Data Analysis Plan

The quantitative research aspect of this study will be empirically tested using various data analysis techniques. The various techniques have its specific purpose and will be discussed in the subsequent sub-sections. Data collected from the administration of the survey (either through online or hardcopy) will be captured in SPSS. All the statistical analysis will be carried out either using SPSS or AMOS. Specifically, SPSS is used to for analysing the descriptive statistics of the data collected, testing of assumptions for normality, reliability analysis and EFA while AMOS is used for CFA and to test the measurement model based on SEM.

123 The data analysis techniques to be used in this study include the following:

 Descriptive statistics of background information of the organisations.

 Descriptive statistics for all variables.

 Testing of assumptions for normality (which includes skewness and kurtosis), homoscedasticity, linearity and multicollinearity.

 Reliability analysis.

 EFA for process flexibility.

 CFA for independent variables which includes structural alignment, strategic alignment, IT capability and process flexibility.

 CFA for SCA which is the dependent variable.

 2nd order assessment of model for BPM.

 Assessment of the structural model.

 Assessment of the structural model for moderating effect of IT investment.

Data Coding & Treatment of Missing Data

Once data collection phase is complete, the responses for the survey will be captured in SPSS. The data needs to be checked for correctness and completeness. This is to ensure that no error during data entry took place (Hair, Black, Babin, Anderson, & Tatham, 2006). Respondents who mailed back incomplete responses will be contacted and the questionnaire can be completed through telephone interview. However, this is not possible for those respondents who completed the questionnaire using the online medium. For incomplete questionnaires via the online medium, the data needs to be removed from the overall list of data. The final number of data after coding and treatment of missing data will be analysed.

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Procedure for Descriptive Statistics

Descriptive statistical analysis is carried out to describe the basic statistics and distribution of the organisations. Basic statistics includes percentages and frequencies. This forms the respondent profile of the data collected for this study. The first section of the survey sought responses with regards to the background information of the organisation, such as the following:

 Nature of industry.

 Industry type.

 Age of the organisation.

 Number of employees in the organisation.

 Market share of the organisation.

Subsequently the mean and standard deviations for all variables are computed and presented. This outlines the descriptive statistics for the variables involved in this study.

Procedure for Testing for Normality

Skewness depicts the tendency of the distribution to deviation from the mean while kurtosis is the measure of relative peakedness or flatness of the distribution. Both skewness and kurtosis explores the curve of the distribution and compares to the normal distribution curve. It is noted that when the values of skewness and kurtosis are equal to zero, the distribution is a perfect match to a normal distribution (Hair et al., 2006; Tabachnick & Fidell, 2001). However, it is accepted that the distribution approximates

125 that of a normal distribution when the value of skewness is within ±2.00 of their respective standard errors for significance of 95% and the value of kurtosis is within ±3.00 of their respective standard errors of significance of 95% (Hair et al., 2006).

Nevertheless, a simpler form of determining distribution can be done through visual examination of the histogram (Hair et al., 2006; Tabachnick & Fidell, 2001). Skewness and kurtosis is to be tested on all variables for this study in order to determine the distribution curve.

Next, homoscedasticity is determined through Bartlett’s test. The purpose to test homoscedasticity is to test the assumption that all variables have equal variances. This test is carried out to determine the level of departure from normality (Hair et al., 2006). Homoscedasticity can also be determined through correlation analysis. Correlation is carried out on all the variables. Correlation analysis examines the relationships between the variables. Correlation analysis describes the direction and level of strength of the relationship between the variables. The statistics for correlation is Pearson’s correlation, ρ and tested based on statistical significance (Schumacker & Lomax, 2004; Sekaran, 2003).

Finally, testing for linearity is carried out on all the variables. Linearity is examined based on p-plot. The linearity among the variables is determined by the closeness the plots are to the linear line. Linearity also checks for multicollinearity. Multicollinearity can be a concern if it is discovered that there are high correlations among the variables. Multicollinearity is determined by the level of variance inflating factor (VIF) and tolerance. Ideally, the level of VIF should be less than 10 while the level of tolerance should be greater than 0.1, in order to exhibit low levels of

126 multicollinearity (A. C. Burns & Bush, 2002; Gujarati, 2003; Hair et al., 2006; Malhotra & Birks, 2007).

Procedure for Reliability Analysis

Reliability analysis is concerned with ensuring consistency of the measurement instrument. Reliability analysis is carried out for all the variables in this study. Reliability is measured by the value of Cronbach’s Alpha (α) which is required to achieve a level of greater than 0.7 for the items of the variables to be accepted as reliable (Hair et al., 2006; Malhotra & Birks, 2007; Sekaran, 2003). Otherwise, the items of the variables needs to be deleted as suggested by the analysis until finally the value of α is accepted.

Even though the measurement instrument for the variables are adopted and adapted from past studies (with the exception of process flexibility), reliability analysis is still required to be carried out. This is because, the target population and scope of the study is different from past studies. As such, there could be items that may not be applicable or relevant within the context of this study.

Procedure for Exploratory Factor Analysis (EFA)

Factor analysis is an interdependence technique under the family of multivariate analysis with the purpose to identify from a large set of variables, the salient few that can be used for multivariate analysis (Malhotra & Birks, 2007). There are two types of factor analysis used within the scope of this study. The first is EFA which is carried out

127 for process flexibility. EFA is required for process flexibility as the measurement instrument was developed within this study. For this reason, EFA is not required for the remaining variables in this study as the measurement instruments were adopted and adapted from past studies. EFA is applied to analyse the scale items in order to prove their discriminant validity (Davis & Cosenza, 1993). EFA is measured based on Kaiser- Meyer-Olkin (KMO) measure of sampling adequacy and test of significance at 95%. The instrument is regarded as adequate when the value of KMO is between 0.5 to 1.0 (Hair et al., 2006).

Procedure for Confirmatory Factor Analysis (CFA)

CFA is a member of the factor analysis family with the objective of determining unidimensionality and construct validity of the variables. CFA is carried out for all the variables in this study. CFA covers two aspects. The first is as a measure for item purification while the second aspect is for assessing the measurement model. CFA needs to be carried out for item purification first before proceeding to assessing the measurement model. Items purification is based on maximum likelihood estimation whereby the unsuitable items are deleted and retested until the salient few items remain for the variable. This is an iterative process (Bagozzi, Yi, & Phillips, 1991).

Hair et al. (2006) recommends applying trial and error methods with reference to the modification indices until the modification indices reach a satisfactory level of goodness-of-fit for the measurement model to be acceptable. The modification index needs to be greater than 4 to achieve an acceptable level (Hair et al., 2006). CFA is carried out in three stages. The first stage is for the independent variable which includes

128 structural alignment, strategic alignment, IT capability and process flexibility. Subsequently, CFA is carried out for SCA which is the dependent variable. Finally the 2nd order model for BPM is carried out.

Next, the SEM assumptions are reviewed prior to the assessment of the model which is explained in the subsequent sub-sections.

SEM Assumptions

In order to successfully carry out test on SEM for model assessment, the sample size of the data has to be adequate. It is noted that sample size for SEM testing is required to be much larger as compared to other multivariate tests (Biemer, Groves, Lyberg, Mathiowetz, & Sudman, 1991; Chin, 1998; Hair et al., 2006). The researchers explain that this situation arises because statistical analysis by SEM may be subject to unreliable outcomes with smaller sample sizes. Although Hair et al. (2006) also recommends a minimal sample size of 100 in order to proceed with SEM testing, Iacobucci (2010) believes that a sample size of 100 to 150 is adequate. Therefore it is assumed that the sample size for this study would fulfil the requirements of SEM to assess the model.

Iacobucci (2010) also points out that another important factor for consideration for SEM testing is the number of items used to measure a construct. Ideally, there should be a minimum of three items per construct and last but not least, he also cautions not to be too concerned with fit indices. A good research model should be practically sound and provide good explanations to both academicians and practitioners.

129 The issue of normality is also a very important assumption when testing using SEM. This is because SEM is sensitive to the characteristics of the distribution of the data. Data that severely deviates from a normal distribution may result in inflating chi- square statistics which in return may cause bias in the outcomes of the coefficient significance and standard errors (Hair et al., 2006; Steenkamp & Baumgartner, 2000; Steenkamp & Trijp, 1991).

Procedure for Structural Model Assessment and Hypotheses Testing

Upon completion of the CFA, assessment for the structural model is carried out. The assessment is carried out on three models. The first model is the full model while the second model is the direct relationship model and finally the third model is the full model with the moderating effect of IT investment. The structural models are assessed based on overall model fit indices to assist in drawing to the conclusion of achieving satisfactory goodness-of-fit (Bentler & Bonnett, 1980; Hair et al., 2006). Although a variety of model fit indices are presented, this study will apply a few indices from three main types of measurements.

First type of index measure to be used in this study is the Absolute Fit Index. This type of index examines the level of effectiveness of the specified model in reproducing the observed data. In other words, this index determines how well the theory fits the sample data collected (Hair et al., 2006). The indices involved and selected for Absolute Fit Index in this study are Normed Chi-Square (CMIN/df) and Root Mean Square Residual (RMR).

130 The next type of index measure is the Incremental Fit Index. The purpose of this index is to assess how well the specified model fits relative to the baseline model, which typically is the null model where all the observed variables are assumed to be uncorrelated (Hair et al., 2006). The indices involved and selected for this study are Normed Fit Index (NFI) and Comparative Fit Index (CFI).

Last but not least type of index is the Parsimony Fit Index. This index determines the best model among a set of competing models by comparing its fit relative to the models complexity. Parsimony fit is achieved either through a better fit or a simpler model (Hair et al., 2006). The index involved is Parsimony Normed Fit Index (PNFI). The types of measures along with the fit indices are tabulated in Table 3.4:

Table 3.4: Model Fit Indices (Adapted from Hair, et al. 2006)

Types of Measure Fit Index Acceptable Value

Absolute Fit Index – to examine the level of effectiveness the model reproduces data

Normed Chi-Square (CMIN/df) ≤ 3.0 Root Mean Square of Approximation ≤ 0.08 Incremental Fit Index –

model fit to relative baseline model

Normed Fit Index (NFI) ≥ 0.9 Comparative Fit Index (CFI) ≥ 0.9 Parsimony Fit Index – best

model comparing its fit relative to its complexity

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