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Tydskrif vir die fisiese en ontwikkelingswetenskappe

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Acta Structilia2017:24(2)Tydskrif vir die fisiese en ontwikkelingswetenskappe

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Journal for the physical and development sciences

Acta Structilia is a South African accredited journal for independently adjudicated research articles on any topic in the field of the physical and development sciences. Each peer refereed article is indicated as such in the journal. The editorial staff considers articles in English and Afrikaans, written from any responsible point of view on subjects in any applicable field of scholarship, i.e. architecture, urban and regional planning, quantity surveying, construction management and project management, building economy, engineering and property or community development. Acta Structilia is published biannually by the University of the Free State. The journal is forwarded to all relevant research units and universities, Southern African research libraries, selected research institutions and libraries abroad, and to subscribers. Views and opinions expressed in this journal are those of the author(s). Publication thereof does not indicate that the Editorial Staff or the University of the Free State accept responsibility for it.

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Redaksie • Editorial Staff

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Redaksionele Raad • Editorial Board

Mr MA Aladapo (Chief Executive, Murty International Limited, Nigeria)Prof. G Crafford (Department of Quantity Surveying, Nelson Mandela Metropolitan University, South Africa)Prof. G di Castri (Italian Institute of Chartered Engineers, Milan, Italy)Dr JA Fapohunda (Construction Management and Quantity Surveying, Cape Peninsula University of Technology, South Africa)Prof. TC Haupt (Faculty of Engineering, Mangosuthu University of Technology, Durban, South Africa)Prof. O Joubert (Affiliated Professor at the Architecture Department, University of the Free State, South Africa) Mr A Kerin (President, Slovenian Project Management Association (ZPM) Slovenia)Prof. K London (Property Construction & Project Management, RMIT University, Australia)Emer Prof. MJ Maritz (Department of Construction Economics, University of Pretoria, South Africa)Prof. HJ Marx (Project EnSci, Univerisity of the Free State, South Africa)Prof. G McLachlan (Department of Architecture, Nelson Mandela Metropolitan University, South Africa) Mr I Moss (Department of Construction Management and Quantity Surveying, Walter Sisulu University of Technology, South Africa)Prof. G Ofori (School of Built Environment and Architecture, London South Bank University, London, United Kingdom)Dr S Ramabodu (Chairperson) (QS-online Quantity Surveyors, Bloemfontein, South Africa) Prof. JJ Smallwood (Department of Construction Management, Nelson Mandela Metropolitan University, South Africa)Dr P Smith (Program Director of Construction Project Management in the School of Building at UTS, Australia)Prof. J Tookey (Programme leader in MCM, AUT University, New Zealand)Mr K Trusler (Edutech Director, The Association of South African Quantity Surveyors, South Africa)Mr B van den Heever (Bert van den Heever QS Incorporated, Quantity Surveyors and Project Managers, South Africa)Prof. C Vosloo (Department of Architecture, University of Johannesburg, South Africa) Prof. BG Zulch (Department of Construction Economics, University of Pretoria, South Africa)

Acta Structilia Jaargang 24 Volume

Nommer 2 Number Desember 2017 December

Inhoud • Contents

Navorsingsartikels • Research articles

Deciphering priority areas for improving project Chipozya Kosta 1 risk management through critical analysis of Tembo-Silungwe pertinent risks in the Zambian construction Nthatisi Khatleli industry

An analysis of the use of mass appraisal Kobus van der Walt 44 methods for agricultural properties Douw Boshoff

A post-contract project analysis of material Ibrahim Saidu 77 waste and cost overrun on construction sites Winston Shakantu in Abuja, Nigeria

Stakeholders’ perception of critical success Bankole Awuzie 106 factors for sustainable facilities management Rasheed Isa practice in universities in sub-Saharan Africa

Oorsigsartikels • Review articles

Building an infrastructure project performance Davison Murwira 128 in the North-West Province Department of Michiel Bekker Public Works and Roads

Investigating alternative dispute-resolution Tariene Wilcocks 146 methods and the implementation thereof Jacques Laubscher by architectural professionals in South Africa

Inligting aan outeurs • Information for authors 168

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The South African Council for the Quantity Surveying Profession (SACQSP) has simplified the submission and assessment of Continuining Professional Development (CPD) requirements of registered persons. CPD submission now requires disclosure of the number of hours invested meaningfully in activities in two main categories. Category 1 activities are those arranged or presented by or to ‘external’ organisatins such as participation in conferences, congresses, workshops or seminars, presentation of lectures, external examination for academic programmes, publication of articles in journals or magazines, other similar activities. Category 2 activities are less formal ‘internal’ activities such as in-house training or seminars, small group discussions, self-study of journals, magazines, articles on web pages, etc.

To assist registered persons with access to journal articles related to quantity surveying and, more generally, built environment issues, the SACQSP at its meeting in March 2007 adopted a recommendation to endorse the journal, Acta Structilia, which publishes quality, peer-reviewed articles and is accredited by the Department of Education.

Council encourages registered persons to peruse Acta Structilia and similar peer-reviewed journals as one of the alternative options to accumulate CPD credits in Category 2 activities. For a limited period, Council will encourage the circulation of Acta Structilia to registered persons.

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Royal Institution of Chartered Surveyors (RICS) supports Acta Structilia

Royal Institution of Chartered Surveyors (RICS) supports the aims and objectives of Acta Structilia and welcomes the efforts being made to improve our knowledge and understanding of the built environment, particularly in an African context.

1


Deciphering priority areas for improving project risk management through critical analysis of pertinent risks in the Zambian construction industry

Peer reviewed and revised

AbstractRisk identification is the first step in the risk-management process. A plethora of current studies in literature dwell overwhelmingly on risk identification much to the exclusion of the source, and the possible mitigation interventions. In a limited effort to address this deficiency in the body of knowledge, this article reports the results of a study conducted using 15 purposive semi-structured interviews and 198 questionnaires targeting clients, contractors and consultants in the building sector in Zambia. This study uses threats to identify improvement areas in the Zambian Construction Industry (ZCI). As a consequence, this research uses the pertinent risk factors as a point of critical analysis to recommend improvement areas for project risk management.Findings show that most of the risks could be categorised as managerial, technical and finance related and could severally be associated with clients, consultants, and contractors compared to project managers. These could be mitigated in the pre-contract phase and construction phase, with the most deficient knowledge areas being cost management, procurement management, integration management, communication man-age ment, and scope management. This article provides areas of focus for built environ ment professionals to improve project delivery and thereby enhance project execution efficiency.Keywords: Building sector, risk identification, Pareto analysis, project risk management, Zambia

Chipozya Kosta Tembo-Silungwe

Mrs Chipozya K. Tembo-Silungwe, School of Construction Economics and Management, University of the Witwatersrand, Johannesburg, South Africa. Phone: +27 11 717 7625, email: <[email protected]>

Nthatisi Khatleli

Dr Nthatisi Khatleli, Senior lecturer, School of Construction Economics and Management, University of the Witwatersrand, Johannesburg, South Africa. Phone: +27 11 717 7651, email: <[email protected]>

DOI: http://dx.doi.org/10.18820/24150487/as24i2.1ISSN: 1023-0564e-ISSN: 2415-0487Acta Structilia 2017 24(2): 1-43© UV/UFS

mailto:[email protected]


http://dx.doi.org/10.18820/24150487/as24i2.1



Acta Structilia 2017: 24(2)

2

AbstrakRisiko-identifikasie is die eerste stap in die risikobestuursproses. ’n Oorvloed bestaande literatuurstudies handel oorweldigend oor die identifisering van risiko’s tot die uitsluiting van die bron en moontlike versagtende intervensies. In ’n beperkte poging om hierdie tekort aan kennis aan te spreek, het hierdie studie 15 doelgerigte semi-gestruktureerde onderhoude en 198 vraelys-opnames gedoen wat kliënte, kontrakteurs en konsultante in die bousektor in Zambië teiken. Bevindinge toon dat meeste van die risiko’s gekategoriseer kan word as bestuurs-, tegniese en finansies verwant en kan afsonderlik met kliënte, konsultante en kontrakteurs geassosieer word met projekbestuurders. Dit kan verminder word in die voorkontrakfase en konstruksiefase, met die mees gebrekkige kennisareas, naamlik kostebestuur, verkrygingsbestuur, integrasiebestuur, kommunikasiebestuur en omvangsbestuur. Hierdie artikel bied fokusareas vir geboue in die omgewing om projeklewering te verbeter en sodoende die doeltreffendheid van projekuitvoering te verbeter.Sleutelwoorde: Bou-sektor, risiko-identifikasie, Pareto-analise, projek risikobestuur, Zambië

1. IntroductionAlthough risk abounds in all spheres of life, the construction industry has the worst record, as it is only surpassed by mining as the most dangerous industry (Ardeshir, Mohajeri & Amiri, 2016: 2546). Al-Bahar and Crandall (1990: 534) define risk as “the exposure to the chance of occurrences of events adversely or favorably affecting project objectives as a consequence of uncertainty”. Cano and de la Cruz (2002: 473) define risk as “an uncertain event that, if it occurs, has a positive (opportunity) or negative (threat) effect on a project objective”. This definition, therefore, entails that the current body of knowledge stresses risk as an occurrence or event, which can present threats and/or opportunities. Chapman and Ward (2003: 98) and Smith, Merna and Jobling (2014: 2) posit that the manner in which risks are managed determines whether the risk would be an opportunity or a threat. Lehtiranta (2014: 647) is of the view that opportunities in project teams are rarely seen. This lack could explain why the perception of risk in projects is normally negative and the emphasis is on dealing with negative risk events as opposed to the opportunities that could be harnessed from the risk events. Project risk management is, therefore, the logical method of establishing the context, identifying, analysing, evaluating, treating, monitoring and communication of risk associated within any activity, function or process in a way that enables losses to be minimised and opportunities to be maximised (Australian and New Zealand Risk Management Standard-AS/NZ 4360 1999: 4). One of the most noted barriers to risk management is lack of knowledge (Chileshe & Kikwasi, 2014: 2; Dey,

Tembo-Silungwe & Khatleli • Deciphering priority areas ...

3

2001: 634; Choudry, Aslam, Hinze & Arain, 2014: 1-9; Lyons & Skitmore, 2004: 60).

The construction industry in Zambia is characterised by quality shortfalls, cost and time overruns as well as project abandonment (Kaliba, Muya & Sichombo, 2009a; Muya, Kaliba, Sichombo & Shakantu, 2013; Auditor General’s Office, 2006-2012: Online). The Auditor General Reports focus on public-sector projects carried out by various government ministries, agencies and authorities. In addition, the Zambia Development Agency reports that the Zambian Government procures over 70% of work in the Republic (ZDA, 2013: Online).

Mañelele and Muya (2008) found that community projects in Zambia (a subsector of the building sector projects) underperform, due to poor risk identification. The following risks were identified: participation, project initiation, budget and finance, skilled labour, material procurement, technical supervision, and quality control.

Studies by Kaliba et al. (2009a) and Muya et al. (2013) on engineering and road projects in Zambia revealed the following major causes of cost escalation in Zambia’s road-construction projects: inclement weather such as heavy rains and floods; scope changes; environmental protection and mitigation costs; schedule delay; strikes; technical challenges; inflation, and local government pressures. Time overruns were attributed to delayed payments, financial processes and difficulties on the part of contractors and clients, contract modification, economic problems, materials procurement, changes in drawings, staffing problems, unavailability of equipment, poor supervision, construction mistakes, poor coordination on site, changes in specifications, labour disputes, and strikes in road-construction projects. These were grouped into four categories: poor financial planning and management; poor change management; lack of capacity, and poor schedule management. In addition, Kaliba, Muya and Sichombo (2009b) found that, in the final analysis, incomprehensible risk identification is a contributing factor to poor project delivery, contributing to poor risk allocation.

Another study by Sibanyama, Muya and Kaliba (2012) on risk factors that result in claims found that claims are rampant in the Zambian construction industry and cited poor risk-sharing as one of the contributing factors. Similarly, in a study targeted at investigating unethical practices contributing to poor project delivery in the ZCI, Mukumbwa and Muya (2013) found that construction contracts in Zambia are characteristically one-sided, with risk mainly shifted to the contractor.


4

Given the aforementioned, risks are prevalent in the ZCI and are affecting performance. In terms of volume, building sector projects are the majority (ZDA, 2013: online), yet few studies in the Zambian context have identified risks affecting this sector, apart from risks identified by Mañelele and Muya (2008) from a subsection of the sector. This study is, therefore, justified by focusing on the whole building sector where it is unclear in which areas the knowledge is lacking and where professionals should focus to alleviate the situation.

Given this knowledge gap, a literature study on project cycle and knowledge areas, risk categories as well as on risk factors in the construction industry of developing countries helped identify risks and assist in categorising these risks into various risk categories determined by the identified risk factor. By categorising the risks identified from the literature review, combined with the risks identified from semi-structured interviews, this study listed pertinent risk factors which were ranked in a questionnaire survey as a point of critical analysis to recommend improvement areas, possible mitigation and alleviation for project risk management in the Zambian Construction Industry.

2. Nature of risksRisks can be known or unknown (Chapman & Ward, 2003: 98). Unknown risks are referred to as uncertainties. Jaafari (2001: 89) defines uncertainty in project contexts as “an unknown probability of impact of a project variable on its objective function”. A further extension of Jafaari’s (2001: 89) postulation is that certain events have a 100% probability chance of occurrence, while totally uncertain events have 0% probability chance of occurrence. Risk has to be understood as the uncertainty that can be measured, while uncertainty is the risk that cannot be measured (Serpella, Ferrada, Howard & Rubio, 2014: 655). Nonetheless, both have an impact on project delivery if unmanaged (Hilson, 2002: 239). This study focuses on the known risks influencing performance in the ZCI. Anything that increases risk or susceptibility is a risk factor (Zou, Zhang & Wang, 2007: 605). Risk factors usually have measureable characteristics or elements (Business Dictionary, 2016: Online), especially when they pertain to volatile issues such as exchange rate, interest rate, labour shortage, or market price.

2.1 Risk categories in the construction industry

Risk categorisation or classification is important as it helps identify the possible root cause for a risk factor (Chapman & Ward, 2003: 99). For instance, political risk may indicate instability in a given area.


5

Therefore, contracts for use in such an area should cover political risks. The classifications or categorisations of risks may occur in various forms such as political, economic, social, technological, legal, and environmental. Others are impact related, such as insurable or uninsurable; acceptable or unacceptable. Another classification could be positive or negative (Ebrahimnejad, Mousav & Seyrafianpour, 2010: 577). Some of these risks could emanate from a contractual relationship, while others are non-contractual (Murdock & Hughes, 2001: 83). Zou et al. (2007: 605), on the other hand, categorise the risks as quality related, cost related and time related. However, the broader classification of risk is internal and external (Tah & Carr 2000: 492; Barlish, Marco & Thaheem, 2013: 709). Furthermore, the concept of risk owner may be used as a classification method; such categorisations have been used by Jarkas and Haupt, (2015: 175-177) who categorise the risks as client related, contractor related, consultant related only when they pertain to internal risks. In this categorisation, risks external to the project team are classified under the umbrella term of external risks. When risks eventuate, more than one party may be affected. In this article, the concept of risk owner is used to refer to who is supposed to manage a particular risk (Smith et al., 2014: 4).

Internal risks could be local (labour, plant, subcontractors, materials, and site) and global (construction, design, financial [company/project] location, precontract, client, contractual, environmental, management, and time frame). External risks include economic, physical, political and technological (Tah & Carr, 2000: 492). Zavadskas, Turskis and Tamoscitience (2008: 351) suggest internal risks (stakeholders, designers, contractors, subcontractors and suppliers) and external risks (economic, social, weather, protetivism). Barlish et al. (2013: 709) formulate a risk taxonomy/category for the construction industry as internal (client/owner, design, job site related, subcontractor, operational, and managerial) and external risks (political, financial/economic, social, cultural, technological, legal regulation, and environment).

A synthesis of studies by Tah and Carr (2000: 492); Lam, Wang, Lee and Tsang (2007: 491; Rezakhani (2012: 33); Tadayon, Jaafar & Nasri (2012: 57, 66); Charoenngam and Yeh (1999: 32), as well as Barlish et al. (2013: 709) shows that the universal risk categories are financial, economic, environmental, legal, and political risks. In addition, further categories are formulated to determine risks present at different levels. For instance, Zhi (1995: 232) proposes categories for use on overseas projects with risk categorisation for national/regional, construction, company, and project level. This discussion


6

provides evidence that risk categories are selected, based on the nature of information needed.

Risk factors can cause many risks and form a causal network with the risks (Tah & Carr, 2000: 500). Moreover, risks are triggered by risk factors (Ebrahimnejad et al. 2010: 576). Various research has been conducted on risk factors affecting the construction industry. Table 1 shows the various risk categories and risk factors found in previous research. Internal risk (within the control of a project team) categories include design risks, productivity risks, client-related risks, contractor risks, and management risks. External risk (beyond the control of a project team) categories include economic, legal, force majeure, political, and social (Table 1).

2.2 Risk factors in the construction industry

Table 2 highlights the various risk factors found in the construction industry as identified in different developing countries, namely China, Egypt, Ghana, India, Indonesia, Jordan, Malaysia, Mozambique, Nigeria, Pakistan, Poland, South Africa, Sri-Lanka, Swaziland, Uganda, Vietnam, and Zambia.

Knowledge of the risk factors affecting other developing countries provides a basis for risk mitigation or alleviation of similar risks. Some of the risks identified are construction sector specific, while others apply to a whole industry. Financial, planning and operational risks seem rampant in developing countries (see Table 2).

Table 1: Common risk factors and their categories in the construction industry

Risk category Risk factors Authors

Client related Client interference, design change, improper intervention

El-Sayegh (2008: 437); Tadayon et al. (2012: 57-69); Santoso et al. (2003: 46-53)

Contractor related

Contractor capabilities: inexperience, contractor liability, defective construction, subcontractor failure, subcontractor default, novel construction methods

Lam et al. (2007: 491); Tadayon et al. (2012: 57-69); Tsai & Yen (2006: 396); Ghosh & Jintanapakanont (2004: 637-640); Nieto-Morote & Ruz-Villa (2011: 227); Wiguna & Scott (2006)

Coordination and cooperation

Cooperation, poor communication, teamwork between contractor and consultant

Mahamid (2011: 611); Tsai & Yen (2006: 396); Santoso et al. (2003: 46-53); Enshassi et al. (2009); Hwang et al. (2013: 120)

Corruption Bribe, fraudulent practices Baloi & Price (2003: 264)


7


Cost Cost overruns, estimator related, poor cost control

Baloi & Price (2003: 264); Zou et al. (2007: 605); Wiguna & Scott (2006); Medda (2007: 216)

Delay

Delay in resolving disputes, time overruns, tight project schedules, time constraints, unrealistic schedules

El-Sayegh (2008: 437); Tadayon et al. (2012: 57-69); Goh & Abdul-rahman (2013: 25); Turkey (2011: Online); Ghosh & Jintanapakanont (2004: 637-640); Medda (2007: 216)

Design

Design changes, design defects, delay in producing detailed drawing, design issues, engineering design

Santoso et al. (2003: 46-53); Oztas & Okmen (2005: 234); Enshassi et al. (2009); El-Sayegh (2008: 437); Medda (2007: 216); Chung et al. (2010: 47-53); Kuo & Lu (2013: 602-614); Nieto-Morote & Ruz-Villa (2011: 227); Wiguna & Scott (2006); Oztas & Okmen (2005: 234); Medda (2007: 216)

Economic and financial

Inflation rates, delayed payment, market, exchange rates, financial failure of client, price inflation, uncertainty in price, level of competition, market, unavailability of funds, financial failure of contractor

Wiguna & Scott (2006); Ghosh & Jintanapakanont (2004: 637-640); Baloi & Price (2003: 264); Zou et al. (2007: 605); El-Sayegh (2008: 437); Goh & Abdul-rahman (2013: 25); Mahamid (2011: 611); Turkey (2011: Online); Kuo & Lu (2013: 602-614); Chung et al. (2010: 47-53); Medda (2007: 216); Lam et al. (2007: 491); Xu et al. (2012: 896); Ebrahimnejad et al. (2010: 581)

EnvironmentInclement weather, unforeseen site ground conditions

Wiguna & Scott (2006); Zou et al. (2007: 605)

Force majeure Invasions, natural hazardsKuo & Lu (2013: 602-614); Enshassi et al. (2009); Chung et al. (2010: 47-53)

LegalDifficulty in obtaining permits, frequent changes in law and statutory regulations

Lam et al. (2007: 491); Tsai & Yen (2006: 396); Xu et al. (2012: 896)

ManagementConstruction management, project management, site management

Nieto-Morote & Ruz-Villa (2011: 227); Dikmen & Birgnoul (2007: 60-66); Kuo & Lu (2013: 602-614); Santoso et al. (2003: 46-53)


8


Political

Frequent changes in statutory laws, government action and regulations, change in law, public risk

Enshassi et al. (2009); Mahamid (2011: 611); Tsai & Yen (2006: 396); Lam et al. (2007: 491); Tadayon et al. (2012: 57-69); Chung et al. (2010: 47-53); Medda (2007: 216)

Productivity

Shortage of labour, lack of manpower, inadequate staff by contractor, low equipment efficiency, equipment unavailability, construction equipment maintenance, labour productivity, resource risk, construction delay

Santoso et al. (2003: 46-53); Kartam & Kartam (2001: 329-333); Wiguna & Scott (2006); Oztas & Okmen (2005: 234); Turkey (2011: Online); Mahamid (2011: 611); El-Sayegh (2008: 437); Zeng & Smith (2007: 589-600); Dikmen & Birgnoul (2007: 60-66); Hwang et al. (2013: 120); Enshassi et al. (2009); Zou et al. (2007: 605); Ghosh & Jintanapakanont (2004: 637-640); Kuo & Lu (2013: 602-614); Mahamid (2011: 611)

Project related

Engineering risks, inadequate site investigation, project complexity, site factor physical/technical, unclear scope

Nieto-Morote & Ruz-Villa (2011: 227); Lam et al. (2007: 491); Dikmen & Birgnoul (2007: 60-66); Zeng & Smith (2007: 589-600); Xu et al. (2012: 896); Tadayon et al. (2012: 57-69); Wiguna & Scott (2006); Oztas & Okmen (2005: 234); El-Sayegh (2008: 437)

Third parties Right-of-way problemsGhosh & Jintanapakanont (2004: 637-640); Turkey (2011: Online)

Social Culture, human factorsLam et al. (2007: 491); Chung et al. (2010: 47-53); Zeng & Smith (2007: 589-600)

.


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10

2.3 Risks, project cycle and knowledge areas

Given that the gap identified is a knowledge gap, it is important to have a basic understanding of construction stages and processes in order to determine where the knowledge gaps could reside. Different risks factors affect a project at different stages of the project, while some risk factors may permeate all stages and processes of the project. The generic stages of the project include: 1 - Pre-project stage, 2 - Pre-construction stage, 3 - Construction stage, 4 - Post-construction stage (Kagioglou, Cooper, Aouad & Sexton, 2000: 148-150), while PMBOK (2008: 18) and ISO 21500 (2012: online) highlight the following processes: 1 - Initiate, 2 - Plan, 3 - Execute, 4 - Controlling, 5 - Close out. Though risks mostly eventuate and manifest themselves in the construction phase (Lehtiranta, 2014: 129; Osipova & Eriksson, 2011: 1151), this does not entail that this is the stage where the risks have their source.

The PMBOK (2008: 67-69) further outlines knowledge areas for practice and application as shown below. Other knowledge areas are also considered:

• Integration Management (PMBOK 2008: 71-101): The knowledge area is devoted to identifying and defining the work in all project phases. This knowledge area deals with efficiently integrating changes into the project at all stages.

• Scope Management (PMBOK, 2008: 103-128): This knowledge area deals with the project scope, project requirement scope, project work, making the work breakdown structure, making the scope baselines, and managing the scope of the project. This area aims to plan the ways in which to keep the project within the established boundaries. This is applied at initiation, planning, and control/monitoring.

• Time Management (PMBOK, 2008: 129-163): The project managers/leaders estimate the duration of the tasks in this knowledge area. Tasks are sequenced here and the choices of resources required for achieving the objective of the project are made. The schedule is monitored and managed to keep the project on track. This knowledge area permeates planning, execution, and control/monitoring.

• Cost Management (PMBOK, 2008: 165-187): Budget baseline is established and costs are estimated in this knowledge area. The plan to manage the costs is categorised in the cost management knowledge area. This knowledge area permeates planning, control/monitoring, and execution.


11

• Quality Management (PMBOK, 2008: 189-213): This is the knowledge area where the quality requirements for project deliverables are planned and tracked. In this area, all the quality issues are monitored and fixed. This is applied to planning, execution, and monitoring/control.

• Human Resources Management (PMBOK, 2008: 215-241): This comprises the essential processes to define the ways in which human resources are utilised, developed, acquired, and managed. This is dealt with in the planning and execution phases.

• Communications Management (PMBOK, 2008: 243-270): The knowledge area defines how communications within the project will work. The project manager/leader makes the communication management plan, ensures the plan is followed, and controls information flow within the project. The knowledge area permeates all phases of a project.

• Risk Management (PMBOK, 2008: 273-311): This consists of identifying risks, planning risk management, conducting risk assessments, and controlling risks. This knowledge is used in the planning and control/monitoring phases. The area concentrates on identifying, analysing, and planning responses to both ‘threat risks’ (negative) and ‘opportunity risks’ (positive).

• Procurement Management (PMBOK, 2008: 313-340): This deals with the processes, which project managers/leaders usually follow to acquire the material required for the successful completion of the project. In this knowledge area, project managers/leaders come up with the plan for conducting procurements, controlling the procurements, and closing out the procurements. This is utilised in the planning, execution, controlling/monitoring, and closing out phases.

• Stakeholder Management (PMBOK, 2008: 261-265): The area encompasses all the processes used by a project manager/leader for recognising and satisfying those who are affected by the project. The affected party can be either internal or external in nature. Close attention needs to be paid to stakeholders who have a powerful positive or negative impact on the project. This is applied throughout the project cycle.

• Claim Management (Lichtenthaler, 2017: online): This is the process of systematically and efficiently managing claims (construction defects) in building projects. Claims run through


12

the phases of detection, examination, and correction of the defects. This occurs in the construction phase.

• Safety management defines the safety obligations on all duty holders, including the client, project supervisor for design process and construction process (Smart market report, 2013: 15). This is normally planned in the pre-contract stage by either the design team or the contractor submits a risk management plan at the tender stage. It is executed in the construction phase.

• Project Financial Management: This process brings together planning, budgeting, accounting, financial reporting, internal control, auditing, procurement, disbursement, and the physical performance of the project with the aim of managing project resources properly and achieving the project’s objectives (NCTC, 2017: online). This is normally planned in the pre-contract stage and executed in the construction phase. It is done in the form of an audit to check the planned against the actual work completed.

• Environmental management (He, 2010: 208). This is the management of the impacts of a project management’s activities on the environment. It provides a structured approach to planning and implementing environment-protection measures. This is normally planned in the pre-contract stage and executed in the construction phase.

The knowledge areas are important for managing a project, as project management is the application of knowledge to achieve the project objective(s). Therefore, these knowledge areas were used as the basis for determining knowledge gaps.

3. MethodologyThis article is part of a bigger study on risk allocation on building projects; therefore, the methodology reported here reflects what was done for the whole study. Saunders, Lewis, and Thornhill (2012: 138) suggest that the methodology comprises the research philosophy, approaches and strategies, choices in methods, time horizons, techniques and procedures for data collection and analysis. The research used a pragmatism philosophy.

Pragmatism entails focusing on problems, and problem-solving to inform future practice (Saunders, Lewis & Thornhill, 2016: 137). The philosophy normally allows mixing of qualitative and quantitative data or several approaches. The strategies employed in this study are interviews and surveys in a sequential manner, as shown in


13

Figure 1. The methods chosen are semi-structured interviews and questionnaire surveys collected in a cross-sectional manner. Therefore, the research is both qualitative and quantitative, because the approach makes use of both numbers and words in determining the pertinent risks impacting on the Zambian building sector and further, deciphering the root causes of such risks. This approach was deemed appropriate, as the nature of the problem is a practical one. The importance of this worldview is that it focuses attention on the research problem and uses several approaches to derive knowledge about the problem (Creswell & Clark, 2011: 45-46). A deeper understanding of the nature of the risks enabled the identification of possible target areas for improvement. The study respondents were consultants, clients and contractors involved in building projects in the Zambian construction industry.

Research flow diagram

Research questions

What are the important risk factors? How can they be categorised to offer meaningful mitigation measures?

Semi-interviews

Consultants, clients and contractors

Questionnaire survey

Consultants, clients and contractors

Literature review

Zambian construction industry, risk management and construction stages and process

Results

Interviews Questionnaire

Discussion

Figure 1: Research flow diagram applicable to this article

3.1 Sampling method

The construction industry in Zambia is, comparatively speaking (vis-à-vis South Africa, for instance), very small. The people who were approached were from the public and private sectors working in the construction industry. Sampling is the process or technique of selecting a suitable sample for determining parameters or characteristics of the entire population (Adams, Khan, Raeside & White, 2012: 87).

The study took each sub-population (clients, professionals and contractors) (see Table 4) as manifesting different characteristics and dynamics and, as such, different sampling techniques were employed to acknowledge these differences, in order to enable credible data to be elicited from the different groups/sub-populations. Four sampling methods were used based on


14

probability (random sampling and stratified random sampling) and non-probability (purposive and census) for the various categories of respondents as shown in Tables 3 and 4.

3.1.1 Semi-structured interviews

The purposive method was employed for the semi-structured interviews to select participants, with at least 10 years’ experience in the construction industry, from diverse backgrounds, professions and project experiences. The purposive heterogeneous sampling was intentionally selected for this purpose. This method of sampling enables accessing respondents from diverse backgrounds with in-depth knowledge about a particular issue (Adams et al., 2012: 87: Babbie, 2013: 126). Leedy and Ormrod (2014: 196) propose a sample of five to 25 participants for semi-structured interviews. In this research, 15 respondents participated, as shown in Table 3. Triangulation of risk factors from different respondents was used as a measure of validity for the interviews.

3.1.2 Questionnaire survey

The questionnaire survey utilised three types of sampling: simple random sampling for consultants who are ordered or arranged according to services (for example, Architecture, Quantity Surveying and Engineers), while a simple census was done for clients and project managers, as these populations were less than 30 (Saunders et al., 2012: 266). For contractors, stratified random sampling was used, as the contractors targeted were listed in different building categories and had different capacities, thereby presenting heterogeneous characteristics across groups and homogeneous characteristics within groups. Each contractor grade (grades 1-3) was treated as a stratum where proportional samples were drawn (Adams et al., 2012: 89). For building category grade 1, limitation of contract value to be tendered is over K40Million (US$ 4 M); grade 2 between K20M and K40 (US$ between 2M & 4M), while the grade 3 category is K10M and K20M (US$ between 1M & 2M) using an exchange rate of 1US$=K9.77.

3.2 Sample size

The sample sizes are shown in Tables 3 and 4 for the respective data-collection methods used. The sample size for the interviews is 15 and for the questionnaire survey the sample is 198. Table 3 further shows the nature of building projects in which the interviewees have participated.


15

Table 3: Respondent profile for interviewees sampled purposivelyRespondent no. Sector Years of

experienceRole in construction

Nature of building projects engaged in

1 Public 15 Quantity surveyor Offices, houses, schools

2 Public 12 Civil engineer Offices, hospitals, schools

3 Public 10 Procurement officers Offices, houses

4 Public 20 Quantity surveyor

Housing units, offices, health facilities, hospitals

5 Public 19 Architect Schools, offices, border infrastructure, houses

6 Private 10 Contractor Houses, student hostels, high-rise buildings

7 Private 32Quantity surveyor/Project Manager

Offices, hospitals, residential, banks, filling stations, stadia, factories

8 Public 21 Client org Primary schools, secondary schools, colleges, houses

9 Public 23 Project manager

Prisons, military installations, houses, rural health centres, flight terminals, border facilities, offices

10 Private 30 Engineer consultant

Showrooms, schools, filling stations, hospitals, hotels, office buildings

11 Private 29 Contractor Housing, offices, banks, schools, hostels

12 Private 10 Contractor High schools, maternity wards, student hostels, offices

13 Private 10 Client org Markets, fire stations, bus shelters, houses

14 Public 15 Procurement officer

Office blocks, houses, farm layouts and different buildings, lodges, banks

15 Private 10 Architect Houses, offices, shops, farm buildings, banks

Table 4: Respondent profile for questionnaire survey

CategorySampling/selection strategy

Subgroup Population Responses Response rate %

Contractors as at 14 August 2014

Stratified random sampling

Group 1 51 22 43.1

Group 2 30 15 50.0

Group 3 69 43 62.3

Consultants (firms) engaged in buildings

Random sampling

Quantity surveyor 36 32 88.9

Engineers 32 28 87.5

Architects 54 38 70.4


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CategorySampling/selection strategy

Subgroup Population Responses Response rate %

Project managers (firms)

Census Project managers 17 14 82.0

Clients CensusPublic (ministries)Private

65

42

66.740.0

Total 300 198 66.0

The sample size for construction-related professionals was calculated in accordance with the table recommended by Krejcie & Morgan (1970: 608). The table gives recommended sample sizes for general research activities, applicable to any defined population. From the table, the recommended sample size for a population of 300 is 169, for 10 000 it is 370, and for 1 000 000 it is 384. This recommendation validates the sample size of 198 as efficient for the population of 300.

3.3 Data collection

Data collections were twofold: semi-structured interviews and a self-administering questionnaire. An interview protocol was used to collect the primary data for the semi- structured interviews in a face-to-face interaction.

The interview protocol had three main sections. The first section included questions regarding the background of the respondent; the second section, questions on the risks perceived as pertinent to building projects, and the last question addressed the possible risk management and mitigation measures for the risks used in practice. This article deals with only the analysis on pertinent risks. During the interview sessions, probing questions were asked to gain a deeper understanding (Babbie, 2013: 253). The interviews ranged between 30 minutes and 70 minutes. The interviews were captured using a digital recorder. Back-up notes were taken during the session in case any problems occurred with the audio-taping and for respondents who did not agree to be audio-recorded. The recording was then transcribed prior to the commencement of the analysis.

Respondent bias during the questionnaire survey was reduced with closed ended questions (Bryman & Bell, 2015: 175). The nature of questions for the survey was similar to that of the semi-structured interviews, starting with background or demographic information, risk practices and measures taken for mitigation and management


17

of encountered risks. A reliability test using Cronbach’s Alpha was calculated for the 55 items and the reliability test scored was 0.96. According to Reynold and Santos (1999: 35-36), a Cronbach’s alpha value greater than 0.7 implies that the instrument is reliable.

3.4 Response rate

Of the 222 questionnaires distributed, 198 completed questionnaires were returned, resulting in an overall response rate of 66% (see Table 3). This is beyond the response rate, recommended by Moyo and Crafford (2010: 68) for the built environment, of between 7% and 40%. The number of purposive interviews was well within the range of recommended numbers advocated by Leedy and Ormrod (2014: 196) of between five and 25 respondents (see Table 3). The response rates demonstrate a high level of reliability.

3.5 Data analysis

The research for the semi-structured interviews used content analysis to determine pertinent risk factors and other categories arising. For the questionnaire survey, descriptive statistics in the form of frequency/counts, mean and standard deviation were calculated. In addition, Pareto analysis was conducted to determine the stage at which most of the risks could be mitigated, the owners of the risks considered pertinent, the possible deficiency in knowledge areas using the project management body of knowledge (PMBOK, 2008) and to determine the risk categories of the pertinent risks. A total of 31 pertinent risk factors were generated from a list of 55 important risks generated from the literature and semi-structured interviews. From the literature, over 100 risk factors were generated; some of these were eliminated because they do not apply either to the context of building projects or in the Zambian built environment, e.g. snow, and so on.

3.5.1 Qualitative data analysis from the interviews

The qualitative data arising from the interviews was transcribed and a manual 10-step thematic content analysis, adapted from Burnard (1991: 463-466), was conducted and strictly followed as follows:

1. Notes were made after the interviews regarding topics discussed during the interviews. The interest was mainly in areas considered pertinent to the study.

2. The reading of transcripts elicited themes which were then categorised to map out some trends in the data.


18

3. The categories were revisited in order to expand on their meaning in terms of what they were telling the researchers.

4. Any commonalities between categories were identified and the categories were ranked according to whether they were major or minor categories in terms of the themes they encapsulated.

5. Once the ranking was done, the categories were compared and contrasted, in order to merge and observe any inclinations.

6. A similar approach was done for all the transcripts.7. The categories from different transcripts were further

juxtaposed to identify any fits or divergences, in order to elicit any useful signals from the data.

8. The next stage was simply to check if there was uniformity in all the transcripts in the data.

9. Once discrepancies were dealt with, some categories were merged and some had to be subcategories, as they could, in the whole, be subsumed by major categories.

10. In the last stage, the transcripts were revisited to find out whether all that needs to be done was done, and to avoid any major mistakes in recording data.

In addition to the thematic analysis after the risk factors were identified, a process of categorisation took place. The project management body of knowledge (2008) processes and knowledge areas were used to map out the processes and knowledge areas of the project in which the identified risks could be mitigated. Kagioglou et al.’s (2000: 148-150) stages of contract were also used to map out the stages in the construction process where the risk factors reside. This was done to give an indication of the project process and stages where a risk could be alleviated or eliminated.

3.5.2 Quantitative data analysis (Questionnaire)

For the questionnaire survey, a 5-point Likert-scale measurement was used to obtain perceptions of the respondents on risks considered pertinent and affecting performance. The scale was ordinal in nature, where 1 was not important at all and 5 exceptionally important. The quantitative data was imputed and analysed using Excel and SPSS 20 programs. Interpretations were then made to make meaning of the data. The mean score and standard deviation were used to determine the factors considered pertinent, as the approach has been applied in other similar research [see Shehu et al. (2014: 61-63);


19

Mbachu & Taylor (2014: 29); Wang & Yuan (2011: 214)]. In addition, Pareto analysis was used. This technique prioritises possible changes by identifying the problems that might be resolved by making the proposed changes. The analysis is based on the Pareto principle, also known as the 80/20 rule, based on the idea that 20% of the causes generate 80% of the results (Kendrick, 2010: online). The Pareto is guided by the following procedure:

• Identify and list problems (risk factors);• Identify the root cause for each (knowledge area, phase in

project, risk category);• Score problem;• Group problems together by root cause;• Add up the score of each group (Kendrick, 2010: online)

4. Results and discussion

4.1 Respondentprofile

The respondents for the interviews had an average of 17 years’ experience, a median of 15 years, and a mode of 10 years. The minimum qualification was first degree, except for procurement officers who had training at advanced diploma level. Table 4 shows that the respondents have been involved in various types of projects with a mix of small- to large-scale building projects.

The qualifications of the respondents were as follows; for clients, all had the minimum of first degree, with over 80% having 6-10 years’ experience. For project managers, the majority (79%) had first degree, while 21% had qualification at masters level. For consultants, first degree qualification (70%), Masters degree (20.4%), Diploma (5%) and certificate (1%). Lastly, contractors’ academic qualifications were as follows: first degree qualification (74%), Masters degree (6%), Diploma (13%), and certificate (6%). Project managers had on average 10 years’ experience, whereas clients, contractors and consultants had an average of nine years’ experience each. The respondents are of acceptable experience levels.


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4.2 Nature of building works engaged in 4.2 Nature of building works engaged in

Figure 2: Building works by volume

New Construction

34%

Renovation 23%

Demolition 13%

Rebuilding 14%

Extension 16%

Building work by volume

New construction Renovation Demolition Rebuilding Extension

Figure 1: Building works by volume

The findings from the questionnaire survey show that the majority of works, in which consultants, project managers and contractors are engaged, are new works (34% by volume). Renovations and refurbishments (23% by volume) of existing buildings are also common, followed by extension (16% by volume) of existing buildings (Figure 2). The least practised are rebuilding works (14%), which are often necessitated by demolition works (13%).

4.3 Types of buildings

There are various types of buildings. Table 3 shows the nature of building projects on which various interview respondents have worked, while Figure 2 shows the nature of building projects from the questionnaire survey data.


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0 5 10 15 20

Residential

offices

shops/markets

hospitality

Stadia

Schools

Clinics

Hospitals

fire stations

Factories

Prisons

Police

Filling station

Banks

Farm buildings

Airport

Building types professionals are engaged in

Projectmanager

Client

Figure 2: Types of buildings - Questionnaire survey

On examining the two data sets closely (Table 3 and Figure 2), it appears that the common building types in the Zambian construction industry are residential, offices, schools, fire stations and health facilities such as clinics, health centres and hospitals.

4.4 Pertinent risks in the building sector

To gain an understanding of the pertinent risks in the Zambian building sector, the respondents were asked to identify pertinent risks, which have been categorised according to prevalence, depending on the count from the content analysis. Table 5 shows the results from the interview data.

Table 5: Risk factors from interviewees

Risk prevalence Risk factors

High prevalence (indicated by 10-15)

Late payment; contractors’ financial difficulty; lack of inspection, monitoring and supervision by contractor and consultants; lack of adherence to contractual provisions by public client


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Risk prevalence Risk factors

Moderate prevalence (indicated by 5-9)

Change in material prices and exchange rates; slow and bureaucratic decision-making process; poorly skilled artisans and poor workmanship; incomplete and insufficient designs; extension of time without costs; unavailability of funds/budget; changes in scope

Low prevalence (indicated by 0-4)

Lack of interest on delayed payment; poor quality works; non-compliance with tender requirements on site; difficulty in implementing clauses by contracting parties; poorly prepared contract documents; poor quality of materials; unavailable material; contractors’ lack of skill and experience; difficult access to site; late site hand-over by client; corruption; poor interpretation of contract; poor safety on site; disputes; high taxes; political interference; inclement weather; low level of subcontracting; inadequate site investigation

Table 5 shows that the majority of the pertinent risks categorised as highly prevalent are financial in nature, while those categorised as moderately important are mainly linked to scoping and technical know-how. Lastly, the low importance category includes diverse risks that could be termed project specific. These could be linked to managerial or operational risks. The interview data helped generate risks for the questionnaire.

Interviews revealed that clients and consultants (60%) perceived that contractors account for more risks, followed by external risks. In addition, the findings from the interviews and questionnaire point out that stages in the risk management include risk identification (using brainstorming, checklist, local knowledge, and expert judgement) and qualitative risk (brainstorming and expert judgement) analysis, communication and occassional monitoring, especially in the public sector, due to lack of finance and inadequate personnel.

4.5 Pertinent risk categories and risk owner

Fifty-five risk factors identified by source (client related, contractor related, consultant related, and external risks) were used in the questionnaire to determine the pertinent risk factor in the industry. The perceptions of respondents were indicated using the 5-point Likert scale where 1 has no importance on performance and 5 is exceptionally important to performance. The mean and the standard deviations of each factor are calculated to determine the rank. If two or more factors have the same mean item score value, then the one with a lower standard deviation was considered more important. The risk factors with mean item score values greater than the average value of all mean values (3.81) are classified as important/pertinent


23

risk factors affecting construction in the Zambian building sector. The pertinent factors are shown in Table 6.

Table 6: Pertinent risks factors in the Zambian building sector from questionnaire survey

Risk factor N Mean Std. deviation

Risk rank

Lack of clarity of drawings and technical specifications 180 4.328 .775 1

Contractor’s underestimate of construction cost 194 4.309 .793 2

Client’s financial stability 197 4.254 .787 3

Contractor’s financial difficulties 191 4.157 .904 4

Defective workmanship and rework 193 4.124 .767 5

Poor supervision 184 4.103 .878 6

Poor quality materials 193 4.067 .872 7

Errors and omissions in design drawings 192 4.057 .875 8

Unclear scope of works 185 4.054 .901 9

Inadequate site investigation 191 4.037 .903 10

Poor coordination and communication 192 4.036 .808 11

Poor supervision on site 190 4.016 .826 12

Inadequate budgeting and contingencies 194 4.016 .908 13

Poor planning of resources - materials, labour, equipment 195 4.010 .919 14

Delay in payment process by the client 194 3.990 .846 15

Lack of inspection of works 189 3.990 .881 16

Delay in consultant’s approval of materials submission 197 3.980 .926 17

Inadequate specification 194 3.974 .890 18

Escalation in material prices 191 3.974 .986 19

Lack of coordination among design disciplines 190 3.963 .939 20

Delay in contractor’s payment certification by the consultant 193 3.953 .909 21

Poor labour productivity 192 3.938 .890 22

Omission in design contract documents 189 3.937 .873 23

Holding key decisions in isolation 191 3.911 .851 24


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Risk factor N Mean Std. deviation

Risk rank

Delay in consultant’s approval of shop drawings 193 3.907 .953 25

Delay in consultant’s response to requests for information 196 3.898 .900 26

Unstable exchange rates 191 3.895 .906 27

Ineffective monitoring of risks 193 3.855 .935 28

Late delivery of materials 192 3.849 .900 29

Lack of experience in similar projects 194 3.835 .860 30

Frequent change of orders by client 192 3.823 .921 31

Though not all respondents ranked each risk, the responses obtained present a reliable overview. The respondents were diverse in nature and some decided not to provide a response for areas outside their expertise, as they feel that they are not qualified to rank the risk or that their knowledge on the influence of a particular risk was limited.

Table 7: Pertinent risk factors in the Zambian building sector

Risk factor Internal External Risk category Risk owner

Lack of clarity of drawings and technical specifications X Design Consultant

Delay in consultant’s approval of materials submission X Managerial Consultant

Inadequate site investigation X Technical Consultant

Inadequate specification X Technical Consultant

Omission in design contract documents X Design Consultant

Delay in contractor’s payment certification by the consultant X Managerial Consultant

Delay in consultant’s response to requests for information X Managerial Consultant

Delay in consultant’s approval of shop drawings X Managerial Consultant

Poor supervision X Managerial Consultant

Errors and omissions in design drawings X Design Consultant

Poor labour productivity X Technical Contractor/ Consultant


25

Risk factor Internal External Risk category Risk owner

Poor quality materials X Technical Consultant/ Contractor

Poor supervision on site X ManagerialContractor/ Project manager

Poor planning of resources - materials, labour, equipment X Managerial Contractor

Contractor’s underestimate of construction cost X Financial Contractor

Late delivery of materials X Managerial Contractor

Lack of experience in similar projects X Technical Contractor

Contractor’s financial difficulties X Financial Contractor

Defective workmanship and rework X Technical Contractor

Unstable exchange rates X Economic Client/ Contractor

Escalation in material prices X Economic Client/ Contractor

Client’s financial stability X Financial Client

Delay in payment process by the client X Managerial Client

Frequent change orders by client X Managerial Client

Inadequate budgeting and contingencies X Technical Client/

Consultant

Unclear scope of works X Managerial Client

Poor coordination and communication X Managerial Project

manager

Lack of inspection of works X ManagerialProject manager/ Consultant

Lack of coordination among design disciplines X Managerial Project

manager

Holding key decisions in isolation X Managerial Project manager

Ineffective monitoring of risks X Managerial Project manager


26

4.5.1 Risk categories

Figure 3 shows the risk categories for the pertinent risks, with over half (54.84%) of the risks being managerial/operational in nature. The lowest categories are economic risks (which is normally grouped with financial risks) and design. Economic risks are normally external in nature, while financial risks are normally internal, hence the need to separate them in this instance. The Pareto chart shows that the managerial, technical and financial risks are risk categories associated with 80% of the pertinent risks.

Figure 4: Risk categories

1724 27 29 31

54.84 %

77.42%87.10 %

93.55%100.00%

0

20

40

60

80

100

120

Frequency

Cumulative frequency

Cummulativepercentage

Figure 3: Risk categories

These results are not different to risks categories shown in Tables 1 and 2 for risks found in other countries. Therefore, the planning phase and monitoring phase should be given particular attention to these categories of risk. Moreover, going by the Pareto analysis, it can be argued that paying particular attention to these processes will result in mitigation of 80% of the risks (Kendrick, 2010: online).

4.5.2 Risk owner

The risk owners for most of the pertinent risks are the consultant and the contractor. However, this basically depends on the procurement method used. But, in this instance, the traditional method is applied, as it is the most prevalent procurement method in the ZCI.


27

Figure 5: Risk owner analysis

1324

313721.21%

54.55%

63.64%

100.00%

0

20

40

60

80

100

120

Frequency



Figure 4: Risk owner analysis

The Pareto chart in Figure 4 shows all project participants as risk owners of pertinent risks at 80%. However, consultants and contractors account for over 50% of the risks. This means that both parties’ contractor and consultant (normally acting on behalf of clients) should contribute more in managing risk.

4.6 Pertinent risks according to project stage and knowledge area

The mitigation stages for the pertinent risks shows that the majority of the risks occur in the construction phase, while others occur in the planning stage-pre-contract phase (see Table 10).

Table 10: Pertinent risks according to project stage, knowledge area and process

Risk factor

Stage in the project when risk could have been mitigated using generic stages

Process in the project management knowledge areas that could be a possible problem area (PMBOK 5)

Process mapping using PMBOK 5

Errors and omissions in design drawings Pre-contract Procurement Planning

Unclear scope of works Pre-contract Scope Planning


28

Risk factor




Inadequate site investigation Pre-contract Scope Planning

Inadequate budgeting and contingencies

Pre-contract Cost Planning

Inadequate specification Pre-contract Procurement Planning

Omission in design contract documents Pre-contract Procurement Planning

Clarity of drawings and technical specifications

Pre-contract Procurement Planning

Lack of experience in similar projects

Tender stage-Pre-contract Human resources Planning

Contractor’s underestimate of construction cost

Tendering-Pre-Contract Cost Planning

Contractor’s financial difficulties Construction Cost Execution

Defective workmanship and rework

Construction Quality Monitoring and control

Poor supervision by consultants Construction Integration Monitoring

and control

Poor quality materials Construction Quality Execution

Poor coordination and communication Construction Communications Whole project

cycle

Poor supervision on site - contractor Construction Integration Execution

Poor planning of resources - materials, labour, equipment

Construction Procurement Execution

Delay in payment process by the client Construction Cost Execution

Lack of inspection of works Construction Integration Monitoring

and control

Delay in consultant’s approval of materials submission

Construction Time Execution


29

Risk factor




Escalation in material prices Construction Cost Execution

Delay in contractor’s payment certification by the consultant

Construction Cost Monitoring and control

Poor labour productivity Construction Human resources Execution

Holding key decisions in isolation Construction Communication Execution

Delay in consultant’s approval of shop drawings

Construction Time Execution

Delay in consultant’s response to requests for information

Construction Integration Execution

Unstable exchange rates Construction Cost Monitoring

and control

Ineffective monitoring of work Construction Integration Monitoring

and control

Late delivery of materials Construction Procurement Execution

Lack of coordination among design disciplines

Project cycle Communication Monitoring and control

Frequent change orders by client Project cycle Scope Execution

Client’s financial stability Project cycle Financial Whole project

cycle

4.6.1 Risk stage

The Pareto analysis in Figure 5 shows that 80% of the risks are caused by poor mitigation in the construction phase and pre-contract phase; very few risks could be said to occur on account of the tendering stage.


30

Figure 6: Risk stage

1928 31

61.29%

90.32%100.00%

0

20

40

60

80

100

120

Frequency



Figure 5: Risk stage

4.6.2 Knowledge area

Several knowledge areas are essential for managing projects. The pertinent areas that seem to be attributed to the risks are shown in Figure 6.

Figure 7: Knowledge area

712

16 19 22 24 26 28 30 3122.58%

38.71%

51.61%

61.29%

70.97%77.42%

83.87%90.32%

96.77% 100.00%

0

20

40

60

80

100

120

Frequency


Cummulative percentage

Figure 6: Knowledge area


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The Pareto analysis in Figure 6 shows that 80% of the pertinent risks are caused by inappropriate application and/or lack of knowledge in cost management, procurement management, integration management, human resources management, communication management, time management, and scope management. The knowledge areas accounting for over 50% of performance are cost, procurement, scope, and integration management.

4.6.3 Process management

The Pareto analysis in Figure 7 shows that 80% of the pertinent risks are caused by inappropriate application and/or lack of knowledge in project execution, project planning, and monitoring and control. The processes accounting for over 50% of ineffective processes are execution and planning.

29.03%

54.84%

93.55%100.00%

1221

29 31

0

20

40

60

80

100

120

Frequency


Cumulativefrequency

Figure 7: Processes

5. DiscussionThis section discusses the pertinent risks in view of their nature, the risk owner, the stages of the project and the processes during a project where care needs to be taken. In addition, the applicable knowledge area associated with the risk is identified.


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5.1 Nature of pertinent risks and risk owner

The results show that the majority of the pertinent risks are internal in nature. This implies that the mitigation of these risks lies within the project team on a given project. Therefore, risks can be reduced by careful risk planning (Goh & Abul-Rahman, 2013: 21) by the project team. This can be coupled with the selection of participants with capabilities to mitigate such risks. The important risks are from the managerial, technical and financial categories. This implies that the contracting parties must pay particular attention to these categories of risk and sharpen their skills in the aforementioned management areas. This could be dealt with during the pre-contract phase.

The results in section 4.5.2 show that all project participants, to some extent, account for the risks experienced on projects. Nevertheless, both parties (client and contractor) should carry out their roles to mitigate risks (Lehtiranta & Junnonen, 2014: 143; Mu, Chen, Chohr & Peng, 2014: 453) and improve performance. From a traditional procurement perspective, the client needs to put more effort into risk mitigation. This is important, because the interviews revealed that clients and consultants (60%) perceived that contractors account for more risks, although the analysis by risk owner proves otherwise. It has been argued that perceptions influence how risks are responded to and planned for (Lehtiranta, 2014: 641). Moreover, Amundurud and Aven (2015: 43) argue that decisions on risk are strongly dependent on perception. Consequently, imperfect perceptions may account for improper risk response and planning (Floricel, Bonneau, Aubry & Sergi, 2014: 1093). The imperfect perception by parties in the building sector could account for the poor performance in the sector. The findings provide evidence that both parties contribute to undesirable performance (quality shortfalls, cost/time overruns). This calls for the parties to manage their risks better.

5.2 Stage of project where risks should be mitigated

The findings show that most of the risks occur in the construction phase and more measures should be put into this phase. This is congruent with the finding of Osipova and Eriksson (2011: 1151) who point out that most of the risks eventuate in the construction phase. Risks during this stage are normally due to poor monitoring and control of risks (Goh & Abul-Rahman, 2013: 21). In this instance, the interview data provided evidence of poor control and monitoring, due to poor funding and inadequate personnel. This is most prevalent in the public sector. In addition, the findings suggest that planning carried out in the pre-contract phase is inadequate, as risks such as


33

unclear specifications and omissions in design eventuate. Planning should, therefore, be effectively carried out to reduce risks in the construction stage.

5.3 Thedeficientknowledgeareas

The PMBOK (2008: 69) posits that effective management of projects requires the application of all knowledge areas. While risk management is a distinct knowledge area, it has been demostrated, in this instance, that other knowledge areas must be applied, in order to manage risks in the construction industry, as the findings show that, for effective mitigation to be in place, all other knowledge areas might need to be applied. The Zambian building sector professionals need to gain more knowledge in cost management, procurement management, integration management, communication manage-ment, time management, human resources management, and scope management, in order to mitigate 80% of the eventuating risks. It has been argued that risks occur on projects due to lack of skill in risk management (Dey, 2001: 634; Chileshe & Kikwasi, 2014). The findings show that skill is also needed in other knowledge areas. This view is supported by Perez, Gray & Martin (2016: 8) who identify project management, technical and business management skills as skills needed for effective risk management in Queensland. In addition, the findings from the interviews and questionnaire point out that stages in the risk management conducted include risk identification and qualitative risk analysis, communication and occassional monitoring, especially in the public sector, due to lack of finance. It was clear that quantitative risk analysis is rarely done, due to lack of knowledge. This implies that the posibility of occurrence and impacts of such risk rarely have values attached to the possible loss.

6. ConclusionThe empirical findings show the pertinent risks (managerial, technical and financial) encountered in the Zambian building sector. Most of these risks are consultant and contractor related, mainly resulting from imperfect planning and monitoring in the pre-construction and construction phases of the project, respectively. Furthermore, the results show that 80% of the pertinent risks point to deficiency or imperfect application of knowledge in cost management, procurement management, integration management, communi-cation management, and scope management. The findings presented in this study contribute significantly to local know ledge in the building sector, as this is the first such study in Zambia to analyse


34

the pertinent risks and to point out possible mitigation and alleviation area for risks.

The mentioned knowledge areas, coupled with an improvement of skill in quantitative risk analysis and risk monitoring by contractors and consultants, could improve project delivery in the building sector. However, the gaps identified in this study may not be the same for other sectors of construction, such as roads, bridges, and so on. Therefore, a similar methodology could be applied to other sectors to decipher the knowledge areas needed in relation to the risks faced in the specific sector, in order to improve project delivery.

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An analysis of the use of mass appraisal methods for agricultural properties


AbstractThere are numerous factors that influence the price of a farm and some of these factors are not monetary related. This makes the task of the valuer complex and increases the possibility of large differences in the estimated market value determined and the actual selling price.This article reports the results of a study that analysed the unique and distinctive attributes of farms, in order to determine whether it is possible to develop a linear multiple regression model for the valuation of farms (which satisfies accuracy requirements) with reasonably available data. The improvement of accuracy levels of Multiple Regression Analysis (MRA) models as well as the limitations of using these MRA models during farm evaluations was also studied.By following a stepwise regression approach, 60 farms, primarily located in the eco-zone “mixed bushveld” western area of the Limpopo province, were analysed using ten independent variables. Three models have been developed. The results showed that a fairly accurate regression model could be developed. However, a model that achieves a high level of accuracy could not be developed, due to multifaceted reasons, including non-farm factors and the size of the geographical areas.Accurate MRA valuation estimates will be to the advantage of individual farm owners regarding their municipal tax assessments. It will lead to a wider use of MRAs for the valuation of farms, but great circumspect should be taken when using MRA models in farm valuations. This is due to the possibility that the MRA models do not satisfy minimum accuracy requirements.It is difficult, but possible, to develop a fairly accurate MRA model for the valuation of farms. Therefore, if currently used MRA models are not fairly accurate for municipal valuation purposes, it should be possible to improve the accuracy. Further research is recommended in the use of

Kobus van der Walt

Mr Kobus van der Walt, M.Sc. (Real Estate) graduate, Department of Construction Economics, University of Pretoria, Lynnwood Road, Hatfield, 0002, South Africa. Phone: +27 82 804 8555, email: <[email protected]>

Douw Boshoff

Dr Douw (G.B.) Boshoff, Senior Lecturer, Department of Construction Economics, University of Pretoria, Lynnwood Road, Hatfield, 0002, South Africa. Phone: +27 12 420 3781, email: <[email protected]>









van der Walt & Boshoff • An analysis of the use of mass appraisal ...

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other regression techniques such as non-linear, geographic weighted regression and quantile regression. These other techniques would, however, require a larger data sample, in order to provide meaningful results.Keywords: Agricultural property, Automated Valuation Methodology (AVM), farm valuation, mass appraisal, valuation methodology

AbstrakDaar is veelvuldige faktore wat die prys van ’n plaas beïnvloed, wat nie noodwendig suiwer finansieël van aard is nie. Dit maak die taak van die waardeerder moeilik en kompleks, wat weer veroorsaak dat die moontlikheid bestaan dat daar groot verskille tussen die gewaardeerde waardes en die verkooppryse van plase voorkom.Hierdie artikel rapporteer die resultate van ’n studie wat die unieke en onderskeidende eienskappe van plase ontleed ten einde te bepaal of dit moontlik is om ’n liniêre regressie-analiese model te ontwikkel (wat aan minimum akkuraatheidsvlakke voldoen) met redelik beskikbare inligting, vir die waardasie van plase. Die verbetering van die akkuraatheidsvlakke van MRA-modelle sowel as die beperkings van hierdie MRA-modelle, vir gebruik in plaas waardasies, is ook nagevors.Deur middel van ’n stapsgewyse regressie-analiese metode, is 60 plase wat hoofsaaklik in die “gemengde bosveld” ekosone, in die westelike gedeelte van die Limpopo-provinsie geleë is, ontleed deur van 10 onafhanklike veranderlikes gebruik te maak. Drie modelle is ontwikkel. Die resultate het aangetoon dat ’n regressiemodel ontwikkel kan word wat redelik akkuraat is, maar ’n model met ’n hoë mate van akkuraatheid kon nie ontwikkel word nie as gevolg van veelvuldige redes, insluitend redes wat nie direk verwant is aan plaasfaktore nie en die grootte van die geografiese gebiede. Alhoewel akkurate MRA-waardasies tot voordeel sal wees van plaas eienaars vir die bepaling van munisipale belastingwaardes en dit sal lei tot ’n wyer en meer algemene gebruik van MRA’s vir plaaswaardasies, moet groot versigtigheid aan die dag gelê word met die gebruik van MRA-modelle in plaaswaardasies omdat dit waarskynlik is dat die MRA-modelle nie aan minimum akkuraatheidvereistes voldoen nie.Dit is moeilik, tog moontlik, om ’n MRA-model vir plaaswaardasies te ontwikkel wat redelik akkuraat is. Dus, waar MRA-modelle vir munisipale waardasies gebruik word, wat nie redelik akkuraat is nie, behoort die akkuraatheid verhoog te kan word. ’n MRA-modelwaardasie sal egter nooit die waardasie van ’n ervare en kundige professionele waardeerder kan vervang, wanneer ’n maksimale akkurate waardasie benodig word nie.Sleutelwoorde: Landbou-eiendom, waardasie metodologie, massa waardasie, plaaswaardasie, ge-outomatiseerde waardasie metodologie (AVM)

1. IntroductionThe value of a specific agricultural property is determined by a wide variety of factors.

Barry, Ellinger, Hopkin & Baker (1995: 344) pointed out that land values are influenced by many special factors that may differ among potential buyers. To illustrate, an agricultural producer with excess


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machinery capacity may place greater value on a new tract of land than will a neighbour who must buy more machinery to operate the added land. Some non-monetary factors are pride of ownership, family tradition, hobby farming, and rural living.

No two farms are ever the same or entirely homogeneous. No two farms are ever alike in terms of (i) the basic resources (land, labour, or capital) that are available, (ii) the way these resources or factors of production are combined (iii) in terms of the amounts of various crops and livestock produced.

Suter (1992: 39-41) stated that a professional valuer who values farms has specialised knowledge and skills regarding farms. The valuer has acquired skills regarding agronomy, engineering, animal and crop science, economics, law and psychology. As a valuer walks a given subject property, he develops an overall comprehension of factors such as soils, topography, drainage, irrigation facilities and the practices influencing the crops raised in the area. The valuer understands the contribution of various buildings and improvements and whether the farm’s resources, as an operating unit, are balanced. An understanding of the farm real-estate market and for factors such as product prices, costs, earnings, rental rates, government regulations and the idiosyncrasies of both buyers and sellers of farms in his area is evident.

Factors such as the number of years of farming experience of the buyer, and if the buyer owns the adjoining farm, have an impact on the price the buyer is willing to pay (Bourhill, 1998: 80).

Van Schalkwyk (1992: 62) determined that the correlation between the debt per hectare, the population density and farm values is significant. Farm values are also highly correlated with the gross farm income.

When valuing an agricultural property, an important part of the valuation process is to do a thorough property inspection, in order to verify first hand all the relevant factors and data, which can influence the value of the property. This physical inspection has the distinct advantage that the heterogeneous factors applicable to a specific agricultural property are taken into proper account.

The primary objectives of this article are:

• To determine if it is possible to develop a linear multiple regression model for the valuation of farms (which satisfies accuracy requirements) with data of an acceptable quality that is readily available, given the fact that farms are very


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heterogeneous and that a professional valuer of farms must have very specific skills and knowledge;

• To contribute to the knowledge regarding the improvement of the accuracy levels of MRA models in farm valuations, and

• To determine the limitations that these MRA models might have regarding their applicability to farm valuations.

Accurate MRA valuation estimates will be to the advantage of individual farm owners regarding their municipal tax assessments. It will lead to a wider use of MRAs for the valuation of farms, with the associated benefits of lower valuation costs and speedier valuations, especially by financial institutions.

2. FactorsinfluencingthevalueoffarmsFarms have numerous unique factors and attributes that influence their value.

A MRA model, which is inclined to satisfy accuracy requirements, will have to successfully take into account these value-influencing factors and distinctive attributes.

2.1 Market value and the characteristics of agricultural property

Suter (1980: 3) stated that farms are bought and sold as businesses, as enjoyable places to live, as investments, or as insurance against a declining currency value.

Van Schalkwyk (1992: 36-41) commented that the factors that influence the supply-and-demand function of farmland can be allocated in three categories, namely:

• Farm resource factors such as topography, soil potential, percentage of farm that is arable, extent of irrigation, and average rainfall;

• Non-farm factors such as debt per hectare and population density, and

• Interest rates.Bourhill (1998: 92) mentioned that the most important determinant of land value (within a relatively homogeneous area) is the size of the farm. A review of the factors affecting land prices shows that external (non-farm) and non-economic factors complicate the analysis and cause a gap between market value and productive value, which, in turn, varies from submarket to submarket. He concludes by stating


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that, in South Africa, land prices are driven by factors that are difficult to predict and to quantify (Bourhill, 1998: 94).

Pienaar (2015: 71-84) discussed 12 factors that influence a specific farm’s value in addition to the factors that influence the farm values of an area:

• The unique combination of natural resources on a farm, namely the land type, soil form, and grazing capacity;

• The topography of the specific farm;• The presence of rights, servitudes and endorsements;• The level of infrastructure development has a direct influence

on the value;• The utility of the land;• Location in relation to markets and input suppliers;• Access to the farm;• Farm shape and outlay;• Farm extent;• Condition of the veld;• Labour versus capital intensity, and• The potential of the specific farm.

2.1.1 Highest and best use principle

The highest and best use principle is important to the potential value of farm properties.

Rainfall, temperatures, topography and soil types (Murray, 1969: 385-392) typically determine the highest and best use of farmland; this includes the highest and best combination of enterprises. There are usually various alternative enterprises as well as various alternative improvements that could be considered. The valuer must ascertain if the subject property is developed and farmed according to the highest and best norms of the area where the farm is situated. If not, the farm must be valued as if it is developed to its highest and best use, and the cost to develop it as such should be deducted to determine the market value.

The Appraisal Institute (2000: 149) stated: “... thus, an analysis of a property’s highest and best use is truly a property-specific economic study of market forces”.

Gildenhuys (2001: 306) alluded to the Town of Dieppe v Snitch (1997) case where the judge commented: “It is not enough that the lands


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have the capability of rezoning. In my opinion, probability connotes something higher than a 50% possibility”.

2.1.2 Irrigation

The presence of developed or potential irrigation on a farm has a large influence on the value of the farm.

It is not easy to determine if the irrigation on a subject farm is legal. The valuer must have specific knowledge regarding the legality requirements of the irrigation as well as technical knowledge regarding the type of irrigation system that is used.

According to Pienaar (2015: 184), there are four potential possibilities regarding irrigation land on a farm:

• Irrigation land;• Equipped land;• Potentially irrigable dry land, and• Potential irrigable veld.

Each of the above possibilities has a different effect on the market value of the farm.

Furthermore, the efficiency to which water is used also leads to different values per hectare for different irrigation systems.

2.1.3 The use of different valuation methods

With farm valuations, due to the heterogeneity of farms, a combi-nation of all valuation methods is required to account for all components of the farm such as land only, income-production capabilities, and improvements not otherwise accounted for. A MRA model, which endeavours to satisfy accuracy requirements, will have to reflect the fact that all the various valuation methods are used to develop credible farm valuations.

i. Comparable market transaction method (direct sales comparison)

According to Gildenhuys (2001: 216), the comparable market transaction method is not reliable when not enough comparable market transactions have taken place, or when too many adjustments are needed. The prerequisite to use this method is the data availability of sufficient comparable transactions. The lack of farm transactions in a specific area leads to limited comparable transactions, resulting in the adjustment of the criteria in the valuation of the property.


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Ratterman (2007: 53) made the point that the valuer should try to replicate the market conditions at the time of the transaction. Thus, the valuer must assess which attributes of the farm influenced the buyer to pursue the sale transaction. These attributes must be taken into account when adjustments are made to estimate a market value of the subject property. The adjustment process requires considerable in-depth agricultural knowledge as well as the ability to skilfully evaluate and correctly interpret the relevant attribute(s) of each comparable transaction. The adjustments needed are part of the valuation process, which cannot be done by a person who does not possess this knowledge and skill regarding agriculture properties, in particular. This is also the part of the process where mistakes are easily made and thus lead to a wrong valuation of the subject farm.

ii. Productive value (income capitalisation) method

Reliable comparable transactions are not always available, hence often the productive value method becomes the preferred method to use. With climate playing a large role in the income-production possibilities of a farm, annual variances in the local climate can influence yields from year to year. The challenge lies in deciding on a realistic and sustainable yield. Long-term average yields for the immediate area should be used, but the specific farm’s soil potential and its nutrition levels should be taken into account.

This is further complicated by the abilities of the farmer or manager. An average farmer’s abilities should be used in the measurement of returns not that of an above average performer. It is not correct to value a farm based on yields that are achieved by an outstanding farm manager, but, according to Murray (1969: 381-382), yields must be used that will be achieved by a typical manager.

Furthermore, farming income and costs can vary substantially from year to year, making it difficult to implement the productive value approach.

According to Van Schalkwyk (1995: 124), one of the major concerns of using the income approach in the valuation of farmland relates to the use of the appropriate capitalisation rate. Pienaar (2015: 103) mentions that, in order to determine the capitalisation rate, 21 (meaning a large number) assumptions have to be made. It is often not possible, due to a lack of farm transactions, to determine the correct capitalisation rate in the market.


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iii. Depreciated replacement cost method

Many farm valuers find this to be a vital approach, because, in numerous cases, no comparable properties (with similar buildings and improvements in terms of extent and quality) are available. Therefore, to value the land as if vacant and to add the depreciated market value of the buildings is a practical and very often the only approach that can be used (Pienaar, 2015: 91, 100).

iv. Proactive comparable method

Pienaar (2015: 106) explained that this method could be used when there is a lack of reliable comparable transactions. Guidelines for an area are developed proactively (annually). The guidelines for a specific area are then used as benchmarks for farmland values. The guidelines are derived from actual transactions that have been analysed and evaluated. Examples of guidelines are, among others, value/ha for a specific soil type where dry land crop production is done, value/ha for each of a specific veld type, and value/ha for centre pivot irrigation.

It is important for relevant stakeholders such as valuers, financial institutions, agricultural cooperatives and land-reform offices to participate in the preparation of guideline values.

v. Land residual method

This method is used to determine the value of the land only. The value of the land, considered as vacant, is calculated by deducting the value of the improvements from the total value. The land residual method may not be a preferred method but, at times, it prevails as the only alternative method available to value unimproved land (Jonker, 2014: 87).

vi. Partial takings

Farmland is often subjected to the taking of a limited property right such as the requirement to erect high-voltage power lines or a road on the farm, with subsequent registration of a power-line servitude or right-of-way servitude. This is referred to as a partial taking: the whole property is not taken, only certain specific property rights on a specific geographic area of the farm.

Pienaar (2015: 74) described the influence of power-line servitudes on the value of a farm in three ways. First, it is the loss of full utilisation of the specific affected land. Secondly, the area is occupied by pole structures and, lastly, the subjective issue related to the spoiling of the


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scenery. Pipelines and canal servitudes are very similar to power-line servitudes.

Gildenhuys (2001: 338) stated that, in the majority of Anglo-American judicial systems, the before-and-after method is considered the preferred valuation method, because it leads to the most equitable value. He alludes to the comment in the “Uniform Eminent Domain Code”, where it is spelled out that the before-and-after valuation method is usually the most equitable.

The before-and-after method is complex and only a valuer with specific knowledge and skill regarding farms can apply this method.

2.2 Automated Valuation Models

There is a paucity of literature regarding mass valuations and the use of Automated Valuation Methods (AVMs), specifically for the valuation of agricultural property.

The Appraisal Institute (2013: 295-296) commented that property tax assessors have, for many years, used regression models for mass appraisal, especially in highly developed residential markets. Regression analysis models form the basis for many AVMs.

Thompson (2008: 1) argued that quantitative methods are under-going a massive renaissance, specifically pointing out that a homogeneous valuation method is required worldwide, due to economic globalisation.

2.2.1 Mass valuations

Valuation of properties is, by nature, an estimation of the value of a property as of a given date, and the precision demanded needs to be weighed against the cost of producing value (Bond & Dent, 1998: 373).

Thompson (2008: 31 & 41) stated that stratification of a residential market into rational market segments is the key to producing usable models and establishing the proper sub-populations for valuation, using comparable sales.

The SAIV (2014: 14-2) mentioned that, in a mass appraisal environment, valuation models must be developed that replicate the forces of supply and demand over an entire area.

Des Rosiers and Thériault (2008: 111) wrote that hedonic price modelling is popular for two main reasons: “First, it rests on multiple regression analysis which is a conceptually sound and very powerful


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analytical device that combines probability theory with calculus, thereby allowing the sorting out of crossed influences that affect property values. Second, it perfectly fits the very definition of market value, expressed as the most probable price that should be paid for in a competitive and transparent market setting.”

Gilbertson & Preston (2005: 129) commented that the real danger remains that automated products will be confused with traditional valuations when this is not the case. Valuations are a professional opinion and must be clearly distinguished as such.

Tretton (2007: 488, 505) wrote that AVMs are in use across the world with varying degrees of sophistication. Most of these AVMs value residential property.

2.2.2 Advantages and shortcomings of AVMs

Tretton (2007: 505-508) mentioned and described the advantages of AVMs:

• Full transparency and public access facilitated;• Low cost;• Consistency;• Speed, and• Annual revisions are possible.

He also stated that considerable criticism, in the US, can be found of AVMs used by commercial companies for loans:

• Concern that the public does not appreciate/understand the difference between an automated valuation and a conventional valuation, which involves a physical inspection, understanding of the market condition, and a careful examination of comparable evidence close to the property;

• The use of outdated or very limited data. A recurring theme being the lack of data available outside the public sector;

• Failure to take account of all the variables affecting value – a lack of individual inspection of the property, and

• There is greater confidence in a “real person” undertaking the valuation.

Gilbertson and Preston (2005: 127-129) commented that the fact that a valuer has hardly, if any, input to an AVM has the advantage of eliminating human error and bias, and the disadvantage that it could also eliminate the physical property inspection, the skill, judgement and experience of the valuer.


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Robson and Downie (2008: 6) referred to specific constraints on AVM use:

• Data limitations: AVMs depend on the accuracy, comprehensiveness and timeliness of the data they use; without sales or value data, they cannot produce a result. They are most reliable when valuing typical properties in homogeneous neighbourhoods at prices close to the median for the locality. Subsequently, these models are less reliable when there are incomplete data records, few sales in a geographical area, unique properties, or unique local markets. The difficulty of modelling purchasers’ preferences for non-physical property characteristics such as views, gardens and sunshine is mentioned.

• Risk acceptance: The main impediment to further using AVMs is caution over inaccuracy. Where accuracy is less critical, for instance when credit capacity is good, and where the physical property has already been checked, as for second mortgages, AVMs may be judged acceptable, despite this concern.

Thompson (2008) wrote that the phrase “garbage in/garbage out” captures the key message that the quality of the values produced is directly impacted by the quality of the data analysed and used to produce the property value estimates.

2.2.3 Municipal taxation

One of the major problems that municipalities face is the extraordinary high administration cost to determine the values of agricultural properties (Fisher, 1996: 314). Therefore, there is a very real cost benefit for municipalities to use AVMs for agricultural valuations.

2.2.4 Trends and opportunities in the use of AVMs

Robson and Downie (2008: 4) found that AVMs are in use throughout the world. This includes India, Russia, South America, and many smaller countries. Some countries are “early-stage” users, while others such as Sweden, the US and Canada are “established” users. The established users have confidence in its use for second mortgage purposes and have also started to use it for first mortgage purposes.

They also found that a successful AVM model in one country would not necessarily be applicable in another country. It has to be adapted to local market conditions that drive values.


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If AVMs are properly understood and used, they will become a valuable part of the valuation process rather than the process itself (Gilbertson & Preston: 2005, 128).

In a survey undertaken by Robson and Downie (2008), with the participation of 473 valuers across the world, 44% of the respondents believe that they can benefit by using AVM data.

However, 87% of the respondents believe that conventional valuations are more accurate than AVMs because of valuers’ local knowledge. Of the respondents, 90% agreed that valuers’ ability to evaluate comparables is a major advantage over AVMs.

Gilbertson and Preston (2005: 127) indicated that there is not enough access to highly comparable data, which makes the application of mass valuation technology much more complex in the commercial property sector than in the residential property sector.

Tretton (2007: 488 & 505) stated that the importance of [data] quality cannot be over-emphasised. It is no coincidence that the most highly developed commercial AVM appears to exist in Hong Kong, where 99% of property is rented and the Commissioner’s knowledge of transactions is very high. The key is data. The poorer the quality and quantity of data, the less feasible automation becomes.

Boshoff and De Kock (2013: 12-13) found that 50% of the professional valuers they interviewed in South Africa were of the opinion that AVMs can only be used for residential valuations, to a certain degree of accuracy. In their opinion, commercial property is a much more involved valuation exercise and the risks associated with this type of property need to be balanced and managed.

2.2.5 Accuracy requirements

According to Pienaar (2015: 55), there is a general belief in the valuation industry that it is acceptable to have different valuations of the same property. These can differ by 10% in the value estimates. From personal communications with a number of valuers, who specialise in valuing farms, the common opinion is that, since farms are much more difficult to value than a residential property, the tolerable accuracy should preferably be within 15%. If it exceeds 20%, the valuation is not considered credible.

Crosby, Lavers and Murdoch (1998: 305) mentioned that “the margin of error” concept involves the proposition that, in considering whether a valuer exercised reasonable care and skill in carrying out a valuation, it is important to determine the extent to which that


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valuation departs from the “true value” [selling price] of the property. Crosby (2000: 321-324) stated that they researched 120 pairs of fairly typical commercial investment valuations. They determined that 65% of the valuations is within 10% of each other and that 90% is within 20% of each other. Furthermore, they refer to a study by Hutchison (1996) where the valuation estimates were compared with the actual selling price. The results of the research were similar: 65% of the valuations had a margin of error of less than 10%, and 90% of the valuations had a margin of error of less than 20%.

Crosby (2000: 14-15) refers to cases decided in the High Court in Britain, between 1977 and the year 2000, in which the margin of error was the matter of contention. “In the majority of cases in which the judge has ruled on the extent of the bracket, the result lies between 10 per cent and 15 per cent either side of what is found to be the ‘true value’ (or either side of the midway point in cases where no decision was reached as to the true value). Moreover, while individual experts may occasionally demand (or concede) a wider bracket, there is no recorded instance of anyone favouring a figure in excess of ±20 per cent. It appears, therefore that, to date, ±20 per cent has been universally regarded as the absolute limit”.

According to the IAAO (2013: 13), the most generally useful measure for uniformity is the Coefficient of Dispersion (COD). However, it is important to take cognisance of the fact that ratio studies cannot be used to judge the level of appraisal of an individual property (IAAO, 2013: 7).

Rossini and Kershaw (2008: 1) conducted research to establish minimum requirements for accuracy in AVMs in the greater Adelaide metropolitan area. They used 2 538 transactions that took place in 2005 and 2006 in their database. Their research focused on establishing a set of standards for the accuracy of individual valuation. Rossini and Kershaw (2008: 8) concluded that for a “reasonable level of acceptance” of accuracy, the AVM should have a minimum of 90% of the individual estimates within a 20% accurate range and the COD should be less than 10%.They stated that, if only 80% of the individual estimates are within a 20% accurate range and the COD is more than 13, the AVM is “of no real value to users”. The major advantage of Rossini and Kershaw’s (2008) study is that they established guidelines for the accuracy of individual properties’, within a group of properties, appraisal accuracy.

The IAAO (2013: 17) prescribes specific maximum COD levels for specific types of properties:


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• Residential property, a COD of maximum 15%, and• Income-producing property, a COD of maximum 20%.

However, there is no maximum COD level specified for a developed farm. However, based on the above information, the researcher concluded that a COD of less than 10% and a 90% of individual estimates within 20% accuracy qualifies as a high degree of accuracy and a COD of 10%-15% and 80% of individual estimates within 20% accuracy as indicative of a fair degree of accuracy.

2.2.6 The use of regression analysis in the valuation of agricultural properties

Murray (1969: 276-285) described nine different studies done in the U.S.A. In the first study, the sale prices of 160 farms from 1916 to 1919 in Minnesota were analysed. The last study he referred to was done in 1965 in the Mississippi River Delta, where 1 378 land transfers were analysed. Multiple regression equations were developed in each study, which typically took variables such as the depreciated cost of buildings, land classification index, soil productivity index, land slope, drainage, water supply, distance to the market, distance to town, size of the farm, and other variables into account.

Murray concluded that the statistical approach could explain about three-fourths of the variations in values, but there was always a level of variance that was not explained by statistical analysis. There are similar complexities in the characteristics of both farmland and commercial property, which make the application of AVMs in the valuation of agricultural property more challenging. Arguably, even more difficult than in the case of commercial property.

• Farms are highly heterogeneous, and• The quality and availability of data to develop successful

AVM models to use in the valuation of agricultural property are often poor and scarce.

3. MethodologyThe stepwise regression method was used to develop three MRA models. In this process, all the candidate independent variables in the model are checked to determine if their significance has been reduced below the specified tolerance level. If a non-significant variable is found, it is removed from the model (NCSS, 2015: online). By following this process, the regression model has been improved by removing the independent variables that have a non-significant influence on the dependent variable.


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3.1 Sampling method and size

The aim of the researcher was to use data, as far as possible, from a homogeneous area regarding its natural habitat. Sixty eco-zone farms (“mixed bushveld”) (South African National Biodiversity Institute, 2005: 26) in the JR, KR, KQ, LR, LS and MT registration divisions in the western area of the Limpopo Province were used. It can be described as the area north of the road from Bela-Bela (Warmbaths) to Northam, and west of the road from Bela-Bela (Warmbaths) to Makhado (Louis Trichardt (see Figure 1). The only exception is four farms that are close to Letsitele in the eastern part of the Limpopo Province. Twenty-four farms have Thabazimbi as the nearest town, four farms have Vaalwater, eight have Mookgopong (Naboomspruit), four have Alldays, four have Warmbaths (Bela-Bela), four have Makhado (formerly Louis Trichardt), four have Letsitele, and eight farms have Lephale (formerly Ellisras) as the nearest town.

Figure 1: Study area

Source: Google Maps

Eco-zone “mixed bushveld” is described as: altitude of 700-1 100 m; rainfall 300-500 mm, mostly in the form of thunderstorms. The summers are hot, reaching temperatures of 35°C and more by day, with only occasional frost during winter nights. Due to the low rainfall, grasses do not form dense uniform stands. Grass types are mainly a mix


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between types with a higher grazing value and types with a lower grazing value.

3.2 Data acquisition

The data used for empirical analysis consisted of 15 valuations, plus three comparable transactions per valuation. Thus, a total of 15 valuations plus 45 real transactions, giving 60 data sets regarding 60 farms. A quantity of 60 observations and 10 independent variables gives a ratio of 6:1 (observations: independent variables), which is considered to be sufficient. A ratio of 4:1 is considered the minimum (Australian Property Institute, 2015: 489).

3.3 Dataanalysisandinterpretationoffindings

In this study, a number of statistical tests and indicators are used to analyse and evaluate the accuracy, applicability and statistical significance of the regression model(s).

3.3.1 Pearson rcorrelationcoefficient

Correlation coefficients measure the strength of linear association between two variables (Gujarati & Porter, 2009: 20).

It can vary numerically between -1.0 and 1.0. The closer the correlation is to 1.0 or to -1.0, the stronger the relationship between the two variables. A correlation of 0.0 indicates the absence of any relationship.

3.3.2 R²

The R² is the square value of the r correlation value. It is also called the coefficient of determination.

The R² can vary numerically between 0.0 and 1.0. A value, for example, of 0.65 means 65% of the variation in the dependant variable is accounted for by the independent variables in the model. It also implies that 35% of the value of the dependent variable is not accounted for by the model (Gujarati & Porter, 2009: 493).

3.3.3 Adjusted R²

For comparative purposes, the adjusted R² is a better measure than the R². The adjusted R² value is a calculated value that adjusts the analysis model if independent variables are added to increase the R² (Gujarati & Porter, 2009: 493). When a variable is added to a model and the adjusted R² does not increase, the new variable indicates no


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additional influence than would be explained by adding any totally irrelevant random variable (Wolverton, 2009: 296).

3.3.4 Standard Error of the Estimate

This is also referred to as the root mean squared error. It is the standard deviation of the error term (SPSS, [n.d.]: online). When data is normally distributed, it is expected that approximately 67% of the data lie within ±1 standard deviation of the mean (Australian Property Institute, 2015: 471)

3.3.5 CoefficientofDispersion(COD)

According to the IAAO (2013: 13), the most generally useful measure of variability or uniformity is the COD. It is also the most important measurement for uniformity. The COD measures the average percentage deviation of the assessed values to the selling prices, from the median ratio (assessed value to selling price) and is calculated using equation 1.

COD = 100Rm N

N

1∑ | Ri - Rm |

................................................................. (1)

Where:

COD = coefficient of dispersion, i.e., the average per cent of dispersion around the median assessment ratio;

Rm = median assessment ratio;

Ri = observed assessment ratio for each parcel;

N = number of properties sampled.

According to Wolverton (2009: 86), the COD is often used as a measure of uniformity in tax assessment studies to reflect the relationship between assessed value and actual value, or price.

3.3.6 t-value(SignificantTesting)

The t-value is a statistical test indicating the significance in the difference between the mean of the actual selling prices and the mean of the estimated values, calculated by the regression analysis model (Gujarati & Porter, 2009: 4).


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A t-value of 0 indicates that the value of the dependent variable is not dependent on the independent variable (Wolverton, 2009: 255). In a regression equation with 17 independent variables (as in Model 1), a t-value of 1.740 and higher indicates a p-value of .05 and lower.

3.3.7 p-value(exactlevelofsignificance)

In statistics, the term ‘significant’ means it is ‘probably true’. The p-value indicates how likely it is that something is not true. A p-value of 0.05 means that there is a 5% probability that something is not true. Thus, it has a 95% probability of being true, and the null hypothesis can be rejected with 95% certainty. The p-value is defined as the lowest significance level at which the null hypothesis can be rejected (Gujarati & Porter, 2009: 835).

3.3.8 F-test

The F-test is a test to determine the overall significance of the estimated regression analysis. It indicates significance of the coefficients in the model for the number of independent variables used in the analysis (Gujarati & Porter, 2009: 240-242).

3.3.9 Durbin-Watson

Autocorrelation is the measure of a correlation between the error terms (Gujarati & Porter, 2009: 412). Autocorrelation is tested by way of the Durbin-Watson test. The value is always between 0 and 4. A value of 2 means that there is no autocorrelation in the sample. Values approaching 0 indicate positive autocorrelation, and values toward 4 indicate negative autocorrelation (Gujarati & Porter, 2009: 434-435).

An underlying assumption of regression models is that the error terms are independent (Australian Property Institute, 2015: 487).

3.3.10 Multicollinearity

Multicollinearity is when two or more independent variables have a strong correlation to each other. This implies that they overlap strongly in measuring the same attribute.

The use and interpretation of a multiple regression model depend on the assumption that the independent variables are not interrelated (Australian Property Institute, 2015: 487).


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3.3.11 Variance-InflatoryFactor(VIF)

The speed with which variances of a variable increase can be seen with the VIF. It shows how the variance of a variable is inflated by the presence of multicollinearity. The extent of collinearity increases as the variance of a variable increases (Gujarati & Porter, 2009: 328).

A common rule applied is, if the value of the VIF index exceeds 10, that variable is highly collinear (Gujarati & Porter, 2009: 340).

3.3.12 Heteroscedasticity

This tests the variance of errors over a sample. If the variance of error is unequal, the sample is heteroscedastic (Gujarati & Porter, 2009: 65).

It can be visually evaluated. When a graph of the regression analysis shows a systematic narrowing or widening of the range of the estimated values, it is an indication of heteroscedasticity (Australian Property Institute, 2015: 487-488).

An underlying assumption of regression models is that the variance of the error is homoscedastic, meaning the variance of the errors is equal (Australian Property Institute, 2015: 487).

3.4 Model descriptions

3.4.1 Dependent variable used in the model

This study aims to develop a regression model that calculates the total market value of the subject property, with the required accuracy. In model 1, the dependent variable is estimated value.

In models 2 and 3, the dependent variable is vacant land value. The reason for using vacant land value as the dependent variable rather than the total value is that the Depreciated Value of Improvements (DVI), which is to be added to the vacant land in order to calculate total value, depends on how accurate the valuer estimated these values. DVI has a very high probability of significance (p = .000 and t = 4.085) on the dependent variable. The value of the DVI variable, therefore, has a high significance on the dependent variable. The risk of a wrong DVI estimate is eliminated by using Vacant Land Value as the dependent variable.

3.4.2 Independent variables used in the model

A number of authors (Woolford & Cassin,1983: 214, 216; Bourhill, 1998: 81; Pienaar, 2015: 71-84) have identified independent variables that have a significant influence on a farm’s value.


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The following variables, which were identified by the abovemen-tioned authors, are used in this study:

• Date of sale;• Size of the farm;• DVI;• Quality of the grazing;• The number of hectares per livestock unit needed;• The number of hectares legally under irrigation;• Distance from the farm to the nearest town;• Tourism infrastructure on the farm;• Topography of the terrain, and• Security in terms of game fence.

3.5 Accuracy requirement

Rossini and Kershaw (2008: 8) concluded that, for a “reasonable level of acceptance” of accuracy, the AVM should have a minimum of 90% of the individual estimates within a 20% accurate range and the COD should be less than 10.

They also stated that, if only 80% of the individual estimates are within a 20% accurate range and the COD is 13, the AVM is “of no real value to users”. Based on the above information, the researcher concluded that a COD of less than 10% and a 90% of individual estimates within 20% accuracy qualifies as a high degree of accuracy; a COD of 10%-15% and 80% of individual estimates within 20% accuracy is indicative of a fair degree of accuracy.

4. Results and discussion

4.1 Model 1

Dependent variable: Total value.

Independent variables: All 10 of the abovementioned independent variables.

Table 1: Summary of statistical indicators, Model 1

Model R R² Adjusted R²

Std. Error of the Estimate Durbin-Watson COD

1 0.945 0.893 0.850 R1 748 826 1.915 14%


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The R² value of 0.893 is fairly high. This means that 89.3% of the variation of the dependent variable is accounted for by the model. The adjusted R² value of 0.85 implies that 15% of the variation is not accounted for by the model. The standard deviation of the error term (value estimates) indicates that approximately 33% of the individual estimates differs more than R1,748,826 from the real value (if the data is normally distributed), indicating a questionable accuracy of the model. The COD is 14%. This is lower than the maximum COD of 15%, which is the requirement for fair accuracy. However, out of the sample of 60 farms, 23 individual farm estimates have an error term of more than 20%. Therefore, only 62% of the individual estimates have an error term less than 20%. This indicates that this model is, in fact, not sufficiently accurate. The Durbin-Watson value of 1.915 indicates that there is no autocorrelation present.

Table 2: Model 1 ANOVA test results

Model 1 df F p-Significance

Regression 17 0.642 .000

Residual 42

Total 59

The calculated F-value of 20.6 is more than eight times the critical F-value of 2.52; therefore, the level of significance of the results of the multiple regression model, given the number of independent variables used in the analysis, is high.

Overall the model is statistically significant (F = 20.6, p = .000).

Table 3: Model 1 regression correlations and coefficients

Independent variable VIF Pearson correlation t-Value p-Significance

Grazing 1.773 0.166 -.366 .716

Date 2.779 -0.263 -1.689 .099

DVI 1.760 0.512 4.000 .000

Size 2.363 0.606 9.641 .000

Ha/LSU 9.161 -0.139 -1.568 .124

Irrigation ha 1.255 0.450 8.340 .000

Town distance 2.552 0.245 -.144 .910

Tourism 2.934 0.201 .347 .730

Game fence 2.449 0.193 .476 .636


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Topography 1.488 -0.039 2.099 .042

Vaalwater 5.349 0.103 -.870 .389

Mookgopong 3.348 -0.232 -1.428 .161

Alldays 1.633 -0.161 -1.512 .138

Letsitele 4.967 0.253 -1.191 .241

Bela-Bela 3.743 0.085 .545 .588

Makhado 3.000 -0.119 -2.243 .030

Lephalale 2.276 0.160 -.974 .335

The VIF values are all well below 10, indicating that there is little or no multicollinearity present. The Ha/LSU variable is the highest, with a VIF value of 9.161, which is still below 10.

The size variable with a value of 0.606 has the highest correlation; the DVI variable with a value of 0.512 has the second highest correlation, and the irrigation ha variable with a value of 0.450 has the third highest correlation.

The topography has the lowest correlation. The date variable has a negative correlation, because the older the transaction, the lower the impact on the dependent variable. Thus, it indicates that the price of the farms increased over time. Although it would be beneficial for the dependent variable to be time adjusted, a lack of accurate information for this purpose precludes this option. The use of a date variable as independent partly solves the difference in dates of sale, although it assumes a linear relationship between time and price. The date variable should be replaced by various date categories, if more data is available, in order to be more accurate, but would have to be considered in future research. The Ha/LSU variable has a negative correlation, because, as the number of hectares to sustain one livestock unit increases, the value of the farms decreases. Thus, it demonstrates that the price of the farms decreases when the carrying capacity decreases, which confirms the market value expectation.

The independent variables size, DVI and irrigation ha have p-values of .000, which indicates 100% probability of significance that the dependent variable is dependent on these independent variables. The t-values of all three are statistically significant at the p = 0.001 level.


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Topography with .042 and Makhado with .030 have values with a higher than 95% probability of significance. Their t-values indicate statistical significance at the p = 0.025 level.

Town distance with p = 0.910 has a less than 10% probability of significance. It has the lowest significance. The t-value is only -.144, which is statistically insignificant at a p = 0.25 level.

Visual inspection of the model indicated homoscedasticity (not shown). Although the COD of 14% is within the maximum of 15, only 62% of individual estimates are within 20% accuracy, indicating that the model is not sufficiently accurate. There is a possibility that the model is sufficiently accurate for the purpose of preliminary investigations or budget purposes. However, great care should be taken to avoid pitfalls because of the relative inaccuracy of the model.

4.2 Model 2

Dependent variable: Vacant Land Value..

Independent variables: DVI and grazing were excluded in this model. This is advocated, as the value of the DVI is not included in the Vacant Land Value and the DVI was also excluded as an independent variable.




4 0.931 0.867 0.822 R1 713 133 1.910 20 %

The R² value of 0.867 is marginally lower than in Model 1. This means that 86.7% of the variation of the dependent variable is accounted for by the model. The adjusted R² value of 82.2% is also marginally lower than Model 1. This implies that 17.8% of the variation is not explained by the model.

The standard deviation of the error term of R1 713 133 is very similar to the number in Model 1.

The COD is 20%, which is higher than Model 1. It is higher than the maximum COD of 15% for fair accuracy. Furthermore, out of the sample of 60 farms, 28 individual farm estimates have an error term of more than 20%. Thus, only 53% of the individual estimates have an error term of less than 20%. Both these values indicate that this model is not fairly accurate.


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The Durbin-Watson value of 1.910 indicates that there is no auto-correlation present.




Residual 44

Total 59

The F-value is marginally lower than in Model 1. The calculated F-value of 19.134 is more than seven times the critical F-value of 2.52; therefore, the level of significance of the results of the multiple regression model, given the number of independent variables used in the analysis, is high. Overall, the model is statistically significant (F = 19.1, p = .000)



Date 2.730 -0.221 -1.770 .084

Size 2.173 0.657 10.168 .000

Ha/LSU 8.032 -0.109 -1.568 .124

Irrigation ha 1.117 0.432 9.167 .000

Tourism 2.539 0.304 .568 .573

Game fence 2.202 0.137 .399 .692

Topography 2.346 0.183 2.151 .037

Vaalwater 1.479 -0.035 -.813 .421

Mookgopong 4.773 0.104 -1.458 .152

Alldays 2.742 -0.255 -1.596 .118

Letsitele 1.563 -0.108 -1.144 .259

Bela-Bela 3.869 0.213 .724 .473

Makhado 3.389 0.090 -2.369 .022

Lephalale 2.567 -0.117 -1.048 .300

Town Distance 1.597 0.218 -.119 .906

The VIF values are all well below 10, indicating that there is hardly any or no multicollinearity present.


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The Pearson correlation values are similar to the values of Model 1; therefore, the comments made in Model 1 are also applicable to Model 2. The independent variables size and irrigation ha have p-values of .000, which indicates 100% probability of significance. Both the t-values are statistically significant at the p = 0.001 level.

Date with .084, game fence with .037, and Makhado with .022 have values with a higher than 90% probability of significance. Their t-values indicate statistical significance at the p = 0.05 level.

Game fence with a p = 0.692 has a 30.8% probability of significance. It has the lowest significance of the independent variables. The t-value is only -.399, which is statistically insignificant at a p = 0.25 level.

A visual inspection of the actual versus the predicted values revealed that no severe heteroscedasticity is evident. All the statistical indicators show that model 1 is more accurate than model 2. There is a possibility that model 2 is sufficiently accurate for the purpose of preliminary investigations or budget purposes. However, great care should be taken to avoid pitfalls because of the relative inaccuracy of the model.

4.3 Model 3

Dependent variable: Vacant Land Value.

Independent variables: reduced to only five, namely topography, irrigation ha, tourism, date, and size. These variables have the lowest p-values and the highest t-values, as indicated in Models 1-3.

A quantity of 24 observations and five independent variables gives a ratio of 5:1 (observations: independent variables), which is considered to be sufficient. A ratio of 4:1 is considered the minimum (Australian Property Institute, 2015: 489). All the transactions that do have Thabazimbi as its nearest town were used. This implies that the geographic area is as homogeneous as possible (with the data available to the researcher). The aim of model 3 is to do a regression analysis regarding an area that is as homogeneous as possible, where the most significant independent variables are used, and which is not influenced by the DVI.




7 .971 .943 .927 R1 055 333 1.889 14%


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The R² of 0.943 and the adjusted R² value of 0.927 imply a high correlation between the dependent and the independent variables. The fact that the adjusted R² is higher and the number of variables is lower indicates that, in the previous models, variables were used that did not explain more than what is explained by adding any totally irrelevant random variable.

The standard deviation of the error term of R1 055 333 is the lowest of all the models. This indicates that approximately 33% of the individual estimates differs more than R1 055 333 from the real value (if the data is normally distributed).

The COD of the error term is 14%. This is lower than the maximum COD of 15%, which is the requirement for fair accuracy. Out of the sample of 24 farms, four individual farm estimates have an error term of more than 20%. Thus, 83% of the individual farm estimates is within 20% of the actual selling price. Therefore, this model is fairly accurate, but it still does not satisfy a high degree of accuracy or a “reasonable level of acceptance”. The Durbin-Watson value of 1.889 indicates that it is undecided whether any autocorrelation is present or not.




Residual 18

Total 23

The F-value of 59.614 is the highest of all the models. Overall, the model is statistically significant (F = 59.6, p = .000). The calculated F-value of 59.614 is more than 13 times the critical F-value of 2.80; therefore, the level of significance of the results of the multiple regression model, given the number of independent variables used in the analysis, is very high.



Date 1.424 0.095 -1.756 .096

Size 1.756 .493 9.170 .000

Irrigation ha 1.045 .756 14.416 .000

Tourism 1.310 .178 .424 .677

Topography 1.189 -.072 3.305 .004


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The VIF values are all well below 10, indicating that there is hardly any or no multicollinearity present.

The independent variables size and irrigation ha have p-values of .000, which indicates 100% probability of significance. Both their t-values are statistically significant at the p = 0.001 level.

Topography with .004 has a p-value with a higher than 95% probability of significance. The t-value indicates statistical significance at the p = 0.025 level.

Tourism with p = 0.677 has the lowest probability of significance. The t-value indicates statistical insignificance at a p = 0.25 level.

The independent variables size, irrigation ha and topography have p ≤ .05 values, which indicates a significance of at least 95%. All the t-values are statistically significant at the p = 0.001 level.

However, the tourism variable is insignificant with p = 0.677. The t-value is only -.424 which is statistically insignificant at a p = 0.25 level.

The irrigation ha variable with a value of 0.756 has the highest correlation. This indicates how valuable the presence of irrigation is to the value of a farm. The size variable with a value of 0.493 has the second highest correlation, which indicates the fact that the bigger the farm, the higher is the value. The fact that it is not the highest correlation may be indicative of the phenomenon that the bigger the farm, the lower is the value per hectare, with the result that a log transformation on the data in this variable should be considered.

The topography variable has a negative correlation, because the value of the farms decreases when the farm consists of a substantial area of mountainous terrain. Thus, it confirms the a priori market value expectation.

A visual inspection of the model output indicated homoscedasticity (not shown) and, therefore, satisfies one of the underlying assumptions of regression analysis. The COD of 14% and the 83% individual estimates that are within the 20% accuracy requirement indicate that this model is fairly accurate.

Limitations of model 3 are that it covers only a relatively small geographic area, namely the area around Thabazimbi. One of the biggest limitations of model 3 is that the dependent variable is the value of the vacant land; it is thus not the total value of the farm. It is the value without the depreciated value of the buildings.

In practice, it seldom happens that a farm has no buildings as improvements. This, therefore, implies that further research will have


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to be done to develop a model that will accurately use variables for the depreciated value of the buildings, to enable the model to estimate the total value of a farm.

Table 10: Summary of variables used

ModelNo. of

farms in sample

Dependent variable Independent variables

1 60 TVDVI, date, size, grazing, ha/LSU, irrigation ha, town distance, tourism, game fence, topography, all the towns

2 60 VLV As in model 1, without grazing and DVI

3 24 VLVDate, size, irrigation ha, tourism and topography and with only the Thabazimbi farms.

Legend

TV = Total Value VLV = Vacant Land ValueVLV/ha = Vacant Land Value/haDVI = Depreciated Value of Investment

Table 11: Summary of the most important statistical values

Model Adjusted R²

Std. Error of the Estimate F- value COD %

% of individual valuations that are

within 20% accuracy

1 0.850 R1 748 826 20.6 14 62

2 0.822 R1 713 133 19.1 20 53

3 .927 R1 055 333 59.6 14 83

The R² of 0.943 and the adjusted R² value of 0.927 is the highest of all the models.

The COD of 14% and the 83% individual estimates that are within the 20% accuracy requirement are also the highest of all the models and indicates that model 3 is the only model that is fairly accurate. However, it still does not satisfy a high degree of accuracy or a “reasonable level of acceptance”, which is defined by Rossini & Kershaw (2008: 8) as a COD of less than 10 and 90% of individual estimates to be within 80% of accuracy. It should, however, be noted that these international benchmarks were initially designed for the US market, and for homogeneous property type. Although, they are


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the only standards set, they may not necessarily be applicable in the South African context, or specifically to agricultural property.

5. Limitations of the studyThere is a paucity of literature available regarding the application of MRA models in the valuation of farms, other than some literature in the quarterly publications of the International Association of Assessing Officers (IAAO), which is only accessible by members at the cost of an expensive membership fee. The latter is not available to scholars. It is, however, suggested that the findings of this study be compared to those of the IAAO.

The availability of sufficient relevant and accurate data to develop MRA models for farm valuations is a severe limitation.

6. ConclusionsA valuer has hardly, if any, input when using a MRA and this is deemed a double-edged sword. It eliminates human error and bias and substitutes the physical property inspection as well as the valuer’s skill, judgement and experience.

It is important to understand the factors that influence farm prices and the various unique and distinctive attributes that are inherently part of farms. These should be taken into consideration when valuing agricultural property. These value-influencing factors and distinctive attributes cause farm valuations to be complex and make it relatively difficult to satisfy accuracy requirements.

The accuracy of a MRA relies heavily on the quality and accuracy of the data used. Thus, the availability of quality and accurate data has a significant impact on the potential accuracy of a MRA. The use of AVMs in the South African residential property market is common. The results of this stepwise regression analysis showed that it is difficult to access appropriate and accurate data to develop a regression model for agricultural property, which satisfies accuracy requirements.

Model 3 does satisfy the accuracy requirements for fairly accurate estimates, but it is not sufficiently accurate to satisfy high accuracy requirements. The reasons for the difficulty to acquire sufficient accurate data in order to be able to develop a MRA model that is sufficiently accurate to satisfy accuracy requirements, are multifaceted. Some of these reasons are non-farm factors that are


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difficult to translate into appropriate and accurate quantitative data in a MRA model.

An alternative approach to developing a MRA model (which is as accurate as possible) is to ensure that the geographic area is as homogeneous as possible; the geographic area must be very small, in order to avoid the inclusion of too many changes in the inherent characteristics of data points due to location. This will imply that multiple MRA models have to be developed for each municipal area. This will be a costly exercise and, therefore, contradict the cost efficiency of MRA. To eliminate the negative impact of too much heterogeneity, further research should be done on more advanced models such as geographic weighted regression and quantile regression techniques.

7. RecommendationsAll the models, except one, that were developed during the stepwise regression process are not fairly accurate. No model has a high degree of accuracy.

It is difficult, yet possible, to develop MRA models that are sufficiently accurate. Therefore, if the MRA models that are currently used are not sufficiently accurate for municipal valuation purposes, it should be possible to improve the accuracy. Valuers should be cautious when using MRA models in municipal farm valuations, because it is possible that the MRA models do not satisfy minimum accuracy requirements. Under the methods applied in this research, a MRA valuation cannot replace a valuation done by a skilled and knowledgeable professional valuer, when high accuracy is required. This does, however, not preclude further research in the application of more advanced methods.

ReferencesAppraisal Institute. 2000. The appraisal of rural property. Chicago, Illinois: Appraisal Institute.

Appraisal Institute. 2013. Appraisal of real estate. 14th edition. Chicago, Illinois: Appraisal Institute.

Australian Property Institute. 2015. The valuation of real estate. 2nd edition. Canberra, Australia: Appraisal Institute.

Barry, P.J., Ellinger, P.N., Hopkin, J.A. & Baker, C.B. 1995. Financial management in agriculture. 6th edition. Danville, Illinois: Interstate Publishers Inc.


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A post-contract project analysis of material waste and cost overrun on construction sites in Abuja, Nigeria


AbstractMaterial waste and cost overrun have been identified as common problems in the construction industry. These problems occur at both pre- and post-contract stages of a construction project. As a result of a dearth of empirical research and low level of awareness, the majority of managers of construction projects in Nigeria pay hardly any attention to material waste issues that affect cost overrun. This article examines the material waste issues that affect cost overruns at the post-contract stage of building projects.The study covers building construction projects in Abuja, Nigeria. In-depth interviews were conducted with professionals using purposive sampling technique. It is purposive, because only building professionals handling projects that are worth over eight million USD are consulted/interviewed. The professionals included 15 project managers, nine quantity surveyors, five site engineers and one senior technical officer of a waste management department/unit. The interviews were on issues relating to material waste and cost overruns at the post-contract stage of a project. The collected data were analysed manually, using the deductive approach. This involves constant comparative analysis of the data to generate common patterns on material waste and cost overrun.The research found that poor quality-of-procurement management, construction management, and site management would cause material waste, which contributes to project cost overruns. A good-quality procurement management entails procuring the appropriate materials, at the right time and in accordance with specifications. Rework, site accidents, inadequate site security/fencing, poor site organisation and discipline,

Ibrahim Saidu

Dr Ibrahim Saidu, Department of Quantity Surveying, Federal University of Technology, P.M.B. 65, Minna, Niger State, Nigeria. +23 48037796321, email: <[email protected]>

Winston Shakantu

Prof. Winston (M.W.) Shakantu Department of Construction Management, Faculty of Engineering, the Built Environment, and Information Technology, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa. Phone: +27 785147492, email: <[email protected]>










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construction-site disputes, lack of experience, and lack of co-ordination among the parties all contribute to material waste and cost overruns.It can be concluded that proper attention to material waste issues has the potential to minimise the rate of cost overrun at the post-contract stage of a project. It is recommended that careful attention should be paid to the issues identified in this study, as they would help reduce the rate of material waste and cost overrun for projects.Keywords: Cost overrun, construction industry, material waste, post-contract stage

AbstrakMateriaalafval en koste-oorskryding word geïdentifiseer as algemene probleme in die konstruksiebedryf. Hierdie probleme vind plaas in beide voor- en na-kontrakstadiums van ’n konstruksieprojek. As gevolg van ’n gebrek aan empiriese navorsing en lae vlak van bewustheid, gee die meeste bestuurders van konstruksieprojekte in Nigerië min aandag aan materiaalafvalkwessies wat koste-oorskryding beïnvloed. Hierdie artikel ondersoek die materiaalafvalkwessies wat koste-oorskryding in die na-kontrakstadium van bouprojekte beïnvloed.Die studie dek boukonstruksieprojekte in Abuja, Nigerië. In-diepte onder-houde met professionele persone is gehou met behulp van doelgerigte steekproefnemingstegnieke. Dit is doelgerig omdat slegs professionele persone wat projekte hanteer wat meer as agt miljoen dollar werd is, geraadpleeg/ondervra is. Die professionele persone het 15 projekbestuurders, nege bourekenaars, vyf terreiningenieurs en een senior tegniese beampte van ’n afvalbestuursafdeling/-eenheid ingesluit. Die onderhoude het gegaan oor die kwessies wat verband hou met wesenlike afval en koste-oorskryding by die na-kontrakstadium van ’n projek. Die versamelde data is geanaliseer deur die deduktiewe benadering te gebruik. Dit behels konstante vergelykende analise van die data om algemene patrone op materiaalafval en koste-oorskryding te genereer.Die navorsing het bevind dat swak gehalte-van-verkrygingsbestuur, konstruksie-bestuur en terreinbestuur materiaalafval tot gevolg sal hê, wat bydra tot die koste van die projekkoste. Goeie gehalte-verkrygingsbestuur behels die verkryging van toepaslike materiale, op die regte tyd en in ooreenstemming met spesifikasies. Herstelwerk, terreinongelukke, onvoldoende terreinbeveiliging/-heining, swak terreinorganisasie en dissipline, geskilpunte op die bouplek, gebrek aan ondervinding en gebrek aan koördinasie tussen partye dra alles by tot materiaalafval en koste-oorskryding.Daar kan tot die gevolgtrekking gekom word dat behoorlike aandag aan materiaalafvalkwessies die potensiaal het om die koers van koste-oorskryding in die na-kontrakstadium van ’n projek te verminder. Daar word aanbeveel dat deeglik aandag gegee word aan die probleme wat in hierdie studie geïdentifiseer word, aangesien hulle sal help om die hoeveelheid afval en die koste-oorskryding vir projekte te verminder.Sleutelwoorde: Oorskryding van kostes, konstruksiebedryf, materiaalafval, na-kontrakstadium

Saidu & Shakantu • A post-contract project analysis of material waste...

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1. IntroductionThe construction industry, which plays a leading role in improving the quality of the built environment, is faced with the problems of waste, time overrun and cost overrun (Osmani, Glass & Price, 2008: 1147; Saidu & Shakantu, 2016a: 124). Material waste has become a serious problem and requires urgent attention in the construction industry (Adewuyi & Otali, 2013: 746). The majority of this waste has not been well managed, thus causing health and environmental problems (Imam, Mohammed, Wilson & Cheeseman, 2008: 469) and affecting the performance of many projects (Ameh & Itodo, 2013: 746; Saidu & Shakantu, 2016b: 555). Several authors reporting on the situation have disclosed the problem of material waste. For instance, 10%-15% of materials delivered to construction sites in the United Kingdom (UK) end up as waste (Osmani, 2011: 209; Saidu, 2016: 12). The United States (US) generates 164m tonnes of construction waste annually, representing 30%-40% of the country’s municipal solid waste (Osmani, 2011: 209). In Malaysia, 28.34% of the total waste sent to landfills originates from construction activities (Begum, Siwar, Pereira & Jaafar, 2007: 190). For every 100 houses built in Nigeria, there is adequate waste material to build another 10 houses (Ameh & Itodo, 2013: 748).

Cost overrun is a global problem which makes it difficult for many construction projects to be completed within budget (Ameh & Itodo, 2013: 748; Memon, Abdul-Rahman, Zainun & Abd-Karim, 2013). Of construction project owners in the UK, 33.3% are faced with the problems of cost overrun (Abdul-Rahman, Memon & Abd. Karim 2013: 268). Flyvbjerg, Holm & Buhl (2004: 6) conducted a global study on cost overruns and concluded that cost overruns were found across 20 nations and five continents of the world, thus affecting 90% of completed projects in the world (Saidu & Shakantu, 2015: 95). The argument on how to totally remove cost overruns from projects has been on-going among the built environment professionals for the past seventy years (Apolot, Alinaitwe & Tindiwensi, 2013: 33).

Relating material waste to cost overrun, Ameh & Itodo (2013: 748) believe that building material wastage on construction sites accounts for cost overruns. For instance, material waste accounts for an additional 15% of project-cost overruns in the UK; 11% in Hong Kong, and between 20% and 30% in The Netherlands. The majority of these findings were survey based. However, Saidu & Shakantu (2016c: 99) investigated the contributions of material waste to cost overruns in Abuja, Nigeria, using field measurement of onsite material waste and determination of amount of cost overrun for each project.


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The research concluded that building material waste contributes to approximately 4% of cost overrun.

The problems of material waste and cost overrun are occasioned by several causes at the pre- and post-contract stages of projects. The identification of these causes at these stages and the application of relevant control measures to minimise their occurrence is a step towards alleviating the consequences (Mou, 2008: 20; Oladiran, 2009: 2; Nagapan, Abdul-Rahman, Asmi & Hameed, 2012: 23; Saidu & Shakantu, 2015: 96).

This research addresses the problem of hardly any attention being paid by the majority of managers of construction projects to the effects of material waste on cost overruns. Many studies have been conducted in this field. For instance, Tam, Shen & Tam (2007: 1471) assessed the levels of material wastage affected by sub-contracting relationships and projects types with their correlations on construction site; Ameh & Itodo (2013: 748) assessed professionals’ views of material wastage on construction sites and cost overruns. The study adopted a survey (questionnaire) research approach. Saidu & Shakantu (2015: 96) examined the relationship between quality of estimating, construction material waste generation and cost overruns in Abuja, Nigeria; Saidu & Shakantu (2016a: 124) examined the relationship between material waste and cost overrun in the construction industry using a thorough literature search and recommended further empirical investigations. Saidu & Shakantu (2016b: 555) developed a framework and an equation for managing construction-material waste and cost overruns but these are not empirically inclined. There is need for a research that provides an unprejudiced assessment of the material waste issues that have effects on cost overruns at the post-contract stage of a building project. Hence, this research examines the material waste issues that have effects on cost overruns at the post-contract stage of building projects by determining the material waste issues that relate to cost overruns at: a) the procurement stage of a project; (b) the site management stage of a project, and (c) the construction management stage of a project.

2. Literature reviewIt is important to note that Figure 1 is not all about construction waste, but that it attempts to show the root cause of ‘material waste’ and ‘cost overrun’ from construction waste. Moreover, Tables 1, 2 and 3 contain information about material waste that relates to cost overrun and not only construction waste. Therefore, information about material waste dominated the entire literature review.


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2.1 Material waste and cost overrun

Construction waste is a global challenge facing construction practitioners. It can have a significant impact on time, cost, quality and sustainability, as well as the success of projects (Nagapan et al., 2012: 22). It is the difference between purchase and actual use (Al-Hajj & Hamani, 2011: 2). Construction waste has been described as any constituent generated, as a result of construction work, and abandoned, whether or not it has been processed or stocked up before being abandoned (Yuan, Lu & Hao, 2013: 484; Hussain, Abdul Rahman & Memon, 2013: 32).

Cost is considered one of the most significant issues and a driving force of project success. It has been regarded as a major concern throughout the project management life cycle. In spite of its recognised significance, it is common for a construction project to fail to achieve its goals within the budget. Cost overrun, according to Azhar, Farooqui & Ahmed (2008: 499), is simply an occurrence, where the final or actual cost of a project surpasses the original or initial estimates. Therefore, cost overrun is a very common issue that affects most of the projects in the construction industry (Azhar et al., 2008: 499), while waste can have a significant effect on the success of a construction project, since it specifically has a major impact on the construction costs (Nagapan, Abdul-Rahman, Asmi & Hameed 2012: 22).

In Nigeria, the lowest average reported percentage of cost overrun on a project was 14% (Hussain et al., 2013: 32; Ameh, Soyingbe & Odusanmi, 2010: 49). This problem, according to Ogunsemi & Jagboro (2006: 257), is attributed to a wrong cost-estimation method adopted at the early stage of building projects.

Ameh & Itodo (2013: 749) assert that material wastage on site leads to an increase in the final cost of the building project. As materials are wasted, more are procured, thus affecting the estimated cost (Teo, Abdelnaser & Abdul, 2009: 257). Ameh and Itodo (2013:754) highlighted that wastages from the following materials contribute to the total project cost: concrete 4%; blockwork 10%; waste from screeding and plastering 15%; packaging 5%, and formwork is based on the number of times it is re-used. Furthermore, research evidence has shown (see Tables 1, 2 and 3) that the main factors causing construction material waste are similar to those causing construction-cost overruns on site; hence, Nagapan, Abdul-Rahman & Asmi (2012: 2-3) (see Figure 1) categorised cost overruns and time overruns as part of non-physical waste, and other material waste as physical waste on a construction site. This shows that cost overruns, time overruns


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and construction material waste are generally categorised as waste. This is supported by Ma (2011: 118) who defines waste as anything that does not add value. Therefore, cost overruns, material waste and material waste that may be lost to landfills do not add value to projects. Therefore, Nagapan, Abdul-Rahman & Asmi (2012: 2-3) assert that construction waste is not all about the quantities of materials that are wasted, but that it is also focused on factors such as overproduction, waiting time, material handling, inventories, and the unnecessary movement of workers that constitute a significant part of non-physical waste, to which the construction industry always pays the least attention.

It is clear from Figure 1 that, since construction waste entails both the physical and the non-physical waste, there is, therefore, a relationship between cost overrun emanating from the non-physical waste and material waste from the physical waste, as they both originate from the same waste family (Saidu & Shakantu, 2015: 97).

Figure 1: Classification of construction waste Figure 1: Classification of construction waste

Adapted from: Nagapan, Abdul-Rahman & Asmi, 2012: 2

Furthermore, the causes of material waste and those of cost overruns identified from the literature are similar. These causes occur as a result of one, or a combination of several causes at different stages of a project (the pre- and the post-contract stages), and they are crucial in identifying effective cost performance and sustainable construction (Saidu, 2016: 61).

2.2 Material waste and cost overrun at the post-contract stage of projects

The causes of material waste and cost overruns at the post-contract stage of projects are identified in three major phases, namely the quality-of-procurement management, the quality-of-construction management, and the quality-of-site management. Tables 1, 2 and 3 present the results of different studies on the causes of material


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waste and those of cost overruns at these three phases of the post-contract stage. In all three tables, columns 2-3 are combined under the heading “material waste”, while columns 4-5 are combined under the heading “cost overrun”. These columns depict that each material waste cause listed in column 1 was identified and linked under a source and the location/country in columns 2-3 as well as in columns 4-5.

2.2.1 Procurement management phase

Table 1 lists the causes of material waste related to the causes of cost overruns with respect to the procurement management phase of a building construction project.

Table 1: The material waste causes that are similar to those of cost overruns with respect to quality-of-procurement management

Causes of material waste related to the causes of cost overruns with respect to the procurement management phase of a project

Material waste Cost overruns

Author and date Location Author and

date Location

Procurement and transportation

Errors/mistakes in material ordering/procurement

Nagapan, Abdul-Rahman, Asmi & Hameed (2012: 23)

Malaysia Allahaim & Liu (2012: 5-6) Saudi Arabia

Procuring items not in compliance with specification

Adewuyi & Otali (2013: 748); Osmani et al. (2008: 23)

Rivers, Nigeria; UK

Allahaim & Liu (2012: 5-6) Saudi Arabia

Errors in shipping/supply

Osmani et al. (2008: 1149); Nagapan et al. (2012: 23)

UK; Malasia Nega (2008: 48) Ethiopia


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date Location

Mistakes in quantity surveys: Poor estimate for procurement (over-procuring)

Nagapan et al. (2012: 23)

Malaysia

Aziz (2013: 57); Allahaim & Liu (2012: 5-6)

Egypt; Saudi Arabia

Wrong material delivery procedures


Malaysia Aziz (2013: 57) Egypt

Delivery of substandard materials


Malaysia Nega (2008: 48) Ethiopia

Damage of material during transportation

Osmani et al. (2008: 1149) UK Nega

(2008: 48) Ethiopia

Late delivery/Inadequate delivery schedule

Nguyen, Gupta & Faniran (nd: 6)

Geelong, Australia

Al-Najjar (2008: 51); Abdul Rahman, Memon & Abd. Karim (2013: 1965)

Gaza Strip; Malaysia

Poor material handling


UK; Malaysia

Ameh, Soyingbe & Odusanmi (2010: 61-62)

Nigeria

Poor protection of materials and damage during transportation

Osmani et al. (2008: 1149); Aiyetan & Smallwood (2013: 1168)

UK; Lagos, Nigeria Nega (2008) Ethiopia

Over-allowance (difficulties in ordering less)


UK; Malaysia Allahaim & Liu (2012: 5-6) Saudi Arabia


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date Location

Frequent variation orders

Nguyen et al. (nd: 6)

Geelong, Australia

Aziz (2013: 57); Baloyi & Bekker (2011: 55)

Egypt; South Africa

Poor product knowledge


Malaysia Jackson (2002: 4) Reading

Difficulties of vehicles in accessing site


UK; Batu, Malaysia

Allahaim & Liu (2012: 5-6); Zewdu & Aregaw (2015: 185)

Saudi Arabia; Ethiopia

Manufacturers

Poor quality of materials

Adewuyi & Otali (2013: 748)

Nigeria Ameh et al. (2010: 61-62) Nigeria

Non-standard sizes of materials

Osmani (2011) UK

Le-Hoai, Lee & Lee (2008: 370)

Vietnam

Poor product information



Lack of awareness

Al-Hajj & Hamani (2011: 221)

UAE Ameh et al. (2010: 61-62) Nigeria

Suppliers

Poor supply chain management


UAE Ameh et al. (2010: 61-62) Nigeria

Supplier errors

Odusami, Oladiran & Ibrahim (2012: 63)

Nigeria Nega (2008: 48) Ethiopia

Poor product incentive




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date Location

Poor handling of supplied materials

Osmani et al. (2008: 1149); Ameh & Itodo (2013: 753)

UK; Nigeria Ameh & Itodo (2013: 753) Nigeria

Poor methods of unloading materials supplied in loose form

Adewuyi & Otali (2013: 748)

Nigeria Nega (2008: 48) Ethiopia

2.2.2 Construction management phase

Table 2 lists the causes of material waste related to the causes of cost overruns with respect to the construction management phase of a building project.

Table 2: The relationship between the causes of material waste and cost overruns with respect to the quality of construction management

Causes of material waste related to the causes of cost overruns with respect to the construction management phase of a project


Author and date Location Author and date Location

Contractors

Incorrect scheduling and planning

Osmani et al. (2008: 1149) UK

Abdul Rahman et al. (2013: 1965)

Malaysia;

Inappropriate contractor’s policies

Nagapan et al. (2012: 23) Malaysia Aziz (2013: 57) Egypt

Lack of awareness

Al-Hajj & Hamani (2011: 221-228)

UAE Aziz (2013: 57) Egypt


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Lack of experience


Malaysia

Abdul Rahman et al. (2013: 1965); Ameh et al. (2010: 62)

Malaysia; Nigeria

Poor site management and supervision

Nagapan et al. (2012: 23); Ameh & Itodo (2013: 753)

Malaysia; Nigeria

Le-Hoai et al. (2008: 370); Allahaim & Liu (2012: 6)

Vietnam; Saudi Arabia

Poor building techniques



Incompetent subcontractor/supplier


Malaysia Ameh et al. (2010: 61-62) Nigeria

Poor financial controls on site


UAEShanmugapriya & Subramanian (2013: 737-738)

India

Use of unskilled labour to replace skilled ones


Malaysia Memon et al. (2013: 10) Malaysia

Culture

Lack of incentive


UAE Memon et al. (2013: 10) Malaysia

Lack of training and development

Al-Hajj & Hamani (2011: 221); Adewuyi & Otali (2013: 748)

UAE; Nigeria Olawole & Sun (2010: 522) UK

Lack of support from senior management


UAEAziz (2013: 57); Allahaim & Liu (2012: 5-6)

Egypt; Saudi Arabia

Lack of awareness among practitioners on waste management


UAE Ameh et al. (2010: 62) Nigeria


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Workers

Workers’ mistakes or errors during construction


UAEShanmugapriya & Subramanian (2013: 737-738)

India

Incompetent workers


MalaysiaAziz (2013: 57); Olawole & Sun (2008: 522)

Egypt; UK

Poor workers’ attitude



Lack of experienced workers


Malaysia

Shanmugapriya & Subramanian (2013: 737); Love, Edward & Irani (2011)

India; UK

Shortage of skilled workers


Malaysia

Abdul Rahman et al. (2013: 1965); Olawole & Sun (2010: 522)

Malaysia; India; UK

Inappropriate use of materials and equipment

Wahab & Lawal (2011: 252)

Nigeria Allahaim & Liu (2012: 5-6)

Saudi Arabia

Poor workmanship

Odusami et al. (2012: 63) Aiyetan & Smallwood (2013: 1168)

Nigeria; Lagos, Nigeria Nega (2008: 48) Ethiopia

Damage caused by workers

Nagapan et al. (2012: 23); Al-Hajj & Hamani (2011: 221)

Malaysia; UAE Allahaim & Liu (2012: 5-6)

Saudi Arabia

2.2.3 Site management phase

Table 3 lists the causes of material waste related to the causes of cost overruns with respect to the site management phase of a building construction project.


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Table 3: The relationship between the causes of material waste and cost overruns with respect to quality of site management

Causes of material waste related to causes of cost overruns with respect to site management phase of a project



Wrong material/equipment storage/stacking


MalaysiaUbani, Okorocha & Emeribe (2011: 76)

Nigeria

Transfer of materials from storage to application

Osmani et al. (2008: 1149) UK Ubani et al.

(2011: 76) Nigeria

Damage of materials by other trades

Aiyetan & Smallwood (2013: 1168)

Lagos, Nigeria Jackson (2002: 4) Reading

Poor site storage area

Osmani et al. (2008: 1149); Odusami et al. (2012: 63)

UK; Nigeria Jackson (2002: 4) Reading

Long distance from storage to application point


Damage by weather

Osmani et al. (2008: 1149); Wahab & Lawal (2011: 252)

UK; Nigeria

Allahaim & Liu (2012: 6); Memon et al. (2013: 10)

Saudi Arabia; Malaysia

Security

Inadequate site security/Fencing

Nguyen et al. (nd)

Geelong, Australia


Theft Osmani et al. (2013: 1149)

UK; Nigeria


Vandalism, sabotage pilferage, and material damage

Osman et al. (2008: 1149); Ameh & Itodo (2013: 753)

UK; Nigeria


Power and lighting problems on site

Nguye et al. (nd: 6)

Geelong, Australia



90




Site conditions

Poor site management

Odusami et al. (2012: 63)

Nigeria Abdul Rahman et al. (2013: 288) Malaysia

Poor site and unforeseen ground conditions

Wahab & Lawal (2011: 252); Aietan & Smallwood (2013: 1168)

Nigeria; Lagos, Nigeria

Aziz (2013: 57); Allahaim & Liu (2012: 5-6)

Egypt; Saudi Arabia

Leftover materials on site

Osmani (2011: 38) UK Ubani et al.

(2011: 76) Nigeria

Waste resulting from packaging

Osmani (2011: 38) UK Allahaim & Liu

(2012: 5-6) Saudi Arabia

Lack of environmental awareness


Malaysia Ubani et al. (2011: 76) Nigeria

Difficulties in accessing construction site



Site congestion and Interference of other crews

Osmani (2011: 38) UK Le-Hoai et al.

(2008: 370) Vietnam

Inadequate site investigation


Shanmugapriya & Subramanian (2013: 737-738)

India

Disputes on siteAdewuyi & Otali (2013: 748)

Nigeria Allahaim & Liu (2012: 5-6) Saudi Arabia

Extra materials ordered are discarded instead of carrying over to next site

Oladiran (2009: 2) Nigeria Allahaim & Liu

(2012: 5-6) Saudi Arabia

Equipment failure on site

Adewumi & Otali (2013: 748)

NigeriaShanmugapriya & Subramanian (2013: 737-738)

India


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Rework

Al-Hajj & Hamani (2011: 225); Adewuyi & Otali (2013: 748); Oladiran (2009: 2); Ameh & Itodo (2013: 753)

UAE; Rivers, Nigeria; Nigeria; Nigeria

Shanmugapriya & Subramanian (2013: 737-738); Le-Hoai et al. (2008: 370)

India;Vietnam

Site accidents Odusami et al. (2012: 63)

Nigeria

Allahaim & Liu (2012: 5-6); Le-Hoai et al. (2008: 370)

Saudi Arabia; Vietnam

Lack of communication

Wahab & Lawal (2011: 252)

NigeriaAbdul Rahman et al. (2013: 1965)

Malaysia

3. Research methodologyAlthough the research analysis was done using a deductive approach, the research method applied for this research was inductive reasoning. This is a reasoning strategy that intends to learn about the phenomena under investigation by applying a less structured methodology in order to obtain richer and more detailed information (Sutrisna, 2009: 9). To achieve this, a qualitative research method that is rooted in the phenomenological research paradigm was applied. This helped the researchers study the attitudes and behaviours of the research subjects within their natural settings (Babbie & Mouton, 2010: 51). This qualitative method involves analysing words; it refers to issues relating to people, objects and situations, and it focuses on naturally occurring, ordinary events in their natural settings (Farrell, 2011: 6). This enables the researchers to examine the material waste issues that affect cost overruns at the post-contract stage of a construction project. Based on the research problem advanced in this study, for instance, the majority of managers of construction projects pay hardly any attention to the effects of material waste on project cost overrun. This has prompted


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the researchers to apply the qualitative method, in order to identify and examine these issues.

The study population consists of building construction projects in Abuja, the Federal Capital Territory of Nigeria. Abuja was selected because it is one of the metropolitan cities of Nigeria with the highest population of professionals in the built environment and with many ongoing construction projects.

3.1 Sampling method

The sampling method was purposive/judgmental, because only building-construction professionals handling projects that are worth 1.6 billion Naira/eight million USD and above were consulted/interviewed. Unlike projects of lesser value, those worth eight million USD and above are categorised as big projects that are likely to be handled by more experienced professionals, who might be more familiar with the issues leading to material waste and cost overruns (Saidu & Shakantu, 2016c: 104). Through purposive sampling, the research targeted the most visible and experienced leaders.

3.2 Sample size

Leedy & Ormrod (2014: 220) believe that the size of a purposive sampling technique for a phenomenological research ranges between five and 25 participants. For this research, semi-structured, in-depth interviews were conducted with 30 construction professionals, comprising 15 project managers, nine quantity surveyors, five site engineers and one senior technical officer of a waste-management department on the issues that relate to material waste and cost overruns at the post-contract stage of a construction project.

3.3 Data collection

The research instrument (interview guide) enabled the researchers to be consistent with the questions posed to the respondents. It also enabled the collection of data based on the perception of the construction professionals in Abuja with regard to the issues that link material waste to cost overruns in the construction industry. Twelve questions based on the objectives of this research were generated. The interview guide was structured in three major groups, namely quality of procurement management, quality of construction management and quality of site management (see Appendix). Probing questions were asked during discussion with the interviewees, in order to obtain further information. An average of 35 minutes was spent in conducting each interview. The interviews


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were conducted between December 2014 and March 2015. The approximate conversion rates as at November 2014 were Nigerian Naira to US dollar = ₦200 = 1USD. All 30 respondents identified in this research through the purposive sampling method responded to all the questions presented for discussion.

3.4 Data analysis

The recorded, transcribed and interpreted interview data were analysed, using the ‘deductive approach’. The application of the ‘deductive analysis’ of data in qualitative research enabled the researchers to extensively condense raw data into a brief and summary format, and to establish clear links between the research purpose and the summary findings derived from the raw data (Dey, 2005: 55).

The data in this analysis was done manually after each respondent’s views were coded and similar views were brought together under a theme/heading. This method involves constant comparative analysis of the data after it has been sorted and coded to generate knowledge about any common pattern in the interviewees’ evidence on material waste and cost overrun. The analysis began by comparing the opinions of the first two interviewees. The process continued with a comparison of the data from the comments and inputs from each new interviewee, until all the responses had been compared with each other. The similarities and differences between the interviewees’ responses were used to develop a conceptualisation of the possible association between the various data items. The results are presented under subthemes under the following headings: quality of procurement management, quality of construction management, and quality of site management.

4. Results and discussionThe results are the summary of the interviews conducted with the 30 respondents after linking the similarities together. These are the key issues realised from the interviews. They are not literatures, but qualitative results. For presentation and discussion purposes, a summary of the interview results is presented under the following headings/themes, which are in line with the set objectives of the research, namely quality of procurement management; quality of construction management, and quality of site management.


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4.1 Quality of procurement management

4.1.1 The quality of procurement management in the respondents’ organisations

The majority of organisations/firms procure materials strictly in accordance with project specifications; with an efficient and well-organised procurement management; they have the know-how of what to procure, what quantity to procure, at what cost to procure, and where to procure. Some companies have the knowledge of current material prices, both locally and internationally, while few lack such knowledge. Some have a network of procurement departments, both locally and internationally, in case the projects require foreign materials.

4.1.2 Contribution of materials procurement to waste-generation and cost overruns

Procuring the appropriate materials, at the right time, in accordance with the specifications, and proper material handling and good product knowledge would reduce material waste and cost overruns. This result corroborates the findings of Osmani et al. (2008: 1147) and Nagapan et al. (2012: 22) who highlighted poor material handling as a cause of material waste, and Ameh et al. (2010: 49) who also noted the same issue as a cause of cost overrun.

4.1.3 Contributionsofqualityoffirms’procurementmanagementtomaterial-waste generation and cost overruns

A good quality-procurement management team should envisage better transportation of materials, order the appropriate quantity of materials, and provide easy access roads. Where these cannot be envisaged, waste would inevitably occur and contribute to cost overruns.

In the absence of a competent and experienced procurement management, a job would probably be given to an incompetent contractor, who might end up wasting materials, thus leading to cost overruns. Moreover, lack of quality control in procurement and adequate estimation for procurement, as stated in project specifications, may result in wastage of materials and contribute to cost overrun. This finding also supports the literature identified in section 2 of this study.


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4.1.4 Material waste causes on project cost-overrun with respect to quality of procurement management

The following material-waste causes resulted in cost overruns at this stage: procuring items not in compliance with the specifications; engaging inexperienced personnel in estimation and procurement; procuring wrong quantity of materials; errors in shipping; damage of material during transportation, and lack of awareness. These results are in line with the causes of material waste that are similar to the causes of cost overrun identified in section 2 of this research study.

4.2 Quality of construction management

4.2.1 Quality of construction management based on the respondent’s experience

Quality of construction management entails managing the entire construction process from inception to completion with all the necessary management tools. Some respondents believe that it is the practical way of achieving design reality through co-ordinating, controlling, organising, communicating, scheduling, motivating, proper building techniques, and good workmanship. Some respondents view construction management as the pillar of every construction work, which has to do with the management of people, plant, materials, equipment, money, time, and the entire construction process.

4.2.2 Relationshipbetweentheintervieweefirms’constructionmanagement, material-waste generation and cost overruns

The respondents were not fully satisfied with their organisations’ approach to construction management. Some disclosed that their firms/organisations were operating far below the average level in terms of construction management; some at the average level, while some still have a very long way to go. The reason for this is that there are situations where projects are not delivered on time, and sometimes within the budgeted cost. However, very few were doing above average. These are experienced and always plan ahead; hence, they generate less waste and cost overruns.

4.2.3 Contribution of subcontractors and suppliers to material-waste generation and cost overruns

Both the subcontractors and the suppliers contribute to material-waste generation and cost overruns. Subcontractors are profit-oriented individuals and the waste they generate directly affects


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their profits. Most of the contract agreements require subcontractors to generate waste at their own risk, which makes them more careful about the amount of waste they generate.

For the suppliers, the quality control department evaluates the supplied product to ensure that they conform with the project’s specification.

4.2.4 Impact of rework and mistake/error on material-waste and cost overruns

Inexperienced professionals/personnel or working contrary to project specification/contract lead to rework and mistakes/errors. An abortive work is already a waste, and it would require the same type of materials, the same labour, and the same costs to re-build. This result corroborates the findings of Aziz (2013: 52) who concluded that abortive works contribute to cost overruns.

4.2.5 Material-waste causes that affect cost overruns with respect to quality-of-construction management

The following material-waste causes affect project-cost overrun with respect to quality-of-construction management: engaging incompetent workers; rework; incorrect scheduling and planning; shortage of skilled workers; lack of experience; poor financial controls on site; poor staff workers’ relationship; lack of awareness of waste management; lack of incentive, and the use of unskilled labour to replace skilled ones. These results corroborate the findings outlined in section 2 of this research.

4.3 Quality of site management

4.3.1 Respondents’ understanding of site management and its contributions to material waste and cost overrun

Site management is an aspect of construction management that deals with the planning, controlling, co-ordinating, communicating, motivating, scheduling, and organising of the entire activities on the site, including the 5Ms (men, machines, money, materials, and management) to achieve the desired project objectives; it involves site security, access road, minimisation of wasteful time, timely provision of materials, and site safety; it has to do with the management of the routine activities on site, and it includes a certain group of people that administer the day-to-day running of a site from inception to completion of a project. Therefore, site management contributes to material waste and cost overruns when


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the management of the site issues is poor or not properly managed or addressed.

4.3.2 Contributions of site security, site accident, and site disputes to material-waste generation and cost overruns

Inadequate site security would lead to pilfering/thefts and damage/sabotage of materials on site. When the site is not properly organised and disciplined, accidents are bound to occur, and these might affect the workers, the structure, or even both.

4.3.3 Material waste causes that affect cost overruns with respect to quality-of-site management

The following material-waste causes affect cost overruns with respect to quality of site management stage: inadequate site security/fencing; poor site organisation and discipline; construction-site disputes; poor site management and the 5Ms; lack of experience; poor construction planning and control; lack of co-ordination among the parties; poor site storage area; communication problems and poor site supervision; problems relating to on-site health and safety; wrong location of cranes on site; inappropriate records of materials, and lack of environmental awareness. These results support the findings highlighted in section 2 of this study.

5. Conclusion and recommendationsMaterial waste and cost overrun are common problems in the construction industry at both the pre- and post-contract stages of a construction project. As a result of a dearth of empirical research and a low level of awareness, the majority of managers of construction projects in Nigeria pay hardly any attention to material waste issues that affect cost overrun. This article examined the material waste issues that affect cost overruns at the post-contract stage of building projects.

The research concludes that both the literature and the empirical findings from the study have established a link between the issues on material waste and cost overruns at the post-contract stage of a project (procurement, construction management, and site management stages).

It is concluded that proper management of procurement, construction-management and site-management processes, as well as their related material waste causes would reduce the rate of cost overruns for projects.


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Based on these, it is recommended that proper attention to material waste issues at the post-contract stage of any project has the potential to minimise the rate of cost overrun. Therefore, careful attention should be paid to the issues identified in this study, as they would assist in reducing the rate of material waste and cost overrun for a project. Construction professionals should be well informed of these issues at an early stage of a project, to enable them (professionals) to evaluate the extent to which their consequences could be minimised.

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Appendix

AN INTERVIEW GUIDE on

“A post-contract stage analysis of material waste and cost overrun on construction

sites in Abuja, Nigeria”

SECTION A: (Preliminary questions) Name of the person being interviewed __________________________

Position ________________________________________________________

Nameofthefirm/organisation __________________________________

Name of the project ___________________________________________

Project location ________________________________________________

Project value (₦) ______________________________________________

Years of experience in the industry ______________________________

Highesteducationalqualification _______________________________

Please describe your role in the organisation ____________________

SECTION B: (Main research objectives)

a) Quality of procurement management 1. Can you tell me about the quality of procurement manage-

ment in your organisation/industry? 2. Does the quality of materials procurement contribute to

material waste-generation? What about cost overruns?3. Does the quality of your firm’s procurement management

contribute to material-waste generation and cost overruns?4. Based on your experience, what are the material waste

causes that affect cost-overrun with respect to quality of procurement management?


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b) Quality of construction management1. Based on your experience, what is the quality of construction

management?2. How can you relate the quality of your firm/organisation’s

construction management to material waste generation and cost overrun

3. Do sub-contractors and suppliers in any way affect the material waste generation and cost overrun?

4. Does rework have any impact on the material waste generation and cost overrun? What about mistakes/errors?

5. What are the material waste causes that affect cost overruns with respect to the quality of construction management?

c) Quality of site management 1. What is your understanding of ‘site management’? How

does site management contribute to material waste and cost overrun?

2. How do the site security, site accident and site dispute affect material waste generation and cost overrun?

3. What are the material waste causes that affect cost overruns with respect to the quality of site management?

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Stakeholders’ perception of critical success factors for sustainable facilities management practice in universities in sub-Saharan Africa


AbstractThe development of an optimal sustainable facilities management (SFM) strategy for university-built assets in sub-Saharan Africa (SSA) is desired. However, this requires an in-depth understanding of the perspectives of different stakeholders on the probable success factors. The elicitation of such perspective is considered imperative, as it allows Facilities Managers to engage with effective SFM planning in a manner that caters to the interest of these stakeholder groups. This study seeks to identify and, subsequently, assess these success factors, according to stakeholders’ perspectives.A sequential mixed method research design is utilised wherein 29 semi-structured interviews were conducted initially, followed by a question-naire survey conducted with 113 respondents, in the second stage. Interviewees were purposively selected from a university of technology (UoT) in South Africa, whilst respondents were drawn from universities within SSA. Data from the first stage was analysed, using qualitative content analysis, and subsequently applied towards questionnaire development. The questionnaires appraised stakeholder perceptions of the criticality of success factors identified during the interviews. The Mean Item Score (MIS) was used to rank the responses.Results from the analysis indicate that ‘presence of a well-articulated FM plan for a specified interval’ and ‘adherence to the tenets of the SD agenda (supply chain)’ were selected as most critical of the success factors identified.

Bankole Awuzie

Dr Bankole Awuzie, Senior Lecturer, Department of Built Environment, Central University of Technology, Private Bag X20539, Bloemfontein, South Africa. Phone: +27(0)51 507-3532, email: <[email protected]>

Rasheed Isa

Dr Rasheed Isa, Lecturer, Department of Building, Federal University of Technology, Minna, Nigeria. Phone: +23 4805114689, email:<[email protected]>








Awuzie & Isa • Stakeholders’ perception of critical success factors ...

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It is expected that the study’s findings will contribute to the development of a viable SFM strategy in SSA universities.Keywords: Facilities management, sub-Saharan Africa, success factors, sustainable development, universities

AbstrakDie ontwikkeling van ’n optimale volhoubare fasiliteitsbestuur (VFB)-strategie vir universiteitsgeboude bates in sub-Sahara-Afrika (SSA) word benodig. Dit vereis egter ’n grondige begrip van die perspektiewe van verskillende belanghebbendes oor die waarskynlike suksesfaktore. Hierdie perspektiewe word as noodsaaklik beskou, aangesien dit Fasiliteitsbestuurders in staat stel om effektiewe VFB-beplanning te kan doen op ’n wyse wat omsien na die behoeftes van hierdie belangegroepe. Hierdie studie poog om hierdie suksesfaktore te identifiseer en te evalueer, volgens belanghebbendes se perspektiewe.’n Opeenvolgende gemengde metode navorsingsontwerp word gebruik waar 29 semi-gestruktureerde onderhoude aanvanklik uitgevoer is, terwyl ’n vraelys-opname met 113 respondente in die tweede fase gedoen is. Onderhoudvoerders is doelbewus gekies uit ’n universiteit van tegnologie (UoT) in Suid-Afrika, terwyl respondente van universiteite binne SSA getrek is. Data uit die eerste fase is geanaliseer met behulp van kwalitatiewe inhoudsanalise en daarna op die ontwikkeling van vraelyste toegepas. Die vraelyste het belanghebbendes se persepsies van die kritisiteit van suksesfaktore wat tydens die onderhoude geïdentifiseer is, beoordeel. Die gemiddelde item telling (MIS) is gebruik om die antwoorde te rangskik.Resultate uit die analise dui aan dat ’n teenwoordigheid van ’n goed-geartikuleerde FM-plan vir ’n bepaalde interval ‘en’ nakoming van die beginsels van die SD-agenda (toevoerketting) gekies is as die mees kritiese van die geïdentifiseerde suksesfaktore.Daar word verwag dat die studie se bevindings sal bydra tot die ontwikkeling van ’n lewensvatbare VFB-strategie in SSA-universiteite.Sleutelwoorde: Afrika suid van die Sahara, fasiliteitsbestuur, suksesfaktore, volhoubare ontwikkeling, universiteite

1. IntroductionRecent developmental patterns depict a quest for transformation from consumption patterns, hitherto described as unsustainable, towards sustainability. Noticeably, society’s quest to attain sustainable development (SD) has continued to gain momentum (Sarpin & Yang, 2012: 602). The increasing nature of the advocacy is buttressed by the rise in the number of publications on sustainability science (Bettencourt & Kaur, 2011: 19541). Organisations have concerned themselves with (re)designing their business models to contribute towards this aspiration. Considering their reputation as societal change agents, universities are assuming a pivotal role in SD implementation (Cortese, 2003: 16; Stephens, Hernandez, Román, Graham & Scholz, 2008: 320). Extant studies highlight the role of universities in mainstreaming SD ethos into their core activities and


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within the broader societal context (Ferrer-Balas, Lozano, Huisingh, Buckland, Ysern & Zilahy, 2010: 607-610). These activities are usually embedded within the teaching and learning, research, and operations facets, respectively.

Universities have been admonished to do more concerning the systemic integration of sustainability ethos across every facet of their endeavour (McMillin & Dyball, 2009: 57). Literature points out that the majority of universities are concerned with attaining SU status through the embodiment of SD ethos not only in teaching, learning and research aspects, but also across the operational facets of which FM forms an integral part (Swearingen, 2014: 235). However, whereas appreciable efforts by universities in developing countries have been observed in the integration of SD into their curriculum and research activities, not a great deal has been reported about the operational aspects. This is particularly the case in sub-Saharan Africa (SSA). This deficiency deters the quest by these universities to achieve SU status. Efforts towards the attainment of SU status will be successful if these universities achieve a systemic integration of SD ethos across their organisational facets (Ferrer-Balas et al., 2010: 608).

Facilities management (FM) bodes immense potentials for the attainment of sustainability gains in organisations (Elmualim, Shockley, Valle, Ludlow & Shah, 2010: 58). This is due to its vast scope which usually transcends the boundary of property management within organisations such as universities. Despite the recognition of FM’s significance, a paucity of studies exploring its role in the attainment of SU status for universities has been observed. Such paucity is quite pronounced within SSA, hence necessitating this study.

In its contribution towards resolving this impasse, this study seeks to identify and assess the success factors for sustainable FM (SFM) practice in universities from a stakeholder’s perspective. It is expected that the findings will contribute to extant literature by highlighting how different stakeholders understand and assess SFM performance in SSA universities. Furthermore, it will provide a platform for the design of a robust SFM strategy by facilities managers working in universities which will cater to the needs of the stakeholders.


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2. Theoretical perspective

2.1 Sustainability, sustainable development and universities

In acceptance of the leadership role in society (Cortese, 2003: 15), universities have, over the past three decades, committed themselves towards making significant contributions towards society’s SD aspirations. Accordingly, they have signed onto various declarations, charters and initiatives (DCIs), adopting SD as a significant aspect of their institutional strategy (Lozano, Lukman, Lozano, Huisingh, & Lambrechts, 2013: 11). Universities within the SSA context have not been left out of these DCIs. A commendable number of these universities have signed up to DCIs at regional, national and international levels. Yet, Lozano, Ceulemans, Alonso-Almeida, Huisingh, Lozano, Waas, Lambrechts, Lukman & Hugé (2015: 2) admit that being signatories to such DCIs does not automatically guarantee attainment of SU status and subsequent contribution to the attainment of SD aspirations in the wider society, reiterating that only optimal implementation of the tenets of such DCIs will. The paucity of studies reporting on SD implementation performance of universities within the SSA context further lends credence to this assertion.

2.2 Need for sustainable facilities management practice in universities

Amaratunga and Baldry (2000: 293) describe FM as an integrated approach for engendering the maintenance, improvement, and adaptation of an organisation’s buildings in order to create the desired ambience required to support the attainment of the organisation’s core mandate. Yim Yiu (2008: 502) reiterates that FM marks a shift from operational services towards strategic resource management, thus distinguishing it from property management. Judging from these strategic roles, FM’s significance within organisations cannot be overemphasised. Effective FM practice influences organisational success factors such as profit determination, productivity, manage-ment of energy and waste, employee welfare, and public perception (Awang, Mohammad, Sapri & Rahman, 2014: 71-72). Accordingly, these scholars have called for the incorporation of SD ethos into FM practices at organisational level. The incorporation of SD ethos into organisational FM, it has been argued, will contribute to the attainment of the organisation’s SD objectives. Such advocacies have resulted from the paradigmatic shift towards SD in organisations such as universities.


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Universities are noted for their ability to provide conducive environ-ments for scholarship to thrive (Cortese, 2003: 15; Stephens et al., 2008: 321; Escrigas, Polak & Jegede, 2011: 13). As a result, they invest considerably in campus infrastructure renewal/redevelopment/development programmes. Arguably, there is an increasing need for these universities to showcase their SD credentials through the nature of the built assets procured. Different phases of the life cycle of these built assets are often superintended by the institutions’ FM directorates (Wright & Wilton, 2012: 119). This makes the adoption of SFM practice in the management of these assets, imperative.

Ikediashi, Ogunlana, Oladokun & Adewuyi (2012: 169) trace the advancement of the SFM concept to the need to contribute to a reduction of the built environment’s debilitating impact on the environment. Over the past few decades, a trend signalling this growing recognition of FM in universities has been observed (Amaratunga & Baldry, 2000: 293-294). Perhaps this recognition can be attributed to the views similar to those espoused by Price, Matzdorf, Smith & Agahi (2003: 213), wherein they observe the potential of the facilities available to a university to considerably impact on student choices. They also add that the nature of the work environment provided by universities can limit their ability to attract the kind of personnel they desire. The aspiration of several SSA universities of assuming an SU status in the not too distant future is significantly dependent on their FM department’s ability to apply SD tenets to its entire operations. These operations consist of the four cardinal roles of FM identified by Yim Yiu previously.

Although the significance of SFM has been noted, the corpus of relevant literature appears silent on the success factors required to achieve this feat within the context of universities, especially in SSA. This study seeks to contribute towards bridging this gap.

2.3 Critical success factors for sustainable facilities management

According to Müller and Jugdev (2012: 758), success factors can be described as project elements possessing the likelihood of bringing about successful project outcomes, if managed effectively. They insist that these factors and the success criteria have formed an integral aspect of the project performance debate and should be taken seriously if the desire for project success is to be accomplished. Therefore, critical success factors (CSF), as the name implies, represents project elements that are critical to the success of any project delivery programme (Müller & Jugdev, 2012: 758). As such, the facilities manager’s ability to identify these CSFs and assess their


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significance from the perspectives of the stakeholders in the university context will contribute to the development of an SFM strategy.

In the absence of relevant literature on CSFs for SFM in universities, this study derives its success factors from a list of barriers (failure factors) to effective SFM in conventional organisations. Based on the description of success factors and the CSFs, it can be deduced, in the absence of established CSFs for SFM in universities, that the CSFs for SFM will be the opposite of the identified barriers (see Table 4). The authors relied on this list of barriers to the implementation of sustainable FM practices in organisations, as compiled by Sarpin and Yang, to derive potential CSFs (Sarpin & Yang, 2012: 604) (see Table 1). This kind of scenarios abound in the literature (see Zhou, Huang & Zhang (2011: 244-246); Babatunde, Akintayo & Akinsiku (2012: 215-222)).

Table 1: Barriers to the implementation of sustainable FM practices in organisations

Barriers Past research Main barriers

1 Knowledge

Elmualim et al. (2010) Lack of knowledge

Nielsen et al. (2009) Limited knowledge regarding environmental themes

Elmualim et al. (2009) Knowledge chasm

Shah (2008) Management of sustainability knowledge

Lai and Yik (2006) Low knowledge level regarding sustainability

Hodges (2005) Discrepancy in knowledge

2 Capability

Shah (2008) Lack of capabilities/skills

Hodges (2005) Lack of capabilities/skills

Elmualim et al. (2010)

Time constraint, lack of senior management commitment, diversity of FM roles, undervaluation of contribution to organisation success

3 Management

Nielsen et al. (2009) Lack of incentives to create routines on environment issue

Nielsen et al. (2009)Shah (2008)

Too little time and few resources to implementAwareness on whole-life value, increasing liability

Hodges (2005) Unwillingness to implement sustainability, lack of financial support

Nielsen et al. (2009) Limited data on local consumption of energy, water

4 AuthorityShah (2008) Performance indicatorsBosch & Pearce (2003) Lack of guidance documents

Source: Sarpin & Yang, 2012: 604


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3. Research methodologyThe objective of this study is twofold, namely to identify CSFs for SFM and to assess the significance of these so-identified CSFs from the stakeholders’ perspectives. This provides veritable information and a platform upon which the development of an SFM strategy in SSA universities will be predicated.

3.1 Research design

To achieve the aforementioned objectives, this study adopts a sequential mixed method research design. Mixed method research designs are renowned for their ability to enable a juxtaposition of data collection and analysis instruments in the conduct of a particular research project (Leech & Onwuegbuzie, 2009: 266). Furthermore, proponents of the research design opine that it allows the shortcomings of the data collection and analysis instruments, belonging to either the qualitative or quantitative genres, to be overcome by the strengths of the other genre being applied therein (Hesse-Biber & Johnson, 2015: 10). The mixed method design in this study is considered sequential, due to the utilisation of different data-collection and analysis techniques belonging to each of the two major genres at two distinct, but interlinked phases of the study (Hesse-Biber & Johnson, 2015: 10).

3.2 Sampling method and sample size

A mixture of purposive, snowballing and convenience sampling techniques was adopted in the selection of 29 interviewees from a South African UoT (Denscombe, 2014: 46). Such sampling techniques enabled the authors to select only stakeholders who had some knowledge concerning FM, SFM and SD at the university. In all, 29 individuals were successfully recruited for the interview sessions held in the first stage (see Table 2).

Table 2: Interviewee demographics

Stakeholder group Position/Job description Code

Management Sustainability manager SM

Support Director of maintenance DFM

Support Clerk of works CoW

Support Assistant clerk of works ACoW

Contractor/Consultants/Suppliers Consultant CIDP


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Stakeholder group Position/Job description Code

Contractor/Consultants/Suppliers Project manager PM

Academic staff Lecturer LAS1-4

Academic staff Senior lecturer SLAS1-2

Contractor/Consultants/Suppliers Subcontractors SCC

Student Postgraduate PGS1-7

Student Undergraduate US1-10

Total 29

Source: Authors’ field work (2016)

In what may seem to be a limitation, these interviewees were recruited from a UoT in South Africa based on convenience sampling. Further, the students were selected from the Department of Built Environment’s B.Tech. class, whereas the postgraduate students were selected from a cohort carrying out sustainability-themed research at Masters and Doctoral degree levels in the Faculty of Engineering and Information Technology at the UoT. Therefore, these interview sessions can be considered at best, exploratory.

For the second phase, respondents were recruited through the participating universities mentioned in the GUNi, IAU and AAU joint survey report on “the promotion of sustainable development in Higher Education Institutions in Sub-Saharan Africa” (Escrigas et al., 2011: 99-101). Universities, which posted questionnaire completion and response rates surpassing 80%, were selected. It was expected that completion rates beyond 80% implied a reasonable level of awareness among various stakeholders within such institutions on the sustainability theme. Of the universities, 41 met this criterion. In addition, it was noted that some of the institutions were situated within French-speaking parts of the SSA, hence the need to adopt English and French in the preparation of the questionnaires.

Gatekeepers were identified and approached in 32 out of 41 universities through the use of snowballing sampling technique. But, after a prolonged duration of engagement and discussions, only gatekeepers from 21 universities were recruited to not only participate, but also assist in the identification of individuals to be issued with the questionnaire alongside their email addresses. Based on a subsisting agreement pertaining to confidentiality, the names of the universities, from which the respondents were recruited, cannot be mentioned. However, the number of questionnaires issued was dependent upon the availability of email addresses. A total of 215


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questionnaires were administered electronically with the aid of SurveyMonkey, a software package that allows a researcher to administer a survey exercise to a vast majority of respondents via email. Care was taken to ensure that respondents were sourced from various stakeholder groups present in the universities, namely management staff, academic staff, support staff (comprising of non-academic staff and staff of the facilities/works department, where possible), students, and contractors/suppliers. An interval of two months was given for the collation of responses. However, reminders were sent to the respondents at the end of the first month on the need to complete and submit the completed questionnaires. In total, 113 respondents were drawn from universities in Ghana, Kenya, Nigeria, and South Africa, respectively (see Table 3).

Table 3: Distribution of respondents per stakeholder group

Stakeholder group Number of respondents

Management 16

Academic 23

Support 21

Student 26

Contractors/Suppliers 27

Total 113

Source: Authors’ compilations (2017)

3.3 Data collection

In the first phase, semi-structured interview sessions were conducted with interviewees, individually, at different times. This phase was conducted as a pilot study to identify the CFSs for SFM practice in a university context, hence the suitability of the utilisation of the number of interviewees. Interview sessions were stopped upon attainment of theoretical saturation (Fusch & Ness, 2015: 1409-1410; Guest, Bunce & Johnson, 2006: 60-62).

Questions asked during these interview sessions focused on the elicitation of interviewees’ perception concerning the success factors against which SFM practice performance in the university can be benchmarked. These interviews lasted for an average of between 30 and 45 minutes each. With the permission of interviewees, the sessions were recorded and subsequently transcribed. For reasons


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bordering on confidentiality, the names of the interviewees were anonymised using relevant codes.

The second phase of this study was predicated on the findings of the thematic analysis of the results generated from the first phase (see Table 4). In this phase, the need to obtain perspectives of a larger sample of stakeholders within the SSA region prompted the decision of the authors to develop a structured questionnaire for conducting a survey of stakeholders within the study context.

In preparing the questionnaire, the guidelines presented by Choi and Pwak (2005) were adopted to eliminate respondents’ bias. These guidelines indicate how the researcher can prevent respondent bias through proper question and questionnaire design as well as during the administration of the questionnaire (Choi & Pwak, 2005: 1-13). The questionnaire consisted of four parts. The first section contained a bio-data section with questions pertaining to the respondent’s attributes. Questions in the second section dwelt on establishing the respondent’s level of understanding of sustainability, sustainable development, facilities management and sustainable facilities management. The third section covered questions regarding the respondent’s perception of sustainable facilities management implementation performance. Lastly, section four encompassed a list of success factors identified from interview sessions conducted in the preliminary study. However, it should be noted that only data from sections 1 and 4 was utilised for this study. The questionnaire was intended for the wider study. In section 4, the respondents were expected to indicate the success factors that were critical to optimal SFM performance, based on a 5-point Likert scale measurement ranging from 1 to 5.

3.4 Response rate

A total number of 141 responses were collated. This represented a response rate of 65.58%. Although this number was unevenly spread among the stakeholder groups present within universities (see Table 3), it was deemed sufficient for the study. Thereafter, returned questionnaires were checked for completeness. It was discovered that 28 questionnaires were not completed and did not add value to the objective of the data-collection exercise. This left the authors with a balance of 113 usable questionnaires.


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3.5 Data analysis

In analysing the qualitative data, the authors relied on the pre-set themes deduced from the list of barriers mentioned in Table 1. The transcripts were read and re-read severally by the authors, independently. Consequently, they coded aspects of the manuscripts and jointly compared the codes. Emergent themes surfaced during the comparing of notes between them. This led to the determination of CSFs for SFM practice in universities. This speaks to the benefits accruable from multi-investigator triangulation, as espoused by Patton (1999: 1192-1193).

In the second phase, the responses obtained from the questionnaires were analysed using descriptive statistics approach - the mean item score (MIS). This approach was deemed appropriate for achieving the study’s objective which was to assess and rank the perceptions of university stakeholders on the various CSFs for SFM practice identified previously.

According to Audu and Kolo (2007: 124), MIS entails the process of assigning numerical values to respondents’ ratings of variable’s importance, for example very high influence (5 points), high influence (4 points), in this order. The MIS of every importance was computed using equation (1)

MS =∑ (fxS)

N 1≤MS≤5 ............................................................................... (1)

Where:

S = the score assigned to each factor by the respondents, it ranges in dependent on the ordinal scale in use (in this case 1-5)

F = frequency of responses to each rating (1-5)

N = total number of responses in the respective score.

Table 4 presents the MIS ranking, based on stakeholders’ perceptions, as reflected on a 5-point Likert scale measurement where 1 indicates not important, 2 indicates rarely important, 3 indicates neutral, 4 indicates important, and 5 indicates very important.

4. PresentationoffindingsThe findings from the first phase of the study are presented in Table 4, and the findings from the second phase of the study are presented in Table 5.


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Table 4: List of CSFs identified from the interviews

CSF category CSFs identified Interviewees (codes)

Knowledge-related/ Authority-related

Establishment of proper framework for sharing SD-based knowledge between various stakeholders within the university

CIDP, SLAS, LAS, ACoW, SM

Knowledge-related/Management-related

Consultative forums to debate new developments and for the development of SD-based knowledge within the university

CIDP, SLAS, LAS, PGS, SM

Knowledge-relatedImproved face-to-face communication about the use of facilities

US, PGS, SLAS, SM, ACoW

Knowledge-related

Effective information systems to provide up-to-date information on the use of existing and new structures

SLAS, DFM, CIDP, LAS


Proper communication of the university’s SD Policy guidelines, if any, to various stakeholders within the university

CIDP, SLAS, SM, CoW, DFM, US, PGS

Knowledge-related/Capability-related

Constant site meetings with contractors and FM department to share lessons learnt as it pertains to SD in their respective projects

DFM, CIDP, CoW, FC, SCC,


Presence of a well-articulated FM plan for specified intervals CIDP, SLAS, LAS, SM

Authority-related/Management-related

Integration of smart and sustainable FM principles into planning stages for the procurement of new infrastructure projects as well as maintenance at the university

DFM, CIDP

Knowledge-related

Development and dissemination of a set of clear SD policy guidelines to be adopted in the maintenance and delivery of infrastructure projects.

CIDP, SLAS, SM

Management-related/Authority-related

Provision of incentives for proper use of workspaces and other types of building stock

SLAS, LAS, SM, CIDP

Authority-related/Capability-related.

Demand for adherence to the tenets of the SD agenda in the selection of supply chain members (sustainable procurement)

CIDP, DFM, FC, SCC

Capability-related CSFs

Provision of financial and organisational support for knowledge and capability development workshops on SD within the FM department

SM, SLAS


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CSF category CSFs identified Interviewees (codes)

Capability-relatedPresence of required competencies for delivering on smart and sustainable FM

CIDP, SLAS, LAS,

Capability-related/Authority-related/Management-related

Presence of a sustainability champion CIDP

Capability-relatedUse of appropriate contracting strategy for project delivery and maintenance

CIDP, DFM

Capability-related/Authority-related

Adoption of a set of standards to ensure compliance by end users and contractors alike.

CIDP

Authority-related/Capability-related

Early engagement of contractors during the procurement of new infrastructure or during the planned phased maintenance of existing building stock

CIDP, DFM

Capability-relatedDevelopment of a skills database for the institution’s supply chain

CoW, CIDP, DFM

Management-related Adequate timespan for the budget implementation DFM, CIDP

Management-related Adequate funding DFM, CIDP, SLAS

Source: Authors’ compilation (2016)

Based on the findings from the interview sessions, a list of 20 CSFs for SFM practice in universities was identified (see Table 4). Some CSFs, which did not form part of the initial pre-set themes identified prior to the commencement of the interview sessions, were also realised from the data. Whilst efforts were made to group these CSFs as mentioned previously, it was discovered that the majority of CSFs identified, overlapped.

Going by the CSFs listed therein, the absence of a consensus among all stakeholders on what the CSFs for SFM entailed, was observed. From the data, it was obvious that stakeholders were only interested in CSFs from which they were able to derive benefits. This culminated in the decision of the authors to assess and rank these identified CSFs from the viewpoints of an enlarged stakeholder audience. The results from this exercise are reported in Table 5.


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Tabl

e 5:

Ra

nkin

g of

crit

ical

succ

ess f

acto

rs fo

r sus

tain

able

faci

litie

s man

agem

ent b

y st

akeh

old

ers

Crit

ical

succ

ess f

acto

rs

Man

agem

ent

Aca

dem

ic

staf

fSu

ppor

t st

aff

Stud

ent

Con

tract

or

(Sup

plie

r)Su

mm

ary

Mean

Rank

Mean

Rank

Mean

Rank

Mean

Rank

Mean

Rank

Mean

Rank

Wel

l-arti

cula

ted

FM

pla

n fo

r spe

cifie

d in

terv

al3.

8110

3.74

104.

671

3.50

114.

224

3.98

1

Ad

here

nce

to th

e te

nets

of t

he S

D a

gend

a (s

uppl

y ch

ain)

4.13

23.

2618

4.38

34.

085

4.11

63.

981

Cle

ar S

D p

olic

y fo

r del

iver

y an

d m

aint

enan

ce o

f pro

ject

s4.

371

3.09

194.

383

3.69

94.

332

3.95

3

Effe

ctiv

e co

mm

unic

atio

n sy

stem

on

new

faci

litie

s/bu

ilt a

sset

s3.

0618

4.74

13.

0018

4.04

64.

155

3.93

4

Pres

ence

of a

sust

aina

bilit

y ch

ampi

on4.

132

3.83

83.

7611

4.27

23.

5914

3.90

5

Dev

elop

men

t of a

skills

dat

abas

e fo

r sup

ply

chai

n3.

947

3.30

174.

482

3.88

83.

8910

3.88

6

Com

pete

ncie

s for

del

iver

ing

on sm

art a

nd su

stai

nabl

e FM

4.00

63.

838

3.95

73.

5810

4.07

83.

837

Ad

equa

te fu

ndin

g3.

888

4.22

33.

2417

3.12

184.

631

3.83

7

Impr

oved

com

mun

icat

ion

on fa

cilit

ies

3.50

123.

5214

4.14

63.

927

3.33

173.

779

Supp

ort f

or k

now

led

ge a

nd c

apac

ity d

evel

opm

ent o

n SD

4.13

24.

174

3.43

153.

3115

3.70

133.

7210

Early

inte

grat

ed sm

art a

nd su

stai

nabl

e pr

inci

ple

3.44

133.

6511

3.57

123.

4212

4.33

23.

7011

App

ropr

iate

con

tract

ing

stra

tegy

for p

roje

ct d

eliv

ery

3.44

133.

6511

3.57

123.

4212

4.11

63.

6612

Ince

ntiv

e fo

r pro

per u

se o

f wor

k sp

ace

3.13

174.

005

3.33

164.

124

3.48

153.

6513

Ad

optio

n of

a se

t of s

tand

ard

s to

ensu

re c

ompl

ianc

e3.

4413

3.48

153.

868

3.27

163.

969

3.61

14

Prop

er c

omm

unic

atio

n of

SD

pol

icy

3.88

83.

916

2.71

204.

541

2.89

193.

5815

Ad

equa

te ti

mes

pan

for t

he b

udge

t im

plem

enta

tion

3.31

163.

3916

3.81

103.

1917

3.85

123.

5216

Con

sulta

tive

foru

m fo

r new

dev

elop

men

t deb

ate

4.06

53.

916

2.71

194.

233

2.70

203.

5017

Fram

ewor

k fo

r kno

wle

dge

shar

ing

2.88

194.

652

3.86

82.

8820

2.93

183.

4318

Early

eng

agem

ent o

f con

tract

ors d

urin

g th

e pr

ocur

emen

t2.

7520

3.00

204.

383

2.96

193.

8910

3.42

18

Con

stan

t site

mee

ting

with

con

tract

ors a

nd F

M st

aff

3.63

113.

6113

3.52

143.

3814

3.41

163.

3720

Sour

ce: A

utho

rs’ o

wn

com

pila

tion

(201

7)


120

Table 5 is self-explanatory and details the various mean item scores ascribed to each of the 20 CSFs, as ranked by respondents. The first column provides the list of CSFs, whereas the succeeding columns present both the mean item scores and ranks given by respondents from each stakeholder group for these CSFs. The last columns, entitled Summary, provide an aggregation of the entire MIS and ranking of the CSFs as carried out by the various stakeholder groups surveyed. From the rankings, the presence of a well-articulated FM plan for a particular duration was deemed as the most significant CSF for engendering SFM practice in universities in the SSA region, whereas constant site meetings with contractors and FM staff was ranked as the least CSF by the stakeholders.

5. DiscussionoffindingsDiscussions will seek to focus on the perceptions and the ranking accorded to the CSFs by the respective stakeholder groups involved in the study. It is expected that the discussions therein will provide readers with an insight into the rationale behind the ranking of the CSFs.

5.1 Management staff perspective

In the study, management staff are considered to consist of staff members who carry out administrative responsibilities in the university system. They can be situated at any position between the middle management and the strategic management ends of the continuum. This cadre of staff are responsible for the daily running of the university and the formulation of policy. Such policies will usually consist of the SD plan and the associated implementation framework.

Studies have shown that this stakeholder category remain pivotal to the success or otherwise of the SFM practice in organizations such as universities (Elmualim et al., 2010: 57). The position of these scholars was further alluded to by several interviewees during the interview process, as presented in Table 4. However, when asked to assess and rank the identified CSFs, the respondents from the management stakeholder group ranked the presence of clear SD policy for delivery and maintenance of projects, the presence of a sustainability champion in strategic management cadre, and support for knowledge and capacity development on SD in first and joint second positions, respectively. They also ranked the early engagement of contractors as the least CSF for ensuring SFM practice in their universities.


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Yet a closer observation of the operational structure of the majority of the universities, from which the respondents were sourced, indicates the absence of sustainability managers or champions at the strategic level. Whilst some institutions have closed down the position as a result of cost-containment measures, others have not allowed such a position to thrive. In addition, the absence of clear and explicit SD-oriented policy guidelines was observed in a majority of these institutions. A cursory review of documents made available to the authors and commentaries from the interviewees showed a clear lack of such policies at the university level. The development and dissemination of this document happens to be the exclusive preserve of the management staff. Whilst it is good to note that the respondents consider these CSFs as central to optimal SFM practice, it behoves them to carry on with the development of these policies and guidelines, as this will not only assist the institution of SFM practice, but also engender enhanced institutional contribution towards SD.

5.2 Academic staff perspective

The core activity of universities worldwide revolves around knowledge creation and dissemination through teaching and learning as well as research activities (Lukman & Glavič, 2007: 107-110). Therefore, for this category of stakeholders, their interest lies in the provision of a conducive environment for them to function along these enunciated roles. They will also expect their research outcomes to be implemented in the university’s drive for SU status through improved SFM practice. Therefore, it is not surprising that respondents in this category will ascribe the first and second position to ‘effective communication system on new structure’ and ‘the presence of a framework for knowledge sharing’ among constituents of the university community. This was the case in this study.

Furthermore, their non-involvement in the procurement processes in the university is observed from their ranking of the CSFs bothering on procurement of built assets and their subsequent maintenance. For instance, they ranked ‘clear SD policy for delivery and maintenance of projects’ and ‘early engagement of contractors during the procurement’ in position 19 and 20, respectively. Accordingly, the ranking of the CSFs for SFM from an academic staff perspective, as presented in the findings, appears justifiable.


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5.3 Support staff perspective

As mentioned in the preceding section, the provision of support for the conduct of core activities in organisations such as universities through the management of non-core activities is not only imperative, but also viewed as the central role of FM and SFM (Amaratunga & Baldry, 2000: 293). Stakeholders grouped under this category participate in the provision of these non-core activities. Accordingly, their views are critical to the enthronement of optimal SFM in the university. As part of the facilities and services delivery team in a university’s operational apparatus, they interface with suppliers and contractors during the project-delivery and maintenance cycle. Apparently, this justifies their proclivity to ranking CSFs pertaining to maintenance of assets higher than others. Based on the available data sets and the MIS computed and highlighted in Table 5, respondents within this stakeholder group ranked CSFs ‘well-articulated FM plan for specified interval’, ‘development of a skills database for supply chain’ and ‘early engagement of contractors during the procurement’ in the first, second and third positions.

Interestingly, the CSF pertaining to the ‘choice of an appropriate contracting strategy’ ranked a dismal 12th position despite being within the purview of the support staff. Perhaps, this may have resulted from the manner in which the procurement of projects is alleged to have been carried out, wherein the management staff have full control over the choice of contracting strategy without input from the designated support staff. This much was obtained during the interview sessions. Yet, much of the perspectives espoused through the ranking by this stakeholder group seem to be valid.

5.4 Student perspective

Students make up a significant proportion of the university community population (Nejati & Nejati, 2013: 102). Besides this, this era of increasing cost of education at the tertiary level and attendant degree of unaffordability among many of the urban populace make the provision of facilities that are apt for scholarship in these institutions, essential. Views held by students cannot be overlooked (Price et al., 2003: 213). Although an effort was made to get across to a larger sample of the student population during the respondent recruitment exercise, the respective gatekeepers reported a lack of interest on the part of the students to complete the questionnaires. Whilst this apathy can be attributed to the low levels of awareness concerning sustainability and SD among these students, as reported


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in the findings made by Escrigas et al. (2011: 67), it is beyond the remit of this study to proceed with an investigation along this route.

Unsurprisingly, the students ranked ‘efficient communication of SD policy within their institutions’ as being cardinal to the successful implementation of SFM practice in their institutions. Obviously, this cannot be farther from the truth, as these sets of individuals require information concerning what sustainability and SD connotes in order to be able to contribute to its attainment during their interaction with the built assets, waste disposal systems and green areas in their respective campuses. Yet, it is befuddling to observe from the ranking that the students who have so highly rated the need for effective communication will turn around to rate the presence of a knowledge-sharing framework as the least ranked CSF. It is expected that this aspect will be investigated further.

5.5 Contractors/supplier perspective

Contractors and suppliers play a significant role in the delivery of FM-related services in universities, hence their inclusion in the interviews and surveys thereafter. It goes without saying that any attempt at securing a transformation towards SFM practice and, eventually, an SU status will entail the transformation of the FM supply chain towards SD-oriented tenets. According to the responses obtained from respondents from this stakeholder group, ‘adequate funding’ was ranked in the first position. This is typical of the contractors and suppliers, as the issue of funding remains critical to them. The ranking of CSFs such as ‘clear SD policy for delivery and maintenance of projects’ and ‘early integrated smart and sustainable principle’ in joint second position does not come as a surprise, as this group of stakeholders usually seek clarity of specifications at an early stage as well as certainty of workflow over a defined period to enable them to plan accordingly.

Yet, the poor ranks allotted to CSFs such as ‘presence of an SD knowledge-sharing framework’, ‘proper communication of SD strategy’ and ‘the establishment of a consultative forum for debate on new development’ by these respondents leaves room for more in-depth studies into the probable causes of such perceptions.

It is obvious that, with the exception of a few cases, a significant number of respondents promoted aspects of SFM CSFs which they considered to be critical to their performance or success. This is not unexpected. However, the aggregation of these perceptions vis-à-vis the ranking indicated in the last two columns provides a veritable platform for the development of an SFM implementation framework


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in universities in the sub-Saharan Africa context. The reason for this is that the ranks accorded to the various CSFs will enable strategic facilities managers operating within this context to prioritise during the development of this framework for optimal SFM performance, directly, and the attainment of SU status in their respective universities, indirectly.

6. Concluding remarksThe potential of universities to contribute towards the actualisation of society’s SD aspirations has been observed. The adoption of SFM practice has been identified as capable of making significant contributions towards achieving this feat. But, a review of relevant literature also revealed that this aspect was being undermined by universities worldwide in comparison to the aspects of teaching and learning as well as research. Opinions had been expressed concerning this deficiency, wherein several commentators have sought to blame the absence of success factors and criteria for assessing the integration of SD ethos into operational aspects of university activities, particularly, facilities management.

Buoyed by this observation and associated commentaries, this study set out to contribute towards bridging this gap by identifying and assessing the CSFs for engendering SFM in these universities from the perspectives of relevant stakeholders. The study achieved its objective through an overt reliance on a sequential mixed method research design. Whereas semi-structured interviews were used to elicit data in the first phase, the use of email-based questionnaires was utilised in the second phase. Data resulting from the first phase was used to develop questionnaires for the second phase, thus indicating complementarity. Data sets from the second phase were categorised according to the perceptions of the individual stakeholder groups initially, prior to the subsequent aggregation of the perceptions of these stakeholder groups into a unified set of CSFs with their associated ranking. The ‘presence of a well-articulated FM plan for a specified interval’ and ‘adherence to the tenets of the SD agenda (supply chain)’ ranked as joint 1st CSFs for SFM, whereas ‘constant site meeting with contractors and FM staff’ ranked the least.

Summarily, this study provides a platform for further studies into the concept of SFM in universities. Such studies may explore the possibility of determining probable reasons behind the ranks ascribed to the CSFs by different stakeholder groups and perhaps, determine any latent relationships therein. In addition, the information provided


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can support the development of an SFM implementation framework within these universities by their strategic facilities managers.

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Building an infrastructure project performance in the North-West Province Department of Public Works and Roads


AbstractBuilding and infrastructure projects at the North-West Province Department of Public Works and Roads (NW DPWR) often perform poorly in terms of overrunning both the original approved timeline and the budget. Adding to poor time and cost performances, these projects often do not meet the desired functional requirements. This article reports on findings of a study which investigated the causes of these poor performances in the NW DPWR. Fifty potential causes of poor performance were identified from literature. These factors were grouped under three main related categories of owner-related, contractor-related and consultant-related and were subjected to a questionnaire survey to identify the most critical causes of failure. The results were analysed using the Relative Importance Index (RII) and Spearman’s Rank Correlation Coefficients. The results indicated that the most significant causes of poor building and infrastructure project performance in the NW DPWR include underestimation of project cost, the lack of experience in executing projects, contractor’s cash-flow constraints, corruption and bribery during the bidding and contract award phase, as well as poor site management and supervision. Recommendations are made to prevent similar causes of projects failure in the NW DPWR in future.Keywords: Building, construction projects, cost overruns, infrastructure, project performance, Relative Importance Index, schedule delays

Davison Murwira

Mr Davison Murwira, M.Eng. student, Graduate School of Technology Management, University of Pretoria, Lynnwood Road, Pretoria, 0002, South Africa. Phone: +27 (0)12-420-2822.

Michiel Bekker

Dr Michiel (C.) Bekker, Senior Lecturer, Graduate School of Technology Management, University of Pretoria, Lynnwood Road, Pretoria, 0002, South Africa. Phone: +27 (0)12-420-2822, email: <[email protected]>






Murwira & Bekker • Building an infrastructure project

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AbstrakBou- en infrastruktuurprojekte by die Noordwes Provinsie se Departement van Openbare Werke en Paaie (NW DPWR) ondervind gereeld tydvertragings in projekvoltooiing sowel as probleme soos oorspandering en die gebrek aan voldoening aan funksionele spesifikasies. Hierdie artikel rapporteer resultate van ’n studie wat onderneem is ten einde die redes waarom projekte in die NW DPWR swak presteer, te ondersoek. Vanuit literatuur is vyftig potensiële redes vir projekfaling geïdentifiseer. Die redes is in drie verwante kategorieë gegroepeer, naamlik eienaarverwante, kontrakteurverwante en konsultantverwante kategorieë. Deur middel van ’n vraelysopname is die mees kritieke redes vir projekfaling geïdentifiseer. Die resulate is ontleed deur gebruik te maak van ’n Relatiewe Sterkte Indeks asook die Spearman Rangorde Korrelasie Koëffisiënte. Die resultate toon dat die mees beduidende redes vir projekfaling in NW DPWR sluit onderberaming van projekkoste, gebrekkige ervaring in projekimplementering, beperkte kontantvloei deur kontrakteurs, korrupsie en omkopery tydens die tenderproses en kontrakaanstellings, sowel as swak terreinbestuur en toesig in. Aanbevelings is gemaak om soortgelyke probleme met toekomstige projekte in die NW DPWR te beperk.Sleutelwoorde: Gebou, konstruksieprojekte, koste-oorskryding, infrastruktuur, projekprestasie, Relative Importance Index, skedulevertragings

1. IntroductionThe North-West Provincial Government (NWPG) has a constitutional responsibility to provide better services to the people of the North-West Province. The NWPG, through the Department of Public Works and Roads (DPWR), is mandated to provide office, residential and other service delivery facilities to provincial departments and political office-bearers. The initial policy of the NW DPWR, in full support of the objectives and targets of the government’s Reconstruction and Development Programme (RDP), is to ensure that all South Africans have access to basic infrastructure (RDP, 1994: online). The Chief Directorate Infrastructure in the Department is responsible for infrastructure planning, design and project implementation of infrastructure assets to meet the needs identified by the client departments under the capital expansion (Capex) programme. The implementation of projects by the NW DPWR is based on the North-West Infrastructure Delivery Management System (NW IDMS), which provides a systematic approach to infrastructure delivery, covering the full life cycle from needs identification, planning and budgeting to procurement, construction, handover, operations and maintenance.

The NW DPWR often perform poorly in the delivery of all construction and maintenance projects on time, within budget and in accordance to the pre-determined requirements. A study undertaken by the South African Government in 2002 to determine the issues and gaps in the delivery of infrastructure reported that there was a shortfall in


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effective and systematic delivery systems, as well as a shortage in skills to deliver projects as per requirements (SAICE, 2016: 2). The most critical problems facing Government’s infrastructure service delivery programme are the following:

• Delayed infrastructure investment, also known as the ‘blocked infrastructure project pipeline’, often due to inadequate planning allocation of resources, as well as excessive bureaucracy;

• Infrastructure delivery backlogs, particularly in respect of buildings infrastructure;

• Budgetary challenges in addressing backlogs in infrastructure delivery;

• Inheritance of unequal spatial distribution of infrastructure resulting in rural areas with limited access to basic, social and economic services;

• Underspending of capital expenditure;• Poor application of project management practices, and• Poor time management.

The South African Government has identified infrastructure development as a means to stimulate the economy (NPC, 2011: 137). The Government is the most significant construction client, contributing between 40% and 50% of the entire domestic construction expenditure (Dlungwana, Nxumalo, Van Huysteen, Rwelamila & Noyana, 2002: 2). According to Ramokolo and Smallwood (2008: 46), South Africa aims to invest 5.1% of South Africa’s Gross Domestic Product (GDP) in construction. They also indicated that 45% of the more than 500,000 people, employed in the construction industry, are estimated to be working in the formal sector. In order to address infrastructure backlogs across the country, the Government is committed to invest in infrastructure development in order to achieve economic growth and address the backlog.

Approximately 60% of the projects being implemented by the NW DPWR are not completed on time and on budget, often resulting in service delivery protests by local communities. In a bid to get to uncover some of the problems experienced in the delivery of building infrastructure projects by the NW DPWR, a preliminary internal evaluation and analysis of project performances and operational deficiencies in the NW DPWR was done. It was evident that there were various challenges facing the NW DPWR in delivering building and infrastructure projects, including termination of contractors for poor performance, cost and schedule overruns on projects such as


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Vryburg Mini Garona Office Park, Vryburg Hospital, and DPWR Head Office in Mmabatho. These problems were further exacerbated by poor procurement practices and poor controls in the delivery process. Despite overall poor performance, some projects were delivered successfully, namely Tlakgameng Community Library, new hostels at Bophelong Special School, and Tlou le Tlou Traditional Offices. The discrepancies in overall project performance prompted the researcher to investigate and identify the causes of infrastructure project failures in the NW DPWR.

This study aims to address the following three questions related to projects at NW DPWR:

• What are the general factors causing the time and cost overrun of building and infrastructure projects?

• What are the critical factors causing the time and cost overrun of building and infrastructure projects?

• What are the perceptions of owners, contractors and consultants regarding the causes of time and cost overrun of building and infrastructure projects?

2. Literature surveyConstruction projects worldwide often suffer from poor performance in terms of time delays, cost overruns and quality defects. Pheng and Chuan (cited in Adebowale & Ayodeji, 2015: 1118) stated that, traditionally, successful delivery of a construction project hinges on the performance of the project manager, who must consider delivery time, budgeted cost and expected quality. However, with the delivery of projects predominantly a team effort, the allocation of single accountability for project performance to one individual might not achieve the desired results.

In the past, various studies investigated and analysed factors causing poor performance on construction-related projects (Table 1). The majority of these studies focused on identifying the major causes of time and schedule overruns, challenges facing contractors, as well as quality management in various construction projects. Table 1 provides a summary of some of the common causes for poor performance of construction-related projects as per categories identified.


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Table 1: Summary of typical factors causing the poor performance of construction-related projects

Critical performance factors Authors

Owner-related factors

Delayed monthly payments for completed work

Assaf & Al-Hejji (2006: 349); Frimpong, Oluwoye & Crawford (2003: 321); Fugar & Agyakwah-Baah (2010: 111); Mansfield, Ugwu & Doran (1994: 254); Odeh & Battaineh (2002: 67); Sambasivan & Soon (2007: 526); Sweis, Sweis, Hammad & Shboul (2008: 671)

Owner’s cash-flow problems

Al-Momani (2000: 51); Assaf & Al-Hejji (2006: 349); Kaliba, Muya & Mumba (2009: 522); Koushki, Al-Rashid & Kartam (2005: 294); Monyane & Okumbe (2012: 192); Sweis et al. (2008: 671)

Scope changes from owner

Al-Momani (2000); Assaf & Al-Hejji (2006: 349); Kaliba et al. (2009: 522); Koushki et al. (2005: 294); Monyane & Okumbe (2012: 192); Sweis et al. (2008: 671)

Delays in decision-making

Chan & Kumaraswamy (1997: 55); Monyane & Okumbe (2012: 192)

Contractor-related factors

Inadequate and poor planning

Assaf & Al-Hejji (2006: 349); Dlungwana, Nxumalo, Van Huysteen, Rwelamila & Noyana (2002: 25-26); Sambasivan & Soon (2007: 526); Sweis et al. (2008: 671)

Contractor’s financial difficulties

Aibinu & Odeyinka (2006: 667-677); Frimpong et al. (2003: 321); Sweis et al. (2008: 671)

Shortage of skilled labour

Baloyi & Bekker (2011: 63); Dlungwana et al. (2002: 25-26); Sweis et al. (2008: 671); Thwala & Phaladi (2009: 533)

Poor site management and supervision

Assaf & Al-Hejji (2006: 349); Chan & Kumaraswamy (1997: 55); Kaliba et al. (2009: 522); Sambasivan & Soon (2007: 526)

Underestimation of project cost

Dlakwa & Culpin (1990: 239); Fugar & Agyakwah-Baah (2010: 111); Mansfield et al. (1994: 254); Thwala & Phaladi, (2009: 535)

Lack of experience in executing projects

Koushki (2005: 294); Muhwezi, Acai & Otim (2014: 21); Nguyen & Chileshe (2015: 398); Sambasivan & Soon (2007: 526); Thwala & Phaladi (2009: 533)

Increase in material cost Baloyi & Bekker (2011: 62); Dlakwa & Culpin (1990: 239); Koushki et al. (2005: 294); Mansfield et al. (1994: 254)

Consultant-related factors

Poor design capacity and design changes

Al-Momani (2000: 51); Baloyi & Bekker (2011: 63); Jackson (2002: 4); Nguyen & Chileshe (2015: 398); Muhwezi et al. (2014: 13-23)

Incomplete designs by architect and engineering disciplines

Aibinu & Odeyinka (2006: 667-677); Baloyi & Bekker (2011: 63); KPMG International (2013: 4); Muhwezi et al. (2014: 13-23)

Architect’s incomplete drawing Aibinu & Odeyinka (2006: 675)

Design error made by the designers

Muhwezi et al. (2014: 13-23); Tumi, Omran & Pakir (2009: 268)


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3. Research methodologyDue to the number of projects and stakeholders involved, it was decided to conduct a quantitative research design rather than a qualitative approach. As stated by Cooper and Schindler (1998: 21), a questionnaire survey assists with the standardisation of data gathering, decreases non-response errors and increases response rates. The number of people and entities participating in NW DPWR projects are numerous, and it is believed that the inputs and views of as many participants as possible will be valuable to objectively identify the key factor that leads to poor project performance. The structured survey questionnaire invitations were sent via e-mail. For this investigation, an e-mail distribution method offered the opportunity to access a bigger group of potential research participants.

3.1 Sampling method

A list of 310 approved consultants (258) and contractors (52) were obtained from the internal NW DPWR procurement database. This database is merely a list of consultants and contractors that are eligible to work for the Department and contains the contact their names, telephone numbers and email addresses. A total of 258 consultants were listed in the database and included architects, civil and structural engineers, electrical engineers, mechanical engineers, and quantity surveyors. A total of 52 contractor names were retrieved. Introductory letters and questionnaires were sent to all companies listed.

Owner participants included employees from NW DPWR who interfaced directly with consultants and contractors on projects and had the mandate to provide managerial and technical guidance on the projects as well as approve or disapprove project deliverables. A total of 45 project owner participants were identified to participate. A simple random sampling selecting method resulted in a sample size of 355, representing project owners (45), contractors (52), and consultants (258).

3.2 Data collection

A structured questionnaire was distributed electronically, via email, to a total randomly selected sample of 355 project owners, contractors and consultants involved in building construction projects under the NW DPWR Infrastructure Chief Directorate in South Africa. The major causes of construction projects failure in the NW DPWR topics listed in the questionnaire were extracted from reviews of the literature, resulting in the formulation of a questionnaire divided into two


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sections, namely the respondent’s profile, and a ranking list of the 50 critical project failure factors where respondents were requested to rank each of the 50 critical project failure factors. The profile section consisted of questions pertaining to general demographics about the respondents. It was divided into three subsections: Type of organisation owner, consultant or contractor; the respondent’s designation, and the professional experience of each respondent. To reduce the respondent’s biasness, and facilitate coding of the questionnaire, closed-ended questions were preferred (Akintoye & Main, 2007: 601).

3.3 Response rate

A total of 100 validly completed responses were received, representing a response rate of 28.2%. According to Moyo & Crafford (2010: 68), contemporary built-environment survey response rates range from 7% to 40% in general. It is significant in respect of the reliability of the response rate that, although the number of questionnaires distributed to consultants seems disproportionally high, the response rate from this category was relatively low, thus not causing any bias towards the results. The questionnaire return rate is provided in Table 2.

Table 2: Questionnaire return rate

Respondents Questionnaires distributed Responses returned Response rate (%)

Owner 45 30 66.67

Contractors 52 26 50.00

Consultants 258 44 17.05

Total 355 100 28


A 5-point Likert scale was used to measure the opinions of the respondents. Likert-type or frequency scales use fixed choice response formats and are designed to measure attitudes or opinions (Bowling, 1997; Burns & Grove, 1997). For the purpose of analysis and interpretation, the following scale measurement was used: 1 - no contribution to failure; 2 - slight contribution to failure; 3 - significant contribution to failure; 4 - very significant contribution to failure, and 5 - major cause of project failure. From this general data, each of the 50 critical project failure factors could be ranked from having no, slightly, significant, very significant or major contribution to project failure.


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To interpret the general data, a combination of the following three descriptive statistical analysis methods was used:

• Relative Importance Index (RII);• Spearman’s Rank Correlation (rs), and• Probability values (p-values).

3.4.1 Relative Importance Index

The Relative Importance Index (RII) is commonly used to assess comparative results from research in the field of project performance (Aibinu & Odeyinka, 2006; Baloyi & Bekker, 2011; Chan & Kumaraswamy, 1997; Kikwasi, 2012; Muhwezi et al., 2014). For this study, RII was used to determine the ranking of different causes of building construction projects failure from the point of view of owners, contractors and consultants.

RII = ∑ W ∕ (A x N), (0 ≤ RII ≤ 1) (1)

Where:W = the weight given to each factor by the respondents and ranges from 1 to 5 as per the Likert scale.A = is the highest weight (i.e. 5 in this case).N = is the total number of respondents.

The cause with the highest index is the most important, and with the smallest number the least important. The rankings made it possible to cross-compare the failure factors as perceived by the three groups of respondents.

3.4.2 Spearman’s Rank Correlation

This study used the Spearman’s Rank Correlation (rs) to identify and test the strength of a relationship between the rankings of any two parties for a single failure cause, while ignoring the ranking of the third party (Assaf & Al-Heijj, 2006; Fugar & Agyakwah-Baah, 2010; Odeh & Battaineh, 2002). The correlation coefficients are calculated using the following formula (2):

rs = 1-6∑d2

(n3 - n) .......................................................................................... (2)

Where:rs = Spearman’s Rank Correlation Coefficient.d = the difference in ranking between any two parties.n = the number of causes of failure, which in this case is 50.


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The correlation coefficient varies between +1, which implies a perfect positive correlation (agreement) and -1, which implies a perfect negative correlation (disagreement). Values close to unity in magnitude imply good correlation, whereas those near zero indicate little or no correlation (Assaf & Al-Heijji, 2006). When r = 0, it means that there is no correlation (Assaf & Al-Heijji, 2006).

3.4.3 Probability values

The p-value is the probability of observing a sample value as extreme as, or more extreme than the value actually observed, given that the null hypothesis is true (Kamanga & Steyn, 2013: 82). To determine whether the parties displayed significant agreement in their rankings, the null hypothesis stated as owner and contractors, contractors and consultants, and owner and consultants do not agree on ranking of the causes of construction projects failure in the NW DPWR was tested at a 95% confidence level (2 tailed tests). The p-value indicates if the correlation is statistically significant. The analysis was aided by the use of MoonStats statistical software.

4. Results

4.1 Relative Importance Index

Table 3 shows a complete set of the survey results illustrating the RII as well as the ranking order where 1 shows the factors contributing the most to failure.


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Table 3: Overall RII and rank of construction projects failure according to owner, contractors and consultants

ID Causes of failure

Owner Contractors Consultants All parties

RII Rank RII Rank RII Rank Average RII Rank

Owner-related factors

1 Late payment of completed works 0.759 13 0.792 7 0.682 28 0.744 16

2 Owner’s cash-flow problems or non-access to funds constraints 0.753 14 0.769 13 0.758 10 0.760 12

3 Late or delayed contract award by owner 0.593 45 0.536 50 0.526 49 0.552 49

4 Late reviewing and approval of design documents 0.620 44 0.608 42 0.600 43 0.609 44

5 Difference between selected bid and consultants’ estimates 0.552 49 0.592 43 0.662 32 0.602 45

6 Delays in decision-making 0.733 19 0.746 23 0.702 24 0.727 18

7 Unrealistic design development period 0.653 40 0.738 25 0.714 20 0.702 30

8 Late issue of instructions 0.703 29 0.677 35 0.718 17 0.700 31

9 Owner interference 0.717 24 0.669 39 0.643 35 0.676 35

10 Poor project scope definition by owner 0.767 12 0.762 15 0.753 11 0.761 11

11 Owner initiated changes during implementation 0.703 29 0.592 43 0.645 34 0.647 39

12 Awarding of contracts primarily on price 0.772 11 0.776 12 0.823 5 0.791 6

13 Corruption and bribery during the bidding and contract award phase 0.857 4 0.800 5 0.809 6 0.822 4

14 Poor information dissemination by owner 0.640 42 0.685 34 0.679 29 0.668 36

Contractor-related factors

15 Late payment of subcontractors for completed works by contractor 0.827 6 0.769 13 0.716 19 0.770 10

16 Fluctuations in material, labour and plant cost 0.669 38 0.631 41 0.609 41 0.636 41

17 Contractor’s cash-flow constraints 0.867 1 0.815 4 0.842 2 0.841 3

18 Underestimation of project cost 0.867 1 0.877 1 0.836 3 0.860 1

19 Shortage of skilled labour 0.793 8 0.762 15 0.786 9 0.780 8

20 Increase in material cost 0.683 33 0.585 45 0.595 45 0.621 42

21 Delay by subcontractor 0.673 35 0.672 38 0.636 38 0.661 37

22 Poor site management and supervision 0.747 16 0.862 2 0.832 4 0.813 5

23 Underestimation of time for completion by contractor 0.793 8 0.777 10 0.791 7 0.787 7


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ID Causes of failure

Owner Contractors Consultants All parties

RII Rank RII Rank RII Rank Average RII Rank

24 Contractor work overload 0.720 23 0.715 31 0.712 21 0.716 27

25 Lack of experience in executing projects 0.867 1 0.824 3 0.870 1 0.853 2

26 Unforeseen ground conditions 0.673 35 0.577 46 0.614 40 0.621 42

27 Inclement weather 0.513 50 0.546 49 0.507 50 0.522 49

28 Poor planning of material acquisition (shortage of available steel, concrete) 0.697 31 0.792 7 0.691 25 0.727 18

29 Poor risk management by contractor 0.733 19 0.704 33 0.740 13 0.726 21

30 Inadequate contingency allowance 0.587 47 0.569 47 0.595 45 0.584 48

31 Defective works and reworks 0.752 15 0.754 20 0.750 12 0.752 15

32 Incompetent subcontractor 0.747 16 0.731 28 0.791 7 0.756 14

33 Lack of effective communication by contractor 0.747 16 0.715 31 0.712 21 0.725 23

34 Deficiencies in the initial bill of materials 0.707 25 0.738 25 0.641 36 0.695 32

Consultant-related factors

35 Discrepancy between design specification and building code 0.673 35 0.677 35 0.600 43 0.650 38

36 Incomplete designs by engineering disciplines 0.833 5 0.762 15 0.718 17 0.771 9

37 Incomplete design by architect 0.807 7 0.731 28 0.738 14 0.759 13

38 Poor quality of tender documents 0.707 25 0.754 20 0.738 14 0.733 17

39 Poor design capacity 0.733 19 0.762 15 0.676 31 0.724 24

40 Non-adherence to project schedule 0.707 25 0.777 10 0.595 45 0.693 33

41 Complexity of building design 0.587 47 0.677 35 0.536 48 0.600 46

42 Delays in issuing information to contractors 0.690 32 0.762 15 0.686 26 0.713 28

43 Owner-initiated changes during design 0.593 45 0.562 48 0.609 41 0.588 47

44 Lack of project coordination and integration 0.647 41 0.754 20 0.709 23 0.703 29

45 Poor project conceptualisation and design 0.680 34 0.800 5 0.686 26 0.722 25

46 Poor project scope definition by owner 0.660 39 0.785 9 0.735 16 0.726 21

47 Risk identification and allocation 0.628 43 0.646 40 0.636 38 0.637 39

48 Poor constructability 0.733 19 0.746 23 0.679 29 0.720 26

49 Poor stipulation of quality parameters 0.707 25 0.728 30 0.638 37 0.691 34

50 Lack of effective communication by consultants 0.793 8 0.733 27 0.656 33 0.727 18


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The RII of each cause, as perceived by all respondents, was used to illustrate the relative ranking. The results revealed that the three groups of respondents differ in the factors they identified as causing construction projects failure in the NW DPWR and in their ranking.

4.1.1 Owners’ viewpoints

The top five causes of construction projects failure in the NW DPWR identified by the owner representatives were the following:

• Contractor’s cash-flow constraints;• Underestimation of project cost;• Lack of experience in executing projects;• Corruption and bribery during the bidding and contract

award phase, and• Incomplete designs by engineering disciplines.

4.1.2 Contractors’ viewpoints

The contractors perceived the top five major causes of construction projects failure in the NW DPWR to be the following:

• Underestimation of project cost;• Poor site management and supervision;• Lack of experience in executing projects;• Contractor’s cash-flow constraints, and• Corruption and bribery during the bidding and contract

award phase.

4.1.3 Consultants’ viewpoints

The top five causes of construction projects failure in the NW DPWR identified by the consultants were the following:

• Lack of experience in executing projects;• Contractor’s cash-flow constraints;• Underestimation of project cost;• Poor site management and supervision, and• Awarding of contracts primarily on price.

Notably, consultants did not rank any consultant-related factors in the top five.


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4.1.4 Overall viewpoints

The top five overall views of all three parties to the survey were as follows:

• Owner and contractors ranked underestimation of project cost by the contractor as the major cause of construction projects failure in the NW DPWR;

• Owner and consultants ranked contractors’ lack of experience in executing projects as the major cause of construction projects failure;

• Owner and consultants ranked contractors’ cash-flow constraints as another major cause of construction projects failure in the NW DPWR;

• According to owner and contractors, corruption and bribery during the bidding and contract award phase by the owner is among the major causes of construction projects failure in the NW DPWR, and

• Based on their viewpoints, contractors claimed that poor project conceptualisation and design are major causes of project failure.

All three parties agree that the following causes are the least important:

• Inclement weather;• Late or delayed contract award by owner;• Inadequate contingency allowance by the contractor;• Owner-initiated changes, and• Complexity of building design.

4.2 Spearman Rank Correlation and p-values

Table 4 provides the values of correlation coefficients among the parties and their corresponding p-values.

Table 4: Correlation test of all factors among respondents

Owner and contractors Contractors and consultants Owner and consultants

Spearman Rank Correlation Coefficient

p-valueSpearman RankCorrelationCoefficient

p-valueSpearman Rank Correlation Coefficient

p-value

0.694 0.000 0.736 0.000 0.763 0.000


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The rank correlation coefficients calculated for all factors among the respondents were: 0.694 for “owner and contractors”; 0.736 for “contractors and consultants” and 0.763 for “owner and consultants”, respectively. These values show that there is a positive correlation between the three groups. The p-values for the three groups were 0.000, denoting a significant relationship between the causes of construction project failure ranked by these three respondent groups. All the groups generally agreed on the ranking of the causes of construction projects failure in the NW DPWR.

5. ConclusionsTable 5 reveals the overall ranking of the top ten most important factors causing construction projects failure in the NW DPWR. All the major stakeholders agreed that seven out of the top ten causes of construction projects failure are linked to contractor-related factors. Four of the top five causes of construction projects failure in the NW DPWR are all contractor related. The highest ranked owner-related cause of construction projects failure is corruption and bribery during the bidding and contract award phase; incomplete designs by engineering disciplines is the highest ranked consultant-related factor. All the top ten factors are linked to the traditional view of project success/failure, which hinges on the ‘iron triangle’ parameters of time, cost and quality.

Table 5: Top ten factors causing failure of construction projects

ID Causes of failure Average RII Rank Related

category

18 Underestimation of project cost 0.860 1 Contractor

25 Lack of experience in executing projects 0.853 2 Contractor

17 Contractor’s cash flow constraints 0.841 3 Contractor

13 Corruption and bribery during the bidding and contract award phase 0.822 4 Owner

22 Poor site management and supervision 0.813 5 Contractor

12 Awarding of contracts primarily on price 0.791 6 Owner

23 Underestimation of time for completion by contractor 0.787 7 Contractor

19 Shortage of skilled labour 0.780 8 Contractor

36 Incomplete designs by engineering disciplines 0.771 9 Consultant

15 Late payment of subcontractors for completed works by contractor 0.770 10 Contractor


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6. RecommendationsGiven the results of the research the following recommendations are proposed:

• The NW DPWR should be wary of awarding tenders where the price of the recommended tender is below the pre-tender estimate by the quantity surveyor. The reason for low tender values could be due to various reasons such as desperation to get a contract and substitute the price with change order or an underestimation of the price of the works.

• Contractors should critically evaluate their ability and competency to successfully complete the required assignment. It is important for contractors to ensure that they understand the requirements of the project during the pre-contract and bidding period so that they go for works for which they have a competitive advantage.

• Contractors’ cash flow should be evaluated prior to bud evaluation and should also be part of the evaluation criteria.

• Consultants should be encouraged to improve upfront planning. A schedule should be set to complete design documents on time, and the Department must ensure that they adhere to the agreed schedule in order to avoid delay of work completion. An agreed turnaround time for document reviews should be confirmed upon project kick-off.

• All parties should put in place policies that will help retain their valuable human resources thereby avoiding high staff turnover.

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Investigating alternative dispute resolution methods and the implementation thereof by architectural professionals in South Africa


AbstractGiven the number of role players and the complexity of the building process, disputes are inevitable. As an alternative to litigation, which is often costly and time consuming, Alternative Dispute Resolution (ADR) methods could be used. Arbitration, mediation, negotiation and adjudication are a few examples of ADR and, if understood correctly and implemented effectively, ADR could prove beneficial to all parties involved in disputes.This article investigates the current knowledge, implementation and benefits of ADR within the South African built environment. The focus population of the study is architectural professionals, as defined by the South African Council for the Architectural Profession (SACAP). A questionnaire was distributed among 581 architectural professionals to determine how informed these architectural professionals are about the different ADR methods, their implementation and resultant consequences.The real-world problem is that architectural professionals do not apply ADR methods because of the lack of knowledge regarding the implementation and benefits of ADR. It is considered that the unique contribution of this article lies in the fact that all architectural professionals in South Africa were asked to participate in the survey regarding ADR, its methods, implementations, and their knowledge thereof. This is the first evidence of many anecdotal statements made on the lack of implementation and knowledge regarding ADR methods within the architectural profession of South Africa. The findings reveal that the

Tariene Wilcocks

Ms. Tariene Wilcocks, Part-time Lecturer, Department of Architecture, Tshwane University of Technology, No 201 Fairfield, 1164 Dormer Avenue, Queenswood, South Africa. Phone: + 27 63 695 4584, email: <[email protected]>

Jacques Laubscher

Prof. Jacques Laubscher, Head of Department, Department of Architecture, Tshwane University of Technology, PO Box 95469, Waterkloof, 0145, South Africa. Phone: + 27 12 382 5252, email: <[email protected]>








Wilcocks & Laubscher • Investigating alternative dispute-resolution ...

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majority of the respondents are not familiar with the term ADR and could not provide a clear definition; 69.4% of architectural professionals do not discuss ADR methods with their client before entering into an agreement, and 58.4% of the population have hardly any or no knowledge regarding the methods and benefits of ADR.These observations indicate that a significant portion of architectural professionals are currently in breach of the SACAP Code of Conduct and could potentially lose their professional license. These results indicate a possible way forward to facilitate a larger implementation of ADR in future building projects.Keywords: South African Council for the Architectural Profession (SACAP), alternative dispute resolution (ADR), litigation.

AbstrakGegewe die aantal rolspelers en kompleksiteit van die konstruksiebedryf is dispute onvermydelik. As ’n alternatief tot litigasie, wat soms duur sowel as tydrowend kan wees, kan alternatiewe geskilbeslegtingsprosedures (ADR) gebruik word. Arbitrasie, bemiddeling/mediasie, onderhandeling en beoordeling is ’n paar voorbeelde van alternatiewe prosedures en, as dit korrek verstaan en geïmplementeer word, kan dit voordelig wees vir alle partye betrokke in die dispuut.Die studie ondersoek die huidige kennis, implementering en voordele van ADR. Die populasie vir die studie is gefokus op professionele persone in die boukunde, soos beskryf deur die Suid-Afrikaanse Raad vir die Argitek-tuurprofessie (SACAP). ’n Vraelys is versprei na 581 boukundiges om vas te stel wat boukundiges in die bedryf se huidige kennis is rakende die verskillende ADR-metodes, die implementering daarvan en die bypassende gevolge.Die werklike probleem is dat boukundiges nie die ADR-metodes toepas nie, weens die gebrek aan kennis rakende die implementering en voordele van ADR. Die unieke bydrae van hierdie artikel lê in die feit dat alle boukundiges in Suid Afrika gevra is om aan die opname oor ADR, metodes, implementering en hul kennis daarvan deel te neem. Dit is die eerste bewys van baie anekdotiese stellings oor die gebrek aan implementering en kennis aangaande ADR-metodes in die boukunde van Suid Afrika. Die resultate bewys dat die meerderheid boukundiges nie vertroud is met die term ADR nie en dus ook nie ’n duidelike definisie daarvan kan gee nie; 69.4% van die boukundiges bespreek nie ADR-metodes met hul kliënte voordat hulle ’n ooreenkoms teken nie. Daar is ook bewys dat 58.4% van die boukundiges min of geen kennis van die metodes en hul bypassende voordele het nie.Hierdie waarnemings dui daarop dat ’n merkwaardige hoeveelheid boukun-diges huidiglik die SACAP-gedragskode oortree en dat hul professionele lisensies potensieel weggeneem kan word. Die resultate dui ook daarop dat daar ’n moontlike pad vorentoe is om ’n groter implementering van ADR in toekomstige bouprojekte te fasiliteer.Sleutelwoorde: Suid-Afrikaanse Raad vir die Argitektuurprofessie (SACAP), alternatiewe geskilbeslegtingsprosedures (ADR), litigasie.


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1. IntroductionChapter 2 of the Constitution of the Republic of South Africa makes provision for ADR in Section 34 (South Africa, 1996: 14). It grants parties the right to have a dispute resolved by means of a public hearing as an alternative to legal proceedings. Arbitration, mediation, negotiation and adjudication are some of the alternative methods that can be used when resolving disputes in South Africa. This is, therefore, also applicable to the construction industry. ADR has a variety of important attributes, including cost effectiveness, time saving, confidentiality, privacy, and the preservation of business relationships. If both parties agree to the method of procedure, the process of ADR could be entered into on a voluntary basis (WIPO, 2012: 5). The complex and specialised nature of construction projects contributes to disputes arising between two parties (Maritz, 2007: 78). According to Bvumbwe and Thwala (2011: 35) and Botha (2000: 1), the major causes of disputes are the following:

• Use of improperly or poorly drafted contracts;• Financial issues and claims;• Poor communication from client and contractor;• Poor management – time, funds and programme, and• Emotions – the ability to handle stress.

This article is limited to the built environment professional’s know-ledge regarding the implementation and benefits of ADR methods, with specific focus on architectural professionals in South Africa. The Architectural Profession Act 44 of 2000 states that all registered persons are obligated to comply with the SACAP Code of Conduct (Board Notice 154 of 2009). Among others, Board Notice 154 of 2009 states that all architectural professionals should enter into professional service agreements that provide for dispute resolution. It is cause for concern that architectural professionals in South Africa often overlook the necessity of professional service agreements that make provision for ADR, despite the obvious risks involved. As a result, these professionals have no recourse for dispute resolution, should it be required.

In 2008, the South African Institute of Architects (SAIA) published a professional service agreement titled the SAIA Client-Architect Agreement (SAIA CAA 2008). This agreement makes provision for mediation and arbitration when disagreements arise during the construction process. Despite its availability, a large number of architectural professionals still do not make use of the SAIA CAA 2008. In a study, Pelser (2013: 36) concluded that, for architectural projects,


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the SAIA CAA 2008 was only used in 3% of construction projects documentation in Pretoria over the past five years. One of the reasons for this is architectural professionals’ general lack of knowledge concerning the content of such agreements. It could be argued that a greater awareness of ADR by architectural professionals could benefit all those involved in the building process. However, the lack of understanding of professional service agreements has a direct impact on the implementation of ADR.

It is, therefore, important to investigate the use and knowledge of ADR in the built environment of South Africa by introducing the origin of ADR and how it relates to the built environment; collecting and analysing data from architectural professionals on the knowledge, implementations, consequences and benefits of ADR, in order to inform architectural professionals on appropriate ADR methods and the importance of the implementation thereof.

This article aims to determine

• the extent of the knowledge and implementation of architec-tural professionals on ADR;

• whether architectural professionals discuss ADR with their clients prior to the commencement of a project, and

• whether architectural professionals are informed about the different methods and benefits of ADR.

2. A brief history of ADRThis article presents a brief historical overview of the origins of ADR in the South African legal system as well as its relevance to the built environment. When faced with a dispute, built environment professionals could use this literature to obtain pertinent information on ADR methods, implementations as well as some advantages and disadvantages of the different methods. The literature review also serves as a background to the study.

2.1 Origin of ADR in the South African legal system

During the 1980s, ADR was used increasingly in the United States (US) in an attempt to resolve court backlogs. Subsequently, different forms of ADR evolved to suit specific needs in different sectors (Freyer, 1997: 108).

In South Africa, the existence of informal methods of ADR can be traced back to the 1960s, when traditional African communities had disputes over food, land and partners (Barrett & Barrett, 2004: 10).


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These disputes were often brought before a traditional leader during a meeting of the community. This method of resolving disputes is closely associated with adjudication, mediation or arbitration.

In South Africa, arbitration is governed by the Arbitration Act 42 of 1965. This Act provides citizens with legal rights to have disputes settled by arbitration tribunals (Ntuli, 2013: 9). This Act applies to domestic and international arbitration proceedings and is of the opinion that foreign arbitration awards are accepted in South Africa. When parties agree to submit any dispute arising out of the contract, the dispute has to be referred to one of the official arbitration bodies in South Africa. There are four main arbitration bodies in South Africa:

• The Arbitration Foundation of South Africa (AFSA);• The Association of Arbitrators (Southern Africa) (AOA);• The Commission for Conciliation, Mediation and Arbitration

(CCMA), and• Africa Alternative Dispute Resolution (AADR).

Disputes occur in all facets of life. ADR is rapidly expanding into all sectors of the economy, including divorce and child custody, educational settings, as well as commercial, engineering and construction disputes (Trollip, 1991: 8). As some of these disputes are complex in nature and involve large amounts of money, ADR can assist the parties involved to settle, or narrow down the issues of the dispute. It is also beneficial to parties with personal or business relationships that require confidentiality to favour early settlement in order to achieve a positive solution for all involved.

The use of ADR methods has established a mechanism to avoid formal court litigation and is expected to have a positive outcome. However, ADR holds many challenges and has not yet been fully embraced by professionals in the South African built environment (De Oliveira, 2012: 80). It is evident that awareness should be created to ensure that ADR becomes an essential part of the legal system of South Africa.

2.2 ADR in the South African built environment

The built environment of South Africa has developed the process of ADR over a period of three decades, when Quail (1978: 165) established the introduction of the mediation process in 1976. The built environment of South Africa is a large industry that brings together a variety of different professionals. Verster (2006: 13) states that the built environment should address the possible risk of disputes that have an impact on the time and cost of building projects. It


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has been reported that problems associated with payments have affected the supply chain of the construction industry and negatively impacted on the contracting environment (Maritz, 2007: 419).

In the construction industry, disputes are a common occurrence and affect the growth and performance of the built environment (Thumbiran, 2015: 1). Consequently, it is necessary to examine traditional means of resolving disputes, as litigation is too costly and time consuming. The development and implementation of ADR methods often increase with accelerated rates of construction, design and procurement documents (Finsen, 2005: 214-216). Finsen (2005: 216) and Verster (2006: 17) argue that ADR is an essential part of the management of construction projects and plays a fundamental role in the successful completion of these complex projects, although it is not implemented throughout the South African construction industry. There are many other sectors in the economy where ADR is expanding rapidly, providing parties with positive solutions to disputes. The ADR Network South Africa is one of many agencies providing assistance in ADR to both the public and the private sectors. However, there is a major need for appropriate resources and awareness regarding ADR methods specific to the built environment of South Africa.

As mentioned earlier, the construction industry is known for its complex nature; it must, therefore, provide an expert facilitator to settle disputes. According to Povey (2005: 2), the facilitator may be a currently practising professional with the necessary experience, or a retired professional in the industry. Facilitators must also be registered with the AOA. Verster (2006: 17) suggests that ADR methods should be applied more effectively, resulting in more time being allocated to productively manage the project. It is necessary to understand that new procedures, methods and increased awareness may be vital, in order to expand the fast-track nature of the construction industry.

3. ADR methods commonly used in the built environmentSince the late 1980s, standard forms of ADR have evolved, each with their own characteristics, as a result of a search for quicker and cheaper alternatives to litigation (Chong & Zin, 2012: 433). According to Motiwal (1998: 117), ADR techniques have been developed by leading universities and ADR centres in the US, Great Britain, Canada and Australia. In support of this statement, Paul Pretorius, advocate and editor of Dispute Resolution 1993, undertook two study tours in the US, in order to learn about alternate forms of dispute resolution.


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Pretorius (1990: 39) concluded that as far as the US is concerned, significant academic resources have been devoted to the scientific study of conflict and the development of appropriate institutions and practices to establish appropriate ADR methods. A study conducted by Van Zyl, Verster & Ramabodu (2010: 521) revealed that the following are the most preferred ADR methods in South Africa’s built environment: negotiation, conciliation, mediation, adjudication, and arbitration.

3.1 Negotiation

Negotiation is one of the most commonly used ADR methods for resolving disputes, because it is an informal method used as a preventative measure to avoid fully fledged disputes between parties (Chong & Zin, 2012: 430). Fisher, Ury & Patton (1991: 6) define nego-tiation as “a basic means of getting what you want from others”. It is a process whereby parties attempt to reach a settlement without involving an independent third party (Ramsden, 2009: 2). Negotiation is convenient, unstructured and often preserves working relationships.

The simplest way of settling disputes is by means of negotiation, because the parties themselves are in the best position to know the strengths and weaknesses of their own cases (Wang, 2000: 191). However, negotiation does not always guarantee success when attempting to settle disputes between parties. According to Pretorius (1993: 38), this may be caused by a general lack of knowledge, in conjunction with parties being too subjective and emotionally involved to make rational decisions.

PROPOSAL AND

PARTY A PARTY B

NEGOTIATION

COUNTER PROPOSAL

INTERACTION BETWEEN PARTIES

SUCCESSFUL/UNSUCCESSFUL

Figure 1: Summarised illustration of negotiationSource: Wilcocks, 2017: 22


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3.2 Mediation

Mediation is an ADR method in which a neutral third party, known as the mediator, seeks to resolve a dispute between the parties in conflict (Chong & Zin, 2012: 430). According to Goldberg, Sander and Rogers (1992: 103), “mediation is an assisted and facilitated negotiation carried out by a third party”. Mediation is also used in other sectors of the economy, namely commercial, civil, labour, family, interpersonal, community, complex public disputes, environmental cases and a wide range of other disputes (Brown & Marriot, 1993: 291).

Mediation proceedings may only take place if the parties are in agreement and willing to assist in reaching a settlement. If a settlement is reached, the mediator will provide the parties with a written agreement that will become legally binding once it has been signed by both parties (Ramsden, 2009: 3). Chong and Zin (2012: 40) and Bollen, Euwema & Muller (2010: 420) argue that the success of mediation depends on its fairness, simultaneously with the cooperation of both parties during the mediation.

MEDIATOR

NON BINDING OPINION

PARTY AINTERACTION BETWEEN PARTIES

MEDIATION

OWN SOLUTION

PARTY B

Figure 2: Summarised illustration of mediationSource: Wilcocks, 2017: 23

3.3 Conciliation

Conciliation is a process that is voluntarily entered into by the disputing parties; it involves an impartial third party. According to Stewart (2006: online), conciliation is “a settlement out of court, usually by the assistance of a neutral third party”. Loots (1991: 1012) further states that the method of conciliation is flexible and that the


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outcome of the dispute is entirely dependent on the willingness of the parties to participate.

Previous parties have stated that their favoured method of ADR is either mediation or conciliation. These methods are often more effective in resolving disputes than litigation, because the outcome of disputes is interest based for both parties rather than rights based and the process of conciliation is not as prolonged and costly as the process of litigation (Rao, 2009: 320).

CONCILIATOR

SETTLEMENT PROPOSAL

CONCILIATION

INTERACTION BETWEEN PARTIES PARTY BPARTY A

Figure 3: Summarised illustration of conciliationSource: Wilcocks, 2017: 25

3.4 Adjudication

The OED (2016: 16) states that the generally accepted definition of adjudication is as follows: “the process of making an official decision about who is right in a disagreement between two groups or organisations”. According to Hibberd and Newman (2001), the process of adjudication is an accelerated form of ADR whereby a neutral or independent third party makes a binding determination on the dispute, unless it is overturned by an arbitrator.

However, the parties involved may not proceed with arbitration or litigation until 28 days after the adjudicator has made the determination. This method of ADR is less disruptive, as parties can continue with construction work and meet their obligations. Adjudication is an ADR method that allows for business relationships to continue while parties resolve their disputes.


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CASE PRESENTATION

ADJUDICATOR

ADJUDICATION

R = REPRESENTATIVE

DETERMINATION

REFERRAL

ADJUDICATION NOTICE

RESPONSE

PARTY A PARTY BR R

CASE PRESENTATION

Figure 4: Summarised illustration of adjudicationSource: Wilcocks, 2017: 26

3.5 Arbitration

Arbitration is considered to be one of the most common methods of ADR in the built environment. Arbitration can be defined as “a judicial and more formal process where the disputing parties present their cases to an independent third party of their choice, known as the arbitrator” (Pretorius, 1993: 5). This method was known to the Romans, used by the Dutch and English during the period of colonial expansion, and extensively used in the construction industry (Finsen, 1999: 203-204).

The process of arbitration is similar to court procedure and may be associated with a formal trial. However, it can be more informal and relaxed, as it can be modified by the agreed parties. Brown and Marriot (1993: 288) state that arbitration is a suitable method of ADR, because a neutral third party with highly specialised knowledge on the subject matter makes a final and binding award, unlike other ADR methods.


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ARBITRATION

R = REPRESENTATIVE

ARBITRATORS(S)

FINAL DETERMINATION

PARTY A PARTY BR R

FINAL DETERMINATION

CASE PRESENTATION

Figure 5: Summarised illustration of arbitrationSource: Wilcocks, 2017: 27

4. MethodologyThe study used a quantitative research approach, as the research involves numbers and measurement (data to be collected), thus emphasising frequencies and statistics (expressed in numbers) (Struwig & Stead, 2001: 7; Leedy & Ormrod, 2005: 179). Because the study focuses on information-seeking behaviour around the current knowledge that architectural professionals have regarding ADR and the implementation thereof, as provided for by the SAIA CAA 2008, a survey research method would be the most appropriate for this study (Courtright 2007: 273).

Williams (2007: 67) classifies a survey as a standard quantitative research method. Survey research involves acquiring information about one or more groups of people, perhaps about their opinions, characteristics, attitudes, or previous experiences, by asking questions and tabulating the answers. The ultimate goal is to learn about a large population by surveying a sample thereof (Leedy & Ormrod, 2005: 183). Survey instruments can be broadly classified into two categories, namely questionnaires and interviews (Boubala, 2010: 55). This study used structured questionnaires to collect and analyse data obtained from architectural professionals in South Africa.


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4.1 Data collection

The questionnaire contained 17 main questions on whether architec-tural professionals implement ADR; discuss ADR prior to entering into an agreement with a client, and whether architectural professionals are informed about the different methods and benefits of ADR. The questionnaire was designed to test the individual experience and understanding of professionals on the current knowledge, implementations, consequences, and benefits of ADR.

The questionnaire was used as a survey method, with two data-collection options. These data-collection methods were chosen to collect data from as many participants as possible. An online questionnaire, created using SurveyMonkey, was emailed to the specific target audience. This is a popular web-based survey software that provides for data collection, data analysis and brand management (SurveyMonkey, 2016).

The second data-collection method is in hardcopy format. This was made available to professionals who found the online survey method impractical, or did not have access to the internet or email. The hardcopies were distributed where necessary, in order to reach as many participants as possible. The questionnaire addresses the relevant research questions developed by the researcher, in order to establish the current knowledge on ADR.

4.2 Design of the questionnaire

The questionnaire was aligned with the specific research subproblems:

• Subproblem 1 - Are architectural professionals familiar with, and well informed about ADR?

• Subproblem 2 - Do architectural professionals discuss ADR prior to entering into an agreement with a client or only once disputes arise?

• Subproblem 3 - Are architectural professionals informed about the different methods and benefits of ADR?

The questionnaire was also discussed with the study leader, an independent statistician and an architectural practice specialist to ensure that the information stated in the questionnaire is relevant and meaningful. The questionnaire is divided into three sections:

Section A: General Information

This section covers the participants’ personal information as well as background on their education and category of SACAP registration.


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Section B: Alternative Dispute Resolution (ADR)

This section addresses subproblems 1, 2 and 3, by establishing the knowledge and implementation of ADR by architectural professionals in South Africa. The researcher also aims to determine whether architectural professionals discuss ADR prior to entering into an agreement with a client and whether architectural professionals are informed about the different methods and benefits of ADR.

Section C: The South African Institute of Architects (SAIA)

This section focuses on the basic knowledge that architectural professionals have regarding the methods and benefits of ADR, as specifically provided for in the SAIA CAA 2008.

The initial questionnaire was pre-tested by circulating it to 10 registered members of SACAP who formed part of the target population. The objective of the pre-test was to determine any uncertainty and/or errors. It also served as a testing mechanism to ensure that all of the subproblems were addressed effectively. This exercise also confirmed the estimated time required to complete the questionnaire. These questionnaires do not form part of the data for the main study, because they were only used to develop the questionnaire.

4.3 Sampling size and its level of presentation of the population

The SAIA agreed to make their database of contacts available. However, this had an impact on the representativity of the data, as SAIA is mainly focused on architects, not on all architectural professionals. Therefore, the researcher approached SACAP to ensure that all architectural professionals in South Africa were reached (13 622 members).

The sampling size was determined by the number of registered SACAP members in South Africa. The 2014/2015 annual SACAP report listed 10 525 registered members in South Africa (SACAP, 2015: 27). However, SACAP agreed to assist the researcher with the study and later informed the researcher that the survey was sent out to 13 622 professionals (Van Stade & Chiunda, 2016). SACAP distributed the questionnaire with only the contact details of the researcher and the supervisor, without placing other limitations on its 13 622 members. The sampling size was ultimately defined by the reality that all the architectural professionals in South Africa were reached.

The survey response from the sampling size totalled 581 partici-pating architectural professionals. Krejcie and Morgan (1970: 608) recommend that, for general research activities in the


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construction-related professionals, a sample size of 370 is valid for a population of 15 000. This recommendation validates the sample size of 581 as efficient for a population of 13 622.

The 581 participants represent 4.27% of the registered architectural professionals. Of the 581 responding participants, 396 completed Section A and B of the questionnaire, consequently representing 2.91% of the architectural population in South Africa. In total, the questionnaire was fully completed by 346 out of 581 research participants who, at the time of undertaking the research, represent 2.55% of registered SACAP members in South Africa.


Biographical information on the research participants was required, in order to draw conclusions regarding the age and gender groups that might prefer to use ADR. This could also determine whether or not a participant’s level of education impacts on the use of ADR.

The questionnaire also made use of three different categories of a 5-point Likert scale, in order to obtain and analyse the respondents’ opinions. Likert-type or frequency scales use fixed choice response formats and are designed to measure attitudes or opinions (Bowling, 1997; Burns & Grove, 1997). For the purpose of analysis and interpretation, the relevant categories of Likert scales were measured by occurrences, agreement and content. Occurrences is measured where 1 is never (10% of times), 2 rarely (25% of times), 3 sometimes (50% of times), 4 mostly (75% of times), and 5 always (100% of times). Agreement is measured where 1 is strongly disagree, 2 disagree, 3 don’t know, 4 agree, and 5 strongly agree. Content is measured where 1 is never heard of it, 2 seen it before, 3 read it, 4 know most of it, and 5 know all of it. By using the various Likert scales, the researcher was able to analyse the respondents’ opinions, knowledge and implementation.

The researcher obtained the raw data from the online platform SurveyMonkey, upon which the findings were reviewed against the foregoing literature review. The data was then processed using the Microsoft Excel® (Microsoft Office® suite 2007) software program, in order to present statistical graphics.

4.5 Comparative data

The researcher grouped certain categories of possible answers together, using the online platform SurveyMonkey. This enabled the researcher to test the statistical significance from respondents,


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regarding age, gender, qualification and category of SACAP registration. These categories are presented in Table 1.

Table 1: Grouping of various categories

Age25-45 years

46+

GenderMale

Female

QualificationDegree (Bachelors or Masters)

No degree

SACAP category of registrationProfessional architect

Professional (Senior) architectural technologist

The comparative data will identify the relationship between various questions. The findings of the data might also identify possible gaps and problems within the profession. The comparative data will only make use of simplified answers by viewing strongly agree and agree as one, and strongly disagree and disagree as one.

Although it is not the main aim of this study, the link between gender and/or age (with a resultant level of experience) and the implementation of ADR are questioned. Should correlations become evident, shortcomings could possibly be addressed through mentorship programmes and Continuing Professional Development (CPD) programmes.

5. FindingsThe researcher analysed and interpreted the data gathered from the participants by graphically summarising each question individually, in order to formulate preliminary findings and subsequently proffer recommendations and conclusions.

In this article, the major findings are addressed according to the specific subproblems and summarised graphically to assist the reader on the significant parts of the study.

5.1 Results

The following bar graph indicates the summarised results of architectural professionals in South Africa on the implementation, knowledge, benefits and methods of ADR as well as whether ADR is discussed prior to entering into an agreement with a client. This


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will mainly represent the overall knowledge of, and implementation of ADR methods by architectural professionals in South Africa. This is followed by explaining each subproblem and its relevant results.each subproblem and its relevant results.

Not Familiar60.7%

No69.4%

Little/None58.4%

Familiar 39.3% Yes

30.6%

Moderate/Fair

41.6%

0%10%20%

30%40%50%60%70%80%90%

100%

Familiarity with ADR Discuss ADR beforeentering into an agreement

Knowledge on ADRmethods and benefits

Implementation and knowledge on ADR

Figure 6: Graphic summary of the implementation and knowledge regarding ADR in the SAIA CAA 2008

5.2 Subproblem 1

Are architectural professionals familiar with, and well informed about ADR?

Research revealed that the majority of the respondents are not familiar with the term ADR and could not provide a clear definition. Only 39.3% of the population is familiar with ADR. This suggests that if the majority of the target population does not have appropriate knowledge on this topic, they most definitely do not implement ADR.

The research indicates that the current lack of knowledge on ADR is cause for concern, considering that the built environment is often faced with disputes arising between the professional and the client. The researcher is able to conclude that, due to the lack of knowledge, a large number of professionals do not implement ADR, although it is provided for in various professional service agreements.

5.3 Subproblem 2

Do architectural professionals discuss ADR prior to entering into an agreement with a client, or only once disputes arise?


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The questionnaire revealed that 69.4% of architectural professionals do not discuss ADR methods with their client prior to entering into an agreement. Comparative data done by the researcher also revealed that age, qualification and category of SACAP registration may also impact on the participant’s response. Further cross-referenced data also revealed that participants who have been involved with disputes are more likely to discuss ADR with potential clients. Of all the required terms of confirming employment, the least likely item to be discussed is the provision for dispute resolution.

The researcher concludes that the majority of architectural professionals do not discuss ADR with their clients prior to entering into an agreement. This observation could indicate that a large number of professionals are in breach of the SACAP Code of Conduct, Rule 4.1. Not only will professionals be at risk of possible formal court proceedings, but they will also run the risk of losing their professional license.

5.4 Subproblem 3

Are architectural professionals informed about the different methods and benefits of ADR?

Data from the questionnaire indicates that 58.4% of the population have hardly any or no knowledge regarding the methods and benefits of ADR. This indicates that professionals have a misconception about the benefits and regulations of ADR. However, participants are able to identify some of the definitions of ADR provided.

The researcher concludes that the majority of architectural professionals are not informed about most of the matters, including the methods and benefits, of ADR, although it is provided for in many professional service agreements used in the built environment.

6. Discussion and conclusionThe study set out to establish the existing knowledge that architectural professionals in South Africa have about ADR methods. The SACAP Code of Conduct lists eight compulsory requirements in a written agreement. The article focused on the provision for dispute resolution and stated that ADR shall form part of a formal agreement between contracting parties.

The research has proved that architectural professionals have hardly any knowledge regarding ADR methods, specifically the implementation thereof. It is concerning that 69.4% of architectural


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professionals do not discuss ADR methods with their client prior to the commencement of a project. The significant impact of this article lies in the fact that this study reached out to all the possible architectural professionals in South Africa. Although previous studies have addressed the many causes and results of ADR, this study is unique, due to the fact that it specifically investigates the knowledge and implementation of ADR by architectural practitioners in South Africa. This is the first published evidence of many anecdotal statements on the lack of implementation and knowledge regarding ADR methods in the architectural profession of South Africa.

The following main issues were derived from the analytical survey:

• A large number of architectural professionals do not imple-ment ADR, and

• A large number of architectural professionals are not familiar with the term ADR and its associated benefits.

The evidence indicates that a significant portion of architectural professionals are currently in breach of the SACAP Code of Conduct and could potentially lose their professional license. As a result, it is recommended that architectural professionals familiarise themselves with the SACAP Code of Conduct and other enforceable laws.

The researcher recommends that regulatory bodies, voluntary associations (VAs) and tertiary institutions urgently address these matters. It is recommended that regular workshops be held on this topic and that VAs communicate the relevant documents to their members electronically. These actions could be in the form of regulated enforcement and, therefore, educating potential architectural professionals in practice-specific matters.

Professionals should also familiarise themselves with the relevant professional service agreements that are available and that provide ADR methods as a means for resolving disputes. The built environment of South Africa makes provision for ADR methods in a variety of contracts such as the JBCC PBA (2007: 30-31), CIDB (2009: 12), FIDIC (2010: 65-70), PROCSA (2015: 11), and the SAIA CAA (2008: 3). Further investigation is necessary to establish the current implementation of ADR in professional service agreements and how to address this matter.

By creating awareness on the lack of knowledge regarding ADR and the benefits thereof, professionals are bound to familiarise themselves with this topic and discuss ADR methods with clients at the onset of a building project. This will make architectural professionals


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better informed and equipped when disputes arise within the built environment.

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https://doi.org/10.1111/j.1571-9979.2010.00282.x

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https://doi.org/10.1108/09699981211237120

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1. Acta Structilia publiseer artikels in Afrikaans en Engels. Die verlangde lengte vir ‘n artikel is tussen 4 000 en 12 000 woorde.

2. Voornemende outeurs moet ’n elek-troniese kopie van ’n artikel in MS Word formaat (oorspronklike word deur die outeur bewaar) voorlê deur dit per e-pos aan te stuur (sien e-pos adres by punt 24.).

3. Enige toepaslike artikel, in ’n ver-staanbare skryfstyl, duidelik uit eengesit en reeds taalversorg, word verwelkom. Diagramme, sketse en foto’s (jpeg of tif-formaat teen 300dpi) mag ingesluit word.

4. Die redakteur behou hom die reg voor om veranderinge, indien nodig, wat betref styl en aanbieding aan te bring. Sou aansienlike wysigings deur referente voorgestel word, sal ’n artikel na die outeur terugverwys word.

5. Kopiereg word aan die outeur(s) by aan-vaarding van die artikel oorgedra.

6. Teksgedeeltes van artikels moet in Microsoft Word, Ariel skrif, font grootte 12, enkel spasiëring gedoen word.

7. Die titel van die artikel moet kort en bondig wees en in beide Afrikaans en Engels aangebied word.

8. ’n Kort opsomming in Afrikaans en Engels moet aan die begin van die artikel aangebring word.

9. Toepaslike sleutelwoorde in Afrikaans en Engels moet onder die opsomming aangebring word.

10. Opskrifte en onderskrifte word in Arabiese syfers genommer, geskei deur ’n punt en hoogstens tot drie vlakke, waarna ’n letter in hakies gebruik word, bv. 1. en 1.1 gevolg deur 1.1.1 en daarna a) ens.

11. Bronverwysings in die teks geskied volgens die Harvardstyl van verwysing: (Outeur, datum: bladsynommer[s]): bv. (Schleien, 2004: 20-23).

12. Net egte voet- en eindnote met die tersaaklike inligting moet gebruik word. Die Harvardstyl van verwysing is hier van toepassing.

13. Die verwysingslys volgens die Harvardstyl van verwysing moet volledig in alfa-betiese volgorde aangebied word. Bv. Sun, M. & Howard, R. 2004. Under-standing I.T. in construction. London: Spon Press.

14. ’n Kopie van alle internet dokumente wat in die teks verwys en gelys is in die bibliografie, moet die artikel vergesel.

15. Aanhalings word nie kursief gedruk nie, en word in dubbelaanhalingstekens aan -gedui. Invoegings binne aanhalings word in blokhakies aangedui. Aanhalings wat langer as drie reëls is, word ge ïndenteer en het nie aanhalings tekens nie.

16. Slegs standaard-afkortings word aan-beveel. Afkortings vir instellings kan gebruik word nadat dit vir die eerste keer volledig uitgeskryf is, met die afkorting daarna in hakies, vir terugverwysing.

17. Kursief moet nie oormatig gebruik word nie, indien wel, slegs vir konvensionele Latynse uitdrukkings bv. per se en vir woorde in ander tale.

18. Beklemtonings kry en kel aanhalings tekens.

19. Besonderhede van die oorsprong van ’n artikel moet aangedui word, soos in die geval van ’n kongresreferaat. Artikels word net vir keuring oorweeg indien vergesel van ’n verklaring dat dit nie elders vir publikasie voorgelê is, of reeds gepubliseer is nie.

20. Artikels word anoniem gerefereer. Die outeur(s) kan die name en adresse van tot drie vakkundiges (nie aan outeur[s] se eie instansie van werk verbonde nie) voorstel wat as referente sou kon optree.

21. Die outeur(s) van artikels wat geplaas word, sal elk twee komplimentêre kopieë van die betrokke uitgawe van Acta Structilia ontvang.

22. ’n Artikel moet vergesel word van die volledige titel, kwalifikasie en affiliasie, adres, telefoon- en faksimiliee-nommers en indien moontlik ’n e-posadres van die betrokke outeur(s).

23. Neem kennis dat ’n publikasieheffing van R50-00 per bladsy op die artikels wat gepubliseer word, betaalbaar is. ’n Fak-tuur sal aan die hoofouteur gestuur word.

24. Rig u bydrae(s) aan: Die Redakteur: Acta Structilia

Interne Posbus 47 Universiteit van die Vrystaat Posbus 339 Bloemfontein, 9300 Suid-Afrika

E-pos adres: [email protected]

ACTA STRUCTILIA

INLIGTING AAN OUTEURS


169

1. An article may be submitted in Afrikaans or English. The desired length for an article is between 4 000 en 12 000 words.

2. A copy of the typed article must be submitted (authors keep the original) in electronic format (MS Word) forwarded via email (see point 24.). The format must be kept as plain as possible for extracting and printing purposes.

3. An edited (proofread) article on any relevant topic, well presented and written in easy understandable style, will be considered for publishing. Diagrams, sketches and photos (jpeg or tif format at 300 dpi) may be included.

4. The editor reserves the right to alter the article(s) where necessary with regard to the style and presentation of the publication. If extensive alterations are advised by adjudicators the article(s) will be returned to the author.

5. Copyright is transferred to the author(s) when an article is accepted for publication.

6. Article content must be written in Microsoft Word, Ariel, font size 12, single spacing.

7. Titles must be short and concise, but informative. Supply suitable headings and sub-headings where necessary. The title must be in both Afrikaans and English.

8. A short summary, in both Afrikaans and English, must be provided at the beginning of the text.

9. Applicable keywords in Afrikaans and English must be given after the summary.

10. Use Arabic numbers with full stops in between for headings and subheadings, i.e. 1. followed by 1.1 and 1.1.1 up to a maximum of three levels. After that use a) etc.

11. Source references in the text must be in the Harvard style of referencing (Author, date: pages). i. e. (Schleien, 1996: 20-40)

12. Foot- and endnotes are likewise done in the Harvard style of referencing.

13. The references list (Harvard style of referencing) should contain all the relevant information, and be listed alphabetically according to the names of the authors. i. e. Sun, M. & Howard, R. 2004. Understanding I.T. in construction. London: Spon Press.

14. A copy of internet documents cited in the text and listed in the references must accompany the article.

15. Quotations are not in italics and must be written in double inverted commas. Inserts in quotations are placed in block brackets. Quotations longer than three lines are indented and are placed without quotations marks.

16. Avoid uncommon abbreviations and acronyms. Abbreviations should be limited to those in general use. Names of corporations, etc are at first written out in full with the abbreviation in brackets after which the abbreviated form may be used.

17. Italics are preferred for stereotyped Latin terms such as per se and for words in other languages.

18. Use single inverted commas to emphasise words or phrases.

19. Details concerning the origin of the article should be indicated, i.e if it was presented at a congress. An article will only be referred to the panel of referees if the author clearly states that it had not received prior publication and is not under consideration for publication elsewhere; also that the research has not been submitted for publication nor has it been published in whole or in part elsewhere.

20. Authors may submit the names and addresses of three scholars (experts) in his field (not members at own place of work) as possible adjudicators.

21. The author(s) will receive two complimentary copies of the relevant issue of Acta Structilia.

22. The article must contain the title, qualifications and affiliations of the author(s), the address, telephone and facsimile numbers and if possible, the email address.

23. Note that a publication fee of R50-00 per page is payable for every article published. An invoice will be sent to the main author.

24. Editorial address: The Editor: Acta Structilia Internal Post Box 47 University of the Free State PO Box 339 9300 Bloemfontein South Africa

E-mail address: [email protected]

ACTA STRUCTILIA

INFORMATION FOR AUTHORS

Referente en konsultante • Referees and consultants

Emeritus Prof. Basie Verster (Director: VersterBerryVerster QS, Bloemfontein, South Africa)

Prof. Fanie Buys (Department of Quantity Surveying, Nelson Mandela Metropolitan University, South Africa)

Prof. Chris Cloete (Department of Construction Economics, University of Pretoria, South Africa)

Prof. Fidelis Emuze (Department of Built Environment, Central University of Technology, Free State, South Africa)

Prof. Theo Haupt (Faculty of Engineering, Mangosuthu University of Technology, South Africa)

Prof. Tinus Maritz (UP) (Department of Construction Economics, University of Pretoria, South Africa)

Prof. Innocent Musonda (Department of the Quantity Surveying and Construction Management, University of Johannesburg, South Africa)

Prof. Eric Nealer (Department: Public Administration and Management, Unisa, South Africa)

Prof. Gerrit van der Waldt (Public Governance, North-West University, South Africa)

Prof. Roelf Van Niekerk (Industrial and Organisational Psychology, Nelson Mandela Metropolitan University, South Africa)

Dr Kofi Agyekum (Department of Building Technology, Kwame Nkrumah University of Science and Technology, Ghana)

Dr Justus Agumba (Department of Construction Management and Quantity Surveying, University of Johannesburg, South Africa)

Dr Giel Bekker (Graduate School of Technology Management, University of Pretoria, South Africa)

Dr Gerrit Crafford (Department of Construction Management, Nelson Mandela Metropolitan University, South Africa)

Dr Richard Jimoh (Department of Building, Federal University of Technology, Nigeria)

Dr Manya Mooya Department of Construction Economics & Management, University of Cape Town, South Africa)

Mr Daniel Huggett (Department of Quantity Surveying and Construction Management, University of the Free State, South Africa)

Mr Gaushal Meelun (CAMA/Valuation Analyst, City of Cape Town, South Africa)

Mr Pine Pienaar (Alpix Agri and Land Price Index Board member, South Africa)

Inhoud • Contents

Navorsingsartikels • Research articlesDeciphering priority areas for improving project Chipozya Kosta 1 risk management through critical analysis of Tembo-Silungwe pertinent risks in the Zambian construction Nthatisi Khatleli industry

An analysis of the use of mass appraisal Kobus van der Walt 44 methods for agricultural properties Douw Boshoff

A post-contract project analysis of material Ibrahim Saidu 77 waste and cost overrun on construction sites Winston Shakantu in Abuja, Nigeria

Stakeholders’ perception of critical success Bankole Awuzie 106 factors for sustainable facilities management Rasheed Isa practice in universities in sub-Saharan Africa

Oorsigsartikels • Review articlesBuilding an infrastructure project performance Davison Murwira 128 in the North-West Province Department of Michiel Bekker Public Works and Roads

Investigating alternative dispute-resolution Tariene Wilcocks 146 methods and the implementation thereof Jacques Laubscher by architectural professionals in South Africa

Inligting aan outeurs • Information for authors 168

Acta Structilia 2017:24 (2) ISSN 1023-0564 e-ISSN 2415-0487

Acta Structilia is endorsed by the South African Council for the Quantity Surveying Profession (SACQSP) for promoting research and Continuing Professional Development (CPD).