Reliability

When a research tool is consistent and stable, for this reason, predictable and accurate, it is said to be reliable. As a result, the greater the degree of consistency and stability of a method, the greater is its reliability (Kumar, 2005).

Equally, for a research to be reliable, it must adopt data collection methods and analysis procedures, which are bound to produce consistent findings. The consistency referred to points to the degree to which the questions below can be answered:

 Will the measures used produce the same results if used on other occasions?

 Will other researchers’ produce the same results if they adopt the same methods and procedures?

 Will those interpreting the research clearly understand how conclusions were drawn from the data collected (Saunders & Lewis, 2012)?

The adopted research strategy, data collection methods, and analysis techniques were carefully chosen to ensure reliability. In answering the questions above, the responses obtained from interviewees even though from different banks provided similar results. Thus, using the same research questions to obtain responses from different interviewees (experienced bank executives) will generate the similar results. More so, the technique used to analyse data (content analysis, DEA window analysis, and multiple regression analysis) can be replicated. While enumerating the strengths of content analysis, Vitouladiti (2014), suggested that establishing reliability is easy and straightforward. Whereas, the DEA window analysis technique will reveal the same results as long as the same input and output variables are used to examine the efficiency of banking institutions. Equally, the multiple regression analysis carried out will present the same results as long as the same variables and tests are employed. As already discussed in the literature chapter, the techniques utilized for analysing data have been used severally in the estimation of bank performance. Hence results and findings obtained via the utilization of the DEA window analysis and multiple regression

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analysis can be relied upon. Additionally, the conclusions to be drawn in this study will be able to be understood in line with the collected data.

Conclusively, proponents of mixed research methods of which this study adopts are of the view that triangulation leads to thicker and richer data and complementary data analysis techniques which ensure validity and reliability (Onwuegbuzie et al., 2011).

4.10 Chapter Conclusion

This chapter centered on the comprehensive presentation of the procedures adopted to achieve the aims and objectives of this study. The philosophical stand of the research study; the adopted research approach; the appropriate research strategies; the research study period; and the data collection and analysis techniques employed in this study were examined in this chapter. In summary, this research leans towards the pragmatic paradigm as it relies on elements of both the positivist and interpretivist in order to answer the research questions. In like manner, the abduction research was also discussed as the preferred research approach as it seesaws between induction and deduction in order to achieve the aims and objectives of the study. Progressively, the chapter also highlighted triangulation of data collection methods as qualitative and quantitative research strategies were employed to obtain data for analysis. Furthermore, presentations that center on the three data analysis techniques were made. Firstly, content analysis is used in this study to analyse interview responses and regulatory documents. Secondly, DEA window analysis which is anchored largely on either the CCR model (constant returns to scale assumption) and the BCC model (variable returns to scale assumption) are comprehensively discussed, before indicating that the BCC efficiency scores are relied upon instead of the CCR efficiency scores due to the inability of the CCR model to identify scale effects. Thirdly, panel data variant of multiple regression estimations is used to ascertain the relationship between the dependent variables proxies of bank efficiency, bank performance, and bank stability, and the independent variables proxies of capital adequacy, asset quality, management quality, earning capacity, liquidity, sensitivity to market risk, bank size, and GDP. Finally, the validity and reliability of this are justified to round up the chapter.

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Chapter Five: DEA Window Analysis Technique

5.1

Introduction

This chapter dwells on the analysis of the performance of Nigerian DMBs based on efficiency scores obtained through the utilisation of the DEA window technique. As mentioned in previous chapters of this thesis, the DEA window method measures the performance of a DMU against other DMUs and itself within a particular window. Therefore, the performances of Nigerian Deposit Money Banks (DMBs) are analysed in twelve windows (window 1 – window 12) to ascertain the effects of the 2005 and 2009 banking reforms, and the global financial crisis on efficiency. Put differently; this chapter examines efficiency scores based on the inputs and output relationship influenced by the 2005 and 2009 banking reforms, the event of the global financial crisis, the bailout strategy and the bridge banking mechanism. Therefore, the performance of Nigerian Tier I (big DMBs) and Tier II (medium & small sized DMBs), bailed-out DMBs, and bridge banks are x-rayed within the period of 2000 - 2013.

As presented and discussed in the literature and methodology chapters of this study, the basic DEA models are based on two broad assumptions i.e. the constant returns to scale (CCR) and the variable returns to scale (BCC). This study initially set out to ascertain the efficiency of Nigerian banks based on the calculated CCR and BCC efficiency scores for the period of 2000 to 2013. However, this section relies primarily on the BCC efficiency scores to draw conclusions owing to the similarity in the pattern of efficiency scores. More so, due to the premise that CCR indicates the overall technical efficiency (OTE) of banking institutions, while BCC is more comprehensive as it decomposes overall technical efficiency (OTE) into pure technical efficiency (PTE) and scale efficiency (SE). The BCC model estimates whether organisations are operating under increasing, decreasing or constant returns to scale. Therefore, the BCC model is picked over the CCR model in the assessment of the efficiency of Nigerian DMBs.

Consequently, BCC efficiency scores obtained from the interaction of Nigerian DMBs inputs (interest expenses, non-interest expenses, and total deposits) that produced outputs (interest income, non-interest income, and total loans) are trusted in the evaluation of the performance

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of individual DMBs and classes of DMBs in the Nigerian banking sector from 2000 – 2013. The analysis of the efficiency scores of DMBs, therefore, shows the extent to which the initiatives and strategies of Nigerian regulators influenced inputs and output production. In addition, the similarity in the slope of the aggregate efficiency scores of both the CCR and BCC models as depicted in the diagram below affirms the position of the study to rely on the efficiency scores of one out of the two models.

Figure 5.1 BCC and CCR Aggregate Average Efficiency Score

The superior slope of the BCC is an indication that the BCC (VRS) efficiency scores are higher than the CCR (CRS) efficiency scores. Hence, when the results of the BCC model are compared against those of the CCR model, the DMUs under the BCC model show higher degrees of efficiency, although there are instances where they are the same. Therefore, the number of efficient banks (DMUs), the percentage of efficient banks, and the average efficiency score under the BCC model are higher than those of the CCR model.

Although the efficiency scores under the constant returns to scale assumption (CCR) are not discussed in conjunction with the adopted variable returns to scale assumption model (BCC),

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the CCR efficiency scores are presented in the appendix section to validate the conclusions of the study. In addition, the CCR efficiency scores are attached to increase the robustness of our results. Likewise, only the BCC efficiency scores are used as dependent variables in the estimation of the effects of bank-specific CAMELS variables, size and GDP on efficiency.