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RICS COBRA 201823 – 24 April 2018RICS HQ, London, UK

In association with

RICS COBRA 2018The Construction, Building and Real Estate Research Conference of the Royal Institution of Chartered Surveyors

Held in London, UK in association with University College London

23 – 24 April 2018RICS HQ, London, UK

© RICS, 2018ISBN: 978-1-78321-293-4ISSN: 2398-8614

Royal Institution of Chartered Surveyors Parliament SquareLondonSW1P 3ADUnited Kingdom


The papers in this proceeding are intended for knowledge sharing, stimulate debate, and research findings only. This publication does not necessarily represent the views of RICS or University College London.

163 A comparative analysis of factors influencing construction productivity in selected South African provinces


COBRA 2018 A COMPARATIVE ANALYSIS OF FACTORS INFLUENCING CONSTRUCTION PRODUCTIVITY IN SELECTED SOUTH AFRICAN PROVINCES Oluseyi Adebowale1 and John Smallwood2 1,2 Department of Construction Management, Nelson Mandela University, Port Elizabeth, South Africa ABSTRACT

The South African government has evolved diverse policies to enhance the competitiveness of South African construction organisations. Notwithstanding these policies, there are long lasting productivity challenges confronting the South African construction sector in which there is evidence to establish the existence of these challenges. To address this issue, it is essential to identify the underlying factors that contribute to poor productivity on South African construction projects. In response to this, a sample of 664 contractors that are registered with the CIDB (Grades 5-9) in four South African provinces was surveyed. The data obtained from respondents was analysed with Microsoft Excel while a measure of central tendency in the form of a mean score (MS) was used to rank factors. It was determined that inadequate workers’ skills, poor leadership, political activities resulting in industrial action, low education level of workers, and inadequate contractors’ experience in project planning are typical construction productivity influencing factors in the four provinces. Addressing these key areas would contribute to improving productivity in South African construction, while productivity improvement would further enhance the satisfaction of construction stakeholders, and ultimately contribute to South African economic development. Keywords: construction, economic development, productivity, South Africa, stakeholders’ satisfaction

INTRODUCTION South Africa is one of the countries in the world with growing infrastructure (Terblanche, 2009). The country consists of nine provinces, namely Eastern Cape, Free State, Gauteng, KwaZulu-Natal, Limpopo, Mpumalanga, Northern Cape, North West, and Western Cape. These provinces are all part of a federal parliamentary republic, under the central governance of a bicameral national legislature. The South Africa population is approximately 56 million (Statistics, 2016), while approximately 80% of the population is Black, 9% White, 9% Coloured (mixed race), and 2% Asian. The South African government has adopted diverse policies that focus on enhancing economic growth for an enabling environment in terms of competitiveness among the South African construction organisations (Cottle, 2014). The establishment of the Construction Industry Development Board (CIDB), the establishment of registered contractors, and creation of formidable programs that support the emerging black group form the key components of these formulated policies. Notwithstanding these policies, there are long lasting challenges confronting the South African construction sector in which there is evidence to establish the existence of


these challenges. A CIDB report states that 13% of the surveyed clients were unsatisfied with the performance of contractors, 24% of the surveyed contractors were unsatisfied with the performance of clients, and 9% of the surveyed projects had levels of defects which were regarded as inappropriate (CIDB, 2012). Furthermore, the South African Workforce Management Group Adcorp (WMGA) estimated that labour marginal productivity in the South African construction industry has been poor for the last four decades (Chingara and Moyo, 2014). This is because South African construction is confronted with unique performance challenges and deliberate efforts are being made on a broad front to address these challenges. Consequently, this study seeks to identify and compare the diverse construction productivity influencing factors across four provinces of South Africa with the highest construction capital outlay. Productivity influencing factors that are common in these provinces can be deemed essential to evolving productivity enhancement strategies. Table 1: Construction capital outlay in 2014 – CIDB (2015: 9)

Province Percentage per sector (%) Building Civil Total

Eastern Cape 16 19 17 Gauteng 43 15 31 Kwazulu Natal 10 29 18 Western Cape 12 9 10 Northern Cape 3 2 2 Free State 4 8 5 Mpumalanga 4 5 5 Limpopo 6 8 7 North West 3 5 4 Total 100 100 100

Productivity in context Productivity is a multi-dimensional concept that could be understood in different contexts depending on the objectives involved; the objective, in turn, defines the parameters involved in its assessment in relation to the benchmark used (Durdyev and Mbachu, 2011). Production methods are usually non-standardised as different organisations measure workers performance at different levels (CIDB, 2015). Many authors have provided succinct and elaborate definitions of productivity relative to their studies. Economists and accountants define productivity as the ratio between the total input of resources and total output of product (Enshassi et al. 2007). The American Association of Cost Engineers (AACE) defines productivity in construction as a relative measure of labour efficiency, either good or bad, when compared to an established base or norm. Nasirzadeh and Nojedehi (2013) define construction labour productivity as the ratio between completed work and expended work hours to execute the project. Single factor productivity Single factor productivity (SFP) considers only one organisation’s resource such as labour, plant, or capital as the input resource (Park, 2006). In economics, labour productivity represents one of the most widely used metrics. This is because there is a relationship between economic growth and labour productivity. The change in labour productivity multiplied by the rise (or decline) in total hours worked in an economy


equals the national output. Park (2006) posits that SFP is frequently adopted in the construction industry, where the actual work hours (input) is divided by the quantity installed (output). Multi-factor productivity Multi-factor productivity (MFP) is determined by dividing the index of actual output by the index of the combined units of labour input, capital services, and intermediate inputs. MFP is used for measuring the combined effects of technological change, efficiency improvements, reallocation of resources, and other factors relative to economic growth. Chau (2009) defines MFP as “the ratio of the total gross output of the construction industry to all the resources required to produce the output within the same time interval”. MFP is commonly expressed as the combined efficiency of labour inputs and capital inputs relative to the growth in GDP or value added. This concept involves multi-factors such as labour, equipment, materials, and capital as the inputs. This concept is commonly employed in economics studies (Park, 2006), and has been widely recognised as the only sustainable source of long-term economic growth. Table 2 presents a review of construction productivity related studies.


LITERATURE REVIEW Table 2: A review of construction productivity related studies (Authors’ construct)

Year Authors

Argument Country

Respondents Instrument

Major findings

1987 Olomolaiye, et al.

Unproductive time negatively influence productivity

Nigeria Craftsmen

Observation and questionnaire

Lack of material, lack of tools, repeat work, instruction delays, and inspection delays

1995

Lim and Alum Technology issue and inadequate skill workers negatively influence productivity

Singapore Contractors

Questionnaire survey

Difficulty in recruitment of supervisors, difficulty in recruitment of workers, high rate of labour turnover, absenteeism at the worksite, and communication problems with foreign workers

1997

Kaming, et al.

Construction productivity research is typically in its infancy with regards to developing countries

Indonesia Craftsmen

Observation and questionnaire

Lack of material, lack of tools, equipment breakdown, rework, changing of workers, interference, absenteeism, and supervision delay

2004 M

akulsawatudom, et al.

Productivity improvement will assist the industry and nations to make significant savings

Thailand Project managers

Structured questionnaire survey

Lack of material, incomplete drawings, incompetent supervisors, lack tools and equipment, and absenteeism

2007

Chan and Kaka

There is a need to consider the differences in the perspectives of W

hite-collar managers and Blue-collar workers

United Kingdom

Managers and

Employees Questionnaire and observation

Poor supervision, simplicity of building design, level of site experience, information flow, and communication with sub-contractors

2007 Dai, et al.

Craftworkers’ perceptions on productivity is rarely considered

United States of America

Journeymen, apprentices and foremen

Focus group and questionnaire

Lack of monetary bonuses for good performance, younger craft workers are not as motivated as the older ones, delays in work because of the absenteeism of other workers, errors on drawings, and lack of materials

2007 Enshassi, et al.

Positive and negative productivity-influencing factors should be identified and controlled

Palestine Contractors

Qualitative questionnaire

Material shortages, lack of labour experience, lack of labour

surveillance, miss-understanding between labour and superintendents, and drawings and specifications altered during execution


Table 2 continued

Year

Authors

Argum

ent C

ountry R

espondents Instrum

ent M

ajor findings

2009 D

ai, et al. Craftw

orkers’ perspective on productivity w

ill enhance their m

otivation

United

States of A

merica

Craft workers


Construction equipment, project m

anagement,

craftworkers’ qualifications, training, and forem

an com

petency

2010 D

ai and Goodrum

There is a need to examine different

languages of construction workers

United

States of A

merica

Craft workers


Errors on the drawings, late response to draw

ing related questions, project m

anagement pays

monetary bonuses for good perform

ance, inadequate inform

ation from supervisors, other than

toolbox meetings, and there is no health and safety

training on this project

2011 D

urdyev and M

bachu

Addressing on-site productivity

constraints will provide the m

uch-needed productivity im

provement

New

Zealand

Project managers,

contractors and sub-contractors

Interview and

questionnaire

Project managem

ent / project team characteristics,

project finance, workforce, project characteristics,

and unforeseen events

2011 Rivas, et al.

There is no enough research on factors influencing productivity in construction industry

Chile Craftsm

en Q

ualitative questionnaire

Material related problem

s, tools related, equipment

and truck related, rework, and interference am

ong crew

s

2013 Thom

as and Sudhakum

ar

Perception of both upper managem

ent and low

er managem

ent employees w

ill better contribute to productivity im

provement

India

Project manager,

site engineer, site supervisor and labour

Questionnaire

survey

Unavailability of m

aterial on time at the w

orkplace, delayed m

aterial delivery by the supplier, unavailability of draw

ings on time at the w

orksite, equipm

ent necessary to do the job not available on tim

e, and poor pay

2015

Jarkas, et al.

Schedule overruns and cost overruns are still predom

inant in the construction sector

Om

an Contractors

Structured questionnaire

Unrealistic design schedules im

posed on designers, construction m

ethods, unrealistic scheduling, low

design fees, and payment delays

2016 H

iyassat, et al.

Due to the cultural differences betw

een countries, the findings of research conducted in one country m

ay not be applicable to another

Jordan Surveying engineer and forem

en Q

uestionnaire survey

Planning, worker‒m

anagement relationships,

education and experience, climate, and technology

and equipment


RESEARCH METHODOLOGY Based on previous labour productivity related studies and the input of construction experts, thirty construction productivity influencing factors were identified. These factors constitute the questionnaire that was used to obtain the perceptions of study participants regarding construction productivity influencing factors. The questionnaire survey comprised an ordinal measurement scale ranking the extent at which the identified factors influence productivity on construction projects. The scale adopted was 1 (Minor) to 5 (Major), while respondents were also provided with an option of selecting ‘unsure’ should respondents be uncertain with respect to a factor. The data related to this enquiry was collected by a structured, closed-ended questionnaire survey. The study population included contractors that are registered with the CIDB within the categories of grade five to grade nine. The population of the contractors was obtained from the CIDB official page, while the sample represented in the study was randomly drawn. Data for the study was primarily collected through an internet survey, while a few questionnaires were administered on construction projects to supplement the low response rate achieved during the internet survey. The Eastern Cape (EC), Gauteng, Kwazulu-Natal (KZN), and Western Cape (WC) predominate in terms of construction capital outlay (Table 1). Hence, the survey was conducted among contractors in these provinces. The data collected was analysed with Microsoft excel while a measure of central tendency in the form of a MS was used to rank factors. 664 Contractors constituted the sample stratum, and 78 questionnaires were returned completed, and included in the analysis of the data. This represents a response rate of 11.7%. The average length of time participants’ organisations had been involved in construction is approximately 23 years, while the average length of time that the respondents have worked in construction is 18 years. Both represent a considerable length of time, which indicates that respondents are likely to understand the factors that influence construction productivity. DISCUSSION OF RESULTS Table 3 presents a comparative analysis of productivity in the four provinces considering labour, materials, and plant resources. The WC records the highest average productivity level of 54.8%, which is possibly attributable to inadequate workers’ skills not being among the highest ranked factors in this province (Table 4). KZN and Gauteng have average productivity levels of 52.7%, and 42.9% respectively. The EC, in which case inadequate workers’ skills is ranked first (Table 4) has the lowest average productivity level of 39.5% Table 3: Measuring productivity on South African construction projects Resource Productivity (%)

Eastern Cape Gauteng Kwazulu-Natal Western Cape Labour 76.0 72.7 74.3 73.3 Materials 72.9 81.7 89.0 88.3 Plant 71.3 72.3 79.7 84.7 Average 39.5 42.9 52.7 54.8 Based upon the MSs, Table 4 presents the ranking of thirty productivity influencing factors in four South African provinces with the highest construction capital outlay. It is essential to consider the highest ranked factors in each province, and subsequently determine which factors have the most influence in at least three of the four provinces.


Such factors can be deemed to be essential in addressing productivity in South African construction. Table 4 reveals that inadequate workers’ skills, the practice of awarding contracts to lowest bidders, poor supervision, poor leadership, and payment delays by contractor are factors that mostly influence productivity in the EC. Respondents indicated that inadequate workers’ skills, payment delays by contractor, corruption, non-involvement of contractors in the design phase, and political activities resulting in industrial action are factors influencing productivity in Gauteng. However, the factors that influence construction productivity in KZN are political activities resulting in industrial action, inadequate workers’ skills, inadequate clients’ briefs, workers, workers’ absenteeism, and poor leadership. Table 4 further reveals that corruption, the practice of awarding contracts to lowest bidders, material delivery problems, political activities resulting in industrial action, and inadequate contractors’ experience in project planning are the major factors influencing construction productivity in the WC. As indicated in the table, there are productivity influencing factors that are considered to have a high impact in at least three of the four provinces surveyed. Political activities resulting in industrial action has the highest mean MS of 4.13. This factor is rated among the most impacting factors in the four provinces. This suggests that there are political issues in South African construction. Poor leadership is also identified as a major productivity issue, which is identified in the four provinces. The factor is ranked third with a mean MS of 3.87. Inadequate workers’ skill is ranked second in terms of the mean MS, namely 4.06. The Table reveals that the factor is an issue in three provinces, namely the EC, GP, and KZN. This suggests that inadequate workers’ skills are a critical factor confronting South African contractors. Corruption with a mean MS of 3.86 is identified as a major issue in three provinces (Gauteng, KZN, and WC). In terms of the mean MS, it is ranked fourth. Corruption can also be related to economic and political issues, which is indicated as the most influencing factor when considering the four provinces. Low education of workers and inadequate contractors’ experience in project planning have mean MSs of 3.70 and 3.78 respectively. Low education of workers is a key factor in three provinces (ECP, KZNP, WCP), and inadequate contractors’ experience in project planning is also a key factor in three provinces (ECP, GP, WCP), while both are ranked tenth and eighth in terms of mean MSs.


Table 4: Construction productivity influencing factors across South African provinces

Productivity factor E

astern Cape

Gauteng

Kw

azulu-Natal

Western C

ape M

ean M

S R

ank M

S R

ank M

S R

ank M

S R

ank M

S R

ank Political activities resulting in industrial action

3.77 7

3.87 5

4.46 1

4.42 4

4.13 1

Inadequate workers’ skills

4.40 1

4.08 1

4.31 2

3.46 16

4.06 2

Poor leadership 3.93

4 3.55

10 3.85

5 4.13

7 3.87

3 C

orruption 3.71

11 3.93

3 3.27

9 4.54

1 3.86

4 W

orkers’ absenteeism

3.70 13

3.45 13

4.12 4

3.92 10

3.80 5

Inadequate clients’ briefs 3.53

19 3.64

8 4.17

3 3.82

13 3.79

6= Late delivery of m

aterials 3.60

17 3.42

15 3.62

6 4.50

3 3.79

6= Inadequate contractors’ experience in project planning

3.84 6

3.69 7

3.17 11

4.42 4

3.78 8

The practice of awarding contracts to low

est bidders 4.06

2 3.74

6 2.65

19 4.54

1 3.75

9 Low

education level of workers

3.77 7

3.41 16

3.58 7

4.05 9

3.70 10

Poor supervision 3.99

3 3.48

11 2.96

14 3.82

13 3.56

11 Paym

ent delays by contractor 3.93

4 3.95

2 2.27

27 3.92

10 3.52

12 N

on-involvement of contractors in the design phase

3.47 21

3.92 4

2.67 18

3.73 15

3.45 13

Faulty equipment

3.65 15

3.40 17

2.38 25

3.88 12

3.33 14

Poor wage policies

3.58 18

3.01 23

2.92 15

3.42 17

3.23 15

Inappropriate construction methods

3.76 9

3.37 18

3.33 8

2.40 27

3.22 16

Job dissatisfaction 3.76

9 3.02

22 2.81

16 3.09

21 3.17

17 N

on-conformance w

ith specifications 3.71

11 2.81

26 3.04

12 3.08

22 3.16

18 C

hange orders 3.36

25 3.59

9 2.42

24 3.14

19 3.13

19 Poor m

otivational system

3.48 20

3.15 20

1.46 30

4.36 6

3.11 20

Inadequate constructability reviews

3.24 28

3.36 19

2.71 17

3.10 20

3.10 21

Design com

plexity 3.40

23 3.43

14 3.02

13 2.50

26 3.09

22= Poor health and safety regulations

3.29 26

2.93 24

3.27 9

2.88 23

3.09 22=

Inclement w

eather conditions 3.42

22 3.47

12 2.46

23 2.71

24 3.02

24 C

onflict of interests among stakeholders

3.40 23

2.83 25

1.60 29

4.13 7

2.99 25

Cultural differences

3.29 26

2.45 29

2.65 19

3.41 18

2.95 26

Poor site conditions 3.69

14 3.15

20 2.33

26 2.25

28 2.86

27 A

ccidents on sites 3.64

16 2.63

28 2.48

22 2.58

25 2.83

28 Lack of w

orkers’ participation in decision making

3.23 29

2.67 27

1.88 28

1.54 30

2.33 29

Religious differences

2.46 30

1.93 30

2.52 21

1.86 29

2.19 30


CONCLUSIONS AND RECOMMENDATIONS The study concludes that the key issues confronting productivity in the EC are inadequate workers’ skills, and the practice of awarding contracts to lowest bidders. In Gauteng, they are workers’ skills, and payment delays by contractors. Political activities resulting in industrial action, and inadequate workers’ skills are the key issues hampering productivity in KZN, while the WC is mostly influenced by the practice of awarding contracts to lowest bidders, and corruption. However, in general it is concluded that South African construction productivity is influenced by political activities resulting in industrial action, inadequate workers’ skills, poor leadership, corruption, low education level of workers, and inadequate contractors’ experience in project planning. Based on this premise, it is recommended that political factors resulting in industrial action should be addressed by extensive engagement of construction stakeholders, especially labour unions. This study further advocates the reinstatement of the apprentice system, and adequate training of construction workers. The challenge relative to skill shortage and low education level of workers can be considerately mitigated with the reinstatement of the apprentice system and proper engagement of contractors with their employees. Furthermore, leadership and management oriented curricula in tertiary education institutions would assist construction managers to develop better leadership and management skills. Corruption relative to construction project delivery should be mitigated by invoking the relevant legislation. REFERENCES Chan, P.W. and Kaka, A., 2007. Productivity improvements: understand the workforce perceptions of productivity first. Personnel Review, 36(4), 564-584. Chau, K.W., 2009. Explaining Total Factor Productivity Trend in Building Construction: Empirical Evidence from Hong Kong. International Journal of Construction Management, 9(2), 45-54. Chingara, B. and Moyo, T. 2014. Factors affecting labour productivity on building projects in Zimbabwe. International Journal of Architecture, Engineering and Construction, 3(1), 57-65. Construction Industry Development Board (CIDB). 2012. The construction industry indicator summary results. Pretoria: CIDB, 1-10. Construction Industry Development Board (CIDB). 2015. Construction monitor and employment, Pretoria: CIDB, 1-33. Cottle, E. 2014. Bargaining indicators, twenty years – A labour perspectives twenty years of transformation of the construction sector in South Africa since the end of Apartheid. Labour Research Service, 139-150.


Dai, J. and Goodrum, P.M., 2010. Differences in perspectives regarding labour productivity between Spanish-and English-speaking craft workers. Journal of Construction Engineering and Management, 137(9), 689-697. Dai, J., Goodrum, P.M. and Maloney, W.F., 2007. Analysis of craft workers' and foremen's perceptions of the factors affecting construction labour productivity. Construction Management and Economics, 25(11), 1139-1152. Dai, J., Goodrum, P.M. and Maloney, W.F., 2009. Construction craft workers’ perceptions of the factors affecting their productivity. Journal of Construction Engineering and Management, 135(3), 217-226. Durdyev, S. and Mbachu, J., 2011. On-site labour productivity of New Zealand construction industry: Key constraints and improvement measures. Construction Economics and Building, 11(3),18-33. Enshassi, A., Mohamed, S., Mustafa, Z.A. and Mayer, P.E., 2007. Factors affecting labour productivity in building projects in the Gaza Strip. Journal of Civil Engineering and Management, 13(4), 245-254. Hiyassat, M.A., Hiyari, M.A. and Sweis, G.J., 2016. Factors affecting construction labour productivity: a case study of Jordan. International Journal of Construction Management, 16(2), 1-12. Jarkas, A.M., Al Balushi, R.A. and Raveendranath, P.K., 2015. Determinants of construction labour productivity in Oman. International Journal of Construction Management, 15(4), 32- 344. Kaming, P.F., Olomolaiye, P.O., Holt, G.D. and Harris, F.C., 1997. Factors influencing craftsmen's productivity in Indonesia. International Journal of Project Management, 15(1), 21-30. Lim, E.C. and Alum, J., 1995. Construction productivity: issues encountered by contractors in Singapore. International Journal of Project Management, 13(1), 51-58. Makulsawatudom, A., Emsley, M. and Sinthawanarong, K., 2004. Critical factors influencing construction productivity in Thailand. The Journal of KMITNB, 14(3),1-6. Naoum, S.G., 2016. Factors influencing labour productivity on construction sites: A state-of-the-art literature review and a survey. International Journal of Productivity and Performance Management, 65(3), 401-421.


Nasirzadeh, F. and Nojedehi, P., 2013. Dynamic modeling of labour productivity in construction projects. International Journal of Project Management, 31(6), 903- 911. Olomolaiye, P.O., Wahab, K.A. and Price, A.D., 1987. Problems influencing craftsmen's productivity in Nigeria. Building and Environment, 22(4), 317-323. Park, H.S., 2006. Conceptual framework of construction productivity estimation. KSCE Journal of Civil Engineering, 10(5), 311-317. Rivas, R.A., Borcherding, J.D., González, V. and Alarcón, L.F., 2011. Analysis of factors influencing productivity using craftsmen questionnaires: Case study in a Chilean construction company. Journal of Construction Engineering and Management, 137(4), 312- 320. Statistics, S.A., 2014. Statistical release P0302: mid-year population estimates, 1-18. Terblanche, L.S., 2009. Labour welfare in South Africa. Journal of Workplace Behavioral Health, 24(1-2), 205-220. Thomas, A.V. and Sudhakumar, J., 2013. Critical analysis of the key factors affecting construction labour productivity–An Indian Perspective. International Journal of Construction Management, 13(4), 103-125.

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