Abstract
Background. Given that the market environment is becoming specialized in different sectors, it is necessary to identify specialized indicators of customer loyalty and their priority to improve the provision of complementary health insurance services. This study aimed to identify, prioritize, and measure the loyalty indicators in people covered by supplemental insurance.
Methods. An exploratory study was conducted using a mixed-method (qualitative-quantitative) approach. In the qualitative section, 20 interviews were conducted with faculty members, managers, and experts of insurance companies in Ahvaz (Khuzestan province, Iran). In the quantitative section, 380 interviews were conducted with the staff of supplementary health insurance. In the qualitative section, the data were first extracted using scoping review methodology, then coded and classified using the data-driven method. Next, each indicator was prioritized using the fuzzy analytical hierarchy process (AHP). Data were analyzed using structural equation modeling (SEM) and Smart PLS software.
Results. We included a total of 38 articles in the scoping review. After the semi-structured interview, five indicators were identified, including customer satisfaction, customer trust, repurchase intention, recommendation to others, and brand preference. Moreover, customer satisfaction (0.269), repurchase intention (0.233), brand preference (0.196), customer trust (0.155), and recommendation to others (0.147) had the greatest impact on customer loyalty.
Conclusion. According to the results, customer satisfaction was the most important factor influencing loyalty. Therefore, the managers of insurance companies should pay more attention to increase satisfaction of insured individuals and improve customer loyalty in companies
Extended Abstract
Background
Due to the market environment and the increasing number of insurance customers, insurance companies are trying to find new ways to retain customers and simplify communication channels with the customers to prevent losing customers, and ultimately their income. Since the cost of attracting new customers in unstable market environments is very high, customer retention is one of the most important functions of the concept of customer loyalty. Also, the current customers will be more profitable for the insurance company due to the continuation of business relations with the company. Customer orientation is one of the pillars of any Islamic organization whose main goals are to retain existing customers (not to turn to competitors) and attract new customers. Therefore, by formulating and presenting a comprehensive and specialized model in the field of customer loyalty of supplemental health insurance, using the opinion of insurance industry experts, the existing capacities in this concept can be used to strengthen the effective relationship between the company and the insured individuals. This study aimed to identify, prioritize, and measure the loyalty indicators in people covered by supplemental insurance.
Methods
This was an exploratory qualitative-quantitative study. In the qualitative section, the samples included 20 faculty members, managers, and experts of insurance companies in Khuzestan province (Ahvaz city of Iran). The sampling was conducted through purposive sampling. In the quantitative section, the samples included 380 employees of the supplemental insurance of Ahvaz Jundishapur University of Medical Sciences. Using the random sampling method, the sample size was determined using Morgan’s table. To extract initial indicators and the effective factors, first, the research question was raised (What are the factors related to the loyalty of supplementary health insurance customers?). Then, the keywords related to customer loyalty were searched. The literature search was performed in electronic databases, including IranDoc, Civilica, Google Scholar, SID, Scopus, Emerald, Elsevier, and the related articles were extracted from 2015 to 2020. The abstracts were reviewed to identify potentially eligible articles. Then, the full-texts of these articles were examined. Unstructured interviews were also conducted to complete the results of the review study. Next, using the framework extracted from the review study and unstructured interviews, an interview guide was designed, and finally, the experts were interviewed. Experts' opinions were extracted through data coding (using the data-driven method), and the results were given to the experts to categorize the data. In this way, the dimensions and required indicators to measure customer loyalty were identified. Data were analyzed using structural equation modeling (SEM) and Smart PLS software.
Results
Out of 52 retrieved studies, 14 duplicates were removed, leaving 38 articles for review. Of 20 experts, 18 were males and two were females. More than half of the employees were female (52%). After the three stages of semi-structured interviews, five indicators were identified, including customer satisfaction (11 items), customer trust (4 items), repurchase intention (2 items), recommend to others (3 items), and brand preference (2 items). In our review, the average of all variables was ≥.0.4, indicating the high convergent validity of the variables. Considering the results obtained from the correlations and the square root of AVE, we can conclude the divergent validity of the model at the structural level according to the Fornell-Larcker Criterion. In our review, the impact factor (IF) and T-statistic of dimensions were as follows: customer satisfaction (IF= 0.44 and T-statistics=4.5), brand preference (IF= 0.44 and T-statistics=8.78), customer trust (IF= 0.29 and T-statistics=3.57), repurchase intention (IF=0.38 and T-statistics=8.5), and recommend to others (IF=0.35 and T-statistic =2.58). According to the value of T-statistics (1.96) in our review, all dimensions of customer loyalty were confirmed. Regarding indicators’ prioritizing and weighting, the results showed that customer satisfaction (0.269), repurchase intention (0.233), brand preference (0.196), customer trust (0.155), and recommendation to others (0.147) had the greatest impact on customer loyalty. Furthermore, in our review, the importance coefficient, the coefficient of determination, the coefficient of predictive power, and the model fit of all variables were estimated at T = 1.96, R2 = 0.992, Q2 = 0.2, and GOF = 46.0, respectively.
Conclusion
The retention of customers and increasing customers’ loyalty have always been the focus of attention of companies. Loyal customers can have a crucial role in reducing companies’ losses. After comparing the indicators obtained in this study with previous studies, it can be concluded that these indicators should be considered when measuring the concept of customer loyalty.