Outsourcing logistics for smarter railways: quantitative evidence on cost, delivery and service performance

Railway transportation plays a central role in modern logistics systems by enabling the efficient movement of freight and passengers across regional and international networks (Wang & Chen, 2025). In an era characterized by globalization, increasing trade volumes and rising customer expectations, railway operators are under growing pressure to enhance operational performance while maintaining cost efficiency (CE), reliability and high service quality (SQ) (Shan, Bešinović, & Schönberger, 2024). These challenges are further intensified by the complexity of railway logistics operations, which involve interconnected activities such as terminal management, intermodal coordination, rolling stock allocation, cargo handling, warehousing interfaces, freight consolidation and last-mile delivery (Karam, Jensen, & Hussein, 2023). Managing all these functions internally requires substantial financial investment, advanced technological infrastructure and highly specialized human resources, making operational optimization a strategic priority for railway organizations operating in increasingly competitive transport environments.

In response to these challenges, logistics outsourcing (LO) has emerged as a strategic approach widely adopted across transportation and supply chain sectors (Abbasi Sıcakyüz, Gonzalez, & Ghasemi, 2024). By delegating noncore logistics activities to specialized third-party logistics (3PL) providers, railway operators can concentrate on their core responsibilities, including train operations, railway traffic management, infrastructure maintenance, network planning and safety management. In contrast, operational activities such as terminal operations, cargo handling, warehousing, intermodal coordination, freight consolidation, customs support and last-mile freight distribution can be performed more efficiently by specialized logistics providers possessing dedicated expertise, advanced technologies and scalable resources. Such collaboration enables railway organizations to improve operational flexibility, optimize resource utilization, reduce operational costs and enhance service delivery while maintaining strategic control over their core railway activities (Mageto, 2022). Although LO has been widely investigated in manufacturing and general supply chain contexts, comparatively little empirical attention has been devoted to railway systems, despite their unique operational characteristics, including fixed infrastructure, strict scheduling constraints, high safety requirements and strong interdependence among network components.

Theoretical perspectives such as transaction cost economics and the resource-based view provide a robust foundation for understanding the strategic role of LO in railway operations. Transaction cost economics suggests that outsourcing allows firms to reduce coordination, monitoring and operational costs by relying on external providers that can perform specific activities more efficiently (Patil et al., 2024). Meanwhile, the resource-based view argues that organizations can strengthen their competitive advantage by accessing valuable external capabilities, advanced technologies and specialized expertise that would otherwise require considerable internal investment (Helfat et al., 2023). In the context of railway logistics, these theories suggest that LO is associated with improved operational performance primarily through the enhancement of intermediate operational capabilities rather than through an immediate direct effect.

Among the various dimensions of operational performance, three factors are particularly critical in railway logistics systems: CE, delivery reliability (DR) and SQ (Muni, Khan, Zafri, & Chowdhury, 2024). CE reflects the ability of railway operators to optimize resource utilization and reduce operational expenses in a capital-intensive environment (Benga, Delgado-Rodriguez, & De Lucas-Santos, 2023). DR refers to the consistency and punctuality of transport services, which are particularly important in highly interconnected railway networks where operational disruptions frequently propagate across multiple logistics processes. SQ encompasses cargo handling accuracy, safety, responsiveness, shipment visibility and customer support, all of which directly influence customer satisfaction and competitiveness (Twaha & Taifa, 2025). Collectively, these 3 operational dimensions provide a comprehensive framework for understanding how LO may contribute to railway operational performance (ROP).

Despite the growing adoption of outsourcing practices, empirical research examining how LO influences ROP through these intermediate dimensions remains limited, particularly in emerging markets. Existing studies have primarily focused on manufacturing industries, general supply chains or multimodal logistics, while comparatively few investigations have examined railway logistics, where infrastructure constraints, scheduling complexity, operational interdependence and safety requirements create a distinct managerial environment. Morocco provides a particularly relevant context for addressing this research gap. The Moroccan railway sector has undergone significant development in recent years through infrastructure expansion, increasing freight transportation demand, stronger integration with international logistics corridors and closer connections with ports and industrial platforms. Nevertheless, Moroccan railway operators continue to face important challenges related to operational costs, DR and SQ, making LO an increasingly relevant strategic option. At the same time, the specific institutional and operational characteristics of the Moroccan railway sector suggest that the findings of this study should be interpreted within the context of an emerging railway market, providing opportunities for future comparative research across different geographical settings.

Accordingly, this study aims to examine the relationships between LO and ROP in Morocco, with a particular focus on the mediating roles of CE, DR and SQ. Using a quantitative approach and structural equation modeling (SEM), the study examines both the direct association between LO and operational performance and the indirect relationships operating through these 3 operational dimensions. By doing so, it contributes to the existing literature in several ways. First, it extends LO research to the relatively underexplored railway sector by providing empirical evidence from an emerging economy. Second, it advances transaction cost economics and the resource-based view by demonstrating how LO influences operational performance through multiple operational mechanisms. Third, it provides practical guidance for railway managers and policymakers regarding the strategic allocation of logistics activities between internal operations and specialized logistics providers to improve organizational efficiency and competitiveness.

The remainder of this article is structured as follows. Section 2 reviews the relevant literature and develops the research hypotheses, focusing on LO and its operational impacts. Section 3 describes the research methodology, including the research design, study context, data collection procedures and analytical approach. Section 4 reports and discusses the empirical findings in light of the proposed hypotheses. Finally, Section 5 concludes the study by summarizing key insights, outlining theoretical and managerial implications, acknowledging the study's limitations and suggesting directions for future research.

Railway logistics operations have become increasingly complex due to globalization, evolving market demands, multimodal integration and rapid technological advancements in the transport sector (Li, Xue, Shao, Zhu, & Liu, 2023; El Moussaoui & El Moussaoui, 2026a). Modern railway operators are responsible not only for moving freight or passengers but also for managing intricate logistics networks that encompass terminal management, yard operations, intermodal freight transfers, rolling stock scheduling, cargo handling, warehousing, inventory coordination, customs support and last-mile delivery coordination (Çelebi, 2023). These activities require continuous coordination among multiple stakeholders, including railway operators, terminal managers, freight forwarders, customs authorities and 3PL providers, making railway logistics one of the most operationally intensive segments of contemporary supply chains. The internal management of all these activities requires extensive capital investment, sophisticated technological systems and specialized human resources. When these operations are managed solely in-house, railways often face challenges such as high fixed costs, limited operational flexibility, suboptimal asset utilization, capacity constraints and delayed responses to operational disruptions (Schofer, Mahmassani, & Ng, 2022).

In response to these challenges, LO has emerged as a strategic approach enabling railway operators to delegate selected noncore logistics activities to specialized 3PL providers while concentrating internal efforts on strategic railway functions such as train operations, network planning, infrastructure management, traffic control and safety assurance (Akbari, 2024; El Moussaoui & El Moussaoui, 2026b). Rather than replacing the strategic role of railway operators, outsourcing allows organizations to redistribute operational responsibilities according to the comparative advantages of specialized logistics partners. Activities such as terminal operations, freight consolidation, cargo handling, warehousing, intermodal coordination, customs documentation, container management and last-mile freight distribution are frequently outsourced because they require highly specialized operational expertise, dedicated equipment and flexible resource allocation. Meanwhile, railway operators continue to retain strategic decision-making responsibilities involving infrastructure investment, train scheduling, traffic regulation, rolling stock management, network expansion and safety governance. This division of responsibilities enables railway organizations to focus on their core competencies while leveraging the technological capabilities, operational expertise and economies of scale offered by specialized logistics providers (Kljaić et al., 2023). Outsourcing also allows operators to scale their logistics capacity in response to demand fluctuations, manage congestion at terminals more effectively, optimize train scheduling and improve resource utilization without requiring substantial additional internal investment, thereby providing both operational and financial flexibility (Zhu, Ng, Wang, & Zhao, 2017; Benatya Benatiya Andaloussi, 2024).

The strategic rationale for LO is strongly supported by both transaction cost economics and the resource-based view (Park, Woo, Yun, & Lee, 2025). Transaction cost economics argues that organizations achieve superior efficiency when external providers can perform operational activities at lower coordination, monitoring and execution costs than internal departments (Ye, Peng, Fan, & Narayanan, 2022). Within railway logistics, internally managing freight consolidation, terminal throughput, intermodal transfers and cargo handling often generates substantial coordination costs because of the high interdependence between train schedules, infrastructure availability, rolling stock allocation and workforce planning (Rong, Li, Zhang, & Wang, 2025). Outsourcing these activities to specialized logistics providers reduces operational complexity, improves coordination efficiency, minimizes service interruptions and decreases the likelihood of cascading delays across railway networks (Karami, 2026).

Complementing this perspective, the resource-based view emphasizes that long-term competitiveness depends on an organization's ability to concentrate internal resources on strategically valuable activities while accessing complementary resources from external partners whenever these resources are difficult, costly or time-consuming to develop internally. Railway operators increasingly rely on specialized logistics providers because these organizations possess advanced digital platforms, automated cargo handling systems, predictive maintenance technologies, intelligent warehouse management systems and highly specialized logistics expertise that may not be economically feasible to develop internally (Qorry, Suroso, & Taryana, 2026). Consequently, outsourcing should not be viewed solely as a cost-reduction strategy but rather as a capability-enhancing mechanism through which railway operators improve operational agility, technological readiness, service responsiveness and organizational flexibility. This combination of efficiency, scalability, technological innovation and specialized expertise makes LO an increasingly strategic choice for railway operators seeking to optimize both operational performance and customer satisfaction in freight and passenger services.

Despite the growing strategic importance of LO, empirical research specifically addressing outsourcing within railway logistics remains relatively limited compared with manufacturing, maritime transport or general supply chain management. Most previous studies have primarily examined outsourcing from cost or operational efficiency perspectives, whereas comparatively little attention has been devoted to understanding the multiple operational mechanisms through which outsourcing contributes to railway performance. Furthermore, the railway sector presents distinctive operational characteristics – including fixed infrastructure, high capital intensity, strict scheduling requirements, safety regulations and strong interdependence among operational activities – that distinguish it from other logistics environments and limit the direct transferability of findings from manufacturing or general logistics research.

Railway operations are inherently interconnected, meaning that delays or inefficiencies occurring in one operational segment – such as congested freight terminals, delayed rolling stock or inefficient intermodal transfers – can rapidly propagate throughout the network and affect multiple stakeholders. By outsourcing selected logistics activities, railway organizations gain access to specialized operational knowledge, advanced technological systems and standardized logistics processes that improve coordination across the supply chain. For example, 3PL providers managing intermodal freight hubs can optimize cargo flows between rail, road and maritime transport, coordinate train departures to minimize idle time, improve warehouse synchronization and respond rapidly to operational disruptions. Moreover, the growing digital transformation of railway logistics has further increased the strategic importance of outsourcing, as specialized logistics providers increasingly integrate artificial intelligence, predictive analytics, Internet of Things technologies, digital twins and real-time visibility platforms into railway logistics operations. These digital capabilities enable railway operators to monitor logistics activities continuously, anticipate operational disruptions, optimize resource allocation and improve service transparency for customers (Sarp, Kuzlu, Jovanovic, Polat, & Guler, 2024; Rodríguez-Hernández, Crespo-Márquez, Sánchez-Herguedas, & González-Prida, 2025).

Therefore, LO should be viewed not merely as an operational decision aimed at reducing costs, but as a strategic collaborative mechanism capable of strengthening operational resilience, increasing organizational flexibility, enhancing SQ and supporting the digital transformation of railway logistics systems. Nevertheless, despite these theoretical arguments, empirical evidence explaining how LO improves ROP through specific operational dimensions remains scarce, particularly within emerging railway markets. Addressing this gap provides the principal motivation for the present study.

LO is increasingly recognized as a strategic mechanism through which railway operators can improve operational performance by strengthening several interrelated operational capabilities rather than generating immediate performance improvements. Consistent with transaction cost economics, outsourcing enables organizations to reduce coordination costs, improve operational specialization and increase resource efficiency through collaboration with specialized logistics providers (Tsay, Gray, Noh, & Mahoney, 2018). Simultaneously, the resource-based view suggests that access to external capabilities, advanced technologies and specialized expertise enables organizations to strengthen their internal competencies and improve their competitive position. Within railway logistics, these theoretical perspectives indicate that the operational benefits of outsourcing are primarily realized through improvements in CE, DR and SQ, which together constitute the cost-delivery-service triad underlying ROP.

Among these operational dimensions, CE represents one of the most immediate and observable outcomes of LO. Outsourced logistics providers frequently serve multiple clients, enabling them to exploit economies of scale, optimize equipment utilization and implement advanced technological solutions such as automated yard management systems, predictive maintenance software, intelligent warehouse management systems and optimized routing algorithms. Such improvements allow railway operators to reduce operating costs while allocating internal resources more strategically toward infrastructure modernization, network expansion, digital transformation and core transport activities (Alotaibi, Quddus, Morton, & Imprialou, 2022; Thai, Rahman, & Tran, 2022; Godovany & Zharikova, 2022). Furthermore, outsourcing reduces redundant investments in logistics infrastructure and specialized equipment while increasing asset utilization through shared logistics resources. These advantages are particularly relevant in railway systems, where infrastructure investments and rolling stock maintenance require substantial financial resources. Accordingly, drawing upon both transaction cost economics and the growing empirical evidence on LO, the following hypothesis is proposed:

DR constitutes another critical operational capability influenced by LO. Railway logistics systems operate within tightly interconnected transport networks in which delays occurring at one operational stage – such as freight terminals, rolling stock allocation or intermodal transfers – can rapidly propagate throughout the network, affecting service punctuality and customer satisfaction. Outsourced logistics providers contribute to improving DR by leveraging predictive analytics, real-time monitoring systems, intelligent scheduling platforms and dynamic resource allocation capabilities. Terminal operators and 3PL providers optimize freight sequencing, coordinate train departures with connecting transport modes and proactively manage operational bottlenecks to maintain schedule adherence (Thai et al., 2022; Abu-Aisha, Audy, & Ouhimmou, 2024). The integration of digital technologies within outsourced logistics activities further enhances operational visibility, allowing railway operators to anticipate disruptions, optimize decision-making and improve network responsiveness under uncertain operating conditions. Consequently, improved DR strengthens customer confidence, operational continuity and overall logistics efficiency. Therefore, the following hypothesis is proposed:

SQ represents another fundamental dimension through which LO contributes to railway performance. SQ encompasses cargo safety, handling accuracy, shipment visibility, responsiveness to customer requests, operational transparency, and compliance with safety and regulatory standards. Railway operators frequently face significant challenges in maintaining consistently high service standards because of increasing freight complexity, growing customer expectations and operational uncertainty. Specialized logistics providers often possess dedicated infrastructure, highly trained personnel, standardized operational procedures and integrated digital information systems that enable accurate cargo handling and continuous shipment monitoring (Shan et al., 2024; Taifa & Twaha, 2026). Moreover, outsourcing facilitates the adoption of customer-oriented logistics practices, including real-time tracking, digital communication platforms and faster problem resolution, thereby improving both operational responsiveness and customer satisfaction. Consequently, outsourcing contributes to strengthening SQ across multiple dimensions of railway logistics operations. Therefore, the following hypothesis is proposed:

Although CE, DR and SQ represent distinct operational dimensions, they collectively contribute to superior ROP. Rather than acting independently, these 3 capabilities complement one another in improving organizational effectiveness, resource utilization, operational resilience, customer satisfaction and long-term competitiveness. CE enables railway operators to optimize the utilization of financial, technological and human resources while reducing unnecessary operational expenditures. These improvements generate additional investment capacity for infrastructure modernization, digital innovation and service development, thereby strengthening overall ROP (Křižan, Vojtek, Široký, Gašparík, & Dedík, 2024).

Similarly, DR contributes to operational performance by improving schedule adherence, reducing service disruptions, minimizing cascading delays and strengthening coordination across multimodal logistics networks. Reliable railway services also increase customer confidence, facilitate long-term business relationships and improve supply chain integration, all of which contribute to sustainable operational performance (Sharma, Hossain, & Kumar, 2024). Likewise, SQ enhances operational performance by ensuring accurate cargo handling, timely deliveries, operational transparency and greater customer satisfaction. High-quality logistics services improve organizational reputation, increase customer retention and strengthen the competitive position of railway operators within increasingly integrated logistics markets (Muni et al., 2024).

Taken together, these arguments suggest that improvements in CE, DR and SQ represent the principal operational mechanisms through which LO contributes to ROP. Consequently, the following hypotheses are proposed:

The research model illustrating the proposed relationships among LO, CE, DR, SQ and ROP is presented in Figure 1.

A quantitative approach is adopted for this research because it allows for the objective measurement of complex latent constructs, statistical testing of theoretically derived hypotheses and the examination of interrelationships among multiple variables through SEM. A quantitative research design is particularly appropriate for this study because the proposed conceptual framework comprises several latent constructs and multiple mediating relationships that require simultaneous estimation within an integrated analytical framework. Compared with conventional multivariate statistical techniques, SEM enables the simultaneous assessment of both the measurement model and the structural relationships among the constructs, thereby providing a rigorous evaluation of the proposed conceptual model. Given that the study investigates multiple mediating mechanisms linking LO to ROP, the structured and measurable nature of a survey-based quantitative design ensures precision, consistency and reproducibility of the empirical analysis. Furthermore, by capturing data at a specific point in time, the study provides a cross-sectional assessment of current outsourcing practices, operational effectiveness and SQ perceptions within Moroccan railway operations.

The research adopts a deductive approach grounded in the theoretical perspectives of transaction cost economics and the resource-based view. Transaction cost economics explains LO as a strategic mechanism through which organizations reduce coordination costs, improve operational efficiency and access specialized external capabilities, whereas the resource-based view emphasizes the strategic value of combining internal resources with complementary external competencies to strengthen organizational competitiveness. These theoretical perspectives justify the selection of the study constructs and provide the conceptual foundation for examining the relationships among LO, CE, DR, SQ and ROP. Accordingly, the proposed conceptual model was operationalized by translating these theoretical assumptions into measurable constructs and empirically examining their relationships using SEM. By linking established organizational theories to observable operational practices, this study establishes a coherent methodological framework for investigating LO within the Moroccan railway sector.

Finally, the research design aligns with previous empirical studies investigating LO and operational performance within transportation and supply chain management while adapting their methodological frameworks to the specific characteristics of railway logistics systems. Unlike many logistics environments, railway operations are characterized by fixed infrastructure, high capital intensity, strict operational scheduling, strong interdependence among logistics activities and complex multimodal coordination requirements, making outsourcing decisions particularly strategic. These distinctive characteristics justify the need for a railway-specific empirical investigation rather than directly extrapolating methodological approaches from manufacturing or general logistics contexts. By employing a rigorous theory-driven quantitative framework supported by SEM, the present study provides a robust methodological basis for empirically examining the proposed conceptual model within the railway logistics context.

The study focuses on the Moroccan railway sector, encompassing both passenger and freight transport operations conducted under specific infrastructural, regulatory and operational conditions. Morocco has experienced significant railway development over the last decade through continuous infrastructure modernization, stronger integration with national logistics corridors, increasing freight transportation demand and closer connectivity between industrial zones, logistics platforms and major seaports. These developments have considerably increased the operational complexity of railway logistics while simultaneously raising expectations regarding CE, DR and SQ. Consequently, the Moroccan railway sector provides an appropriate empirical setting for examining LO practices and their relationships with operational performance within an emerging railway market.

The organizations targeted in this study include the Office National des Chemins de Fer (ONCF), regional freight operators and private logistics service providers involved in railway logistics activities. These organizations represent the principal stakeholders responsible for planning, coordinating and executing railway logistics operations throughout Morocco. Their outsourcing practices include terminal operations, intermodal coordination, cargo handling, warehousing support, freight consolidation, customs-related activities and last-mile freight distribution. By focusing on professionals working within these organizations, the study ensures that the collected data are obtained from respondents possessing substantial practical experience in outsourcing decisions and railway logistics management. Consequently, the respondents were well positioned to evaluate the operational implications of LO based on their direct professional experience rather than on subjective assumptions.

The Moroccan railway system presents several characteristics that distinguish it from more mature railway markets. Railway operators continue to face operational challenges related to infrastructure utilization, demand variability, multimodal coordination, rolling stock allocation and increasing customer expectations regarding logistics services. Under these conditions, LO has progressively become an important managerial strategy for improving operational efficiency while avoiding excessive investment in internal logistics resources. Moreover, the coexistence of public railway operators and specialized private logistics providers creates an operational environment in which collaborative logistics practices are increasingly implemented, making Morocco an appropriate empirical setting for investigating LO.

Furthermore, Moroccan railway logistics require continuous coordination among multiple stakeholders, including railway operators, freight terminals, port authorities, customs administrations, freight forwarders and 3PL providers. Such operational interdependence increases the importance of effective coordination, service reliability and operational responsiveness. Outsourcing selected logistics activities, particularly terminal handling, cargo management, warehousing and intermodal coordination, may contribute to reducing operational bottlenecks, improving schedule adherence and strengthening logistics integration across the railway network. This operational environment therefore provides a suitable context for empirically examining the relationships among LO, CE, DR, SQ and ROP.

The study employed a structured questionnaire specifically designed to examine the operational implications of LO within the Moroccan railway sector. The questionnaire was originally developed in French, which is the primary professional and operational language used by railway operators and logistics practitioners in Morocco. The use of French was intentionally chosen to maximize respondents' comprehension, reduce potential interpretation bias and ensure that the wording of the measurement items accurately reflected the operational terminology commonly employed in Moroccan railway organizations. Administering the questionnaire in the respondents' professional language also minimizes measurement errors associated with language translation and contributes to the reliability and validity of the collected data. Before its large-scale distribution, the questionnaire was pre-tested with 10 experienced railway managers to evaluate the clarity, wording, sequence and contextual relevance of all measurement items. Based on their feedback, minor linguistic refinements were introduced to improve readability while preserving the theoretical meaning and conceptual equivalence of all measurement items. The final questionnaire required approximately 15–20 minutes to complete, providing sufficient detail while minimizing respondent burden.

The questionnaire consisted of four main sections. Section 1 collected demographic and organizational information, including respondents' job title, department, years of professional experience and company type. Section 2 measured LO practices, including the extent of outsourcing activities, their frequency, strategic motivations and perceived benefits. Section 3 assessed the operational performance dimensions associated with outsourcing, namely CE, DR and SQ. Finally, Section 4 evaluated overall ROP through indicators such as operational efficiency, customer satisfaction, profitability and resource utilization.

All measurement items were assessed using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). A 5-point Likert scale was selected because it provides an appropriate balance between measurement sensitivity, respondent comprehension and response consistency, while remaining one of the most widely adopted scaling techniques in logistics, transportation and supply chain management research. Furthermore, the use of a common response format for all constructs improves the consistency of respondents' evaluations and facilitates subsequent multivariate statistical analyses.

The measurement constructs were adapted from well-established logistics and supply chain management studies and carefully contextualized to reflect the characteristics of Moroccan railway operations. As presented in Table 1, the LO construct comprised four items, CE three items, DR four items, SQ five items and overall ROP four items. The number of indicators associated with each construct reflects its conceptual scope rather than an attempt to standardize the number of measurement items across constructs. SQ, for example, encompasses several complementary operational dimensions – including cargo handling accuracy, responsiveness, shipment visibility and operational safety – and therefore requires a broader representation than CE. The questionnaire items were designed to capture observable operational practices and organizational performance outcomes, including punctuality, operational cost reduction, cargo handling accuracy, customer responsiveness and overall operational effectiveness. Accordingly, the measurement items were formulated to evaluate respondents' professional assessments of organizational practices and operational conditions based on their direct managerial and operational experience, thereby ensuring that the constructs reflected organizational realities rather than individual opinions.

Data were collected using the structured questionnaire described in the previous section from professionals working in the Moroccan railway sector. The survey targeted managers, supervisors, logistics coordinators and operational personnel who were directly involved in LO decisions or railway logistics activities. A purposive sampling strategy was adopted to ensure that respondents possessed the professional knowledge and practical experience necessary to provide reliable evaluations of LO practices and their operational implications. Consequently, only professionals with a minimum of three years of experience in logistics, transport, or railway operational functions were considered eligible to participate. This eligibility criterion was established to ensure that respondents were sufficiently familiar with outsourcing practices and capable of evaluating organizational operational performance based on direct professional experience.

The questionnaire was distributed to 1,021 railway professionals through multiple complementary channels, including professional networks, direct corporate contacts, industry mailing lists and LinkedIn groups dedicated to railway logistics professionals. Data collection was conducted over a four-month period, from November 2025 to February 2026, allowing adequate time to reach respondents representing different organizations and operational functions throughout Morocco. To enhance participation and reduce the likelihood of nonresponse bias, personalized invitation messages and periodic follow-up reminders were sent to potential respondents throughout the data collection period.

Following the completion of data collection, all returned questionnaires were carefully screened before statistical analysis. The data screening procedure involved verifying questionnaire completeness, identifying duplicate submissions, detecting inconsistent response patterns and excluding questionnaires containing excessive missing data or evidence of inattentive responding. After applying these quality control procedures, 937 valid questionnaires were retained for the final empirical analysis. The final sample size was considered adequate for SEM, providing sufficient observations to estimate the proposed conceptual model and ensuring robust statistical analysis.

Before completing the questionnaire, all participants were informed about the objectives of the research and were assured that their participation was entirely voluntary. Respondents were also informed that all responses would remain anonymous and confidential and would be used exclusively for academic research purposes. No personally identifiable information was collected at any stage of the survey, thereby protecting respondents' privacy and encouraging accurate and unbiased responses. These procedures ensured compliance with accepted ethical research standards while strengthening the quality and credibility of the collected data.

The collected data were analyzed using (SEM) because this analytical technique is particularly suitable for simultaneously examining multiple relationships among latent constructs while accounting for measurement error. Given that the proposed conceptual framework comprises several interrelated constructs and multiple mediating relationships, SEM provides a comprehensive analytical framework for evaluating both the measurement model and the structural model within a single analysis. The statistical analyses were performed using IBM SPSS Statistics for preliminary data screening and descriptive statistics, whereas SmartPLS 4 was used to estimate the measurement and structural models.

The analysis followed the widely recommended two-stage procedure for partial least squares SEM (PLS-SEM). The first stage involved evaluating the measurement model to assess the psychometric properties of the measurement scales. Internal consistency reliability was assessed using Cronbach's alpha and composite reliability (CR), while convergent validity was evaluated through the average variance extracted (AVE). Discriminant validity was assessed using both the Fornell–Larcker criterion and the heterotrait–monotrait (HTMT) ratio, following current recommendations for PLS-SEM research.

Following the assessment of the measurement model, the structural model was evaluated to test the hypothesized relationships among LO, CE, DR, SQ and ROP. The structural model assessment involved estimating standardized path coefficients and examining the statistical significance of the proposed relationships. In addition, the explanatory power of the endogenous constructs was assessed using the coefficient of determination (R²), while the relative contribution of each predictor was evaluated through effect size (f²) values. Predictive relevance (Q²) and the standardized root mean square residual (SRMR) were also examined to provide a comprehensive evaluation of the proposed conceptual model.

Although demographic and organizational information (e.g. professional experience, organizational role, education level and organization type) was collected and descriptively analyzed, these variables were not included as control variables in the structural model. The primary objective of this study was to test the theoretically specified relationships among the core constructs derived from transaction cost economics and the resource-based view. Consequently, the structural model was intentionally specified to focus on these hypothesized relationships without the inclusion of additional control variables.

Finally, mediation analysis was conducted to examine whether the relationships between LO and ROP were transmitted through CE, DR and SQ. The indirect relationships were assessed using the nonparametric bootstrapping procedure with 5,000 resamples, allowing the mediating roles of the 3 operational dimensions to be examined within the proposed structural model.

Because all variables were measured using a single self-administered questionnaire completed by the same respondents, procedural and statistical approaches were adopted to minimize the potential influence of common method bias (CMB), following the recommendations of Podsakoff, MacKenzie, Lee, and Podsakoff (2003). Procedurally, respondents were informed that participation was entirely voluntary and that all responses would remain anonymous and confidential. They were also assured that there were no right or wrong answers and were encouraged to provide honest and objective responses based on their professional experience. In addition, the questionnaire employed clear and concise wording, and all measurement items were adapted from previously validated scales to reduce ambiguity and improve construct validity.

To statistically assess the potential presence of CMB, Harman's single-factor test was performed. This procedure evaluates whether a single latent factor accounts for the majority of the covariance among the measurement items. In addition, full collinearity variance inflation factors (VIFs) were examined as an additional diagnostic to detect potential common method variance. The results of these assessments are presented in the following section.

The empirical analysis of this study is based on 937 valid responses collected from professionals working in Moroccan railway logistics and operational management, providing a robust representation of the individuals directly involved in LO decisions and railway operational processes. The diversity of respondents is particularly important because outsourcing strategies within railway systems typically involve both strategic planning and operational execution. Senior managers often define outsourcing strategies, negotiate contracts with external logistics providers and evaluate financial implications, while operational staff coordinate daily logistics activities such as terminal operations, cargo handling and intermodal scheduling. This comprehensive representation strengthens the validity of the findings because it reflects the real operational context in which outsourcing strategies are implemented and evaluated in railway organizations.

The demographic composition of the sample reflects the typical characteristics of professionals involved in Moroccan railway logistics operations. As shown in Table 2, male respondents represent 61% of the sample, while 39% are female, indicating a gradual diversification of gender participation within logistics management roles in the railway sector. Although historically dominated by male professionals due to the technical nature of railway operations, the increasing presence of female professionals highlights a shift toward more inclusive workforce dynamics in logistics and transportation management. The age distribution of respondents ranges from 25 to 55 years, with an average age of approximately 36 years, indicating that the majority of participants are mid-career professionals actively engaged in operational decision-making.

Professional experience within the sample further demonstrates the reliability of the dataset. Respondents report between 3 and 25 years of experience, with an average of 8.5 years, suggesting that most participants possess substantial familiarity with railway logistics operations. Experience plays a crucial role in assessing outsourcing outcomes because professionals are better positioned to evaluate the efficiency of logistics providers, identify performance improvements and recognize operational challenges associated with outsourcing arrangements. The distribution of experience levels presented in Table 2 indicates that 36% of respondents have between 6 and 10 years of experience, while 25% possess between 11 and 20 years of experience. This demonstrates that the majority of respondents are not entry-level employees but rather experienced professionals capable of providing reliable insights into the operational effects of outsourcing.

Educational background further illustrates the diversity of professional expertise within the sample. As reported in Table 2, 54% of respondents hold university degrees, primarily in logistics, engineering or transport management, while 30% possess technical or vocational training relevant to operational logistics roles. Additionally, 16% hold postgraduate degrees, including master's or doctoral qualifications, indicating that a segment of the workforce engages in more analytical or strategic decision-making activities. This diversity of educational backgrounds is particularly relevant in the context of LO because outsourcing decisions require both operational expertise and strategic evaluation. Technical specialists understand the practical implications of logistics processes such as cargo handling or yard management, while managerial personnel evaluate outsourcing from financial and strategic perspectives. The combination of these viewpoints ensures that the data capture both operational realities and strategic considerations within Moroccan railway organizations.

The professional roles represented in the dataset further support the credibility of the research findings. According to Table 2, supervisors accounted for 32% of the respondents, followed by logistics managers (28%), while operations coordinators and other managerial roles each represented 20%. This distribution ensures balanced representation across different organizational levels involved in LO decisions. Managers often define outsourcing strategies and evaluate performance outcomes, while supervisors and coordinators oversee daily interactions with external logistics providers. Consequently, the dataset incorporates both strategic and operational perspectives, providing a comprehensive understanding of how outsourcing influences railway logistics performance.

Geographical distribution of respondents also contributes to the representativeness of the sample. As shown in Table 2, 43% of respondents are located in major urban centers such as Casablanca, Rabat and Tangier, where railway traffic volumes and logistics activities are highest. Another 38% operate in regional logistics hubs, which serve as critical nodes for freight consolidation and intermodal coordination. The remaining 19% are located in semi-urban or rural areas, where railway logistics operations face different operational challenges, including infrastructure limitations and lower traffic density. This geographic diversity allows the research to capture outsourcing dynamics across different operational environments within the Moroccan railway system.

Finally, the survey results reveal that 68% of respondents report regular outsourcing practices, particularly in activities such as freight handling, terminal management and last-mile coordination, while 32% report selective outsourcing strategies. This pattern confirms that LO has become a widely adopted operational strategy within Moroccan railway operations. The prevalence of outsourcing practices further justifies the relevance of this research, as understanding how LO affects CE, DR and SQ is essential for improving ROP.

Descriptive statistical analysis was conducted to obtain an initial overview of respondents' perceptions regarding LO and its influence on ROP. Descriptive statistics provide valuable insight into the central tendencies and variability of the constructs examined in the study, offering a preliminary understanding of how railway professionals evaluate outsourcing practices and operational outcomes. The constructs analyzed in Table 3 include LO, CE, DR, SQ and overall ROP.

Among the constructs, LO exhibits the highest mean score, suggesting that respondents strongly agree that outsourcing practices are widely implemented and perceived as beneficial within Moroccan railway operations. This high evaluation reflects the increasing reliance of railway operators on external logistics providers to manage specialized activities such as terminal operations, cargo handling and intermodal coordination. Outsourcing these activities allows railway organizations to access specialized expertise, advanced technologies and operational resources that may not be available internally. As a result, railway operators can improve operational efficiency while focusing on their core competencies.

The construct CE also receives a relatively high mean score, indicating that respondents perceive outsourcing as an effective mechanism for reducing operational costs and improving resource allocation. Railway logistics operations involve substantial fixed costs associated with infrastructure maintenance, rolling stock management and labor-intensive handling processes. Outsourcing certain logistics functions enables operators to convert fixed operational expenses into variable costs while benefiting from the economies of scale achieved by specialized logistics providers. These providers often serve multiple clients, allowing them to distribute operational costs more efficiently and invest in advanced logistics technologies that improve productivity. Consequently, outsourcing contributes to greater financial flexibility for railway operators, enabling them to allocate resources toward strategic investments such as infrastructure modernization and network expansion.

Similarly, the construct DR receives a strong evaluation, reflecting the perception that outsourcing contributes positively to improving punctuality and schedule adherence in railway logistics operations. Reliability is a critical performance dimension within railway systems because delays in one operational segment can propagate across the network and disrupt multiple logistics processes. Outsourced logistics providers often utilize advanced scheduling systems, real-time monitoring technologies and specialized operational expertise that help reduce operational disruptions. These capabilities enable railway operators to improve the accuracy of train schedules, reduce delays and enhance coordination between different transport modes within intermodal logistics networks.

The construct SQ also demonstrates a positive evaluation, indicating that outsourcing contributes to improved cargo handling accuracy, responsiveness to customer inquiries and operational safety. External logistics providers frequently possess specialized infrastructure and trained personnel dedicated to logistics operations, allowing them to maintain higher service standards than internally managed logistics processes. Improved SQ is particularly important for railway operators seeking to strengthen customer satisfaction and maintain competitiveness within the broader transport sector.

Overall, the descriptive statistics presented in Table 3 reveal that railway professionals perceive outsourcing as a multifaceted operational strategy capable of improving several key performance dimensions simultaneously. The relatively balanced mean values across all constructs suggest that outsourcing is not viewed solely as a cost-reduction tool but rather as a comprehensive operational approach. Furthermore, the skewness and kurtosis values reported in the table fall within acceptable statistical ranges, confirming that the dataset meets the normality assumptions required for SEM.

Before estimating the structural relationships among the constructs, a Pearson correlation analysis was conducted to explore the preliminary associations between LO and the operational performance dimensions examined in this study. Correlation analysis provides an initial understanding of whether the relationships proposed in the conceptual framework are supported by the observed data. It also allows researchers to evaluate the strength and direction of associations between variables before proceeding to more complex structural modeling techniques such as SEM. In the context of railway logistics operations, examining these relationships is particularly important because operational performance dimensions are inherently interconnected. Improvements in 1 dimension often influence others, creating a network of relationships that collectively shape the performance of railway logistics systems.

The results presented in Table 4 reveal several statistically significant positive correlations among the constructs included in the study. LO demonstrates strong positive correlations with CE (r = 0.52), DR (r = 0.49) and SQ (r = 0.47), all of which are statistically significant at the p < 0.001 level. These findings provide preliminary empirical support for the theoretical arguments developed in the literature review, suggesting that outsourcing logistics activities contributes to improvements in key operational capabilities within railway systems.

The strong relationship between LO and CE indicates that outsourcing is widely perceived by railway professionals as an effective strategy for reducing operational costs and optimizing resource utilization. This result aligns with the principles of transaction cost economics, which argue that organizations can achieve greater efficiency by outsourcing activities to specialized providers that possess superior operational capabilities and economies of scale. Besides, the positive association between LO and DR suggests that external logistics providers play an important role in enhancing punctuality and operational coordination within railway logistics networks. By leveraging advanced scheduling systems, predictive analytics and real-time tracking technologies, outsourced providers can help railway operators minimize delays and maintain consistent service schedules. Similarly, the positive correlation between LO and SQ indicates that outsourcing contributes to improved customer-oriented logistics services, including accurate cargo handling, enhanced shipment visibility and faster response to customer inquiries. These improvements are particularly important in railway logistics operations, where customer satisfaction and service reliability directly influence the competitiveness of rail transport compared to other modes such as road or maritime transport.

In addition to the relationships between outsourcing and the intermediate operational constructs, the correlation matrix reveals strong associations between the mediating variables and ROP. CE demonstrates the strongest relationship with ROP (r = 0.57), followed by DR (r = 0.55) and SQ (r = 0.53). These results indicate that improvements in these operational capabilities are closely linked to broader performance outcomes such as operational efficiency, customer satisfaction and profitability within railway systems. The correlations among the mediating constructs themselves are also significant, suggesting that CE, DR and SQ are interdependent dimensions of operational performance. For example, improved logistics efficiency can reduce delays and improve schedule adherence, which in turn enhances SQ. This interconnected pattern reinforces the importance of analyzing these variables within an integrated structural model rather than examining them independently.

Overall, the correlation results provide strong preliminary evidence supporting the conceptual framework proposed in this study. The significant and positive relationships among the constructs suggest that LO is associated with improvements in key operational capabilities, which in turn contribute to enhanced ROP. These findings justify proceeding with the SEM analysis to examine the causal relationships among the variables and to test the hypotheses developed in the theoretical framework.

Because all variables were collected using a single self-administered questionnaire completed by the same respondents, the potential influence of CMB was first examined as part of the measurement model assessment. Following the recommendations of Podsakoff et al. (2003), Harman's single-factor test was conducted to determine whether a single latent factor accounted for the majority of the covariance among the measurement items. As reported in Table 5, the first unrotated factor explained 34.72% of the total variance, which is below the recommended threshold of 50%, indicating that CMB is unlikely to represent a serious concern in this study. Furthermore, the full collinearity VIF values ranged from 1.34 to 2.18, remaining well below the recommended threshold of 3.30. These findings provide additional evidence that common method variance did not substantially influence the observed relationships among the constructs.

Before testing the structural relationships among the constructs, it is essential to evaluate the reliability and validity of the measurement model to ensure that the latent variables used in the analysis are accurately measured by their corresponding indicators. Measurement model assessment is a critical step in SEM because it verifies that the observed variables reliably represent the theoretical constructs included in the conceptual framework. In this study, the constructs evaluated include LO, CE, DR, SQ and ROP. To assess the measurement model, several widely accepted statistical criteria were employed, including Cronbach's alpha, CR and AVE.

The results summarized in Table 6 demonstrate strong reliability across all constructs. Cronbach's alpha values range between 0.82 and 0.90, exceeding the commonly accepted threshold of 0.70, which indicates high internal consistency among the measurement items. High Cronbach's alpha values suggest that respondents interpreted the survey items in a consistent manner and that the items measuring each construct capture a coherent underlying concept. For example, the LO construct includes items related to outsourcing terminal operations, accessing external technology, improving operational flexibility and focusing on core activities. The strong internal consistency observed in the reliability analysis confirms that these items collectively represent a unified concept of LO within railway operations.

CR values further confirm the robustness of the measurement model. As shown in Table 6, CR values range from 0.87 to 0.93, well above the recommended threshold of 0.70. CR is particularly useful in SEM because it accounts for the contribution of individual indicator loadings when assessing construct reliability. The high CR values observed in this study indicate that the measurement items contribute strongly to their respective constructs and that the constructs can be reliably used in the subsequent structural model analysis.

Convergent validity was evaluated using AVE, which measures the extent to which a construct explains the variance of its indicators. The AVE values reported in Table 6 range from 0.57 to 0.66, exceeding the recommended threshold of 0.50. This result confirms that the constructs capture more than half of the variance in their respective measurement items, indicating strong convergent validity. Establishing convergent validity is essential because it ensures that the indicators measuring a specific construct are closely related and accurately reflect the theoretical concept they represent.

Discriminant validity was also assessed to ensure that each construct is empirically distinct from the others. In logistics research, operational dimensions may appear conceptually related, making it necessary to verify that they represent separate constructs rather than overlapping measurements. The analysis confirms that each construct exhibits adequate discriminant validity, meaning that the constructs capture different aspects of ROP. This distinction is particularly important for this study because the conceptual framework assumes that operational dimensions act as separate mediating mechanisms linking LO to ROP.

Overall, the measurement model assessment confirms that the constructs used in this study exhibit strong reliability and validity. These results provide a solid methodological foundation for proceeding with the structural model analysis and hypothesis testing, ensuring that the relationships observed in the structural model reflect genuine theoretical relationships rather than measurement errors.

After confirming the reliability and validity of the measurement model, the structural model was estimated to evaluate the hypothesized relationships among LO, the operational performance dimensions and overall ROP. SEM allows simultaneous estimation of multiple causal relationships, making it particularly appropriate for analyzing complex systems such as railway logistics networks where operational capabilities interact with each other. The structural model assessment focused on estimating the path coefficients, determining the statistical significance of relationships and evaluating the explanatory power of the model through the coefficient of determination (R²) for the endogenous constructs. These indicators collectively provide a comprehensive understanding of how LO influences railway performance through the operational mechanisms proposed in the conceptual framework. Given the cross-sectional nature of the data, the estimated relationships should be interpreted as statistical associations rather than definitive causal effects. Consequently, the findings indicate significant relationships among the study constructs but do not establish temporal causality, which should be investigated in future longitudinal research.

The R² values reported in Table 7 demonstrate that LO explains a substantial proportion of the variance in the intermediate operational constructs. In particular, outsourcing accounts for a significant share of the variation in CE, DR and SQ, confirming that outsourcing practices play a central role in shaping operational capabilities within railway logistics systems. Most notably, the R² value for overall ROP reaches 0.61, indicating that the conceptual model explains 61% of the variance in operational performance outcomes. According to widely accepted guidelines in logistics and supply chain management research, an R² value above 0.50 represents strong explanatory power. These findings demonstrate that the proposed conceptual model possesses substantial explanatory power within the Moroccan railway context. Nevertheless, because the analysis is based on cross-sectional survey data collected at a single point in time, the reported relationships should be interpreted as associations rather than evidence of direct causal effects.

The results summarized in Table 8 demonstrate that LO exerts a strong and statistically significant positive effect on CE, providing empirical support for Hypothesis 1. This finding indicates that outsourcing logistics activities such as freight handling, terminal management and intermodal coordination contributes to reducing operational expenses and improving resource utilization within railway organizations. External logistics providers often possess specialized infrastructure, optimized operational processes and economies of scale that enable them to perform logistics activities more efficiently than internal teams. As a result, railway operators can reduce fixed operational costs associated with labor, equipment maintenance and facility management while simultaneously improving productivity. This outcome aligns with the theoretical perspective of transaction cost economics, which suggests that organizations outsource activities when external providers can perform them more efficiently, thereby minimizing coordination and monitoring costs. In the context of railway logistics, the ability to reduce operational costs is particularly valuable given the capital-intensive nature of railway infrastructure and rolling stock management. Furthermore, the finding is also consistent with the Resource-Based View, which argues that organizations can strengthen their operational capabilities by accessing complementary external resources, specialized logistics expertise and advanced technologies that would otherwise require substantial internal investment.

The structural model also confirms that LO has a significant positive effect on DR, supporting Hypothesis 2. DR represents a critical operational capability in railway logistics systems because delays in one segment of the network can propagate across the entire system, affecting multiple operational processes and reducing customer satisfaction. Outsourcing logistics functions to specialized providers enables railway operators to benefit from advanced scheduling technologies, real-time monitoring systems and predictive analytics that enhance operational coordination. For example, terminal operators and logistics service providers can optimize cargo sequencing, manage yard congestion more effectively and coordinate train departures with connecting transport modes. These improvements reduce the likelihood of operational disruptions and help maintain consistent train schedules. Consequently, the findings suggest that outsourcing logistics activities contributes not only to cost reduction but also to improved punctuality and reliability in railway operations. This result further supports the resource-based view by indicating that specialized logistics providers contribute valuable operational capabilities, digital technologies and coordination expertise that enhance DR throughout railway logistics networks.

Similarly, the results confirm that LO has a significant positive impact on SQ, providing support for Hypothesis 3. SQ in railway logistics encompasses several operational aspects, including cargo handling accuracy, shipment visibility, safety compliance and responsiveness to customer requests. External logistics providers frequently invest in specialized equipment, digital tracking platforms and trained personnel dedicated to maintaining high service standards. By outsourcing specific logistics functions, railway operators can leverage these capabilities to improve the overall quality of services delivered to customers. Improved SQ is particularly important in the context of modern logistics markets, where customers increasingly demand transparency, reliability and responsiveness in freight transport services. The findings therefore suggest that outsourcing logistics operations can strengthen customer satisfaction and enhance the competitiveness of railway transport relative to alternative transport modes. The result also reinforces the theoretical assumptions of the resource-based view, suggesting that collaboration with specialized logistics providers enables railway organizations to access superior service capabilities and operational competencies that contribute to improved service performance.

In addition to examining the effects of outsourcing on operational capabilities, the structural model analysis also evaluated how these capabilities influence ROP. The results indicate that CE, DR and SQ all exert significant positive effects on operational performance, supporting Hypotheses 4, 5 and 6. Among these relationships, CE demonstrates the strongest effect, highlighting the importance of financial optimization in railway operations. Given the substantial infrastructure and maintenance costs associated with railway systems, improving CE directly enhances profitability and resource utilization. DR and SQ also contribute significantly to operational performance by ensuring consistent service delivery, reducing disruptions and enhancing customer satisfaction.

The stronger association observed between CE and ROP reflects the operational characteristics of the Moroccan railway sector. Railway operators continue to face considerable financial pressures associated with infrastructure maintenance, rolling stock management, energy consumption and network expansion. Consequently, improvements in CE generated through LO are more immediately reflected in overall operational performance than improvements in other operational dimensions. This finding is consistent with transaction cost economics, which emphasizes that organizations improve performance by minimizing transaction and coordination costs through the efficient allocation of operational activities to specialized external providers.

Although DR and SQ also exhibit significant positive relationships with ROP, their effects are comparatively smaller. This does not imply that these operational dimensions are less important; rather, it suggests that the benefits associated with service improvements generally materialize more gradually. In the Moroccan railway context, logistics service providers have made substantial progress in cargo handling, warehousing support and intermodal coordination. However, continuous improvements remain necessary in areas such as digital integration, real-time shipment visibility, customer responsiveness and advanced logistics technologies. These contextual factors may explain why SQ contributes positively to ROP while exhibiting a relatively smaller effect than CE.

Overall, these findings provide empirical support for both transaction cost economics and the resource-based view. While Transaction Cost Economics explains the operational benefits associated with reducing coordination and operational costs through outsourcing, the resource-based view highlights the strategic value of accessing complementary external capabilities, specialized logistics expertise and advanced technologies that strengthen organizational performance. Together, these theoretical perspectives suggest that LO is associated with improved ROP primarily through the enhancement of key operational capabilities rather than through an immediate direct effect.

The combined effect of these relationships results in a substantial explanatory power for the structural model, with the operational performance construct exhibiting a high R² value, indicating that the proposed model explains a large proportion of the variance in ROP. This level of explanatory power suggests that the cost-delivery-service triad represents a robust framework for understanding how outsourcing strategies translate into performance improvements within railway logistics systems.

Thus, these different values make it possible to present the research model, in its validation version (Figure 2).

Overall, the structural model results provide strong empirical support for all 6 proposed hypotheses. More importantly, they indicate that LO is positively associated with ROP through improvements in CE, DR and SQ. The specific indirect relationships through which these operational capabilities transmit the influence of LO are further examined in the following mediation analysis.

To further understand the mechanisms through which LO influences ROP, a mediation analysis was conducted. The objective of this analysis was to determine whether this relationship operates directly or indirectly through intermediate operational capabilities. Examining these mediation pathways is particularly important in LO research because outsourcing decisions typically affect organizational performance by modifying internal processes rather than by producing immediate performance outcomes. In other words, outsourcing improves performance by enhancing the efficiency and effectiveness of logistics operations.

The mediation results presented in Table 9 reveal that LO exerts significant indirect effects on ROP through all three operational dimensions. Among these mediation pathways, CE represents the strongest indirect effect, suggesting that outsourcing improves railway performance primarily by reducing operational costs and improving financial efficiency. This finding reinforces the importance of cost management in railway logistics systems, where high infrastructure and operational costs often limit the financial flexibility of railway operators. By outsourcing certain logistics activities, railway organizations can reduce overhead expenses, optimize resource allocation and improve overall operational efficiency.

The predominance of the indirect effect through CE can be explained by the operational characteristics of the Moroccan railway sector. Railway organizations continue to allocate substantial financial resources to infrastructure maintenance, rolling stock management, network modernization and energy consumption. This finding is consistent with transaction cost economics, which argues that organizations enhance performance by allocating noncore activities to specialized external providers capable of performing them at lower coordination and operating costs.

DR also plays a substantial mediating role in the relationship between outsourcing and performance. Outsourcing logistics operations to specialized providers helps reduce these disruptions by improving scheduling accuracy, increasing operational flexibility and enabling faster responses to unexpected events. As a result, improved reliability contributes to enhanced system performance by reducing delays and maintaining consistent service delivery.

This result also supports the resource-based view, suggesting that specialized logistics providers contribute valuable operational capabilities, digital scheduling systems and coordination expertise that strengthen the reliability of railway logistics operations. Rather than relying exclusively on internally developed capabilities, railway operators benefit from complementary external resources that improve operational coordination across the logistics network.

SQ represents another important mediation pathway, highlighting the role of customer-oriented logistics services in improving railway performance. Outsourced logistics providers often implement advanced digital tracking systems, automated cargo handling equipment and specialized training programs for logistics personnel. These capabilities allow railway operators to deliver higher-quality logistics services, including improved cargo safety, enhanced shipment visibility and faster response to customer inquiries. Such improvements strengthen customer trust and satisfaction, which ultimately contribute to improved operational and financial outcomes.

Although SQ exhibits the smallest indirect effect among the 3 mediators, the relationship remains statistically significant. Within the Moroccan railway context, this comparatively lower effect may reflect the fact that several customer-oriented logistics capabilities – including real-time shipment visibility, integrated digital communication platforms and advanced service customization – are still evolving. Consequently, improvements in SQ may require longer implementation periods before generating substantial organizational performance improvements. Nevertheless, the positive and significant relationship confirms that SQ remains an essential operational mechanism through which LO contributes to ROP.

The mediation analysis further indicates that the direct effect of LO on ROP decreases when the mediating variables are included in the model, suggesting that the influence of outsourcing on performance operates primarily through these intermediate operational capabilities.

To further examine the nature of the mediation, both the direct and indirect effects were considered. The results indicate that the direct relationship between LO and ROP becomes nonsignificant after the inclusion of CE, DR and SQ in the structural model, whereas all indirect effects remain statistically significant. These findings provide empirical evidence of full mediation, indicating that LO is associated with ROP primarily through improvements in these three operational capabilities rather than through a direct relationship.

The mediation results therefore provide strong empirical support for the theoretical assumptions developed in this study. Consistent with transaction cost economics and the resource-based view, the findings suggest that LO should not be viewed as an operational strategy that directly improves railway performance. Instead, its contribution is realized through the enhancement of operational capabilities, namely CE, DR and SQ, which collectively translate outsourcing practices into superior organizational performance.

Although the present findings are based on cross-sectional data, the benefits of LO are likely to become more pronounced over time as collaborative relationships between railway operators and specialized logistics providers mature. Long-term partnerships may facilitate continuous process improvement, knowledge sharing, digital integration and operational learning, thereby further strengthening CE, DR and SQ. Future longitudinal studies are therefore recommended to examine how these operational capabilities evolve over time and whether the observed relationships remain stable throughout different stages of outsourcing partnerships.

The findings also suggest that LO should complement rather than replace the strategic role of railway operators. Core functions such as train operations, traffic management, infrastructure maintenance, network planning, rolling stock management and safety governance are expected to remain under the direct responsibility of railway organizations. In contrast, operational logistics activities including terminal operations, cargo handling, warehousing, intermodal coordination, customs support, freight consolidation and last-mile distribution appear particularly suitable for outsourcing to specialized logistics providers. This division of responsibilities enables railway operators to concentrate on their core competencies while benefiting from the operational expertise of external partners.

Although SQ and ROP were measured using perceptual assessments, the respondents consisted of experienced railway managers, supervisors and logistics professionals directly involved in outsourcing decisions and operational activities. Their evaluations therefore represent informed professional judgments based on extensive practical experience rather than purely subjective opinions. Moreover, the satisfactory reliability and validity results of the measurement model further support the appropriateness of using perceptual measures to assess these organizational constructs.

To ensure the robustness of the empirical findings, the overall quality of the structural model was evaluated using several model validation indicators commonly applied in SEM. These indicators include the effect size (f²) values for individual structural relationships, and global model fit indices such as the SRMR and the normed fit index (NFI). Evaluating these metrics provides insight into the explanatory power of the model and the relative importance of each hypothesized relationship within the structural framework.

The effect size (f²) analysis presented in Table 10 further highlights the importance of LO as a driver of operational capabilities. The relationship between LO and CE exhibits a large effect size, indicating that outsourcing decisions play a particularly significant role in improving financial efficiency within railway operations. The relationships between LO and DR and SQ also demonstrate meaningful effect sizes, confirming that outsourcing contributes to improvements in operational capabilities.

Finally, global model fit indices were evaluated to confirm the overall adequacy of the structural model. As shown in Table 11, the SRMR value of 0.054 falls well below the recommended threshold of 0.08, indicating a satisfactory model fit. Similarly, the NFI value of 0.92 exceeds the commonly recommended threshold of 0.90, confirming that the proposed model provides a good representation of the empirical data.

Overall, the findings extend both transaction cost economics and the resource-based view within the railway logistics context by demonstrating that LO contributes to ROP primarily through improvements in CE, DR and SQ rather than through a direct relationship. This evidence contributes to the relatively limited empirical literature on LO in railway systems, particularly within emerging economies.

This study investigated the relationship between LO and ROP by examining the mediating roles of CE, DR and SQ within the Moroccan railway sector. Drawing upon transaction cost economics and the resource-based view, the study proposed and empirically tested a conceptual framework explaining how LO is associated with operational performance through key operational capabilities. Using SEM and data collected from 937 railway professionals, the findings provide robust empirical support for the proposed research model.

The results demonstrate that LO is positively associated with CE, DR and SQ, all of which contribute significantly to ROP. Among these operational dimensions, CE emerged as the strongest mediator, highlighting the critical importance of financial optimization within capital-intensive railway operations. DR and SQ also play important mediating roles by strengthening operational coordination, improving service consistency and enhancing customer-oriented logistics performance. Collectively, the findings indicate that the benefits associated with LO are realized primarily through improvements in these operational capabilities rather than through a direct relationship with ROP.

From a theoretical perspective, this study makes several important contributions to the LO literature. First, it extends the application of transaction cost economics within the railway logistics context by demonstrating that outsourcing decisions are associated with improved operational performance through reductions in coordination costs and more efficient allocation of noncore logistics activities. Second, the findings enrich the resource-based view by providing empirical evidence that access to complementary external capabilities, specialized logistics expertise and advanced operational technologies strengthens organizational performance through multiple operational mechanisms. Finally, the study contributes to the relatively limited empirical literature on railway LO in emerging economies by proposing and validating an integrated mediation framework linking LO, operational capabilities and ROP.

From a managerial perspective, the findings provide several practical implications for railway operators and policymakers. Rather than considering LO solely as a cost-reduction strategy, railway organizations should adopt a strategic approach that develops long-term collaborative relationships with specialized logistics providers. Core railway functions, including train operations, traffic management, infrastructure maintenance, rolling stock management and safety governance, should remain under the direct responsibility of railway operators, whereas operational logistics activities such as terminal operations, cargo handling, warehousing, intermodal coordination, customs support and last-mile freight distribution may be outsourced to specialized partners. In addition, railway managers should continuously monitor outsourcing performance through operational indicators related to CE, DR and SQ while investing in digital coordination platforms and information-sharing mechanisms to maximize the long-term benefits of outsourcing partnerships.

Despite these contributions, several limitations should be acknowledged. First, the study is based on cross-sectional survey data collected from railway professionals operating within the Moroccan railway sector. Consequently, although significant associations were identified, the findings should not be interpreted as evidence of causal relationships, and their generalizability to other national railway systems should be considered with caution. Second, the study relied on perceptual measures reported by experienced railway professionals. Although the reliability and validity analyses confirmed the adequacy of the measurement model, future research could complement perceptual assessments with objective operational indicators such as financial performance, punctuality records, freight throughput or customer service metrics. Third, organizational characteristics such as firm size, outsourcing duration, contract type and digital maturity were not incorporated as control variables and may also influence ROP.

Future research may extend the present study in several directions. Comparative investigations across different national railway systems would improve the external validity of the proposed framework and provide insights into how institutional and operational environments influence outsourcing outcomes. Longitudinal research designs would also be particularly valuable for examining how outsourcing partnerships evolve over time through continuous process improvement, knowledge transfer, digital integration and organizational learning. Finally, future studies may investigate additional mediating or moderating variables, including digital logistics technologies, supply chain integration, organizational resilience, innovation capabilities, environmental sustainability and inter-organizational trust, in order to develop a more comprehensive understanding of LO and ROP.