فصلنامه مدیریت زنجیره تامین
پیاپی 84 (پاییز 1403)

Switching cost refers to the cost of losing a business partner and is applied with the aim of attracting and retaining the other party across the entire supply chain, from attracting and retaining suppliers to marketing, sales, and customer retention. Switching cost is applicable in both industrial marketing (B2B) and consumer marketing (B2C); however, this article focuses on industrial marketing due to the characteristics of the research field. The significance of switching cost lies in maintaining stakeholders, including suppliers, buyers, and intermediaries, while also bringing benefits and profitability. Various factors influence the determination of switching costs, which are addressed in this article. Although similar studies have been conducted in previous research, no comprehensive framework has yet been provided. The purpose of this research is to design a switching cost model using a qualitative-qualitative method. In the first qualitative phase, 113 open codes, 29 axial codes, and 6 selective codes were extracted from the literature and research background using the meta-synthesis method. Then, in the second qualitative phase, thematic analysis of interviews reduced the open codes to 87, and revisions were made to localize and practically adapt the extracted codes. To validate and ensure the reliability of both methods, inter-coder reliability was employed. The final framework includes six dimensions of switching cost in industrial marketing and supply chain management: (1) environmental variables, (2) strategic variables, (3) commercial variables, (4) beneficial switching cost, (5) technical switching cost, and (6) behavioral switching cost.

Given the crucial role of management control systems in managing costs and creating value, supply chain integration can enhance operational efficiency and foster a competitive edge. This study aims to explore the impact of supply chain integration on the relationship between management control systems and healthcare supply chain performance, considering a contingency approach. This study employs a descriptive-correlational and structural equation modeling approach. The population consists of hospital accountants, and a sample size of 215 participants was selected through convenience sampling. Data was collected using a questionnaire during the second quarter of 2024. Structural equation modeling was used to analyze the research data. In this study, management control systems (broad scope, timeliness, integration, and aggregation) served as the independent variable, supply chain performance (cost-effectiveness, asset utilization, flexibility, and quality) as the dependent variable, and supply chain integration as the moderating variable. Sixteen hypotheses were tested to examine the relationship between the independent and dependent variables. Three of these hypotheses were not supported, indicating no significant relationship between aggregation and the three variables of asset utilization, supply chain flexibility, and supply chain quality. Subsequently, the moderating effect of supply chain integration on the 16 relationships between independent and dependent variables was assessed. The findings revealed that supply chain integration did not moderate five of these relationships, including the relationship between accounting information aggregation and supply chain quality. Overall, the findings of this research provide a clear understanding of how supply chain integration, through management control systems, impacts hospital performance. Additionally, the research findings suggest a strong relationship between hospitals and suppliers.

Given the uncertainties and shortages in the healthcare supply chain, organizations involved in this chain have increasingly adopted coopetition strategies. However, some coopetition efforts have failed, often due to neglecting essential criteria for selecting partners. As no prior research has comprehensively addressed the factors influencing partner selection for coopetition, this study was conducted to identify and model these factors, aiming to determine the most fundamental ones. In the first phase of the study, using thematic analysis, 38 factors influencing partner selection were identified and categorized into 15 groups: "complementary services and resources", "accurate and experienced human resources", "adherence to ethical principles", "providing accurate, prompt, and high-quality services", "acceptance of changes and innovation", "brand reputation", "presence in the private sector", "strategic alignment with our organization", "geographical proximity", "appropriate human resource management", "having sufficient working capital to manage changes", "information sharing", "similar organizational culture", "organizational discipline and coherence", and "service capacity." In the second phase, the factors were modeled using the fuzzy total interpretive structural modeling method. The results showed that "presence in the private sector" and "geographical proximity" are the most fundamental factors influencing other factors. In the next level, "accurate and experienced human resources", "adherence to ethical principles", and "having sufficient working capital to manage changes" were identified. Based on the identified criteria, organizations are advised to prioritize these factors in selecting coopetition partners. Additionally, organizations should strive to enhance these attributes internally to become more attractive partners. To this end, recommendations include reducing administrative bureaucracy, strengthening organizational culture, improving information infrastructure, and enhancing human resource management.

The traceability system minimizes unsafe and low-quality food products during production, processing, and distribution stages. To implement a traceability system, it is crucial to understand the drivers that enable traceability in the food supply chain. This study aims to analyze the drivers for implementing traceability capability in the food supply chain. The research method is applied in terms of purpose and survey-based in terms of data collection. The statistical sample consists of 20 food industry experts. Based on literature review and screening, 11 drivers for establishing a food traceability system were identified. A pairwise comparison questionnaire using interpretive structural modeling (ISM) was employed by the experts. To analyze the collected data, ISM was utilized to prioritize the drivers for implementing traceability capability in the food supply chain, resulting in a nine-level prioritization. In the structural model, the adoption of Industry 4.0 was identified as an influential driver, while market support and competitive advantage were highly influenced drivers. The MICMAC matrix was used to analyze the interrelationships between the drivers. Subsequently, the structural equation modeling (SEM) method using Smart PLS 3.0 software was employed to fit the obtained structure. A questionnaire containing 33 Likert scale questions was designed and completed by 170 food industry employees. The validity and reliability of the questionnaire, as well as the model fit, were confirmed. The results of this study can serve as a guideline for the sustainable implementation of the traceability system in the country's food industries.

Performance evaluation and ranking of units is one of the most important actions of any organization to identify complications and adopt appropriate policies. Many organizations pay attention only to the evaluated unit in performance evaluation and determine the evaluation indicators only with regard to the same unit and do not pay attention to other members of the supply chain. While only paying attention to the evaluated unit may lead to errors in evaluation and local optimization. In this research, by examining the supply chain of insurance services, appropriate indicators were selected to evaluate the representatives of the selected insurance company. Then, using one-stage and two-stage DEA model, 36 representatives of selected insurance companies were evaluated. The results of this research showed that the two-stage DEA model performs better than the one-stage DEA model due to the more complete ranking of the units and also showing the strengths and weaknesses of each insurance agency. The ranking results with and without considering the supply chain are significantly different from each other.

The agricultural food supply chain plays a fundamental role in ensuring food security and societal sustainability. This study aims to design a sustainable supply chain model in the food and agriculture industries with a focus on economic, social, and environmental dimensions. Initially, a comprehensive literature review and analysis of previous studies identified 16 key factors. Data were collected through purposive sampling from 15 experts and academics in the field and subsequently screened based on their insights. In the qualitative phase, data were analyzed using thematic analysis and fuzzy Delphi methods, and a conceptual model was developed using MAXQDA 2022 software. The model's validity was confirmed through fuzzy Delphi methodology.In the quantitative phase, an ISM questionnaire was developed to evaluate the relationships among criteria and determine their influence levels, which was then administered to the experts. The MICMAC analysis classified the criteria into four categories: autonomous, dependent, linkage, and independent factors. The results of the study identified and validated 16 key factors in the agricultural food supply chain and established their levels and interrelationships using fuzzy Delphi and interpretive structural modeling (ISM)The findings reveal that critical factors such as customer responsiveness, packaging, branding, and traceability contribute significantly to the improvement of the supply chain. Moreover, fundamental factors like production and operations, social responsibility, and product safety and health were recognized as essential components of supply chain sustainability. Additionally, emerging factors such as trade sanctions and the formation of unions were identified as influential in enhancing competitiveness and optimizing resource distribution.This research highlights the effectiveness of fuzzy Delphi and MICMAC methodologies in clarifying the relationships among criteria and determining their hierarchical positions. The insights derived from this study provide a robust foundation for improving the sustainability of food and agricultural supply chains.