This chapter presents the results obtained from the 146 respondents of the survey questionnaire and also the results of interviews. First, the demographic information about the subjects is presented, and then the data interpretation is stated according to the sequence of the research questions. The final phase presents the discussions on the results.
Results
The results section will be divided in many sections, the descriptive statistics and the results for each research questions.
Characteristics of the Participants
This section describes the characteristics of the HR managers who responded to the survey and also the characteristics of the interviewees. The demographic information of the respondents includes: area of the HR discipline, years of experiences, and education levels.
Survey respondents.
During the data collection process 200 questionnaires were distributed, and 146 were returned.
From the 146 responses only 139 (69.5%) were valid and therefore were used for the following analysis.
From the 139 respondents of the survey, 63.3% (n=88) were male and 36.7% (n=51) were female. Regarding the area of work, the main HR discipline of the respondents was in HRM (74.8%, n=104) followed by training (10.8%, n=15), then the HR generalists (8.6%, N=12). With respect to professional experiences in HR, the majority of the respondents had less than five years of experience (56.8%, n=79). In terms of education degrees, 65.5% of respondents had an equivalent to a master degree (n=91). The table 4.1 presents the detailed information about the respondent.
44 Table 4.1.
Demographic Characteristics of the Survey Respondents
Demographics N %
Gender Male 88 63.3
Female 51 36.7
Years of Experience 0-4 79 56.8
5-9 33 23.7
10-14 16 11.5
+15 11 7.9
Area of Discipline Training 15 10.8
OD 8 5.8
HRM 104 74.8
Generalist 12 8.6
Other - -
Education Associate 9 6.5
Bachelor 34 24.5
Master 91 65.5
Doctorate 2 1.4
Other 3 2.1
Participants of interviews.
For the interviews, 4 experts in the HR field were interviewed; table 4.2 gives a description of the demographics of experts.
Table 4.2.
Demographic Characteristics of the Survey Interviewees
Interviewee Gender Years of
Experience
Area of
Discipline Highest Diploma
A M 10 HRM Master
B F 11 HRM Master
C F 28 HRM Master
D M 15 HRM Master
Descriptive Statistics of the Questionnaire
The table 4.3describes the mean of importance and expertise level of each AOEs as well the standard deviation. From the computation above the study research questions 1 to 4 have been answered in the following lines.
45 Table 4.3.
Mean Score and Standard Deviation of HRD Competencies
Competency Frequency Mean SD
Importance Expertise Importance Expertise Performance
Perceived importance of HRD competency domains.
Table 4.4 below, displays the competencies ranked form perceived importance highest mean score to lowest.
Table 4.4.
Perceived Importance of HRD Competencies Ranking
HRD Competency Mean Rank
Coaching 3.95 1
Performance Improvement 3.87 2
Instructional Design 3.79 3
Training Delivery 3.79 4
Integrated Talent Management 3.69 5
Knowledge Management 3.66 6
Managing Learning Programs 3.64 7 Evaluating Learning Impact 3.64 8
Change Management 3.52 9
Learning Technologies 3.44 10
According to the table above the highest rated competency is coaching (M=3.95, SD= .77) followed by performance improvement (M=3.87, SD= .85), training delivery (M=3.79, SD= .93) and instructional design (M=3.79, SD= .93). Although being the highest rated competencies, we can say that almost all the competencies have been rated above the importance score (3.0).
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Therefore we can safely assume that the respondents consider the HRD competencies assessed as relevant to their current function. The lowest rated competency for importance is learning technologies (M=3.44, SD= .92)
Perceived level of expertise in the HRD competency domains
.
Table 4.5 describes the rank of each competency from the highest expertise level score to the lowest expertise level. The highest rated competencies is coaching (M=3.57, SD=.81), followed by training delivery (M=3.45, SD=.95). The lowest rated competencies are learning technologies (M=2.86, SD=.97) and change management (M=3.09, SD=.96). Altogether, the researcher can say that HR managers participating to this study perceive fairly their abilities in the HRD competencies as all competencies were rated below M=4.0, which indicates the proficient level in the scale of measurement.
Table 4.5
Perceived Expertise Level of HRD Competencies Ranking
HRD Competency Mean Rank
Coaching 3.57 1
Training Delivery 3.45 2
Performance Improvement 3.39 3
Instructional Design 3.32 4
Integrated Talent Management 3.3 5 Evaluating Learning Impact 3.24 6 Managing Learning Programs 3.17 7
Knowledge Management 3.15 8
Change Management 3.09 9
Learning Technologies 2.86 10
HR managers in Burkina Faso development needs.
The figure 4.1 represents the development needs of HR managers following the model combining importance and expertise level.
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Figure 4.1. Importance and expertise level of HRD competencies.
As the self-assessment showed and graphed by figure 4.1, HR manager perceive all AOEs as relevant to their job function (M ≥ 3). Also almost all the AOEs except learning technologies were rated above competent level (M≥ 3). For the expertise level of learning technologies, M=2.86 (shown by the arrow), the rating was the lowest rated competency; therefore priority should be given to improve the proficiency of HR managers in the area. Although other competency ranked above the average score of 3.0 for expertise level, they still can be improved.
In order to prioritize the development needs a quadrant model was drawn for the HRD competencies in figure 4.2. The quadrant was drawn in accordance with the mean score of importance and expertise level perception of the AOEs by the HR managers in Burkina Faso. The numbers on the quadrant are the order of priority of needs. The quadrant has been divided into not a priority, first priority, second priority, third priority.
Not a priority refers to competencies that should not be taking into account while prioritizing the competencies to develop. The competencies in this section are competencies which rated for importance M=1 to < 3 and for expertise level M= >3 to 5
0 1 2 3 4 5
Coaching Performance Improvement Instructional Design Training Delivery Integrated Talent Management Knowledge Management Managing Learning Programs Evaluating Learning Impact Change Management Learning Technologies
Expertise Importance
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First priority refers to competencies that should be developed first while prioritizing the competencies to develop. The competencies in this section are competencies which rated for importance M= >3 to 5and for expertise level M= 1 to < 3. In this study only learning technologies can be labelled as first priority.
Second priority refers to competencies that should be developed in second positionwhile prioritizing the competencies to develop. The competencies in this section are competencies which rated for importance M=1 to < 3 and for expertise level M= 1 to < 3.
Third priority refers to competencies that should be developed in third position while prioritizing the competencies to develop. The competencies in this section are competencies which rated for importance M= >3 to 5and for expertise level M= >3 to 5.
Figure 4.2. Prioritizing HRD competencies development needs 1
Not a priority Third priority
Second priority First priority
-Performance Improvement -Training Delivery
-Instructional design
-Integrated Talent Management -Coaching
-Knowledge Management -Change Management
-Learning Technologies
1 3 5
5
1
Expertise Level
Importance Level
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Difference in the perception of demographics groups towards HRD competency expertise level.
This section is divided into the different demographics variables which are: gender, years of experience, work area and education level.
Gender of the respondents.
Table 4.6
Independent Samples Test of Gender on HRD Competencies
Levene's Test for Equality
of Variances t-test for Equality of Means
F Sig. t df
Sig.
(2-tailed)
Mean Difference
Std. Error Difference
Learning Technologies
Equal variances
assumed 3.12 0.079 2.22 136 0.028* 0.37 0.169 Equal
variances not
assumed 2.33 120.99 0.021* 0.37 0.16
Note. * P< .05
From the Table 4.6 it appears that gender only have an impact on how respondents perceive their expertise level in learning technologies competency (t=2.2, p <.05). Therefore we can conclude that there is a statistically difference in the way Male and Female HR managers in Burkina Faso perceive their ability level in learning technologies competency. To ascertain the difference in scoring between male and female HR managers in Burkina Faso, the mean score of each category has been calculated. For learning technologies males scored M= 3.11 and females scored M=2.72.
50 Years of work experience of the respondents.
Table 4.7
Years of Work Experience Groups Scores on HRD Competencies
0-4 5-9 10-14 +15
M SD M SD M SD M SD
Performance Improvement
3.13 0.8 3.67 0.64 3.8 0.54 3.86 1.08
Instructional Design 3.14 1 3.78 0.73 3.85 0.67 4.03 0.96 Evaluating
Learning Impact
3.01 0.8 3.46 0.67 3.66 0.74 3.69 0.82
Training Delivery 3.02 1 3.72 0.68 3.72 0.8 3.72 1.1 Learning
Technologies
2.56 0.9 3.09 0.84 3.44 0.8 3.52 1.19
Managing Learning Programs
2.92 0.8 3.46 0.81 3.42 0.67 3.76 1.08
Integrated Talent Management
2.97 1 3.46 0.96 3.45 0.57 5.02 4.2
Coaching 3.37 0.9 3.63 0.69 4.07 0.46 4.14 0.58 Knowledge
Management
2.95 1 3.34 0.8 3.34 0.73 3.7 0.85
Change Management
2.88 1 3.18 0.97 3.54 0.89 3.65 0.96
After computing the scores of each category, it is perceptible that the different groups have different scoring in the different competencies. To determine if those differences are statistically different, One Way ANOVA has been computed.
51 Table 4.8.
One Way ANOVA of Work Experience Groups on HRD Competencies Sum of
52 perceive their performance improvement (F=7.38, p < .01),training delivery (F=7.17, p < .01) , Evaluating Learning Impact (F=6.01, p < .01) , instructional design (F=6.63, p < .01), learning technology (F=7.73, p < .01) , managing learning programs (F=5.93, p < .01), integrated talent management (F=6.71, p < .01), coaching (F=5.99, p < .01), change management (F=3.35, p < .05) , and knowledge management (F=3.51, p < .05) competencies. To ascertain at which level the differences are located, a Scheffé test was ran.
For performance improvement, there is a significant difference in perceived mean score of expertise level between the category 0 to 4 years of work experience and the category 5 to 9 years (-.54,p < .05), 10 to 14 years (-.66,p < .05) and more than 15 years (-.73,p < .05). This shows that HR managers who have less than 5 years of work experience perceive their performance improvement competency lower than other categories.
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For training delivery, there is a significant difference in perceived mean score of expertise level between the category 0 to 4 years of work experience and the category 5 to 9 years (-.63,p < .05), 10 to 14 years (-.71,p < .05) and more than 15 years (-.88,p < .05). This shows that HR managers who have less than 5 years of work experience perceive their training delivery competency lower than other categories.
For evaluating learning impact, there is a significant difference in perceived mean score of expertise level between the category 0 to 4 years of work experience and the category 10 to 14 years (-.65, p < .05). This illustrates that HR managers who have less than 5 years of work experience perceive their measuring and evaluating competency lower those who have between 10 and 14 years.
For instructional design, there is a significant difference in perceived mean score of expertise level between the category 0 to 4 years of work experience and the category 5 to 9years (-.70, p
< .01). This illustrates that HR managers who have less than 5 years of work experience perceive their instructional design competency lower those who have between 5 and 9 years.
For learning technologies, there is a significant difference in perceived mean score of expertise level between the category 0 to 4 years of work experience and the category 5 to 9 years (-.53, p
< .05), 10 to 14 years (-.88, p < .05) and more than 15 years (-.96, p < .05). This shows that HR managers who have less than 5 years of work experience perceive their learning technology competency lower than other categories.
For managing learning programs, there is a significant difference in perceived mean score of expertise level between the category 0 to 4 years of work experience and the category 5 to 9 years (-.54, p < .05), 10 to 14 years (-.50, p < .05) and more than 15 years (-.84, p < .05). This shows that HR managers who have less than 5 years of work experience perceive their managing learning programs competency lower than other categories.
For integrated talent management, there is a significant difference in perceived mean score of expertise level between the category 0 to 4 years of work experience and the category more than 15 years (-2.04, p < .05). Also there is also a significant difference in mean score between the category +15 and the category 5-9 (-1.56, p < .05). This illustrates that HR managers who have less than 5 years of work experience perceive their integrated talent management competency lower those who have 15 or more years of work experience; in addition HR managers who have
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more than 15 years of work experience perceive their integrated talent management competency lower those who have 5 to 9 years of work experience.
For coaching, there is a significant difference in perceived mean score of expertise level between the category 0 to 4 years of work experience and the category 10 to 14 years (-.69, p< .05) and more than 15 years (-.76, p < .05). This shows that HR managers who have less than 5 years of work experience perceive their performance improvement coaching competency lower than other categories.
Although the one way ANOVA test showed statistical significant differences among demographics for some competencies, the Scheffé test could not find statically different scores between the different categories.
Work area of the respondents.
Table 4.9.
Discipline Area Groups Scores on HRD Competencies
Training OD HRM Generalist
M S.D M S.D M S.D M S.D
Performance Improvement
3.37 0.98 3.91 1.25 3.41 0.72 2.98 0.95 Instructional Design 3.76 0.97 3.83 0.99 3.4 0.9 3.17 1.23 evaluating learning
impact 3.06 0.88 3.83 0.83 3.25 0.81 3.02 0.68
Training Delivery 3.53 0.88 3.95 1.25 3.28 0.96 3 0.85 Learning
Technologies 2.93 1.04 3.38 1.38 2.83 0.95 2.73 0.71 Managing Learning
Programs 3.09 0.98 3.77 1.06 3.17 0.85 2.9 0.84
Integrated Talent
Management 2.91 1.14 5.46 4.87 3.27 0.94 2.62 0.56
Coaching 3.36 0.91 4.1 0.67 3.6 0.8 3.28 0.75
Knowledge
Management 3.08 1 3.75 0.71 3.14 0.92 2.89 0.9
Change Management 3.2 1.22 3.78 0.77 3.06 1.03 2.72 0.61
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After computing the scores of each category, it is perceptible that the different groups have different scoring in the different competencies. To determine if those differences are statistically different, One Way ANOVA has been computed. Only integrated talent management showed a statistically significant mean score difference.
Table 4.10.
One Way ANOVA of Discipline Area Groups on HRD Competencies Sum of
Squares
df Mean Square
F Sig. Summary of the Scheffé Comparisons Integrated
Talent Management
Between Groups
45.34 3 15.11 7.29 .000** OD>Training (2.55**)
OD>HRM(2.19**) OD>Generalist (2.84**) Within
Groups 279.94 135 2.07 Total
325.29 138 Note. ** p< .01, * P< .05
From the table above it appears that the area work discipline has a very significant impact on how respondents perceive their integrated talent management competency F= 7.29(p = 000).
To ascertain at which level the differences are located a Scheffé test was ran. From the Scheffé test results, there is a significant difference in the mean scoring on integrated talent Management between managers who work in organizational development (OD) area and those who work for the training area with mean score difference of 2.55 (p < .01), the human resource management (HRM) area with a mean score difference of 2.65*(p < .01), and the HR generalist with a mean score difference of 3.24*(p < .01).
56 Education level of the respondents.
Table 4.11.
Education Level Groups Scores on HRD Competencies
Associate Bachelor Master Doctorate Other
M S.D M S.D M S.D M S.D M S.D
Performance
Improvement 2.94 0.84 3 0.67 3.58 0.82 3.88 0.88 3.25 0.25 Instructional
Design 2.85 1.23 3.1 0.73 3.65 0.94 3.83 0.7 2.44 0.5 Evaluating
Learning
Impact 2.85 0.94 3 0.7 3.41 0.81 3.5 0.23 2.55 0.38 Training
Delivery 2.44 0.97 3 0.84 3.55 0.92 4 0.47 2.11 1.17 Learning
Technologies 2.44 0.76 2.7 0.83 2.98 1.02 3.38 1.23 2.08 0.8 Managing
Learning
Programs 2.37 0.97 2.7 0.61 3.45 0.85 3.4 0.56 2.53 0.5 Integrated
Talent
Management 2.86 0.95 2.6 0.79 3.63 1.71 3.375 1.23 2.41 0.52 Coaching 3.22 0.7 3.3 0.74 3.71 0.82 4.1 0.14 3.8 0.72 Knowledge
Management 2.83 0.89 2.7 0.87 3.35 0.89 4.125 0.17 2.58 0.38 Change
Management
2.63 0.57 2.7 0.87 3.28 1.05 4 1.06 2 0.86
After computing the scores of each category, it is perceptible that the different groups have different scoring in the different competencies. To determine if those differences are statistically different, One Way ANOVA has been computed. Only integrated talent management showed a statistically significant mean score difference.
57 Table 4.12
One Way ANOVA of Educational Level Groups on HRD Competencies Sum of
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From the table 4.12 it appears that years of educational level has an impact on how respondents perceive their performance improvement (F=4.37, p < .01); training delivery (F=4.08, p < .01);
evaluating learning impact (F=3.41, p < .05); instructional design(F=6.17, p < .01); managing learning programs (F=8.95, p < .01); integrated talent management (F=3.43, p < .05); coaching (F=2.68, p < .05); knowledge management(F=4.84, p < .01) and change management (F=3.79, p
< .01).To ascertain at which level the differences are located a Scheffé test.
For performance improvement, there is a significant difference in the mean scoring between managers who bachelor degree and those who possess a master degree.The mean score difference between the two categories are of .58 (p < .05). This results shows that HRs that have a bachelor perceive their expertise level in performance improvement competency lower than the manager who have a master degree.
For instructional design, there is a significant difference in the mean scoring between managers who associate degree and those who possess a master degree. The mean score difference between the two categories are of 1.11 (p < .05). This results shows that HRs that have an associate degree perceive their expertise level in instructional design competency lower than the manager who have a master degree.
For managing learning programs, there is a significant difference in the mean scoring between managers who possess an associate degree, a bachelor degree, and those who possess a master degree. The mean score difference between Bachelor degree holders and master degree holders is of 1.08 (p < .05).Also the mean score difference between associate degree holders and master degree holders is of .78(p < .05). This results shows that HRs that have an associate degree and
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the bachelor degree holders perceive their expertise level in managing learning programs competency lower than the manager who have a master degree.
For integrated talent management, there is a significant difference in the mean scoring between managers who bachelor degree and those who possess a master degree. The mean score difference between the two categories are of 1.01 (p < .05). This results shows that HRs that have a bachelor perceive their expertise level in performance improvement competency lower than the manager who have a master degree.
For knowledge management, there is a significant difference in the mean scoring between managers who bachelor degree and those who possess a master degree. The mean score difference between the two categories are of .67 (p < .01). This results shows that HRs that have a bachelor perceive their expertise level in performance improvement competency lower than the manager who have a master degree.
Although the one way ANOVA test showed statistical significant differences among demographics for some competencies, the Scheffé test could not find statically different scores between the different categories.
In summary we can say that, apart from gender, there are significant differences in scoring the perceived expertise level in HRD competencies among some subcategory of demographic groups.
Best Development Channel for the HR Managers
This section transcribes the results from the interview of experts. The results are divided into three parts. The first part discusses what should be done from the experts’ point of view to improve HR managers’ competency expertise level, the second part discusses the practical application of the proposed solutions and the last part discusses how to evaluate if development objectives have been achieved. The interviews have been conducted in order to support the study on how to develop the competency of HR managers in HRD.
Enhancing HR managers HRD competencies.
From thepoint of view of expert, in order to enhance The HR managers’ competencies, HRD concepts should be more often taught in schools which have HR programs, and also personalized trainings according to needs could be implemented. Experts also pointed out that seminar on HRD could be organized to enhance the competencies of HR managers. Also interviewee C pointed out
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that it important to sensitize organizations on why it is important to develop HR managers HRD competencies. Interviewee D stressed out the fact that public organization HR managers could be trained in the Ecole Nationale d’ Aministration et de Magistrature (ENAM), as the professional school has a rich curriculum which could enhance HR managers’ competencies.
Implementing development activities.
For this question experts pointed out that , first specific training needs has to be found and this could only done by involving all stakeholders. Also external resources could be used to implement trainings or seminars. Specialization courses could be taken home or abroad by the HR managers, if the budget allows it. Interviewee D pointed that HRD short courses could be taken by HRs in various institutions. To finished, experts pointed out that HRD specialists could be recruited to coach HR managers in big organizations.
Evaluating knowledge acquired.
In order to assess whether or not the objectives of the different training objectives have been achieved, experts re commended immediate practice of learned concepts, by practical exercise on the field. As matter fact interviewee D stated that: “When you train somebody, the least you can ask from the trainee is to put in practice what has been learned”. Practice was referred by “hot”
evaluation by interviewee C, he also added that the “Cold” evaluation would be the long term evaluation by the observation of performance of the HR manager in his human resource development work. Interviewee A also suggested that a professional exam be set to evaluate the knowledge acquired.
The results from the interview can be linked to existing literature, as matter of fact competency development results from a variety of different learning activities (Poel, Van Dam, & van den Berg, 2004). According toTannenbaum & Yukl (1992),training plays an important part of competency development within organizations. Also according to the United Nations competency development guide, four ways have been stressed out as activities that could enhance competencies.
The four competencies are:
Learning by Doing: this refers to taking part of job activities, job simulations and job aids Learning through Training: refers to attending training courses
Learning by Listening/Watching: it refers to observing others and watching video based development.
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Learning by Reading: it refers to reading books on required competencies and skill briefs.
As we can observe, to enhance competencies training still remain relevant.
Discussions
The results of the data analysis put to light the development needs of the HR managers who
The results of the data analysis put to light the development needs of the HR managers who