Data Analysis Partial Least Square Path Modeling

Partial least square path modeling (PLS-PM) is used for complex cause and effect relationship model (Joe Hair et al., 2011; Williams et al., 2011). The PLS-PM is a variance based approach and differs from covariance based structural equation modeling. The PLS-PM approach is to maximize of variance explained and is more to a prediction model.

In the multiple regression analysis there is too much error in estimating the standardized beta coefficient or regression coefficient (Hair et al., 2011; Williams et al., 2011). SEM is used to minimize the measurement error and gives a better estimation to a data set. This is the reason for SEM selection compared to multiple regression analysis (MRA) in Koha OSLIS research (Joe Hair et al., 2011; Williams et al., 2011). SEM is a class of multivariate techniques that combines the aspects of factor analysis and

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regression which enable the researcher to simultaneously examine the relationship among measured variables and latent variables as well as between latent variables. The latent variables (constructs) are used to measure the concept that is abstract, complex and cannot be observed directly. The latent variables are represented in path models as blue circles and are measured by means of multiple items (survey questions). Indicators (manifest variables) are directly measured observation and known as the raw data set or items or manifest variables and represented in path models as yellow rectangle. The error terms is used to capture the unexplained variance in constructs and indicators when the path models are estimated.

There are two main terms used widely in PLS-PM which is the exogenous latent variables and endogenous latent variable. The exogenous latent variables are latent variables that serve only as independent variables in a structural model. The endogenous latent variables are latent variables that serve only as dependent variables or as both independent and dependent variables in a structural model. The predictive relationship in the path modeling is referred to causal links as the UTAUT model support the causal relationship (Min et al., 2008) used in Koha OSLIS research.

In a path model, the constructs used are relevant to Koha OSLIS research and is defined clearly in definition of terms, Chapter 1. The measurement for independent (exogenous) and dependent (endogenous) variables are clearly defined with expert validation (Appendix E) for the items and constructs used in Koha OSLIS research. The relationship is either positive or negative as well as the direction is hypothesized based on unified theory of technology acceptance and also based on literature discussed in Chapter 2. The unified theory of technology acceptance by (Venkatesh et al., 2003) explains the positive relationship to be exist in the Koha OSLIS research. An OSIS- UTAUT theoretical framework is used to explain the hypothesized relationship. A

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parsimonious approach to the theoretical specification is far more powerful than the broad application of a shotgun (Min et al., 2008).

There are two sub model in the PLS-SEM. The measurement model and structural model. The measurement model indicates the relationship between the observed data (item constructs or indicators) and latent variables in Koha OSLIS research. The structural model indicates the relationship between the Koha OSLIS latent variables. The latent variables in Koha OSLIS research are performance expectancy (PE), effort expectancy (EE), information technology skill (ITS), system quality (SQ), information quality (IQ), cost (C), social influence (SI), self-efficacy (SE), attitude towards using technology (ATUT) and acceptance of Koha open source library information system (ATUKOSLIS). Partial is used to explain the algorithm which solves the SEM (Joe Hair et al., 2011; Williams et al., 2011). In the measurement and structural model the partial algorithm is used to estimate the latent variables. The algorithm is repeated until a level where the convergence is obtained.

Partial least square path modeling is famous and widely used to test and analyze the well-established model such as UTAUT model by Venkatesh et al. (2003) and underlying theory such as unified theory of technology acceptance that combine 8 other theories in the technology acceptance model. The PLS-SEM is preferably used by researchers when the research data set is common factor based. The common factor for behavioral study is related to technology acceptance.

3.8 Summary

Chapter 3 is the structure for the overall research strategy that addresses the research problem in a theoretical framework. This chapter defines the study type, hypotheses, independent and dependent variables and data collection technique. This research reveals the direct cause and effect influencing factors for the technology acceptance for Koha

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OSLIS using the OSIS-UTAUT model. The measure is explaining the variables used in this research. The difficulty to obtain the content analysis due to the model UTAUT which focused on users compared to the direct users of a particular system. The findings of UTAUT is bias to system developers (Gallego et al., 2008). The instrumentation validity is performed by experts in the behavioral aspects of study. There are 61 items in the survey instrument which are adopted and modified (Delone & McLean, 2003; Venkatesh et al., 2003). Pre-test is conducted with 30 respondents within the sampling frame with random sampling technique. Non- response bias does not exist for the pre-test. The data analysis technique will be using the partial least square path modelling (PLS-PM). Some terminologies and introduction to PLS-PM is explained. The instrument is considered reliable with the Cronbach’s alpha’s > 0.7 (Nunnally et al., 1967). 61 items are accepted to be used in main study. The respondent demographic is presented and attached in Appendix G.

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