مدیریت نوآوری دیجیتال در صنعت خرده‌فروشی با تمرکز بر قابلیت‌های کلیدی اینترنت اشیا و رتبه‌بندی فروشگاه‌های پیشرو

Abstract
This study aims to identify, analyze, and prioritize the capabilities of the Internet of Things (IoT) and to rank selected smart retail stores using a hybrid multi-criteria decision-making approach in a fuzzy environment. In the first step, based on a systematic literature review and expert opinions, 14 key IoT capabilities were extracted. The conceptual framework was then validated using confirmatory factor analysis (CFA) and data collected from 138 senior and middle managers of the Etka chain stores in Tehran. In the second step, the fuzzy DANP technique was applied to analyze the causal relationships among the components and to calculate their final weights. The results indicated that capabilities such as "Personalized Services," " Customer Experience Enhancement," and " Delay Reduction " were of the highest importance. In the final step, the fuzzy MARCOS method was employed to rank 16 Asian smart stores in terms of their utilization of IoT capabilities. The results showed that the Tachikawa Concept Store ranked first, while the AEON LakeTown was placed last. The findings of this study can serve as a strategic reference for managers in the Iranian retail industry. By learning from the experiences of leading Asian stores and considering the identified IoT capabilities along with their causal relationships and assigned weights, managers can pinpoint existing digital gaps, prioritize strategic technology investments, and take effective actions toward developing intelligent retail infrastructures.
 
Introduction
The rapid growth of digital technologies, especially the Internet of Things, is reshaping retail by connecting devices, sensors, and systems into intelligent networks that enable real-time monitoring, data collection, automation, and data-driven decision-making. In this context, IoT enhances supply chain transparency, inventory optimization, operational efficiency, and personalized customer experiences. However, adoption in developing economies such as Iran remains limited due to technological, organizational, and infrastructural barriers, emphasizing the need to identify and prioritize high-impact IoT capabilities. To address this gap, this study proposes an integrated framework combining CFA, Fuzzy DANP, and Fuzzy MARCOS to validate, weight, and rank key IoT capabilities. Furthermore, by benchmarking leading Asian smart retail stores under uncertainty, the proposed approach identifies successful implementation patterns and provides practical guidance for data-driven strategic digital transformation in retail.
Case study
The case study focuses on Etka Chain Stores in Tehran, where 138 senior and middle managers informed the confirmatory factor analysis and nine IoT and retail experts supported the fuzzy analyses . This real-world Iranian retail context strengthens the practical relevance of IoT capability evaluation and underpins strategic digital transformation decisions for emerging economies.
Materials and Methods
This study adopted a structured mixed-method approach to identify, validate, and prioritize IoT capabilities in retail and to evaluate the performance of selected chain stores. The research was conducted in three sequential phases to ensure methodological rigor.
In the first phase, the conceptual structure of the 14 IoT capability components was validated through measurement model assessment using variance-based Structural Equation Modeling (PLS-SEM) in SmartPLS. A researcher-developed questionnaire comprising 42 items was designed through a systematic literature review and refined by eight academic and industry experts. Data were collected from 138 senior and middle managers across 64 Etka chain stores in Tehran using simple random sampling. Convergent and discriminant validity, as well as reliability, were confirmed through factor loadings, AVE, Fornell–Larcker criteria, and Cronbach’s alpha.
In the second phase, causal relationships and relative importance of the IoT capabilities were analyzed using Fuzzy DANP method. Nine purposively selected experts evaluated pairwise influences using linguistic scales converted into triangular fuzzy numbers. The process included constructing and normalizing relation matrices and deriving final weights through a converged weighted supermatrix.
In the final phase, Fuzzy MARCOS was applied to rank selected retail stores based on the derived weights. A fuzzy decision matrix was normalized, ideal and anti-ideal solutions were determined, and utility degrees were calculated to obtain the final rankings.
Discussion and Results
This study provides an integrated and systematic understanding of how IoT capabilities support digital transformation and operational efficiency in the retail sector. The analysis followed three sequential stages: validation of the conceptual model, examination of causal relationships and capability prioritization, and benchmarking smart retail stores under uncertainty.
First, the conceptual framework comprising 14 IoT capability dimensions was validated using CFA in SmartPLS. The results confirmed the robustness of the measurement model, as all indicators demonstrated high and statistically significant factor loadings. Convergent validity and reliability were established through strong AVE and composite reliability values, while discriminant validity was verified using the Fornell–Larcker criterion. In addition, the SRMR value (0.058) indicated a satisfactory model fit, supporting the suitability of the identified IoT capability structure for subsequent causal and ranking analyses.
Second, the interdependencies and relative importance of IoT capabilities were examined using the Fuzzy DANP approach. Expert evaluations enabled the construction of fuzzy direct and total relation matrices, from which cause–effect relationships were derived. The findings identified “Smart Monitoring,” “Identification and Tracking of Aggregated Units,” and “Unique Identification” as key driving capabilities that influence other dimensions. In contrast, capabilities related to customer-centric and sustainability outcomes were found to be more dependent. The final priority weights highlighted “Personalized Services,” “Customer Experience Enhancement,” “Delay Reduction,” “Identification and Tracking of Aggregated Units,” and “Safety Management and Situational Awareness” as the most critical capabilities for retail digital transformation.
Finally, the Fuzzy MARCOS method was applied to rank sixteen smart retail stores with different operational models. The results revealed substantial variation in IoT capability utilization, with utility scores indicating clear performance gaps among stores, with the Tachikawa Concept Store achieving the highest rank and AEON LakeTown occupying the lowest position.
Conclusion
This study developed and empirically validated a comprehensive hybrid framework to identify, analyze, and prioritize Internet of Things (IoT) capabilities in retail. By integrating CFA, FDANP, and FMARCOS, the research ensured both conceptual rigor and robust multi-criteria decision-making under uncertainty. Benchmarking sixteen Asian smart retail stores revealed substantial disparities in IoT adoption, highlighting the strategic role of advanced digital and automation technologies in enhancing competitiveness, operational efficiency, and customer engagement. For Iranian retailers, particularly Etka Chain Stores, the findings offer a practical roadmap to guide technology investments, optimize retail operations, and support data-driven digital transformation. The study provides both theoretical and practical contributions by establishing a systematic framework and empirical benchmarks for IoT capability evaluation. Future research could explore dynamic interactions among IoT, artificial intelligence, and blockchain technologies to further understand their combined impact on sustainable smart retail innovation.