Journal of Quality Engineering and Management

Investigating the impact of productivity quality management indicators on increasing service production efficiency considering the importance of artificial intelligence in Pasargad insurance

Document Type : Original Article

Authors

Ahmad Moaledji oureh 1
Seyed Ahmad Ghasemi 2
Elsa Shakrolehpour 3

1 Department of Industrial Management, Production and Operations Orientation, Roudehen Branch, Islamic Azad University, Iran.

2 Assistant Professor, Institute of Humanities and Social Studies, University of Tehran, Iran.

3 Department of Production and Operations Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Iran.

https://doi.org/10.48313/jqem.2026.561555.1585
Abstract
Purpose: One of the most important competitive challenges for insurance companies these days is to provide services that can increase productivity with better quality. This research aimed to investigate the impact of productivity quality management indicators and artificial intelligence on increasing the productivity of service production in Pasargad Insurance.
Methodology: The statistical population of the quantitative part of this research includes all personnel working in the central building of Pasargad Insurance.  Due to the large size of the statistical population, a classified questionnaire based on the results of the qualitative phase of the research was prepared and distributed among the personnel to increase the generalizability of the results. The sample size of this study was initially estimated to be 1,300 people using the Cochran formula, and after final calculations, the final sample size was determined to be 297 people. Since this research was conducted using a survey method, the data were analyzed using descriptive and inferential statistical methods. Then, in the inferential statistics section, after determining the distribution of variables in the population, more advanced analyses were performed. For this purpose, structural equation modeling was used with Smart PLS software, as well as descriptive statistical tests to examine demographic data and analyze research variables in SPSS software.
Findings: According to the findings of this study, it can be concluded that combining productivity quality management indicators with modern artificial intelligence technologies plays a significant role in improving performance and increasing service productivity in the insurance industry, especially in companies such as Pasargad Insurance.
Originality/Value: Therefore, the productivity quality management model and artificial intelligence on increasing the productivity of service production in Pasargad Insurance presented in this research is a scientific and practical step towards moving the insurance industry towards technological transformation, organizational agility, and long-term competitiveness.

Keywords

Organizational culture optimization
Service improvement
Cultural indicators
Subjects
[1]    Bartoli, J. (2021). The effects of company culture on company profitability [Tehesis]
[2]    Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological forecasting and social change, 168, 120783. https://doi.org/10.1016/j.techfore.2021.120783
[3]    Ogbeide, S. O., Adesuyi, I. O., & Adeoye, I. A. (2023). Service quality and customer satisfaction in some selected insurance firms in Akure Metropolis in Ondo state, Nigeria. Economic insights-trends and challenges, (1), 83–93. https://doi.org/10.51865/EITC.2023.01.07
[4]    Hira Hameed, L., Usman, M., Kazim, J. U. R., Assaleh, K., Arshad, K., Hussain, A., … ., & Abbasi, Q. H. (2024). Artificial intelligence enabled smart mask for speech recognition for future hearing devices. Scientific reports, 14(1), 1–11. https://doi.org/10.1038/s41598-024-81904-y
[5]    Radu, N., & Alexandru, F. (2022). Process optimization models using artificial intelligence and digital transformation of the insurance industry. Proceedings of the international conference on business excellence (pp. 1283–1294). Sciendo. https://doi.org/10.2478picbe-2022-0117
[6]    Blasco-Blasco, O., Demeter, M., & Goyanes, M. (2024). A contribution-based indicator of research productivity: Theoretical definition and empirical testing in the field of communication. Online information review, 48(4), 823–840. https://doi.org/10.1108/OIR-11-2022-0634
[7]    Clegg, B., MacBryde, J., Ball, P., Masi, D., Mullen, H., & Despoudi, S. (2025). Where next for manufacturing productivity research? Propositions based on exploratory empirical investigation. International journal of productivity and performance management, 74(3), 1026–1051. https://doi.org/10.1108/IJPPM-05-2023-0229
[8]    Al-Smadi, M. O. (2025). Insurance sector readiness for digital transformation: Empirical evidence from Jordan. Insurance markets and companies, 16(1), 33–41. http://dx.doi.org/10.21511/ins.16(1).2025.03
[9]    Soler, R., Cardona, C., & Toledo, B. (2023). Next in insurance. https://www.pwc.com/mt/en/publications/insurance/next-in-insurance-2023.html
[10] Eling, M., Nuessle, D., & Staubli, J. (2022). The impact of artificial intelligence along the insurance value chain and on the insurability of risks. The geneva papers on risk and insurance - issues and practice, 47(2), 205–241. https://doi.org/10.1057/s41288-020-00201-7
[11] Capgemini. (2024). World property and casualty insurance. https://www.capgemini.com/insights/research-library/world-property-and-casualty-insurance-report-2024/?
[12] Mohammadi, M. (2018). Identifying and prioritizing strategic levers in managing insurance for the studied individuals, pasargad insurance. The first national conference on research and development in management and resistance economics, Tehran, Iran. Civilica. (In Persian). https://civilica.com/doc/787705
[13] Ghorbani, Z., & Mosleh, M. (2021). Identifying and prioritizing life insurance risks in pasargad insurance company using multi-criteria decision-making and data mining techniques. National conference on tourism management and industry (pp. 36–46). Tehran, Iran. Civilica. (In Persian). https://www.noormags.ir/view/en/articlepage/1868883
[14] Khadem, E. N. (2023). The impact of marketing excellence on the insurance service engagement: An applied study AT the national insurance company. World bulletin of social sciences, 20, 4–12. https://www.neliti.com/publications/602999/the-impact-of-marketing-excellence-on-the-insurance-service-engagement-an-applie#cite
[15] Ghasemi, S. A., Hasangholipor, T., Esfidani, M. R., & Abbasian, E. (2022). Applications of intelligent marketing information system (Case study of insurance industry in Iran). Turkish journal of computer and mathematics education (TURCOMAT), 13(1), 255–262. https://doi.org/10.17762/turcomat.v13i1.12049
[16] Holden, M., Ogada, E., Hebron, C., Price, P., & Potokar, T. (2022). Quality improvement training for burn care in low-and middle-income countries: A pilot course for nurses. Burns, 48(1), 201–214. https://doi.org/10.1016/j.burns.2021.04.002
[17] Holland, C. P., Mullins, M., & Cunneen, M. (2021). Creating ethics guidelines for artificial intelligence (AI) and big data analytics: The case of the european consumer insurance market. Patterns, 2(10), 1-14. https://doi.org/10.1016/j.patter.2021.100362
[18] Purwanto, A. (2020). The impacts of leadership and culture on work performance in service company and innovative work behavior as mediating effects. Journal Of Reseacrh In Business, Economics, And Education, 2(1), 283-291.  https://ssrn.com/abstract=3985680
[19] Prentice, C., Weaven, S., & Wong, I. A. (2020). Linking AI quality performance and customer engagement: The moderating effect of AI preference. International journal of hospitality management, 90, 102629. https://doi.org/10.1016/j.ijhm.2020.102629
[20] Yan, K., Li, G., & Cheng, T. C. E. (2020). The impact of service-oriented organizational design factors on firm performance: The moderating role of service-oriented corporate culture. International journal of production economics, 228, 107745. https://doi.org/10.1016/j.ijpe.2020.107745
[21] Dutt, R. (2020). The impact of artificial intelligence on healthcare insurances. In Artificial intelligence in healthcare (pp. 271–293). Academic Press. https://doi.org/10.1016/B978-0-12-818438-7.00011-3
[22] Alipour Pijani, A., & Sattari, S. (2023). Presenting a model of how artificial intelligence capabilities affect the quality of organizational productivity. The second national conference on quality and reliability management and engineering, Tehran, Iran. Civilica. (In Persian). https://civilica.com/doc/2018954
[23] Nemati Ghareh-Tapeh, H., Asgarnejad Nouri, B., Zarei, Q., Nemati Ghareh-Tapeh, H. (2021). Exploring the effects of artificial intelligence on developments in digital marketing technologies. The 8th national conference on economics, management and accounting, Shirvan, Iran. Civilica. (In Persian). https://civilica.com/doc/1234958
[24] Yadegari, M., Sohrabi, Z., Sheikhkanluymilen, S. (2021). The impact of artificial intelligence on digital marketing. The first international conference on electrical, computer, mechanical and artificial intelligence engineering, Mashhad, Iran. Civilica. (In Persian). https://civilica.com/doc/1443837
[25] Fallah, A., Karimi T, R., Shayesteh Tabakh, G. (2019). Presenting a model of medical equipment supply chain integration and its impact on improving health-oriented services; A case study of one of Tehran hospitals. Health research journal, 4(4), 211-219. (In Persian). https://www.magiran.com/paper/2040965/
[26] Asghari Zadeh, E. (2018). Identifying and prioritizing the dimensions of milad hospital service quality using the swara approach.
[27] Sarmad, z., Bazargan, A., hejazi, E. (2025). Research methods in behavioral sciences. Agah. (In Persian). https://B2n.ir/ee4654
[28]            Betsch, C., Böhm, R., Airhihenbuwa, C. O., Butler, R., Chapman, G. B., Haase, N., … ., & Uskul, A. K. (2016). Improving medical decision making and health promotion through culture-sensitive health communication: an agenda for science and practice. Medical decision making, 36(7), 811–833. https://doi.org/10.1177/0272989X15600434
Journal of Quality Engineering and Management
Volume 15, Issue 4
Winter 2026
Pages 398-414

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