Fuzzy Network Modeling and Analysis of Service Productivity in Retail with a Focus on Social Responsibility

Document Type : Original Article

Authors

1 Department of Industrial Engineering, Management and Industrial Engineering Complex, Malek Ashtar University of Technology, Tehran, Iran.

2 2. Department of Industrial Engineering, Management and Industrial Engineering Complex, Malek Ashtar University of Technology, Tehran, Iran.

Abstract

In the dynamic retail service industry, enhancing productivity not only improves organizational performance but also plays a crucial role in fulfilling social responsibility. This study models and analyzes the network of service productivity components and ranks selected units of Etka chain stores using a hybrid Fuzzy DANP and Fuzzy WASPAS approach. Seventeen key components were identified across three dimensions—employee support, service process, and external service quality—based on the literature, with data collected through a fuzzy pairwise comparison questionnaire. First, causal relationships among components were analyzed, and final weights determined using the Fuzzy DANP method. Key drivers identified include authentic leadership, empowering organizational culture, and customer feedback, while managerial assessment and planning and systemic service design emerged as primary improvement targets. In the second step, six selected stores were ranked using Fuzzy WASPAS based on extracted weights. Results showed significant differences in productivity, with stores having data-driven feedback systems and cohesive customer interactions ranking higher. This causal-fuzzy model provides managers with an effective tool to design targeted programs for enhancing productivity, improving service quality, and strengthening organizational social responsibility. The study’s main innovation lies in applying a hybrid Fuzzy DANP and WASPAS approach, enabling simultaneous identification of driver and consequence factors and offering a practical pathway for productivity improvement in turbulent service environments. Moreover, fuzzy modeling bridges the gap between theory and practice, providing a precise and flexible decision-making tool for managers.

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