درخت حوزه‌های تخصصی

Objective : This study aims to identify and prioritize cybersecurity risks associated with IoT applications in the retail sector, an area critical to digital transformation and operational resilience. Given the challenges managers face in evaluating threats under uncertainty, the study introduces a novel methodological framework to enhance risk-based decision-making and strategic resource allocation.  Methodology: A hybrid approach combining Pythagorean fuzzy SWARA (PF-SWARA) and an alternative ranking technique based on adaptive standardized intervals (PF-ARTASI) within the FMEA framework is proposed. PF-SWARA is used to weight evaluation criteria, and PF-ARTASI ranks the identified risks. The model is applied to a case study in Iran’s retail sector. Sensitivity and comparative analyses are conducted to validate the robustness and effectiveness of the method. Results : The findings show that "Insecure Firmware/Software and Inadequate Patch Management" is the top cybersecurity risk, followed by "Lack of Standardization and Interoperability Issues" and "Physical Security concerns". The proposed PF-SWARA–ARTASI approach outperforms traditional FMEA and PF-MOORA methods in terms of result consistency, robustness, and practicality under uncertain conditions. Conclusion : This research makes four contributions: (1) It proposes the first integration of PF-SWARA and PF-ARTASI within FMEA; (2) applies a novel ranking method for risk prioritization; (3) provides an actionable list of prioritized cybersecurity risks in IoT-enabled retail; and (4) validates the model through extensive sensitivity and comparative analysis. The study provides a valuable decision-making tool for IT managers and contributes to the existing literature on fuzzy risk assessment in retail contexts.

Purpose : This research aims to develop an advertising policy model in digital marketing based on eye tracking. Method : The research method is qualitative and based on grounded theory. Semi-structured interviews were used to collect information, and data analysis was performed using the Strauss and Corbin method and the paradigm model. Sampling was theoretical sampling using targeted (judgmental) techniques, based on which 15 interviews were conducted with managers and marketing and advertising experts. Findings : The findings of the research show that during the process of open, central and selective coding, the advertising policy model based on the use of eye tracking consists of Causal categories that include Advertising content, Quality of environmental advertising and Promotional features; contextual categories consist of advertising slogans,  billboard elements, analysis of customer eye movements;  intervening categories consist of online advertising,  advertising costs, advertising through media and mass communication; Central categories that consist of environmental advertising status, development of advertising influence, effective advertising on customers' intentions; category of strategies includes advertising message attractions, evaluation of target environmental advertising selection process, advertising based on customers' taste; consequence category includes competitive advantage, currency innovation, Promotion of strategic marketing decisions. Conclusion : The study concludes that the eye tracking-based advertising policy model provides a comprehensive framework for enhancing advertising strategies. The model emphasizes the importance of considering various factors such as advertising content, consumer behavior, and contextual elements in developing effective advertising campaigns. The research highlights the potential of eye tracking in optimizing advertisements and gaining a competitive edge in the market. From a managerial perspective, the model offers actionable insights for marketers to improve customer engagement and increase advertising effectiveness by leveraging eye tracking data. It also suggests that advertisements should be tailored to consumer preferences and the context in which they are viewed. The study advocates for further research in this area to broaden the application of eye tracking technology in other marketing domains and service sectors. From an academic standpoint, this research contributes to the limited body of knowledge regarding environmental advertising and the use of eye tracking in marketing. It provides a foundation for future studies to explore the relationship between visual marketing and consumer behavior, offering insights that could foster innovative advertising strategies in the digital marketing field.