Question

Question 4 Read the following case extract and answer ALL questions that follow. The survival of businesses in the 21st century depends largely on organisational climate. The organisational competencies and capabilities that steered companies to success in the past will almost certainly not guarantee any success going forward. Today, managers are grappling with the multiple challenges of delivering higher economic returns, improving productivity and reducing cost. In this regard, a deeper understanding of the dynamics of performance management has the potential to help steer an organisation towards an environment that has a competitive advantage (Rowland & Hall, 2012). Employee performance is defined as the outcome or contribution of employees that enable them to attain goals (Herbert, John & Lee 2000). The literature presents evidence of an existence of obvious effects of training and development on employee performance. Some studies have proceeded by looking at performance in terms of employee performance in particular (Purcell, Kinnie & Hutchinson 2003; Harrison 2005) while others have extended to a general outlook of organisational performance (Guest 1997; Swart et al. 2005). In one way or another, the two are related in the sense that employee performance is a function of organisational performance since employee performance influences general organisational performance. In relation to the above, Wright & Geroy (2001) noted that employee competencies change through effective training programs. It therefore not only improves the overall performance of the employees to effectively perform their current jobs but also enhances the knowledge, skills an attitude of the workers necessary for the future job, thus contributing to superior organisational performance. Zambesia Limited is an Information Technology company operating in a Southern African country. As part of a broad set of plans to sharpen the company’s competitive edge, the new Chief Technology Officer (CTO) of the company, Mr Trevor Ncube, has hypothesised that a continuous improvement programme (CIP) consisting of several specialised training sessions for employees would improve employee performance in the long run. A pilot study premised on three specialised training sessions for 200 employees who were randomly selected from the company’s 500 workforce took place during the 2020 fiscal year, at the end of which the impact of the programme was evaluated to determine its efficacy. At the beginning of the exercise, the 200 employees were randomly selected out of 410 individuals who volunteered to take part in the study. The 200 employees were tested for their baseline competence on a scale of 1 to 10 (where 1 denotes the lowest possible score and 10 the highest possible score), using a standardised measurement instrument. The 200 employees were subsequently tested using the same instrument at the end of the first, second and third training sessions. The scores obtained in each of the tests were recorded as shown below in Table 4.1. There was zero participant attrition throughout the continuous improvement programme. Table 4.1: An excerpt of the data collected as part of the CIP (Note: the scores of only ten participants are shown here) Respondent # Pre-training score Post-training1 score Post-training 2 score Post-training 3 score 1 5 5 6 7 2 4 6 7 8 3 5 5 7 7 4 4 7 7 8 5 5 5 6 8 6 5 6 6 7 7 4 5 5 6 8 6 7 7 7 9 5 6 8 8 ... ... ... ... ... 200 5 7 8 8 The data was analysed using IBM SPSS Statistics version 25 and the output showed in Table 4.2 to Table 4.6 was produced. Table 4.2: Test of Normality of Employees’ Competence Scores measured on 4 occasions Tests of Normality Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. Pre_Training_Score .090 200 .004 .969 200 .022 Post_Training1_Score .126 200 .079 .967 200 .488 Post_Training2_Score .072 200 .200* .992 200 .900 Post_Training3_Score .092 200 .257 .964 200 .437 . This is a lower bound of the true significance. a. Lilliefors Significance Correction Table 4.3: Dependent Variables measured on 4 occasions Within-Subjects Factors Measure: MEASURE_1 Time Dependent Variable 1 Pre_Training_Score 2 Post_Training1_Score 3 Post_Training2_Score 4 Post_Training3_Score Table 4.4: Descriptive Statistics of the Dependent Variable measured on 4 occasions Descriptive Statistics Mean Std. Deviation N Pre_Training_Score 5.010 .81438 200 Post_Training1_Score 5.705 .55054 200 Post_Training2_Score 7.240 .66558 200 Post_Training3_Score 7.530 .79055 200 Table 4.5: Multivariate Tests Multivariate Tests Effect Value F Hypothesis df Error df Sig. Partial Eta Squared Time Pillai's Trace .643 21.252 3.000 197.000 .000 .643 Wilks' Lambda .455 21.252 3.000 197.000 .000 .643 Hotelling's Trace 1.152 21.252 3.000 197.000 .000 .643 Roy's Largest Root 1.152 21.252 3.000 197.000 .000 .643 a. Design: Intercept Within Subjects Design: Time b. Exact statistic Table 4.6: Test of Sphericity Mauchly's Test of Sphericity Measure: MEASURE_1 Epsilon Within Subjects Effect Mauchly's W Approx. Chi-Square df Sig. Greenhouse-Geisser Huynh-Feldt Lower-bound Time .878 25.232 5 .104 .661 .723 .333 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a. Design: Intercept Within Subjects Design: Time b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. Table 4.7: Test of Within-Subjects Effect Tests of Within-Subjects Effects Measure: MEASURE_1 Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Time Sphericity Assumed 423.021 3 141.007 58.228 .000 .542 Greenhouse-Geisser 423.021 1.762 237.366 58.228 .000 .542 Huynh-Feldt 423.021 1.845 229.280 58.228 .000 .542 Lower-bound 423.021 1.000 423.021 58.228 .000 .542 Error(Time) Sphericity Assumed 1445.504 597 2.422 Greenhouse-Geisser 1445.504 354.971 4.077 Huynh-Feldt 1445.504 367.210 3.938 Lower-bound 1445.504 199.000 7.265 Table 4.8: Test of Within-Subjects Effect Pairwise Comparisons Measure: MEASURE_1 (I)Time (J) Time Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval for Difference Lower Bound Upper Bound 1 2 -1.745 .123 .003 -1.986 -1.504 3 -2.230 .098 .000 -3.432 -1.028 4 -2.520 1.158 .000 -5.190 -2.500 2 1 1.745 .123 .003 1.504 1.986 3 -.485 .938 .042 -1.725 .755 4 -1.175 .331 .000 -2.350 .552 3 1 2.230 .098 .000 1.028 3.432 2 .485 .938 .042 -.420 2.100 4 -.690 .754 .033 -2.245 .865 4 1 2.520 1.158 .000 .830 5.190 2 1.175 .331 .000 .650 3.000 3 .690 .754 .033 -.865 2.245 Based on estimated marginal means . The mean difference is significant at the .05 level. a. Adjustment for multiple comparisons: Bonferroni. 4.1 Specify and elaborate on the type of research conducted by Mr Ncube in the study and the research design employed. (4 marks) 4.2 Specify the independent and dependent variables implicit in Mr Ncube's study. (2 marks) 4.3 Formulate the null and alternative hypotheses for Mr Ncube's study. (2 marks) 4.4 On the basis of the output, discuss whether the impact evaluation was performed using a parametric test or a non-parametric test. Substantiate your answer. (3 marks) 4.5 With the aid of the statistical decision making tree, specify the main inferential statistical test which was performed as part of the data analysis to evaluate the impact of the continuous improvement programme on employees’ performance and substantiate the appropriateness of this test. (2 marks) 4.6 Provide a comprehensive interpretation of the output, stating the primary findings from the study, and specify whether the training programme (i.e., the intervention) yielded a significant improvement in employee scores? (4 marks) 4.7 On the basis of a preliminary literature review, state ANY TWO (2) actions that Mr Ncube could have taken to address the problem at Zambesia Limited without conducting the study. (2 marks)

Question 4
Read the following case extract and answer ALL questions that follow.
The survival of businesses in the 21st century depends largely on organisational climate. The organisational competencies and capabilities that steered companies to success in the past will almost certainly not guarantee any success going forward. Today, managers are grappling with the multiple challenges of delivering higher economic returns, improving productivity and reducing cost. In this regard, a deeper understanding of the dynamics of performance management has the potential to help steer an organisation towards an environment that has a competitive advantage (Rowland & Hall, 2012).
Employee performance is defined as the outcome or contribution of employees that enable them to attain goals (Herbert, John & Lee 2000). The literature presents evidence of an existence of obvious effects of training and development on employee performance. Some studies have proceeded by looking at performance in terms of employee performance in particular (Purcell, Kinnie & Hutchinson 2003; Harrison 2005) while others have extended to a general outlook of organisational performance (Guest 1997; Swart et al. 2005). In one way or another, the two are related in the sense that employee performance is a function of organisational performance since employee performance influences general organisational performance. In relation to the above, Wright & Geroy (2001) noted that employee competencies change through effective training programs. It therefore not only improves the overall performance of the employees to effectively perform their current jobs but also enhances the knowledge, skills an attitude of the workers necessary for the future job, thus contributing to superior organisational performance.
Zambesia Limited is an Information Technology company operating in a Southern African country. As part of a broad set of plans to sharpen the company’s competitive edge, the new Chief Technology Officer (CTO) of the company, Mr Trevor Ncube, has hypothesised that a continuous improvement programme (CIP) consisting of several specialised training sessions for employees would improve employee performance in the long run. A pilot study premised on three specialised training sessions for 200 employees who were randomly selected from the company’s 500 workforce took place during the 2020 fiscal year, at the end of which the impact of the programme was evaluated to determine its efficacy.
At the beginning of the exercise, the 200 employees were randomly selected out of 410 individuals who volunteered to take part in the study. The 200 employees were tested for their baseline competence on a scale of 1 to 10 (where 1 denotes the lowest possible score and 10 the highest possible score), using a standardised measurement instrument. The 200 employees were subsequently tested using the same instrument at the end of the first, second and third training sessions. The scores obtained in each of the tests were recorded as shown below in Table 4.1.
There was zero participant attrition throughout the continuous improvement programme.
Table 4.1: An excerpt of the data collected as part of the CIP (Note: the scores of only ten participants are shown here)
Respondent # Pre-training score Post-training1 score Post-training 2 score Post-training 3 score
1 5 5 6 7
2 4 6 7 8
3 5 5 7 7
4 4 7 7 8
5 5 5 6 8
6 5 6 6 7
7 4 5 5 6
8 6 7 7 7
9 5 6 8 8
...
...
...
...
...
200 5 7 8 8
The data was analysed using IBM SPSS Statistics version 25 and the output showed in Table 4.2 to Table 4.6 was produced.
Table 4.2: Test of Normality of Employees’ Competence Scores measured on 4 occasions
Tests of Normality
Kolmogorov-Smirnov Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Pre_Training_Score .090 200 .004 .969 200 .022
Post_Training1_Score .126 200 .079 .967 200 .488
Post_Training2_Score .072 200 .200* .992 200 .900
Post_Training3_Score .092 200 .257 .964 200 .437
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
Table 4.3: Dependent Variables measured on 4 occasions
Within-Subjects Factors
Measure: MEASURE_1
Time Dependent Variable
1 Pre_Training_Score
2 Post_Training1_Score
3 Post_Training2_Score
4 Post_Training3_Score
Table 4.4: Descriptive Statistics of the Dependent Variable measured on 4 occasions
Descriptive Statistics
Mean Std. Deviation N
Pre_Training_Score 5.010 .81438 200
Post_Training1_Score 5.705 .55054 200
Post_Training2_Score 7.240 .66558 200
Post_Training3_Score 7.530 .79055 200
Table 4.5: Multivariate Tests
Multivariate Tests
Effect Value F Hypothesis df Error df Sig. Partial Eta Squared
Time Pillai's Trace .643 21.252 3.000 197.000 .000 .643
Wilks' Lambda .455 21.252 3.000 197.000 .000 .643
Hotelling's Trace 1.152 21.252 3.000 197.000 .000 .643
Roy's Largest Root 1.152 21.252 3.000 197.000 .000 .643
a. Design: Intercept
Within Subjects Design: Time
b. Exact statistic
Table 4.6: Test of Sphericity
Mauchly's Test of Sphericity
Measure: MEASURE_1
Epsilon
Within Subjects Effect Mauchly's W Approx. Chi-Square df Sig. Greenhouse-Geisser Huynh-Feldt Lower-bound
Time .878 25.232 5 .104 .661 .723 .333
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.
a. Design: Intercept
Within Subjects Design: Time
b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table.
Table 4.7: Test of Within-Subjects Effect
Tests of Within-Subjects Effects
Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
Time Sphericity Assumed 423.021 3 141.007 58.228 .000 .542
Greenhouse-Geisser 423.021 1.762 237.366 58.228 .000 .542
Huynh-Feldt 423.021 1.845 229.280 58.228 .000 .542
Lower-bound 423.021 1.000 423.021 58.228 .000 .542
Error(Time) Sphericity Assumed 1445.504 597 2.422
Greenhouse-Geisser 1445.504 354.971 4.077
Huynh-Feldt 1445.504 367.210 3.938
Lower-bound 1445.504 199.000 7.265
Table 4.8: Test of Within-Subjects Effect
Pairwise Comparisons
Measure: MEASURE_1
(I)Time (J) Time Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval for Difference
Lower Bound Upper Bound
1 2 -1.745 .123 .003 -1.986 -1.504
3 -2.230 .098 .000 -3.432 -1.028
4 -2.520 1.158 .000 -5.190 -2.500
2 1 1.745 .123 .003 1.504 1.986
3 -.485 .938 .042 -1.725 .755
4 -1.175 .331 .000 -2.350 .552
3 1 2.230 .098 .000 1.028 3.432
2 .485 .938 .042 -.420 2.100
4 -.690 .754 .033 -2.245 .865
4 1 2.520 1.158 .000 .830 5.190
2 1.175 .331 .000 .650 3.000
3 .690 .754 .033 -.865 2.245
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
a. Adjustment for multiple comparisons: Bonferroni.
4.1 Specify and elaborate on the type of research conducted by Mr Ncube in the study and the research design employed. (4 marks)
4.2 Specify the independent and dependent variables implicit in Mr Ncube's study. (2 marks)
4.3 Formulate the null and alternative hypotheses for Mr Ncube's study. (2 marks)
4.4 On the basis of the output, discuss whether the impact evaluation was performed using a parametric test or a non-parametric test. Substantiate your answer. (3 marks)
4.5 With the aid of the statistical decision making tree, specify the main inferential statistical test which was performed as part of the data analysis to evaluate the impact of the continuous improvement programme on employees’ performance and substantiate the appropriateness of this test. (2 marks)
4.6 Provide a comprehensive interpretation of the output, stating the primary findings from the study, and specify whether the training programme (i.e., the intervention) yielded a significant improvement in employee scores? (4 marks)
4.7 On the basis of a preliminary literature review, state ANY TWO (2) actions that Mr Ncube could have taken to address the problem at Zambesia Limited without conducting the study. (2 marks)

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