REFINEMENT OF THE CONSTRUCTS

Before the regression analysis commenced, the current study clearly defined the individual constructs and examined the reliability and validity of the respective measurement scales. According to Hair et al., (2006) it is important to refine all constructs before the data analysis. Following this view, exploratory factor analysis (EFA) was used to select items that loaded on a factor in order to reduce the number of items. Factor analysis is a technique employed to gauge the extent to which measurement overlap (Field, 2009) with the view to reducing measurable and observable variables to fewer latent variables that share a common variance (Bartholomew, Knott and Moustaki, 2011). Similarly, EFA is a procedure used to discover the number of factors influencing variables and to examine the variables that move together (DeCoster, 1998).

Following this notion, EFA with an oblimin Kaiser Normalisation rotation was used to allow for a particular item to load on multiple factors, thus highlighting its true influence across all factors (e.g., Hair et al., 2006; Samiee and Chabowski, 2012). In extracting the factors, the widely accepted principal component analysis criterion (i.e. eigenvalue ˃ 1) was used. Given sample restriction, EFA was performed on each construct. To implement this strategy, EFA on business growth items was performed first. Second, EFA was performed on political ties items. Third, entrepreneurial passion items were analysed. Fourth, environmental dynamism

147

items were analysed. For completeness and in order to demonstrate support for the robustness of the items used in the study, EFA was performed on all constructs involving all good items (items that had factor loadings exceeding 0.40).

7.3.1 Scale for Business Growth

This scale contained items tapping one component of business growth. Initially, factor analysis was conducted in the bunsiness growth scale to discover the variables that load on the factor. Two items namely; sales growth rate and employee growth loaded on the same factor. The first item relates to employee growth in accordance with literature measuring business growth (e.g., Zahra, 1993; Vaessen and Keeble, 1995; Peters and Brush, 1996; Delmar, 1997; Davidsson and Wiklund, 2000). The second item relates to sales growth. The literature has acknowledged the use of sales growth in measuring business growth (e.g., Miller, 1987; Dunne and Hughes, 1996; Ardishvili et al., 1998; Weinzimmer et al., 1998). Accordingly, the current study assessed these items in a factor. All the two items comprising the factor measuring growth were therefore run in a single exploratory factor analysis. The principal component analysis extraction method and direct oblimin rotation technique were employed. The result of the exploratory factor analysis of business growth scale is reproduced in Table 7.7.

Table 7. 7: Factor Matrix of the Scale for Firm Growth

Items Factor Loadings

GROW1: Employee growth .758

GROW2: Sales growth .714

Eigenvalue: 3.21

% Variance explained: 60.28 KMO: 0.76

Barlett’s Test: 567.82 (Sig. 0.000)

Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization. a. 1 component extracted.

148

As can be seen from Table 7.7, EFA retained all the two items loading in one factor. In all, a total of 60.28% cumulative extracted variance was obtained. As such the growth scale was taken to the next stage of the analysis.

7.3.2 Scale for Political Ties

Exploratory factor analysis was conducted in the scale items for political ties in a single EFA. The EFA results represented in Table 7.8 revealed that all the four items for the political ties scale were loaded on a single factor. This study followed previous research and measured political ties (e.g., Acquaah, 2007; Acquaah and Eshun, 2010). In all, a total of 50.91% cumulative extracted variance was obtained. The factor matrix is reproduced in Table 7.8.

Table 7. 8: Factor Matrix of the Scale for Political Ties

Items Factor Loadings

POL_TIE2 .798 POL_TIE3 .697 POL_TIE4 .693 POL_TIE1 .658 Eigenvalue: 2.04 % Variance explained: 50.9 KMO: 0.717

Bartlett’s Test: 204.36 (Sig. 0.000)

Extraction Method: Principal Component Analysis. a. 1 component extracted.

7.3.3 Scale for Entrepreneurial Passion

The three domains of entrepreneurial passion were measured by a total of thirteen items. The analysis of EFA revealed that four items tapped intense positive feelings (IPF) inventing and one item tapped identity centrality (IC) inventing whilst three items captured ipf founding with one item capturing IC founding. The last domain (passion for developing) was captured with four items with three items capturing IPF developing and one item tapping IC developing. All the thirteen items were entered into a single EFA and the results revealed a three-factor solution. All items loaded were on these three domains with loading ˃0.40. These three domains of entrepreneurial passion have received much attention in the literature

149

(e.g., Cardon et al., 2009a; Cardon et al., 2013; Cardon and Kirk, 2015). The three-factor solution is reported in Table 7.9. From Table 7.9, a three-factor solution showing the three domains of passion for inventing, founding and developing was obtained with a cumulative extracted variance of 63.61%. The eigenvalues were 3.33 (passion for invent), 2.85 (passion for founding) and 2.08 (passion for developing). Table 7.9 below shows the factor matrix of the scale for entrepreneurial passion.

Table 7. 9: Factor Matrix of the Scale for Entrepreneurial Passion

Items Factor Loadings Invent Found Devel

Ipf-INVENT1 .846 Ipf-INVENT2 .833 Ic-INVENT1 .811 Ipf-INVENT4 .802 Ipf-INVENT3 .767 Ipf-FOUND3 .826 Ipf-FOUND2 .798 Ipf-FOUND1 .788 Ic-FOUND .713 Ipf-DEVEL2 .833 Ipf-DEVEL3 .827 Ipf-DEVEL1 .817 Ic-DEVEL1 .659 Eigenvalue 3.33 2.84 2.09 % Variance explained 25.65 21.89 16.07 KMO: .791

Bartlett’s Tests: 1706.123 (Sig. 0.000) % of Variance Extracted 63.61%

Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.

Note: IPF=Intense positive feelings; IC=identity Centrality

7.3.4 Scale for Environmental Dynamism

The scale for environmental dynamism was analysed in a single EFA. The EFA results depicted in Table 7.10 shows that only three items out of four items loaded on a single factor. Thus, one of the four environmental dynamism items was removed due to high cross-loadings with other factors. The one factor solution for environmental dynamism was obtained with a cumulative extracted variance of 62.89%. As has been argued in the entrepreneurship and

150

small business literature, this factor is important for measuring environmental dynamism which refers to the rate of change and degree of unpredictability of factors within an environment (e.g., Duncan, 1972; Daft and Weick, 1984; Miller and Friesen, 1982a; Miller, 1987; Sakarya, Eckman and Hyllegard, 2007; Fini et al., 2009; Fini et al., 2012). Accordingly, the environmental dynamism scale was taken to the next stage of the analysis (simultaneous analysis of all items). Table 7.10 below depicts the factor matrix of the scale for environmental dynamism.

Table 7. 10: Factor Matrix of the Scale for Environmental Dynamism

Items Factor Loadings

DYNM2 .772 DYNM1 .736 DYNM3 .671 Eigenvalue: 2.58 % Variance explained: 62.89% KMO: 0.811

Bartlett’s Test: 83.14 (Sig. 0.000)

Extraction Method: Principal Component Analysis. a. 1component extracted.

7.3.5 Passion, Political Ties, Dynamism and Growth Scales

Having performed EFA to assess the individual scales and having selected the items that loaded well (loadings ˃.40) on their respective factors, it is important to examine the extent to which each item performed in relation to other items. Accordingly, this section provides an account of simultaneous analysis of all items in a single exploratory factor analysis. The EFA results produced six factors in line with what was predicted. Thus, no item was further deleted as all the returned items produced factor loadings ˃.40. The retained items explained 60.21% of the total variances. As a result, the retained items were considered in the regression analysis. Table 7.11 below depicts the pattern matrix for the EFA for all scales (passion, political ties, environmental dynamism and business growth).

151

Table 7. 11: EFA for Passion, Political Ties, Dynamism and Business Growth Items Factor Loadings

INVENT FOUND DEVEL POL DYNM GROW

Ipf-INVENT1 .838 Ipf-INVENT2 .827 Ic-INVENT1 .809 Ipf-INVENT4 .802 Ipf-INVENT3 .781 Ipf-FOUND3 .822 Ipf-FOUND2 .801 Ipf-FOUND1 .792 Ic-FOUND1 .679 Ipf-DEVEL2 .828 Ipf-DEVEL3 .801 Ipf-DEVEL1 .795 Ic-DEVEL1 .692 POL2 .810 POL3 .723 POL1 .630 POL4 .621 DYNM2 .776 DYNM1 .722 DYNM3 .629 GROW1 .751 GROW2 .613 Eigen value 3.42 3.16 2.36 1.91 1.50 1.06 % Explained Variance 15.46 14.26 10.52 8.53 6.64 4.80

Note: IPF=Intense positive feelings; IC=identity Centrality KMO: 0.75

Bartlett’s Test: 2342.08 (Sig. 0.00) Only factor loadings > .40 are shown.

Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization. % Total cumulative variance explained: 60.21%