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Article

The Role of IT Flexibility in Enhancing Supply Chain Resilience in the Oil Products Distribution Sector

by
Hayder Abdulmohsin Mijbas
1,2,
Muhummad Khairul Islam
3 and
Mohamed Khudari
1,*
1
College of Graduate Studies, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
2
College of Administration and Economics, Mustansiriyah University, Baghdad 00964, Iraq
3
Institute of Energy Policy and Research, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(5), 2295; https://doi.org/10.3390/su17052295
Submission received: 13 January 2025 / Revised: 26 February 2025 / Accepted: 28 February 2025 / Published: 6 March 2025
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Abstract

:
Business organisations are working to improve their IT flexibility to gain a competitive advantage in the evolving business landscape. This study aims to assess the influence of IT flexibility in its three dimensions (compatibility, connectivity, modularity) on supply chain resilience in the petroleum products distribution sector. It will also examine the connection between these dimensions and supply chain resilience at both the macro and micro levels. The research population represented the managers of fuel stations in the petroleum products distribution company with 2215 managers. A sample of 327 managers was selected using the G*Power program. The results of the regression analysis indicate that IT flexibility has a significant impact on supply chain resilience. Among the dimensions of IT flexibility, the modularity dimension was found to be the most influential. This research contributes to clarifying the role of IT infrastructure flexibility in enhancing supply chain resilience, which in turn contributes to the continuity and growth of organisations. The research emphasises the importance of focusing on collaboration and knowledge sharing to achieve greater flexibility in facing challenges, as well as investing in opportunities.

1. Introduction

The petroleum products distribution sector is one of the vital sectors that plays a strategic role in supporting the national economy and meeting the energy needs of various economic sectors [1]. As a result of the rapid changes in the business environment, this sector, like other economic sectors, faces many challenges [2], including technological challenges, market fluctuations, and customer needs and desires that vary in diversity and intensity [3,4]. Contemporary business organisations seek to adopt an effective strategic approach that ensures supply chain resilience, maintains the continuity of their operations, and protects them from interruption, considering the dynamic changes witnessed by the contemporary business environment [5]. Many studies have indicated that the effectiveness of supply chain resilience is not limited to being a management responsible for confronting interruptions in the supply chain. Still, it goes beyond that to play a crucial role in improving operational efficiency and enhancing competitiveness [6,7]. IT flexibility is one of the important tools that companies rely on to face challenges and disruptions in the supply chain [8]. It provides the ability to quickly redesign systems, enhance communication between stakeholders, and support decision-making processes in the face of uncertainty [9,10]. In addition to innovative solutions that improve the flow of information across different parts of the supply chain [11]. IT flexibility also plays an important role in enhancing integration between different systems and facilitating rapid response to sudden changes that may occur in the market [12,13]. Despite the literature’s discussion of the concept of IT resilience, the knowledge gap exists in understanding how IT resilience can play a critical role in making the supply chain more resilient, especially in the petroleum products distribution sector, especially in developing economies such as Iraq [14,15,16,17,18,19,20]. Iraq’s petroleum products distribution sector suffers from many complexities in the government-controlled regulatory environment, in addition to the challenges of the petroleum refining industry’s infrastructure and seasonal demand fluctuations [21]. This research aims to analyse the impact of IT flexibility on improving supply chain resilience, focusing on its key dimensions: compatibility, connectivity, and modularity [22,23,24]. The construction of each dimension is presented in Table A1 and Table A2 (Appendix A). Additionally, it seeks to identify the factors that enhance operational performance and enable organisations to respond swiftly to market changes. The research also offers practical recommendations to assist decision-makers in making effective choices in this crucial area.

2. Literature Review

The concept of IT flexibility refers to the ability of an organisation’s IT infrastructure to improve and adapt quickly and effectively to rapid changes in the internal and external environment [25]. IT flexibility provides the ability to support innovation, improve operational efficiency, and enhance integration between systems by reconfiguring IT resources [26]. Additionally, IT flexibility minimises the risk of disruptions and strengthens business continuity, providing organisations with a competitive advantage in rapidly changing environments [27]. Therefore, IT flexibility is one of the key pillars for achieving continuity and sustainability in operational performance and enhancing digital growth processes amid rapid developments in IT [28]. According to Duncan’s classification, IT flexibility is measured by its dimensions: compatibility, connectivity, and modularity [29,30].
Compatibility refers to the effective communication between IT components [31]. Different IT platforms make information exchange available and flow smoothly within an organisation [32]. For example, systems designed using a service-oriented architecture have each layer of the technology stack loosely coupled while adhering to strict industry standards [33]. This approach ensures that technology components with similar features can communicate effectively [22].
Connectivity is the possession of the IT infrastructure for a variety of platforms that enable an organisation to use and benefit from them [22]. Connectivity promotes the effective exchange of information within and outside the organisation [32]. Improving connectivity creates better alignment, allowing organisations to create stronger links between IT and other corporate strategies by improving planning, coordination, and task execution [24]. Enterprise application integration (EAI) illustrates this concept by promoting a more connected environment. It breaks down application and data silos by taking communications and data flows out of the applications themselves [34].
Modularity is a vital element of IT flexibility, referring to the ability of technology systems to adapt quickly to changes by using multiple integrated technologies [35]. Through this capability, the organisation can meet the needs of evolving businesses, which is positively reflected in the adaptation and speed of response to environmental challenges imposed by the technological environment [36]. As with some companies with more adaptable technology infrastructures, they leverage scalable software platforms or cloud computing technologies to accommodate new requirements without modifying their infrastructure [37]. Therefore, IT flexibility must include modularity, which allows companies to respond quickly and effectively [38].
Supply chain resilience is the ability of a supply chain to recover from disruptions and emergencies and to adapt and respond quickly to ensure the continued efficient flow of products and services [39]. That makes resilience essential to ensure business sustainability in complex and evolving environments [40]. The concept of supply chain resilience involves a set of strategic dimensions that contribute to enhancing the supply chain’s ability to resist and withstand the crises and disruptions it faces. Among these dimensions is early risk sensing, which helps provide early warning of risks and disruptions before they occur, which helps in taking the necessary precautions that increase the possibility of quickly adapting to emergency conditions and challenges, thus maintaining the continuity of the chain’s operation and quickly restoring its normal state [41]. Continuous cooperation and information exchange among the various parties in the supply chain help to increase the effectiveness of coordination [42], which in turn has a positive impact on the overall sustainability of the system [43,44]. Together, these dimensions form a strong foundation for building a resilient supply chain that can effectively face future challenges [45]. The literature review indicates that previous studies have advanced our understanding of the dimensions of IT flexibility and their significance in improving supply chain resilience. However, there is still a need for an integrated framework that effectively links these dimensions and clarifies their collective impact on operational performance and an organisation’s ability to adapt to rapid market changes. This research is an important step toward bridging this gap by providing a comprehensive analysis and practical recommendations that enhance decision-making in this critical area. It offers a unique contribution and adds value to the existing literature.

3. Research Model and Hypotheses

Supply chain resilience is of great importance to many companies that are always seeking to adapt to the challenges they face in the business environment [46,47], and these challenges are often of a changing and unpredictable nature [48]. IT flexibility and its dimensions of compatibility, connectivity and modularity play a pivotal role in enhancing companies’ ability to adapt to the rapid and unexpected changes they face through communication and coordination across the parts of the supply chain and its basic components [49,50]. IT flexibility increases the speed of companies’ responses to unexpected disruptions and challenges [51]. IT flexibility is expected to play a positive role in supply chain resilience. Therefore, the first hypothesis will be formulated as follows:
H1. 
IT flexibility has a significant impact on supply chain resilience.
IT compatibility is critical to enhance coordination between IT systems within companies; this is reflected in the different parties within the supply chain [52]. The greater the compatibility between these systems, the more efficient the flow of data and information is, and it has the advantage of improving supply chain resilience [53]. Therefore, the second hypothesis can be formulated as follows:
H2. 
Compatibility has a significant impact on supply chain resilience.
The rapid and effective exchange of information between the parties that make up the supply chain is very important [54]. Effective connectivity enhances the ability to rapidly make decisions to face changes in the business environment [55], thus increasing the ability of the supply chain to withstand and adapt to challenges [56]. Accordingly, the third hypothesis can be formulated as follows:
H3. 
Connectivity has a significant impact on supply chain resilience.
Modularity offers IT greater flexibility, allowing companies to swiftly adjust and modify their systems in response to changes in their environment or market challenges [57,58]. This quick adaptation improves the supply chain’s response to environmental shifts [58]. Based on this understanding, we can formulate the following fourth hypothesis:
H4. 
Modularity has a significant impact on supply chain resilience.
Figure 1 depicts the relationship diagram illustrating the connections between the research variables and their influence based on the research hypotheses. The study focuses on the impact of information technology flexibility on the overall resilience of the supply chain and its dimensions. The independent variable in the study is information technology flexibility, which comprises three dimensions: compatibility (COM), connection (CON), and modularity (MO), as well as supply chain resilience (SCR).

4. Methodology

To implement the research, a questionnaire consisting of 28 paragraphs was designed and divided into three main sections: the first section dealt with demographic variables, while the second section included the independent variable (IT flexibility) and its three dimensions (compatibility, communication, and modules) [36]. The third section focused on the dependent variable (supply chain flexibility) [37,38]. The questionnaire was designed using a five-point Likert scale (1 = strongly disagree, 5 = completely agree). The study population was determined to include gas station managers working in the oil products distribution company, numbering 2215 managers. The G*Power program was used to determine the total sample size needed, where the total sample size was determined at 327 managers, considering a statistical significance level of 0.05, a statistical power of 0.95, and a medium effect size. To ensure the recovery of a sufficient number of valid questionnaires, 400 questionnaires were distributed, and 350 questionnaires were recovered, of which 334 were valid for statistical analysis after excluding incomplete or invalid questionnaires. The reliability and validity of the questionnaire were evaluated using Cronbach’s alpha coefficient. The results shown in Table 1 show high Cronbach’s alpha coefficients for the different dimensions, indicating the high reliability of the questionnaire. The value of Cronbach’s alpha coefficient for the IT flexibility variable was 0.957, and for the supply chain flexibility variable, 0.963. The sub-dimension coefficients ranged between 0.772 and 0.899, which confirms that the questionnaire has a high and acceptable degree of reliability for all its dimensions.
The data collected from the survey conducted for this research revealed the demographic details, as shown in Table 2. The survey was comprised of 100% male participants. The largest portion of the sample, 37.13%, fell within the 30–40 age group, followed by the 41–50 age group at 27.84%, the over-50 age group at 23.35%, and the under-30 age group at 11.68%. Regarding academic qualifications, 48.80% of the sample held a diploma, making it the largest group. Holders of a preparatory certificate followed this at 25.75%, a bachelor’s degree at 19.16%, a higher diploma at 5.69%, and a master’s degree at 0.60%. Regarding familiarity with the technologies used in fuel distribution operations, 30% of the participants had a basic level of familiarity, 50% had an intermediate level, and 20% had an advanced level. Regarding participation in supply chain training programs or seminars, 50% had participated in fewer than five programs, while 40% had participated in five or more.

5. Results and Discussion

The results indicate that the arithmetic means of all variables are higher than the neutral value (3). In Table 2, the mean arithmetic mean value of IT flexibility was 3.630. Furthermore, the arithmetic mean values of its dimensions were as follows: compatibility 3.635, connectivity 3.639, and modularity 3.615. The arithmetic mean value of supply chain resilience was 3.617. This indicates a relatively positive evaluation by the sample members of these variables compared to the hypothetical average of 3. This evaluation reflects the participants’ interest in the importance of IT flexibility and its role in enhancing the sustainability of logistics operations. Additionally, the standard deviation value of the IT flexibility variable at both the overall and dimensional levels and supply chain resilience was greater than or equal to 0.60, indicating a greater relative homogeneity in the participants’ opinions regarding their supply chain resilience assessment compared to IT flexibility. These results reflect an awareness of the importance of investing in flexible technologies to enhance the supply chain’s ability to adapt to challenges and improve operational efficiency.
Structural equation modelling (SEM) is a statistical technique used to evaluate and examine the causal link between dependent and independent variables [59]. In this study, the measurement model and the structural model were assessed using Smart-PLS 3.0. A measurement model defines the link between the latent variable and the indicator or manifest variable. The Partial Least Squares (PLS) measurement model is analysed using Principal Component Analysis (PCA). Because the study’s variables were reflective, the measurement model was also assessed for internal consistency, indicator reliability, convergent validity, and discriminant validity [60].
Composite reliability is used to gauge internal consistency [61]. To be more precise, according to Hair et al. (2014), it is deemed acceptable if the composite dependability is 0.70 or above [62]. Table 3 displays the composite reliability (CR) statistics. Every CR value was discovered to be more than 0.70. Put another way, all the variables included in this study were deemed reliable since the results met the overall recommendations of Hair et al. (2019) [63].
Convergent validity is the positive association between a measure and further measurements of the same variable. Validity assessment of the reflective variables is crucial [62]. Table 4 presents the evaluation of the measurement model in terms of factor loadings and AVE. Hair et al. (2014) state that all items should ideally have factor loadings of at least 0.70 [63]. As a result, 12 items with values below 0.70 were eliminated as they could not account for the variable in the specified factor. It may be inferred that the factor takes enough variation from the variable because the other factor saturations were higher than 0.70. The variation that the variable in the given factor represents is explained by factor saturation. Furthermore, every Average Variance Extracted (AVE) value for these items was greater than 0.50, indicating that the variables in this study had convergent validity.
Discriminant validity evaluates the degree to which items distinguish between ideas or variables [64]. According to Hair et al. (2014), a variable that demonstrates discriminant validity is unique and able to capture the phenomenon of interest that is not reflected by other variables in the same model [62]. This investigation assessed discriminant validity using Fornell and Larcker’s criteria (see Table 5).

5.1. Structural Model Path Coefficients (Model 1)

 Hypothesis (H1). 
IT flexibility significantly affects supply chain resilience.
The direct influence of the IT flexibility (ITF) variable, which consists of compatibility, connection, and modularity dimensions, on supply chain resilience (SCR) was examined using a route analysis model, as shown in Figure 2. With a coefficient of β = 0.880 and p < 0.01, Table 6 and the model in Figure 2 show that the IT flexibility variable has a positive influence on supply chain resilience, indicating that increased IT flexibility improves supply chain resilience. The high impact factor shows that investing in IT flexibility is not just a technical improvement but a strategic factor that enhances supply chain resilience, underscoring its critical role in dynamic and complex business environments such as the petroleum products distribution sector.

5.2. Model Fit Indices and Statistical Reporting

Structural Equation Modelling (SEM) was employed using Smart-PLS 3.0 to evaluate the model’s explanatory power and goodness of fit, ensuring methodological rigour. The key model fit indices are reported in Table 7.
These results indicate that IT flexibility contributes only marginally to variations in supply chain resilience, emphasising the need to consider additional variables that might mediate or moderate this relationship.

5.3. Structural Model Path Coefficients (Model 2)

 Hypothesis (H2). 
The impact of compatibility on supply chain resilience is significant.
The compatibility variable (CPT) has a positive effect on supply chain resilience (SCR), as demonstrated in Table 7 and illustrated in the model in Figure 3, with β = 0.3019 and p < 0.01. In other words, an improved level of compatibility would lead to increased resilience.
 Hypothesis (H3). 
The impact of connectivity on supply chain resilience is significant.
Table 7 and Figure 3 illustrate that the connectivity variable (CNT) exerts a positive influence on supply chain resilience (SCR), with β = 0.304, p < 0.01. In other words, increased connectivity would lead to a more resilient supply chain.
 Hypothesis (H4). 
The impact of modularity on supply chain resilience is substantial.
The modularity variable (LC) has a positive effect on supply chain resilience (SCR), as demonstrated in Table 8 and illustrated in the model in Figure 3, with β = 0.316 and p < 0.01. In other words, an increased modularity level would lead to improved supply chain resilience.

5.4. Testing of Hypotheses

Geisser and Stone created the bootstrapping method to test the study’s hypotheses. Table 9 shows the outcomes of this study’s hypothesis testing.
The results of the path analysis indicated that IT flexibility has a strong overall effect on supply chain resilience (β = 0.88). However, the effects of its dimensions—compatibility (β = 0.319), modularity (β = 0.3015), and connectivity (β = −0.304)—were found to be weak. This means that IT flexibility significantly enhances the ability of the supply chain to adapt to challenges and disruptions. At the same time, the individual dimensions do not exert the same level of influence. This indicates that IT flexibility at the aggregate level plays a crucial role in increasing the ability of the supply chain to adapt to disruptions and challenges, which enhances its resilience. The current research results are consistent with what was discussed in the previous literature regarding IT flexibility as a concept consisting of several interconnected dimensions, each contributing to improving organisational performance and the ability to coexist and adapt to changes in the business environment [6,31,32]. Although the dimensions of IT flexibility are essential pillars of information systems’ resilience, their impact individually remains limited unless they are effectively integrated. This is confirmed by previous literature, which finds that an integrated technological infrastructure that combines these dimensions is the most efficient and effective in enhancing supply chain resilience [33,34]. The information in Table 7 shows that the overall effect of IT flexibility on supply chain resilience is weak, suggesting the need to explore additional factors. Conversely, Table 9 demonstrates a strong overall impact of IT flexibility; however, when the individual dimensions are evaluated separately, their effects are weak. This highlights the importance of combining the different dimensions of IT flexibility to effectively improve supply chain resilience.
The results indicate that IT flexibility at the aggregate level significantly affects supply chain resilience. However, when its dimensions (compatibility, connectivity, and modularity) are analysed individually, they appear to have a smaller influence. This phenomenon can be attributed to the synergistic interaction between these elements. Compatibility helps systems integration, which enhances connectivity and enables seamless and efficient data exchange. At the same time, modularity allows for rapid adaptation to changes in demand or disruptions within the supply chain. However, compatibility and connectivity are critical to ensuring operational continuity. Ultimately, these dimensions are most effective and impactful when they work together in an integrated manner, enhancing rapid response to challenges, which is the logical explanation for the power of their combined effect.
The accuracy of the analysis of the results does not prevent some limitations from being considered. The most prominent of these limitations is the use of a Likert scale in collecting data, which may lead to biases in the participants’ responses, as these responses are based on self-assessment that may be affected by the respondents’ perceptions or previous experiences in the field of information technology. In addition, the sample used in the research, which was limited to managers of fuel stations in Iraq, limited the process of generalising the results. Therefore, the current research allows for future studies that take different samples in different geographical areas to expand the scope of generalising the results. Moreover, from the management side, companies should focus on integrating the components of IT into a technology strategy that achieves an effective impact that ensures the efficiency of the supply chain and enhances its resilience.

6. Conclusions

This study examined the relationship between IT flexibility and supply chain resilience in Iraq’s petroleum products distribution sector. The three dimensions of IT flexibility, compatibility, modularity, and connectivity were explored to analyse their contribution to improving the ability of supply chains to cope with changes and disruptions. The results indicate that IT flexibility as an overarching factor is critical in enhancing supply chain resilience. At the same time, the individual dimensions showed varying and relatively weak effects, reflecting the need for their integration to achieve greater effectiveness. Theoretically, these results are consistent with previous literature that emphasises the importance of IT flexibility in enhancing operational efficiency and rapid response to market changes. However, the study added a new dimension by testing the effect of each dimension of IT flexibility separately, which contributed to highlighting the limited effectiveness of these dimensions when they are not integrated. The study has several limitations that should be considered. First, the sample was limited to fuel station managers in Iraq, which may limit the possibility of generalising the results to other industrial sectors or geographic regions. Second, the study relied on a Likert scale survey instrument, which may be affected by inaccurate subjective assessments. Finally, the study did not consider the impact of other environmental and organisational factors that may affect supply chain resilience. Based on these limitations, the study recommends future research considering diverse samples covering different sectors and using more comprehensive research methodologies, such as longitudinal studies, to track the impact of IT flexibility on supply chain resilience over time. Studying the impact of environmental and organisational factors, such as mediating or moderating variables, is also suggested. In practical terms according to the research findings, to improve the resilience of the supply chain, companies operating in the oil products distribution sector should rely on practical strategies that focus on developing IT flexibility by first having the company conduct an assessment of its technological infrastructure to identify weaknesses in compatibility and connectivity between different parts, and then work on investing in systems that can compatibility and improve flow such as enterprise resource planning (ERP) systems. In addition, companies can implement standard technology solutions, such as smart and customisable inventory management systems, which allow for rapid adaptation to changes in demand or disruptions in the supply chain. Finally, the company should subject its employees to training courses on how to operate these new systems to ensure maximum benefit from the capabilities available in these systems. This is reflected positively in the efficiency of supply chain operations and their adaptability. By implementing these strategies, the oil products distribution company will be able to build a more resilient and capable supply chain in the face of future challenges.

Author Contributions

Conceptualisation, H.A.M. and M.K.I.; methodology, H.A.M. and M.K.; investigation, H.A.M.; resources, H.A.M. and M.K.I.; data curation, H.A.M.; writing—original draft preparation, H.A.M.; writing—review and editing, M.K.I. and M.K.; visualisation, M.K.; supervision, M.K.I. and M.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Multiple dimensions of IT flexibility.
Table A1. Multiple dimensions of IT flexibility.
DimensionsQuestions
Compatibility
1. 
Our company uses enterprise systems to achieve integration.
2. 
Our business is not limited by our choice of operating systems (e.g., UNIX, Windows).
3. 
Software applications can be easily transported and used across multiple platforms.
4. 
Our company makes extensive use of middleware to integrate key enterprise applications.
5. 
Our company offers multiple interfaces or entry points (e.g., Internet) to external users.
6. 
Our IT department has sufficient entry points to share information with stakeholders
1. 
Our systems are sufficiently flexible to incorporate electronic links to external parties.
Connectivity
2. 
Our company has a high degree of systems interconnectivity.
3. 
All remote offices and mobile personnel can connect to a central office.
4. 
Our firm applies open systems network mechanisms to boost connectivity.
5. 
Company databases are accessed through many different protocols.
6. 
Our IT systems can be easily connected/integrated with third parties (suppliers, partners, etc.)
Modularity
1. 
Legacy systems within our firm do not hamper the development of new IT applications.
2. 
Functionality can be quickly added to critical applications based on end-user requests.
3. 
Data is captured and made available to everyone in the company in real-time.
4. 
Data rules and relations are not hard coded into applications.
5. 
Reusable software modules are widely used throughout our systems development group.
6. 
Organization and integration of our IT systems allow for quick changes
Table A2. Multiple dimensions of supply chain resilience.
Table A2. Multiple dimensions of supply chain resilience.
DimensionsQuestions
Agility
1. 
The company is sensitive to the opportunities and threats in the business environment.
2. 
The company can rapidly respond to the changing market.
3. 
The company reserves extra capacity in response to the rapidly changing market.
4. 
The company’s employees can execute multiple kinds of tasks and jobs.
5. 
The company fully authorizes its managers to make private places to provide comfort and wait for customers.
6. 
When selecting collaborative partners, the company prioritizes agility and responsiveness.
7. 
The company frequently adjusts the course of the truck in response to the rapidly changing market.
8. 
The company’s employees can execute multiple kinds of tasks and jobs.
Collaboration
1. 
We effectively employ collaborative demand forecasting techniques using shared data.
2. 
Our data flows transparently between supply chain members, with full access by all firms to facilitate collaborative decision-making.
3. 
Our company has increased operational flexibility through collaboration with suppliers.
4. 
Our company invests in facilities and equipment at suppliers’ plants and is prepared to share risks with both suppliers and customers.
5. 
We are willing to make cooperative changes.
6. 
In dealing with our major supplier, we have a mutual understanding of what will happen in the case of events occurring that were not planned.
7. 
When our major supplier incurs problems, we try to help.
8. 
Company’s problems are joint responsibilities no matter who is at fault.
Flexibility
1. 
Our supplies are used in multiple finished goods.
2. 
We can quickly vary outsourced storage, distribution, and other services.
3. 
We have a sophisticated inventory management system that regularly computes both safety stock and cycle stock at all storage and retail locations.
4. 
We can quickly change the routing and mode of transportation for outbound shipments.
5. 
We have flexibility in production in terms of the volume of orders and production schedule.
6. 
Our supply contracts can be easily modified to change specifications, quantities, and terms.
7. 
Our finished goods use modular designs.
8. 
We can quickly reallocate orders to alternate suppliers and reallocate jobs between different production units.
Redundancy
1. 
The company uses advanced techniques or best practices in inventory management to ensure redundancy.
2. 
The company conducts periodic analyses to estimate future needs for petroleum products and ensure they are met.
3. 
The company has backup plans to deal with any potential challenges affecting redundancy such as disturbances in supplies or price fluctuations.
4. 
The company adopts initiatives to move towards sustainability about the petroleum products it distributes.
5. 
The company cooperates with reliable suppliers to ensure the redundancy of oil resources in a sustainable manner.
6. 
The company evaluates the quality of its redundancy and the extent to which it meets customer and market needs.
7. 
The company is interested in providing clear information about the redundancy and sustainability of petroleum products to its customers and business partners.
8. 
The company has future improvements that you plan to implement to ensure redundancy and sustainability in its operations
Redundancy
1. 
The company uses advanced techniques or best practices in inventory management to ensure redundancy.
2. 
The company conducts periodic analyses to estimate future needs for petroleum products and ensure they are met.
3. 
The company has backup plans to deal with any potential challenges affecting redundancy such as disturbances in supplies or price fluctuations.
4. 
The company adopts initiatives to move towards sustainability about the petroleum products it distributes.
5. 
The company cooperates with reliable suppliers to ensure the redundancy of oil resources in a sustainable manner.
6. 
The company evaluates the quality of its redundancy and the extent to which it meets customer and market needs.
7. 
The company is interested in providing clear information about the redundancy and sustainability of petroleum products to its customers and business partners.
8. 
The company has future improvements that you plan to implement to ensure redundancy and sustainability in its operations
Information
Sharing
1. 
Our company exchanges relevant information with suppliers.
2. 
Our company exchanges timely information with suppliers.
3. 
Our company exchanges accurate information with suppliers.
4. 
Our company exchanges complete information with suppliers.
5. 
Our company exchanges confidential information with suppliers.
6. 
Sharing information about product enhances transparency in the sector.
7. 
The company should increase its efforts in sharing information about the impact of the oil and gas industry on the environment.
8. 
I find it important to know more about the safety and quality procedures your company follows in its distribution operations.
visible
1. 
Inventory levels are visible throughout the supply chain.
2. 
Demand levels are visible throughout the supply chain.
3. 
Do you think that the company seeks to achieve customer satisfaction effectively?
4. 
Do you think the company supports innovation, research and development?
5. 
Do you think that the company is interested in developing and improving the skills of its employees?
6. 
Do you believe that the company is committed to complying with the laws and regulations relevant to its activity?
7. 
Do you think that the company seeks to achieve transparency in its dealings?
8. 
Based on your view of our current activities, do you believe that the company is committed to social responsibility.

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Sustainability 17 02295 g001
Figure 1. A conceptual model depicting the relationship between IT flexibility and supply chain resilience and the associated hypotheses.
Figure 1. A conceptual model depicting the relationship between IT flexibility and supply chain resilience and the associated hypotheses.
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Figure 2. Path coefficient Model 3 results for the effect of IT flexibility on supply chain resilience.
Figure 2. Path coefficient Model 3 results for the effect of IT flexibility on supply chain resilience.
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Figure 3. Path coefficient Model 4 results for the effect of IT flexibility dimensions on supply chain resilience.
Figure 3. Path coefficient Model 4 results for the effect of IT flexibility dimensions on supply chain resilience.
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Table 1. Study scale concerning survey sections, sources, and Cronbach’s alpha.
Table 1. Study scale concerning survey sections, sources, and Cronbach’s alpha.
SectionVariablesSourceItemsCranach’s Alpha
Section one Participant and company background informationResearcher developed 4
Section twoCompatibility[17]
[39]
[40]		60.874
Connectivity60.844
Modularity60.877
IT Flexibility180.947
Section threeSupply chain resilience[37]
[38]
[41]		420.963
Table 2. Descriptive results.
Table 2. Descriptive results.
Measurement ItemsMeanSD
CPT3.6350.693
CNT3.6390.704
MOD3.6150.701
ITF3.6300.656
SCR3.6170.579
Table 3. Results of composite reliability.
Table 3. Results of composite reliability.
VariableComposite Reliability
ITFCPT0.89
CNT0.874
MOD0.89
SCRAG0.898
COL0.823
FIX0.899
RDY0.828
IS0.898
VIS0.825
Table 4. Results of convergent validity.
Table 4. Results of convergent validity.
First-Order VariablesSecond-Order VariablesItemsLoadingsAVE
CPTITFCPT1 <- CP 0.7790.632
CPT2 <- CP 0.778
CPT3 <- CP 0.794
CPT4 <- CP 0.79
CPT5 <- CP 0.832
CPT6 <- CP 0.794
CNTCNT1 <- CNT0.8090.647
CNT2 <- CNT0.792
CNT3 <- CNT0.829
CNT4 <- CNT0.788
CNT5 <- CNT0.798
CNT6 <- CNT0.81
MODMOD1 <- MOD0.7250.614
MOD2 <- MOD0.801
MOD3 <- MOD0.807
MOD4 <- MOD0.814
MOD5 <- MOD0.804
MOD6 <- MOD0.745
AGSCRAG1 <- AG0.7820.585
AG2 <- AG0.772
AG3 <- AG0.767
AG4 <- AG0.723
AG5 <- AG0.771
AG6 <- AG0.721
AG7 <- AG0.779
AG8 <- AG0.798
COLCOL-1 <- COL0.580.559
COL-2 <- COL0.646
COL-3 <- COL0.627
COL-4 <- COL0.789
COL-5 <- COL0.797
COL-6 <- COL0.738
COL-7 <- COL0.755
COL-8 <- COL0.388
FIXFIX-1 <- FIX0.7940.589
FIX-2 <- FIX0.768
FIX-3 <- FIX0.773
FIX-4 <- FIX0.736
FIX-5 <- FIX0.777
FIX-6 <- FIX0.712
FIX-7 <- FIX0.777
FIX-8 <- FIX0.79
RDYRDY-1 <- RDE0.5960.585
RDY-2 <- RDE0.666
RDY-3 <- RDE0.653
RDY-4 <- RDE0.789
RDY-5 <- RDE0.799
RDY-6 <- RDE0.745
RDY-7 <- RDE0.702
RDY-8 <- RDE0.407
ISIS-1 <- IS0.7960.586
IS-2 <- IS0.778
IS-3 <- IS0.772
IS-4 <- IS0.737
IS-5 <- IS0.778
IS-6 <- IS0.721
IS-7 <- IS0.779
IS-8 <- IS0.753
VISVIS-1 <- VIS0.590.559
VIS-2 <- VIS0.656
VIS-3 <- VIS0.639
VIS-4 <- VIS0.78
VIS-5 <- VIS0.797
VIS-6 <- VIS0.734
VIS-7 <- VIS0.734
VIS-8 <- VIS0.40
Table 5. Results of Fornell–Larcker ‘s criterion.
Table 5. Results of Fornell–Larcker ‘s criterion.
ITFCPTCNTMODSCRAGCOLFIXRDYISVIS
IT0.746
CPT0.9490.795
CNT0.9460.8610.805
MOD0.9220.810.7990.783
SCR0.8790.8280.8320.8160.639
AG0.870.8350.8290.7860.9180.765
COL0.6630.6190.6330.6170.840.5930.677
FIX0.870.8350.8360.7790.9220.9670.5920.766
RDY0.6560.5910.5990.6620.8470.5910.940.5860.68
IS0.8670.8280.8280.7870.9250.9750.5920.9920.5980.765
VIS0.660.5970.6110.6550.8450.5840.9420.590.9890.5890.677
Table 6. Modelling the coefficients of the path model for the effect of IT flexibility on supply chain resilience.
Table 6. Modelling the coefficients of the path model for the effect of IT flexibility on supply chain resilience.
PathBetaSt. ErrorT
ITF -> SCR0.880.03412.654
Table 7. Model fit indices for IT flexibility and supply chain resilience.
Table 7. Model fit indices for IT flexibility and supply chain resilience.
Model Fit IndexValueInterpretation
R2 (R-squared)0.0041Indicates IT flexibility explains 0.41% of the variance in supply chain resilience.
Adjusted R20.0010Accounts for the number of predictors, confirming minimal explanatory power.
SRMR (Standardized Root Mean Square Residual)0.576Represents residual variance; lower values indicate a better model fit.
f2 (Effect Size)0.0041Small effect size, suggesting weak predictive power.
Table 8. Modelling the coefficients of the path model for the effect of IT flexibility dimensions on supply chain resilience.
Table 8. Modelling the coefficients of the path model for the effect of IT flexibility dimensions on supply chain resilience.
PathBetaStd. ErrorT
CPT -> SCR0.3190.0575.623
CNT -> SCR0.3040.0545.638
MOD -> SCR0.3160.0466.823
Table 9. Results of hypotheses testing.
Table 9. Results of hypotheses testing.
Research HypothesesPathTSt. ErrorResult
H1IT flexibility significantly affects supply chain resilience0.88012.6540.034Accept
H2Compatibility significantly affects supply chain resilience.0.3195.6230.057Accept
H3Connectivity significantly affects supply chain resilience.0.3045.6380.054Accept
H4Modularity significantly affects supply chain resilience.0.3166.8230.046Accept
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Mijbas, H.A.; Islam, M.K.; Khudari, M. The Role of IT Flexibility in Enhancing Supply Chain Resilience in the Oil Products Distribution Sector. Sustainability 2025, 17, 2295. https://doi.org/10.3390/su17052295

AMA Style

Mijbas HA, Islam MK, Khudari M. The Role of IT Flexibility in Enhancing Supply Chain Resilience in the Oil Products Distribution Sector. Sustainability. 2025; 17(5):2295. https://doi.org/10.3390/su17052295

Chicago/Turabian Style

Mijbas, Hayder Abdulmohsin, Muhummad Khairul Islam, and Mohamed Khudari. 2025. "The Role of IT Flexibility in Enhancing Supply Chain Resilience in the Oil Products Distribution Sector" Sustainability 17, no. 5: 2295. https://doi.org/10.3390/su17052295

APA Style

Mijbas, H. A., Islam, M. K., & Khudari, M. (2025). The Role of IT Flexibility in Enhancing Supply Chain Resilience in the Oil Products Distribution Sector. Sustainability, 17(5), 2295. https://doi.org/10.3390/su17052295

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