Impact of demographic variables on customers’ perceived service

Descriptive analysis was also used to identify if the respondents´ characteristics profile in term of gender, age, education and income can influence the dimensions of service quality.

This study first applied a normality test given that outliers often have a dramatic effect on the fitted model. The univariate skewness and kurtosis were computed to test the normality of each variable. The results show skewness was ranging from -.13 (Education) to .79 (Age) and from -1.99 (Gender) to .32 (Age) for kurtosis, were within the maximum limits of an absolute value of two for skewness and seven for kurtosis, as recommended by West et al.

(1995). It was also clear that most variables were slightly off-center. Thus, the characteristic variables data were further grouped to meet the normality criteria condition as follow: The three subgroups of age respondents´ (45-54; 55-64, and 65 and above) were brought together under the subgroup “45 and above”. Moreover, this study put together the subgroups with

3,1 3,2 3,3 3,4 3,5 3,6 3,7 3,8

LOY1 LOY2 LOY3 LOY4 LOY5

Mean scaore of respondents´loyalty

Mean score of respondents´loyalty

very small data by grouping some elements of the variables of education, income and usage.

Consequently, this study shrinked the usage data into 3 categories “less than 1 week”; “over 1 week-1month” and “over 1 month”. Furthermore, the subgroup “others” was rejected from education categories because the number of respondents were insignificant and the remaining were grouped as “college and below” “University degree” “Master degree and above”. The same was applied for incomes categories and they were grouped as follow “less than 12000”

“12001-24000” and “more than 24001”. Finally, it was necessary to do a data check to see if there was any error. The overall results were perfect and the statistical normality test was positive.

Thereafter, the sample was subjected to statistical analysis using T-student and ANOVA tests. Since both can detect any interaction effects between variables and examine comparison of means among the two or more independent samples groups. Generally, T- student is use to compare two subcategories, while ANOVA test is often use to compare more than two subgroups. Besides, the theory acknowledged that means of two groups are significantly different from each other if F-distribution is greater than 1 or P-value is lower than 0.05.

Drawing on what was obtained from the T-student and ANOVA tests, the overall results show respondents’ gender, age, education and income are not factors that can influence customers’ perception of service quality in Spanish online banking setting (See arrays of all the analyses in Appendix 6 and 7). However, 12% of female have different view on EFF7 and PRI1 variables, and 16 % have different view on PRI3 variable than men. More explicitly, those female were generally pleased with their online bank website as they do not shows their online banking behavior as well as protecting information about their credit cards and debit cards. These findings are consistent with the study of Bigne et al. (2005) who found that men and women did not show significantly different behaviors in shopping through the mobile technology for users in Spain with 86% of penetration rate. According to the review of literature in online shopping acceptance model, Zhou et al., (2007) also concluded that the effect of age on consumers’ intention to purchase online remains unclear. Likewise, Ganesan- Lim et al., (2008) found no differences in the perception of service quality based on gender.

Moreover, Straughan & Albers-Miller (2001) found no relationship between age and domestic retail store loyalty in traditional service.

However, the extant literature indicates that some studies found different results. For example, according to (Eagle, 2009) gender is important in the Arab world, for instance females prefer to go to banks that have dedicated female branches because they are in line with social and religious values. Likewise, Spathis et al., (2004) intimates that male customer of Greek banks have a more positive perception of the quality of service they receive than women. Besides, male customers perceived receiving a higher level of quality than female in 29 of the 31 banking service quality items. Once more men ranked effectiveness and reliability highest and assurance second; whereas, for women, price ranked first and access ranked second. Moreover, the review of literature in online shopping acceptance model observed that male consumers make more online purchases and spend more money online than females; they are equally or more likely to shop online in the future, and are equally or more favorable of online shopping. Besides, women have a higher-level of web apprehensiveness and are more skeptical of e-business than men (Zhou et al., 2007). This finding may be justified by the study of Swaminathan et al. (1999) in traditional service who stressed that shopping orientation was found to influence consumers’ shopping activities, interests, and opinions. Men and women were found to have different shopping orientations;

men were more convenience-oriented and less motivated by social interaction, while women were just the opposite. Moreover, Zhou et al., (2007) observed that online shopping is a relatively easy task, which does not require higher education. Given that even though some studies identified a positive relationship between education and the time and money consumers spent online, the majority did not.

Furthermore, according to Rashid and Bangladesh (2009) men and women used different criteria in selecting banks in Bangladesh. Besides, customers with undergraduate level found Corporal Efficiency was the most influential factor, whereas those who have completed postgraduate put close importance to corporal efficiency and Confidence. They have also chosen Core Banking services at the second stage important factor have completed postgraduate, put close importance to Corporal efficiency. Moreover, Donthu and Garcia (1999) found that men appeared to make more purchases and spend more money online than women.

Also in contrast to this study, customers with high income might favor traditional retailers with high levels of service quality while customers with low incomes might be more tolerant to lower levels of service quality in Hong-Kong (Sum & Hui, 2009). This view was further emphasized by Meng et al., (2009) who found Chinese consumers shop at different stores

based on their income level implicating that income level might be a source of discrepancies in the perception of service quality in China. Likewise, Homburg and Giering (2001) found income has a relationship with purchasing decisions and that high income customers gather information prior to buying a product which may have an influence on satisfaction.

Moreover, most popular items purchased online, including books, CDs, holiday and leisure travel, PC hardware, and software, are all “normal goods”, those for which demand increases as income increases (Donthu and Garcia, 1999; Swaminathan et al., 1999; Zhou et al., 2007;

Sum & Hui, 2009)

According to Zhou et al., (2007) literature review on online shopping adoption some study used10-year span, whereas others used 15-year span or 20-year span. Hence, according to the authors there was no standard age categorization scheme making cross-study comparisons impossible. However, Homburg and Giering (2001) intimated that there is a relationship between age and service quality dimensions. In the same vein, Ganesan-Lim et al., (2008) found that age has a big influence on the perception of service quality. In addition, Rashid and Bangladesh (2009) found in their study that Compliance with banks Cost-Benefit were first priority for the age group of 31-40 years and Confidence level of the customer with banks was the most important factor for respondents from 40+ age category.

Additionally, according to Rashid and Bangladesh (2009), respondents who have completed postgraduate, put close importance to banks Corporal efficiency and Confidence. They have also chosen Core Banking services at the second stage important factor. Likewise, Chau &

Lai (2003) identified that several individual differences including level of education and extent of prior experience have significant effects on ATM’s beliefs.

7 CHAPTER 7: RESULTS

As explained earlier, this section first presents the objective and methodology of each sub- model of the thesis. Given that all five proposed sub-models are different from each other, it was considered necessary to introduce and justify the motivation of the proposed model.

Moreover, given that the literature review discussed in chapter 3 of the present thesis was guided toward the introduction of main framework, it was judge appropriate to extend, discussed in depth and narrowed the existing literature toward each proposed sub-model.

Besides, it was deemed necessary to justify the rationale for each assumption in the sub- model by means of brief literature review. In addition, each proposed sub-model required different statistical analyzes and the use of different software package. Therefore, it was deemed necessary to describe and include all the steps used. Furthermore, since this study have no hypothesis about the nature of the underlying factor structure of the measures; this study deliberately used SPSS 19 to unveil the factor structures of the reported dimensions.

Hence, all the values reported for EFA were calculated using SPSS software because PLS does not carry out EFA (Gefen, 2005). Likewise, the CFA outer loadings of the same data from PLS are slightly different from those of SPSS loadings in general term. Hence, AVE and CR assessment based on those values may be slightly different from those reported on the model fitness using PLS. Because the AVE and CR reported are based on the model fitness, which in turn is reported based on the outer loadings. Therefore, justifying why the outcome of EFA, AVE and CR may be slightly different from those reported in some sub- models.