Analysis procedure

An overview of data analysis was provided in Chapter One, Section 1.5 and a brief review of data analysis approaches and techniques is also presented in this Section. The detailed discussion of the data analysis procedure is presented in Chapter Six. Data analysis technique depends on whether the data collection method is quantitative or qualitative (Neuman 2012). As this study employed surveys to collect the data to test the research model presented in Chapter Four, Section 4.3 and the hypotheses developed in Sections 4.2.2 to 4.2.4, quantitative analysis was applied (Cavana et al. 2001). The first stage in data analysis included descriptive analysis to identify the firmographic profile of the sample. Following this stage, reliability and validity assessments as well as factor analysis of measure were undertaken (Schumacker and Lomax 1996; Byrne 2001).

Testing the main and moderation effects. To test the direct effects and

moderation effects as outlined in the hypotheses, this research followed the procedure proposed by Slotegraaf and Atuahene-Gima (2012), Ngo and O’Cass (2013) and O’Cass et al. (2013) and applied Partial Least Squares (PLS). PLS is recognised as a suitable method to assess the research model and relationships due to different reasons. PLS is a variance-based structural equation modelling (SEM) technique that is more advantageous than covariance-based SEM approaches when measures are not well established (Fornell and Bookstein 1982). In this context, PLS provides measurement assessment, which is essential when new numbers of items are and refined measures are developed (see Dawes, Lee and Dowling 1998; Smith and Barclay 1997). The underlying reason is that in the early stages of model development, it is appropriate to determine causality from the measures to the construct and PLS is more suitable to measure causality (Henseler et al. 2009).

Further, PLS-SEM maximises the explained variance of the endogenous latent variables by estimating partial model relationships in an iterative sequence of ordinary least squares (OLS) regressions. CB-SEM estimates model parameters so that the discrepancy between the estimated and sample covariance matrices is minimised. In

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this study, the primary concerns are maximising the prediction of dependent endogenous constructs, including employee brand building behaviour, CCSP, service solution superiority, customer based brand equity and firm based brand equity. Moreover, as PLS allows the examination of measures and theory simultaneously (e.g., Fornell and Bookstein 1982). It was used for examining the measurement properties (outer-measurement model) and hypotheses (inner-structural model) which provides specification through two sets of linear equations namely outer-measurement model and inner-structural model (Fornell and Cha 1994). The outer-measurement model specifies the relationships between observed indicators and their respective constructs, while the inner-structural model specifies the relationships between latent constructs (Falk and Miller 1992; Fornell and Cha 1994; Hulland 1999). The last advantage of using PLS in this research is that PLS is suitable for sample size less than 200 (Hair et al. 2011; Slotegraaf & Atuahene-Gima, 2011 ; Rodríguez-Pinto et al. 2008).

Further, in addition to above mentioned advantages of PLS, it also has a number of specific advantages over other SEM statistical approaches, such as LISEREL and AMOS. PLS as a variance-based structural equation modelling avoids many of the assumptions and chances that improper solutions will occur as in the case of covariance-based approaches via LISREL or AMOS analyses (Henseler et al. 2009; Bagozzi et al. 1991). Underlying this advantage is the fact that PLS underestimates path coefficients compared to LISREL and AMOS (Henseler et al. 2009; Dijkstra 1983). Further, PLS produces a conservative test of the substantive relationships. Given the nature of the study and the benefits of PLS-SEM outlined here, this study employs PLS- SEM, specifically Smart-PLS v2 to evaluate the adequacy and validity of research model and hypothesis testing (Henseler et al. 2014; Henseler 2012; Hair et al. 2011; Wetzeles et al. 2009).

The last stage of research design is reporting the result as presented in Figure 5.1. The reporting stage includes interpreting statistical indexes and their meanings, which are presented in Chapter Six. The second stage of reporting involves discussing the findings and exploring the theoretical reasons for identified relationships between constructs. In Chapter Seven, theoretical and managerial implications are discussed and limitations of the study to open up future researches are explored.

5.6. Conclusion

This chapter has provided a comprehensive discussion of the research methodology applied in this study. It introduced the stages that the student researcher went through to design and implement the research. This chapter also provided the justification for the

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research methodology adopted and detailed the stages of the research design. To obtain the most reliable and valid data and decrease common method variance the design covered specific issues such as clarity of items, employing multiple informant design and having marker variables in surveys. Regardless of the disadvantages of online survey methods such as lack of physical presence and motivation, an online survey was considered to be more suitable due to faster response and cost, which could overcome two strong limitations of PhD research (small funds, limited time to complete the research). In Chapter Six, attention is given to analysing the data and reporting the results to test the hypotheses.

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Chapter Six