DESIRABLE CONSTRUCTION MANAGEMENT SKILLS IN THE

SOUTH AFRICAN CONSTRUCTION INDUSTRY:

METHODOLOGY FOR ASSESSMENT

Dr Nicholas Chileshe1, Ph.D., MSc (Eng), MSc(Mgt), MCIOB, MBEng, MCIM, PgCertLT, ILTHEM

School of Environment & Development, Sheffield Hallam University, UK

N.Chileshe@shu.ac.uk 1Visiting Research Fellow, SABERC, Cape Peninsula University of Technology

Chileshen@cput.ac.uk

Ferdi Fester

University of Johannesburg

ffester@twr.ac.za

Dr Theo C. Haupt, Ph.D., M.Phil., FCIOB, MASI

Cape Peninsula University of Technology

hauptt@cput.ac.za

ABSTRACT

Within the South African Construction Industry and indeed worldwide,

the issue of what should be the desirable skills that construction management

graduates should posses varies between academia and industry's expectations.

Despite the existence of a range of intervention strategies used to assess the

quality of construction graduates emanating from universities in the

disciplines of construction management, little research has been undertaken to

examine if these interventions can be applied to the South African

Construction Industry. Thus the noted omission has been the motivation for

this research project. The main aims of this paper are twofold; firstly, to

identify the major skills desirable for construction management graduates,

secondly to develop a technique for evaluating the construction management

graduate skills.

Through a survey of 58 South African construction related organisations,

this study identifies the 37 skills that are lacking from technikon graduates in

construction management disciplines. The survey results indicate that

industry rate trust and honesty as the most desirable skill that graduates

should posses. Ability to conduct research is the least ranked skill by industry.

The findings indicate that the methodology adopted is reliable, valid and

undimensional.

This paper concludes by presenting the instrument which contributes to

the formulation and design of improved model of delivery that addresses the

perceived gap between industry and academia.

KEYWORDS: Construction Industry, Construction Management, Skills,

South Africa,

1.0 INTRODUCTION

The South African educational landscape has changed to accord with government’s

commitment to transformation and increased access to higher education by all its citizens.

Consequently, institutions previously known as Technikons are being transformed into

Universities of Technology, charged with the responsibility of delivering instructional

offerings that are both relevant and responsive to the needs and priorities of the national

economy. These institutions are expected to offer career-oriented educational programs

designed to meet the needs of industry and commerce in a hi-tech global economic

environment. Their approach to education is practical and outcomes-based, with the intent

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

2

that their graduates are immediately employable and productive. A key component that

ensures this productivity is the inclusion into technikon programmes of periods of experiential

learning spent in industry (Haupt 2003). This collaborative approach to education, also known

as co-operative education, has 3 stakeholders namely, the institution, industry and students.

Co-operative education includes periods of academic study alternating with a period of related

work experience and prepares the student well for the class-to-work transition. For this goal to

be reached it is necessary that Technikons offer relevant training to adequately prepare their

graduates for the workplace. As observed by McCaffer (2004), "universities and companies

have to work together to promote degree programmes..." (page 19). One way of achieving this

goal would be through the incorporation and integration of Courses offered at National

Building diploma into Construction Management and Quantity Surveying learning outcomes.

Watson and Chileshe (2004) provided a 'unit guide learning outcomes frameworks' using the

example of Project Management drawing some of the experiences from the Chartered

Institute of Building (CIOB) code of Practice for Project Management for Construction and

Development (1996). Furthermore, experiential learning has long been recognized as among

the most effective means of acquiring professional education and training (Tinker and Tramel,

2002; Beliveau and David, 2002, Hicks, 1996)

The views of samples were canvassed nationally using self administered structured

questionnaires of 162 first year students drawn from three institutions and two disciplines

namely construction management and quantity surveying on the nation diploma program.

This paper reports on the findings of the first year students in the National Diploma for the

B.Tech programs. The findings are statistically reported descriptively together with measures

of central tendency, particulaly the means of responses. Equations are also computed to

generate various ranges of indices to enable the assessment of readiness of the building

diplomates. One of the objectives of this study was to examine the levels of knowledge and

skills and attributes needs of industry vis-à-vis Technikon education in the fields of

construction management in order to establish the extent of the perceived mismatch between

sector needs and academic programs at Universities of Technologies within the South African

Construction Industry.

1.1 Academic Subjects Offered in the Built Environment Programme

It is generally accepted that industries that make up the built environment need professionals

with qualifications to increase their effectiveness and create a well trained and motivated

workforce. South Africa is no different, as such during the 1980s the employment market

identified the skills that technicians should possess. These skills were quantified in terms of

time required to acquire each critical skill, and the best place at which to acquire them. It is

against this background that for all engineering programs, approximately one third of the

required skills could be learnt in the work place, and the remainder in the classroom.

2.0 METHODOLOGY

For any research, it is recommended that the response rate should be reported to indicate the

extent of the sample frame polled. What then should be the acceptable response rate? Also,

what should be the appropriate mechanism for managing the questionnaires? According to

Moser and Kalton (1979), the probable numbers of refusals and non-contacts can be roughly

estimated from the pilot survey and the effectiveness of various ways of reducing non-

responses can be compared. It is generally acknowledged that the main problem with mail

surveys is that of getting an adequate response rate. Moser and Kalton (1979) identified

among the factors influencing the response rate as that of its sponsorship, population and

subject matter. Depending on the nature of research, response rates may vary. However for

survey-based research, a 20% response rate is acceptable. (Malhotra and Grover, 1998).

Based on Frolich (2002), the following techniques were adopted to improve the response rate:

existing scales, results, pre-notice and multiple mailings.

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

3

2.1 Missing Data It is acknowledged by various researchers that missing and incomplete data of any studies

could lead to wrong conclusions being drawn. Missing data is normally of two types, one

called item non-response, where a question is left unanswered, and unit non-response, where

the whole questionnaire is omitted. The first type was resolved by a commonly used method

known as ‘simple mean imputation’. Alternatively, the approach would be to exclude the

data, though this approach could be misleading. Where a value for a sliding scale was

missing, the ‘median’ for that particular question was included. The median was used for a

number of different reasons. Firstly, the rest of the questions were all whole numbers and to

include the ‘mean’ would have moved away from this. Secondly, a number of questions

were skewed (see the results of the descriptive statistics in the appendix D), with a number of

outlying responses lowering the mean. Therefore the median value can be seen to give a

more accurate picture of what the average respondent thought.

2.2 Data Analysis

2.2.1 Cluster Analysis Using Discriminant Analysis and Canonical Correlation In additional to the traditional methods of analysis such as Analysis of Variance (ANOVA)

and Multivariate Analysis of Variance MANOVA, further analytic methods used in order to

address the comments are Discriminant Analysis (DA) and Canonical Correlation.

Based on the objectives of the research as highlighted in the introduction section, a general

question facing this area of inquiry was how to organise the observed data into meaning

structures that is to develop taxonomies. The general approach has been to classify the skills

and attributes on a Yes/No basis. Through cluster analysis the adequacy of skills and

attributes necessary for Construction Management Graduates are classified according to their

levels of adequate and relevant knowledge of skills and attributes. These have been

categorised into three levels, namely high, medium and low.

DA has the added advantage over ANOVA and MANOVA in that it can actually put cases

into groups on a discriminating function identified classification. For example, in this study

the case of the South African construction related organisations were discriminated into the

following two functions; Size, Business Orientation and Location (Provinces). The Courses

and graduates offered by the Universities of Technology were also discriminated into the

following two functions of ND Buildings and Graduates. Furthermore DA makes an effort to

interpret the patterns of differences among the predictors.

Table 1.0: Summary of Discriminating Functions

Cases Discrimination Function

Size (Turnover and Number of

Employees) Size - Turnover ( Medium versus Small)

Size - Number of Employees ( Macro, Small,

Medium and Large)

Business Orientation (n = 6) Contractor; Engineer (Consultant) Project; Manager

(Consultant) Quantity; Surveyor (Consultant);

Subcontractor; Other (Employers Organisation and

Education)

Location (n = 8) Eastern Cape, Free State, Guateng, Kwazulu-Natal,

Northern Cape, North West, Western Cape, Various

Satisfaction Levels of Employers High, Medium and Low

ND Buillding Students

Diplomates

Graduates B.Tech Construction Management

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

4

2.2.2 Computation of Relative Adequacy Subject Indices

The relative adequacy subject index (RASUI) derived to summarize the adequacy of each

academic subject area and was computed as

RASUI = AxN

w .……………………..Equation 1.0

Adopted from Pheng and Gracia (2002)

Where:

w = weighting as assigned by each respondent in a range 1 to 5, where 1 implies

'extremely inadequate' and 5 implies 'extremely adequate';

A = the highest weight (5);

N= the total number in the sample.

A low relative adequacy index indicates that the academic subject is least adequately prepared

by Universities of Technology as perceived by the employing organisations, whereas a high

index indicates that the adequacy of the academic subject is high.

Where the RAIs were the same for two or more academic subjects (variables), rank

differentiations are achieved by examining the distribution of the rating against such variables.

Kumaraswamy and Chan (1998) to compute a mean score used a similar formula

2.2.3 Computation of Relative Relevancy Subject Indices

The relative relevancy subject index (RRSI) derived to summarize the relevancy of each

academic subject area and was computed as

RRSI = AxN

w .……………………..Equation 4.2

Adopted from Pheng and Gracia (2002)

Where:

w = weighting as assigned by each respondent in a range 1 to 5, where 1 implies

'extremely irrelevant' and 5 implies 'extremely relevant';

A = the highest weight (5);

N= the total number in the sample.

A low relative relevancy index indicates that the academic subject is least perceived to be

relevantly prepared by Universities of Technology as perceived by the employing

organisations, whereas a high index indicates that the relevancy of the academic subject is

high.

Where the RRIs were the same for two or more academic subjects (variables), rank

differentiations are achieved by examining the distribution of the rating against such variables.

2.2.4 Computation of Relative Adequacy Skills Index (RASI)

The relative adequacy skill index (RASI) derived to summarize the adequacy of each

academic subject area and was computed as

RASI = AxN

w .……………………..Equation 2.0

Adopted from Pheng and Gracia (2002)

Where:

w = weighting as assigned by each respondent in a range 1 to 5, where 1 implies

'extremely inadequate' and 5 implies 'extremely adequate';

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

5

A = the highest weight (5);

N= the total number in the sample.

A low relative skills adequacy index indicates that the construction management skill is least

adequately prepared by Universities of Technologies as perceived by the staff and employing

organisations (Industry), whereas a high index indicates that the adequacy of the skill is high.

Where the RASIs were the same for two or more construction management skills (variables),

rank differentiations are achieved by examining the distribution of the rating against such

variables. Kumaraswamy and Chan (1998) to compute a mean score used a similar formula

2.2.5 Computation of Relative Importance Skills Index (RISI)

The relative importance skills index (RISI) derived to summarize the importance of each of

the 37 construction management skills and was computed as

RISI = AxN

w .……………………..Equation 3.0

Adopted from Pheng and Gracia (2002)

Where:

w = weighting as assigned by each respondent in a range 1 to 5, where 1 implies

'extremely unimportant' and 5 implies 'extremely important';

A = the highest weight (5);

N= the total number in the sample.

A low relative importance index indicates that the construction management skill is perceived

to least important by either Industry or staff, whereas a high index indicates that the

importance of the construction management skill is high.

2.2.6 Computation of the Level of Preparation of Academic Subjects in

Universities of Technology

In order to assess the adequacy and relevancy levels of the necessary skills and attributes of

Construction Managers, an average value for all the six factors was deemed to represent the

levels of adeqaucy / relevancy of Construction Management Skills. This approach of

adopting the vector was used by Saraph et al (1989); Watson and Chileshe(2004)

2.2.7 Levels of Skills / Attributes of Construction Managers = N

Wi …..Equation 4.0

Where:

∑Wi = The sum of the average of each factor (construct)

N= the total number of the Implementation Constructs (N = 6).

2.2.8 Construct Centre of Gravity

The centre of gravity gives an indication of the overall weight within the Construction

Management Skills and Attributes Model (CM-SAM) that can be apportioned by the construct.

It can be defined as the ratio of the construct to the overall construct relative advancement

indices. The Construct centre of gravity can be computed as follows:

ǾRAI = ( ( ( WiRAI / NA)/n) ……………………...........................……Equation 5.0

Where ǾRAI = Centre of Gravity of the Construct

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

6

Table 2.0: Scoring the Levels of CM-SA Preparation

Average Score (∑Wi) RAI/RRI CMS Level

4.0 to 5.0 0.8 to 1.0 High (H)

3.0 to < 4.0 0.6 to < 0. 8 Medium (M)

1.0 to < 3. 0 0.2 to < 0.6 Low (L)

The generated scores are from the relative advancement indices and the mean values. These

values will form the basis for the classification of the proposed new assessment and

monitoring tool.

3.0 METHODOLOGY FOR THE PROCESS MATRIX

The Construction Management Skills and Attributes-Subject Performance Index Model uses

the Quality Function Deployment approach as illustrated in the following flow chart in Figure

1.0

Figure 1.0: Steps in the Formulation of the CMSA-SA Matrix

The main steps involved in the formulation of the CMSA-SA Matrix are summarised as a

flow diagram shown in Figure 1.0. Issues in applying the methodology in each of the steps of

Figure 1.0 are addressed in the following:

Step 1 - Data Matrix: Information for the raw data is the importance of the skills and subjects

studied in the Universities of Technology.

Step 2 and 3 - Process Matrix: The matrix is illustrated in Table 3 and a description of the

elements contained in the rows and columns are provided for in the methodology for the

Construction Management Key Skills and Attributes-Subject Adequacy sub section.

The Total Skills and Attributes-Subject Importance Indicator Matrix (TSA-SII) model uses

the QFD approach as shown in Table 3.0

Step 1: Data Matrix

Step 2: Process Matrix

Step 3: Process Matrix 2

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

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Table 3.0 : Data Matrix- The Maximum Achievable Level of Construction Management

Graduates

Eight Key Subject Areas

Max

imu

m P

erfo

rman

ce

Construction

Management

Skills and

Attributes

Importance

Weights Status

Maximum Level of Performance

The steps and details in the calculation are as follows:

Column includes 37 key 'skills and attributes', which were modified from the

design by Haupt and Smallwood (2005) measurement of the success of CM and

assessment of performance satisfaction levels. These indicators represents the

outcomes of the preparation of Graduates by Universities of Technology. The relative

importance were reported by the Industry in a questionnaire survey on a scale of 1 to

5 where 1 represents 'very inadequately, 2 = inadequately, 3 = neutral, 4 = adequately,

and 5 = very adequately.

Row : includes 'eight subjects' as reported in Haupt et al (2005a, b); Chileshe et al

(2004). They represent the critical success factors necessary for the training of

Construction Management students. The relative importance of CM constructs were

reported by various respondents of SA Construction Related SMMIs in a

questionnaire survey on a scale of 1 to 5 where 1 represents very inadequately, 2 =

inadequately, 3 = neutral, 4 = adequately, and 5 = very adequately.

Matrix : represents the strength of the relationships between the CM deployment

constructs (column ) which relates to the process element and the business and

organisation performance indicators ( row ) which relates to the outcomes of the

implementation process.

Column : features the critical weight factors for the business and organisation

performance indicators as computed in Equation 7.2

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

8

Column : represents the status of the key 37 skills and attributes performance

indicators under the conditions of the world class Universities of Technologies ( i.e.

maximum score of 5)

Row : features the critical weight factors for the Construction Management

deployment subjects as computed in Equation 2.2

Row : represents the status of the adequacy of Construction Management

deployment subjects under the conditions of the world class Universities of

Technologies ( i.e. maximum score of 5)

Matrix : the point scores were calculated by the synthesis of the information in

attributes , , , , and according to equation 1.11

Cell : in Table 3.0 is the actual level of the Construction Management Key Skills

and Attributes (37) - and Subject Adequacy Indicator Level under the actual

conditions for the Organisation 8 which is computed the procedure outlined.

Table 4.0: Relevancy of Topics Data

Rank Topic Mean CWF Std.

Dev.

CV

1 PM = Project Management 4.35 0.1384 0.87 20.0

2 CT = Construction Technology 4.25 0.13522 0.97 22.82

3 Management Principles, Theories and Practices 4.05 0.1288 0.83 20.49

4 CL = Construction Law 3.96 0.1259 0.96 24.24

5 CBE = Construction Business Environment 3.91 0.1244 0.84 21.48

6 CE = Construction Economics 3.89 0.1237 0.96 24.67

7 CS = Construction Science 3.85 0.1224 0.92 23.89

8 RM = Research Methodology 3.17 0.1008 1.12 35.33

Total 31.43 1.000

Table 5.0: Adequacy of Preparation by Universities of Technology in Topics Data

Rank Topic Mean CWF Std.

Dev.

CV

1 CT = Construction Technology 3.57 0.1369 1.08 30.25

2 Management Principles, Theories and Practices 3.44 0.1320 0.99 28.78

3 Project Management 3.37 0.1293 0.99 29.38

4 CBE = Construction Business Environment 3.25 0.1247 0.96 29.54

5 CL = Construction Law 3.22 0.1235 0.98 30.43

6 CE = Construction Economics 3.20 0.1227 0.97 30.31

7 CS = Construction Science 3.16 0.1212 0.94 29.74

8 RM = Research Methodology 2.85 0.1093 0.95 33.33

Total 26.06 1.0000

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

9

Table 6.0: The Process Matrix: Skills Adequacy-Subject Product Matrix (MP Scores)

Imp

ort

ance

ran

kin

g

Skills/attribute

Co

nst

ruct

ion

tec

hn

olo

gy

Man

agem

ent

Pri

nci

ple

s,

Th

eori

es a

nd

Pra

ctic

e

Pro

ject

man

agem

ent

Co

nst

ruct

ion

Bu

sin

ess

En

vir

on

men

t

Co

nst

ruct

ion

Law

Co

nst

ruct

ion

Eco

no

mic

s

Co

nst

ruct

ion

sci

ence

Res

earc

h M

eth

od

olo

gy

IR Skills/Attribute CT MP PM CBE CL CE CS RM

cw

f

1 Trust and honesty

2 Acceptance of responsibility 3 5 5 2 4 2 3 4

3 Problem solving skills 4 3 3 3 3 3 3 3

4 Worker safety and health awareness 3 5 5 3 3 3 1 3

5 Time management - 5 5 - 2 1 - -

6 Decision making 4 3 3 3 3 3 3 3

7 Verbal communication skills (oral and

written)

1 1 1 1 1 1 1 1

8 Planning, scheduling and controlling

construction operations and activities

- 5 5 - - - - -

9 Practical building knowledge 5 5 3 1 3 4 2 4

10 Interpersonal skills 4 3 3 3 3 3 3 3

11 Measurement, costing and estimating 4 - - - - 4 - -

12 Familiarity with construction quality

management

2 2 2 2 2 2 2 2

13 Ability to work autonomously 4 4 4 4 4 4 4 4

14 Team building capability 4 4 4 2 2 2 2 3

15 Adaptability to changing work

environment

3 3 3 3 3 3 3 3

16 Leadership capability 4 3 3 3 3 3 3 3

17 Active listening skills 2 3 3 3 3 4 3 4

18 Ability to resolve conflicts and disputes 3 3 3 3 3 3 3 3

19 Ability to exercise professional judgement 2 1 1 1 1 1 2 1

20 Creativity and innovation

21 Supervisory skills and ability to train others 3 2 2 2 2 1 2 3

22 Numeracy 4 4 4 4 4 4 4 4

23 Computer literacy 3 5 5 1 3 4 3 4

24 Ability to use surveying and levelling

equipment

5 - - - - - - -

25 Up-to-date professional knowledge 3 3 3 3 3 3 3 3

26 Managerial knowledge 3 3 3 3 3 3 3 3

27 Financial management 1 3 3 4 2 4 1 1

28 Familiarity with workings and intricacies

of industry

4 3 3 3 4 4 2 4

29 Negotiating skills 1 3 3 1 1 1 1 1

30 Environmental awareness

31 Academic achievement 4 4 4 3 3 4 4 3

32 Entrepreneurship 1 3 2 4 2 3 1 1

33 Marketing skills 1 3 3 4 1 3 1 2

34 Work study 1 3 1 1 1 1 1 1

35 Systems development ability 4 4 4 4 3 4 3 3

36 Ability to conduct statistical analysis 2 3 3 3 2 3 3 4

37 Ability to conduct research 1 1 3 1 1 3 1 5

This table provides the overall rating on a scale of 1 to 5, how adequately each of the skills

and/or attributes (rows) arranged in order of importance according to employers of CM

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

10

graduates are addressed in each of the subject areas (columns), where 1 = very inadequately, 2

= inadequately, 3 = neutral, 4 = adequately, and 5 = very adequately. These ratings were

obtained by work-shopping the matrix with a sample of educators, alumni and employers.

Clearly there are areas of deficiency in the present subject offerings relative to the desirable

skills and attributes that graduates need to perform as CMs.

3.1 Numerical Example for the Application of the Skills and Attributes - Subject

Performance Index

The importance weights or centroid of gravity (critical weight factors) for the key skills and

attributes desirable in Construction Managers and Subject Indicators taught in the Universities

of Technology are combined to produce a matrix.

The results are indicated in Table 4.0 which is the actual level of the Construction

Management Graduate performance, The South African Construction Related SMMIs would

expect to achieve from the employment of Universities of Technologies graduates. The key

subject areas taught in the Universities of Technology are in the horizontal column

abbreviated as CT for Construction Theories, MP = Management Principles, Theories and

Practice, PM = Project Management, CBE = Construction Business Economics, CL =

Construction Law, CE = Construction Economics, CS = Construction Science and RM =

Research Methodology. The vertical column represents the 37 key skills and Attributes

desirable in a Construction Manager. The values in the matrix are obtained from the average

of the sum of the importance weightings multiplied by the status figure, which in both cases

equals to 5.00. The value of 5.00 is the highest the respondent could score on the Likert scale

of 1 to 5 for the adequacy of preparation by Universities of Technology in topics, relevance of

topics and 1 to 5 for the desirable skills and attributes of Construction Management graduates.

For a world class organisation University of Technology, the maximum highest score

expected in each subject preparation would be a perfect 5, which would equate to the

importance weighting or centroid value of 0.125. The matrix generated the overall maximum

level of Construction Management skills development equal to 112.50 from the South African

related SMMIs in the sample. The value of 112.50 is obtained from n (n=8)*5 + (n=37)*5 =

40 + 185 = 225/2 = 112.50. Therefore, the generated Total Construction Management Skills

and Attributes - Subject Performance Index (CM-SAPI) for the sample is 112.50.

Table 6.0 provides the overall rating shows the application of any University of Technology

against the median of the Industry, in the case the scores for the Organisation No 8 are

generated as shown in Table 7.2. The total score achieved is 35.77. This value can now be

used to compare against the Industry median as follows:

CM-SAPI =

Actual Level of CM Skills and Attributes Performance Equation 6.0 Maximum Industry Median

The Equation 6.0 is adapted from Arditi and Lee (2003) who used it as a mechanism for

corporate service quality performance measurement model. The only difference is that the

matrix was generated by including the strength of relationships between the vertical and

horizontal variable from the independent assessors. Specifically, the tool was developed for

construction owners to rank the design and build (D/B) firms relative to corporate service

quality, as well as D/B firms to benchmark themselves against their competitors. However,

this method of Quality Function Deployment has been used before by other researchers. This

is normally called the “house of quality” in other studies such as Shillito (1994), Hoyle (1998)

CM-SAII =

148.33 * 100 = 80.18% 185.00

Chileshe, Fester and Haupt- Desirable Construction Management Skills in the South African

Construction Industry: Methodology for Assessment

11

What is novel about its application to this study, is that in this particular example, all the

Construction Management Key Skills and Attributes are assumed to carry equal weightings in

the contributions to the development and preparation of Universities of Technolgies

Graduates.

The results of the process matrix indicate that the "Academic Achievement" key skill and

attribute has the highest (mean = 3.64) impact in terms of importance on the development of

Construction Managers whereas Construction Technology is considered by Industry as a key

subject areas in terms of preparing the adequacy (mean= 3.57) of graduates. This is indicated

by the total value which is obtained by summing up the product of the matrix for each

individual construct. In this case, "Academic Achievement" has a total median score

of 3.11 (Table 5.20a). On a comparative basis, the industry has a score of 3.04 (Table 7.19)

which is above the Industry median. Similarly, the total score in the first subject, Construction

Technology which is designated as “CT” has a total score of 15.17 which is more than the

Industry median of 14.06. The potential of application of this method would be competitive

benchmarking across the Universities of Technology by the CIOB Africa Accreditation Panel.

The competing UoTs would compare their scores against the industry median, thus assist

management in making strategic decisions. From the matrix, it can be noted that the

"Marketing Skills" is low in terms of contributing to the overall performance as the score of

2.98 is the lowest across all the thirty-seven skills whereas the adequacy of "Research

Methodology" also ranked lowly (total mean = 12.62) out of a possible 14.06.

4.0 CONCLUSIONS

5.0 REFERENCES

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