Overview of the Analysis

The models outlined in Chapter 3 have been analysed using two methods i) OLS regression and ii) PLS path modelling. The individual models are tested initially to confirm/reject the proposed hypothesis. Following this, the integrated model has been assessed to understand the inter-linkages between the different groups of predictor variables.

PLS path modelling was introduced by Wold (1974) for analysing high dimensional data in a low structure environment and has undergone various extensions and modifications” (Henseler et al., 2009, p. 284). In contrast to the covariance based Structural Equation Modelling (CB-SEM), PLS path modelling (PLS-SEM) is based on variance based techniques (Henseler et al., 2009). In the last few years, PLS path modelling has been very prevalent in marketing research (Henseler et al., 2009; Hair et al., 2012) in addition to strategic management (Hulland, 1999) and other related fields. In fact, Long Range Planning had a special issue in 2012 devoted towards PLS modelling in strategic management which indicates its growing importance. One of the advantages of PLS-SEM over OLS regression and CB-SEM is that it is not rigid when it comes to the assumptions with respect to multivariate normality (Hair et al., 2012). Further, the other advantages of this soft modelling technique are the fact that it does not impose stringent restrictions as in CB-SEM on smaller samples and complex models, which suits this particular study. Further it can incorporate both reflective and formative scales when compared to CB-SEM which is very restrictive when it comes to formative scales. PLS-SEM is more predictive in nature when compared to CB-SEM which is more confirmatory in nature. It also works well with nominal, ordinal, interval and ratio scaled data. PLS estimates latent variable scores as linear combinations of their manifest variables or indicators (Hair et al., 2012). All manifest variables (MV) are also given weights and all MVs for a construct do not have equal weights. MVs with weaker relationship with the construct and other MVs for the construct are given lesser weights.

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However, the disadvantages of this soft modelling technique are with respect to the absence of a global optimization criterion which implies a lack of fit good model fit (Hair et al., 2012; Henseler et al., 2009). Although there are measures like GoF index (Tenehaus et al., 2004), f2 (Cohen, 1988), R2 (Hair et al., 2010) and Q2 (Henseler et al. 2009; Chin 1998), several questions are posed on the effectiveness of these measures and how stringent they are (Hulland, 1999). Another concern is the fact that the “parameter estimates are not very optimal when it comes bias and consistency” (Hair et al., 2012 p. 416, Reinartz et al., 2009). This bias is greater when it comes to more complex models. The strengths and weakness with PLS- SEM should be well understood (Jöreskog and Wold 1982; Sosik et al. 2009) before using the same. Further, PLS does not provide significance levels and a bootstrap or jack-knife procedure has to be run to get the t-statistic values which could then be used to check if the estimates are significant. For these reasons, this study focuses on OLS regression results but also provides PLS path modelling output to further substantiate the findings from the study in terms of validating both measurement and structural aspects of the model.

For the PLS analysis, the results from both the measurement model (outer model) and the structural model (inner model) have been presented. For the measurement model, the tables represent the extent to which the individual items load on to the construct (outer loading) they intend to capture and the average variance extracted (AVE – which indicates convergent validity for the construct with its items). In addition, the outer model analysis also provided the reliability of the scales with composite reliability (CR) and Cronbach’s alpha. The structural model results are presented in terms of path coefficients (which are nothing but the regression coefficients) and their significance along with R2 which indicates the explanatory power of the model. R2 values of .67, 0.33 and 0.19 are indicative of substantial, moderate and weak PLS models (Chin, 1998). Additionally, the goodness of fit measure (GoF) has also been provided for all of the models. GoF is the geometric mean of average communality and R2. GoF values of .1, .25 and .36 (Tenenhaus et. al., 2004) indicate small, medium and large values of GoF (Tenenhaus et. al., 2004). For performing PLS path modelling, SmartPLS (Ringle et al., 2005) has been used (with path weighting scheme and missing values replaced by EM method) to test the measurement (outer) and structural model (inner). Contrary to CB-SEM, in PLS

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path model the measurement model and structural model gets assessed simultaneously. Hence for each model that has been analysed, both the measurement model and structural model are assessed. Additionally, the significance levels for the estimates provided by SmartPLS have been arrived at following the bootstrapping with 500 samples as discussed earlier.

In terms of presentation of the results of OLS regression analysis, the tables provided in the subsequent sections include the regression coefficients (standard and unstandard) and the significance of these coefficients. In addition, to ensure that multicollinearity amongst the variables (since the independent variables are also correlated as shown in Table 11) does not pose a problem in any of the models considered, the VIF (Variance Inflation Factor) and tolerance values have also been reported. The other reported variables include R2 and F values which indicates the effectiveness of the model in accounting for the variance in the dependent variable (explanatory power). The assumptions related to OLS regression with respect to linearity, normality and homoscedasticity have been checked with P-P, Q-Q, scatter and residual plots and have been provided in Appendix C.

The following sections detail the analysis of the individual models as explain above followed by the analysis of the integrated model.

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