نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران.

2 گروه مدیریت بازرگانی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

10.22105/dmor.2021.299446.1468

چکیده

هدف: امروزه شرکت‌های بیمه با رقابتی ‌گسترده برای جذب و نگهداری مشتریان وفادار رو‌به‌رو هستند؛ بنابراین اهمیت مدل‌های پیش‌بینی وفاداری مشتریان بیش از گذشته نمایان شده است و می‌تواند موجب سهم بازار گسترده‌تری برای شرکت‌ها می‌شود. هدف اصلی پژوهش حاضر شناسایی عوامل مؤثر بر وفاداری مشتریان و توسعه مدلی جهت پیش‌بینی میزان وفاداری آن‌ها در صنعت بیمه و در شرکت‌های بیمه است.
روش‌شناسی پژوهش: این پژوهش ازنظر رویکرد، کمی، ازنظر گردآوری اطلاعات، پیمایش و ازنظر نتایج حاصله، کاربردی است. در چارچوب این پیمایش از تحلیل عاملی تاییدی و شبکه‌های عصبی مصنوعی استفاده‌شده است. به‌منظور بومی‌سازی عوامل مستخرج از ادبیات نظری و همچنین رفع تناقضات موجود در مؤثر بودن یا نبودن عوامل به‌دست‌آمده از ادبیات پژوهش، در ابتدا عوامل با استفاده از تحلیل عاملی تاییدی و نرم‌افزار SMART PLS3 موردبررسی قرار گرفتند و تاثیرات آن­­ها بر وفاداری مشتریان سنجیده شدند. سپس عوامل تایید‌شده به‌عنوان ورودی برای آموزش شبکه‌ی عصبی مصنوعی با نرم‌افزار MATLAB R2019b در نظر گرفته شد.
یافته‌ها: در این پژوهش حجم نمونه بر اساس جدول مورگان (حجم جامعه نامحدود و سطح خطای %0.05)، 384 نفر در نظر گرفته شده است. تعداد 436 پرسشنامه به‌صورت تصادفی ساده بین بیمه‌گذاران چهار شرکت بیمه شامل بیمه ایران، شرکت بیمه آسیا، شرکت بیمه البرز، شرکت بیمه پارسیان توزیع گردید و 384 پرسشنامه کامل دریافت شد. پس از تحلیل نتایج حاصله از روش تحلیل عاملی تاییدی، عوامل تعهد، کیفیت ادراک‌شده، اعتماد، ارزش ادراک‌شده، همدلی، تصویر برند، جذابیت گزینه‌های دیگر، رضایت مشتری بر وفاداری مشتریان در شرکت‌های بیمه ایران تاثیر داشتند و عامل هزینه جابجایی بر وفاداری مشتری تاثیر ناچیزی داشت. درنهایت مدل مورد هدف پژوهش برای پیش‌بینی وفاداری با 8 نورون ورودی، 110 نورون لایه میانی و 1 خروجی با سطح خطای 0.00992 و رگرسیون 0.98694 طراحی گردید.
اصالت/ارزش افزوده علمی: برونداد این پژوهش، مدلی جهت پیش‌بینی وفاداری مشتریان شرکت‌های بیمه‌ای در کشور ایران فراهم می‌کند تا این شرکت‌ها بتوانند بر عواملی که منجر به حفظ وفاداری مشتریان می‌شود سرمایه‌گذاری کنند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Identifying factors and forecasting customer loyalty- using confirmatory factors analysis and artificial neural network modeling

نویسندگان [English]

  • Mohamad Ali Khatami Firoz Abadi 1
  • Mona Jahangir Zade 2
  • Amir Mazyaki 1
  • Seyed Soheil Fazeli 1

1 Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.

2 Department of Management, Faculty of Economic and Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

چکیده [English]

Purpose: Nowadays insurance companies, same as other companies, are facing massive competition. This issue indicates the value of customer loyalty also a predictive model. Customers play a crucial role in the sustainability of organizations by constant repurchasing. Companies with loyal customers have more market share, and more money may return on investment. This article's main aim is to identify the factors affecting customer loyalty in insurance companies.
Methodology: This research was quantitative, analytical-descriptive. In gathering information, Data was collected through the survey, and the findings are practical. In this way, two methods, Confirmatory Factor Analysis (CFA) and Artificial Neural Networks (ANN) were used. For localizing the factors extracted from other similar prior literature, first, the elements were examined by CFA with SMART PLS application due to some conflicts in the literature to evaluate whether each factor affects customer loyalty or not. Then, the elements were introduced to the ANN for training by this program.
Findings: In this article, by using the MORGAN table, the sample size detected 384 people in 0.05 error. Questionnaires were distributed randomly between four Iranian insurance companies, ASIA insurance company, ALBORZ insurance company, and PARSIAN insurance company. Based on Confirmatory Factor Analysis, elements of commitment, perceived quality, trust, perceived value, empathy, brand image, the attraction of other alternatives, and customer satisfaction impact the customer loyalty of insurers in these companies. The cost of change, nevertheless, did not have a significant effect on customer loyalty. Then, the factors used as inputs for the multi-layer perceptron training also customer loyalty are indicated as output. The model was designed with eight inputs, 110 nodes in the hidden layer, and one output the error was E= 0.00992 and the regression = 0.98684.
Originality/Value: the finding of this research is, expanding a model for predicting customer loyalty in Iranian insurance companies.

کلیدواژه‌ها [English]

  • Customer loyalty
  • Artificial neural network
  • Confirmatory factors analysis
  • Insurance companies
  • Aaker, J. L., Rudd, M., & Mogilner, C. (2011). If money does not make you happy, consider time. Journal of consumer psychology, 21(2), 126-130.‏ https://doi.org/10.1016/j.jcps.2011.01.004
  • Alehosseini, A., Sarabi Jamab, M., Ghorani, B., Kadkhodaee, R., & Wongsasulak, S. (2017). Evaluating the performance of artificial neural networks (ANNs) for predicting the effect of polymer concentration and operating voltage on the physical properties of electrosprayed particles. Innovative food technologies, 4(4), 31-43. (In Persian). DOI: 22104/jift.2016.409‏
  • Al-Zoubi, M. R. (2013). Service quality effects on customer loyalty among the Jordanian telecom sector empirical study. International journal of business and management, 8(7), 35-45.‏
  • Ansari, A., & Riasi, A. (2016). Modelling and evaluating customer loyalty using neural networks: evidence from startup insurance companies. Future business journal, 2(1), 15-30.‏ https://doi.org/10.1016/j.fbj.2016.04.001
  • Ashraf, S., Ilyas, R., Imtiaz, M., & Ahmad, S. (2018). Impact of service quality, corporate image and perceived value on brand loyalty with presence and absence of customer satisfaction: a study of four service sectors of Pakistan. International journal of academic research in business and social sciences, 8(2), 452-474.‏
  • Bahrami Farsi, S., & Khoshsima, R. (2015). Identifying factors affecting customer loyalty in Asia insurance company. The second national conference and the second international conference on management and accounting in Iran, Tehran, Iran. Civilica. (In Persian). https://civilica.com/doc/484717/
  • Bahramzadeh, M. M., & Shokati Moghareb, S. (2009). Identifying and ranking the factors affecting customer loyalty of private banks in Khouzestan province. Second international conference of financial services marketing, Tehran, Iran. (In Persian). https://civilica.com/doc/96832/
  • Bilgili, B., Candan, B., & Bilgili, S. (2014). A research study on the relationship among relational benefit, perceived quality, image and customer loyalty in different hospitality businesses. International journal of management cases, 16(3).‏ https://www.circleinternational.co.uk/wp-content/uploads/2021/01/16.3.pdf#page=20
  • Dhasan, D., & Aryupong, M. (2019). Effects of product quality, service quality and price fairness on customer engagement and customer loyalty. ABAC journal, 39(2).‏ http://www.assumptionjournal.au.edu/index.php/abacjournal/article/view/3959
  • Gallan, A. S., Jarvis, C. B., Brown, S. W., & Bitner, M. J. (2013). Customer positivity and participation in services: an empirical test in a health care context. Journal of the academy of marketing science, 41, 338-356.‏ https://doi.org/10.1007/s11747-012-0307-4
  • Gavanda, S., Geisler, S., Quittmann, O. J., & Schiffer, T. (2019). The effect of block versus daily undulating periodization on strength and performance in adolescent football players. International journal of sports physiology and performance14(6), 814-821.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.‏
  • Graham, J. M. (2008). The general linear model as structural equation modeling. Journal of educational and behavioral statistics, 33(4), 485-506.‏ https://doi.org/10.3102/1076998607306151
  • Habibi, A., & Adanvar, M. (2018). Structural equation modeling and factor analysis (practical training of LISREL software). Publisher of Academic Jihad Publishing Organization. https://www.gisoom.com/book/
  • Hadiyat, M. A., & Prilianti, K. R., (2012). Comparing statistical feature and artificial neural networks for control chart pattern recognition: a case study. 3rd international conference on technology and operation management (pp. 83-88). University of Surabaya Repository. http://repository.ubaya.ac.id/905/
  • Hadiyat, M. A. (2019). Combined structural equation modelling–artificial neural networks model for predicting customer loyalty. IOP conference series: materials science and engineering (p. 012024). IOP Publishing.‏ DOI: 1088/1757-899X/703/1/012024
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. Journal of marketing theory and practice, 19(2), 139-152.‏ https://doi.org/10.2753/MTP1069-6679190202
  • Han, H., & Hyun, S. S. (2014). Medical hotel in the growth of global medical tourism. Journal of travel & tourism marketing, 31(3), 366-380.‏ https://doi.org/10.1080/10548408.2013.876955
  • Hayes, B. E. (2008). Measuring customer satisfaction and loyalty: survey design, use and statistical analysis methods (3rd Ed.). Quality Press. Milwaukee, WI. https://businessoverbroadway.com/resources/books/measuring-customer-satisfaction/
  • Heaton, J. (2018). Ian goodfellow, yoshua bengio, and Aaron courville: deep learning. In Genetic programming and evolvable machines (pp. 305-307). The MIT Press.‏ https://doi.org/10.1007/s10710-017-9314-z
  • Howat, G., & Assaker, G. (2013). The hierarchical effects of perceived quality on perceived value, satisfaction, and loyalty: empirical results from public, outdoor aquatic centres in Australia. Sport management review, 16(3), 268-284.‏
  • ‏ Jani, D., & Han, H. (2014). Personality, satisfaction, image, ambience, and loyalty: testing their relationships in the hotel industry. International journal of hospitality management, 37, 11-20.‏ https://doi.org/10.1016/j.ijhm.2013.10.007
  • Katra, R., & Lupetki, J. (2018). The effect of weeds on cropping system for sustaining food security. Medbiotech journal, 2(02), 50-53.‏
  • Khan, Y., Shafiq, S., Naeem, A., Ahmed, S., Safwan, N., & Hussain, S. (2019). Customers churn prediction using artificial neural networks (ANN) in telecom industry. International journal of advanced computer science and applications, 10(9), 132-142.
  • Kim, M. K., Park, M. C., & Jeong, D. H. (2004). The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications policy, 28(2), 145-159.‏ https://doi.org/10.1016/j.telpol.2003.12.003
  • Kishada, Z. M. E., Wahab, N. A., & Mustapha, A. (2016). Customer loyalty assessment in Malaysian Islamic banking using artificial intelligence. Journal of theoretical and applied information technology, 87(1), 80-91.‏ https://oarep.usim.edu.my/jspui/handle/123456789/2026
  • Lakshmanan, S. (2019). How, when, and why should you normalize/standardize/rescale your data?. https://towardsai.net/p/data-science/how-when-and-why-should-you-normalize-standardize-rescale-your-data-3f083def38ff
  • Lee, H. N., Lee, A. S., & Liang, Y. W. (2019). An empirical analysis of brand as symbol, perceived transaction value, perceived acquisition value and customer loyalty using structural equation modeling. Sustainability, 11(7), 2116.‏ https://doi.org/10.3390/su11072116
  • Leong, L. Y., Hew, T. S., Lee, V. H., & Ooi, K. B. (2015). An SEM–artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline. Expert systems with applications, 42(19), 6620-6634.‏ https://doi.org/10.1016/j.eswa.2015.04.043
  • Lopes, E. L., de Lamônica Freire, O. B., & Lopes, E. H. (2019). Competing scales for measuring perceived quality in the electronic retail industry: a comparison between ES-Qual and E-TailQ. Electronic commerce research and applications, 34, 100824.‏ https://doi.org/10.1016/j.elerap.2019.100824
  • Mahdavi, Gh., & Abed, M. (2014). Identifying factors affecting customer loyalty in life insurance industry. International conference on insurance and development. (In Persian). https://www.sid.ir/paper/833521/fa
  • Meeboonsalang, W., & Chaveesuk, S. (2019). An integrated model of customer loyalty in automobile insurance in Thailand. Asia-pacific social science review, 19(3), 203-216.‏
  • Menhaj, M, B. (2014). Basics of neural networks. Amirkabir University of Technology Publishers. (In Persian). https://www.gisoom.com/book/
  • Mohselnin, S. H., & Esfidani, M. R. (2014). Structural equations based on the partial least squares approach using software Smart-PLS. Publisher of Mehraban Publishing Institute. (In Persian). https://www.gisoom.com/book/
  • Molaee, M., & Parsa, S. (2016). Predicting customer behavior using artificial neural network technique. Shabak magazine, 2(3), 11-15. https://www.sid.ir/paper/508461/fa
  • Najafi, A. (2019). Predictability of loyalty and separation of self-insurance Persons of Social Security Organization based on data mining method. Social security journal, 15(1), 88-109.‏ (In Persian). http://qjo.ssor.ir/article_96131.html?lang=en
  • Nielsen, M. A. (2015). Neural networks and deep learning (Vol. 25). San Francisco, CA, USA: Determination Press.‏
  • Oliver, R. L. (1999). Whence consumer loyalty?. Journal of marketing, 63(4_suppl1), 33-44.‏ https://doi.org/10.1177/00222429990634s105
  • Pirayesh, R., & Alizadeh, M. (2016). The role of insurance on a country's economy development. Journal of management, 187, 722. (In Persian). https://www.magiran.com/paper/1657392
  • Rahi, S. (2016). Impact of customer perceived value and customers perception of public relation on customer loyalty with moderating role of brand image. The journal of internet banking and commerce, 21(2).‏ https://www.icommercecentral.com/open-access/impact-of-customer-perceived-value-and-customers-perception-of-public-relation-on-customer-loyalty-with-moderating-role-of-brand-image.php?aid=74335
  • Rezvani, M., Rezaee, M., & Tanhapoor, K. (2020). Customer loyalty model in emerging organizations based on artificial neural networks (case‎ study: emerging private banks‎. New marketing research journal, 10(1), 63-82.‏ https://nmrj.ui.ac.ir/article_24686_en.html
  • Sanaullah, A., Fatema, N., Ather, M., Sanaullah, A., & Malik, H. (2022). Analyzing impact of relationship benefit and commitment on developing loyalty using machine intelligence approach. Journal of intelligent & fuzzy systems, 42(2), 699-712.‏ DOI: 3233/JIFS-189742
  • Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour & information technology, 36(10), 1053-1066.‏ https://doi.org/10.1080/0144929X.2017.1340973
  • Siemes, T. (2016). Churn prediction models tested and evaluated in the Dutch indemnity industry. International conference on computer science and information engineering. Open Universiteit Nederland. https://core.ac.uk/download/pdf/80496548.pdf
  • Singh, B. (2021). Predicting airline passengers’ loyalty using artificial neural network theory. Journal of air transport management, 94, 102080.‏ https://doi.org/10.1016/j.jairtraman.2021.102080
  • Soltani Lifshagerd, S., Shahroodi, K., & Chirani, E. (2020). Designing an analytical model to determine the factors affecting insurer churn by neural network technique. International journal of finance & managerial accounting, 5(19), 85-98.‏ https://ijfma.srbiau.ac.ir/article_16891.html
  • Van Tonder, E., & Petzer, D. J. (2018). The interrelationships between relationship marketing constructs and customer engagement dimensions. The service industries journal, 38(13-14), 948-973.‏ https://doi.org/10.1080/02642069.2018.1425398
  • Verma, G., & Sharma, K. (2017). The role of quantitative techniques in business and management. Journal of humanities insights1(01), 24-26.
  • Walter, A., Mueller, T. A., Helfert, G., & Wilson, D. T. (2002). Delivering relationship value: key determinant for customers' commitment. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=16da8f7e36cf08ef79b694075637918bc0
    1b8e31
  • Wang, S., Lee, Y. K., & Kim, S. H. (2018). Effects of franchise restaurant selection attributes on perceived value, customer satisfaction and loyalty. The Korean journal of franchise management, 9(4), 7-19.
  • Zaree, A., Motameni, A. R., Feyz, D., Kordnaeech, A., & Farsizadeh, H. (2017). The role of export entrepreneurship-oriented behavior in firms' export performance promoting by clarifying mediator role of competitive advantage. Public management researches, 9(34), 169-193. DOI: 22111/jmr.2017.3107
  • Fida, B. A., Ahmed, U., Al-Balushi, Y., & Singh, D. (2020). Impact of service quality on customer loyalty and customer satisfaction in Islamic banks in the Sultanate of Oman. Sage open, 10(2), 2158244020919517. DOI: 1177/2158244020919517
  • Wong, H. S., Wong, R. K., & Leung, S. (2019). Enhancing sustainability in banking industry: factors affecting customer loyalty. Academy of accounting and financial studies journal23(3), 1-12.
  • Kumar, V., Dalla Pozza, I., & Ganesh, J. (2013). Revisiting the satisfaction–loyalty relationship: empirical generalizations and directions for future research. Journal of retailing89(3), 246-262.
  • Wang, T., Zhou, J., Zhang, G., Wei, T., & Hu, S. (2019). Customer perceived value-and risk-aware multiserver configuration for profit maximization. IEEE transactions on parallel and distributed systems, 31(5), 1074-1088.