Readiness Assessment for Big Data Analytics in Citizen Relationship Management: a Case Study of Tehran Governorate

Document Type : Original Article

Authors

1 Human Resource Management, Faculty of Management. Kharazmi University, Tehran, Iran Corresponding Author E-mail: rnoori@khu.ac.ir

2 Information Technology and Operation, Faculty of Management. Kharazmi University, Tehran, Iran E-mail: farrokh@khu.ac.ir

3 Faculty of Management, Kharazmi University, Tehran, Iran E-mail: vahid.h1996@gmail.com

Abstract
This research examined how external organizational factors influence the acceptance and readiness assessment for integrating big data analytics into citizen relationship management (CRM) at Tehran Governorate. The study employed Davis's Technology Acceptance Model (TAM) in an applied, field-based design. Data were collected using a standardized questionnaire based on TAM, with a sample of 105 managers and experts from Tehran Governorate, and analyzed using Structural Equation Modeling (SEM) in SPSS and SmartPLS software. The questionnaire comprised five main dimensions, with validated reliability and validity. Results indicated that external factors (scalability, data storage and processing, data analysis capabilities, flexibility, and reliability), perceived ease of use, and perceived usefulness significantly impact the acceptance of big data analytics. Furthermore, organizational external components such as data storage and processing, flexibility, and reliability lead to satisfaction and intention to utilize big data analytics in managing citizen relations by creating perceived usefulness and confirming user expectations. These findings corroborated the previous research and demonstrated that strategic attention to training, expert recruitment, hardware development, infrastructure enhancement, and information security can facilitate effective adoption of big data analytics, thereby creating opportunities for research development in legal, economic, and other fields.

Keywords

Subjects

REFERENCE
Al Batayneh, R. M., Taleb, N., Said, R. A., Alshurideh, M. T., Ghazal, T. M., & Alzoubi, H. M. (2021, May). IT governance framework and smart services integration for future development of Dubai infrastructure utilizing AI and big data, its reflection on the citizens standard of living. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) (pp. 235-247). Springer International Publishing.
Al-Ateeq, B., Sawan, N., Al-Hajaya, K., Altarawneh, M., & Al-Makhadmeh, A. (2022). Big data analytics in auditing and the consequences for audit quality: A study using the technology acceptance model (TAM). Corporate Governance and Organizational Behavior Review, 6(1), 64-78. https://doi.org/10.22495/cgobrv6i1p6.
Aldholay, A., Isaac, O., Jalal, A. N., Anor, F. A., & Mutahar, A. M. (2022). Factors that accelerate the rise of acceptance of big data platforms for academic teaching: Personal innovativeness as moderating variable. In Proceedings of International Conference on Emerging Technologies and Intelligent Systems (pp. 227-243). Springer International Publishing.
Al-Dossari, S., Mokhtar, U. A., & Abdul Ghani, A. T. (2023). Factor influencing the adoption of big data analytics: A systematic literature and experts review. SAGE Open, 13(4), 21582440231217902. https://doi.org/10.1177/21582440231217902.
Allen, B., Tamindael, L. E., Bickerton, S. H., & Cho, W. (2020). Does citizen coproduction lead to better urban services in smart cities projects? An empirical study on e-participation in a mobile big data platform. Government Information Quarterly, 37(1), 101412. https://doi.org/10.1016/j.giq.2019.101412.
Alyusuf, I. Y., & Al-Rahmi, W. M. (2022). Big data analytics adoption via lenses of Technology Acceptance Model: Empirical study of higher education. Entrepreneurship and Sustainability Issues, 9(3), 399-417. https://doi.org/10.9770/jesi.2022.9.3(25).
Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2018). Applied computing and informatics.
Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101. https://doi.org/10.1016/j.aci.2018.12.001.
Arghashi, V., & Yuksel, C. A. (2022). Interactivity, inspiration, and perceived usefulness! How retailers' AR-apps improve consumer engagement through flow. Journal of Retailing and Consumer Services, 64, 102756. https://doi.org/10.1016/j.jretconser.2021.102756.
Asadpoor, E., Mohammadi, F., & Rahimi, A. (2024). Acceptance of artificial intelligence among medical students in Iran: A Technology Acceptance Model approach. Medical Education Journal, 18(2), 45-58.
Ashiku, L., Al-Amin, M., Madria, S., & Dagli, C. (2021). Machine learning models and big data tools for evaluating kidney acceptance. Procedia Computer Science, 185, 177-184.
Aworh, M. K., Kwaga, J. K. P., & Okolocha, E. C. (2021). Assessing knowledge, attitude, and practices of veterinarians towards antimicrobial use and stewardship as drivers of inappropriate use in Abuja, Nigeria. One Health Outlook, 3(1), 25. https://doi.org/10.1186/s42522-021-00058-3.
Barham, H., & Daim, T. (2020). The use of readiness assessment for big data projects. Sustainable Cities and Society, 60, 102233. https://doi.org/10.1016/j.scs.2020.102233
Baycan, T., & Yigitcanlar, T. (2024). Smart city governance: Assessing modes of active citizen engagement. Regional Studies, 59(1), 2399262. https://doi.org/10.1080/00343404.2024.2399262.
Beesmart City. (2024, February 8). How smart cities are boosting citizen engagement. https://www.beesmart.city/en/smart-city-blog/how-smart-cities-boost-citizen-engagement.
Beesmart City. (2025, January 15). 5 citizen engagement solutions that smart cities should know. https://www.beesmart.city/en/smart-city-blog/top-civic-engagement-solutions-smart-cities.
Cabrera-Sánchez, J.-P., & Villarejo-Ramos, Á. F. (2020). Acceptance and use of big data techniques in services companies. Journal of Retailing and Consumer Services, 52, 101888. https://doi.org/10.1016/j.jretconser.2019.101888.
Caffaro, F., Micheletti Cremasco, M., Roccato, M., & Cavallo, E. (2020). Drivers of farmers' intention to adopt technological innovations in Italy: The role of information sources, perceived usefulness, and perceived ease of use. Journal of Rural Studies, 76, 264-271. https://doi.org/10.1016/j.jrurstud.2020.04.028.
Cao, Y., & Kang, M. (2025). Digital dialogue in smart cities: Evidence from public concerns, government responsiveness, and citizen satisfaction in China. Cities, 158, 105174. https://doi.org/10.1016/j.cities.2025.105174.
Chamoso, P., González-Briones, A., De La Prieta, F., Venyagamoorthy, G. K., & Corchado, J. M. (2020). Smart city as a distributed platform: Toward a system for citizen-oriented management. Computer Communications, 152, 323-332. https://doi.org/10.1016/j.comcom.2020.01.059.
Civita App. (2025, February 18). Why every city needs a Citizen Relationship Management mobile app. https://civitaapp.com/citizen-relationship-management-mobile-app/.
Costa, R., Fernandes, G., & Silva, M. (2025). Understanding recruiters' acceptance of artificial intelligence: Insights from the Technology Acceptance Model. Applied Sciences, 15(2), 746. https://doi.org/10.3390/app15020746.
Fahmfam Ghodsiyeh and Hamidi Hodjat Fahm Fāmm, & Hamidi. (2018). Factors influencing the development and management of smart cities using an integrated approach of big data technologies, Internet of Things, and cloud computing. Journal of Information Processing and Management, 34(2), 557-584. [in Persian]
Fallahi Modaresi, S., & Zarei, A. (2022). A study on the factors influencing the adoption of big data technology in the tourism industry using the Technology Acceptance TOE framework (case study: Tourism industry jobs in Shiraz). Journal of Tourism and Development. [in Persian]
Faridoon, L., Liu, W., & Spence, C. (2024). The impact of big data analytics on decision-making within the government sector. Big Data, 3(X), 1-17. https://doi.org/10.1089/big.2023.0019.
Galvez Rachel. (2024, July 12). Data governance adoption has risen dramatically - Here's how. https://www.precisely.com/data-integrity/2025-planning-insights-data-governance-adoption-has-risen-dramatically/.
Ghali, E. A. A., Dominic, P. D. D., Fati, S. M., Muneer, A., & Ali, R. F. (2021). The assessment of big data adoption readiness with a technology–organization–environment framework: A perspective towards healthcare employees. Sustainability, 13(15), 8379. https://doi.org/10.3390/su13158379.
Hamta, N., Mohammadzadeh, Y., Hemati, M., & Dehghanzadeh, R. (2020). A study of user attitudes toward the medical imaging storage and transfer system in the educational and medical centers of Qom based on the Technology Acceptance Model (TAM). Qom University of Medical Sciences Journal, 14(6), 1-8. [in Persian]
Hossin, M. A., Du, J., Mu, L., & Asante, I. O. (2023). Big data-driven public policy decisions: Transformation toward smart governance. SAGE Open, 13(4), 21582440231215123. https://doi.org/10.1177/21582440231215123.
Ibrahim, F., Münscher, J.-C., Daseking, M., & Telle, N.-T. (2024). The technology acceptance model and adopter type analysis in the context of artificial intelligence. Frontiers in Artificial Intelligence, 7, 1496518. https://doi.org/10.3389/frai.2024.1496518.
Infosys Public Services. (2024). Big data analytics for government sector. https://www.infosyspublicservices.com/insights/blogs/bytes-better-policies.html.
Iriani, S. S., & Andjarwati, A. L. (2020). Analysis of perceived usefulness, perceived ease of use, and perceived risk toward online shopping in the era of Covid-19 pandemic. Systematic Reviews in Pharmacy, 11(12), 313-320.
Islami, M. M., Asdar, M., & Baumassepe, A. N. (2021). Analysis of perceived usefulness and perceived ease of use to the actual system usage through attitude using online guidance application. Hasanuddin Journal of Business Strategy, 3(1), 52-64.
Ju, J., Liu, L., & Feng, Y. (2018). Citizen-centered big data analysis-driven governance intelligence framework for smart cities. Telecommunications Policy, 42(10), 881-896. https://doi.org/10.1016/j.telpol.2018.01.003.
Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research -- Moving away from the "What" towards the "Why". International Journal of Information Management, 54, 102205. https://doi.org/10.1016/j.ijinfomgt.2020.102205.
Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280. https://doi.org/10.1016/j.techsoc.2020.101280.
Kumar, V., & Reinartz, W. (2018). Customer relationship management. Springer International Publishing.
Lampropoulos, G., Siakas, K., Viana, J., & Reinhold, O. (2022). Artificial intelligence, blockchain, big data analytics, machine learning and data mining in traditional CRM and social CRM: A critical review. In 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (pp. 504-510). IEEE.
Li, C., Chen, Y., & Shang, Y. (2022). A review of industrial big data for decision making in intelligent manufacturing. Engineering Science and Technology, an International Journal, 29, 101021. https://doi.org/10.1016/j.jestch.2021.06.001.
Maroufkhani, P., Tseng, M.-L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, 102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190.
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.103169.
Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings, 12(2), 90. https://doi.org/10.3390/buildings12020090.
Naeem, M., et al. (2022). Trends and future perspective challenges in big data. In Advances in intelligent data analysis and applications (pp. 309-325). Springer Singapore.
Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., ... & De-La-Hoz-Valdiris, E. (2022, November). Trends and future perspective challenges in big data. In Advances in intelligent data analysis and applications: Proceeding of the sixth euro-China conference on intelligent data analysis and applications, 15–18 October 2019, Arad, Romania (pp. 309-325). Singapore: Springer Singapore.
Noori, A., & Emamvirdi, G. (2015). Designing a model to assess readiness for digital/electronic readiness in Iranian service organizations using Data-Driven Theory Construction (DDD) method. Decision Engineering, 3(1), 61-86. [in Persian]
Noori, A., Hatami, Z., & Ebrahimiān, H. (2017). Factors influencing the adoption of information technology and its impact on human resources. Human Resource Management Research, 9(4), 127-153. [in Persian]
Oubdi, L., & El-Mekkaoui, O. (2026). The adoption of AI tools in doctoral studies: An extended TAM framework. In S. D. Carter, A. Smith-Hunter, & L. Best (Eds.), Impact of artificial intelligence (AI) and the global financial crisis on development in Africa (pp. 47-63). Springer International Publishing. https://doi.org/10.1007/978-3-031-94518-2_4.
Pangarkar Tajammul. (2024, August 13). Data governance statistics and facts (2025): Emerging technologies, challenges and adoption, AI, ROI, and data quality insights. https://electroiq.com/stats/data-governance/.
QodeaCTS. (2024). Data analytics in the public sector: Using data to improve services. https://qodea.com/resources/data-analytics-in-the-public-sector-using-data-to-improve-services/.
Rahman, M. N. (2020). Exploring the factors influencing big data technology acceptance [Doctoral dissertation, Portland State University]. PDXScholar.
Randi Vahid, Khoon., Siyavosh Mohsen, & Bamsoomi Behrooz. (2014). Factors influencing online customer purchase behavior in Iran with respect to the Technology Acceptance Model (TAM). Management of Development and Transformation, 109-118. [in Persian]
Rattletech. (2021, October 14). How a Citizen Relationship Management solution can transform your city. https://www.rattletech.com/citizen-relationship-management-solution-transform-city/
Sarker, I. H. (2021). Data science and analytics: An overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science, 2(5), 377. https://doi.org/10.1007/s42979-021-00765-8.
Shukla, D. (2024, January 23). Big but not scary: How to use big data to shape government policy and delivery. Global Government Forum. https://www.globalgovernmentforum.com/big-but-not-scary-how-to-use-big-data-to-shape-government-policy-and-delivery/.
Siagian, H., Tarigan, Z. J. H., Basana, S. R., & Basuki, R. (2022). The effect of perceived security, perceived ease of use, and perceived usefulness on consumer behavioral intention through trust in digital payment platform. Petra Christian University.
Siagian, H., Tarigan, Z. J. H., Basana, S. R., & Basuki, R. (2022). The effect of perceived security, perceived ease of use, and perceived usefulness on consumer behavioral intention through trust in digital payment platform. International Journal of Data & Network Science6(3), 861–874.
Singh, P. D. (2024). Generative AI through the lens of Technology Acceptance Model. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4953174.
Smith, A. M. (2024, September 15). Data governance trends in 2025. DATAVERSITY. https://www.dataversity.net/articles/data-governance-trends-in-2025/.
Xu, Z., Zhang, J., Zhang, Z., Li, C., & Wang, K. (2020). How to perceive the impacts of land supply on urban management efficiency: Evidence from China's 315 cities. Habitat International, 98, 102145. https://doi.org/10.1016/j.habitatint.2020.102145.