List of Abbreviations
Chapter 6: Chapter 6: Findings on the Associations of Organisation Size and Employee Roles with the BSC
6.8 Data analysis
6.8.6 Assessment of nomological validity
6.8.7.2 Modification indices process
Following Hooper et al. (2008), the researcher performed several error term correlations; these were chosen by considering the highest modification index values together with the logical reasoning of the correlation. Having considered these factors, twelve error term correlations were created. The first step in doing so was to create error correlation pairs between observed variables within a single latent variable. This part of the process involved five correlated pairs. The first error term correlation items were knowledge about organisational KPIs (st2) and knowledge about organisational KPI targets (st3). Both of these variables are within the same latent variable of strategy translation. The two variables correlate with each other in the sense that they express different dimensions of the same object, where st2 concerns the name of the measurement indicator and st3
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concerns the target of the measurement indicator. The second error term correlation items were direct supervisor guidance in achieving my performance goals (li5) and direct supervisor monitoring of my performance (li6). These observed variables are within the same latent factor of leadership involvement.
They measure the subordinate’s perspective of the direct supervisor’s role to encourage and support the performance of subordinates under their supervision.
Hence, the relationship lies in the fact that the direct supervisor not only monitors but also guides the employees’ performance. The third error term correlation items were active dialogue with a direct supervisor when setting up personal KPIs (sj5) and active dialogue with a direct supervisor when setting up personal KPI targets (sj6). These two observed variables are within the same latent factor, strategy as everyone’s everyday job. The two variables are correlated with one other in that they both refer to dialogue regarding performance with the direct supervisor; Item sj5 concerns the setting up of KPIs, while Item sj6 concerns the determining of KPI targets. Hence, the combining of these two variables to characterise not only supervisors’ interest in good performance measures but also their level of concern regarding the target for the performance measures. The fourth pair of error term correlation items includes KPI quality (sj2) and realistic KPI targets (sj3). These observed variables are both within the same latent factor of strategy as everyone’s everyday job. The two variables are correlated in the sense that they both express characteristics of performance measures, where sj2 concerns the quality of individual KPIs and sj3 concerns individual KPI targets. The fifth error term correlation is between alignment of individual and organisational KPIs (sj1) and individual KPI targets as supporting the accomplishment of organisational targets (sj4). These two observed variables are both within the same latent factor, strategy as everyone’s everyday job. They were both intended to measure whether personal KPIs support the organisation, both in terms of the use of performance measurements and KPIs’ relationship with organisational targets. The combining of these two observed variables indicate the extent to
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which individual KPIs and their targets are in line with organisational KPIs and their targets.
The second part of error term correlation in this research consisted of seven correlations involving observed variables represented by different latent variables.
The occurrence of such error term correlations is likely in the presence of covariance amongst all five latent variables (SFO variables) under the framework of the BSC. The sixth error correlation term concerned active dialogue with a direct supervisor when setting up individual KPIs (sj5) direct supervisor guidance for achieving individual performance goals (li5). The correlation of these two observed variables was based on the fact that they both have the same object (individual KPIs) with different emphases on the role of the direct supervisor: at the beginning (when setting up KPIs) and during the process of accomplishing KPI targets. The seventh error correlation term paired personal KPIs as supporting organisational strategic objectives (st4) with personal KPIs as supporting organisational targets (sj4). These two observed variables were paired based on the consideration that both are concerned with the same dimension, namely, individual KPIs as supportive of organisational KPIs. The eighth error term correlation was between active dialogue with a direct supervisor when setting up individual KPI targets (sj6) and direct supervisor guidance in achieving individual performance goals (li5).
Though the two items are represented by different latent variables, they portray similar dimensions of the direct supervisor role in terms of employee performance. The ninth error term correlation concerned the linkage of the reward system to KPI target achievement (sj7) and the alignment of employee competence development with organisational strategy (sp1). Both observed variables express the dimension of BSC usage as related to the organisational rewards system and personal competence development, respectively. The tenth error term correlation combined the items regarding the link between individual KPIs and organisational strategic objectives (st4) and the alignment of individual KPIs with organisational KPIs (sj1). Both observed variables mention the same
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dimension of measurement (individual KPIs) to organisational goals, thus demonstrating the extent of individual KPI support of organisational KPIs. The eleventh error correlation term pair, the alignment of individual KPIs with those of colleagues at the same level (sa3) and the alignment of individual KPIs with organisational key performance indicators (sj1), were linked because all employees’ individual KPIs are presumed both to align with other colleagues’ at the same level and to support organisational KPIs. The twelfth correlation error term linked active dialogue with a direct supervisor when setting up personal KPIs (sj5) and direct supervisor monitoring of performance (li6). The justification for correlating these two observed variables was that both variables express the same issue: the direct supervisor’s role in setting up individual KPIs and monitoring subordinate performance using those KPIs.
Finally, the results from the CFA showed a good fit, particularly given the RMSEA of 0.069, CFI of 0.930, TLI of 0.915 and SRMR of 0.055, all of which are under the standard threshold (Hu and Bentler 1999; Steiger 2007; Hair et al. 2010, p. 667).
The SFO principles model was then used in further analysis examining the associations with organisational factors, organisational commitment and PSM.
Table 6.18 Comparison of initial and modified models Fit
indices
Initial model
Modified model
Cut-off value
Reference
RMSEA 0.107 0.069 0.07 Hair et al. 2010, p. 667; Steiger 2007
CFI 0.820 0.930 ≥ 0.90 Hu and Bentler 1999
TLI 0.794 0.915 ≥ 0.90 Hu and Bentler 1999
SRMR 0.094 0.055 ≤0.08 Iacobucci 2010; Hooper et al. 2008; Hu and Bentler 1999
Note: Coefficients are statistically significant at ρ <0.01
As can be seen in Tables 6.18 and 6.19, the root mean squared error of approximation (RMSEA) is reported at 0.069; hence it represents a good fit (Steiger 2007; Hair et al. 2010, p. 667). For the standardised root mean squared residual
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(SRMR), the results show the figure 0.055, which is under the maximum (0.08) suggested by Hu and Bentler (1999), Hooper et al. (2008) and Iacobucci (2010).
The CFI value index was 0.930 and the TLI value was 0.915. According to Hair et al.
(2010, p. 669), a cut-off criterion of ≥ 0.90 in both CFI and TLI indicates that the model is a good fit. Thus, according to the fit index results, the modified model of BSC implementation shows a good fit. Table 6.19 provides comprehensive results of the measurement model estimates and the fit statistics of the BSC implementation model. The final results of CFA model are provided in Appendix 8.
Table 6.19 CFA measurement model and fit statistics of BSC implementation
Measurement model estimates
Strategy translation -> st1 0.77(0.12) ‡
Strategy translation -> st2 0.68(0.02) ‡
Strategy translation -> st3 0.70(0.02) ‡
Strategy translation -> st4 0.62(0.02) ‡
Strategy alignment -> sa1 0.79(0.01) ‡
Strategy alignment -> sa2 0.86(0.01) ‡
Strategy alignment -> sa3 0.69(0.01) ‡
Strategy as job -> sj1 0.67(0.01) ‡
Strategy as job -> sj2 0.73(0.01) ‡
Strategy as job -> sj3 0.65(0.02) ‡
Strategy as job -> sj4 0.67(0.02) ‡
Strategy as job -> sj5 0.64(0.02) ‡
Strategy as job -> sj6 0.62(0.02) ‡
Strategy as job -> sj7 0.58(0.02) ‡
Strategy process -> sp1 0.73(0.01) ‡
Strategy process -> sp2 0.78(0.01) ‡
Strategy process -> sp3 0.78(0.01) ‡
Strategy process -> sp4 0.68(0.01) ‡
Leadership involvement -> li1 0.75(0.01) ‡
Leadership involvement -> li2 0.78(0.01) ‡
Leadership involvement -> li3 0.77(0.12) ‡
Leadership involvement -> li4 0.81(0.01) ‡
Leadership involvement -> li5 0.58(0.02) ‡
Leadership involvement -> li6 0.58(0.02) ‡
Fit statistics
Root mean squared error of approximation (RMSEA) 0.069
CFI (CFI) 0.930
Tucker – Lewis Index (TLI) 0.915
Standardised root mean squared residual (SRMR) 0.055
Note: Reported are standardised coefficients (standard errors). Sig.: * = 10% or less; † = 5% or less; ‡ = 1% or less.
The findings provided in Table 6.19 are further discussed in Chapter 9. The final BSC model resulting from the CFA was used in the subsequent analysis, which
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examined the association of BSC implementation with organisational size and strategic employee roles by means of SEM.