CONFIRMATORY FACTOR ANALYSIS

7.4.1 An Overview

Confirmatory factor analysis (CFA) operates on the notion that it can be used to assess the relationships between a set of observed variables and a set of continuous latent variables (Diamantopoulos and Siguaw, 2009). Thus, CFA is used to examine whether measures of a construct are in line with researcher's conception of the nature of that construct and to assess unidimensionality/consistency of the constructs.

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Therefore, in order to assess unidimensionality/consistency of the constructs, CFA was employed. CFA has become widespread in quantitative research in the social sciences (Netemeyer, Bearden and Sharma, 2003; Diamantopoulos and Siguaw, 2009). A key feature of CFA is that the technique is used to test an existing theory (Ping, 2004). Thus, CFA is a statistical technique for testing how well measured variables represent a small number of constructs. The rationale behind factor analytic methods is to reduce the number of variables and examine structure in the relationships between variables. As has been argued by some scholars (e.g., Netemeyer, Bearden and Sharma, 2003; DeVellis, 2003), CFA can aid statistical efforts to reduce scale items. Through CFA, scale reliability can be established in the form of composite reliability (CR) and average variance extracted (AVE). Furthermore, CFA can establish scale validities including convergent validity and discriminant validity (Chou and Bentler, 1995; Ping, 2004).

Accordingly, all the scales that passed the EFA analysis were examined using the CFA technique using the maximum likelihood (ML) method. Indeed, there are several model testing and estimation procedures to scholars in CFA; namely generalised lest square (GLS), asymptotic distribution free (ADF), partial least squares (PLS) and maximum likelihood methods. The current study adopted the ML method as it has been found to be robust under moderate violation of normality (Chou and Bentler, 1995; Boso, Story and Cadogan, 2013). Notwithstanding this notion, the current study did not violate any normality assumption.

A number of scholars have suggested that in assessing the model fit, several fit indices must be reported and these include Chi-square (χ2_{) statistic, Root Mean Square Error of} Approximation (RMSEA), Non-normed Fit Index (NNFI), Comparative Fit Index (CFI), Joreskog Goodness Fit Index (GFI) and Incremental Fit Index (IFI) (Hu and Bentler, 1995; Hoyle and Panter, 1995; Hair et al., 2006). These fit indices are recommended in the

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literature as acceptable ways to evaluate overall fit measurement models (Diamantopoulos and Siguaw, 2009). There is no absolute value for these fit indices to suggest a good fit, yet there are guidelines available for this task. Table 7.12 provides guidelines for some of these indices.

Table 7. 12: Summary of Guidelines for CFA Model Fit Indices

Fit Indices Guidelines

Chi-square (χ2) Insignificant results

Root Mean Square Error of Approximation (RMSEA) Value ˂ 0.05

Normed Fit Index (NFI) Value ˃ 0.90

Non-normed Fit Index (NNFI) Value >0.90

Comparative Fit Index (CFI) Value ˃ 0.90

Joreskog Goodness Fit Index (GFI) Value ˃ 0.90

Standardized Root Mean Square Residual (SRMSR) Value .08 or ˂

Source: Hair et al., (2006) with insights from Boso, Story and Cadogan (2013)

7.4.2 Measure Construction

To assess unidimensionality/consistency, CFA was performed to estimate the full measurement model with all the items that performed well in the EFA. To achieve this objective, the indicators were constrained to load only on their hypothesised underlying factors (Sirdeshmukh, Singh and Sabol, 2002). Following this view, all the multi-item scales that passed the EFA evaluation were, therefore, entered into CFA model using LISREL 8.7 (Joreskog and Sorbom, 2004). The CFA model in Table 7.13 returned a converged solution with a good fit. This approach is consistent with prior scholarly developments (e.g., Cadogan et al., 2006, Boso, Story and Cadogan, 2013). Table 7.13 presents the results of the full measurement model for the CFA.

According to Baker and Sinkula (1999), items that are conceptually related must be analysed together. Each item was allowed to only load on one construct for which it was an indicator. The first set included the three components of passion: inventing, founding and developing.

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The second set comprised political ties, environmental dynamism and business growth. The third set contained the retained in set through set two and these items were modelled simultaneously.

Table 7. 13: Results of CFA Model for the Analysis of all Scales

Construct Variable Factor Loadings (with t-values) Standard Errors

Passion for Inventing Ipf-INVENT1 0.79 (fixed) -

Ipf-INVENT2 0.92 (15.33) 0.06

Ipf-INVENT3 0.96 (13.71) 0.07

Ipf-INVENT4 0.93(13.28) 0.07

Ic-INVENT1 0.98(12.25) 0.08

Passion for Founding Ipf-FOUND1 0.86(fixed) -

Ipf-FOUND2 0.89(9.88) 0.09

Ipf-FOUND3 0.87(8.70) 0.10

Ic-FOUND4 0.90(11.25) 0.08

Passion for Developing Ipf-DEVEL1 0.72(fixed) -

Ipf-DEVEL2 0.96(13.71) 0.07

Ipf-DEVEL3 0.79(11.28) 0.07

Ic-DEVEL4 0.64(10.66) 0.06

Political Ties POL1 0.84(fixed) -

POL2 0.90(10.00) 0.09 POL3 0.97(10.77) 0.09 POL4 0.73(9.12) 0.08 Environmental Dynamism DYNM1 0.69(fixed) - DYNM2 0.88 (9.77) 0.09 DYNM3 0.83(11.85) 0.07

Business Growth GROW1 0.86 (fixed) -

GROW2 0.94 (10.44) 0.09

Fit indices: χ2_{= 319.80; df=194; p-value=0.00; RMSEA= 0.04; NFI=0.92; NNFI=0.93;}

CFI=0.94; IFI=0.94; GFI=0.96; SRMSR=0.046 Note: IPF=Intense positive feelings; IC=Identity centrality

As can be seen from Table 7.13, the model returned a good solution and all the factor loadings were positive and significant (p˂.01). This CFA model included items of the

hypothesised theoretical constructs: passion for inventing, passion for founding, passion for developing, political ties, environmental dynamism and business growth. Each construct was

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allowed to only load on one construct for which it was an indicator. With regards to the fit indices, the model demonstrated a good fit (i.e. RMSEA= 0.04 NFI=0.92; NNFI=0.93; CFI= 0.94; IFI=0.94; GFI=0.96 and SRMSR=0.05) except the χ2= 319.80 (df=194; p-value=0.00) which was significant at 1% level (p˂.01). The NFI is 0.92 which is greater than 0.90 indicating a reasonable fit (Baggozi and Yi, 2012; Boso, Story and Cadogan, 2013).

Table 7. 14: Fit indices for the measurement models

CFA Models χ2 _{DF χ}2_{/DF p-} Value RMSE A SRMSR NNFI CFI GF I Measurement (set 1) 146.49 92 1.59 .13a _{.03 .04 .96 .96 .98} Measurement (set 2) 163.46 53 3.08 .01 .05 .03 .98 .98 .95

Full measurement (set 3) 319.80 194 1.65 .00 .04 .04 .93 .94 .96

Measurement (set 1): Passion for inventing, passion for developing and passion for founding Measurement (set 2): political network ties, perceived environmental dynamism and firm growth Measurement (set 3): all items retained in set 1 through to 2 were modelled simultaneously RMSEA: root mean square error of approximation

NNFI: non-normed fit index CFI: comparative fit index

SRMSR: standardized root mean square residual a Not significant at α=0.05

Moreover, the standard errors for the items were quite low. As a consequence, the model result provides support for the robustness of the measurement items used (Ping, 2004). Following this view, the present study depended on the parameters from CFA model for further analysis.