List of Abbreviations
Chapter 6: Chapter 6: Findings on the Associations of Organisation Size and Employee Roles with the BSC
6.8 Data analysis
6.8.4 Assessment of convergent validity
According to Bartholomew et al. (2002), factor loading is the correlation between the observed variables and a latent variable. If high convergent validity occurs, high loadings in a factor would indicate that they converge on a common point, the latent construct (Hair et al. 2010). Table 6.15 provides complete information regarding factor loading of all observed variables.
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Table 6.15 Factor analysis of the variables (rotated factor loading)
Latent Construct Code Observed Variable Factor
Strategy translation
st1 Our organisation’s strategy map helps me to better understand our organisation’s strategic objectives
0.61 st2 I know all about our organisation’s key performance indicators 0.90 st3 I know all about our organisation’s key performance indicator
targets
0.92 st4 My KPIs relate to the organisation’s strategic objectives 0.54 Strategy
alignment
sa1 The organisational KPIs are cascaded proportionately 0.75 sa2 The organisational KPI targets are cascaded proportionately 0.82 sa3 My KPIs are aligned with those of other colleagues at the same level 0.64 sj1 My KPIs are in line with organisational KPIs 0.68
sj2 I have good quality KPIs 0.76
sj3 My KPI targets are realistic 0.71
sj4 My KPI targets support the accomplishment of organisational targets
0.69 sj5 There is active dialogue with my direct supervisor when
determining my KPIs
0.73 sj6 There is active dialogue with my direct supervisor when
determining my KPI targets
0.69 sj7 The rewards system is linked to KPI target achievement 0.52 sp1 My competence development is aligned with organisational
strategy
0.68 sp2 BSC management is supported with sound information technology 0.79 sp3 I find the performance reporting procedure easily understandable 0.78
sp4 Performance reporting requires solid proof 0.65
Leadership involvement
li1 The head of the organisation educates me about performance management based on the BSC
0.71 li2 The head of the organisation provides sufficient information
regarding the organisation’s strategies and goals
0.76 li3 I know all about our organisation’s objectives 0.71 li4 The head of the organisation motivates me to accomplish my
targets
0.80 li5 My direct supervisor actively guides me to reach my performance
goals
0.69 li6 My direct supervisor monitors my performance through my KPIs 0.68
Table 6.16 shows that all loading factors are above 0.4, indicating that they are good factors for the purposes of CFA (Hair et al. 2014, p. 136). Given these results, all items were retained at this stage and adequate evidence of validity convergence for the BSC CFA model was considered to have been provided.
159 6.8.5 Assessment of discriminant validity
According to Hair et al. (2010), discriminant validity is the extent to which a latent variable differs from other latent variables. In this research, discriminant validity was examined by comparing the average variance extracted (AVE) estimates for each factor with the squared inter-construct correlations associated with that factor. The rule is that there are no problems with discriminant validity when AVE estimates are greater than the corresponding inter-construct squared correlation estimates (Hair et al. 2010, p. 723). The first step was to calculate the AVE using the following formula:
AVE = ∑ 𝐿𝑖2
𝑛
𝑖=1
Where Li represents the standardised factor loading and i is the number of items.
An AVE should be calculated for all latent variables (Hair et al. 2006, p. 709).
Table 6.16 BSC standardised factor loadings, average variance extracted and AVE estimates
Variable ST SA SJ SP LI
My key performance indicators are aligned with those of other colleagues at the same level
0.61 My key performance indicators are in line with the organisational key performance indicators
0.90 I have good quality key performance indicators 0.92 My key performance indicator targets are realistic 0.54 My key performance indicator targets support the accomplishment of organisational targets
0.75 There is active dialogue with my direct supervisor when
determining my KPIs
0.82 There is active dialogue with my direct supervisor when
determining my KPI targets
0.64
The rewards system is linked to KPI target achievement 0.68
My competence development is aligned with organisational strategy
0.76 BSC management is supported with sound information technology 0.71 I find the performance reporting procedure easily understandable 0.69
Performance reporting requires solid proof 0.73
The head of the organisation educates me about performance management based on BSC
0.69 The head of the organisation provides sufficient information
regarding the organisation’s strategies and goals
0.52
I know all about our organisation’s objectives 0.68
The head of the organisation motivates me to accomplish my targets
0.79 My direct supervisor actively guides me to reach my performance
goals
0.78
160
My direct supervisor monitors my performance through my key performance indicators
0.65 Our organisation’s strategy map helps me to better understand
our organisation’s strategic objectives
0.71
I know all about our organisation’s KPIs 0.76
I know all about our organisation’s KPI targets 0.71
My key performance indicators relate to the organisation’s strategic objectives
0.80
The organisational KPIs are cascaded proportionately 0.69
The organisational KPI targets are cascaded proportionately 0.68
Average variance extracted (AVE) 58% 54.8% 47.13% 52.9% 52.7%
The (AVE) estimates illustrated in Table 6.16 are greater than the corresponding inter-construct squared correlation estimates as shown in Table 6.17. Hence, there is no problem with discriminant validity for the BSC implementation model for this research.
Table 6.17 Construct correlation matrix (standardised)
st sa sj sp li
Strategy translation 1 0.08 0.09 0.14 0.18
Strategy alignment 0.28‡ 1 0.07 0.07 0.08
Strategy as everyone’s everyday job
0.30‡ 0.26‡ 1 0.10 0.1
Strategy as a continuous process 0.38‡ 0.27‡ 0.32‡ 1 0.18 Leadership involvement 0.43‡ 0.29‡ 0.31‡ 0.42‡ 1 Significance levels: * = 10% or less; † = 5% or less; ‡ = 1% or less.
Note: item values below the diagonal are correlation estimates among constructs, diagonal elements are constructed variances, and values above the diagonal are squared correlations.