the dual impact of structural complexity on resilience.

The remainder of this paper is organised as follows: Section 2 covers the theoretical background. Section 3 presents the conceptual model and hypotheses formulation. The research methodology is described in Section 4. Section 5 reports data analysis and results, followed by discussions of implications and future research in Section 6. Finally, the paper is concluded in Section 7.

In this section, we outline each higher-order construct of SCMITI, SCCs and SCR, drawing upon existing literature to establish and define the necessary dimensions for their evaluation.

Supply chains are conceptualised as dynamic networks, with nodes representing supply chain partners and links denoting their business interactions ( Christopher, 2000 ; Lambert et al. , 2005 ; Craighead et al. , 2007 ; Pettit et al. , 2010 ) aimed at fulfilling customer requirements. In this context, SCMITI plays a critical role in facilitating the interaction among supply chain partners across the network to improve the flow of products, services, information, payments and decisions ( Zhao et al. , 2008 ).

A focal supply chain partner with significant influence and resources often leads to initiating a common IT platform by leveraging its position to demonstrate the significant benefits that effective IT integration provides to the whole supply chain. This effort, combined with strategic alignment and collaborative efforts among supply chain partners, can significantly enhance the adoption and effectiveness of SCMITI ( Rai et al. , 2006 ).

SCMITI refers to the extent to which a focal supply chain partner shares information with its upstream suppliers and downstream customers ( Leuschner et al. , 2013 ; Wei et al. , 2020 ). In line with Rai et al. (2006) , we conceptualise SCMITI as a second-order construct that encompasses two fundamental dimensions: Data Consistency (DC) and Cross-Functional Applications Integration (CFAI). DC refers to the extent to which common data definitions and consistency in stored structural data (e.g. inventory levels, demand forecasts, production schedules) have been established across a focal firm’s supply chain. CFAI refers to the extent to which a partner communicates in real-time with other partners within the chain using SCM applications.

Building on the concept of organisational capabilities by Chakravarti and Day (1991) as a complex blend of skills and collective knowledge for achieving strategic goals ( Bharadwaj, 2000 ), SCCs are defined as the ability of a focal supply chain partner to recognise, employ and incorporate internal and external resources to manage supply chain activities ( Wu et al. , 2006 ). These capabilities can be categorised into lower-level operational capabilities and higher-level dynamic capabilities ( Winter, 2003 ; Brusset and Teller, 2017 ).

Operational capabilities contribute to day-to-day operations, while dynamic capabilities enhance superior performance over time ( Winter, 2003 ). Considering today’s dynamic and turbulent business environment that favours dynamic capabilities ( Qaiyum and Wang, 2018 ), this study concentrates exclusively on dynamic SCCs, specifically those driven by IT that contribute to SCR.

Wu et al. (2006) argue that integrated IT systems allow for real-time data sharing across departments, leading to improved decision-making and operational efficiency. Moreover, IT systems allow for advanced analytics and tools to rapidly adjust supply chain processes in response to sudden market changes ( Wu et al. , 2006 ). Additionally, IT systems enhance the ability to sense market changes and seise opportunities, a core aspect of dynamic capabilities ( Wu et al. , 2006 ).

Therefore, the abovementioned examples support that IT systems facilitate data integration and operational adjustments, reflecting the adaptive nature of dynamic capabilities. Through the theoretical lens of the dynamic RBV, we conceptualise IT-driven SCCs in the SCCs-SCR relationship as a second-order construct that encompasses five dimensions: flexibility ( Chowdhury and Quaddus, 2017 ), visibility ( Mandal et al. , 2016 ), responsiveness ( Wu et al. , 2006 ; Yu et al. , 2018 ), collaboration ( Mandal et al. , 2016 ) and integration ( Wu et al. , 2006 ; Yu et al. , 2018 ). These five SCCs stem from the strategic utilisation and integration of IT. We briefly characterise each dimension below.

Supply Chain Flexibility (SCFL) is defined as the ability of a focal supply chain partner to swiftly and efficiently deploy diverse resources to address market changes ( Jüttner and Maklan, 2011 ; Dubey et al. , 2021 ). The resources encompass a diverse array, including financial assets, human capital, technological tools and physical infrastructure. SCFL enables supply chain partners to encounter, resolve and exploit unexpected emergencies in supply, manufacturing and distribution processes ( Sreedevi and Saranga, 2017 ).

Supply Chain Visibility (SCVI) is described as the ability of a focal supply chain partner to have access to or share timely information about its operations ( Jüttner and Maklan, 2011 ; Dubey et al. , 2021 ). It involves the timely exchange of information about entities moving through the supply chain and the planned and actual times of events ( Jüttner and Maklan, 2011 ). This information encompasses real-time inventory levels, demand forecasts, production schedules, shipment statuses and any disruptions or delays, among others. SCVI uncovers the location of resources within the chain and communicates this information to SC partners ( Blackhurst et al. , 2011 ).

Supply Chain Responsiveness (SCRS) is defined as the ability of a focal supply chain partner to adapt to customer requirements and cope with variations in business conditions and market fluctuations ( Klibi et al. , 2010 ; Sabouhi et al. , 2020 ). Using SCRS, supply chain partners can offer product variety to their downstream customers in a time-effective manner (adapted from Holweg (2005) and Reichhart and Holweg (2007) ).

Supply Chain Collaboration (SCCL) is defined as the ability of a focal supply chain partner to make decisions jointly and work with its partners in the chain ( Jüttner and Maklan, 2011 ). SCCL significantly enhances information-processing capacity ( Dubey et al. , 2021 ) by enabling supply chain partners to share information, and jointly plan, coordinate and execute activities. This collaboration aims to achieve a seamless flow of goods, information and finances across the supply chain ( Zhao et al. , 2008 ).

Supply Chain Integration (SCIN) is defined as a supply chain partner’s ability to integrate its internal and external organisational activities with those of its supply chain partners ( Huo, 2012 ). This integration is crucial for effectively synchronising, coordinating and optimising the main flows within the supply chain ( Zhao et al. , 2008 ).

The multidimensional and multidisciplinary concept of resilience in SCM is relatively new, having been introduced in the early 2000s ( Starr et al. , 2003 ; Christopher and Peck, 2004 ; Christopher and Rutherford, 2004 ; Sheffi and Rice, 2005 ). SCR refers to the supply chain’s ability to effectively manage disruption risks and recover from them, returning to the original or a better state ( Christopher and Peck, 2004 ; Chowdhury et al. , 2019 ). In this context, resilience enables supply chain partners to adapt in response to disruptions ( Wieland and Durach, 2021 ). This requires both readiness and the ability to respond and recover, to prevent significant negative impacts on the supply chain.

Robust supply chain processes that can absorb disruptions can be developed through resilience, rather than attempting to resist vulnerabilities ( Pettit et al. , 2010 ). This can be done by aligning resources to adapt to the business environment strategically and outperforming competitors ( Hamel and Välikangas, 2003 ). Drawing on stability- and adaptation-based perspectives of SCR as highlighted by Ivanov (2023) , we conceptualise SCR as a second-order construct reflected in three dimensions: recovery ( Chowdhury and Quaddus, 2017 ), agility ( Liu et al. , 2018 ) and environmental sustainability ( Marchese et al. , 2018 ). Recovery and agility, rooted in the stability-based view, focus on restoring normalcy post-disruption. However, environmental sustainability, reflecting the adaptation perspective, enhances long-term supply chain viability through innovative practices and responsible resource management.

Supply Chain Recovery (SCRE) refers to the ability of a focal supply chain partner to return to a normal level of functionality after a disruption and is widely recognised as a key component of SCR ( Sheffi and Rice, 2005 ). SCRE’s pivotal role in maintaining operational stability and mitigating disruption consequences solidifies its position as a key metric for measuring overall SCR.

Supply Chain Agility (SCAG) refers to the ability of supply chain partners to respond to changes in the external environment effectively ( Liu et al. , 2018 ). Agility triggers unintentional learning across supply chain partners by facilitating the transfer of unconventional knowledge that may not have been formally shared among them before the disruption ( Scholten et al. , 2019 ). This knowledge transformation allows for better coordination and quicker responses to disruptions, thereby enhancing the overall SCR.

Supply Chain Sustainability (SCSU) is described as the ability of a focal supply chain partner to mitigate negative environmental impacts and address sustainability issues, integrating SCM with environmental management ( Fiksel, 2006 ; Kusi-Sarpong et al. , 2019 ; Rajesh, 2021 ). Marchese et al. (2018) argue that a more sustainable system inherently becomes more resilient. Therefore, SCSU’s ability to absorb and mitigate disruptive environmental challenges positions it as a crucial indicator of SCR.

Despite significant growth in the SCR literature, critical gaps persist in understanding how firms develop resilience ( Blackhurst et al. , 2011 ; Ambulkar et al. , 2015 ) and particularly the role of IT resources in enhancing SCR. As Sodhi et al. (2012) highlighted, more empirical research is needed to address these gaps. Moreover, existing studies lack conclusive empirical evidence on the role of SCSC in SCR ( Akın Ateş et al. , 2022 ) and exhibit a knowledge gap regarding the relationships between resilience, risk and SCM ( Blackhurst et al. , 2005 ; Ponomarov and Holcomb, 2009 ).

To bridge these gaps, similar to Wang et al. (2024) and Xia et al. (2024) , this study integrates the RBV ( Wernerfelt, 1984 ; Barney, 1991 ) and DCV ( Teece et al. , 1997 ) as dual complementary theoretical lenses. Within the RBV framework, we identify lower-level tangible IT resources (e.g. technology and IT infrastructure), alongside complementary intangible resources (e.g. human knowledge and skills in IT), that collectively contribute to the development of IT-driven capabilities ( Bharadwaj, 2000 ; Rai et al. , 2006 ).

We then extend this interpretation through the lens of DCV by conceptualising IT-driven SCCs as dynamic capabilities, enhancing SCR and thereby serving as a crucial competitive advantage. In other words, SCR enables the supply chain partners to effectively manage disruptions and meet market demands more efficiently than competitors during times of turbulence ( Dubey et al. , 2021 ). This application of the dynamic RBV, aligned with the DCV principles, aims to offer a clearer understanding of the interplay between resources (i.e. SCMITI), dynamic capabilities (i.e. IT-driven SCCs) and competitive advantage (i.e. SCR).

Consistent with the framework on the relationship between IT, capabilities and competitive advantage outlined by Huang et al. (2023) , Figure 1 illustrates our hypotheses by including higher-order constructs of SCMITI, IT-driven SCCs and SCR as well as SCRM and SCSC as two moderators.

The RBV framework posits that firms gain a competitive advantage through unique, valuable and hard-to-imitate resources ( Wernerfelt, 1984 ; Barney, 1991 ). SCMITI embodies such resources by integrating lower-level tangible IT assets, such as technology and IT infrastructure, with complementary intangible resources, such as human knowledge and IT skills ( Bharadwaj, 2000 ; Rai et al. , 2006 ). Drawing upon RBV theory, we argue that SCMITI leverages these collective resources to foster the development of IT-driven capabilities. According to Bharadwaj (2000) and Wu et al. (2006) , these IT-driven capabilities provide a competitive edge when their replication by competitors becomes more challenging. Therefore, within the RBV framework, we propose the following hypothesis:

Moreover, the DCV ( Teece et al. , 1997 ) extends RBV by emphasising a firm’s ability to integrate internal and external competencies and resources to address rapidly changing environments. In this context, SCMITI provides opportunities for blurring the boundaries between supply chain partners, enabling them to reduce uncertainty by aligning their inter-organisational processes with one another ( Vanpoucke et al. , 2017 ). As an enabler of information exchange, IT plays a major role in supply chain activities integration by allowing supply chain partners to increase the volume and complexity of information exchange and share real-time information ( Vanpoucke et al. , 2017 ). By sharing and exchanging information on various cross-functional and cross-firm business activities (e.g. demand forecasts, inventory levels and customer needs), SCMITI helps supply chain partners collaborate and respond to environmental changes ( Wei et al. , 2020 ). Therefore, within the DCV framework, we propose the following hypothesis:

Drawing upon the DCV ( Teece et al. , 1997 ), which emphasises the integration of dynamic capabilities to create competitive advantage, we posit that IT-driven SCCs significantly enhance firms’ competitive advantage during turbulent times by bolstering SCR. These capabilities include flexibility ( Chowdhury and Quaddus, 2017 ), visibility ( Mandal et al. , 2016 ), responsiveness ( Wu et al. , 2006 ; Yu et al. , 2018 ), collaboration ( Mandal et al. , 2016 ) and integration ( Wu et al. , 2006 ; Yu et al. , 2018 ). Each of these IT-driven SCCs contributes to supply chain partners’ ability to adapt, respond and thrive in dynamic environments, thereby fostering resilience and sustaining competitive advantage.

Specially, IT-driven capabilities provide supply chain partners with real-time tracking of demand and product availability across the supply chain, and in turn, empower faster responses to shifts in demand ( van Hoek, 2020 ). Wu et al. (2006) argue that IT systems facilitate data integration and operational adjustments, reflecting the adaptive nature of dynamic capabilities ( Wu et al. , 2006 ). Moreover, these capabilities are crucial for active learning about customer needs, competitor strategies and market trends, enabling supply chain partners to adapt their operations in response ( Aslam et al. , 2018 ). Therefore, we argue that IT-driven SCCs contribute significantly to SCR by developing and maintaining competitive advantage in dynamic market conditions. Based on this argumentation, we formulate the following hypothesis:

By combining the aforementioned arguments and the one presented in Subsection 3.1, we posit a comprehensive theoretical framework that integrates the RBV and the DCV to explain the relationship between SCMITI and SCR through the development of IT-driven SCCs. Initially, we established that SCMITI contributes to the development of SCCs within the RBV framework. Subsequently, we argued that SCCs, as dynamic capabilities under the DCV, play a crucial role in developing competitive advantage in the form of enhanced SCR. These arguments align with Irfan et al. (2020) , who argue that IT integration enables supply chain flexibility (i.e. a dimension of SCCs), which in turn results in higher supply chain agility (i.e. a dimension of SCR). Therefore, we develop the following hypothesis:

Modern supply chains are ranked among the most vulnerable systems ( Guerra et al. , 2024 ), necessitating robust SCRM. While the literature on SCRM has been extensively explored, there is no clear consensus on its definition ( Sodhi et al. , 2012 ). For this study, we define SCRM as the process of identifying risk factors, assessing them and implementing mitigation strategies ( Braunscheidel and Suresh, 2009 ; Tummala and Schoenherr, 2011 ; Sodhi et al. , 2012 ). As the supply chains rapidly evolve through the integration of IT, the traditional SCRM needs a paradigm shift ( Colicchia et al. , 2019 ; Farrell and Gallagher, 2019 ) and a more holistic and integrated view of the SCRM is required ( Heckmann et al. , 2015 ; Guerra et al. , 2024 ).

The interconnectivity of organisational processes within and across supply chain partners has the potential to foster IT-driven SCCs ( Vanpoucke et al. , 2017 ). However, it could also introduce new types of supply chain vulnerabilities, such as cyber and informational risks ( Creazza et al. , 2022 ). Therefore, SCRM plays a crucial role in the effectiveness of leading SCMITI to IT-driven SCCs by mitigating the risks associated with IT. By providing a robust SCRM framework organisations can proactively manage and mitigate the risks ( Ambulkar et al. , 2015 ) that can potentially undermine the effectiveness of SCMITI in creating SCCs. Therefore, following the key role of SCRM in protecting and strengthening the relationship between SCMITI and SCCs, we formulate the following hypothesis:

Modern supply chains are ranked as some of the most complex in the contemporary world ( Guerra et al. , 2024 ). Complexity in systems can lead to increased uncertainty, which consequently reduces clarity and control, making it difficult for business leaders to navigate and manage their organisations effectively ( Reeves et al. , 2020 ). As businesses diversify their products, adopt new technologies and extend their worldwide supply networks, supply chains will inevitably take more complex structures ( Akın Ateş et al. , 2022 ). Structural complexity is related to uncertainty originating from static characteristics of the supply chain such as the degree of connections among partners and the diversity of partners ( Cheng et al. , 2014 ; Bode and Wagner, 2015 ). In line with Chowdhury et al. (2019) and Craighead et al. (2007) , we define SCSC as a measure of the interconnectedness of a supply chain partner with their upstream suppliers and downstream customers.

According to Akın Ateş et al. (2022) , the impact of SCSC on business performance remains uncertain. Some studies have discovered a negative impact ( Brandon-Jones et al. , 2015 ), while others have observed a positive impact ( Sharma et al. , 2019 ) or no impact at all ( Chaudhuri and Boer, 2016 ). Reeves et al. (2020) argue that SCSC may hinder efficiency but it can improve resilience and adaptability by increasing the diversity of the business partners. It has been observed that complexity can amplify disruptions ( Craighead et al. , 2007 ), worsen operational performance, complicate decision-making and lead to disruptions ( Bode and Wagner, 2015 ). Nonetheless, the empirical evidence concerning the impact of SCSC on SCR remains unresolved. Organisations can leverage IT-driven SCCs to enhance the effectiveness of risk-mitigating measures (e.g. multiple sourcing, backup plans, etc.); however, uncertainty can reduce the effectiveness of these measures. Thus, SCSC can weaken the relationship between SCCs and SCR. We propose the following hypothesis:

Consistent with Mabert et al. (2003) and Revilla and Saenz (2017) , we incorporated firm size (FS) as it may influence the main variables in the conceptual model and introduce unwanted sources of variance.

In this study, we employed the questionnaire-based survey method to adopt items from the existing literature ( Dennehy et al. , 2021 ). We measured the constructs using survey items adapted from the literature. The unit of analysis was a random selection of professionals from all tiers of the supply chain, chosen to provide a comprehensive understanding of the supply chain and its performance, as demonstrated in previous research ( Brandon-Jones et al. , 2014 ).

The survey was designed using Qualtrics which is a web-based platform ( Dennehy et al. , 2021 ). Before distribution, the survey was reviewed by three faculty members and pretested then made available online from 18 August 2020, to 18 December 2020. The targeted sample of respondents was composed of supply chain professionals from around the world who were contacted via LinkedIn. A total of 412 respondents completed the survey anonymously, but 18 of them did not fill out the survey completely and were therefore removed due to person-level missingness. The demographic profile of the remaining respondents is provided in Table 1 .

All constructs and survey items were measured using a five-point Likert scale, a common practice in empirical research ( Qin et al. , 2021 ). To enhance scale performance, minor modifications were made to some items ( Dennehy et al. , 2021 ). Details of the survey items and their literature sources are provided in Table A1 in the  Appendix .

Independent variable . In line with Rai et al. (2006) , we modelled the latent construct SCMITI as a second-order construct consisting of the sub-constructs DC and CFAI. We measured DC using a combination of items adapted from van den Hoven (2004) , Rai et al. (2006) , Lu and Ramamurthy (2011) and Guo et al. (2014) . These items related to the use of automatic data capture mechanisms, data definition and storage consistency, the application portfolio and data management and architectures and compliance with international data content standards through the use of RFID technology. We measured the CFAI sub-construct by adapting items from Rai et al. (2006) and Yu et al. (2018) , which related to the applications used for supply chain planning and transactions, as well as their interaction with internal applications and database consistency across functional departments.

Dependent variable . The second-order construct SCR and its three sub-constructs – SCAG, SCSU and SCRE – were modelled. SCAG was measured with five items adapted from Liu et al. (2018) , reflecting a firm’s rapid response to market changes and customer needs. SCSU was measured using six items adapted from Kusi-Sarpong et al. (2019) , emphasising the environmental dimension of sustainability. The items reflect sustainable research and development, sustainable waste management, efficient energy consumption, eco-friendly product distribution, green product market responsiveness and socio-environmental standards implementation. SCRE was measured using four items adapted from Chowdhury and Quaddus (2017) , reflecting the firm’s capability to manage disruptions with minimal losses and quick, cost-effective recovery.

Mediator variable . The second-order construct, SCCs, was modelled in terms of its five sub-constructs: SCFL, SCVI, SCRS, SCCL and SCIN. SCFL was measured using seven items adapted from Chowdhury and Quaddus (2017) , focusing on production, product diversity, workforce, sourcing and product development. SCVI was measured using three items adapted from Mandal et al. (2016) related to the firm’s information accessibility for monitoring, as well as two items adapted from Jüttner and Maklan (2011) related to tracking systems. SCRS was measured with five items adapted from Braunscheidel and Suresh (2009) related to market-responsive order decisions. SCCL was measured using four items adapted from Mandal et al. (2016) related to strategic cooperation with suppliers. Finally, SCIN was measured using three items adapted from Huo (2012) related to real-time internal connectivity, as well as two items adapted from Rai et al. (2006) related to information sharing with partners.

Moderator variables . SCRM was measured using three items adapted from Ambulkar et al. (2015) and one item adapted from Sreedevi and Saranga (2017) . These items collectively evaluate the firm’s capability to manage risks and disruptions within the supply chain. Specifically, they measure the existence of a dedicated department or team for risk management, the utilisation of Key Performance Indicators (KPIs) and metrics for monitoring risks, the deployment of an information system tailored to manage disruptions and the proactive detection of operational risks through supplier monitoring, inspections and tracking. SCSC was measured using four items adapted from Chowdhury and Quaddus (2017) . These items capture the complexity of the supply chain structure by examining the firm’s approach to reducing complexity through direct engagement with suppliers and buyers, the limited bidirectional flow of goods and services, the use of multiple suppliers to mitigate supply risks and the strategy of engaging multiple buyers to minimise disruption risks. The items reflect the organization’s efforts to manage and streamline the intricate dynamics associated with supplier and buyer relationships within the supply chain.

Control variable . In this study, we also included FS as a control variable, as it may influence the main variables in the conceptual model and introduce unwanted sources of variance. Consistent with Mabert et al. (2003) and Revilla and Saenz (2017) , we measured FS using Annual Revenue (AR) as a proxy, as firms with more annual revenue tend to have a stronger financial position and may be able to invest more in IT, potentially leading to higher levels of SCMITI compared to firms with less annual revenue.

Partial least squares-based structural equation modelling (PLS-SEM) was used for the analyses using SmartPLS Version 4 ( Ringle et al. , 2022 ), which is suited for complex models and estimating nomological networks among latent variables and is frequently used in operations management research ( Hair et al. , 2011 ; Doering and Suresh, 2016 ; Akter et al. , 2021 ; Dennehy et al. , 2021 ). To validate the hierarchical component model, the disjoint two-stage approach was used ( Becker et al. , 2012 ; Sarstedt et al. , 2019 ). A measurement model with lower-order components was constructed first in stage one. To achieve satisfactory reliability and validity, seven items were removed from some constructs (the italic rows in Table A1 in the  Appendix ): one item from the DC scale of SCMITI, three items from the SCFL scale of SCCs, one item from the SCRE scale of SCCs, one item from the SCSC scale and one item from the SCAG scale of SCR. After removing these items, the model characteristics yielded sufficient scores for the composite reliabilities of the constructs (greater than 0.70, ranging from 0.77–0.91, but the Cronbach’s alpha was below the desired level) and sufficient convergent validity ( Fornell and Larcker, 1981 ) with AVE’s ranging from 0.51–0.71 (see Table 2 ).

As can be observed in Table 2 , discriminant validity was established using the Fornell–Larcker criterion. The alternative HTMT ratios were investigated as well (not shown). All HTMT ratios were under the 0.9 threshold ( Henseler et al. , 2015 ). No indication was found for concerns of common method variance in the data based on Harman’s one-factor test ( Podsakoff et al. , 2003 ). In addition, no correlation between constructs exceeded 0.90 (see Table 2 ) and no variance inflation factor (VIF) exceeded 3.3 ( Bagozzi et al. , 1991 ). For stage two, scores of all lower-order variables were saved and added as new variables to the data set where the construct scores were used as indicators for the higher-order construct measurement model ( Sarstedt et al. , 2019 ).

For the reflective higher-order constructs, sufficient scores were obtained for the composite reliabilities (> 0.70) and the AVEs ranged from 0.53–0.69 (see Table 3 ). For the formative higher-order construct, both indicators were significant ( p < 0.001), and the VIF values were below 3.3 ( Diamantopoulos and Siguaw, 2006 ). Discriminant validity was also established based on both the Fornell-Larcker criterion and the HTMT ratio ( Fornell and Larcker, 1981 ; Henseler et al. , 2015 ).

To assess the higher-order model, the path effects were estimated with bias-corrected bootstrapping and 5,000 subsamples. The Standardised Root Mean Square Residual for the estimated model was 0.072, indicating an acceptable fit ( Cho et al. , 2020 ). All path effects are presented in Table 4 .

We found SCMITI was a significant predictor of SCCs, γ = 0.42, p < 0.001, which supports H1 . No significant direct effect was found between SCMITI and SCR, γ = 0.03, p = 0.628. Hence, no support was found for H2 . A significant indirect effect was found between SCMITI and SCR, γ = 0.25, p < 0.001, implying full mediation. SCCs were found to be significantly related to SCR, γ = 0.60, p < 0.001, supporting H3. The SCRM was found to be a significant predictor of SCCs, γ = 0.42, p < 0.001.

The SCSC was a significant predictor of SCR with γ = 0.17 and p < 0.001. The SCCs are found to be a significant mediator. The indirect effect of SCMITI on SCR had a coefficient of 0.25 with p < 0.001, indicating full mediation, supporting H4. The interaction effect of SCRM and SCMITI (i.e. SCRM×SCMITI) on SCCs was significant with γ = 0.08, p = 0.032, supporting H5, as visualised in Figure 2 , indicating that a higher level of SCRM strengthens the positive relationship between SCMITI and SCCs.

The proposed interaction effect between SCSC and SCCs (i.e. SCSC×SCCs) on SCR was not significant, γ = 0.03, p = 0.555. Hence, no support was found for H6. The adjusted R-squares were 0.52 and 0.51 for SCCs and SCR, respectively, with all Q_Predict^2 scores above zero ( Shmueli et al. , 2019 ). Moreover, we found that the control variable FS in terms of annual sales has a significant impact on SCMITI, meaning that firms with higher annual sales can achieve a higher level of SCMITI. Figure 3 illustrates the path model of this study analysed using PLS-SEM.

Our analysis yielded several significant findings. Primarily, we discovered that IT-driven SCCs fully mediate the relationship between SCMITI and SCR. This mediation confirms direct relationships between SCMITI and IT-driven SCCs, as well as between IT-driven SCCs and SCR while revealing no direct relationship between SCMITI and SCR.

The confirmed direct relationship between SCMITI and IT-driven SCCs (H1) aligns with existing literature, indicating that IT integration facilitates the development of SCCs by enhancing coordination, information sharing and efficiency. Wu et al. (2006) discovered that advancements in IT and the alignment of IT resources can facilitate the development of SCCs. Irfan et al. (2020) demonstrated that IT is a crucial resource for process integration that leads to flexibility capability. Similarly, Poberschnigg et al. (2020) found that cross-functional integration factors generate specific SCCs that influence resilience. Fawcett et al. (2011) noted that targeted IT investment promotes dynamic collaboration capabilities within the supply chain, thereby enhancing firm performance. Vanpoucke et al. (2017) further emphasised that IT is a crucial enabler for supply chain integration, allowing partners to increase the volume and complexity of real-time information sharing.

Interestingly, we found no direct relationship between SCMITI and SCR (H2). This absence aligns with the findings by Bharadwaj (2000) and Wu et al. (2006) , who argued that IT investments do not inherently lead to competitive advantage unless the firm leverages their investment to create unique capabilities. This finding is also supported by Yu et al. (2018) , who suggested that resources should be used in implementing strategies that are not easy to duplicate by competitors.

Moreover, the direct relationship between IT-driven SCCs and SCR (H3) is supported by literature that underscores the beneficial impact of IT-based decision-making on SCR. Belhadi et al. (2022) demonstrated that IT-enabled decision-making processes enhance resilience by allowing firms to respond more swiftly and effectively to disruptions. Nandi et al. (2020) identified information sharing, collaboration, integration and coordination as key capabilities of blockchain technology that can enhance service quality and flexibility.

According to the discussion above, confirming the fully mediated relationship between SCMITI and SCR by IT-driven SCCs (H4) highlights the crucial role of these capabilities in enhancing SCR. IT-driven SCCs such as flexibility ( Chowdhury and Quaddus, 2017 ), visibility ( Jüttner and Maklan, 2011 ; Mandal et al. , 2016 ), responsiveness ( Braunscheidel and Suresh, 2009 ), collaboration ( Mandal et al. , 2016 ) and integration ( Rai et al. , 2006 ; Huo, 2012 ) serve as essential links in translating IT resources and investments into IT-driven SCCs that ultimately lead to resilience outcomes.

Our analysis revealed a positive moderating effect of SCRM on the relationship between SCMITI and IT-driven SCCs (H5). This finding aligns with the proactive role of SCRM in managing and mitigating risks, as noted by Ambulkar et al. (2015) . They argue that SCRM provides a robust framework for identifying, assessing and mitigating risks. Since the role of IT in SCM is inevitable, balancing IT integration with IT security should be considered to leverage IT capabilities effectively as emphasised by Smith et al. (2007) .

In light of Wang-Mlynek and Foerstl’s (2020) consideration that no single company is immune to risk in today’s dynamic business landscape and the critical shift from an individual to a holistic view of risk management highlighted by Colicchia et al. (2019) , we consider cybersecurity as a “must-have” within SCRM. This shift is particularly crucial given the escalation of supply chain cyber risks, evidenced by high-profile attacks ( Creazza et al. , 2022 ) and hackers’ increased sophistication in breaching IT defences, facilitated by generative AI. This finding underscores SCRM’s role in enhancing IT-driven SCC development and, consequently, improving SCR.

Contrary to expectations, we found that supply chain structural complexity has no significant moderating effect on the relationship between IT-driven SCCs and SCR (H6). This finding aligns with previous research by Caniato and Größler (2015) and Chaudhuri and Boer (2016) , which showed SCSC’s neutral impact on firm performance. Although the impact of SCSC on performance remains inconclusive ( Akın Ateş et al. , 2022 ), it can be argued that the structural dimension of complexity in the supply chain can both weaken and strengthen the relationship simultaneously.

On one hand, as Reeves et al. (2020) argued, structural complexity can enhance resilience by providing more opportunities for adaptability within the ecosystem and increasing resilience. On the other hand, structural complexity increases the frequency of disruptions ( Bode and Wagner, 2015 ), which can lead to less SCR. Therefore, the positive and negative impacts of SCSC may cancel each other out, resulting in no significant moderating effect on the relationship between SCCs and SCR.

This study provides practical insights to supply chain managers, emphasising the critical importance of investing in SCMITI to enhance SCR. Particularly, it highlights that merely investing in SCMITI is not enough and managers need to ensure that IT investments are leveraged to create unique capabilities that enhance resilience, rather than simply acquiring technology. It paves the way for managers to choose appropriate IT-based platforms and tools for enhancing SCR, by suggesting those that enhance IT-driven SCCs such as flexibility, visibility, responsiveness, collaboration and integration across the supply chain.

Moreover, the results indicate that to enhance SCR, business managers need to invest not only in suitable technologies that directly contribute to IT-driven SCCs but also in the security of these digital systems. Managers need to integrate robust risk management practices, including cybersecurity measures, into their IT strategy and ensure the continuation of the IT-driven SCCs that ultimately lead to SCR. With the increasing sophistication of cyber threats, managers should prioritise cybersecurity as an integral part of their SCRM strategy, especially when implementing and leveraging IT systems across the supply chain. This will help in effectively leveraging IT capabilities and enhancing resilience.

Also, managers should be aware that SCSC can have both positive and negative impacts on resilience. Given the interconnected nature of these findings, managers should adopt a holistic approach to SCM. This includes aligning IT investments with capability development, integrating risk management practices and considering the SCSC.

While we are confident in the validity and reliability of our study, some potential limitations should be acknowledged. The data were collected from supply chain professionals in the industry sector, which may limit the generalisability of the findings to the service sector. Moreover, the data collection was done during the COVID-19 pandemic, which may render its findings less applicable over time, despite their relevance to the constructs explored.

Future research can utilise more modern theories on technology integration to delve deeper into the complex interplay among SCSC, SCCs and SCR to provide additional insights into the underlying mechanism of achieving SCR. In light of the growing interest in supply chain digitalisation and the use of generative AI, as well as the consolidation of product design in digital artefacts, examining the integration of cybersecurity with SCR is a crucial research direction. Ghadge et al. (2019) provide multiple future research directions for cybersecurity and SCR, further highlighting the importance of this area. Another area worthy of investigation is the potential of digitalisation to enhance sustainability, despite the significant greenhouse gas emissions that may arise from cloud computing.

The complex global supply chain structure, combined with interconnected IT systems, has made businesses more vulnerable to various disruptions, such as geopolitical tensions and cyberattacks. This study is motivated by the urgent need to understand how SCMITI can enhance SCR, given the frequent disruptions and their significant economic impact on businesses and society worldwide. We utilised both DCV and RBV as the theoretical lens to conceptualise the interplay between SCMITI and SCR. We formulated IT-driven SCCs as mediators and SCSC and SCRM as two moderators. We developed six hypotheses and collected data through a survey from supply chain professionals. We analysed data and tested the hypotheses using PLS-SEM. The outcomes of this empirical research not only provide industry professionals with knowledge on converting SCMITI into SCR but also significantly enrich the academic literature on SCR. Our empirical analysis indicates that IT-driven SCCs serve as essential driving forces in fostering SCR, while SCRM functions as a crucial catalyst that reinforces this association.

Table A1