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Demographic Characteristic of the Sample
The demographic characteristics of the sample, by each type of bank is provided in Table 4.5. It is clear from the figures in Table 4.5 that in terms of their demographic profile and contextual variables, the two groups of customers are remarkably similar. Differences that emerge in their responses to the questions relating to the four constructs can therefore be attributed to differences between the two groups of banks, and not necessarily to demographic differences between the groups of customers.
Minor differences were found in the sample representation with respect to gender. In this aspect, among foreign bank customers, 55% were male, and among local bank customers, 48% were male. Correspondingly, the percentage representation for the female respondents was higher for the local banks compared to the percentage representation of these respondents for the foreign banks. That is, among foreign bank customers, 45% were female, and among the local bank customers, 52% were females. However, when the corresponding significance of the Chi-square test of independence in Table 4.6 is considered, this difference is not statistically significant (p=0.145).
With access to the internet, the majority of customers agreed that they have access to internet. This could be seen in the percentage representation whereby 86% of all the respondents interviewed, responded by saying that they have internet access. In the sample representation in terms of the local banks’ customers and foreign banks’ customers, there was a minor difference between the two groups in terms of access to the internet, as 85% of local bank customers had access to the internet, and 88% of foreign bank customers had access to the internet. As seen in Table 4.6, this difference is also not statistically significant (p=0.342).
However, despite having access to the internet, only 56% of the all the respondents interviewed were comfortable with using the internet for banking services, and this comprised of 58% of the local bank customers and 54% of foreign bank customers. That means, despite the fact that the majority of the customers agreed that they had access to internet services, these customers had their reservations on using this technology to carry out their financial transactions. It seems therefore that there is a general reluctance among Tanzanian bank customers to trust the internet to facilitate their banking activities.
With mobile phone ownership, 98% of the respondents who were interviewed possessed mobile phones. This result is not unusual, since the major part of data collection was conducted in the city centres where the participating banks have their branches, and that is why the majority of the respondents who were interviewed happened to have mobile phones.
Nevertheless, despite the fact that the majority of those who were interviewed had mobile phones, but still only 57% (which comprised 59% of the customers using local banks’ services and 55% of the customers using foreign banks’ services) were comfortable with using mobile phones to facilitate their banking transactions. This reluctance of using mobile phones to facilitate banking activities could possibly be attributed to Tanzanian customers to be seemingly not ready for using mobile phones as a means to facilitate their financial transactions.
Inferential Statistics
An inferential statistical analysis was applied in the data analysis process in order to arrive at conclusions that reflect the outcomes from the sample and to draw inferences about the target population from the sample (Tabachnick & Fidell, 2007; Zikmund, 2003). The main statistical operations that were performed were confirmatory factor analysis (CFA) and, subsequently, structural equation modelling (SEM).
However, before carrying out the CFA, an assessment on normality was first carried out on the four constructs of cultural orientation, service performance, customer satisfaction and corporate reputation. This was done across the two groups of banks so as to examine whether the data in question exhibited multivariate normality, an assumption which is considered to be important when conducting structural equation modelling analyses in general (West, Finch & Curran, 1995). This is due to the fact that statistical research has established that non-normality, specifically the presence of excess kurtosis, the peakedness of a frequency-distribution curve, has an effect on tests of variances and covariances on which structural equation modelling analyses are based. Indications of kurtosis therefore always raise concerns in statistical research that applies structural equation modelling and especially multivariate kurtosis, where a multivariate distribution indicates tails and peaks which are quite different from the characteristics of multivariate normal distribution (Byrne, 2010).
Confirmatory factor analysis has been employed to summarise and reduce the information contained in the large number of variables into a smaller and more easily managed number of factors or latent variables. The purpose is to combine linear combinations of variables that aid in investigating the interrelationships, and to determine the validity of the constructs and the measurement instruments used to collect the data (Zikmund, 2003).
Confirmatory factor analysis has several advantages. Firstly, confirmatory models have the advantage of taking into account prior knowledge when formulating the model and moreover, these models can open up various methods for testing models and hypotheses (Blunch, 2008).
Secondly, confirmatory factor analysis is considered to be an efficient and popular technique in carrying out a validity assessment of the measured construct. Thirdly, confirmatory factor analysis can be used to assess the level of both convergent and discriminant validity of the measurement constructs. Finally, clear statistical research results can be obtained by being able to separate variance into attribute, method and error components, that is to say through squared factor loadings and error variance analysis (Bagozzi, Yi & Phillips, 1991). Confirmatory factor analysis was applied both to investigate the factor loading of the 69 items, which comprised 40 items that define service performance, 16 that define cultural orientation, 10 that define corporate reputation and three that define customer satisfaction. Confirmatory factor analysis was used to examine the degree to which multiple measures of the same construct demonstrate agreement or convergence (Hair, Black, Babin & Anderson, 2010). Lastly, structural equation modelling was also applied to investigate the plausibility of the proposed final conceptual model.
1. CHAPTER 1 INTRODUCTION AND BACKGROUND TO THE STUDY
1.1 A BRIEF HISTORY OF THE TANZANIAN BANKING INDUSTRY
1.2 PROBLEM STATEMENT
1.3 PURPOSE OF THE STUDY
1.4 RESEARCH OBJECTIVES
1.5 IMPORTANCE AND BENEFITS OF THE STUDY
1.6 CONCEPTUAL FRAMEWORK AND HYPOTHESIS DEVELOPMENT
1.7 PERSONAL CULTURAL ORIENTATION, SERVICE PERFORMANCE, CUSTOMER SATISFACTION AND CORPORATE REPUTATION
1.8 SCHOLARLY CONTRIBUTION TOWARDS THE MANAGEMENT OF BANKING SERVICES
1.9 DEFINITIONS OF KEY TERMS
1.10 PLAN AND STRUCTURE OF THE THESIS
2. CHAPTER 2 THE TANZANIAN BANKING INDUSTRY
2.1 INTRODUCTION
2.2 GEOGRAPHICAL LOCATION AND TOPOGRAPHY OF TANZANIA
2.3 POLITICAL CONTEXT
2.4 AN OVERVIEW ON THE COUNTRY’S ECONOMIC FEATURES
2.5 IMPLEMENTATION OF THE IMF AND THE WORLD BANK SUPPORTED REFORM PROGRAMMES
2.6 COMMERCIAL BANKING ACTIVITIES
2.7 REFORMS OF THE ECONOMIC AND FINANCIAL SECTORS DURING THE 1980S
2.8 ASSESSMENT OF THE FINANCIAL SECTOR PROGRAMME
2.9 DEVELOPMENT OF THE TANZANIAN BANKING INDUSTRY
2.10 THE SECOND GENERATION FINANCIAL SECTOR REFORMS
2.11 TANZANIA’S BANKING INDUSTRY OVERVIEW
2.12 COMPETITION AMONG BANKS IN TANZANIA
2.13 CONCLUDING REMARKS
3. CHAPTER 3 LITERATURE REVIEW
3.1 INTRODUCTION
3.2 ELEMENTS OF SERVICE PERFORMANCE
3.3 FEATURES OF SERVICES AND THE CHALLENGES POSED TO A SERVICE FIRM
3.4 SERVICE QUALITY AND SERVICE QUALITY DIMENSIONS
3.5 THE ROLE OF SERVICE QUALITY IN THE FIRM’S PERFORMANCE
3.6 SERVICE INNOVATION
3.7 FACILITIES FOR CASH DISTRIBUTION
3.8 CUSTOMER SATISFACTION
3.9 CORPORATE REPUTATION
3.10 THE RELATIONSHIP BETWEEN SERVICE QUALITY AND CUSTOMER SATISFACTION
3.11 THE RELATIONSHIP BETWEEN SERVICE QUALITY AND CORPORATE REPUTATION
3.12 THE RELATIONSHIP BETWEEN CUSTOMER SATISFACTION AND CORPORATE REPUTATION
3.13 PERSONAL CULTURAL ORIENTATIONS
3.14 CULTURAL INFLUENCES ON PERCEIVED SERVICE PERFORMANCE
3.15 SUMMARY OF LITERATURE REVIEW
4. CHAPTER 4 RESEARCH METHODOLOGY
4.1 INTRODUCTION
4.2 RESEARCH PARADIGM
4.3 DESCRIPTION OF THE INQUIRY STRATEGY AND RESEARCH DESIGN
4.4 FIELD WORK
4.5 RATIONALE OF THE STUDY
4.6 RESEARCH OBJECTIVES
4.8 PILOT STUDY
4.9 STAGES IN THE RESEARCH PROCESS
4.10 RESPONSE RATE
4.11 DATA COLLECTION
4.12 DATA ANALYSIS APPROACH
4.13 DATA ANALYSIS METHODS
4.14 SAMPLE REPRESENTATIVENESS ACROSS TWO TYPES OF BANKS
4.15 MEASUREMENT INVARIANCE TESTING
4.16 SECOND-ORDER FACTOR MODELS
4.17 FIT MEASURES IN STRUCTURAL EQUATION MODELLING
4.18 MODERATION ANALYSIS
4.19 RELIABILITY OF RESEARCH INSTRUMENTS
4.20 VALIDITY OF THE RESEARCH INSTRUMENTS
4.21 FINAL MEASUREMENT INSTRUMENT
4.22 ETHICAL CONSIDERATIONS
4.23 CHAPTER SUMMARY
5. CHAPTER 5 MEASUREMENT MODELS
5.1 INTRODUCTION
5.2 RELIABILITY ANALYSIS
5.3 THE MEASUREMENT MODEL OF SERVICE PERFORMANCE
5.4 THE MEASUREMENT MODEL OF CULTURAL ORIENTATIONS
5.5 THE MEASUREMENT MODEL OF CORPORATE REPUTATION
5.6 THE MEASUREMENT MODEL OF CUSTOMER SATISFACTION
5.7 CHAPTER SUMMARY
6. CHAPTER 6 RESULTS OF THE SUBSTANTIVE ANALYSES
6.1 INTRODUCTION
6.2 HYPOTHESES OF THE RELATIONSHIPS BETWEEN SERVICE PERFORMANCE, CUSTOMER SATISFACTION AND CORPORATE REPUTATION
6.3 THE MULTIPLE GROUP STRUCTURAL EQUATION MODEL OF THE RELATIONSHIPS BETWEEN SERVICE PERFORMANCE, CUSTOMER SATISFACTION AND CORPORATE REPUTATION
6.4 TESTING FOR MODERATION OF TYPE OF BANK IN THE MULTIPLE-GROUP STRUCTURAL EQUATION MODERATION MODEL OF SERVICE PERFORMANCE, CUSTOMER SATISFACTION AND CORPORATE REPUTATION
6.5 HYPOTHESES OF THE RELATIONSHIPS BETWEEN CULTURAL ORIENTATIONS AND SERVICE PERFORMANCE
6.6 THE MULTIPLE GROUP STRUCTURAL EQUATION MODEL OF THE RELATIONSHIPS BETWEEN CULTURAL ORIENTATIONS AND SERVICE PERFORMANCE
6.7 TESTING FOR MODERATION OF TYPE OF BANK IN THE MULTIPLE-GROUP STRUCTURAL EQUATION MODERATION MODEL OF CULTURAL ORIENTATIONS AND SERVICE PERFORMANCE
6.8 SUMMARY OF SUBSTANTIVE RESULTS
7 CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS
7.1 INTRODUCTION
7.2 PURPOSE OF THE STUDY
7.3 IMPORTANCE OF THE CONTEXT OF THIS STUDY
7.4 DISCUSSION OF THE RESULTS ACCORDING TO THE STATED RESEARCH OBJECTIVES
7.5 MANAGERIAL IMPLICATIONS
7.6 ACADEMIC CONTRIBUTION OF THE STUDY
7.7 LIMITATIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH
REFERENCES
ADDENDUM A: DATA COLLECTION INSTRUMENT
ADDENDUM B: INVITATION TO PARTICIPATE IN SURVEY
