277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies of Ethiopia, and evaluation of the results with reference to literatures. Thus, the discussions goes on in two stages : At first, respondents profile presents in order to make comparisons of sample profile between public and private insurance sector, secondly, Relationships between demographic factors and customer loyalty; Determinant factors and customer loyalty and Correlation analysis are presented and thirdly, test for the level of loyalty comparisons between public and private insurance companies is presented. 6.1 Respondents profile in public & private sectors This section presents the respondents' profile by gender group, age group, marital status, education level, current occupation, income group, and how respondents attracted for purchasing policies. For the purpose of comparisons of customer loyalty in public and private insurance sectors respondents attitudes, opinions, and experiences were collected from both public and private insurance sectors. And then inferences are made based on responses and statistical test results. Thus, comparison of sample profile between public and private insurance sector are presented in the following paragraphs.
278 Table 6.1: Comparisons of sample profile: Sector * Gender distribution Gender of respondents Female Male Total Sector Public Count 55 145 200 % within Sector 27.5% 72.5% 100.0% % within sex of respondents 18.3% 20.7% 20.0% % of Total 5.5% 14.5% 20.0% Private Count 245 555 800 % within Sector 30.6% 69.4% 100.0% % within sex of respondents 81.7% 79.3% 80.0% % of Total 24.5% 55.5% 80.0% Total Count 300 700 1000 % within Sector 30.0% 70.0% 100.0% % within sex of respondents 100.0% 100.0% 100.0% % of Total 30.0% 70.0% 100.0% As the above table exhibits relatively large number of females of private sector (24.5%) are participated than public ((5.5%). From the total participants (30%) are female while (70%) are male respondents. Table 6.2: Comparisons of sample profile : Sector * age distribution age of respondents under 25 25-44 45-64 65 or above Total Sector Public Count 8 87 87 18 200 % within Sector 4.0% 43.5% 43.5% 9.0% 100.0% % within age of respondents 100.0% 22.8% 17.9% 14.5% 20.0% % of Total.8% 8.7% 8.7% 1.8% 20.0% Private Count 0 294 400 106 800 % within Sector.0% 36.8% 50.0% 13.2% 100.0% % within age of respondents.0% 77.2% 82.1% 85.5% 80.0% % of Total.0% 29.4% 40.0% 10.6% 80.0% Total Count 8 381 487 124 1000 % within Sector.8% 38.1% 48.7% 12.4% 100.0% % within age of respondents 100.0% 100.0% 100.0% 100.0% 100.0% % of Total.8% 38.1% 48.7% 12.4% 100.0%
279 The above table reports that (.8%) of the young respondents are participated in public where there is no in private insurance sector. On the other hand, oldest participants are from private insurance sector (10.65) when in public sector is (1.8%). Table 6.3: Comparisons of sample profile : Sector * Marital status Marital status Single Married Others Total Sector Public Count 9 152 39 200 % within Sector 4.5% 76.0% 19.5% 100.0% % within Marital status 31.0% 18.8% 23.8% 20.0% % of Total.9% 15.2% 3.9% 20.0% Private Count 20 655 125 800 % within Sector 2.5% 81.9% 15.6% 100.0% % within Marital status 69.0% 81.2% 76.2% 80.0% % of Total 2.0% 65.5% 12.5% 80.0% Total Count 29 807 164 1000 % within Sector 2.9% 80.7% 16.4% 100.0% % within Marital status 100.0% 100.0% 100.0% 100.0% % of Total 2.9% 80.7% 16.4% 100.0% As the above table shows (65.5%) of the married participants are from private sector while (15.2%) married participants are from public insurance sector. Table 6.4: Comparisons of sample profile : Sector * Education level High school or less Education level of respondents undergradua te post graduate or above Sector Public Count 97 72 31 200 Total % within Sector 48.5% 36.0% 15.5% 100.0% % within Education level of respondents 21.1% 19.8% 17.4% 20.0% % of Total 9.7% 7.2% 3.1% 20.0% Private Count 362 291 147 800 % within Sector 45.2% 36.4% 18.4% 100.0% % within Education level of respondents 78.9% 80.2% 82.6% 80.0% % of Total 36.2% 29.1% 14.7% 80.0% Total Count 459 363 178 1000 % within Sector 45.9% 36.3% 17.8% 100.0% % within Education level of respondents 100.0% 100.0% 100.0% 100.0% % of Total 45.9% 36.3% 17.8% 100.0%
280 The table above reports that (14.7%) highly qualified participants is from private insurance sector when (3.1%) 0f the participants are from public insurance sector. Table 6.5; Comparisons of sample profile: Sector * current job distribution current job Business class Salary class Others Total Sector Public Count 80 100 20 200 % within Sector 40.0% 50.0% 10.0% 100.0% % within current job 17.2% 22.9% 20.4% 20.0% % of Total 8.0% 10.0% 2.0% 20.0% Private Count 386 336 78 800 % within Sector 48.2% 42.0% 9.8% 100.0% % within current job 82.8% 77.1% 79.6% 80.0% % of Total 38.6% 33.6% 7.8% 80.0% Total Count 466 436 98 1000 % within Sector 46.6% 43.6% 9.8% 100.0% % within current job 100.0% 100.0% 100.0% 100.0% % of Total 46.6% 43.6% 9.8% 100.0% As the above table shows (38.6%) of participants of the business class are from private insurance sector where (8%) are from public sector. Table 6.6: Comparisons of sample profile : Sector *respondents monthly income Less than Birr 5,000 Monthly disposal income Birr 5001-10,000 Birr 10,001-15,000 15,001 and above Sector Public Count 19 108 65 8 200 Total % within Sector 9.5% 54.0% 32.5% 4.0% 100.0% % within Monthly income 23.5% 23.7% 20.0% 5.8% 20.0% % of Total 1.9% 10.8% 6.5%.8% 20.0% Private Count 62 347 260 131 800 % within Sector 7.8% 43.4% 32.5% 16.4% 100.0% % within Monthly disposal income 76.5% 76.3% 80.0% 94.2% 80.0% % of Total 6.2% 34.7% 26.0% 13.1% 80.0% Total Count 81 455 325 139 1000 % within Sector 8.1% 45.5% 32.5% 13.9% 100.0% % within Monthly disposal income 100.0% 100.0% 100.0% 100.0% 100.0% % of Total 8.1% 45.5% 32.5% 13.9% 100.0%
281 The above table exhibits that (6.2%) less income people are from private sector while (1.9%) is from public income. Table 6.7: Comparisons of sample profile : Sector * How respondents attracted for purchasing policies Friends, relatives, etc. How respondents learn purchasing policies Advertisement s Government/cooper ative advice others Sector Public Count 72 46 50 32 200 Total % within Sector 36.0% 23.0% 25.0% 16.0% 100.0% % within How respondents learn purchasing policies 22.5% 18.9% 21.2% 16.0% 20.0% % of Total 7.2% 4.6% 5.0% 3.2% 20.0% Private Count 248 198 186 168 800 % within Sector 31.0% 24.8% 23.2% 21.0% 100.0% % within How respondents learn purchasing policies 77.5% 81.1% 78.8% 84.0% 80.0% % of Total 24.8% 19.8% 18.6% 16.8% 80.0% Total Count 320 244 236 200 1000 % within Sector 32.0% 24.4% 23.6% 20.0% 100.0% % within How respondents learn purchasing policies 100.0% 100.0% 100.0% 100.0% 100.0% % of Total 32.0% 24.4% 23.6% 20.0% 100.0% As the above table reports most participants of private insurance sector (19.8%) are attracted by insurance advertisements while large number of respondents from public insurance sector (7.2%) is attracted by their friends, relatives and acquaintances. 6.2 Analyzing Relationships Relationships deals to a connection between two or more factors which can be measured statistically, so that we can obtain information about the correspondence of two variables. Variables may be independent or dependent to each other. Variables may not have relationship in one situation while in the other situation one variable may be a cause for the other variable and go in the same direction or in the opposite
282 directions. Thus, in this section statistical test is conducted if there is a relationship exists between Demographic factors and customer loyalty, between determinant factors and customer loyalty in public and private insurance. 6.2.1. Chi-square analysis: Respondents demographic particulars to loyalty in private and public insurance companies. In this section attempts are made to explore the relationship between customers personal profiles and loyalty both in public and private insurance companies. In order to know the variation between demographic factors and loyalty each of the variable respondents response scores are computed as: strongly dissatisfied and dissatisfied come under low category, neither agree nor disagree come under medium and agree and strongly agree put under high. Chi-square analysis is used to identify the significant relationships between demographic factors and loyalty. Chi-square analysis helps us to make decisions about which one reflects fluke differences between demographic factors and loyalty and which one reflects true differences. The demographic variables chosen for this analysis are: respondents Sex, age, income, education level, occupation, and marital status. It was hypothesized that: H o1 : Customers Demographic factors are not related to loyalty in insurance H 11 : Customers Demographic factors are related to loyalty in insurance Ho1a: Customers gender is not related to r loyalty in insurance Ho1 b : Customers Marital status is not related to loyalty in insurance Ho1 c : Customers age is not related to customer loyalty in insurance Ho1 d : Customers educational level is not related to customer loyalty in insurance Ho1 e : Customers current occupation is not related to customer loyalty in insurance Ho1f: Customers income level is not related to customer loyalty in insurance
283 The hypothesis testing was carried at 95% confidence level; two tailed. Hypothesis testing was performed to know if there is relationship between demographic variables and customer loyalty (i.e. behavioral loyalty plus attitudinal loyalty)in public and private insurance companies. The chi-square test result is illustrated as below. Table 6.8: Relationship between gender and Loyalty in Public Insurance Sex Do you recommend insurance products to others? df X 2 Sig Do you intend to purchase additional policies? Will you continue as customer in the company? value low medium High Total Male 29 (14.5%) 22(11%) 94 (47%) 145 (72.55) 16 10.20.856 Female 12 (6%) 8(4%) 35(17.5%) 55 (27.5%) Total 41 (20.5%) 30(15%) 129(64.5%) 200 (100) As the table 6.8 above reported, male of the public insurance company has high level of favorable perception on customer loyalty (47%) than female of (17.5%). Surprisingly, (14.5%) of male respondents disfavor loyalty which is bigger than their counterpart of female (6%). However, Pearson chi-square statistics (10.20, 16), p>.05 suggests that such variation is not statistically significant. This means gender and customer loyalty in public insurance are independent to each other; customer gender does not have association to customer loyalty, so that the Null hypothesis is accepted.
284 Table 6.9: Relationship between gender and loyalty in Private Insurance (α=.05) Sex Do you recommend insurance products to others? df X 2 S ig Do you intend to purchase additional policies? value 2taile Will you continue as customer in the company? d Low Medium High Total Male 149(18.6%) 54(6.7%) 352(44%) 555(69.3%) 14 1.104.000 Female 54(6.7%) 6(.75%) 185(23%) 245(30.6%) Total 203(20.5%) 60(15%) 547(68%) 800 (100) The table 6.9 above exhibits those males respondents response is higher (44%) in favor of loyalty than female respondents response of (23%). Similarly, more of the male respondents (18.6%) express they have no loyalty in their current provider which is higher than of the female respondents (6.7%) Such variation is supported by Pearson chi-square statistics which suggests that such variation is statistically significant. Chi-square (1.104, 14), p<.05. According to this test p-value is lea than.05 and so the Null hypothesis is rejected and alternative hypothesis is accepted. This means gender and customer loyalty are dependent to each other. Customer gender has direct association to loyalty. In contrary to this,as table 6.8 above reported in public insurance sector gender and customer loyalty are not related, they are independent to each other.
285 Table 6.10: Relationship between Respondents Age and loyalty in Public Insurance Respondents Do you recommend insurance products to others? df X 2 Sig Age Do you intend to purchase additional policies? Will you continue as customer in the company? low medium high value Less than 25yrs 2 (1%) - 6 (3%) 25-44 yrs 12 (6%) 16 (8%) 59 (29.5%) 48 45-64 yrs 19 (9.5%) 11 (5.5%) 57 (28.5%) 65 yrs or above 8 (4%) 3 (1.5%) 7 (3.5%) Total 41 (20.5%) 30 ( 15%) 129 (64.5%) 65.2 8.049 As it can be seen in the above table 6.10 the age group of 25-44 years express they are loyal to their current insurance provider (29.5%) than the other age group. The age group of less than 25 years and age group of 65 years or above is found being low in their perception of loyalty. Such variation is supported by chi-square value (65.28, 48), p<.05. According to this test p-value is less than.05 and because of this the Null hypothesis is rejected and alternative hypothesis is accepted which means age and customer loyalty are statistically significant and dependent to each other. This result verified that there is relationship between customer age and customer loyalty in public insurance sector. There is direct association between customer age and loyalty in public insurance sector of Ethiopia.
286 Table 6.11: Relationships between respondents age and loyalty in Private Insurance companies Age Do you recommend insurance products to others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Less than 25yrs 25-44 yrs 57 (7%) 26 (3.3%) 211 (26%) 45-64 yrs 80 (10%) 28 (3.5%) 292 (37%) 65 yrs or above 12 (1.5%) - 94 (11.8%) df X2 Sig 28 2.07.000 Total 149 54 (6.8%) 597 (74.6%) (18.6%) The table 6.11 above presents the age group of 45-64 years express their favorable attitude towards loyalty (37%) than other age group in private insurance companies. Such variation is supported by chi-square value (2.07.28), p<.05. Thus, we can conclude that age and customer loyalty in private insurance are related and so Null hypothesis is rejected and alternative hypothesis is accepted. Table 6.12: Relationship between respondents Marital Status and Customer loyalty in Public Insurance (α=.05) two tailed Respondents Marital status Do you recommend insurance products to others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Single 4(2%) 1(.5%) 4 (32%) df X 2 Sig Married 30 (15%) 24 (12%) 98(49%) 66.70.000 Others (Divorced, 7 (3.5%) 5 (2.5%) 27( 13.5%) 32 Total 41(20.5%) 30 (15%) 129( 64.5%) The table 6.12 above presents that marital status of married group (49%) expresses their favorable attitude towards loyalty than other marital status groups. This variation is
287 supported by chi-square analysis as: Chi-square (66.70, 32), p<.05, hence Null hypothesis is rejected and alternative hypothesis is accepted. This means marital status and customer loyalty are related to each other in the public insurance. Table 6.13: Relationship between Marital Status and Customer loyalty in Private Insurance (α=.05) two tailed Marital status Do you recommend insurance products to df X 2 Sig others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Single 14(1.8%) - 6(.8%) 28 1.32.000 Married 121(15%) 46(5.8%) 488 (61%) Others 14 (1.8%) 8 (1%) 103(12.9%) (Divorced, etc.) Total 149 (18.6%) 54 (6.8%) 591 (73.9%) The table 6.13 above shows that married group of the marital status (61%) expresses their positive attitude towards loyalty which is higher than the other group. Surprisingly, among the groups who express their responses the married group (15%) are not loyal to their providers. Such response variation is supported by chi-square statistics as, (1.32, 28), p <.05 which is statistically significant. So, null hypothesis is rejected and alternative hypothesis is accepted. This means marital status and customer loyalty are related to each other in private insurance sector. As we observed in table 6.5 above marital status and loyalty are also related in public insurance sector.
288 Table 6.14: Relationship between Respondents Educational Level and Customer loyalty in Public Insurance Company (α=.05) two tailed Education level Do you recommend insurance products to others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High High school or less 25 (12.5%) 19 (8.5%) 53 (26.5%) Undergraduate 10(5%) 9 (4.5%) 53 (26.5%) Postgraduate or 6 ( 3%) 2 (1%) 23 (1.5%) above Total 41 (20.5%) 30 (15%) 129(64.5% df X 2 Sig 32 52.0 9 The table 6.14 above gives evidence of response variation according to respondents education level. Respondents of whose qualification is high school or less and under.014 graduate people express their positive attitude towards loyalty than the other group which is (26.5%). Such variation is supported by Chi-square value (52.09, 32), p <.05 which means the variation is statistically significant. Thus, Null hypothesis is rejected and alternative hypothesis is accepted. This means there is relationship between qualification and customer loyalty in the public insurance sector. Table 6.15: Relationship between Respondents Educational Level and Customer loyalty in Private Insurance Companies (α=.05), two tailed Education level Do you recommend insurance products to others? df X 2 Sig Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High 28 1.28.000 High school or less 93 (11%) 7(.9%) 262 (32.7%) Undergraduate 36 (5%) 39 (4.9%) 216 (27%) Postgraduate or 20 (3%) 8 (1%) 119 (14.5%) above Total 149 (19%) 54 (6.7%) 597 (74.6%)
289 The above table6.15 presents, those who are high school or less (32.7%) express their positive view to their current insurance provider than the others. Such variation is supported by chi-square statistics (1.28, 28), p<.05 which means the variation is statistically significant and so that qualification and loyalty are related. Hence, Null hypothesis is rejected and alternative hypothesis is accepted. Table 6.16: Relationship between Respondents Current Job and Customer loyalty in Public Insurance Company (α=.05), two tailed Education level Do you recommend insurance products to others? df X2 Sig Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Total Business class 20 (10%) 12 (6%) 48 (24%) 80(40%) Salary class 19 (9.5%) 17 (8.5%) 64 (32%) 100(50%) Others(agriculture. 2 (1%) 1 (.5%) 17(8.5%) 20(10%) Total 41 (20.5%) 30 (15%) 129(64.5%) 200(100) 32 43.67.082 As the above table 6.16 exhibits salary groups (32%) of respondents perception on loyalty is positive than others. In the contrary (10%) of the business class respondents express their opinion that they disfavor their provider. However, such variation is not supported by x 2 analysis. Chi-square statistics (43.67, 32), p=.082 suggest that there is no relationship between customers occupation and customer loyalty. Thus, null hypothesis is accepted. This means there is no association between customer current job and loyalty.
290 Table 6.17: Relationship between respondents Current Job and Customer loyalty in Private Insurance Companies. (α=.05), two tailed Occupation Do you recommend insurance products to others? df X 2 Sig Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High total Business class 79 (9.8%) 27 (3%) 280 (35%) 386(48%) 28 1.62.000 Salary class 58 (7.3%) 12 (1.5% 266 (33%) 336(42%) Others 12 (1.5 %) 15 (1.9%) 51 (6%) 78(10%) (agriculture..) Total 149 (19%) 54 (8%) 597 (75%) 800(100%) As it can be seen in the table 6.17 above, respondents who are in business group (35%) express their loyalty to the current provider, which is higher than the other group response. Such variation is supported by Chi-square value (1.62, 28), p<.05, so that null hypothesis is rejected and alternative hypothesis is accepted. This means there is relationship between customer occupation and loyalty in the private insurance sector. There is direct association between customer present occupation and loyalty in private insurance sector. In table 6.9 above it was reported that there is no association between current job and loyalty in public insurance sector. Customer current job and loyalty are independent to each other in public insurance sector.
291 Table 6.18: Relationship between Respondents Income and Customer loyalty in Public Insurance Company (α=.05), two tailed Monthly Income Do you recommend insurance products to others? df X 2 Sig level Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Total Less than Birr 5,000 3 (1.5%) 2(1%) 14 (7%) 19 Birr 5001-10,000 30 (15%) 12 (6%) 66 (32%) 108 Birr 10,001-15,000 7 (3.5%) 16 (8%) 42 (21%) 25 Birr 15,001 or above 1 (.5%) - 7 (3.5%) 8 Total 41 (20.5%) 30 (15%) 129 (64.5%) 200 48 90.2.000 As it is reported in the above table 6.18 middle income customers in the group 5001-10,000 observed as (32%) expressed as they are loyalty to their current insurance provider higher than the other group. Surprisingly, this middle income class (15%) expressed that they are not loyal to their current insurance providers which is higher than the other group. Among respondents the high income class (.5%) expressed their disloyalty least of the other group. Such variation is supported by Chi-square value (90.2, 48),p<.05which means there is relationship between customer income and customer loyalty. Thus, Null hypothesis is rejected and alternative hypothesis is accepted. Hence we can infer that customer income level is related to customer loyalty in public insurance sector. Table 6.19: Relationship between Respondents Income and Customer loyalty in Private Insurance Companies. (α=.05), two tailed Income level Do you recommend insurance products to others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Total Less than Birr 7 (.9%) - 55 (6.8%) 62 5,000 Birr 5001-10,000 67 (8%) 21 (2.6%) 259 (32%) 347 df X 2 Sig Birr 10,001-51 (6%) 27 (3%) 182 (22.7%) 260 15,000 Birr 15,001 or 24 (3%) 6 (.7%) 101 (12.6%) 131 above Total 149 (56%) 54 (6.7%) 597 (75%) 800 42 2.31.000
292 The above table 6.19 reported that medium income customers (32%) response express they are loyal to their current provider higher than that of the other group. This variation is supported by b Chi-square calculation (2.31, 42), p<.05 which means there is significant relationship between customers income and loyalty in private insurance sector of Ethiopia. Thus, Null hypothesis is rejected and alternative hypothesis is accepted. To know if relationships exist between demographic variables and customer loyalty in private and public insurance companies, response rate and chi-square test were conducted. Thus, it was found that demographic variables of respondents age, marital status, and education level have related to customer loyalty in both public and private insurance companies. However, gender and current job are found independence to customer loyalty in public insurance while in private insurance they are related to customer loyalty. 6.2.2. Correlation analysis: Among variables in public and private insurance companies In this section efforts are made to know the relationship between variables in both public and private insurance companies. The statistical tools employed for computation relationships is the Pearson correlation statistics. The analysis gives us information whether the relationship is positive or negative, whether the relationship is strong or weak. The table below shows the result of correlation analysis between variables.
293 Table 6.20. Correlation Matrix results in public insurance of Ethiopia. service quality perception customer satisfaction service quality perception N 200 200 200 200 200 **. Correlation is significant at the 0.01 level (2-tailed). As it can be seen in the above table 6.20, variables: service quality, customer satisfaction, customer trust, and customer loyalty are positively and significantly correlated to each other. The above Pearson correlation test shows that the variables are correlated with values of above.60 considered as moderate and strong correlation. The relationship between variables is significant and so that null hypotheses are rejected. This means determinant variables are correlated with customer loyalty significantly. The correlation coefficient between variables tells us the strength of the relationship between the two variables. And also the correlation coefficient between variables tells us respondents responses are positive and linear. The table below shows correlation statistics results of private insurance companies. customer customer satisfaction trust switching cost Customer loyalty Correlation 1.731 **.734 **.713 **.807 ** Sig..000.000.000.000 Correlation 1.727 **.675 **.821 ** Sig..000.000.000 customer trust Correlation 1.736 **.850 ** Sig.000.000 N 200 200 200 switching cost Correlation 1.864 ** Customer loyalty Sig.000 Correlation.807 **.821 **.850 **.864 ** 1 Sig. (2- tailed).000.000.000.000
294 Table 6.21: Correlation Matrix Private Insurance sector service quality perception service quality perception customer satisfaction customer trust switching cost Customer loyalty Correlation 1.770 **.757 **.751 **.800 ** Sig..000.000.000.000 customer Correlation 1.719 **.798 **.821 ** satisfaction Sig..000.000.000 customer trust Correlation 1.715 **.806 ** Sig..000.000 switching cost Correlation 1.901 ** Customer loyalty Sig..000 N 800 800 Correlation.800 **.821 **.806 **.901 ** 1 Sig. (2- tailed).000.000.000.000 N 800 800 800 800 800 **. Correlation is significant at the 0.01 level (2-tailed). As it can be seen from the above table 6.21 there is positive and significant relationships exist between variables. The relationship between variables is strong; above the threshold.70 and it is considered as strong correlation. Results of the correlation analysis shows us the p- value <0.01. Hence, Null hypothesis is rejected and we can conclude that there are relationships between determinant factors and customer Loyalty in private insurance sector. In the above table 6.20 and table 6.21 it is found that determinant variables and customer loyalty are related significantly and positively in both public and private insurance companies. The relationships were observed strong and positive which implies customer responses are in similar trends. However, the correlation value of switching cost and customer loyalty is higher in private insurance sector while the
295 correlation value between customer trust and customer loyalty is higher in public insurance sector than private insurance sector. 6.3. Comparative analysis: loyalty in public and private insurance This study examines if there is differences in level of customer loyalty in public and private insurance sectors. Thus, statistical test is computed to identify the level of policyholders level of loyalty in that of public and private insurance companies. Samples of (200) were taken from public insurance company and 800 samples were taken from private insurance companies. The researcher was in the view that there is no significant difference in customer level of loyalty between public and private insurance companies. Thus, statistical test were conducted to know if customer level of loyalty is the same or different in public and private insurance companies. Here below is the hypothesized null and alternative hypothesize. Ho8: There is no significance difference in level of customer loyalty between public and private in insurance sectors. H18 : There is significance difference in level of customer loyalty between public and private in insurance sectors. Thus, in order to verify the hypothesized statements two levels of tests were performed: simple comparison based on mean & standard deviation, and ANOVA. The test analyses are illustrated below.
296 6.3.1 Comparisons of loyalty using Mean & SD: In public & private insurance companies of Ethiopia In order to know if customer loyalty is similar or different in both sectors a comparative test is employed using mean and standard deviation of each variable. The detail is presented in the table below. Table 6.22: Comparing of Public and Private Insurance( using mean & SD) Sectors Factors Mean Std. Deviation Sample size Public service quality 3.438.5194 200 customer satisfaction 3.225.6446 200 customer trust 3.498.6742 200 switching cost 3.696.6403 200 customer loyalty 3.560.8044 200 Private service quality 3.702.4716 800 customer satisfaction 3.423.5855 800 customer trust 3.644.6097 800 switching cost 3.757.6705 800 customer loyalty 3.761.7075 800 The table 6.22 above gives information of means and standard deviations for each variable group. Mean scores based on a five point scale ranging from 1(strongly disagree) to 5 (strongly agree). It is clear from the table that the mean of variables of public insurance is different from that of private insurance sector.their standard deviation is also different from public to private. Generally, in public insurance there is a bit more dispersion of standard deviation from the mean than private insurance sector. The mean score of private insurance companies in all variables are higher than its counterpart public insurance mean scores. This means the customer perception on
297 loyalty is higher in private insurance companies than public insurance company. From this we can conclude that level of customer loyalty is not similar in both sectors. However, to make a final decision further test is necessary so that ANOVA analysis is performed as below. 6.3.2. Regression weight between public and private insurance companies. Regression weights of public and private insurance companies are illustrated below for comparison in order to differentiate the effects of variables on customer loyalty. Table 6.23: unstandardized Regression Weight Estimates for Public and Private insurance companies. Descriptions Estimates, at 95% Public P.value Private p.value n=200 n= 800 Constant -1.175 -.557 Customer Service.221.000.165.000 loyalty quality Customer Customer.319.000.157.000 loyalty satisfaction Customer Customer.325.000.280.000 loyalty trust Customer loyalty Switching cost.490.000.572.000 As it can be seen in the above table, variables affect customer loyalty significantly in both public and private insurance companies. P-value is less than.05 in both sectors and in all variables so that we can conclude that variables affect customer loyalty significantly. However, the regression weights of service quality, customer satisfaction, and customer trust on customer loyalty are greater in public insurance sector than in private insurance sectors. This means if one variable goes up by one unit
298 in both sectors simultaneously the effects on customer loyalty is higher in public sector than private sector. Exceptionally, regression weight of switching cost on customer loyalty is much higher in private insurance sector than public insurance sector. 6.3.3. Comparisons of respondents perceptions of behavioral & attitudinal loyalty in public & private sectors based on survey The graph below depicts respondents perception of their behavioral and attitudinal loyalty responses in public and private insurance sectors as: Respondents perceptions on recommending company reputation to other people (word of mouth behavioral loyalty) Willing to purchase additional policies from their current provider(crossselling, behavioral loyalty ) Commitment to continue doing business with their insurer (retention, attitudinal loyalty). 100 80 60 40 20 0 51 53.7 80.5 Public n= 200 Private, n= 800 Public n= 200 Private, n= 800 Figure 6.1: Respondents behavioral and attitudinal response in public and private sectors (in percent) 65.9 56.5 55.9 Pledge to advocate Willing for cross-selling Commitment for retention Public private As it can be seen in the above figure respondents perception of recommending company reputation to other people is a bit higher in private (53.7%) than public insurance (51%). In contrary, in regard to intentions for purchasing additional policies from the current provider is much higher in public (80.5%) than in private insurance
299 sector (65.9%). In customer retention response public respondents are a bit higher than private insurance sector. 6.3.4. ANOVA: Comparison of customer loyalty in public & private insurance companies. ANOVA is a general technique that can be used to test the hypothesis set about the mean difference among two or more groups, under the assumption that the sample populations are normally distributed (Malhotra 2008). ANOVA allows us to reject or to retain the null hypothesis, then make inferences about the population. ANOVA is based on two assumptions. 1) The observations should be random samples from normal distributions (normally distributed). 2) The populations have the same variance (possess homogeneity of variance). Therefore, before we carried out ANOVA, we should have to check whether the assumptions are satisfied. As the assumptions are checked and illustrated in chapter four above, we could proceed with the analysis of variance in order to test for significant interaction effects and main effects. Hypotheses testing of main & interaction effects i. Null and alternative hypothesis Ho8: There is no significant difference in level of customer loyalty between public and private in insurance sectors H18 There is no significant difference in level of customer loyalty between public and private in insurance sectors Significance Level: α = 0.05, that is the chance of occurring type I error is 5 %. Critical Value and Rejection Region: Reject the Null hypothesis if p-value 0.05.
300 The ANOVA table below shows test of significance main effects and interaction effects. Table 6.24 Tests of Between-Subjects Effects Source Dependent variable : Level of customer loyalty Type III Sum of Squares df Mean Square F Sig. ηp 2 * Corrected Model 110.493 9 12.277 31.498.000.054 Intercept 40791.637 1 40791.637 1.047E5.000.954 sector 24.188 1* 24.188 62.057.000.012 Factor 59.633 4* 14.908 38.248.000.030 sector * Factor 3.664 4.916 2.350.052.002 Error 1944.987 4990.390 Total 67668.493 5000 Corrected Total 2055.480 4999 * ηp 2 = (Partial Eta Squared) which means estimate of effect size. *Sector has 2 categories (levels) then 2-1 =1 df and factor has 5 categories then 5-1=4 df. ii. Findings of Interaction effect: As it can be seen from the above table, ANOVA result reports that the overall model is statistically significant as F ratio (9, 4990) = 31.948, p <.05). P- Value is less than.05, thus we can conclude that the model is fitting for statistical computation. The table above also tells us the interaction effect and main effects. There is no a significant interaction effect between the level of sector involved and the amount of factor provided to affect customer loyalty as F ratio (4, 4990) =2.350, P- value =0.052 where p-value is greater than 0.05. And also the partial Eta squared (estimate of effect size) is=.002 which means 0.2% of the variability in the dependent variable can be explained by the independent variable so that the size of the effect is very small.
301 iii. Inference: It can be concluded that there is no enough evidence to reject the null hypothesis. We fail to reject the null hypothesis. This means the interaction effect sector*factor is not statistically significant. The interaction effects of sector,( private and public ) and the extent of the level of factor (levels) provided are the same to affect customer loyalty. Findings of Main effects of hypothesis test: In this analysis we have two independent variables sector and factor which further classified in different levels(sectors=2 levels private and public ), factor has 5 levels ( service quality perception, customer satisfaction, customer trust, switching cost and customer loyalty) where level of customer loyalty is dependent variable The factorial two-way ANOVA found that there is a significant( difference) main effect of sector type to affect customer loyalty, F ratio (1,4990) = 62.057, P value <.05, ηp 2 =.012. In addition to this, there is a significant main effect of factors provided to affect customer loyalty, F value (4, 4990) = 38.248, P <.05, ηp 2 =.030. Inference of the main effects: As the findings reports there is enough evidence to conclude that there is difference in mean value of the sectors. That is degree of freedom is 1 and the value of Partial Eta squared is 0.012 which tells us that sector accounts for 1.2% of the total variance in the effect. So that we have enough evidence to make decision that there is difference between means and we can conclude that it is statistically significant and Null hypothesis is rejected at 95% of confidence interval. Thus, we can infer that sector of the main effect is differently affect customer loyalty. The main effect of factor is also statistically
302 significant that shows significance mean difference in the level of factors in order to affect customer loyalty. In this study we are interested in the sector, factor and interactions (sector *factor) effects whether we have significant mean differences between our groups for our two independent variables, sector and factor and for their interaction, sector *factor of levels. Since the interaction effect did not have significant effect, pair wise comparison is performed below. Pair-wise comparison is the mean difference of public and private insurance sectors. The table below shows the Tukey post-hoc test results for the mean difference of the public and private insurance sectors. Table 6.25: Pair wise Comparisons Dependent Variable: Levels of customer loyalty 95% Confidence Interval for (I) (J) Mean Difference Difference Sector Sector (I-J) Std. Error Sig. Lower Bound Upper Bound Govt Pvt -.174 *.022.000 -.217 -.131 Pvt Govt.174 *.022.000.131.217 Based on estimated marginal means *. The mean difference is significant at the.05 level. The above table presents the mean difference is significant at the.05 level. As it can be seen from the above table there is a significance difference between the means of the sectors, public and private. Although, both sector means difference is statistically significant, it is observed that mean of private insurance is higher than public insurance sector. The table below shows means difference between factors of five levels of variables.
303 Table: 6.26: Mean Differences between Determinant factors and level of loyalty (I) factors service quality perception (J) factors Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval for Difference Lower Bound Upper Bound satisfaction.246 *.035.000.177.314 customer Trust -.001.035.971 -.070.067 Switching cost -.157 *.035.000 -.225 -.088 loyalty -.091 *.035.009 -.159 -.023 customer satisfaction service quality -.246 *.035.000 -.314 -.177 customer Trust -.247 *.035.000 -.315 -.178 Switching cost -.402 *.035.000 -.471 -.334 loyalty -.337 *.035.000 -.405 -.268 customer Trust service quality.001.035.971 -.067.070 satisfaction.247 *.035.000.178.315 Switching cost -.156 *.035.000 -.224 -.087 Customer loyalty -.090 *.035.010 -.158 -.021 Switching cost service quality.157 *.035.000.088.225 satisfaction.402 *.035.000.334.471 customer Trust.156 *.035.000.087.224 loyalty.066.035.059 -.003.134 Customer loyalty service quality.091 *.035.009.023.159 satisfaction.337 *.035.000.268.405 customer Trust.090 *.035.010.021.158 Switching cost -.066.035.059 -.134.003
304 As the above table 6.26 presents based on estimated marginal mean, the mean difference is significant at the.05 level. The mean difference of customer satisfaction is significant at 95% confidence interval. The other variables mean difference are significant in some way while in other way is not significant. Moreover, level of loyalty between public and private insurance companies are pictorially illustrated below. The graph is based on two dimensions: horizontally variables are presented and at the vertical axis mean of variables are depicted. The type of sector (private, public) is also delineated in the graph. Thus, using this two-dimensional graph it is possible to identify the level of loyalty in insurance sector both private and public. Figure.6.2: Estimated marginal means of levels of customer loyalty
305 The plot above gives evidence of the level of customer loyalty in public and private insurance sectors. In regard to service quality the private sector has better status while in public as observed less. In all variables customer perception is higher in private sectors than public sector. This means customers are satisfied in private sector than who are in public sectors. Customers have better trust on employees of private companies than public ones. This implies that the private insurers meets customer needs which influence customer loyalty better than public insurance company. 6.4 Chapter Summary Efforts have been made to achieve the research objective of exploring the level of customer loyalty in public and private insurance sectors. Respondents demographic factors and customer loyalty were assessed if relationships exist, so that it is verified that relationships exist between demographic factors and customer loyalty. However, sex and customer loyalty are not related in public insurance company. Private insurance mean score is reported as higher while public insurance is a bit smaller. This implies that the private insurers provide better service quality than the public insurers. Factorial ANOVA was conducted to examine whether there were statistically significant differences in level of customer loyalty between public and private insurance sectors. From the results it is observed that there is strong evidence that the mean of factors varies with the prevailing sectors operated ( df = 1, 4,990, F value=62.057, p-value <.05, ηp2 =.012.There is also significance difference between factors as : ( df =4, F value =38.248, p-value <.05, and ηp2 =.030). However, the combination effect between factors and sectors does not have sufficient evidence to reject null hypothesis (as F value =2.350, p-value=.052 and ηp2 =.002)
306 CHAPTER VII FINDINGS, SUGGESTIONS & CONCLUSIONS The focus area in this study was to examine the influence of determinants on customer loyalty. The literature review highlighted on describing service quality, customer satisfaction, customer trust and switching cost, as antecedents of customer loyalty. In addition to this, efforts were made in exploring the mediation effect of customer satisfaction between service quality perceptions and customer loyalty link. Besides this, efforts were made to identify whether the level of customer loyalty in public and private insurance is similar or not using the Factorial ANOVA. In order to achieve the objective of the study hypotheses were statistically tested. The detail discussions presented in chapter 5 and 6 above. This chapter presents summary of findings & implications for insurers, contribution of the study, conclusions and suggestions. 7.1. Findings & Implications for Insurers Respondents response survey shows that (38.8%) of respondent policyholders (table 5.29 above) believe companies facilities, equipments and communication materials are poor while (58.8%) viewed fulfillment of facilities in their respective companies. This implies insurers should have to fulfill working materials in order to attract customers and stay happily in the company. About (14.2%) of respondents believe that empathy activities are poor in their company; such situation can influence them to view themselves that they have chosen the wrong insurer. This means they will probably searching for a better or start thinking to shift to other providers.
307 Furthermore, the attitude response analysis indicates that (82%) customers are willing to consider repeat purchases from their existing providers (Table 5.33), but (21.6%) respondents are not inclined for cross-selling. On the other hand, the response analysis of Table 5.33 above reveals (21.6%) respondents believe they will not recommend the company good experience to their friends, acquaintance and relatives. Worryingly, (14.7%) of the respondents do not have idea to continue business with their current providers. From this insurers need to be alert, they have to make efforts to meet customer needs in order to survive and prosper. Moreover, the attitude response analysis result indicates that sustainable communication with the provider definitely could increase their satisfaction (Table 5.30). When (54.6%) of respondents believe their interactions with the respective companies are good while (34.2%) respondents revealed they had poor communication with their providers. Among respondents (24.9%) are dissatisfied with their provider due to poor service, (26.6%) of the respondents are not satisfied due to poor treatment from their provider. This can be considered as a forewarn for insurers. Customers have a genuine cause for complaint so that insurers need to listen to them. All staff in contact with customers must at all times be polite and helpful. Getting the service right will improve customer satisfaction and assist in the building of customer loyalty. It is crucial for insurers to maintain a multichannel distribution strategy in order to enhance communication with existing customers. The respondents analysis in (Table 29, 30, 31, 32) above show that improved service quality, customer trust on employees and customer trust on company as well as overall satisfaction with the provider, drive customers towards loyalty. The F- values indicate the relationship between determinant
308 factors and customer loyalty is significant. Therefore, if providers are unable to respond to customer changing needs, it is imminent that customers will switch to other providers. It was hypothesized that there is no relationship between customers demographic factors and customer loyalty in insurance sector of Ethiopia. Chi-square analysis was employed for significance test (Table 5.27 above). Thus, the analysis indicates that the respondents demographic particulars (gender, age, education level, income, marital status, family size, occupation) and customer loyalty are dependent.the relationship is significant at 95% significance level that Null hypotheses are rejected and alternative hypotheses were accepted. This relationship implies insurers should deploy resources based on customers demographic variables to build loyalty for their products. It was also hypothesized that there were no relationship between determinant variables (service quality, customer satisfaction, customer trust and switching cost) and customer loyalty. Hence Pearson correlation coefficient was employed to test the hypotheses. So that the result of hypotheses testing show relationship between determinant factors (service quality, customer satisfaction, customer trust & switching cost) and customer loyalty is positive, strong and significant at the 0.01 level 2-tailed (Table 5.28 above). Relationship is observed as: Service quality and customer loyalty (r=.802), customer satisfaction & loyalty(r=.823), customer trust and loyalty (.818) and switching cost to loyalty (r=.889).the relationship is observed as direct and strong linear relationships exist between variables. This means the increment in one variable would correspond to increase in customer loyalty. Such direct relationship between variables is a message
309 for insurers means that if they want to build customer loyalty they need to focus on determinant factors. Furthermore, respondents analysis of respondents perception on service quality, satisfaction, trust and switching cost and customer loyalty was performed based on response rate and F- statistics. The result of this analysis shows the relationship between determinant variables and customer loyalty is significant. Perceived response scores were recorded, low (strongly dissatisfied & dissatisfied), medium (neither agree nor disagree) and high (agree and strongly agree) and variation observed for each variable. These variations are supported by F- value calculation. The F- value analysis verified that the relationship between service quality, customer satisfaction, customer trust & switching cost and customer loyalty is statistically significant. The results of regression modeling including mediation analysis demonstrate that there is a close relationship between (as perceived by policyholders): Service quality and customer loyalty in the insurance sector of Ethiopia. Service quality and customer satisfaction in the insurance sector of Ethiopia. Customer satisfaction and customer loyalty in the insurance sector of Ethiopia. Customer trust and customer loyalty in the insurance sector of Ethiopia. Switching cost and customer loyalty in the insurance sector of Ethiopia. However, switching cost has the greatest influence on customer loyalty among influencing variables of service quality, customer satisfaction and customer trust.the regression analysis also suggest that customer trust has greater influence on customer loyalty following switching cost. This implies that insurers need to focus on switching cost, customer trust, and service quality and customer satisfaction for customer
310 retention. The other important issue the regression analyses unfold is that R 2 =.879 i.e. 87.9 % of the variation in customer loyalty is explained by determinant factors (effect of service quality, customer satisfaction, switching cost and customer trust on customer loyalty). The regression analysis reveals that the estimate of regression weight as (Table 5.37): When service quality perception goes up by 1, customer loyalty goes up by 0.191. When perceived customer satisfaction goes up by 1, customer loyalty goes up by 0.210. When customer trust perception goes up by 1, customer loyalty goes up by 0.292. When switching cost perception goes up by 1, customer loyalty goes up by 0.531. This means the greater the variables increase the greater is customer loyalty. Switching cost has more influence on loyalty (.531) than the other variables mentioned. This means when the costs of switching costs are high for the customer, there is a greater probability that the customer will remain loyal and purchasing additional policies. The more switching protection available the more customers go with the present providers. The result of regression analysis implies when customers are satisfied with the insurer overall service, when customers have trust on employees and company, they will be motivated for cross-selling. If the insurer is not servicing as customer expects, then all potential cross-selling opportunities will be lost. Therefore, the first point of action should be delivering quality service to the existing customers according to their needs. Efforts need to be made well before the customer lapses, not at the point they are considering leaving. This requires significant investment in understanding customer needs and deploys resources on antecedents of customer loyalty.
311 The positive significant coefficient for customer satisfaction and loyalty relationship suggests higher customer satisfaction on insurance service and the higher the loyalty of customers towards the insurance sectors. Thus, satisfied customer is important in developing a loyal customer. Therefore insurers should always strive to ensure that their customers are very satisfied. The finding suggest that service quality influences customer loyalty in insurance sector which implies for insurers, they need to focus on the quality of service to increase the perceived value of service among the customers mind. The regression analysis also suggest that (figure 5.13above) switching cost affect loyalty uniquely by 7.8%, trust influence loyalty uniquely by 2.2%, customer satisfaction influence loyalty by.92% and service quality influence uniquely loyalty by.53%. These are indications for insurers in which variable to focus more. The mediation analysis suggests that customer satisfaction plays the role of mediation between service quality and customer satisfaction link. The total effect of service quality on customer loyalty is 1.191 in which its direct effect on customer loyalty is.617 and its mediation effect is.574. Customer satisfaction is affected by service quality directly by regression weight of.935 and customer satisfaction affects customer loyalty by regression weight of.614 (Table5.45). The effect size tell us how much of the effect of the service quality on the customer loyalty can be attributed to the indirect path of (service quality customer satisfaction customer loyalty). The direct effect is the size of the correlation between the service quality and the customer loyalty with the mediating variable included in the regression.
312 Hence the statistical significance test of mediation suggest that customer satisfaction significantly mediated between service quality and customer loyalty link that is service quality to satisfaction =.935, p different from zero, service quality affects loyalty=.617, p different from zero, and customer satisfaction affects loyalty =.614, p different from zero (refer Table 5.42).The effect of service quality on satisfaction is positive and significant. The value of path coefficient is 0.935 while the indirect effect of service quality on customer loyalty is.614 but lower than the path between service quality and customer satisfaction. Furthermore, the result unfolds that customer satisfaction partially mediates the relationship between services quality and customer loyalty. The total effect of service quality on customer loyalty is (1.198) while the indirect effect of service quality on customer loyalty is ((.553) and the direct effect of service quality on customer satisfaction is (.909) and the direct effect of customer satisfaction on customer loyalty is (.608). Finally it was hypothesized that level of loyalty is the same both in public and private insurance companies of Ethiopia. Hence mean score comparison of private and public insurance companies analysis shows that private insurance mean(3.65) is a bit higher than public mean score 3.48(Table 6.15 above).this implies private insurance companies are better to satisfy customer than public company. The result of the study further indicates improved quality service and customer treatment, as well as satisfaction with the provider, undoubtedly drive repeat purchases. Meeting customer needs may improve this situation. To meet customer expectations, insurers need a deep understanding of the varying needs of individual customers.
313 Factorial ANOVA was conducted to examine whether there were statistically significant differences in level of customer loyalty between public and private insurance sectors. The ANOVA suggest that the mean of the factor interaction to sector is not statistically significant; F (4, 4990) =2.35, p=.052, ηp2 =.002. This means there is no interaction effect of the sector,(private and public)involved and the amount of factors provided have no different effect on customer loyalty. However, there is a significant main effect of sector to affect customer loyalty. On the other hand, there is also significant main effect of the factors provided affect on customer loyalty. In nutshell, the finding of this study suggest that by improving antecedents of customer loyalty (i.e. meeting customer needs) insurers could maintain significant customer loyalty in the insurance sector. 7.2. Contribution of the Study This study is believed to make contribution to marketing theory and practice. It adds to current theory by bringing together five main influencing factors which had previously been treated separately in some literatures: Demographic factors, Service quality, customer satisfaction, customer trust and switching cost in tandem with mediation analysis. This study provides a synthesized investigation of these activities for better understanding of the drivers of customer loyalty. This research is significant in identification of the drivers of customer loyalty. Insurers now have the possibility to target specific service variables for improvement, which increases the likelihood of positive customer behaviors and attitude by building loyalty, and ultimately positively affects company performance.
314 7.3. Suggestions for future research When conducting this study, determinant of customer loyalty in insurance sector of Ethiopia several new ideas have raised. Some of these are: Conducting an in-depth study about the profitability of loyal customers in comparison to the cost of retaining them. In the study of determinants of customer loyalty the role of employees and organizational behavior need to be considered. The present study is confined to insurance operating in Addis Ababa. Further studies can focus on large scale all over the country. The development of insurance is low in Ethiopia. There are so many factors which can affect insurance development. Thus, situations that could affect the relationships in insurance growths need to be investigated. It is hoped that the findings could stimulate further research In Ethiopian insurance sector. 7.4. Conclusions The problem of customer lapsing in insurance sector of Ethiopian is a severe problem that makes to the decline of premium & low insurance distribution which initiated me to launch this study. The study was aiming at to investigate the influencing factors of customer loyalty. To achieve this objective hypothesis were framed, attitude survey data collected and analyzed with the help of appropriate statistical tools for further interpretation. Hence, the following conclusions have been drawn from the findings of the study.
315 Recommending company products to others It is found that people are attracted towards insurance products more of (32%) by their friends, relatives and acquaintances. These people advisers tell their experience of the company about products, employs of the company trustworthiness, treatment, and transparency, the overall satisfaction of the company, etc. If these people themselves are satisfied in the insurance provider and could develop confidence they could urge more people to join insurance companies. Their role of playing as company advocacy could accelerate in accordance to insurers good reputation. So companies need to identify customers expectations and focus on meeting their current needs. The respondents demographic characteristics analysis indicates customers have different needs and expectations that need a deep understanding of individual customer needs. While older customers(11.8%) willing to recommend company products younger customers(26%) are pledged to recommend company products to others.hence insurers have to give emphasis to discern their current customer needs and expectation and make efforts to meet their customers expectations. In order to recruit new customers to their companies insurers deploy resources for advertisement. However, existing customer can do more, customers need quality service, they need sustainable communication, appropriate technology for their service, well treatment, confidence on the company, etc. If customers are satisfied with the service provided, if they trust the company, they can play the role of advocacy of the company and attract more customers. Instead, if customers are
316 dissatisfied and do not have confidence on the company they can tell more people about their bad experience that could damage customer acquisition. In terms of service quality,( 38.2%) of the respondents claims insurers do not use appropriate technology to provide service,(14.2%) of the respondents also disfavor company employees understanding customer needs (13.7%) respondents believe the company does not provide prompt service to customers. In related to satisfaction (34.2%) respondents are not satisfied in company communication system, (26.7%) of the respondents believe that they do not have overall satisfaction on the company. A Bible saying goes on, A good reputation is worth much more than silver and gold. Hence insurers need to make efforts to meet customer needs. The more customers get quality service the more they abide with the provider. The more they are treated, the more they satisfy with the company, the more they get prompt service they could build confidence on the company and extend company reputation to others. Enhancing cross-selling Cross-selling products to existing customers is the most cost-effective way to achieve sustainable growth of the insurance company. However, it is found that the level of repeat purchase is low i.e. (13.7%) of respondents do not have intention for cross selling. One key way of encouraging customers to repeat purchase from the same provider is improving service quality. Customers need prompt service, they need individual attention, they need error free service, and they need transparency two way communication. Customers could purchase repeat purchase if they trust the company, if they have trust on employees of the company. It is observed from
317 the analysis that satisfaction derive customer for repeat purchase. The more customers satisfy on service quality provided by the company, the more they trust the company, the more they will marsh for repeat purchase. The finding shows that service quality, customer satisfaction and customer trust and barriers for switching initiate customers for repeat purchase. Increasing customer retention Retention pays better than acquisition. Various researches point out to the fact that customer acquisition is five to ten times more expensive than customer retention (Bhattacharijee, 2006). The bucket theory in the literature review depicts this fact clearly. We found that (14.7 %) of respondents are thinking of churn. If things can t improved i.e. customers need can t be fulfilled they will switch. Surprisingly, (15.1%) of the respondents are on the juncture to move back and forth. Hence, insurers need to emphasize on improving service quality, creating satisfaction among customers. Due to the fact that insurance sectors do not provide tangible products but provide promise, their efforts is usually assessed by measures of the expectation-perception model insurers have to identify customer needs and endeavor should be made to meet these customer needs. Service quality and customer satisfaction alone cannot achieve the objective of creating a loyal customer base. Customer trust is also an important antecedent of customer loyalty. Thus, insurers need to focus on the construct of trust which contains belief in the company employees and company as a whole, which provides the customers an assurance of positive outcomes not only for the present but also for the future. As illustrated in the literature, the customers must be making to believe that the
318 company will not behave opportunistically for sake of its own interest; otherwise they will switch to other providers. Other issue for gaining customer loyalty for insurance sector is switching cost. The greater the customer satisfaction & switching cost, the greater will be the customer loyalty (Beerli, 2004). This research has proved that a cost that is associated with the process of switching from one provider to another is a significant factor in customer s retention. The majority of respondents (81.6%) believe it is risky to change the present provider because of this they inclined to stay with the present provider. On the contrary,( 17.4%) of respondents do not see risky to switch to another insurer so that if their needs are not satisfied they can easily lapse. Poor service encourages switching while delivering an efficient service will validate a policyholder s view that they have chosen the right insurer. If providers unable to respond to customer changing needs, they will switch to other providers. Therefore, insurers need to cement their relationship with their customers by improving the service they provide to meet customer needs, develop and employ strategies to satisfy customers, develop trust among customers and use some barriers for switching. All staff in contact with customers must at all times be polite and helpful. Getting the service right will improve customer satisfaction and assist in the building of customer loyalty. It is crucial for insurers to maintain a multichannel distribution strategy in order to enhance communication with existing customers The findings indicate that while service quality is an important driver of customer loyalty, its direct effect is larger than the indirect effect in generating higher customer loyalty. It is important for the insurers to focus on service quality
319 dimensions and make efforts to create satisfaction among customers. Insurers need to develop a systematic assessment programs to monitor service quality, customer satisfaction and customer trust overtime. Company employees should be kept informed of results and be encouraged to take part in figuring out an effective resolution strategy The findings of the study shows that service quality, customer satisfaction, customer trust and switching cost play a vital role in influencing customers towards loyalty in insurance sector of Ethiopia. Pearson correlation coefficient for the relationship between determinant variables and customer loyalty is significant and positive. This tells us that, just as one variable increases, customer loyalty increases that reflect the close connections and influence of customer loyalty. Estimate of regression weight also shows service quality, customer satisfaction, customer trust and switching cost affects customer loyalty. When one variable increases by 1 unit customer loyalty also goes up correspondingly.this means by improving performance of influencing factors customer loyalty can be maintained. The mediation analysis result shows that customer satisfaction partially mediates the relationship between services quality and customer loyalty link The study finding shows that service quality is an important antecedent of customer satisfaction and customer loyalty. Hence, insurers should focus on customer satisfaction for which service quality is an important antecedent. The impact of service quality on customer satisfaction is considerably strong and can lead to a more favorable towards loyalty. The literature review revealed the customer loyalty is the key for company survival and
320 prosperity. So that the study indicates that service quality is one of the significant factors for maintaining customer loyalty. The result shows that mean scores of influencing factors (service quality, customer satisfaction, customer trust and switching cost in private insurance companies are greater than mean scores of public insurance. In nutshell, the more insurers make efforts to improve the service they provide, meet customer needs, able to create trustworthiness among customers and put barriers of shifting the more customers abide with the current provider The study has offered an investigation of determinants of customer loyalty and attitude survey was mainly based on policyholders. As a direct consequence of this methodology, the study encountered a number of limitations, which need to be considered. In order to develop customer loyalty, other variables may affect loyalty as for example employee of the company. Other variables may play the role of mediation in developing customer loyalty. It is hoped that these problems will be solved in the future studies.
321 BIBLIOGRAPHY AcaStat Software (2012). Coefficients for Measuring. Association. Retrieved from: http://www.acastat.com/statbook/chisqassoc.htm Afsar, B. et al. (2010 March 1). Determinants of Customer Loyalty in Banking Sector of Pakistan (Doctorial dissertation, Hazara University). Pakistan. Ahmad, A., & Sungip, Z. (2008). An Assessment on Service Quality in Malaysia Insurance Industry. Communications of the IBIMA.Vo. 1. Akbar, M., & Parvez, N. (2009). Impact of Service Quality, Trust & Customer Satisfaction on Customer loyalty: ABAC Journal, 29(1): 24-38. ALee, J., Lee, L., and Feick, L. (2001). The Impact of Switching Costs on the Customer satisfaction-loyalty link: Mobile phone service in France. Journal of Services Marketing. vol. 15, no. 1, pp. 35-48. Alinvi, F., & Babri,M. ( 2007). Customers Preferences of Insurance Services. International Business Program. Angelova, B., & Zekiri, J.( 2011). Measuring Customer Satisfaction with Service Quality, Using American Customer Satisfaction Model (ACSI Model. International Journal of Academic Research in Business and Social Sciences. Vol. 1, No. 3 American Marketing Association, (2011). The Behavioral Consequences of Service Quality. The Journal of Marketing. Vol. 60, no. 2.pp. 31-46. Retrieved from.http://www.jstor.org/stable/1251929. Anja and Chamberlain. (2010).Opportunities and Challenges for Microfinance in Ethiopia: The Centre for Financial Regulation and Inclusion. USB Bellville Park Campus, Carl Cronje Drive, Bellville, 7530, South Africa. Retrieved from: www.cenfri.org Arkell,J., (2011). The Essential Role of Insurance Services for Trade Growth and Development. The Geneva Association, Swizerland. Aronson, Elliot (1980).The social animal (3rd ed.). San Francisco, CA:W.H.Freeman. Arvidsson, S., (2010). Determinants of Customers Loyalty in Automobile Insurance. Swedish National Road and Transport Research Institute, Stockholm. Ashforth, B.E., & Mael, F., (2001). Social Identity Theory and the Organization. Academy of Management Review, 14(1), 20-39.