Bridging the Gap Between Supply Chain Risk and Organizational Performance Conditioning to Demand Uncertainty

Abstract

This study aims to explore the impact of supply chain risk (SCR) on organizational performance (OP) within the Pakistani auto sector, focusing on the mediating role of supply chain integration (SCI) and the moderating effect of demand uncertainty. The research investigates how effectively managing SCR and integrating supply chain functions can enhance organizational resilience and performance, especially in the context of a volatile market environment. A quantitative research design was employed, with data collected through self-administered questionnaires distributed to 400 supply chain managers and decision-makers in the auto industry. A total of 320 completed questionnaires were returned, resulting in a response rate of 80%. The data were analyzed using Smart PLS to test the proposed hypotheses and examine the relationships between SCR, integration, demand uncertainty, and organizational performance. This study found that SCR negatively impacts organizational performance, while SCI mediates this relationship, enhancing performance outcomes. Additionally, demand uncertainty was found to moderate the relationship between SCR and integration, highlighting the importance of flexibility and adaptability in supply chain management. All hypotheses were supported, confirming the significant role of integration and uncertainty in managing risks effectively. This study contributes to the existing literature by providing empirical evidence from a developing economy, offering valuable insights for practitioners in the automotive industry. This study is a contribution to the supply chain management literature in that it provides empirical evidence regarding supply chain integration as a mediator and demand uncertainty as a moderator of the relationship between the supply chain risk and organizational performance.

1. Introduction

As technology and globalization advance, supply networks become more intertwined [1]. According to [2], managing risks and maintaining long-term effectiveness are essential for business complexity. Supply chain networks become more complex as global trade involves more countries and actors. Each stakeholder introduces new risks, which might delay operations [3]. SCR management is needed after technical failures, geopolitical conflicts, and natural calamities. These trends have led businesses to create complex risk management systems to reduce vulnerabilities and boost resilience. Organizations must prioritize risk management to meet market expectations and stay competitive in the global market. Risk management affects customer happiness, cost efficiency, and supply chain efficiency. Effective risk management requires identifying dangers, assessing their effects, and implementing proactive mitigation techniques. Supplier disruptions can hurt many companies’ productivity and profits; therefore, this protection is crucial [4]. Companies worldwide are investing heavily in modern risk management technology and practices [1]. The automotive industry dominates industrial output and employment in Pakistan [5]. This sector has grown due to rising car sales and infrastructure improvements. Market volatility, regulation, and supply chain disruptions affect industrial growth, as do currency instability, raw material shortages, and geopolitical unpredictability [6]. Supply chain management must be strategic to overcome these challenges and preserve operational resilience. Automobile industry in Pakistan needs supply chain management as local and global markets become more complex. Innovative supply chain solutions reduce risks and maintain competitiveness [7]. Ref. [8] suggest that integrating new technology and increasing stakeholder participation might help firms prevent disruptions. Efficiency and effectiveness help businesses achieve goals with the help of Robust supply chain protocols [9].

The automobile industry in Pakistan, vital to industrial growth and economic stability, is threatened by supply chain fragility [10]. Complex risks arise from macroeconomic instability, supplier unreliability, and demand unpredictability [7]. Disparities between production and supply, which may be altered by consumer tastes and purchasing power, cause supply chain inefficiencies and delays. Infrastructure deficiencies, geopolitical volatility, and global supply chain dependency make suppliers [11]. Currency fluctuations, inflation, and fiscal imbalances hamper Pakistani supply chain management. These considerations have increased automobile sector stakeholders’ worry about SCRs [12]. These risks hinder operations, limiting the capacity of the industry to meet customer expectations, stay profitable, and grow [13]. SCRs have not been tested on automotive organizational performance in Pakistan [14]. Due to research gaps, SCI as a risk mediator is poorly understood [15]. Supply chain resilience is linked to SCI, which synchronizes operations and information sharing among stakeholders [16]. Integration may increase organizational performance and minimize risk in Pakistan; however, this is debatable [13]. SCI improves trust, transparency, and collaboration in developed nations, improving responsiveness and efficiency. While complexity within the supply chain networks of Pakistan has been on the rise, few studies discuss the contribution of SCR to organizational performance in the local automobile industry. Moreover, SCI has been extensively proven as a risk-reducing and performance-enhancing measure in highly developed economies [16]; however, its contribution cannot be measured using infrastructural and institutional constraints [17]. Furthermore, demand uncertainty, influenced by economic fluctuations, seasonality, and demand patterns, has not been investigated significantly in the automobile industry of Pakistan for SCR and SCI [18,19].

This study aims to investigate how effectively managing SCR and integrating supply chain functions can enhance organizational resilience and performance, particularly in Pakistan’s volatile market. Specifically, it examines the following:

• The impact of SCRs on organizational performance in the Pakistani auto sector;
• The mediating role of SCI in the relationship between SCR and performance;
• The moderating effect of demand uncertainty on this relationship.

This study improves emerging market supply chain management and offers automotive industry advice for Pakistan. This study addresses SCR administration issues in developing nations to promote intellectual discourse. Most SCR and organizational performance research is performed in industrialized nations, but this study focuses on Pakistan, a dynamic, growing market with distinct supply chain disruption concerns. Market procedures and technology infrastructure strengthen industrialized nation supply networks [20]. Pakistan has higher SCR due to infrastructure restrictions, sociopolitical instability, and unreliable supplier networks [12]. This Pakistani study sheds light on how SCR affects emerging economies’ operational efficiency and organizational effectiveness. Demand uncertainty and SCI are important but understudied challenges in the auto sector in Pakistan. This research advances theoretical frameworks. These technologies give supply chain management data and help organizations manage risks and uncertainties. Demand unpredictability and SCI are novel ways to measure business reactions to external shocks. Supplier, manufacturer, and distributor integration boosts industrialized economies’ performance and resilience [21].

2. A Literature Review

2.1. Theoretical Background

This study is primarily grounded in the resource-based view (RBV) theory, which serves as the cornerstone for understanding the dynamics of supply chain risk, supply chain integration, and organizational performance. RBV posits that a firm’s competitive advantage stems from its ability to acquire, manage, and deploy valuable, rare, inimitable, and non-substitutable (VRIN) resources [22]. In the context of this research, supply chain integration is conceptualized as a strategic organizational capability that enables firms to effectively manage resources across the supply chain, reducing vulnerabilities and enhancing operational resilience [23]. The RBV suggests that firms with highly integrated supply chains are better positioned to create synergies, streamline operations, and improve decision-making processes. Supply chain integration facilitates seamless communication, effective resource allocation, and collaboration among stakeholders, making it a key enabler of organizational performance. By mitigating the impact of supply chain risks, such as demand fluctuations or supplier unreliability, integration acts as a buffer that ensures stability and efficiency [24]. For instance, firms that adopt integrated systems and processes can leverage real-time information to anticipate disruptions, coordinate responses, and maintain service levels, thus sustaining their competitive advantage in volatile markets. This theoretical lens also explains how supply chain risks undermine organizational performance when integration is lacking. Risks such as economic instability or supplier failures disrupt resource flows and hinder operational efficiency [25]. However, by leveraging the capability of integration, firms can enhance resource utilization and minimize performance losses. The RBV framework, therefore, underpins the mediating role of supply chain integration in translating risk management into performance improvements.

2.2. Supply Chain Risk and Organizational Performance

Supply chain risks like supplier failure, market volatility, and geopolitics interfere with organizational efficiency [26]. Such risks interfere with the free flow of products, information, and capital, which is necessary for consumer satisfaction and operational effectiveness [16]. Inefficient risk management exacerbates production delays, expenses, and supply chain partner uncertainty, leading to operational inefficiencies and vulnerabilities [27]. Disruptions in inefficiently managed supply chains usually amplify each other, affecting performance [28]. Supplier unreliability in just-in-time (JIT) production might affect output, inventories, and market prospects [29]. Such risk can lead to significant financial losses through decreased market competitiveness, disruptions, and margins [30]. Inefficient infrastructure, regulatory uncertainty, and economic instability increase SCR and organizational performance in emerging economies [31]. The automotive industry is subject to monetary uncertainty with unstable supplier structures and shifting government regulations [17]. Supply delivery slackness and deficiency lower operating performance, consumer satisfaction, customer trust, and brand loyalty [32]. Long-term SCR hinders business innovation and market responsiveness [33]. In order to avoid performance disruption due to risk effects, companies will have to place the supply chain process in conjunction with risk management. Automotive supply network problems and disruptions can lead to harm to organizational performance [28]. Building effective supply networks to function in risky climates is a matter of understanding firm dynamics and exposures.

H1: 

Supply chain risk negatively impacts organizational performance.

2.3. Supply Chain Risk and Supply Chain Integration

Economic unpredictability, supplier unreliability, and demand fluctuations can erode integration trust and collaboration. In uncertain times, supply chain stakeholders may hide important information or hinder collaboration, fragmenting the chain [31]. When suppliers miss delivery dates, companies may transfer suppliers or take other actions to minimize production delays [34]. Integration requires steady and predictable supply chain dynamics; thus, interruptions reduce market responsiveness and operational efficiency [35]. Infrastructure constraints, regulatory ambiguity, and SCR make collaborative supply chain strategies challenging in developing nations. Therefore, these dangers impair integration. Legal changes, currency volatility, and market instability affect the automotive supply chain process and resource coordination [33]. These dangers typically disrupt supply chain synchronization, hindering firm integration [36]. Corporations avoid collaborative infrastructure and technology for integration due to perceived risks, restricting long-term collaboration [24]. Prioritizing urgent issues may impair integrated supply chain systems [34]. SCR and integration are cyclical; thus, firms must utilize proactive risk management to reduce risks and improve integration. In uncertain times, this strategy encourages collaboration and resilience.

H2: 

Supply Chain Risk has a negative impact on Supply Chain Integration.

2.4. Supply Chain Integration and Organizational Performance

SCI boosts company performance by improving responsiveness, resource efficiency, and supply chain communication. By sharing real-time data and making choices, integration helps supply chain participants coordinate their systems. These qualities can help organizations anticipate and respond to market changes, reduce operational inefficiencies, and increase customer satisfaction [37]. Inventory, lead times, and manufacturing schedules may be optimized with SCI to increase business performance and cost. Integration helps organizations identify and resolve supply chain issues before they impair performance [38]. Integration improves supply chain productivity, efficacy, and dependability by building trust and collaboration [39]. SCI impacts organizational performance, especially in competitive and unpredictable environments that need adaptation and resilience. Manufacturing process integration improves quality and consistency in interdependent industries like auto manufacturing [40]. Objective alignment and resource aggregation increase integrated supply chain asset utilization and minimize redundancies [34]. Integration boosts supply chain knowledge and best practices, allowing enterprises to create and implement new solutions that provide them an edge [41]. Integration helps growing nations like Pakistan with inefficient supply chains. SCI boosts efficiency, customer loyalty, and market reaction [31]. Interrelated dynamics reveal how SCI improves organizational performance in the short and long term.

H3: 

Supply Chain Integration Positively Impacts Organizational Performance.

2.5. Supply Chain Integration as Mediator

SCI mediates the relationship between organizational performance and SCR, helping organizations turn weaknesses into strengths. By coordinating supply chain processes, integration mitigates risks such as supplier unreliability, demand unpredictability, and logistical interruptions [42]. Interconnected supply chains enable stakeholders to interact, share resources, and address problems in real time during disasters [43]. If a supplier breaks down, integrated systems can quickly find replacements or adapt production schedules [44]. Effective risk management improves cost efficiency, delivery timeframes, and customer satisfaction, enhancing organizational results [45]. Lack of integration can lead to supply chain disconnected responses and competing aims, which increases risk-related performance impacts [46]. SCI is especially important in complex industries like the automotive industry in emerging nations, where companies confront many risks. Integration buffers uncertainty and streamlines planning and execution by building a cohesive network that enhances predictability and visibility [47]. Infrastructure issues, market instability, and legislative changes raise SCR in unstable countries like Pakistan. SCI improves long-term resilience and reduces immediate risk, helping organizations cope with adversity [21]. Technology and risk management frameworks integrate to boost operational efficiency and competitiveness and stimulate innovation [48]. These interactions demonstrate the importance of risk management and integration as strategic tools for long-term organizational performance.

H4: 

Supply Chain Integration Mediates the Relationship Between Supply Chain Risk and Organizational Performance.

2.6. Demand Uncertainty as a Moderator

Demand unpredictability plays a crucial moderating role in the relationship between SCR and SCI, significantly shaping how risks influence the extent and effectiveness of integration. In conditions of high demand volatility, the inherent uncertainty exacerbates SCR, introducing challenges such as supplier unreliability, inventory shortages, and logistical interruptions, all of which impede seamless integration and alignment [49]. These disruptions force firms to adopt reactive measures, such as storing excessive inventories or diversifying suppliers, which can compromise the strategic coordination necessary for integration. Frequent shifts in demand trends necessitate constant adjustments to production schedules, resource allocation, and communication across the supply chain, creating fragmentation in relationships that are critical for SCI. For example, supply chain partners may prioritize urgent, short-term needs over long-term collaboration, thereby undermining the consistency required for integrated strategies [50]. However, demand assurance or better predictability alleviates these challenges by enhancing forecasting accuracy and planning capabilities, enabling organizations to mitigate SCR and foster a more integrated supply chain approach. When demand is stable, businesses can align their operations, optimize resource sharing, and strengthen coordination with supply chain partners, thus reducing the adverse effects of SCR on integration. In volatile industries such as the Pakistani automotive sector, where demand fluctuations are frequent, firms are often compelled to emphasize stability over flexibility, which can come at the expense of SCI [51]. This trade-off reflects the challenges of maintaining coherence and alignment in supply chains when market preferences are erratic and consumer demands fluctuate. Nonetheless, advanced tools like predictive analytics and real-time data sharing offer potential solutions to these issues. By leveraging these technologies, firms can anticipate demand patterns and respond proactively, fostering both risk resilience and enhanced integration [52]. Ultimately, demand unpredictability amplifies the complexities of managing SCR and SCI, particularly in dynamic and competitive environments, highlighting the need for robust strategies that balance flexibility and integration [53]. Businesses that successfully navigate these challenges can achieve greater supply chain resilience and maintain competitive advantages in uncertain markets.

H5: 

Demand Uncertainty Moderates the Relationship Between Supply Chain Risk and Supply Chain Integration.

3. Theoretical Framework

Resource-based view (RBV) theory provides a solid foundation for recognizing how firms apply internal capabilities in order to cope with supply chain risks and enhance organizational performance. RBV holds that highly integrated supply chain firms have a high propensity to generate synergies, reduce complexity in operations, and enhance decision-making [54]. Supply chain integration (SCI) enables effective communication, efficient usage of resources, and coordination of stakeholders, thereby functioning as an organizational performance enabler [37]. In an RBV context, SCI is not only an operation but a strategic capability that maximizes supply chain resilience to risk through enhanced supplier relations, lead-time reduction, and coordination improvement [55]. Firms that possess successful SCI mechanisms have improved supply chain visibility, which enables them to anticipate disruptions and proactively design mitigation strategies [55]. This strategic capability is vital in high-risk supply chain sectors such as the Pakistani automobile industry, where market instability, regulatory unrest, and unreliable suppliers create chronic disruptions [56]. The ability to integrate supply chain functions not only minimizes risk but enhances competitive advantage because firms with functional SCI frameworks can respond to shifts in demand better and ensure continuity in operations [12]. Thus, RBV identifies with this research’s conceptual framework by positioning SCI as a mediating mechanism that translates internal resources into risk-mitigating capabilities, whose influence has a direct bearing on organizational success [57].

Whereas RBV describes the strategic worth of supply chain integration (SCI) in supply chain risk management, Contingency Theory presents an alternative perspective through its focus on the effect of external environmental factors, like demand uncertainty, on supply chain strategy [24]. According to the Contingency Theory, the effectiveness of supply chain integration relies on external factors since companies must continually adapt to fluctuating market forces in order to attain stability and performance [12]. Increased demand uncertainty increases supply chain planning and coordination complexity, making it difficult for companies to forecast demand patterns, lower inventory levels, and coordinate with suppliers [39]. In turbulent environments, SCI will be powerless to counteract supply chain risks unless companies adopt adaptive strategies, such as predictive analytics, adaptive sourcing, and real-time information sharing [38]. The moderating role of demand uncertainty implies that agile supply chain practices are of the highest significance, as companies internalizing contingency-based risk management strategies are more resilient and outperform other companies in the long term [37]. Through the integration of RBV and Contingency Theory, this current study presents an integrative theoretical framework that describes SCI as a strategic resource for supply chain risk management, where demand uncertainty moderates the relationship through the impact on integration activity adaptability. The integration of these theories enhances the current literature on supply chain resilience and provides practical recommendations to companies operating in high-risk sectors, such as the Pakistani automobile industry, where effective risk management techniques are necessary to ensure long-term sustainability [33].

The conceptual framework of this research, shown in Figure 1, illustrates the intricate relationships between supply chain risk (SCR), supply chain integration (SCI), organizational performance (OP), and demand uncertainty. It posits that SCR impacts OP, while SCI serves as a mediating factor that mitigates this adverse effect by enhancing coordination, communication, and resource alignment across the supply chain. Demand uncertainty, as a moderating variable, influences the strength of the relationship between SCR and SCI, either exacerbating or mitigating integration challenges depending on the level of unpredictability. This model provides a comprehensive framework for understanding how effective integration strategies and risk management can optimize performance in dynamic and volatile markets.

4. Methodology

4.1. Population

In this study, Pakistani automotive supply chain administrators and operators are studied. This demography includes procurement, logistics, production planning, inventory management, and distribution workers who directly impact supply chain operations. The right demographic is chosen because it can provide unique insights into SCR, organizational performance, and integration. Automotive workers know that raw material shortages, transportation issues, and market instability affect supply chain efficiency [58]. Due to its supply chain focus, the findings of this study are sector-specific, informative, and transferable. The focus of this study on managers and operators helps it understand how industry organizations address operational problems, allocate resources, and make strategic decisions.

4.2. Sample Size and Sampling Technique

The sample size must be sufficient to identify correlations and significant effects. This study used 200 structural equation modeling (SEM) samples. The sample size decreases Type II errors while maintaining statistical validity. A study by [59] found that 320 respondents met the minimum generalizability and accuracy requirements. A complete SEM examination of 320 samples can correctly depict the Pakistani automotive workforce. The data collection process was completed within a span of four weeks, ensuring adequate time to distribute and collect responses while minimizing delays. This timeline allowed for effective participant engagement and ensured the completion of 320 valid questionnaires for analysis. The large sample size permits investigation of company size, employee position, and organizational structure-related response disparities. If non-responses occur, it is beneficial to evaluate incomplete surveys or missing data to ensure valid and accurate results. The sample size meets the 95% confidence level and 5% margin of error requirements for the precision and dependability of this study. The participants were sampled using a non-probability convenience sampling approach. Convenience sampling in organizational research is prevalent where the population of interest is easily accessible, and the participants are cooperative [60]. Convenience sampling is particularly suited under exploratory and applied research conditions where time limitations, resources, and participant availability make the use of probability sampling impracticable. Convenience sampling, however, has certain limitations, specifically external validity, in the sense that it has the potential to create selection bias and limit the generalizability of findings to sampled organizations. To address such issues, robustness checks were contemplated. Comparison with secondary data sources was first conducted to confirm significant trends and results. Generalizability could also be enhanced by the use of stratified or quota sampling in subsequent studies to provide wider representation across the industry. The demographic profile of the respondents provides a comprehensive understanding of the sample population, highlighting the diversity and representation within the Pakistani auto sector (

Table 1. Demographic Profile of Respondents.

Demographic Variable	Category	Frequency	Percentage (%)
Gender	Male	200	62.50
	Female	120	37.50
Age	20–30 years	100	31.25
	31–40 years	150	46.88
	41+ years	70	21.88
Experience	1–5 years	130	40.63
	6–10 years	110	34.38
	11+ years	80	25.00
Job Role	Managerial	120	37.50
	Operational	150	46.88
	Technical	50	15.63

).

In terms of gender distribution, a significant majority of the participants were male (62.50%), while females accounted for 37.50% of the respondents, reflecting the male-dominated nature of the supply chain roles in the industry. Regarding age, the majority of participants were aged between 31 and 40 years (46.88%), followed by those aged 20–30 years (31.25%) and a smaller proportion aged 41 years and above (21.88%), indicating that mid-career professionals were the dominant group. The respondents’ experience levels showed that 40.63% had between 1 and 5 years of experience, 34.38% had 6–10 years, and 25% had over 11 years of experience, suggesting a well-balanced representation of both relatively new and seasoned professionals. The job roles of the respondents further illustrate this diversity, with 46.88% working in operational roles, 37.50% in managerial positions, and 15.63% in technical roles, ensuring insights from different hierarchical levels and functional areas of the supply chain.

4.3. Data Collection Procedure

Data for this study was collected using a self-administered, in-person questionnaire. In-person surveys resolve questionnaire ambiguities, ensure participant comprehension, and improve data collection control [61]. An in-person approach raises the likelihood of accurate responses and reduces the risk of misinterpretation by giving rapid support if participants have any problems completing the questionnaire. Pakistani automakers’ supply chain staff received 400 questionnaires. After survey distribution, 320 completed responses were received, achieving an 80% response rate. For organizational research, especially in-person surveys, this response rate guarantees that data accurately represents the target population. Despite the benefits of in-person distribution, social desirability bias and self-reported data were considered. To mitigate common method bias (CMB), a single-factor test by Harman utilizing principal component analysis was implemented. The test findings indicated a 34.56% variance in a singular component, with a threshold value of less than 50% [62]. Consequently, no statistical evidence of the CMB was identified.

4.4. Measures

Measurement scales were taken with caution to be adapted from established, validated previous studies so as to guarantee the accuracy, reliability, and validity of the constructs of this study. In this approach, the operationalization of constructs should be performed in a way that is congruent with the research objectives of this study and maintains consistency with established research on supply chain management. Each construct was measured by multiple items to capture its multidimensional nature, which made the data collected more robust. All the measurement items were rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), which is the most commonly used and reliable format for capturing respondents’ attitudes and perceptions.

4.4.1. Supply Chain Risk

Supply chain risk was measured using a 4-item scale developed by [31]. “Preventing operations risk (e.g., select a more reliable supplier, use clear safety procedures, preventive maintenance)” is a sample item from the questionnaire. The Cronbach alpha reliability of the scale was 0.899.

4.4.2. Supply Chain Integration

To measure supply chain integration, a six-item scale adapted by [33] was used. One sample statement from the questionnaire is “Departments in the plant frequently communicate with each other”. The alpha reliability of the scale was 0.933.

4.4.3. Organizational Performance

To measure organizational performance, the 7-item scale developed by [63] was used. A sample item from the questionnaire is “My department adapts quickly to unanticipated changes”. The alpha reliability of the scale was 0.856.

4.4.4. Demand Uncertainty

Demand uncertainty was measured using the 4-item scale by [54]. A sample item from the questionnaire is “The demand for our plant’s products is unstable and unpredictable”. The alpha reliability of this scale was 0.847.

4.4.5. Data Analysis Tools

The online questionnaire data was analyzed using SmartPLS 4.0. According to the research hypotheses, this study can examine complex interactions between latent variables, including direct, indirect, and moderating effects [64]. This is made possible using PLS-SEM. PLS-SEM excels in non-normal data analysis and small sample sizes [62]. The original measurement model inquiry ensured construct reliability, validity, and proper survey scale target variable evaluation. The second structural model analyzed organizational performance, demand volatility, SCR, and integration. This program calculated path coefficients, standard errors, and t-values to assess model relationships. By assessing direct and indirect effects, SmartPLS helps researchers test hypotheses and derive important conclusions from data.

5. Results

5.1. Measurement Model Assessment

Table 2. Construct Reliability and Validity.

Variables	Items	Outer Loading	Cronbach Alpha	CR	AVE
Demand Uncertainty	DU1	0.763	0.847	0.897	0.685
	DU2	0.808
	DU3	0.857
	DU4	0.879
Organizational Performance	OP1	0.820	0.856	0.896	0.565
	OP2	0.854
	OP3	0.818
	OP4	0.850
	OP5	0.796
	OP6	0.770
	OP7	0.721
Supply Chain Integration	SCI1	0.885	0.933	0.947	0.747
	SCI2	0.874
	SCI3	0.857
	SCI4	0.847
	SCI5	0.862
	SCI6	0.861
Supply Chain Risk	SCR1	0.871	0.899	0.929	0.767
	SCR2	0.880
	SCR3	0.897
	SCR4	0.855

and Figure 2 show demand uncertainty, organizational performance, SCI, and risk constructs’ reliability and validity. The table shows outer loadings, composite reliability (CR), average variance, and Cronbach alpha of each construct. Study construct measurement quality must be assessed using these markers. Since all outer loadings for demand uncertainty components (DU1-DU4) exceed 0.7, convergent validity is likely. This model has high internal consistency with Cronbach alpha for demand uncertainty of 0.847 and composite dependability of 0.897. Demand uncertainty has an AVE of 0.685, over the suggested 0.5, indicating that the construct accounts for a large share of component variance.

The organizational performances of items OP1 to OP7 are shown in the table, with outer loadings between 0.721 and 0.854, indicating convergent validity. The organizational performance indicator has 0.856 Cronbach alpha and 0.896 composite reliability, showing high internal consistency. However, the AVE of organizational performance is 0.565, which is beyond the 0.5 criterion. This suggests that some things may not have enough variance. This figure meets social science standards. All item loadings (SCI1–SCI6) exceed 0.847, with the greatest value reaching 0.885 in SCI, demonstrating substantial convergent validity. Cronbach alpha is 0.933, and composite reliability is 0.947, both above acceptable levels. The SCI construct is viable because its average variance extracted (AVE) is 0.747, exceeding the 0.5 criteria. SCR 1–4 have external loadings from 0.855 to 0.897, showing that they accurately represent the construct. The composite reliability of SCR is 0.929, and Cronbach alpha is 0.899, indicating strong internal consistency and reliability. Its AVE is 0.767, which is significantly above the 0.5 threshold, establishing construct validity. Table 2 construct reliability and validity shows that all study components are reliable and valid.

5.2. Discriminant Validity

Table 3. Discriminant Validity (HTMT).

Variables	DU	OP	SCI	SCR
Demand Uncertainty
Organizational Performance	0.893
Supply Chain Integration	0.239	0.388
Supply Chain Risk	0.670	0.781	0.270

shows discriminant validity results from the Heterotrait–Monotrait Ratio (HTMT), which measures study component dissimilarity. This table shows the correlation between each pair of constructs. The HTMT score between demand uncertainty (DU) and organizational performance (OP) is 0.893, exceeding the 0.85 criterion. The two constructs appear closely related, suggesting that they may not be independent, necessitating further study. Demand uncertainty and SCI have HTMT values of 0.239, which are far below the threshold. This shows that these two notions are distinct, supporting discriminant validity. These two constructs are distinct since the HTMT score for demand uncertainty and SCR is 0.670, below 0.85. The HTMT scores for organizational performance for SCI (0.388) and risk (0.781) are well below the 0.85 criteria, indicating discriminant validity. Organizational success is distinct from SCI and risk. HTMT values for SCI and risk are 0.270, indicating independent constructions. Table 2 shows good discriminant validity for the study constructs. No HTMT results above 0.85, confirming the uniqueness of constructs and the resilience of the measuring model.

5.3. Coefficient of Determination and Q²

Table 4. Coefficient of Determination and Q².

Variables	R2	Q2
Organizational Performance	0.500	0.480
Supply Chain Integration	0.123	0.110

shows the explanatory and predictive performance of this model, including R² and Q² for study constructs. In social science research, this model explains 50% of organizational performance variance with an R² value of 0.500. The elements of this model—SCI, risk, and demand uncertainty—explain approximately half of organizational performance variability. A moderate R² value suggests that this model reveals the link between supply chain dynamics and organizational performance despite other factors affecting performance. The Q² score for organizational performance is 0.480, over the threshold of 0, indicating predictive relevance [65]. The capacity of this model to predict organizational performance and the postulated relationships is validated by a Q² score above zero.

The R² of supply chain integration (SCI) is 0.123, which is low explanatory power. This means that part of the most impactful external drivers of SCI is omitted from the model. Omitted variables are supplier relationships, regulatory environment, organizational culture, and technological adoption. Although the R² is low, the model is predictive with a Q² of 0.110. Such variables must be added to subsequent studies to improve the explanatory power and robustness of the model. R² and Q² values indicate that organizational performance outperforms SCI in terms of predictive and explanatory power. Future models may include additional criteria to determine organizational performance and SCI.

5.4. Structural Equation Model

Figure 3 and

Table 5. Direct Path Analysis.

Hypotheses	Relation	Path Coefficient	t-Value	p-Value
H1	SCR -> OP	−0.656 *	14.205	0.000
H2	SCR -> SCI	−0.232 *	3.038	0.001
H3	SCI -> OP	0.146 *	1.847	0.032

Note: * p < 0.05.

provide SCI, OP, and SCR straight-path analyses. The main hypothesis, H1, concerns organization effectiveness and supply chain risks. The t-value of 14.205 is above the key threshold of 1.96, demonstrating the significant impact of SCR on organizational performance. The path coefficient for this connection is −0.656. The p-value for this link is 0.000, well below 0.05. SCR severely reduces organizational efficacy. Disruptions, uncertainties, and inefficiencies caused by SCR hurt corporate performance. This route coefficient shows how SCR improves organizational effectiveness. Risk and SCI are examined in Hypothesis 2, which describes that the path coefficient of −0.232 and t-value of 3.038, which exceeds 1.96, show a statistically significant negative relationship between the two constructs. The p-value for this association is 0.001, which is much lower than the 0.05 threshold, demonstrating that SCR negatively impacts integration. This research implies that uncertainty and risk may make it harder for organizations to build strong connections with suppliers, distributors, and other partners, reducing integration. Trust, communication, and goal–process mismatches can hinder SCI. This shows how risk management affects organizational efficiency and SCI. The third hypothesis, H3, investigates SCI and organizational effectiveness. For this connection, the t-value is 1.847, and the path coefficient is 0.146. It is somewhat higher than 1.96, indicating a statistically significant positive correlation at 0.05. SCI boosts organizational efficacy (p-value 0.032). Due to improved coordination, resource management, and market reaction, organizations with higher SCI perform better. Organizations can improve financial and operational performance by integrating operations, decreasing costs, enhancing product quality, and offering excellent customer service. Despite its small size, the route coefficient emphasizes SCI in organizational success. These findings emphasize the necessity of SCR mitigation and integration for competitive organizational performance.

5.5. Mediation Assessment

In

Table 6. Mediation Analysis.

Hypotheses	Relation	Path Coefficient	t-Value	p-Value
H4	SCR -> SCI -> OP	−0.034 *	1.693	0.045

Note: * p < 0.05.

, the mediation analysis shows how SCR indirectly affects organizational performance (OP) through SCI. The indirect association SCR -> SCI -> Organizational Performance has a route coefficient of −0.034 and a t-value of 1.693, which is beyond the critical threshold of 1.96. At 0.05, the indirect effect is marginally significant. The indirect association has a p-value of 0.045, which is close to the significance criterion. According to this study, SCI may reduce SCR, which can hurt organizational performance. Supply network risk affects organizational performance depending on SCI. Increased dangers reduce SCI, lowering organizational performance. In practice, firms can strengthen SCI strategies, such as enhancing supplier coordination, utilizing digital integration tools, and organizational coordination to reduce supply chain risk. In addition, companies should not solely rely on SCI but should employ common risk management practices to ensure performance. Future research should investigate industry circumstances and marketplace forces beyond the firm to further assess the practical impact of SCI in mitigating supply chain risks.

5.6. Moderation Assessment

For SCI, the moderation study examined the relationship between SCR and demand uncertainty (DU) in

Table 7. Moderation Analysis.

Hypotheses	Relation	Path Coefficient	t-Value	p-Value
H5	DU x SCR -> SCI	−0.098	3.694	0.000

. The interaction impact route coefficient, Demand Uncertainty x SCR -> SCI, is −0.098, and the t-value is 3.694, exceeding the crucial threshold of 1.96. This shows a statistically significant negative association between demand uncertainty and SCI risk. The moderation effect is substantial, with a p-value of 0.000, much below 0.05. This finding shows that demand uncertainty and SCR reduce SCI, with SCR having a greater negative impact under high demand uncertainty. Due to SCR, organizations have trouble aligning operations, coordinating efforts, and communicating with partners when demand is unclear. This finding suggests that higher demand uncertainty reduces the ability of firms to integrate their supply chains effectively under supply chain risk. Practically, risky market firms must invest in adaptive supply chain approaches, such as real-time data analysis, adaptive logistics networks, and flexible sourcing, to counter the negative impact of demand volatility. Risk-sharing contracts with suppliers and predictive forecasting techniques can help firms maintain SCI even in the presence of uncertain demand. Industry-specific solutions to enhance SCI resilience under demand volatility conditions should be investigated in future studies.

6. Discussion

This study reveals the complicated linkages between organizational performance, demand volatility, SCR, and integration in the automobile industry in Pakistan. The research tests all hypotheses to prove that good supply chain management ensures business resilience and profitability in a dynamic and competitive market. This section carefully analyzes the supply chain management results and compares them to those of previous research. The growing literature on the positive association between organizational performance and SCR shows that supply network disruptions hurt corporations. Results supported [12] the premise that risk is critical for supply chain performance, showing that high-risk enterprises have low operational efficiency and financial stability. Supplier unreliability, shifting demand, and external economic volatility can create delays, cost overruns, and inventory depletion, compromising the ability of a company to meet consumer requests, maintain cost efficiency, and deliver things on time. Research by [56] concluded that high-risk enterprises should invest more in buffer inventories, alternative supplier linkages, and risk management. The findings of this study validate the resource-based view (RBV) by establishing that supply chain integration (SCI) is a strategic asset that increases organizational performance and minimizes supply chain risk (SCR). This is consistent with [34], who assume that supply chain integration processes enhance the competitive strength of a company. This study also validates the Dynamic Capabilities Theory, establishing how firms that employ adaptive and proactive integration methods are capable of addressing market uncertainty and demand volatility more effectively [66]. The findings show that supply chain regulations must be integrated to reduce SCR, which lowers organizational performance. SCI improved organizational performance, supporting [40]. They believe that organizations that effectively integrate their supply chains through collaboration and information exchange can reduce risk and disruptions. Automotive manufacturers use a complex network of suppliers, distributors, and logistics providers, so even little delays can cause severe time, cost, and performance losses. SCI allows organizations to quickly and effectively adapt to disturbances, including unexpected economic changes, supplier delays, and demand swings [67]. Integration and this proactive response improve the performance of the firm by minimizing supply chain errors and increasing organizational agility.

SCI improves organizational performance, supporting a resource-based view (RBV), which holds that organizations with superior resources and capabilities, such as effective integration processes, can gain a competitive edge [39]. Integrating supply chain operations creates a strategic asset that helps companies handle disruptions, giving them a sustained competitive edge and improved performance. A study by [36] found that SCI optimizes processes, reduces redundancies, and improves supply chain decision-making. In the automotive business, SCI reduces weather-related disruptions, improves operational efficiency, and boosts customer satisfaction. This paper says that demand uncertainty affects SCR and integration. This study found that SCR affects integration more when demand fluctuates. Thus, enterprises need flexible integration solutions to meet different and changing needs. According to [56], demand is unpredictable; thus, companies must rethink their supply chain strategy to quickly respond to market situations and consumer preferences. Consumer behavior, market conditions, and global upheavals make demand prediction difficult for organizations. They struggle with supply chain planning and management. Smart integration solutions, including adaptive production planning, real-time information transmission, and demand forecasting, are needed to overcome demand unpredictability [55]. By reducing demand volatility, these technologies help firms satisfy customer expectations during tough times. Due to mitigated demand unpredictability, SCI can adjust to changing conditions. Flexible integration solutions are desired by enterprises as demand changes. Real-time data transmission and forecasting may require complex technologies and customer and supplier cooperation. This integrated flexibility ensures a steady supply of goods and services, keeping enterprises competitive regardless of demand. Furthermore, this study agrees with [36] in its belief that SCI enhances organizational performance through encouraging collaboration, sharing information, and managing risk. Due to the intricate supplier–distributor–logistics networks within the automotive sector, small disturbances can result in massive time, cost, and efficiency losses. The authors of [67] felt that integration enables organizations to react swiftly to unexpected issues like economic changes, late delivery by suppliers, and changes in demand. This study corroborates such a belief by supplying empirical proof that SCI does not only decrease disturbances but also increases firm agility to equip organizations with the capability to maintain competitiveness in volatile external environments.

According to this study, demand uncertainty and other external factors reduce SCI disruption risk. The moderating effect of demand uncertainty significantly influences the relationship between SCR and SCI. High demand volatility exacerbates challenges in aligning and coordinating supply chain activities, hindering integration. When demand uncertainty is low, organizations can integrate more effectively, reducing risks. However, in uncertain environments, firms may prioritize risk mitigation over long-term integration, leading to inefficiencies. This highlights the need for agile supply chain strategies that incorporate forecasting and real-time analytics to better manage demand fluctuations and improve overall supply chain performance.

SCI affects organizational performance and risk; another discovery, as shown in this study, is that SCI affects organizational risk and performance. Integration boosts performance and lowers SCR. The authors of [38] say that SCI helps organizations achieve goals and control risks, and integration boosts efficiency and lowers SCR. Networked supply chains allow enterprises to share information and make choices, reducing inefficiencies and costly delays caused by hazards like supply outages and demand volatility. The mediation effect of SCI emphasizes the importance of supply chain management as a strategic asset that boosts competitiveness. Improved departmental communication and SCI can boost company performance. This study found that effective integration strategies reduce SCR and maintain operational efficiency during external disruptions. SCI improves organizational performance and decreases risk, according to strong evidence. The survey says that supply chain management solutions affect long-term performance and reduce risks. Demand unpredictability and enterprise SCI hinder modern supply chain management. Tight integration and flexible risk and uncertainty management can benefit automotive and adjacent sectors. In the supply chain management literature, these findings show how risk, integration, and uncertainty affect organizational performance. The research gives managers tips for navigating a complex and dangerous supply chain. Strategic supply chain management helps organizations stay competitive and perform better during transitions, according to this study.

6.1. Practical Implications

This report benefits automotive managers, legislators, practitioners, and other supply chain-challenged organizations. Data show the importance of SCR management. SCR can significantly hinder performance; thus, businesses must detect and reduce it to preserve continuity and profitability. Automotive supply chain disruptions include economic volatility, supplier shortages, and demand uncertainty in Pakistan. Complex risk management frameworks must handle immediate and long-term issues. These frameworks require better forecasting, demand management, flexible procurement, and supplier diversity. Active risk management improves performance by avoiding supply chain disruptions, financial losses, and operational instability. The impact of SCI on organizational performance and risk management is examined. Integration of procurement, production, logistics, and distribution improves operational efficiency and interruption management. This integration improves information sharing, interdepartmental collaboration, and problem-solving. Pakistani firms need strong supplier–consumer ties to integrate. Supply chain collaboration, cloud data interchange, and ERP systems are examples. SCI improves efficiency, lead times, and customer satisfaction [41]. SCI helps organizations manage uncertainty, especially in conditions with frequent disruptions and high demand fluctuation, by providing more accurate decision-making data.

This study found that demand uncertainty moderates firms in unpredictable markets. This research implies that firms with shifting demands should use more flexible integration solutions. Conventional, rigid supply chain tactics may fail if they cannot quickly adapt to changing consumer expectations or markets. Organizations may use dynamic inventory management, real-time demand forecasting, and flexible production planning to handle demand swings. Through demand-driven supply chain strategies, automakers can adapt manufacturing schedules and inventory in real time to consumer feedback and market trends. Businesses can adjust operations to variable demand, eliminating overstocking and stockouts and improving performance. The analysis links SCI to organizational success and risk. By integrating more, firms can reduce risks and boost efficiency. This shows the strategic importance of SCI in gaining a competitive advantage. SCI improves operational efficiency, coordination, and response times, improving performance. Integrating a complex automotive supply chain in Pakistan can improve responsiveness, decrease redundancy, and speed up operations. Integration can improve supply chain information transfer, helping firms discover and handle issues before they escalate. When combined with effective integration processes, this proactive risk management method can boost the resilience and long-term success of an organization during interruptions.

This study provides a valuable addition to the scholarly body of knowledge through enhanced comprehension of the effects of supply chain risk on organizational performance for the automobile sector in Pakistan. While previous research has contrasted supply chain risks across industries, few empirical analyses have examined the role of integration capability as a mediator and demand uncertainty as a moderator of the relationship. In addressing this knowledge gap, this study contributes to supply chain management theory, and the resource-based view (RBV) in particular, through the illustration of how integration capability can be leveraged as a strategic asset for mitigating risks and enhancing performance. Additionally, the application of SmartPLS to structural equation modeling (SEM) in this study offers a sound methodological framework that enhances the robustness of the findings, which will be of interest to scholars and practitioners. Given that the economic environment is turbulent, and supply chains suffer disruptions in emerging economies like Pakistan, study findings are of particular interest to organizations interested in improving and transforming more resilient supply chain strategies.

From the management perspective, this study focuses on proactive supply chain risk management via better integration practices. Pakistani auto industry managers should consider promoting collaboration with suppliers, enhancing information-sharing processes, and the use of digital technologies such as big data analytics and blockchain to ensure utmost transparency and efficiency. This study also highlights demand uncertainty’s importance in defining supply chain risk response, and firms should possess dynamic and adaptable supply chain strategies to respond to volatility. Practical recommendations include diversifying supplier networks, implementing predictive analytics in demand forecasting, and promoting contingency planning processes to effectively mitigate uncertainties. By applying these practices, firms can reduce business disruption, promote efficiency, and ultimately improve organizational performance in an uncertain and competitive market environment.

6.2. Theoretical Implications

This study supports the resource-based view (RBV) of a company by showing how SCI may increase performance and minimize risk. This is a speculation. According to the resource-based view (RBV), organizations with superior competencies, capabilities, and resources can outperform competitors. SCI improves performance, speeds up operations, and reduces hazards in this inquiry. Organizations can improve SCI to reduce risks and interruptions. A study by [37] found that interconnected supply chains improve performance by enabling informed decision-making and operational optimization. This research supports the resource-based view (RBV) and shows that integration is a crucial ability that helps firms overcome external problems. This study has major supply chain management theoretical implications. It adds to the growing research on SCR, integration, and performance in emerging nations like Pakistan. Prior studies have focused on industrialized nations, neglecting emerging nations, where economic instability, infrastructural shortcomings, and political volatility may increase SCR and uncertainties.

6.3. Limitations and Future Directions

Despite its limitations, this study sheds light on SCR and organizational performance. Cross-sectional data, which provide a snapshot of links, are the main limitations of this study. Cross-sectional studies cannot accurately reflect supply chain dynamics, which may modify risk factors, integration strategies, and performance. This suggests that this analysis ignores risk accumulation and the long-term impacts of SCI. Longitudinal designs may help future studies comprehend SCR, integration, and performance across time and under different conditions. The results benefit the Pakistani automobile industry, but they may not apply to other industries with different economic, political, and cultural circumstances. The automobile industry in Pakistan faces unique challenges, including economic volatility, regulatory changes, and political unrest. Integration solutions and supply chain hazards vary by sector and region. Pharmaceuticals, electronics, and food manufacturing have distinct supply chain dynamics and dangers; therefore, the conclusions may not apply. The parameters of this study may be expanded by studying different industries and regions, allowing for comparisons and revealing the conclusions’ generalizability or specificity. Industry-specific or international research may help explain how SCR and integration techniques affect organizational performance in different contexts. Demand uncertainty, integration, and SCR were the main variables analyzed. These variables are essential for understanding how supply chain management affects organizational performance, although others may also affect results. Technology, supplier relationships, consumer engagement, and innovation can impact supply chain and risk management techniques. Supply chain management increasingly depends on environmental sustainability, automation and AI technology, and global economic conditions. Future research should include these components to improve supply chain dynamics understanding. These larger characteristics may help researchers understand how organizations might use SCI and risk management tactics to gain a competitive edge in a more complex and linked global market. Further research might examine how organizational culture and leadership affect risk mitigation and SCI, improving understanding of internal elements that affect performance.

7. Conclusions

This study found a complex relationship between demand volatility, organizational performance, SCR, and integration in the auto industry of Pakistan. This research supports the idea that SCR hurts company performance, and integration helps. The decline in SCR and integration due to demand uncertainty highlighted the significance of supply chain management flexibility in adjusting to changing market conditions. According to this report, SCI can boost organizational performance and reduce risk. In a competitive and changing market, operational efficiency, cost reduction, responsiveness, and SCI can help businesses succeed. These results show that SCI reduces risk and enhances efficacy. They emphasize the need for market management technologies that can react to demand uncertainty reduction. Automotive companies may use strong SCI methods to strengthen resilience and reduce interruptions. Proactive supply chain management demands agility and integration to reduce risk and unpredictability. This helps organizations stay competitive and viable in challenging business environments.