Trust plays a central role in economic transactions, particularly in the financial services sector where advance payments are common. In the absence of assurance regarding future service fulfillment, consumers are naturally reluctant to commit financially. Trusted institutions, in contrast, attract customers more easily, incur lower promotion costs, and retain existing clients for longer periods. In insurance markets—characterized by complex, heterogeneous, and contingent products—trust becomes even more essential.
Trust is a psychological evaluation by which a subject (the trustor) assesses the reliability, integrity, or benevolence of an object (the trustee). As the Cambridge Dictionary notes, trust reflects the belief that someone is honest, will not cause harm, and will reliably fulfill expectations. Because trust exists between a trusting subject and a trusted object, its level can be shaped by characteristics of either party. Distrust toward an insurer may arise from the insurer’s behavior or reputation, but it may also derive from personal dispositions of the consumer.
In considering whether trust in insurance is predominantly shaped by forces on the supply side or on the demand side, it is illuminating to draw a philosophical parallel that highlights the interplay between subject and object in construction of knowledge, just as Copernicus in astronomy and Immanuel Kant in philosophical understanding of the world. By situating the inquiry into insurance trust within the broader epistemological dialogue, we foreground the significance of both supply-side mechanisms and demand-side interpretations in shaping confidence in the insurance system.
Prior research on trust in financial institutions has largely focused on the consumer side. Studies on banking relationships, for example, show that factors such as financial well-being, debt levels, financial literacy, demographic characteristics, and past experiences affect consumers’ trust in institutions (Shim et al. 2013; Baidoo & Akoto 2019; Jansen et al. 2015; Park 2020). Similar inquiries have been made in insurance (Akotey & Adjasi 2014; Courbage & Nicolas 2019) and securities markets (Guiso et al. 2008; Lachance & Tang 2012).
In contrast, research focusing on the supply side determinants of trust is limited. Existing studies examine country-level trust (Qiu et al. 2019), firm size, profitability, governance, and structural attributes related to corporate trustworthiness (Kang & Kim 2019). Research specific to insurance institutions remains scarce, though some studies have highlighted staff characteristics, service quality, corporate reputation, and profitability as determinants of insurance trust (Chen 2019).
Even fewer studies have simultaneously examined trust antecedents from both consumer and supplier perspectives. To date, Van Dalen and Henkens (2018) provide the only example, investigating trust in pension providers using both consumer traits and pension-system attributes. However, their analysis does not explicitly distinguish between subject-based and object-based dimensions of trust.
Against this backdrop, the present study is the first to examine how trust in insurance relationships is shaped by both consumer characteristics (subjects) and insurer/salesperson characteristics (objects). Specifically, we ask whether distrust toward an insurer is more attributable to consumer dispositions or to insurer-related attributes. Based on survey data from China and Korea, we find that insurer-side characteristics and salesperson attributes play a more decisive role in shaping insurance trust in both countries.
A substantial body of research in economics and finance has examined trust, focusing either on the role of trust (its impact or value) or on the determinants of trust levels. Existing studies can broadly be categorized into those that emphasize the consumer perspective and those that highlight the supplier or institutional perspective.
First, regarding the general value of trust, numerous studies have explored how trust contributes to economic development (Fukuyama 1995; De Smidt & Botzen 2018), business operations (Atchinson 2005; Ensari & Karabay 2016), and financial or insurance markets (Schneider 2005; Dercon et al. 2015; Delis & Mylonidis 2015; Chen et al. 2016; Cline & Williamson 2020; Maarse & Jeurissen 2019; Qiu et al. 2019; Wang et al. 2019). These studies consistently find that trust facilitates access to credit, strengthens work commitment, promotes transactions, and increases the likelihood of successful contract formation—ultimately supporting economic growth.
Second, on the consumer side of financial trust, scholars have examined both the role of trust and the determinants of trust or distrust toward financial institutions. Empirical work in countries such as the Netherlands and Italy (Guiso et al. 2008) highlights how trust shapes consumer financial behavior. Other studies identify factors influencing trust levels—including age, personality traits, gender, income, education, financial literacy, past experiences, and even compensation structures (Lachance & Tang 2012; Chen 2013; Jansen et al. 2015; Fungáčová et al. 2019; Courbage & Nicolas 2019; Park 2020).
Third, although relatively limited in number, some studies examine financial service institutions as objects of trust. These studies emphasize factors such as geographical location, reputation, firm size, and financial structure as determinants of consumer trust. For example, Chen et al. (2011) analyzed survey data from China and Korea to identify common determinants of trust in the insurance industry, examine regional differences between Beijing and Shanghai, and compare trust across distribution channels. The present study draws on the same survey data but extends the analysis by simultaneously examining both trust objects (insurers) and trust subjects (consumers), a component that constituted an important part of Chen’s unpublished doctoral dissertation (Chen 2012).
Fourth, to the best of our knowledge, Van Dalen and Henkens (2018) conducted the only study to date that systematically examines trust from both consumer and supplier perspectives. Focusing on private pension providers in the Netherlands, they identified institutional stability and integrity as the strongest predictors of trust in pension systems. Contrary to common assumptions, transparency—often emphasized as a crucial component of trust building—was found to have little or no significant effect. Importantly, their findings echo the premise of the current study by showing that trust is shaped not only by institutional attributes but also by characteristics of the consumers who evaluate those institutions.
This study seeks to assess the relative influence of consumer characteristics and insurer-side characteristics on trust in insurance companies. We evaluate whether trust is driven more strongly by (a) consumer trust orientation or (b) the capabilities and behaviors of insurers and their salespersons, and whether these relationships differ between China and Korea.
We assume that trust by individual (i) in an insurance company (Y) is a function of Oi, perceived insurer or salesperson traits (object-side determinants) and Si, personality-based trust orientation of the insured (subject-side determinants). Thus,
To measure Yi and its determinants we designed a survey instrument focused on trust, financial behavior, and insurance markets. Survey items were based on validated measures from prior studies on and were reviewed by local insurance experts. The questionnaire was originally in Korean and translated into Chinese for the surveys distributed in that country. To mitigate any confusion related to the translation, we asked three Chinese employees in insurance services to review the questionnaire.
To ensure content validity, a preliminary survey was conducted in Seoul, Shanghai, and Beijing. A total of 30 respondents participated—10 in each city—equally divided between life insurance and automobile insurance customers. The distinction was considered important because automobile insurance is compulsory in both countries, whereas life insurance in China is characterized by lower demand and lower consumer literacy. The pilot survey was administered from May 10 to May 29, 2011, after which the questionnaire was revised for clarity and relevance. The survey questionnaire is available from the authors upon request.
The survey was administered to residents of Seoul, Beijing, and Shanghai who had purchased either life or automobile insurance. Given the large population sizes of China and Korea as well as budget constraints, convenience sampling was employed. A total of 300 questionnaires were distributed in Seoul and its surrounding areas, and 400 each in Beijing and Shanghai. The survey was administered between June and August 2011.
In Seoul, 268 responses were retrieved, of which 19 were excluded due to omissions in key items, resulting in 249 valid cases. In Beijing, 359 questionnaires were returned, with 53 removed for insincere responses, yielding 306 valid cases. In Shanghai, 382 surveys were collected, and 32 were excluded for insincerity, leaving 350 valid responses. Overall, the final dataset consisted of 905 valid responses from Seoul (249), Beijing (306), and Shanghai (350). The demographic characteristics and insurance relationships of the final sample, displayed separately for respondents in Korea and China, are reported in the Appendix.
The dependent variables for the study are the respondent’s level of trust in the insurance company, and the respondent’s commitment to the relationship with the insurance company. Trust in the insurer was assessed in the survey with a single item assessing general subjective trust in the insurance company, measured using a 7-point Likert scale. Relationship commitment was operationalized through two survey items: the intention to renew the insurance contract, and intention to recommend the insurer to others. Both items were assessed using a 7-point Likert scale. To assess respondents’ overall trust in their insurance company, the three variables were also combined into a single global trust measure.
The independent variables measure the characteristics of the respondent (the insured) and of the insurance company. Construction of these variables is described below.
The survey assessed personality characteristics of the insured regarding basic trust levels. According to Giddens (1990), some people tend to trust their neighbor or a public institution more than other people. Thus, we classify the insured’s personality characters into relationship-centered ones and institution-centered ones, using six survey questions regarding their personal humanity, friendship, ethics, trust of social institutions, rules, and documents/contracts. Responses are combined into two trust measures: relational trust orientation and institutional trust orientation.
Expertise or Competence refers to the company’s professional knowledge or skills that are required to provide insurance services. Following Doney and Cannon (1997), we measured expertise with four variables: financial status, expert acquisition, business system,, and R&D. We also considered the sales channel’s practical capability, product knowledge, post-purchase-service, and needs identification.
Benevolence refers to how much an insurance company or its sales channel intends to take care of its customers by providing insurance products or services in their shoes or on their behalf. Among the items used in the studies by Doney and Cannon (1997), Coulter and Coulter (2002), and Jung and Oh (2005), we chose four traits to measure benevolence: customer priority, problem solving, courtesy, and prompt processing.
Honesty measures how accurate or truthful the insurer or its sales channel is in treating its customers before and during the transaction in this study. Our review of previous research, which included Morgan and Hunt (1994), Doney and Cannon (1997), Coulter and Coulter (2002), and Jung and Oh (2005), suggested four traits to measure the level of honesty for the company, consisting of transparent management, corporate social responsibility activity, non-exaggeration, and trustworthy information; and another four items to measure the honesty of the sales channel: personal trust, non-exaggeration, fulfillment of promises, and non-distortion.
Data analysis was conducted using SPSS version 28.0. First, frequency analysis was performed to examine demographic characteristics and to identify potential data entry errors. To assess construct validity, we conducted exploratory factor analysis (EFA), and reliability was evaluated through Cronbach’s α to ensure internal consistency of the measurement scales.
Principal component analysis with Varimax rotation was applied, retaining only factors with eigenvalues greater than 1.0. Items with factor loadings below .50 or with significant cross-loadings were removed to ensure construct clarity. The extracted factor scores were subsequently used as independent variables in the regression analyses. This procedure minimized multicollinearity among conceptually related survey items and enabled more parsimonious model estimation. The results revealed clear and distinct factor structures for trust in insurance companies, trust in insurance salespersons, and individual trust orientation, confirming the multidimensional nature of trust within the insurance market.
Stepwise regression analysis was used to test the research hypotheses. Stepwise regression is well suited to the aims of the present study for two reasons. First, our objective was not only to assess the statistical significance of individual predictors but also to determine the hierarchical importance of trust-related determinants originating from three distinct levels: insurance companies. insurance salespersons, individual (consumer) trust orientation. Stepwise regression allows predictors to enter the model sequentially according to their explanatory power, thereby clarifying which factors contribute most substantially to variations in insurance trust. Second, several predictors in this study were conceptually interrelated. Constructs such as trust in insurance companies, trust in insurance salespersons, and individual trust orientation are theoretically connected, raising the potential for multicollinearity. By using factor scores and applying conservative entry and removal criteria, the stepwise procedure reduced multicollinearity risks and yielded a more parsimonious model without sacrificing explanatory validity.
Additionally, diagnostic tests showed no serious robustness issues in the final model, supporting the appropriateness of the stepwise regression approach used in this study. Overall, this method enabled us to identify the strongest drivers of insurance trust while avoiding model overfitting by excluding variables with limited incremental value.
Exploratory factor analysis extracted two distinct factors for trust in insurance companies, explaining 72.21% of the total variance. As shown in Table 1, the first factor comprises items associated with financial stability, professional competence, and institutional systems, and is therefore labeled (Physical) Capacity of Insurance Companies. The second factor includes items capturing customer-oriented service delivery, ethical and honest management, social responsibility, and transparent communication, and is labeled (Soft) Attitude of Insurance Companies. These findings suggest that trust in insurance companies is shaped by both structural capacity and customer-oriented ethical attitude or behavior, indicating that consumers evaluate insurers across both functional and relational dimensions.
Rotated Component Matrix for Trust in Insurance Companies
| Component | ||
|---|---|---|
| 1 | 2 | |
| 1. Has a strong and stable financial condition. | .311 | .861 |
| 2. Has well-established systems and procedures to handle operations. | .330 | .877 |
| 3. Has secured enough experts needed for insurance business. | .459 | .709 |
| 4. Conducts extensive research to meet customer needs. | .365 | .844 |
| 5. Provides polite and friendly services. | .657 | .343 |
| 6. Considers and respects consumer interests. | .736 | .434 |
| 7. Processes contract changes and other requests promptly. | .624 | .377 |
| 8. Listens carefully to customers’ opinions. | .730 | .463 |
| 9. Conducts business honestly. | .740 | .373 |
| 10. Engages actively in social contribution activities. | .778 | .262 |
| 11. Does not exaggerate services to appeal to customers. | .829 | .259 |
| 12. Does not exaggerate services to appeal to customers. | .800 | .283 |
Factor analysis of trust in insurance salespersons yielded two distinct factors, explaining 66.66% of the total variance. As shown in Table 2, the components of the first factor, labeled Professionalism of Insurance Salespersons, include the salesperson’s product knowledge, ability to provide accurate information, and competence in identifying customer needs. The second factor, labeled Benevolence of Insurance Salespersons, captures interpersonal and relational attributes such as kindness, sincerity, responsiveness, fulfillment of promises, and avoidance of exaggerated or distorted claims.
Rotated Component Matrix of trust for Insurance Salespersons
| Component | ||
|---|---|---|
| 1 | 2 | |
| 1. Fully understands insurance products and is highly professional. | .148 | .882 |
| 2. Provides sufficient explanations and information about the product. | .283 | .819 |
| 3. Continues to provide services and information after contract signing. | .372 | .752 |
| 4. Accurately identifies customers’ needs and preferences. | .362 | .741 |
| 5. Prioritizes customer interests over their own benefit. | .600 | .454 |
| 6. Resolves customer complaints or issues effectively | .789 | .232 |
| 7. Is polite and friendly. | .738 | .232 |
| 8. Handles contract changes and requests quickly and efficiently. | .767 | .254 |
| 9. Is personally trustworthy. | .751 | .349 |
| 10. Does not exaggerate product features to make sales. | .712 | .244 |
| 11. Keeps promises well. | .781 | .198 |
| 12. Does not seem to distort facts. | .748 | .331 |
These findings indicate that customers’ trust in insurance salespersons is grounded in a combination of professional competence and benevolent interpersonal behavior, emphasizing the dual role of salespersons as both technical experts and relational actors in the insurance market.
Factor analysis identified two distinct dimensions of individual trust orientation, accounting for 81.65% of the total variance. As shown in Table 3, the first factor captures interpersonal values such as affection, friendship, and loyalty, and is therefore labeled Relational Trust Orientation (Interpersonal Trust). The second factor reflects trust based on rules, formal contracts, and institutional structures, and is labeled Institutional Trust Orientation. These findings indicate that individuals systematically differ in the foundations upon which they form trust—some rely primarily on personal relationships, whereas others place greater emphasis on institutional mechanisms and formal safeguards.
Rotated Component Matrix of Individual Trust Orientation
| Component | ||
|---|---|---|
| 1 | 2 | |
| 1. I trust the judgment of family members or relatives. | .082 | .884 |
| 2. I trust friendships with schoolmates or neighbours. | .044 | .924 |
| 3. I trust loyalty among friends. | .155 | .875 |
| 4. I trust social organizations. | .926 | .088 |
| 5. I trust social rules and systems. | .874 | .059 |
| 6. I trust written contracts more than verbal promises. | .902 | .137 |
To assess respondents’ overall trust in their insurance company, we included three global evaluative items. As shown in Table 4, all items loaded strongly onto a single factor, indicating that basic/general trust, renewal intention, and recommendation intention constitute a unified evaluative dimension of trust-related behavioral outcomes.
Rotated Component Matrix
| Component | |
|---|---|
| 1. Overall, I find my insurance company trustworthy. | .869 |
| 2. I am willing to renew my contract when it expires. | .893 |
| 3. I am willing to recommend my current insurance company to others. | .874 |
Stepwise multiple regression analysis was conducted to identify the determinants of overall trust in the insurance company. The dependent variable in the analysis was respondents’ combined global evaluation of trust in their current insurance provider. The stepwise procedure sequentially selected predictors that contributed significantly to the explanation of each dependent variable examined. Consistent with the study’s conceptual framework, variables related to salesperson characteristics, company traits, and individual trust orientation were included as candidate predictors. The final model retained only those variables that demonstrated meaningful incremental explanatory power, thereby providing a clear hierarchy of influential determinants. Table 5 presents the results.
Stepwise Regression Results, Global Trust in Insurance Company
| Step | Predictors Added | R2 | ΔR2 | F |
|---|---|---|---|---|
| 1 | Salesperson Professionalism | .327 | -- | 438.728 |
| 2 | Salesperson Benevolence | .458 | .131 | 381.388 |
| 3 | Company Physical Infrastructure | .481 | .023 | 278.013 |
| 4 | Institutional Trust | .497 | .016 | 222.462 |
| 5 | Company Attitude | .508 | .011 | 185.676 |
| 6 | Region | .513 | .005 | 157.713 |
| 7 | Claim Experience | .518 | .005 | 137.767 |
As shown in the table, salesperson professionalism entered the model first and alone explained 32.7% of the variance in overall trust, indicating that technical competence and product knowledge of salespersons constitute the most influential predictor of trust in insurance companies.
Salesperson benevolence was then added, producing a substantial increase in explanatory power (ΔR2 = .131, p < .001; total R2 = .458). This result highlights the essential role of relational, ethical, and interpersonal behaviors in shaping consumer trust, complementing the technical dimension captured in step 1.
In step 3, company physical infrastructure was introduced, resulting in a modest but statistically significant increase in explained variance (ΔR2 = .023, p < .001). This suggests that perceptions of insurers’ structural capacity—such as systems, financial stability, and professional resources—contribute meaningfully, though less strongly than salesperson-related factors.
Step 4 incorporated institutional trust orientation, which added an additional 1.6% to the explained variance (ΔR2 = .016, p < .001). While significant, its effect was comparatively small, indicating that individual-level trust orientation plays a secondary role relative to salesperson and firm characteristics.
In steps 5 through 7 company attitude, region, and claim experience entered sequentially, yielding incremental but limited improvements in model fit. The final model reached an R2 of .514, suggesting that although demographic and experiential factors exert statistically significant effects, their contribution is modest.
Multicollinearity diagnostics confirmed that all variance inflation factor (VIF) and condition index values were within acceptable thresholds, indicating no major collinearity concerns and supporting the robustness of the final model.
To evaluate the robustness of the main findings, additional stepwise regressions were performed using the three component variables used to construct the global trust measure: basic/general trust in insurance companies, intention to renew the contract, and intention to recommend the insurer to others.
Table 6 presents the regression results using general trust as the dependent variable. Consistent with the results for overall trust, salesperson professionalism entered first, explaining 25.7% of the variance, and in step 2 salesperson benevolence produced a large and statistically significant increase in explanatory power (ΔR2 = .193, p < .001; total R2 = .450).
Stepwise Regression Results, Basic Trust in Insurance Company
| Step | Predictors Added | R2 | ΔR2 | F |
|---|---|---|---|---|
| 1 | Salesperson Professionalism | .257 | -- | 313.057 |
| 2 | Salesperson Benevolence | .450 | .193 | 369.508 |
| 3 | Institutional Trust | .476 | .026 | 272.715 |
| 4 | Company Physical Infrastructure | .485 | .009 | 212.173 |
| 5 | Company Attitude | .497 | .003 | 177.887 |
| 6 | Relational Trust | .499 | .002 | 149.362 |
| 7 | Region | .502 | .002 | 128.986 |
Step 3 added institutional trust orientation (ΔR2 = .026, p < .001), followed in sequential steps by company physical infrastructure (ΔR2 = .009), company attitude (ΔR2 = .003), relational trust orientation (ΔR2 = .002), and region (ΔR2 = .002), respectively. Although statistically significant, the incremental contributions of these variables were relatively small.
The final model explained 50.2% of the variance in general trust, with salesperson-level factors again contributing the most. These findings generally accord with the regression results for overall trust and underscore the dominant role of salespersons in shaping trust perceptions.
Table 7 displays the stepwise regression results for intention to renew the insurance contract. As in the previous models, salesperson professionalism entered first and explained 23.2% of the variance. This was followed by salesperson benevolence (ΔR2 = .088, p < .001), confirming that both technical competence and interpersonal sincerity of salespersons significantly reinforce consumers’ willingness to continue their relationship with the insurer.
Stepwise Regression Results, Intention to Renew the Contract
| Step | Predictors Added | R2 | ΔR2 | F |
|---|---|---|---|---|
| 1 | Salesperson Professionalism | .232 | -- | 272.829 |
| 2 | Salesperson Benevolence | .320 | .088 | 211.933 |
| 3 | Company Physical Infrastructure | .336 | .016 | 151.658 |
| 4 | Institutional Trust | .346 | .010 | 119.219 |
| 5 | Claim Experience | .355 | .009 | 99.110 |
| 6 | Kind of Insurance | .360 | .005 | 84.150 |
| 7 | Region | .363 | .003 | 72.965 |
Next, company physical infrastructure (ΔR2 = .016) and institutional trust orientation (ΔR2 = .010) were added, followed by claim experience (ΔR2 = .009), type of insurance purchased (ΔR2 = .005), and finally region (ΔR2 = .003). These variables contributed small but significant improvements to model fit.
The final model explained 36.3% of renewal intention, again demonstrating that salesperson professionalism and benevolence are the primary drivers, with company characteristics and personal factors playing secondary roles.
Finally, Table 8 presents the robustness test using recommendation intention as the dependent variable. Salesperson professionalism entered first (R2 = .269, p < .001), followed by salesperson benevolence (ΔR2 = .049, p < .001). These results again emphasize the importance of both competency and benevolence in shaping positive word-of-mouth intentions.
Stepwise Regression Results, Intention to Recommend the Insurer
| Step | Predictors Added | R2 | ΔR2 | F |
|---|---|---|---|---|
| 1 | Salesperson Professionalism | .269 | -- | 332.499 |
| 2 | Salesperson Benevolence | .318 | .049 | 210.076 |
| 3 | Company Physical Infrastructure | .345 | .027 | 158.416 |
| 4 | Region | .366 | .021 | 129.721 |
| 5 | Company Attitude | .377 | .011 | 108.900 |
In step 3, company physical infrastructure contributed an additional 2.7% to the explained variance (ΔR2 = .027, p < .001). Region (ΔR2 = .021) and company attitude (ΔR2 = .011) were added in subsequent steps, yielding modest but statistically significant improvements.
The final model accounted for 37.7% of the variance in recommendation intention. Thus, across three dependent variables, salesperson attributes consistently had the strongest explanatory power, followed by company-level factors and regional/demographic variables.
Across all robustness checks, the hierarchical pattern of predictors remained stable. The most influential predictors (consistently number 1 and number 2) were Salesperson Professionalism and Salesperson Benevolence. Moderately influential predictors (those that played a secondary role) were Company Physical Infrastructure and Company Attitude. Minor but significant contributors included Institutional Trust Orientation, Claim Experience and Region or Type of Insurance.
The robustness checks confirmed the stability of our main findings. Regardless of whether the dependent variable was basic/general trust, contract renewal intention, or recommendation intention, salesperson characteristics consistently dominated the explanatory models, followed by company traits and then by individual or regional factors. These results reinforce the central conclusion of the study, which is that trust in insurance companies is shaped primarily by the professionalism and benevolence of insurance salespersons, rather than by company-level or individual-level factors alone.
This study examined the determinants of trust in insurance companies by analyzing three levels of trust-related factors: insurance companies, insurance salespersons, and individual trust orientation. Using survey data collected from Korea (Seoul) and China (Beijing and Shanghai), we conducted factor analyses to identify the underlying structures of trust and employed stepwise multiple regression to evaluate the relative influence of various predictors on consumers’ trust, renewal intention, and recommendation intention. Additional robustness checks were performed to assess the consistency of the findings across different trust-related dependent variables.
The factor analyses revealed clear multidimensional structures. Trust in insurance companies consisted of physical capacity and customer-oriented attitude, trust in salespersons reflected professionalism and benevolence, and individual trust orientation divided into relational trust and institutional trust dimensions. These results underscore that trust in the insurance context is both structural and relational, shaped by a combination of organizational capability, interpersonal interaction, and individual predispositions.
The regression analyses yielded a consistent and noteworthy pattern. Across all models, salesperson professionalism emerged as the strongest and most stable determinant of trust, explaining the largest share of variance in overall trust, renewal intention, and recommendation intention. Salesperson benevolence also made substantial additional contributions, demonstrating the crucial importance of ethical, sincere, and responsive interpersonal behavior. Company-level factors—such as physical infrastructure and customer-oriented attitude—had statistically significant but comparatively modest effects. Individual trust orientations and demographic variables further contributed to trust formation but only marginally. This hierarchical pattern of influence highlights a central conclusion of the study: the salesperson serves as the primary gateway through which consumers assess and form trust in insurance companies.
These results carry important implications. Theoretically, the findings contribute to the literature on financial trust by demonstrating that interpersonal trust mechanisms—specifically those mediated by frontline salespersons—play a more decisive role than institutional or demographic factors in shaping trust in insurance providers. Practically, insurers seeking to strengthen customer trust should prioritize enhancing their sales force’s professionalism, product knowledge, ethical standards, and relational competencies. Investment in training, monitoring, and establishing incentives that reinforce customer-oriented behaviors may yield substantial improvements in trust, retention, and positive word-of-mouth.
Despite its contributions, this study has limitations. The data were collected through convenience sampling in three urban areas and rely on self-reported perceptions, which may restrict generalizability. Additionally, the cross-sectional nature of the survey precludes causal inferences. Future research could expand the geographic scope, incorporate behavioral or longitudinal data, and examine potential moderating variables such as online distribution channels or claim severity.
In conclusion, this study demonstrates that trust in insurance companies is shaped most strongly by the supply side (attributes of insurance salespersons, particularly their professionalism and benevolence) rather than demand side. While company and consumer factors matter, it is the human interface—where technical expertise meets ethical engagement—that most powerfully drives consumer trust and shapes their long-term relationship with insurers.