SCALE RELIABILITY AND VALIDITY ANALYSIS

Having obtained EFA and CFA solutions for the scales and having refined the constructs, the respective scale reliabilities were examined. Scale reliability is important in management research since summated scales are an assembly of interrelated items designed to measure underlying constructs (Nunnally, 1978). According to Bryman (2008: 149) ‘‘reliability refers to the consistency of a concept’’. Similarly, Zikmund (2003) indicated that reliability reflects the degree to which measures are free from error with a high degree of producing consistent results. According to Bryman (2008) the use of Cronbach’s alpha to assess internal reliability is common in social science research.

The current study used Cronbach’s alpha and composite reliability methods to assess the reliability of the measures. Cronbach's alpha determines the internal consistency of items to establish its reliability (Cronbach, 1951). Several scholars have suggested that to achieve scale reliability Cronbach’s alpha value should be above 0.60 for exploratory research and 0.70 for confirmatory research (Nunnally, 1978; Nunnally and Bernstein, 1994).

Previous studies have expressed different opinions on an acceptable alpha value. For example, Field (2009) indicated that an alpha value of 0.70 to 0.80 is acceptable value of Cronbach’s alpha in determining the internal reliability of a measure. Bryman (2008) also argued that the figure 0.80 is typically employed as a rule of thumb to indicate an acceptable level of internal reliability. Table 7.15 presents the scale reliability results depicted by

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Cronbach’s alpha values. As shown in Table 7.15 the scale reliability results of the current study range from 0.85 to 0.94. Additionally the current study assessed composite reliability (CR) of the scales used. CR is a structural equation model (SEM) reliability statistic that is similar to Cronbach alpha value (Baum and Locke, 2004). The rule of thumb is that CR values should exceed .60 for exploratory model testing (Hayduk, 1987). CR of the study’s measures was above the threshold of .60. This suggests that the current study achieved high scale reliability for all the studied constructs (Nunnally and Bernstein, 1994).

Regarding the validity of the scales, it has been argued that CFA procedures can be used to examine aspects of validity (Ping 2004). Following this view, the present study conducted CFA of all scales using average variance extracted (AVE) and composite reliability (CR) techniques to establish validity and reliability of the scales. In assessing the reliability of the individual items, the present study calculated a composite reliability value for each latent variable. To achieve this objective, the present study used the information on the indicator loadings and error variances from the completely standardised solution (Diamantopoulos and Siguaw, 2009). In fact, convergent validity of the scales (which refers to the degree to which two measures of constructs that theoretically should be related, are in fact related) were established since all the items loaded on their original constructs without cross loadings and correlated errors (Ping, 2004). It can be observed from Table 7.15 that all composite reliabilities comfortably exceed the 0.60 threshold (Ping, 2004).

AVE refers to the amount of variance that is captured by the latent variable in relation to the amount of variance due to its measurement error (Fornell and Larker, 1981; Dillon and Goldstein, 1984). Thus, AVE measures error-free variance of a set of items. According to Fornell and Larcker (1981) AVE measures can be used to achieve convergent validity. Indeed, AVE is an indication of how much of the variance of latent variable is captured by

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the set of observed variables. This study achieved discriminant validity by calculating the square roots of AVE for all multi-item constructs (See Table 8.1). The results revealed that, for all constructs, each correlation of one construct with another is less than the square root of its AVE, suggesting discriminant validity of the study’s measures (Fornell and Larcker, 1981). Table 7.15 below depicts a summary of predictor measures, scale reliability and validity.

Table 7. 15: Predictor Measures and Scale Reliability and Validity Analysis

Constructs Composite Reliability* Average Variance Extracted (AVE)** Cronbach’s Alpha Research Reference Passion for Inventing 0.87 0.73 0.87 Cardon et al., (2013) Passion for Founding 0.81 0.69 0.91 Cardon et al., 2013 Passion for Developing 0.80 0.63 0.95 Cardon et al., 2013 Environmental Dynamism

0.85 0.71 0.88 Miller and Friesen

(1982a)

Political ties 0.84 0.64 0.85 Acquaah (2007)

Business growth 0.87 0.67 0.89 Anderson and Eshima

(2013)

Prior growth .93 .78 - Low and MacMillan

(1988; Baum and Locke, 2004)

*Composite reliability (CR) = the sum of the square roots of the item-squared multiple correlations squared and divided by the same quantity plus the sum of the error variances (Werts, Linn and Joreskog, 1974). **AVE=Σ[λi2]Var(X)/Σ[λi2]Var(X)+Σ[Var(i)] where λi is

the loading of xi on X, Var denotes variance, i is the measurement error of xi, and Σ denotes a sum (Fornell and Larker, 1981).

As argued by Diamantopoulos and Siguaw (2009), AVE value less than 0.50 suggests that measurement error accounts for a greater amount of the variance in the indicators than does the underlying later variable; hence can raise eyebrows about the soundness of the indicators and the latent variable. As can be observed from Table 7.15, AVE values ranged from 0.63 to 0.78. This indicates that measurement error accounts for a lesser amount of the variance in the indicators than does the underlying latent variable.

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In conclusion, the assessment of the measurement part revealed strong evidence to suggest validity and reliability for the operationalisation of the latent variables. On the whole, the assessment of the measurement part did not highlight any deficiencies.