اعتبارسنجی عوامل موثر بر سیستم اطلاعاتی بازاریابی هوشمند در فروشگاه زنجیره‌ای رفاه

نوع مقاله : مقاله پژوهشی (کمی)

نویسندگان

1 گروه فناوری اطلاعات ،واحد تهران جنوب،دانشگاه آزاد اسلامی، تهران، ایران

2 گروه مدیریت بازرگانی، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران

3 گروه مدیریت بازرگانی،واحد شهر قدس، دانشگاه آزاد اسلامی، شهر قدس، ایران

4 گروه مدیریت صنعتی ،واحد تهران جنوب،دانشگاه آزاد اسلامی، تهران، ایران.

چکیده
هدف این پژوهش اعتبارسنجی عوامل مؤثر بر سیستم اطلاعاتی بازاریابی هوشمند در فروشگاه زنجیره‌ای رفاه می‌باشد. این پژوهش از نظر هدف، کاربردی و روش اجرای آن کمّی با رویکرد توصیفی-پیمایشی می‌باشد. جامعه آماری پژوهش شامل 384 نفر از مدیران و کارشناسان بازاریابی و فناوری اطلاعات شعب منتخب فروشگاه رفاه می‌باشد. نمونه‌گیری در این پژوهش از نوع تصادفی ساده می‌باشد. برای گردآوری داده‌ها از پرسشنامه محقق‌ساخته استفاده شد. برای برازش مدل طراحی‌شده، از مدل‌سازی معادلات ساختاری با نرم‌افزار SmartPLS4 بهره گرفته شد. یافته‌های پژوهش حاکی از آنست که، استراتژی ارزش کسب‌وکار، مدیریت هوشمند محصول، تحلیل رفتار مشتری و روانشناسی خرید، ارتباطات هوشمندمشتری، لجستیک و زنجیره تأمین پاسخگو، تجربه مشتری هوشمند، فناوری‌ها و زیرساخت‌های داده، سنجش عملکرد و متریک‌ها، انطباق پذیری سازمانی، چارچوب اخلاقی و داده بر طراحی سیستم اطلاعاتی بازاریابی هوشمند تأثیر مثبت و معناداری وجود دارد. در نتیجه، طراحی و استقرار چنین سیستمی، نیازمند تلفیق هوشمندانه قابلیت‌های فناوری‌های پیشرفته تحلیلی با استراتژی کسب‌وکار، ملاحظات اخلاقی و آماده‌سازی بستر سازمانی است. این پژوهش چارچوبی عملیاتی را در اختیار مدیران فروشگاه زنجیره‌ای رفاه قرار می‌دهد تا با تبدیل داده‌ها به بینش عملی، نه تنها کارایی عملیاتی و سودآوری را افزایش دهند، بلکه نقش اجتماعی خود در تأمین مطلوب کالاهای اساسی جامعه را نیز به نحو احسن ایفا نمایند.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Validation of factors affecting the intelligent marketing information system in Refah chain store

نویسندگان English

Hafez Roustasekehravani 1
Abdullah Naami 2
Alireza Rousta 3
Kiamars Fathi Hafshejani 4
1 Roustasekehravani Department of Information Technology Management, ST.C.,Islamic Azad University, Tehran, Iran
2 Department of Business Management, ST.C. Islamic Azad University, Tehran, Iran.
3 Department of Business Management, ShQ.C.,Islamic Azad University, Shahr-e Qods, Iran
4 Department of Industrial Management, ST.C.,Islamic Azad University, Tehran, Iran
چکیده English

Abstract
The aim of this study is to validate the factors affecting the smart marketing information system in Refah chain store. This study is applicable in terms of its purpose, and its implementation method is quantitative with a descriptive-survey approach. The statistical population of the study includes 384 managers and marketing and information technology experts of selected Refah store branches. The sampling in this study is simple random. A researcher-made questionnaire was utilized to collect data. Structural equation modeling with SmartPLS4 software was employed to fit the designed model. The research findings indicate that business value strategy, smart product management, customer behavior analysis and shopping psychology, smart customer communications, responsive logistics and supply chain, smart customer experience, data technologies and infrastructures, performance measurement and metrics, organizational adaptability, ethical framework, and data have a positive and significant effect on the design of smart marketing information system. As a result, designing and deploying such a system requires a smart integration of advanced analytical technology capabilities with business strategy, ethical considerations, and organizational context preparation. This study provides an operational framework for convenience store chain managers to transform data into actionable insights to not only increase operational efficiency and profitability, but also to play their social role in providing the desired basic goods to society in an efficient manner.
Introduction
In the digital age, marketing is no longer simply aligned with sales, but has become a comprehensive, customer-centric, and data-driven philosophy in which decisions are made based on evidence, not intuition. In this context, the marketing information system, as one of the fundamental pillars of marketing management, is a vital tool for collecting, organizing, and analyzing marketing-related data that enables managers to make informed decisions (Aggarwal et al., 2025). These systems not only record raw data, but also dissect it and store it for advanced analysis and more accurate decision-making in the future (Abu Anzeh et al., 2024). In today's competitive and dynamic environment, fast and efficient access to marketing information is a strategic source for gaining sustainable competitive advantage. Chain stores, especially in developing countries like Iran, are faced with a huge volume of data generated from daily interactions with millions of customers, thousands of suppliers, tens of thousands of products and hundreds of branches (Habibi Machiani, 2020). Smart management of this data is not only an opportunity to optimize operations, but also the key to a deeper understanding of customer behavior, demand forecasting and designing targeted campaigns. In this regard, intelligent marketing information systems, through advanced data mining techniques and machine learning algorithms, are able to discover patterns hidden in data and provide effective predictions based on customer behavior and market conditions (Liu et al., 2023). The necessity of this research is to design and validate a realistic, adaptable, and localized model that is not only technically feasible, but also aligned with the organizational structure, work culture, and social values ​​of Refah. This model should be able to analyze data related to customers, products, suppliers, and branches in an integrated framework and provide behavioral analysis and practical predictions to improve marketing decisions. Therefore, this article aims to validate the intelligent marketing information system model in Refah chain store. This research is a response to the gap in the literature: designing a model that, while taking advantage of global advances in the field of marketing intelligence, is also compatible with the organizational and social realities of Iran. The results of this study not only provide an operational framework for Rehf, but can also be a model for other retail organizations in the country that are seeking responsible and sustainable digital transformation. Therefore, the main question of the present study is: How to validate the factors affecting the intelligent marketing information system in the Rehf chain store?
Theoretical Framework
Marketing Information System
A marketing information system is a structured set of people, technologies, processes, and procedures designed to collect, analyze, store, and present market-related information in a timely manner to help marketing decision makers identify opportunities, respond to challenges, and design effective strategies. This system is a continuous and dynamic process that transforms raw data into actionable information (Aggarwal et al., 2025).
Agarwal et al. (2025) investigated an unsupervised visual data-based marketing information system for the management of the transportation industry. They showed that the integration of digital technologies in information systems can improve decision-making in operational departments and confirmed its generalizability to other industries (such as hospitals).
In a study in Hormozgan province by Dehghani (2025), a positive and significant relationship was shown between the use of a marketing information system and the improvement of productivity indicators, sales growth, and production capacity in industrial units, and the deployment of such systems was introduced as a key strategy for improving economic performance.
Research Methodology
This research is applicable in terms of its purpose, and its implementation method is quantitative with a descriptive-survey approach. The statistical population of the research includes 384 managers and marketing and information technology experts of selected branches of the convenience store. The sampling in this research is simple random. A researcher-made questionnaire was utilized to collect data.
Research findings
To fit the designed model, structural equation modeling with SmartPLS4 software was applied. The research findings indicate that business value strategy, intelligent product management, customer behavior analysis and shopping psychology, intelligent customer communications, responsive logistics and supply chain, intelligent customer experience, data technologies and infrastructures, performance measurement and metrics, organizational adaptability, ethical framework and data have a positive and significant effect on the design of an intelligent marketing information system. As a result, the design and implementation of such a system requires the intelligent integration of advanced analytical technology capabilities with business strategy, ethical considerations, and organizational platform preparation. This research provides an operational framework for managers of the Refah chain store to transform data into actionable insights, not only to increase operational efficiency and profitability, but also to effectively play their social role in providing the community with the desired basic goods.
Conclusion
The present study was conducted with the aim of validating the factors affecting the smart marketing information system in the Refah chain store. The results of this study are consistent with the results of Agarwal et al. (2025), Dehghani (2025), Torabi et al. (2024), Khojastehpour & Abedi (2024), Trianti et al. (2024), Shekhawat (2023), Liu et al. (2023) Chen & Chang (2023), Iravani et al. (2023), Tarahomi & Keshtgar (2022), Jin et al. (2022), Khojastehkhosro et al. (2022), and Tota & Negro (2021). Tota & Negro (2021) showed that focusing on value proposition and competitive differentiation is the basis for the success of marketing information systems in the competitive retail environment. The study of Jin et al. (2022) emphasized that the pricing strategy based on the perceived value of the customer has a direct impact on the performance of the smart marketing information system. These results indicate that focusing on high-quality basic goods and creating a sense of value for families is the basic foundation for designing the smart marketing information system in convenience stores.
Based on the findings of this study, it is recommended that convenience chain stores set up a smart marketing command center that integrates all the influential factors identified in this study into an integrated platform. This center should enable the implementation of smart and personalized marketing campaigns in real time by creating a live connection between customer data from the loyalty system, supply chain inventory information, and behavior analysis system.

کلیدواژه‌ها English

Intelligent Marketing Information System
Business Value Strategy
Customer Behavior Analysis
Intelligent Customer Experience
Logistics and Supply Chain
Abu Anzeh, A. Y., Basel Abushaweesh, Q., Alfayez, M., & AlQudah, M. Z. (2024). Mapping the future information systems and marketing strategy-a bibliometric analysis of emerging trends. EDPACS, 69(10), 1-29. https://doi.org/10.1080/07366981.2024.2387332
Aggarwal, K., Khoa, B. T., Sagar, K. D., Agrawal, R., Dhingra, M., Dhingra, J., & R, L. K. (2025). Marketing information system based on unsupervised visual data to manage transportation industry using signal processing. Expert Systems, 42(1), e13384. https://doi.org/10.1111/exsy.13384
Anica-Popa, I., Anica-Popa, L., Rădulescu, C., & Vrîncianu, M. (2021). The integration of artificial intelligence in retail: benefits, challenges and a dedicated conceptual framework. Amfiteatru Economic, 23(56), 120-136. https://doi.org/10.24818/EA/2021/56/120
Ashmarina, S., & Zotova, A. (2016). The system of marketing information management: development, assessment, improvement. Economic Annals-XXI, 160(7-8), 51-56. https://doi.org/10.21003/ea.V160-10
Bourg, L., Chatzidimitris, T., Chatzigiannakis, I., Gavalas, D., Giannakopoulou, K., Kasapakis, V., Konstantopoulos, C., Kypriadis, D., Pantziou, G., & Zaroliagis, C. (2023). Enhancing shopping experiences in smart retailing. Journal of Ambient Intelligence and Humanized Computing, 14(12), 15705-15723. https://doi.org/10.1007/s12652-020-02774-6
Bruzzone, A. G., Sinelshchikov, K., Massei, M., & Schmidt, W. (2020). Artificial Intelligence to Support Retail Sales Optimization. European Modeling & Simulation Symposium. https://doi.org/10.46354/i3m.2020.emss.061
Chen, H., Chan-Olmsted, S., Kim, J., & Mayor Sanabria, I. (2022). Consumers’ perception on artificial intelligence applications in marketing communication. Qualitative Market Research: An International Journal, 25(1), 125-142. https://doi.org/10.1108/QMR-03-2021-0040
Chen, J., & Chang, Y.-W. (2023). How smart technology empowers consumers in smart retail stores? The perspective of technology readiness and situational factors. Electronic Markets, 33(1), 1. https://doi.org/10.1007/s12525-023-00635-6
Czerniachowska, K., Hernes, M., & Subbotin, S. (2022). A Business Framework of Architecture for a Retail Information System. European Management Studies, 20(1), 193-210. https://doi.org/10.7172/1644-9584.95.9
Dehghan, T. (2025). Investigating the relationship of marketing information system (MKIS) with total productivity of production factors (TFP). International Journal of Business Management and Entrepreneurship, 4(1), 71-86. https://mbajournal.ir/index.php/IJBME/article/view/58
Del Vecchio, P., Mele, G., Siachou, E., & Schito, G. (2022). A structured literature review on Big Data for customer relationship management (CRM): toward a future agenda in international marketing. International Marketing Review, 39(5), 1069-1092. https://doi.org/10.1108/IMR-01-2021-0036
Habibi Machiani, A., Taghipour, M., Foladi Talari, Y., & Savar Rokhsh, M. (2020). Designing a smart management model for Iranian chain stores based on business intelligence (Case study: Proma chain store). Journal of Advertising and Sales Management, 1(4). [In Persian] https://civilica.com/doc/1228622/
Hair Jr, J., Page, M., & Brunsveld, N. (2019). Essentials of business research methods. Routledge.
Hassan, P., Ali, A. E.-A., Gheith, M. H., & Thabet, I. A. (2023). The Effect of Marketing Information System (MKIS) on Sales Growth and Market Share. Raya International Journal of Commercial Sciences, 2(7), 151-218.
Iravani, M., Sarmad Saidi, S., & Ghasemi, B. (2023). Designing smart marketing strategy model of Saderat Bank in line with regional and geographical planning. Geography (Regional Planning), 13(52), 237-253. [In Persian] https://doi.org/10.22034/jgeoq.2022.255713.2798
Judijanto, L. (2024). Bibliometric Insights into the Development of Real-Time Business Intelligence Systems. The Eastasouth Journal of Information System and Computer Science, 2(1), 1-14. https://doi.org/10.58812/esises.v2i01
Khojastehkhosro, A., Nejad Mohammad Namaghi, A., & Shadenoush, N. (2022). The effect of organizational accountability, customer orientation and innovation strategy on marketing information system in service-oriented companies. Business Management, 55(14), 95-110. [In Persian] https://civilica.com/doc/1921203/
Khojastehpour, D., & Abedi. (2024). The role of marketing intelligence and marketing information system in effective marketing decision making. National Conference on Management and E-Commerce, 7(7), 252-268. [In Persian] https://civilica.com/doc/2091912/
Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Publications.
Kundu, N., Mustafa, F., & Chola, C. (2023). Artificial intelligence in retail marketing. In Artificial Intelligence for Business (pp. 86-107). Productivity Press. https://doi.org/10.4324/9781003358411-6
Liu, Y., Alzahrani, I. R., Jaleel, R. A., & Al Sulaie, S. (2023). An efficient smart data mining framework based cloud internet of things for developing artificial intelligence of marketing information analysis. Information Processing & Management, 60(1), 103121. https://doi.org/10.1016/j.ipm.2022.103121
Maghsoudi, M., & Nezafati, N. (2023). Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach. Telematics and Informatics Reports, 11, 100070. https://doi.org/10.1016/j.teler.2023.100070
Pantano, E., & Timmermans, H. (2019). Smart retailing. Springer. https://doi.org/10.1007/978-3-030-12608-7
Pearlson, K. E., Saunders, C. S., & Galletta, D. F. (2024). Managing and using information systems: A strategic approach (8th ed.). John Wiley & Sons.
Pourbahman, F., Askari Far, K., & Rounaghi, M. H. (2021). Identifying and prioritizing smart retail technologies based on technology features and organizational capabilities (Case study: Refah chain stores). Technology Development Management, 9(1), 135-164. [In Persian] https://sid.ir/paper/1039916/fa
Rousta, A., & Allaf Jafari, E. (2024). Impact of environmental knowledge, responsibility and concern on sustainable consumption behavior: does customer attitude matter?, Management of Environmental Quality: An International Journal, 35(8), 1858-1877. https://doi.org/10.1108/MEQ-06-2023-0166
Shekhawat, S. (2023). Smart retail: How AI and IoT are revolutionising the retail industry. Journal of AI, Robotics & Workplace Automation, 2(2), 145-152.
Tarahomi, S. M. E., & Keshtgar, N. (2022). Marketing intelligence. International Conference on Interdisciplinary Studies in Management and Engineering. [In Persian] https://civilica.com/doc/1507436/
Teubner, R. A., & Stockhinger, J. (2020). Literature review: Understanding information systems strategy in the digital age. The Journal of Strategic Information Systems, 29(4), 101642. https://doi.org/10.1016/j.jsis.2020.101642
Tirno, R. R. (2024). Effect of business intelligence on organizational competitiveness-exploring the mediation of technology anxiety. *Computers in Human Behavior Reports*, 16, 100536. https://doi.org/10.1016/j.chbr.2024.100536
Torabi, M. A., Abbasian, E., & Milani, S. M. S. (2024). Smart marketing in self-sufficient artificial ecosystems. Smart Marketing Management, 5(2), 1-10. [In Persian] https://doi.org/JABM.3.2.15564.358874589687908
Trianti, K., Zaini, M. R., & Zakkiya, Y. N. (2024). Marketing Strategies at Retail Be Smart in Increasing Sales. International Journal of Entrepreneur and Business Administration, 2(2), 58-75. https://doi.org/10.33474/ijeba.v2i2.22428
Veeramuthu, M. (n.d.). A Study on Effectiveness of Indian Marketing Information System (MIS).
Vishniakou, U. (2020). Internet marketing organization with the use of intelligent and block chain technologies. System Analysis and Applied Information Science, (1), 18-23. https://doi.org/10.21122/2309-4923-2020-1-18-23
Wang, L., Pertheban, T. R. A., Li, T., & Zhao, L. (2024). Application of business intelligence based on big data in E-commerce data evaluation. Heliyon, 10(21), e38768. https://doi.org/10.1016/j.heliyon.2024.e38768
Wichmann, J. R., Uppal, A., Sharma, A., & Dekimpe, M. G. (2022). A global perspective on the marketing mix across time and space. International Journal of Research in Marketing, 39(2), 502-521. https://doi.org/10.1016/j.ijresmar.2021.09.001
دوره 5، شماره 2 - شماره پیاپی 13
تابستان 1405
صفحه 296-316

  • تاریخ دریافت 04 اردیبهشت 1405
  • تاریخ بازنگری 25 خرداد 1405
  • تاریخ پذیرش 18 تیر 1405