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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS

COPY RIGHT © 2012 Institute of Interdisciplinary Business Research 174

OCTOBER 2012

VOL 4, NO 6

DECISION MAKER’S PERCEPTIONS ON CONTRACTOR PREQUAIFICATION

CRITERIA

Ajayi, O.M. and Ogunsanmi, O.E.

Department of Building, University of Lagos, Akoka, Lagos, Nigeria.

Abstract

The process of selecting contractors for a proposed project is a major decision which may

influence the progress and success of any construction project. Selecting an inappropriate

contractor for a project could therefore lead to project behind schedule, price changes and

substandard work. The decision make by the client or his representative directly or indirectly

affect the success or otherwise of a project outcome. A descriptive research was used and the

population consists of client’s organization and consulting firms, it comprises of Quantity

Surveyors, Builders and Engineers. A random sampling method is used and a total of 50

questionnaires were distributed, but 17 were received from public client’s organization and 25

from consulting firms. Data collected were analyzed using descriptive and inferential statistic.

From the pilot study carried out, the most prominent prequalification criteria were identified. In

conclusion, clients and consulting firms will be aware of early warning sign of the contractors

who will not be able to perform.

Keywords: Contractor, Criteria, Decision Maker, Perception, Prequalification

1.0 Introduction

Decision making is a process of gathering information from which a decision is taken. It has

becomes mathematical science today (Figuera et al, 2005).It formalizes once thinking so that

the process of taking the decision is transparent. Decision making involves many criteria and

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subcriteria used to rank the alternatives of a decision and so decision maker’s have to be

objective whenever they are taking decision especially on selection of contractors (Saaty,

2008).Biases occur in all stages of decision process. They come in form of information

perception, information processing and in making selection from options. Decision making is

governed by certain heuristics﴾reasoning patterns﴿ and decision frames from which arise

deviations from rational and optimal decisions﴾Cardenas,Halman,Al-

Jibouri,2009.﴿Contractors are those involved in the construction stage of construction

projects and so any decision taken about their selection becomes cushion since it affect the

performance of the project. The process of selecting contractors for a proposed project is a

major decision which may influence the progress and success of any construction project.

Selecting an inappropriate contractor for a project could therefore lead to project behind

schedule, price changes and substandard work﴾.Banaitiene and Banaitis,2006﴿.It is due to

inappropriate criteria been selected when evaluating qualification of contractor, inappropriate

significance attributed to the criteria and inappropriate methodology applied for the

contractor evaluation and selection as identified by Banaitiene and Banaitis﴾2006﴿ in their

study on Lithuania selection and evaluation process. The complexity of contractor selection

is attributed to its uncertainty nature. It is due to fuzziness associated with contractor

performance, experience, prequalification criteria and qualitative judgment﴾Lam et al, 2001﴿.

The decision make by the client or his representative directly or indirectly affect the success

or otherwise of a project outcome (Holt, 1996). Thus, a wrong approach in selection could

lead to project failure. The client is faced with choosing between using competitive bidding

and negotiation to select contractor (Waara, 2006). Most public client organizations adopt a

selective approach for inviting tenderer for construction projects. This help to prevent

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contractor default and associated overhead costs of contractors. It enables the clients to assess

the liability, competency and capability of potential contractors to satisfactorily carry out the

contract. It also minimizes the potential risks involved in the project (Ng and Skitmore,

1999). Odeyinka and Yusuf (1997) opine wrong tendering practice is a major contribution to

inefficiency in Nigeria construction industry. However some contractor selection methods

currently in existence are criticized as incomplete and biased and lacking consideration in

terms of contractor’s performance (Fong and Choi, 2000). In addition, there have been steady

increase in the range of methods used for procurement of construction works in the last two

decades for instance in Nigeria we have “Due process which is meant for transparency and

accountability, but yet there have not be improvement in the success rate of construction

projects. Project management is faced with many problems and so a better means of

managing project delivery is therefore necessary for construction industry to continue to

survive. Therefore the method of ensuring that a contractor is able to execute the assigned

project in accordance to client’s objectives (time, cost and quality standard) is to assess the

contractor’s capabilities at the prequalification and tender evaluation stages (Hatush and

Skitmore, 1997).

Objectives of the study

1. To identify the prominent criteria used for contractor selection in client organization and

consulting firms at prequalification stage.

2. To find out the level importance of identified criteria for contractor selection at

prequalification.

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Hypotheses of the study

1.The most dominant criteria in clients’ organization and consulting firms at prequalification

stage are not financial criteria, experience, managerial capability, health and safety and

contractors reputation and image.

2. There is no significant difference between clients’ organization and consulting firms on the

level of importance of the prequalification criteria and tender evaluation.

2.0 Review of literature

From the design stage of construction project, the client is faced with numerous decisions which

may result to the success or failure of the entire project (Russell et al, 1992). The task of

selecting the appropriate bidders for a particular project is one of such decision. It is one of the

most challenging tasks performed by an owner or his representative. (Holt, 1996). Every

construction project faces adversity and uncertainty which must be overcome. No matter how

meticulous the development of the contract, poor selection of the contractor(s) to execute the

work will surely magnify the problems encountered on the project. In order to overcome these

problems, a competent contractor who will be able to complete the project within cost, time and

quality is required. It is achieved through prequalifying contractor prior to the bidding process

which is the first stage of selection and then through evaluation of tenders. Contractor

prequalification is a process of screening construction contractors by project owners or their

representatives according to a determined set of criteria deemed necessary for successful project

performance, in order to determine the contractor’s competence or ability to participate in the

project bid. It implies that there must be a set of principles (criteria) laid down upon which the

standard of acceptance and performance are to be measured even though such criteria varies in

emphasis according to the characteristics of the project﴾Moore,1985;Odusami,1998;Ng and

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Skitmore,1999;Ng,Skitmore and Smith,1999; Lam,Hu and Ng,2005﴿. Contractors generally

undergo a screening process before the project can be given to them. It enables unqualified

contractors to be eliminated at the early stage of the project and those who meet the required

criteria are qualified for the next stage. Contractors been classified into categories according to

factors such as experience, liquidity and patrimony. Then the prequalified contractors present

their offers. These offers are evaluated under an economic criterion and in some projects, a

technical criterion. After evaluation, the contract is awarded according to the final score (Alarcon

and Mourgues, 2000). According to Lam, Hu, Ng, Skitmore and Cheung (2001) contractor’s

prequalification can be regarded as a complicated two – group non – linear classification

problem. It involves a variety of subjective and certain information extracted from various

parties such as contractors, prequalifies and project teams. Non-linearity, uncertainty, and

subjectivity are the three predominant characteristics of contractor prequalification. This makes

the process more of an art than a scientific evaluation. Efforts have been made to cope with the

non-linearity existing between contractor attributes and the corresponding prequalification

decisions made by the owner (client) this efforts include the development of a range of

nonlinearity models such as programmed evaluation review technique (PERT) (Hatush and

Skitmore, 1997), the artificial neural network model (Lam et al, 2001, 2002; Khorsrowshah,

1999), the analytical hierarchy process (AHP) (Fong and Choi,2000; Al-Harbi, 2001) and the

multi – attribute utility model (Diekmann,1981). In an attempt to reduce the subjectivity and

uncertainty involved in the process of contractor prequalification, a number of techniques have

been investigated, involving varying degree of complexity. Nguyen (1985) in Lameta (2005)

applied fuzzy set theory to the evaluation and selection of bidders based on cost, the presentation

of bid information and past experience. While Russell et al (1990) develop a prototype

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knowledge based expert system that aids construction owners in performing contractor

prequalification. It provides an automated contractor evaluation process through which rational

and consistent prequalification decisions could be made. Ng (1996) in Lam et al (2005)

developed a case – based reasoning system and found that it could capture and reuse

experimental knowledge pertinent to contractor prequalification decisions, and thus help decision

makers to produce more reliable and expeditious decisions for contractor prequalification.

According to Khosrowshahi(1999) the ability to optimize the short listing from a large number of

potential contractors is as important to the final selection of the right bidder. This is because the

quality of the final bidder is as good as those shortlisted.Holt (1998) developed artificial

intelligence(AI).It helps to provide assistance with decisions relating to contractors

prequalification. It is suitable where the problem cannot be expressed totally in algorithmic form.

The most appropriate artificial intelligence (AI) is the artificial neural networks (ANN)

developed by Khosrowshahi (1999). It aids the public client to make rational decision on

contractors prequalification. The screening of contractors is usually based on a set of

prequalification criteria. These prequalification criteria are financial, experience, managerial

capability, health and safety and contractors’ reputation and image.

3.0 Research Methods

For this study the possible observations who made up the population for this study are clients and

consultants. For the purpose of this study public clients are the focus, since most of the projects

carried out by the public clients are owned by the government where accountability is required

and so such projects must go through “due process” and they are always in competition.

The consultants are the construction professionals involve in construction projects. They include

architects, quantity surveyors, engineers and builders. The list of the practicing professionals for

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the study was obtained from their respective professional bodies and they are randomly selected

which implies they all have equal chance of been selected. A descriptive research survey is used

for the study. Data for the research are generated using opinion-based questionnaire survey,

which will serve as the primary data. A closed ended questionnaire design is used for this study

because it helps in obtaining quantitative data which could be used to test the study hypotheses.

The tool for the analyses of the data collected depends on the type of data, the nature of analysis

and the hypotheses to be tested. Descriptive statistical tools such as frequency distribution

percentages and mean were used in analyzing the descriptive data of the respondents’ profile.

Ranking was based on the use of mean to test the level of importance of prequalification. T-test

and analysis of variance was used to test the difference between clients’ organization and

consulting firms.

4.0 Results and Discussions

The analysis of the data collected from the questionnaires administered and the checklist are

presented as follows.

General Information about Respondents.

Table 1 shows the demographic variable of respondents from client’s and consultant

organisation.From both client and consulting organisation,majority of the respondent are

Quantity surveyors﴾70.6%,96%﴿.It is followed by Architect﴾5.9 %﴿,civil Engineer﴾5.9% ﴿and

Builder﴾5.9%﴿ under client organisation.While under consulting organization there were no

respondents from Architecture, Civil Engineering and Building.47.1% of the respondents had

spend 11-20years in the construction industry in the client organisation and 41.7% in the

consulting organisation.It is follow by 23.5% of respondents have spend less than 10years and

21-30years respectively under client organisation.For consultants organisation,29.2% have spend

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less than 10years and 12.5% have spend 12.5%.Respondents that have spend more than 30years

for both client and consultant organisation are 5.9% and 16.6% respectively. The level of

experience of these respondents shows that the data collected are reliable. The highest academic

qualification of respondents for both client and consulting organization is M.Sc.﴾50%, 37.5%﴿.It

shows that all the respondents are knowledgeable and competent to provide the information

required for this study. The highest total value of project executed by client’s organisation is

29.4% of worth 101-500million and 501-1billion naira. It is follow by a project value of over

1billion of 23.5%.For consultant organisation, the highest total value of project executed is

52.2% of worth over 1billion naira. It is follow by project of worth 501-1billion naira﴾30.2%﴿.

Table 4.1 Background Information

Background clients’ consultant

Information organization organization

Frequency % frequency %

Professional Discipline

Architecture 1 5.9 - -

Civil engineering 1 5.9 - -

Building 1 5.9 - -

Quantity surveying 12 70.6 24 96

others 2 11.8 1 4

Total 17 100 25 100

Years in construction industry

Less than 10years 4 23.5 7 29.2

11-20years 8 47.1 10 41.7

21-30years 4 23.5 3 12.5

More than 30years 1 5.9 4 16.6

Total 17 100 24 100

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Table 4.1 continued.: Background Information

Background clients’ consultant

Information organization organization

Frequency % frequency %

Highest Academic Qualification

HND 2 12.5 7 29.2

B.SC 4 25 6 25

M.SC 8 50 9 37.5

MBA - - 2 8.3

PGD 2 12.5 - -

Total 16 100 24 100

Total value of project executed﴾#﴿

51-100million 3 17.6 - -

101-500million 5 29.4 4 17.4

501-1billion 5 29.4 7 30.4

Over 1billion 4 23.5 12 52.2

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Frequency of used prequalification criteria

From table 4.2 the frequently used prequalification criteria are

Table 4.2 Level of frequency of used and not used prequalification criteria by consulting

firms respondents

Consultant prequalification criteria frequency used (%) frequency not used (%)

A1. Financial criteria

Current fixed asset 18(78) 5(22)

Liquidity 13(57) 10(43)

Annual turnover 17(74) 6(26)

A2 Credit rating

Subcontractors 17(74) 6(26)

Supplier 12(57) 9(43)

A3 Banking arrangement and bonuses

Short term borrowing 11(48) 12(52)

Long term borrowing 9(41) 13(59)

Medium term borrowing 9(41) 13(59)

Bonus 6(30) 14(70)

A4 Financial status

Balance sheet statement 19(83) 4(17)

Income statement 17(74) 6(26)

B Technical Ability

B1 Experience criteria

Type of past projects completed 24(100) -

Size of past projects completed 24(100) -

National or local catchments 16(70) 7(30)

Level of technology 18(78) 5(22)

Technical skills 23(100) -

B2 Personal

Availability of supervisors 17(71) 7(29)

Availability of skilled craftsmen 18(82) 4(18)

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Skills including professional 23(100) -

B3 Plant and equipment

Availability of owned construction 23(100) -

B4 Ability

Ability to handle the project 24(100) -

Ability to perform on site 23(100) -

Ability to control and organize contract 23(100) -

Ability to efficiently integrate labour resources 21(91) 2(9)

Ability to meet target dates 21(91) 2(9)

C.Managerial capability

C1 Past performance and quality of work

Past performance 24(100) -

Quality control programme 24(100) -

Possession of quality assurance certificate 14(61) 9(39)

Quality of workmanship 22(92) 2(8)

Confidence in design and flexibility 14(64) 8(36)

D. Health and Safety

Experience in noise control 11(50) 11(50)

Accident book 10(48) 11(52)

Level of adherence to health and safety regulation 23(100) -

Provision of health and safety regulation 20(83) 4(17)

Safety record available 18(82) 4(18)

Company safety policy 20(87) 3(13)

E.Contractors’ reputation and image

Past and present experience 19(83) 4(17)

Financial penalties 21(100) -

Litigation tendency 19(87) 3(13)

E2.Length of time in business

Amount of projects 22(100) -

Capacity of work handled 23(100) -

Permanent place of residence 12(55) 9(45)

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Table 5.17 level of frequency of used and not used prequalification criteria by clients’

organisation respondents

Clients’ prequalification criteria frequency used (%) frequency not used (%)

A1. Financial criteria

Current fixed asset 13(87) 2(13)

Liquidity 13(87) 2(13)

Annual turnover 13(77) 4(23)

A2 Credit rating

Subcontractors 9(64.3) 5(35.7)

Supplier 8(53.3) 7(46.7)

A3 Banking arrangement and bonuses

Short term borrowing 8(40) 9(60)

Long term borrowing 3(20) 12(80)

Medium term borrowing 5(35.7) 9(64.3)

Bonus 1(9) 10(91)

A4 Financial status

Balance sheet statement 9(60) 6(40)

Income statement 9(64.3) 5(35.7)

B Technical Ability

B1 Experience criteria

Type of past projects completed 17(100) -

Size of past projects completed 16(100) -

National or local catchments 10(58.8) 7(41.2)

Level of technology 17(100) -

Technical skills 17(100) -

B2 Personal

Availability of supervisors 15(88.2) 2(11.8)

Availability of skilled craftsmen 14(100) -

Skills including professional technical expertise 16(100) -

B3 Plant and equipment

Availability of owned construction equipment

For quality assurance 16(94.1) 1(5.9)

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B4 Ability

Ability to handle the project 17(100) -

Ability to perform on site 15(93.8) 1(6.2)

Ability to control and organize contract 15(93.8) 1(6.2)

Ability to efficiently integrate labour resources 15(93.8) 1(6.2)

Ability to meet target dates 15(100) -

C.Managerial capability

C1 Past performance and quality of work

Past performance 15(100) -

Quality control programmes and quality of

Works on past project 17(100) -

Possession of quality assurance certificate 11(68.8) 5(31.2)

Quality of workmanship 17(100) -

Confidence in design and flexibility in

Accommodating design input by client 10(62.5) 6(37.5)

D. Health and Safety

Experience in noise control 5(33.3) 10(66.7)

Accident book 5(33.3) 10(66.7)

Level of adherence to health and safety regulation 14(87.5) 2(12.5)

Provision of health and safety regulation 15(88.2) 2(11.8)

Safety record available 9(60) 6(40)

Company safety policy 14(87.5) 2(12.5)

E.Contractors’ reputation and image

Past and present experience in regard to legal claim 8(53.3) 7(46.7)

Financial penalties previously

Levied in respect 8(53.3) 7(46. 7)

Litigation tendency 9(69.2) 4(30.8)

E2.Length of time in business

Amount of projects handled in the past 5years 15(93.8) 1(6.2)

Capacity of work handled presently 17(100) -

Permanent place of residence 10(71.4) 4(28.6)

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Table 5.18 level of importance of prequalification criteria of clients’ organization and

consulting firms

Prequalification consulting firms clients’ organization importance overall

criteria Importance index rank importance index rank index ranking

Ability to handle the project 0.928 1 0.96 1 4.71 1

Type of past projects 0.92 2 0.92 6 4.60 3

Skills including professional 0.917 3 0.86 15 4.37 8

Past performance 0.912 4 0.94 4 4.61 2

Technical skills 0.904 5 0.95 2 4.50 4

Ability to perform on site 0.904 5 0.94 4 4.44 6

Size of past project

Completed 0.896 7 0.88 13 4.45 5

Complexity of work

executed 0.883 8 0.88 13 4.29 11

Ability to control and organize

contract 0.883 8 0.90 9 4.34 9

Ability to efficiently

integrate 0.87 10 0.83 16 4.15 17

Ability to meet target dates 0.87 10 0.95 2 4.39 7

Quality of workmanship 0.87 10 0.91 7 4.32 10

Availability of skilled

Craftsmen 0.858 12 0.89 11 4.24 13

Quality control programme 0.85 13 0.90 9 4.24 13

Availability of owned

construction 0.843 14 0.88 13 4.20 15

Level of technology 0.842 15 0.91 7 4.24 13

Amount of projects handled 0.842 15 0.88 13 4.17 16

Availability of supervisors 0.84 17 0.88 13 4.29 12

Current fixed asset 0.792 18 0.71 20 3.71 18

Company safety policy 0.775 19 0.77 19 3.68 19

Liquidity 0.73 20 0.8 18 3.59 22

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Annual turnover 0.725 21 0.71 23 3.50 24

Experience in noise control 0.717 22 0.60 33 2.79 36

Possession of quality

Assurance 0.717 22 0.81 17 3.68 19

Level of adherence to health 0.712 24 0.73 22 3.60 21

Provision of health and safety

regulation 0.692 25 0.69 29 3.37 26

Confidence in design 0.692 25 0.76 20 3.51 23

Safety record available 0.683 27 0.61 32 3.17 30

Past and present experience 0.648 28 0.68 28 3.31 27

Litigation tendency 0.643 29 0.70 25 3.15 32

Balance sheet statement 0.642 30 0.66 29 3.17 30

National or local catchments 0.617 31 0.69 26 3.31 27

Subcontractors 0.616 32 0.63 30 3.10 33

Accident book 0.611 33 0.54 35 2.43 40

Permanent place of residence 0.609 34 0.61 31 2.90 35

Medium term borrowing 0.591 35 0.50 37 2.59 38

Supplier 0.576 36 0.60 33 2.93 34

Long term borrowing 0.564 37 0.53 36 2.54 39

Short term borrowing 0.522 38 0.63 30 2.68 37

Income statement 0.525 39 0.71 23 3.40 25

Bonus 0.5 40 0.43 38 1.92 41

Capacity of work handled - - 0.89 11 4.27 13

Financial penalties - - 0.63 30 3.25 29

Hypothesis

5.2.20 Perception of clients’ organization, consulting firms and contracting organization on

prequalification and tender evaluation for contractors’ selection

Table 5.28 shows the result of the perception of the respondents of clients’ organization,

consulting firms and contracting organization. Analysis of variance was used to show their

perception. It was realized that Ftab was greater than Fcal, thus there is a significant between their

perceptions and so the alternate hypothesis (H1) is accepted.

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Table 5.27 Descriptive data results of level of importance of prequalification criteria of

clients’ organization, consulting firms and contracting organization

Respondents N mean standard deviation standard error minimum maximum

Contractor 23 172.83 21.27 4.43 132.00 213.00

Consultant 22 153.46 28.14 6.00 54.00 193.00

Client 13 152.31 27.58 7.65 76.00 181.00

Total 58 160.88 26.87 3.53 54.00 213.00

Table 5.28 Anova result of level of importance of prequalification criteria of clients’

organization, consulting firms and contracting organization

Sum of mean

Square DF squares Fcal Ftab sig. pvalue decision

Between

Groups 5450.627 2 2725.314 41.98 724.98 0.02 0.00 HI accepted

Within

Groups 35703.528 55 649.155

Total 41154.155 57

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