PLS-SEM

According to Hair et al. (2011b), PLS-SEM was initially developed by Wold (1975) under the name NIPALS (Non-linear Iterative Partial Least Squares), and was extended further by Lohmoller (1989), as an alternative to CB-SEM that focuses on prediction, meanwhile the requirements of data distribution and specification of relationships are relaxed. Though PLS-SEM is less popular than CB-SEM, its application has recently expanded in marketing research and other business disciplines (Henseler et al., 2009). The increasing application of PLS-SEM can be partly attributed to the recent improvement in the technique itself (Hair et al., 2011b), such as analysing moderating effects (Henseler and Chin, 2010), segmentation techniques such as Finite Mixture Partial Least Squares (FIMIX-PLS), and non-linear effects (Rigdon et al., 2010).

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Despite the increasing popularity of PLS-SEM in other disciplines, very few studies in management accounting research utilise PLS-SEM. In two surveys regarding the application of SEM (Smith and Langfield-Smith, 2004; Henri, 2007) in management accounting research, only five studies (Ittner et al., 1997; Vandenbosch, 1999; Anderson et al., 2002; Chenhall, 2004, 2005) used PLS-SEM during the period from 1980 to 2005. This is fairly surprising given the great advantages of PLS-SEM that seem to be tailor-made for management accounting research (Smith and Langfield- Smith, 2004).

PLS-SEM provides a good opportunity for statistical modelling to move forward without being restricted by large sample size, strong underlying theory and normally distributed data (Smith and Langfield-Smith, 2004). Obviously, there are many areas in management accounting research where theory is underdeveloped or models being tested are very complex, which makes PLS-SEM the appropriate technique for this type of research (Smith and Langfield-Smith, 2004). PLS-SEM is mainly designed for predicting causal relationships in situations of high complexity and low theoretical information (Smith and Langfield-Smith, 2004).

4.6.3.1 Overview of PLS-SEM

As the CB-SEM, the PLS-SEM comprises two components for testing models with latent variables, measurement (outer) model and structural (inner) model (Hair et al., 2011a). The measurement model relates observed (manifest) indicators to their own latent variables, while the structural model relates the endogenous latent variables to other latent variables, including exogenous latent variables (Tennhaus et al., 2005). Endogenous latent variables refer to variables that are explained by other variables through structural model relationships, while exogenous latent variables represent variables that are not explained by any of the model variables and do not have any structural path relationships pointing at them (Hair et al., 2011a).

The measurement model comprises the unidirectional predictive relationship between each latent variable (construct) and its associated observed measures (Hair et al., 2011a). Two possible forms of measurement models can be used in PLS-SEM, reflective and formative models. “Reflective indicators are seen as functions of the latent variables and changes in the latent variable are reflected in changes in the indicator (manifest) variable.... In contrast, formative indicators are assumed to cause a latent variable, and changes in the indicators determine changes in the value of the

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latent variable” (Hair et al., 2011a: p. 141). However, all indicators used in the current model are reflective, not formative, and the procedures followed in evaluating the reflective measurement models are used in this study. The PLS-SEM algorithm uses a two-stage approach in data analysis (Lohmoller, 1989; Hair et al., 2011a). In the first stage, the latent variables’ scores are estimated. In the second stage the outer weights and loadings are finally estimated (Hair et al., 2011a).

4.6.3.2 Assessing the Measurement Model

Before testing the structural model, the reflective measurement model should be first assessed in terms of reliability and validity. The literature suggests some important measures of reliability, such as internal consistency (composite reliability) and indicator (item) reliability (Hair et al., 2011a). Similarly, validity can be assessed through using nomoloigical, convergent and discriminant validity (Hair et al., 2011a). These measures will be discussed in detail in the measurement model analysis (chapter 5).

4.6.3.3 Assessing the Structural Model

As the main goals of PLS-SEM are predicting and maximising the explained variance in the latent endogenous variable, the primary criteria of evaluating the structural model should be R2, path coefficients and the significance levels of path coefficients (Hair et al., 2011a). The significance of path coefficients can be assessed using resample techniques such as bootstrapping or jackknifing, as PLS-SEM puts no distribution assumption for the data used in the analysis. This study uses bootstrapping, as it is regarded as more efficient than jackknifing (Chin, 1998). Further, assessing the structural model entails assessing its ability to predict the endogenous latent variables (Hair et al., 2011a). One of the important measures used in assessing the predictive relevance in a model is the Stone-Geisser Q2 value (Geisser, 1974; Stone, 1974). This value can be calculated using the blindfolding technique, that omits part of the data in a systematic way and uses the resulting estimates in predicting the omitted part (Hair et al., 2011a).

Further, assessing the heterogeneity of observations is an important step in evaluating the structural mode. Failure to assess the heterogeneity of data risk damaging the validity of the PLS-SEM results as different parameters estimates may be obtained for different subpopulations (Hair et al., 2011a). Several tools have been developed in

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PLS-SEM to assess the unobserved heterogeneity, such as FIMIX-PLS (Ringle et al., 2010).

In contrast with CB-SEM, there is no overall goodness of fit measure in the PLS- SEM, as its main objective is different from the CB-SEM (Hulland, 1999; Hair et al., 2011b). Some scholars introduced global measures of fit (e.g. Tenenhaus et al., 2004). However, these measures are not widely accepted and thought to be inconsistent with PLS-SEM assumptions and objectives (Hulland, 1999; Hair et al., 2011b).

4.6.3.4 PLS-SEM Software

While the basic algorithms used in PLS-SEM were developed in the 1970s, the first software packages such as LVPLS (Lohmoller, 1984) and PLS Path (Sellin, 1989) were not publically available before the 1980s (Temme et al., 2010). The limited use of PLS-SEM in the last few decades can be partly attributed to the lack of progress concerning the software’s development in terms of availability, user-friendliness and methodological options (Temme et al., 2010).

However, this situation has recently changed and many alternative software solutions are now available to choose from, such as PLS-GUI, Visual-PLS, PLS-Graph, Smart PLS, SPAD-PLS (Temme et al., 2010). Moreover, each software package has different features in terms of requirements, methodology, options and ease of use (Temme et al., 2010). This study uses both Warp-PLS and Smart-PLS, as each software package has its distinguished features. Warp-PLS is the most recent available software package (Kock, 2011). It offers a number of features, which are largely absent from most, if not all PLS-based SEM software packages currently available (Kock, 2011):

1. It estimates p values for path coefficients automatically, instead of providing only standard errors or t values, and leaves the user to figure out what the corresponding p values are.

2. It estimates several model fit indices, which have been designed to be meaningful in the context of PLS-based SEM analyses.

3. It automatically builds the indicators’ product structure underlying moderating relationships, and goes a little further. It shows those moderating relationships, related path coefficients and related p values in a model graph as they should be shown.

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4. It allows users to view scatter plots of each of the relationships among latent variables (when they are connected through arrows in the model), together with the regression curves that best approximate those relationships, and save those plots as .jpg files for inclusion in research reports.

5. It calculates variance inflation factor (VIF) coefficients for latent variable predictors associated with each latent variable criterion. This allows users to check whether some predictors should be removed due to multicolinearity (this feature is particularly useful with latent variables that are measured based on only one or a few indicators).

However, Warp-PLS does not include tools for assessing predictive relevance (such as blindfolding procedures) or unobserved heterogeneity (such as FIMIX-PLS). These tools are embedded in the Smart-PLS software. Therefore, this software was used in the current study for double checking the results obtained by Warp-PLS and using both the blindfolding procedure and FIMIX-PLS.

4.7 Summary

This chapter discussed in detail the research philosophy and methodology underpinning the current study. After exploring the research paradigms and methodologies used in social sciences in general and accounting research in specific, the study adopted the positivist paradigm and the cross-sectional survey methodology to test the research hypotheses that were previously developed based on the theoretical model in the previous chapter. The chapter also addressed the key issues related to identifying the research context and population, sampling process, data collection using different methods (questionnaire, content analysis and archive data) and, finally, the proposed statistical techniques to be used in the data analysis (e.g. SEM).

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