This construct has 9 (Nine) ordinal variables. Where the respondents selected General Statement 1, they were administered these EO set of questions. 70 (seventy) out of the sample size of 165 (i.e. 42.5%) were categorised as EO type of enterprises. The balance 95 (ninety-five) (57.6%) were categorised as Small Business Owners (SBO) type of enterprises.
5.3.1 Data Screening & Missing Value analysis
The effective sample size univariate (in Diagonal) and Pairwise Bivariate (off Diagonal) shows that there are no missing values.
Innov1 Innov2 Innov3 Proac1 Proac2 Proac3 Risk1 Risk2 Risk3
Innov1 Innov2 Innov3 Proac1 Proac2 Proac3 Risk1 Risk2 Risk3 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70
Table 13: Lisrel 8.8 Data screening output
5.3.2 EO Reliability Tests
Latent Factors What is being measured No. of items
Cronbach Cronbach
if deleted Innovativeness Overall innovativeness 3 .946
Innov1 Strong emphasis on R&D and technological leadership
167
Innov2 New product introduction .907
Innov3 Substantial change in product and service technology .914
Proactiveness Degree of proactiveness
3 .922
Proac1 First movers instead of followers against competitors .854 Proac2 First to introduce new products/services, procedures
and technology
.874
Proac3 Adopt ‘undermine the competitor’ posture .929
Risk Taking Risk taking propensity 3 .917
Risk1 Favour high risk projects (with high return potential) .920
Risk2 Favour bold, proactive and wide-ranging changes rather than incremental changes
.859
Risk3 Adopt bold, aggressive posture to maximise the probability of exploiting potential opportunities
.852
All 9 items Overall Entrepreneurial Orientation of the organisation
9 .971
Table 14: Reliability Tests (Cronbach Alpha)
As evident from the above table, seven (7) of the Nine (9) items consistently reflect the scale used to explain the Entrepreneurial Oriented (EO) construct. Removing the two items Proac3 and Risk1 from their respective sub-constructs would improve the respective reliability scores but this is at best marginal and not significant and so at this stage these two items have been retained.
5.3.3 EO Data Descriptives
The EO construct was measured using a sample of 70 microenterprises. Due to the limited sample size of 70 and the fact that each of the items have been measured on a 7 point Likert scale there are quite a few zero cells in the bivariate
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distribution of the ordinal variables. Joreskog (2005) suggests that there are essentially three options that could be followed under these circumstances:
a) Reduce the number of categories b) Eliminate the most offending variables
c) Replace the assumption of underlying bivariate normality with the assumption of underlying bivariate normality conditional on the covariates.
Since the total sample size used in this research is only 70 effective respondents for the EO construct, option (b) i.e. eliminating the offending variable at this stage may not be feasible. Additionally, given the extent of the zero cells across the nine ordinal variables, it was doubtful whether the inclusion of the covariates (Age of firm, Type, Technology Intensity, Sector) would actually be helpful. In terms of the subsequent use of PCA or Ordinal Regression (OR) analysis, this was not an issue. The PCA by default is a data reduction technique and therefore some of the offending variables would be removed (option b). In the case of Linear or Ordinal Regressions (OR) other covariates (Age of firm, Type, Technology Intensity, Sector) would need to be included as control variables and so the negative impact of zero cells would be mitigated (option c). However, before we can proceed further with this, it is first important to establish whether the EO construct is uni- dimensional or multi dimensional and more importantly, which of the manifest variables from this construct should be eliminated.
5.3.4 Is the EO construct uni-dimensional or multi dimensional?
As an initial step, all nine (9) measured variables derived from the questionnaire were loaded onto the Build Pure Cluster (BPC) algorithm within TETRAD 4.3. As illustrated in Table 15 which summarises the TETRAD output, five (5) out of the nine (9) EO measurements are grouped under one cluster (alpha = 0.05) for
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our sample of 70 EO type microenterprises. Moreover, the reliability of the five items included in the cluster is (Cronbach α= 0.732) is still considerably high. This is evident when in the subsequent analysis a simulated sample of 5000 is used.
Not in clusters Included in Cluster 1 (L_1)
Proac2 Proac3 Risk1 Risk3 Innov1 Innov2 Innov3 Proac1 Risk2
Table 15: Summary of TETRAD Search BPC algorithm for EO construct using observed data. Note: Sample size = 70, Wishart test at Alpha (α) = 0.05
A SEM Monte Carlo simulation was undertaken for a sample size of 5000 in order to confirm that the EO construct is indeed uni-dimensional. As illustrated in Table 16 below, if a simulated sample of 5000 is used, then majority of the EO measurements (i.e. eight out of nine) as suggested by Covin & Slevin (1991) and Runyan et al (2008) actually load onto a singular latent variable. The reliability of the construct derived using simulated data (Cronbach α = 0.729) is still sufficiently high.
Not in clusters Included in Cluster 1 (L_1)
Proac2 Innov1 Innov2 Innov3 Proac1 Proac3 Risk1 Risk2 Risk3
Table 16: Summary of the TETRAD Search BPC algorithm for EO construct using simulated data. Note: Sample size = 5000, TETRAD_Wishart test at Alpha (α) = 0.05
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Therefore, using the simulated data (N=5000) the results confirm that EO is a uni- dimensional construct. However for a small sample size (N=70) not all the manifest variables loaded successfully onto one cluster. It is possible a data reduction technique such as Principle Component Analysis (PCA) used subsequently using a small sample is likely to give us different results. In section 3.2 the following alternative hypothesis was presented
H2(1) The nine measures covering Innovativeness, Proactiveness and risk-taking attributes of a firm used to measure EO cluster around a uni-dimensional construct.
Therefore based on the above results from the simulated data, it is possible to conclude that the Null Hypothesis H2 (0) can be rejected. The alternative
hypothesis H2 (1) that EO is a uni-dimensional construct comprising of Innovativeness, Proactiveness and risk-taking is therefore accepted. We can therefore claim that the construct originally presented by Danny Miller ((1983) and further developed by Covin & Lumpkin (1991) and used by Runyan, et al (2008) is more or less valid when tested using large sample sizes albeit that one of the manifest variables (Proac2) was not included in the cluster.