Background and Objective
Logistics service quality has become one of the critical factors influencing supply chain performance, customer satisfaction, and competitiveness in industrial markets. In today’s dynamic business environment, industrial organizations increasingly depend on efficient logistics systems to ensure timely delivery, reduce operational uncertainties, and strengthen long-term relationships with customers and suppliers. However, industrial markets, particularly in developing economies such as Iran, face several challenges, including infrastructural limitations, information asymmetry, technological constraints, and instability in business conditions. These challenges make it difficult to apply conventional logistics service quality models without considering contextual factors.
Existing logistics service quality models have made significant contributions by identifying important dimensions such as reliability, responsiveness, information quality, timeliness, and order accuracy. Nevertheless, many of these models mainly emphasize general service dimensions and provide limited explanations regarding how operational, technological, relational, and environmental factors interact within specific industrial contexts. In particular, previous models have insufficiently addressed the characteristics of Iran’s industrial markets, including the importance of long-term business relationships, inter-organizational trust, infrastructural constraints, and the need for integrated information systems.
Therefore, the main objective of this study is to develop an indigenous model for enhancing logistics service quality in Iran’s industrial markets. The concept of an indigenous model in this research does not imply the creation of completely new factors; rather, it refers to identifying and explaining the specific configuration and interaction of influential factors within the economic, infrastructural, and relational context of Iran.
Methodology
This study employed a qualitative research approach using the systematic grounded theory methodology. The grounded theory approach was selected because existing models could not fully explain the complex relationships among different dimensions of logistics service quality in Iran’s industrial markets. Therefore, instead of testing a predetermined framework, this research sought to develop a model based on the experiences and perceptions of experts involved in logistics and industrial markets.
The research participants consisted of academic experts and professional specialists in logistics and industrial markets. A purposive and snowball sampling approach was used to identify knowledgeable participants. Data were collected through semi-structured interviews with 17 experts until theoretical saturation was achieved. The collected data were analyzed through three stages of coding: open coding, axial coding, and selective coding.
To ensure research quality, validity was assessed through expert review and confirmation of conceptual categories. Furthermore, the reliability of the coding process was evaluated using inter-coder agreement, resulting in a coefficient of 0.88, indicating an acceptable level of agreement.
Findings
The findings resulted in the development of a five-dimensional indigenous model consisting of causal conditions, contextual conditions, intervening conditions, strategies, and consequences.
The causal conditions included factors related to understanding industrial customers’ needs, the importance of timely supply, and operational requirements of industrial production. These conditions indicate that logistics service quality in industrial markets is closely connected to production continuity and the ability of logistics providers to respond effectively to customer expectations.
Contextual conditions highlighted the importance of information integration, technological capabilities, and coordination among supply chain actors. The findings showed that the availability of accurate and timely information, along with digital technologies such as customer relationship management systems and tracking solutions, can reduce uncertainty and improve logistics performance.
Intervening conditions included relational factors, particularly trust and long-term cooperation among organizations. The results indicated that in Iran’s industrial markets, trust plays a significant role in reducing transaction risks and strengthening cooperation between industrial customers and logistics service providers.
The identified strategies included improving operational processes, increasing responsiveness, enhancing delivery reliability, optimizing transportation routes, and developing technological capabilities. These strategies contribute to improving logistics service quality by addressing both operational and relational requirements.
Finally, the consequences of implementing the proposed model included improved customer satisfaction, stronger industrial relationships, enhanced supply chain performance, and increased competitiveness.
Conclusion
This study developed an indigenous framework for enhancing logistics service quality in Iran’s industrial markets by integrating operational, technological, relational, and contextual dimensions. Unlike general logistics service quality models that primarily focus on universal service dimensions, the proposed model explains how these factors interact under the specific conditions of Iran’s industrial environment.
The contribution of this research does not lie in introducing entirely new components of logistics service quality; rather, its innovation is associated with explaining the relationships and interactions among existing factors within a specific national context. By emphasizing infrastructural conditions, information integration, inter-organizational trust, and industrial relationships, the proposed model provides a more comprehensive understanding of logistics service quality in Iran.
The findings offer practical implications for industrial managers, logistics providers, and policymakers by highlighting the importance of integrated operational strategies, digital capabilities, and relationship-based approaches. The model can support decision-makers in developing more effective logistics systems that are aligned with the realities and requirements of industrial markets in Iran.