Customers’ Perceptions of M-Banking Dissertation

Introduction

To find answers pertaining to the major objectives of the study, the gathered data was analysed using SPSS v.23. All the Likert-based answers were coded from 1=strongly disagree to 5=strongly agree. An exploratory factor analysis was run to group the existing variables into factors, and also to reduce the number of dimensions for further procedures. Then, MANOVA was run to see if scores on the factors significantly differed for different age groups.

Assumptions for the factor analysis were checked (Field 2013). Because the factor analysis requires that no multivariate outliers are present in the data, these were identified using the SPSS syntax provided in Appendix 1; see details in IBM Support (n.d.). As a result, 3 cases were excluded from the analysis. In addition, 7 cases with missing values on any of the dependent variables were also excluded. Thus, N=238 cases were analysed. The KMO test yielded.826, indicating a “meritorious” adequacy of distribution of values for the factor analysis (George & Mallery 2016). Bartlett’s test of sphericity yielded p<.001, meaning that the data was suitable for analysis (Warner 2013).

The varimax rotation method with Kaiser Normalisation was employed; factor scores were extracted and saved using the regression method. Three factors were retained. Their weights with respect to the original variables were as follows:

These three factors explained 75.31% of total variance in the data. The factors were named as follows:

1. f_mbank_liking,
2. f_mbank_trustwor,
3. f_phbank.

It was decided to consider that the factors represented the following, respectively:

1. a general level of liking towards m-banking;
2. a general level of perception of m-banking as trustworthy;
3. use of physical banks.

Next, MANOVA was conducted using age_group as the independent variable (“fixed factor”), and the three factors as dependent variables, to check whether there were differences in the mean factor scores between different age groups. The main assumptions of the MANOVA were met: many of them were checked during the factor analysis; multivariate outliers were removed prior to the factor analysis. Homogeneity of covariance matrices was not violated: Box’s test was not significant (p=.192). Levene’s test of equality of error matrices was not significant for any of the three factors (p=.673, p=.626, p=.413, respectively), so the variances of different factors were homogeneous.

The descriptive statistics for the factors can be found in Appendix 2.

The results of the overall MANOVA were as follows:

Evidently, all the test statistics were significant at α=.05 for the age_group independent variable; the most conservative result was produced by Pillai’s Trace, and it still was significant: F(12, 699)=1.908, p=.03, partial η²=.032, which is a small effect size (Warner 2013).

The results of univariate F-tests are provided in the table:

Apparently, the univariate tests detected significant (at α=.01) differences on the f_mbank_liking factor: F(4, 233) = 4.027, p=.004, partial η²=.065, which is a medium effect size (Warner 2013).

Pairwise comparisons (post-hoc tests with the Bonferroni adjustment) can be found in Appendix 3. As can be seen, significant differences were found on the f_mbank_liking factor between two pairs of age groups: a) 26-35 and 36-45 (p=.035, mean difference=.516, SE=.175), and b) 26-35 and 46-55 (p=.012, mean difference=.674, SE=.206). No other significant differences were detected (either between other age groups or on other factors).

Therefore, the third objective of this study was answered as follows. If millennials are people born between 1980 and 2000, they should be aged 17-37 at the time of the study. The statement that they are generally more hospitable towards m-banking was partially confirmed; statistically significant differences on the “liking” factor were found only between two pairs of groups, as noted in the previous paragraph; but no significant differences were found between e.g. those aged 15-25 and any other group (which, however, can be explained by stating that people aged 15-25 probably had not had much experience of banking). This confirms the findings of e.g. Hanafizadeh et al. (2014) and Laukkanen (2016), who also assert that age plays a significant role in determining clients’ perceptions on e-banking.

Also, curiously, no significant differences were found between those aged 26-35 and those aged 56-65; however, the latter group of respondents only consisted of 9 people, which might have contributed to the extremely high p-value.

As for the second objective of the study, the view that customer’s perceptions of m-banking vary significantly across different ages (Kundu & Datta 2012) was also partially confirmed, as stressed above.

As for the first objective of the study – to consider different impacts fuelled by the transformation in the customers’ perceptions on innovative technologies on the whole and m-banking in particular in the last five years – no conclusions can be made from the gathered data. However, the review of the literature demonstrates that the changes in the clients’ views lead to greater perceived usefulness and ease of use, and lower perceived risks, which stimulates clients to use m-banking (Alalwan et al. 2015). Also, Afshan and Sharif (2016) found out that the characteristics of tasks that are to be done and of the technologies that are to be used in m-banking, as well as the initial customer’s trust towards the bank, and several other factors, influence the clients’ readiness to embrace m-banking.

Conclusion and Recommendations

All in all, the current study investigated the perceptions of some UK customers on the use of mobile banking services. The data collected from the respondents was analysed using two statistical procedures: a) the factor analysis (to reduce the dimensions of the data and make it more manageable in further analyses), and b) the multivariate analysis of variance (to check whether there were statistically significant differences in the extracted factors across several age groups).

Three factors were extracted and saved using the regression method; also, the varimax rotation with Kaiser Normalisation was utilised. These three factors explained nearly 75.31% of the variance in the data. The extracted factors were approximately labelled as 1) a general level of liking for mobile banking; 2) a general level of perceived trustworthiness of mobile banking; and 3) the use of physical banks. It is rather curious that MANOVA found no statistically significant differences between different age groups on the second and third factor. Therefore, it is possible to assert that a) the claim that different age groups view mobile banking as more (or less) trustworthy than the “usual” banking was not supported by evidence in the current study; and that b) the claim that different age groups have different attitudes towards the use of physical banks also was not supported by evidence in this study.

Next, statistically significant differences (α=.05) were found between several age groups when it came to the general liking of m-banking. More specifically, the group of respondents aged 26-35 significantly differed from both those aged 36-45 and those aged 46-55; people aged 26-35 had a greater liking towards m-banking than these other two groups. Curiously, no statistically significant differences were found between other groups. It might be possible to hypothesise that young people (aged 15-25) did not differ much in their liking towards m-banking from other groups because they had not been much exposed to any type of banking due to their young age. As for the perceived absence of significant differences between older individuals (aged 56-65) and the representatives of other age groups, it can be noted that the sample size for the group of people aged 56-65 was only 9, which might have affected the precision of the estimates and the significance of the results.

Therefore, it is possible to state that the current study supported some of the findings made in previous research – for instance, that there are significant differences between different age groups when it comes to the perceptions on mobile banking, and that the “millennials” generally tend to better accept such innovations than individuals of older age (Hanafizadeh et al. 2014; Laukkanen 2016). However, a potentially important finding of this study is that young people (15-25) did not differ much in their perceptions of m-banking from other age groups.

The limitations include the fact that has already been mentioned above, namely, that the size of the group of individuals aged 56-65 was only 9 respondents, which, in fact, was a barrier to making conclusions about this group. Also, some questions asked at the beginning of this paper could not have been answered from the gathered data, so the author had to rely only on the literature on the topic, and there is a dearth of such literature. In addition, the gathered data was not analysed from all the possible angles in this paper; it is possible to further explore this data by including, for example, the variables of gender and education into the statistical procedures. Furthermore, the factor analysis procedure meant that some information gathered via surveying was lost; however, this seems to be almost inevitable, for it is very difficult to manage an analysis involving 8 dimensions.

There are several recommendations to be made on the basis of the study. First, because people aged 26-35 already have positive perceptions on m-banking, further promotion of such services can target this segment of the market to get good results. Also, because individuals aged 15-25 do not differ from other age groups in their perceptions on m-banking, they can also be targeted (at least when they come of legal age) by advertisement, and by some programs or campaigns aimed at attracting new customers; this age group might be receptive to such promotion. In addition, because no significant differences between age groups were found when it came to perceived trustworthiness of m-banking or to the use of physical banks, it e.g. probably does not matter to which age group to appeal when it comes only to increasing perceived trustworthiness of m-banking; however, considering the better liking of m-banking by those aged 26-35, the promotion of perceived trustworthiness may be more worthwhile for that group, because they are already predisposed towards using m-banking services.

On the whole, these recommendations are entrepreneurial rather than innovative, for they are related to attracting new customers to m-banking. However, they might also be considered slightly innovative, because they advise how to promote an innovative service.

Reference List

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Alalwan, AA, Dwivedi, YK, Rana, NP & Williams, MD 2016, ‘Consumer adoption of mobile banking in Jordan: examining the role of usefulness, ease of use, perceived risk and self-efficacy’, Journal of Enterprise Information Management, vol. 29, no. 1, pp. 118-139.

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Hanafizadeh, P, Behboudi, M, Koshksaray, AA & Tabar, MJS 2014, ‘Mobile-banking adoption by Iranian bank clients’, Telematics and Informatics, vol. 31, no. 1, pp. 62-78.

IBM Support n.d., Compute Mahalanobis distance and flag multivariate outliers, Web.

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Laukkanen, T 2016, ‘Consumer adoption versus rejection decisions in seemingly similar service innovations: the case of the Internet and mobile banking’, Journal of Business Research, vol. 69, no. 7, pp. 2432-2439.

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