Journal of Construction

Volume 11 Issue 2

ISSN 1994 – 74022018

OF CONSTRUCTION OF

The Journal of Construction 2018, produced by Master Builders Print Studio. Contact : printstudio@masterbuilders.co.za

The Journal Of Construction (JOC) is the official journal of the Association Of Schools Of Construction Southern Africa (ASOCSA). ASOCSA has committed itself to foster excellence in construction communication, scholarship, research, education and practice and the JOC provides the medium to achieve this commitment. JOC is at this stage a bi-annual refereed journal serving all stakeholders and participants in the building construction and civil engineering sectors.

JOC publishes quality papers written in a conversational style aiming to advance knowledge of practice and science of construction while providing a forum for the interchange of information and ideas on current issues. JOC aims to promote the interface between academia and industry, current and topical construction industry research and practical application by disseminating relevant in-depth research papers, reviews of projects and case studies, information on current research projects, comments on previous contributions, research, innovation, technical and practice notes, and developments in construction education policies and strategies. Some issues might be themed by topic.

Topics in JOC include sustainable construction, education and professional development, service delivery /customer service, information and communication technology, legislation and regulatory framework, safety, health, environment and quality management, construction industry development, international construction, risk management, housing, construction-related design strategies; material, component and systems performance; process control; alternative and new technologies; organizational, management and resource issues; human factors; cost and life cycle issues; entrepreneurship; design, implementing, managing and practicing innovation; visualization, simulation, innovation, and strategies .

In order to maintain and ensure the highest quality in JOC, all papers undergo a rigorous system of blind peer review by acknowledged international experts.

AIMS AND SCOPE

The Journal of Construction 2017, produced by Master Builders Print Studio. Contact : printstudio@masterbuilders.co.za

//OPEN ACCESS

The Journal of Construction is committed to open access for academic work and is, therefore, an open access journal, which means that all articles are avail-able on the internet to all users immediately from the date of publication. This allows for the reproduction of articles, free of charge, for non-commercial use only and with the appropriate citation information. All authors publishing in the Journal of Construction ac-cept these as the terms of publication.

Copyright of the content of all articles and re -views remains with the designated author of the article or review. Copyright of the layout and design of Journal of Construction articles and reviews remains with the Journal of Construc -tion and cannot be used in other publications.

Benefits of open access for authors, include:

- Free access for all users worldwide- Authors retain copyright to their work- Increased visibility and readership- Rapid publication- No spatial constraints

ADVISORY BOARD

Nelson Mandela Metropolitan University South Africa

Dr. Albert ChanThe Hong Kong Polytechnic University China

Prof. Alan GriffithSheffield Hallam UniversityU.K.

Dr. Benedict IlozorEastern Michigan UniversityU.S.A.

Prof. David EdwardsBirmingham City UniversityU.K.

Dr. Dean KashiwagiArizona State UniversityU.S.A.

Prof. Charles EgbuGlasgow Caledonian UniversityU.K.

Prof. Ronie NavonNational Building Research Institute (NBRI) Israel

Prof. Christian KochTechnical University of DenmarkDenmark

Prof. Paulo Jorge da Silva BártolaPolytechnic Institute of LeiriaPortugal

Dr. Faizal Manzoor ArainNational University of SingaporeSingapore

Prof. Kerry LondonUniversity of NewcastleAustralia

Prof. Abdul Rashid bin Abdul AzizUniversiti Sains MalaysiaMalaysia

Dr John EbohonDe Montfort UniversityU.K.

Prof. Kerry LondonUniversity of NewcastleAustralia

Dr. Vicente A. GonzalezThe University of AucklandNew Zealand

Prof. Ahmad RamlyUniversity of MalayaMalaysia

Dr. Nina BakerUniversity of StrathclydeScotland

Prof. James SommervilleGlasgow Caledonian UniversitySotland

Dr. Vian AhmedUniversity of SalfordU.K

Prof. Nicola CostantinoPolytecnico di BariItaly

Prof. Stephen EmmittTechnical University of DenmarkDenmark

Prof. Derek Clements-CroomeUniversity of ReadingU.K

Prof. David BoydUniversity of Central EnglandU.K.

Dr. Peter LoveEdith Cowan UniversityAustralia

Dr. Ravi Srinath PereraUniversity of UlsterNorthern Ireland

Dr. Robert KongNanyang Technological UniversitySingapore

Prof. Stephen OgunlanaAsian Institute of TechnologyThailand

Dr. Wilco TijhuisUniversity of TwenteNetherlands

Dr. Gary SmithNorth Dakota State UniversityU.S.A.

Ms. Jane EnglishUniversity of Cape TownSouth Africa

Prof. Hojjat AdeliOhio State UniversityU.S.A.

Dr. Helen LingardRoyal Melbourne Institute of Technology, Australia

Prof. Low Sui PhengNational University of SingaporeSingapore

Prof. Marton MarosszekyUniversity of New South WalesAustralia

Dr Nicholas ChilesheUniversity of South AustraliaAustralia

Dr. Peter ErkelensEindhoven University of TechnologyNetherlands

Prof. Chris CloeteUniversity of PretoriaSouth Africa

Prof. Martin SextonUniversity of Salford U.K.

Prof. Russell KenleySwinburne University of Technology Australia

Prof. John Smallwood

Kgashane Stephen Nyakala1 & Thomas

Munyai 1, Andre Vermeulen2 & Jan -Harm, C Pretorius2

1Department of Operations Management, Tshwane University of

Technology, South Africa

2Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa

*Corresponding author: KgashaneNyakala

Email: nyakalaks@tut.ac.za

JOURNAL OF CONSTRUCTION

Volume 11Issue 2 ISSN 1994 - 7402

CONTENTS

1

13

25

38

47

EDITORIAL

The 2nd issue of Volume 11 of the Journal of Construction (JoC) comprises four papers which cover various topics in construction discussed below.

Firstly, Chetty and Haupt discuss the learning environment, self-efficacy and performance in mathematics: a Mangosuthu case study. Secondly, Awosina, Ruben Ndihokubwayo and Fapohunda discuss the mitigating factor on construction cost underestimation when implementing design requirements. Thirdly, Nyakala, Munyai, Vermeulen and Pretorius investigate the applications and assessment of a quality assurance process measurement model in construction projects. Finally, Mncwango and Allopi discuss the structural suitability of expanded polystyrene using a comparative analysis.

EDITOR: Dr Nishani Harinarain, University of KwaZulu-Natal, Durban South Africa.

* Ravi Chetty, ** Theodore Haupt* Faculty of Engineering, Department ofConstruction Management & Quantity Surveying, Mangosuthu University of

Technology, Umlazi, Durban 4026, South Africa** School of Engineering, Construction

Management , University of Kwazulu- Natal , Durban , South Africa

Corresponding Author: Ravi Chetty Email: ravi@mut.ac.za

Bonke Mncwango and Dhiren Allopi Department of Civil Engineering and

Surveying Durban University of Technology

P O Box 1334, Durban, 4000, South Africa

Corresponding Author: Bonke Mncwango, Email: bmncwango@gmail.com

2,Abiodun Awosina1, Ruben Ndihokubwayo and 3Julius Fapohunda

1,2,3 Department of Construction Management and Quantity Surveying, Faculty of Engineering, Cape Peninsula University of Technology, South

Africa

1 Corresponding Author: Abiodun Awosina Email: abiodunawosina@nispdsl.co.za

INSTRUCTIONS FOR AUTHORS

Pg. 1

* Ravi Chetty, ** Theodore Haupt* Faculty of Engineering, Department ofConstruction Management & QuantitySurveying, Mangosuthu University ofTechnology, Umlazi, Durban 4026, South Africa

** School of Engineering, Construction Management , University of Kwazulu- Natal , Durban , South Africa

Corresponding Author: Ravi Chetty Email: ravi@mut.ac.za

LEARNING ENVIRONMENT, SELF-EFFICACY AND PERFORMANCE IN MATHEMATICS: A MANGOSUTHU CASE STUDY

ABSTRACT

Purpose

The success of students may depend on factors such as student's motivation for learning and existing support systems. The study analyses the r e l a t i o n s h i p b e t w e e n t h e c l a s s r o o m environments, the student self-efficacy with regard to mathematics as a chosen subject to determine whether the perception of the students about their mathematics classroom climate is related to self-efficacy towards the subject and whether this relat ionship affected their achievement in mathematics.

Methodology

A sample of 89 students from the Department of Construction Management and Quantity S u r ve y i n g a t M a n g o su t h u U n i ve r s i t y o f Technology in South Africa who have completed their mathematics subject was used to undertake this study. The survey was focused on their views and experiences regarding their mathematics classes. The data was generated from a questionnaire survey made up with 16 statements and information about four constructs

Limitations

The study was undertaken on a cohort of MUT students only. It is likely that the findings might be different at other universities.

Findings

The findings reveal that there is a significant relationship between student mathematics self-efficacy and mastery goal structure, appropriate challenges and caring.

Also, a significant relationship was found between self-efficacy and student achievement with slight differences between male and female students.

Originality/value of paper

The findings of the paper provide insight into how s t u d e n t e ffi c a c y a n d a c h i e v e m e n t i n mathematics which is a critical module in the curriculum of construction and engineering programs and important computational competence in the world of work and professional practice.

KEYWORDS:

Academic performance; classroom climate; learning environment; mastery goal structure ; Mathematics; Self-efficacy

1. INTRODUCTION

A significant knowledge of mathematics is a key aspect for engineering students including construction management students due to the mathematical involvement of a number of subjects taught to them. Also, mathematics is needed for many career and job opportunities in

[1].engineering and more especially in construction Therefore, mathematics proficiency is critically important for students in construction studies because it is equipping them with tools that are

[2].needed for design purposes

Mathematics is needed in designing structures and erecting building. Mathematics allows building capacity for students to be to understand various engineering concepts and develop critical thinking to enable them to be creative and innovative. This will help them to enhance their problem-solving skills which is at the core of their preparation as future 21st century construction professionals. Historically, the racial divide between the various races is reflected in the number of students who are encouraged to take up mathematics and science subjects at high school. Rather they are diverted to other subjects which are 'simpler' and more suited to their future careers given the practice of job reservation of the previous political dispensation in South Africa.

In recent years universities have been encouraged to increase access for those less fortunate and historically disadvantaged to register for engineering and construction-related programs. While these programs require mathematics as a prerequisite, the level of mathematics needed was determined by the type of university students were registered at. Consequently, lower levels of mathematic performance at high school was acceptable at universities of technology of which Mangosuthu University of Technology (MUT) is an example. Therefore, it would be expected that institutions such as this would have in place c l a s s ro o m e nv i ro n m e n t s a n d su p p o r t i ve environments created by teaching staff to assist students taking mathematics to be successful. This study examines the impact of these scaffolding efforts in improving mathematics grades at MUT as a precursor to a broader study that will encompass all universities in South Africa. In particular, it examines the relationships between mathematics self-efficacy, instructor mastery goal structure, instructor challenging and instructor caring.

2. BACKGROUND

2.1 Self-efficacy Studies have revealed that the deficiency of self-efficacy related to mathematics is a significant contributor to student's lower performance in

[ 1 ] .mathematics Building a consistent and successful selfefficacy mathematics instruction will require a classroom climate or environment created by competent academics or instructors with the involvement of students in order to properly address the development of self-efficacy in many subjects in general and in mathematics particularly as it is the case for this study. The interventions of instructors must include mastery goal structures, appropriate challenges and caring. There is a very important concept that is very central to this study which is known as self-efficacy, this is in fact linked to the confidence of

[3] the student. Bandura (1977) defined it as the capacity for a student to organize and execute courses of action that is essential to generate specific accomplishments. It refers to beliefs related to anything students are able to accomplish, rather than the skills they believe to

[4]possess . Self efficacy is positively related to deep motivation, selection of career, choice of tasks, task values, and persistence. It also plays an important role in people's lives and this happens on daily basis. Numerous and diverse variables can influence individual's self-efficacy, particularly in

[5]mathematics . The attitudes of many students can be influenced by many different aspects

including the views and opinions of parents, attitudes and behaviour of peers, the type of

[6].school, lecturer, and the classroom climate Self-efficacy has a direct effect on persistence; this means the more persistent a student can be in a subject, the more he develops high level of confidence which is the true reflection of self-

[7].efficacy As one of the affective variables, it is used to clarify in details academic success and was found

[8];[9].to confidently affect academic achievement Therefore, a high self-efficacy in any subject in general and in mathematics particularly implies that the students have reached a certain level of confidence regarding their capability to be successful in the subject. Consequently, this motivates the students to face challenges with determination and boost their ambitions of being

[4].successful Self-efficacy can influence the choices made by people and assess the amount of effort put into the tasks, the relevant thought patterns, and

[ 5 ] [ 1 0 ] .the emotional reactions Self-efficacy in mathematics would determine the level of persistence for students when completing

[11].mathematics course work effectively There are four contributing factors to self-efficacy: firstly, the mastery experiences (performance outcomes) – experiences showing that students can complete a task successfully. Secondly, vicarious experiences based on the fact that by watching other students of similar skill completing a task successfully it makes them feel they can do the same. Thirdly, verbal persuasion based on the fact that when peers or mentors encourage students doing a task that they can do it, finally the physiological feedback (emotional arousal) – positive thinking increases self-efficacy while too much stress lessens self-efficacy. It has been reported that mastery goal structure; challenge and lecturer care significantly influenced mathematics self-

[12].efficacy Where these were present or evident students had higher levels of mathematics self-efficacy than when they were not. Individual efficacy was found to the strongest influence to

[13].student mathematics achievement Self-efficacy can be achieved within a conducive atmosphere or environment which at university is the classroom. It is therefore important to analyse this concept when it comes to the success of students in many su b j e c t s g e n e r a l l y b u t m o re s p e c i a l l y i n mathematics and mathematical subjects.

2.2 Classroom climate

Classroom climate has been defined as the classroom environment, the social climate, the emotional, and the physical aspect of the

[14].classroom It has been described as a learning [15].environment The relationship between the

Pg. 2

Pg. 3

student and instructor or lecturer is therefore very important. If there is effective communication between the instructor and the students the success of students in mathematics or any other subject can be achieved through a less painful process than is sometimes the case. The instructor has an obligation to create a favourable climate in the classroom through goal-setting strategies in order to stimulate the student success process. These strategies imply that the type of climate created in the classroom by the instructor through goal-setting, appropriate challenges and empathy for the students is likely to contribute positively to student achievement which should be one of the ultimate aims for the instructor. Student efficacy or confidence is perceived as the capability of the student for a specific task or subject such as mathematics. There is an opportunity for instructors to influence student self-efficacy through the created classroom climate. This achievement is possible because instructors are seen by students as the source of knowledge, example of achievement and inspiration and role models. Therefore, instructor interactions with students are vital to the perceptions of students. Perhaps, instructors need to be cautious to avoid making mathematics more difficult and stressful for students than it should be. It is suggested to boost their confidence and self-efficacy in mathematics than generating fear and phobia of mathematics.

2.3 Mastery goal structure

It has been reported that mastery goal orientation [15].is strongly related to competence development

Goal theory suggests that the motivation of students is influenced not only by their beliefs from their background and individual dispositions but also by the environment that they find themselves in. A mastery goal involves a perception that the real learning and understanding of students rather than memorisation are valued and that success is accompanied by effort and indicated by personal improvement or by achieving absolute standards. It is also obvious that students in the same class do not share the same perception regarding instructor practices. Instructor mastery goal structure involves the degree to which the instructor wants students to learn and understand the fact and concept for a lesson or to enjoy learning process. Few studies reported positive relations between mastery goal structure and self-

[16] [17][18] .efficacy and achievement Generally, mastery goal structure is linked with the beliefs and

[19].behaviours of students Therefore, in order to create positive learning environments the focus

[15]needs to be on creating a mastery goal structure .

Instructor support, respect and positive affect can be crucial factors in classrooms with high mastery goal structure. Instructors in these types of classrooms tended to encourage students to help each other and explain their reasoning.

2.4 Student and instructor relationship

The motivation of students to undertake schoolwork has been found to be related to the perception they have about their instructors who

[20].are viewed to be emotionally supportive A good student and instructor relationship nurtures development in confidence as well as self-

[1].efficacy

It is important that emotionally supportive attitudes or behaviour includes respect, care,

[ 1 5 ] .warmth, empathy and friendliness The relationship between students and their instructors reflects the potential of classroom interactions to boost their growth. Furthermore, positive relationship between students and instructor can positively be linked with student

[ 2 1 ] .motivation, engagement, and well-being Therefore, it is up to instructors to find the balance between positively challenging students and caring attitude. This can be achieved by believing in their students and assisting them in achieving

[1].their academic goals Students who perceive their instructors to be caring and supportive tend to be more motivated by exerting greater effort and persistence.

2.5 Mathematical anxiety versus achievement

Mathematical anxiety can destroy the self-confidence or self-efficacy and seriously affect the performance of students in the subject and others that require mathematics efficiency. It affects their belief system as well as their attitudes toward their ability to achieve success in mathematics. These attitudes may originate from various aspects in the environment of students as well as school and

[22].home experiences

Mathematical anxiety is defined as the restlessness of students during mathematical operations. It includes their fear of failing the exams and the resulting physical stress that leads to negative mathematical attitudes characterised

[23]. by the dislike of mathematics

Mathematical anxiety is projected negatively by self-efficacy and can be understood as a result of low self-efficacy, according to the social learning

[10][31].theory A student who feels anxious about mathematics classes can easily feel unable of

Pg. 4

doing mathematics. The higher the level of self-efficacy, the more energetic the individual becomes. Consequently, the individual will put more effort toward the assignment and the longer he will persevere to the point of loving and enjoying mathematics; this attitude will vanish the fear and the anxiety and bring more confidence in the individual. Therefore, mathematical anxiety can be a forecaster of self-efficacy by the fact that higher anxiety in mathematics strongly related to

[22].lower levels of self-efficacy This is confirmed by the fact that students presenting signs of mathematical anxiety have a tendency of poor attitudes about mathematics. They have also a tendency of avoiding mathematical courses,

[24].therefore, the result is lower achievement scores There are five areas that contribute to students' mathematical anxiety: teachers/ instructor (lecturer) attitude, curriculum, instructional s t r a t e g i e s , t h e c l a s s r o o m c u l t u r e , a n d

[25].assessment Teacher/ instructor (lecturer) attitude can greatly influence mathematical anxiety and it is the leading factor influencing student attitudes with regard to learning of

[26];[27].mathematics Therefore, a teacher/ instructor (lecturer) has the responsibility to help students remove the fear and phobia of mathematics in order to achieve expected results and produce critical thinkers and quality professionals.

2.6 Teacher/ instructor (lecturer) Attitude

Teaching must stimulate in student a culture of excellency not fear or anxiety, it involves major impacts to make significant changes within any

[28].society when it comes to an education system Even with a major reform for curriculum, lasting changes would not occur without sustained professional development designed to change

[29]teachers' beliefs and attitudes . The belief of teachers can be improved or modified by scrutinizing students' mathematical thinking,

[29].technology, curriculum, and gender Teachers have to play an important role in assuring that their methods of imparting mathematical knowledge are based on sound standards. It is very dangerous and could be very destructive for a student in a classroom facing a teacher with a negative attitude toward mathematics. The consequences are such that it can be transferred into the instruction and discussion made by the teacher. Students, especially girls, pick up on these clues inadvertently given by the teacher and take it on as their own. Parents can reinforce this attitude at home in discussion with the child, as well as

[30].priorities aligned with the family When attitudes are developed to negatively think about mathematics, achievement suffers but also the

confidence is seriously affected and the fear and the phobia of mathematics increases.

3. RESEARCH DESIGN AND APPROACH

This quantitative study sought to describe the connection between classroom environment, student performance and students' self-efficacy in mathematics as well as their impact on the student success. The study had mainly to determine if there is a relationship exists between students' mathematics self-efficacy and their perceived mathematics classroom environment. A sample of 89 students from the Department of Construction Management and Quantity S u r ve y i n g a t M a n g o su t h u U n i ve r s i t y o f Technology in South Africa who have completed their mathematics subject was used to undertake this study. The survey was focused on their views and experiences regarding their mathematics classes. The data was generated from a questionnaire survey made up with 16 statements and information about four constructs described as follows: mastery goal structure, instructor challenge, instructor care and self-efficacy based on Fast's 2010 study with each construct

[12][32].comprising of four statements Data collected from students included the items from both surveys and demographics . Each of the statements required a scaled response of agreement. SPSS v23 was used as statistical tool to analyse the results. The statistical analysis included measures of central tendency and dispersion. The internal validity of scaled responses was determined by the Cronbach's alpha coefficient for validity.

4 RESULTS AND DISCUSSIONS

4.1 Students profile

Most students (86.5%) were second year students and in all probability. their experience of the mathematics module would still be fresh in their minds. Nearly two-thirds of the student sample (64.8%) comprised of civil engineering students. In terms of gender distribution, male students (56.3%) outnumbered the female students. Also, students had either taken MATH1 (64.8%) or Applied Building Science (35.2%). Most male students (81.3%) were in their second year of study, were doing civil engineering studies (81.3%), and had either taken MATH1 (81.3%) or Applied Building Science (18.7%). Almost all female students (92.1%) were in their second year of study. Most were doing civil engineering (47.4%), and had either taken MATH1 (47.4%) or Applied Building Science (56.6%).

4.2 RELIABILITY

Table 1 presents the Cronbach's alpha coefficient for the scaled responses from each construct.

Note: Normal font = sample response, bold font = male student responses and italic font = female student responses.

4.2 Reliability

Construct Cronbach alpha Mathematics self-efficacy 0.854

0.858 0.856

Instructor mastery goal structure

0.758 0.748 0.788

Instructor challenge 0.867 0.821 0.908

Instructor care 0.771 0.721 0.834

4.2 Reliability

Construct Cronbach alpha Mathematics self-efficacy 0.854

0.858 0.856

Instructor mastery goal structure

0.758 0.748 0.788

Instructor challenge 0.867 0.821 0.908

Instructor care 0.771 0.721 0.834

Pg. 5

From Table 1, it is evident that the degree of internal consistency for each set of the scales used is acceptable with Cronbach Alpha coefficients greater than 0.700 which is the benchmark for acceptable internal scale consistency.

Therefore, the scales used can be considered as acceptable regarding the measure of the reliability of the constructs. Also, a strong internal consistency of the scaled responses for mathematics self efficacy across these three groups (entire sample, males and females) was recorded.

More specifically, it was recorded that the internal consistency of female responses was higher than male responses when compared with other constructs for each case.

Table 2a: Mathematics self-efficacy (n=89; n=49; n=38)

Statement Mean Standard deviation Rank Mathematics self -efficacy 5.12

5.40 5.24

1.29 1.31 0.98

3 2 3

I am sure I can learn everything taught in Mathematics

5.62 5.64 5.61

1.30 1.54 0.97

1 1 1

I am sure that I can do even the most difficult work in my Mathematics class

4.94 5.06 4.82

1.55 1.71 1.35

4 4 4

Even if a new topic in mathematics is difficult I am sure that I can learn it

5.28 5.40 5.16

1.48 1.65 1.26

3 3 3

I am sure that I can figure out the answers to problems that my instructor gives me in class

5.49 5.53 5.39

1.21 1.32 1.05

2 2 2

Table 2a: Mathematics self-efficacy (n=89; n=49; n=38)

Statement Mean Standard deviation Rank Mathematics self -efficacy 5.12

5.40 5.24

1.29 1.31 0.98

3 2 3

I am sure I can learn everything taught in Mathematics

5.62 5.64 5.61

1.30 1.54 0.97

1 1 1

I am sure that I can do even the most difficult work in my Mathematics class

4.94 5.06 4.82

1.55 1.71 1.35

4 4 4

Even if a new topic in mathematics is difficult I am sure that I can learn it

5.28 5.40 5.16

1.48 1.65 1.26

3 3 3

I am sure that I can figure out the answers to problems that my instructor gives me in class

5.49 5.53 5.39

1.21 1.32 1.05

2 2 2

Note: Normal font = sample response, bold font = male student responses and italic font = female student responses

Table 2c : Instructor challenge (n=89; n=49; n=38)

Statement Mean Standard deviation Rank Instructor challenge 5.21

5.23 5.98

1.38 1.39 1.40

2 3 2

When I have figured out how to do mathematics problems my instructor gives me more challenging work

5.41 5.04 5.82

1.84 1.94 1.66

2 3 4

My mathematics instructor does not let me get away with doing easy work

5.36 5.02 5.83

1.69 1.64 1.65

3 4 3

My mathematics instructor pushes me to take on challenging work

5.62 5.22 6.08

1.70 1.71 1.62

1 2 2

My mathematics instructor makes sure that the work I do really makes me think

5.12 5.66 6.18

1.53 1.62 1.41

4 1 1

Table 2c : Instructor challenge (n=89; n=49; n=38)

Statement Mean Standard deviation Rank Instructor challenge 5.21

5.23 5.98

1.38 1.39 1.40

2 3 2

When I have figured out how to do mathematics problems my instructor gives me more challenging work

5.41 5.04 5.82

1.84 1.94 1.66

2 3 4

My mathematics instructor does not let me get away with doing easy work

5.36 5.02 5.83

1.69 1.64 1.65

3 4 3

My mathematics instructor pushes me to take on challenging work

5.62 5.22 6.08

1.70 1.71 1.62

1 2 2

My mathematics instructor makes sure that the work I do really makes me think

5.12 5.66 6.18

1.53 1.62 1.41

4 1 1

Table 2b : Instructor mastery goal structure (n=89; n=49; n=38)

Statement Mean Standard deviation Rank Instructor mastery goal structure 5.67

5.81 6.13

1.24 1.23 0.97

1 1 1

My instructor thinks that really understanding the material is the main goal of the class

5.80 5.69 5.92

1.43 1.45 1.44

3 3 3

My instructor thinks it is important to understand the material and not to just memorize it

6.29 6.12 6.47

1.36 1.60 0.98

1 1 1

My instructor thinks how much you improve in Mathematics is really important

6.11 6.04 6.24

1.34 1.55 1.00

2 2 2

My mathematics instructor accepts nothing less than my full effort

5.60 5.37 5.87

1.71 1.88 1.47

4 4 4

Table 2b : Instructor mastery goal structure (n=89; n=49; n=38)

Statement Mean Standard deviation Rank Instructor mastery goal structure 5.67

5.81 6.13

1.24 1.23 0.97

1 1 1

My instructor thinks that really understanding the material is the main goal of the class

5.80 5.69 5.92

1.43 1.45 1.44

3 3 3

My instructor thinks it is important to understand the material and not to just memorize it

6.29 6.12 6.47

1.36 1.60 0.98

1 1 1

My instructor thinks how much you improve in Mathematics is really important

6.11 6.04 6.24

1.34 1.55 1.00

2 2 2

My mathematics instructor accepts nothing less than my full effort

5.60 5.37 5.87

1.71 1.88 1.47

4 4 4

Pg. 6

Note: Normal font = sample response, bold font = male student responses and italic font = female student responses

Note: Normal font = sample response, bold font = male student responses and italic font = female student responses

Table 2e : Grades (n=89; n=49; n=38)

Grade Mean Standard deviation Grade expected 80.60

79.20 78.70

1.08 1.28 1.09

Grade achieved 72.10 70.10 72.90

1.15 3.95 1.21

Table 2e : Grades (n=89; n=49; n=38)

Grade Mean Standard deviation Grade expected 80.60

79.20 78.70

1.08 1.28 1.09

Grade achieved 72.10 70.10 72.90

1.15 3.95 1.21

Pg. 7

4.36 4.74

2.07 1.97

3 3

My mathematics instructor listens to what I Say

5.58 5.51 5.71

1.55 1.50 1.66

2 2 2

I feel that my mathematics instructor will go above and beyond to help students

5.88 5.69 6.11

1.40 1.56 1.18

1 1 1

I feel

Table 2d: Instructor caring (n=89; n=49; n=38)

Statement Mean Standard deviation Rank Instructor caring 4.45

4.95 5.32

1.61 1.37 1.38

4 4 4

My mathematics instructor take a personal interest in students

4.43 4.21 4.73

2.03 2.14 1.88

4 4 4

My mathematics instructor cares about how 4.48 2.03 3 4.36 4.74

2.07 1.97

3 3

My mathematics instructor listens to what I Say

5.58 5.51 5.71

1.55 1.50 1.66

2 2 2

I feel that my mathematics instructor will go above and beyond to help students

5.88 5.69 6.11

1.40 1.56 1.18

1 1 1

I feel

Table 2d: Instructor caring (n=89; n=49; n=38)

Statement Mean Standard deviation Rank Instructor caring 4.45

4.95 5.32

1.61 1.37 1.38

4 4 4

My mathematics instructor take a personal interest in students

4.43 4.21 4.73

2.03 2.14 1.88

4 4 4

My mathematics instructor cares about how 4.48 2.03 3

Note: Normal font = sample response, bold font = male student responses and italic font = female student responses

Note: Normal font = sample response, bold font = male student responses and italic font = female student responses

4.3 OVERALL FINDINGS

A 7-point Likert scale was used where 1 = strongly disagree and 7 = strongly agree to determine to what extent they agreed with 16 statements on s t u d e n t m a t h e m a t i c s s e l f- e ffi c a c y, a n d perceptions of instructor mastery goal structure, instructor challenge and instructor caring and instructor role. The responses from the students are presented in Tables 2a, 2b, 2c, 2d, and 2e. Overall, it was found that student sample as a whole have a tendency to agree with the statements making up the constructs of instructor goal mastery structure (mean=5.67), instructor

challenge (mean=5.21), and mathematics self-efficacy (mean=5.12), this is observed in a general analysis of the Tables 2a, 2b, 2c, and 2d. In general, instructor caring was the construct they agreed least with when comparing all constructs. This finding suggests that this aspect is an area that instructors could pay more attention to. However, it is also likely that the large class sizes restrict the amount of individual attention that instructors could give to students. Some gender differences were observed in the levels of agreement with females were having higher levels of agreement with instructor goal mastery structure (Table 2b), instructor challenge

and instructor caring. On the other hand, male students had higher levels of agreement with mathematics self-efficacy (Table 2a). Regarding mathematics self-efficacy construct, the statement which which they agreed most was: I am sure I can learn everything taught in Mathematics and the statement they least agreed with was: I am sure that I can do even the most difficult work in my Mathematics class ( Table 2a). These findings suggest that students believe in their ability to learn what they are being taught in class. However, they were unsure about their capability when faced with more difficult examples which were more challenging and called for higher levels of thinking and application. It was noticed that male students had higher levels of agreement with all statements making up the mathematics self-efficacy construct

For the instructor goal mastery structure construct (Table 2b), the statement they agreed most with was: My instructor thinks it is important to understand the material and not to just memorize it while the statement they agreed least with was: My mathematics instructor accepts nothing less than my full effort. These findings hint that instructors emphasize the need for students to strive for understanding rather than rote memorization of mathematical concepts. Further, it seems that students were not convinced that their instructors had high levels of mastery expectations from them.

It is possible that this finding correlates with the low levels of instructor caring demonstrated by instructors in class. Female students had higher levels of agreement with all statements in this construct. Regarding the instructor challenge construct (Table 2c), there were mixed levels of agreement with all statements. There was most agreement with My mathematics instructor pushes me to take on challenging work.

This findings suggests that instructors do encourage students to do more difficult mathematical tasks. Female students had higher levels of agreement with all statements in this construct. Regarding the instructor caring construct (Table 2d), there was most agreement with I feel that my mathematics instructor will go above and beyond to help students and least agreement with My mathematics instructor takes a personal interest in students. These findings suggest that while it is accepted that instructors will do more for students without necessarily taking a personal interest in them and their individual needs and performance. Regarding grading (Table 2e), differences between the grade expected and the grades achieved were detected. Female students had the smaller difference (5.8%) than the difference for male students (9.1%). It is evident from these findings that male students also had the higher grade expectancy but the lowest grade achievement.

Pg. 8

4.4 Correlation between the constructs

Construct Mathematicsefficacy

Instructor Goal mastery structure

Instructorchallenge

Instructorcaring

0.287** 0.006

0.305** 0.004

0.306** 0.004

Mathematics efficacy

0.347* 0.015

0.377** 0.007

0.342* 0.016

0.634** 0.000

0.544* 0.000

Instructor goal mastery structure

0.616** 0.000

0.471* 0.001

0.648** 0.000

0.652** 0.000 0.679** 0.000

Instructor challenge

0.665** 0.000 0.696** 0.000

0.236* 0.028

Grade expected

0.324* 0.025

0.384* 0.017 0.214* 0.046

0.319** 0.002

0.314** 0.003

Grade achieved

0.311* 0.031

Table 3. Correlations (n=89; n=49; n=38)

0.356* 0.028

Note: Normal font = sample response, bold font = male student responses and italic font = female student responses

* Correlation is significant at the 0.05 level(2-tailed)** Correlation is significant at the 0.01 level (2-tailed)

Relationships between the various constructs including gender, grade expected and grade achieved were examined. The results using Pearson's correlations are presented in Table 3. It was found that for the entire sample, instructor goal mastery structure (r=0.287, p<0.006), instructor challenge (r=0.305, p<0.004) and instructor caring (r=0.306, p<0.004) were strongly

correlated with mathematics efficacy confirming the findings of previous studies reported in the literature. This finding suggests that if instructors were more involved in teaching mathematics than at present providing challenges and scaffolding support mathematics efficacy would improve. Instructor challenge (r=0.634, p<0.000) and instructor caring (r=0.544, p<0.000) were strongly correlated with instructor goal mastery structure; and instructor caring (r=0.679, p<0.000) was strongly correlated with instructor challenge. Mathematics efficacy (r=0.236, p<0.028) and grade achieved (r=0.286, p<0.007) were strongly

Pg. 9

correlated with grade expected. This finding suggests that student expected achievement in mathematics is influenced by student attitudes toward the subject and actual achievement. It is likely that if regular assessments are done the actual grade expected could be achieved. Mathematics efficacy (r=0.214, 046), instructor goal mastery structure (r=0.319, p<0.002) and instructor challenge (r=0.314, p<0.003) were strongly correlated with grade achieved suggesting that if these are present in the mathematics classroom the grade achieved would improve.

For males, instructor challenge (r=0.347, p<0.015), instructor goal mastery structure (r=0.377, p<0.007) and instructor caring (r=0.342, p < 0 . 0 1 6 ) w e re s t ro n g l y co r re l a te d w i t h mathematics efficacy; instructor challenge (r=0.616, p<0.000) and instructor care (r=0.471, p<0.001) were strongly correlated with instructor goal mastery structure; and instructor care (r=0.665, p<0.000) is strongly correlating with instructor challenge. Grade achieved (r=0.534, p<0.000) is strongly correlating with grade expected suggesting that the higher the goals set for themselves by students supported by self efficacy in mathematics the more likely that their performance in the subject wil l improve. For females, there were no strong correlations with mathematics efficacy. However, instructor challenge (r=0.648, p<0.000) and instructor care (r=0.652, p<0.000) were strongly correlated with instructor goal mastery structure; instructor care (r=0.696, p<0.000) was strongly correlated with instructor challenge suggesting that the more attention instructors gave them the more likely they would respond effectively to the challenges set in the classroom. Mathematics self-efficacy (r=0.384, p<0.017) was strongly correlating with grade expected indicative of the well-established relationship between achievement and efficacy. Instructor challenge (r=0.356, p<0.028) was strongly correlated with grade achieved. It is likely that the more students are challenged to achieve the greater the chances that they will perform better.

These correlations confirm what should be happening on the practical level for student self-efficacy, when the instructor is positively challenging students and caring for them to grasp the concepts, understand the principles and facts. Generally, the confidence of students will be enhanced because they have a feeling of being well coached. Therefore it can be argued that the

classroom climate created by mathematics instructors will help students achieve the expected results. Consequently, the expected results will be the true reflection of how both the instructor and student are working toward a success. These correlations imply that success or high level of student self-efficacy can be achieved via a symbiosis of efforts between the instructor and the students.

CONCLUSIONS 4.5

This study confirmed the positive correlation between self-efficacy and academic achievement as reported in previous and similar studies undertaken[8][9]. In the same way, it was found that there was a positive correlation between mathematics self-efficacy and mastery goal structure, instructor challenge and instructor care. This same outcome was also reported in a similar study completed in which the same constructs were used with a different sample, environment and approach. [12]. Furthermore, this study suggests that student and instructor relationships in the form of these constructs have a positive influence regarding their mathematics efficacy. This relationship was confirmed by previous findings[16][17][18]. Overall, most of the outcomes reported in this study are similar to some studies dealing with student or mathematics self-efficacy. In terms of correlations between the constructs this study concluded the following: mathematics self-efficacy was positively correlated with grade expected and grade achieved despite the grades expected being somewhat higher than the actual grades achieved. Regarding female students, no correlation between mathematics self-efficacy and grade achieved was found. This could be due to the fact that majority of female students are generally not interested in mathematic subjects. However, there was a correlation between mathematics self-efficacy and grade expected. On the other hand, for male students there was a correlation between mathematics self-efficacy and grade achieved. The outcomes of this study indicate that instructors should focus on creating learning classroom environments for mathematics through goal setting, appropriate challenges and empathy. Arguably, student achievement in mathematics will improve. However, this outcome is possible with strong commitment and high motivation for learning from the students inspired by their instructors. The fear and phobia of

Pg.10

mathematics from students needs to be overcome by students with the help of the instructors. It was also found that instructor care was the construct that students least agreed with which is indicative of the opportunity for instructors to improve their relationships with their students characterized by warmth, friendliness, respect, empathy and care. In so doing, it is likely that the student mathematics self-efficacy will improvecommensurately with improved achievement being the outcome. This outcome is possible because the climate and environment in which learning is taking place will be conducive, exciting and will motivate students to learn mathematics, therefore repelling phobia and fear of the mathematics. For future work it is suggested to expand the study to other universities in the country in order to get more understanding on the concept of self-efficacy and mathematic performance.

REFERENCES 4.6

1. Peters, M., (2013). Examining the relationshipsamong classroom climate, self-efficacy, and achievement in undergraduate mathematics: a multi-level analysis, International Journal of Science and Mathematics Education, 11,459-480

2. Augustine, N., (2007). Rising above thegathering storm: emerging and employing America for a brighter economic future. Washington, D.C.: National Academy of Sciences

3. Bandura, A., (1977). Self-efficacy – Toward au n i f y i n g t h e o r y o f b e h av i o u r a l c h a n g e , Psychological Review, 84 (2), 191-215

4. Bandura, A., (1986). Social foundations ofthought and action: A social cognitive theory, Englewood Cliffs, New Jersey: Prentice-Hall

5. Hackett, G., Betz, N. E., (1989). An exploration ofthe mathematics self-efficacy/mathematics performance correspondence. Journal for Research in Mathematics Education, 20, 261-273

6. Klassen, R. M., Usher, E. L., (2010). Self-efficacyin educational settings: Recent research and emerging directions. In T. C. Urdan and S. A. Karabenick (Eds.), Advances in motivation and achievement: Vol. 16A. The decade ahead: Theoretical perspectives on motivation and achievement (pp. 1-33). Bingley, UK: Emerald. doi:10.1108/S0749-423(2010)000016A004

7. Robbins, S., Allen, J., Casillas, A., Petersen, C.and Le, H.,(2006). Unravelling the differential effects of motivational and skills, social and self-m a n a g e m e n t m e a su re s f ro m t r a d i t i o n a l predictors of college outcomes. Journal of Educational Psychology, 98, 598-616

8. Fettahloglu, P., Güven, E., Înce, A., Sert, Ç., andAydogdu, M., (2011). The effect of Science teacher candidates' self-efficacy towards Science education on academic achievement, Ahe Evran University Journal of Kirsehir Education Faculty, 12 (3), 159-175

9 Komarraju, M. and Nadler, D., (2013). Self-efficacy and academic achievement: why do implicit beliefs, goals, and effort regulation matter? Learning and Individual Differences, 25, 67-89

10 Pajares, F., Graham, L.,(1999). Self-efficacy, motivation, constructs, and mathematics performance of entering middle school students. Contemporary Educational Psychology, 24(2), 124-139.

11 Larson, L., Pesch, K., Surapaneni, S., Bonitz, V., Wu, T., and Werbel, J., (2015). Predicting graduation: The role of mathematics/science self-efficacy. Journal of Career Assessment, 23 (3), 399-409

12 Fast, L., Lewis, J., Bryant, M., Bocian, K., Cardullo, R., Rettig, M and Hammond, K. , (2010). Does math self-efficacy mediate the effect of the p e r c e i v e d c l a s s r o o m e n v i r o n m e n t o n standardized math test performance? Journal of Educational Psychology, 102 (3), 729-740

13. Pina-Neves, S., Faria, L., and Raty, H., (2013).Students' individual and collective efficacy: joining together two sets of beliefs for understanding academic achievement. European Journal of Psychology of Education, 28 (2), 453-474

14. Bierman, K., (2011). The promise andpotential of studying the “invisible hand” of teacher influence on peer relations and student outcomes: A commentary. Journal of Applied Developmental Psychology, Special Issue Teachers and Classroom Social Dynamics. 32 (5), 297

15. Patrick, H., Kaplan, A., Ryan, A., (2011).Positive classroom motivational environments: Convergence between mastery goal structure and classroom social climate, Journal of Educational Psychology, 103 (2), 367-382

Pg.11

16. Murdock, T., Hale, A., Weber, M., (2001).Predictors of cheating among early adolescents: Academic and social motivations, Contemporary Educational Psychology, 26, 96-115

17. Nolen, S., (2003). Learning environment,motivation and achievement in high school science. Journal of Research in Science Teaching, 40, 347-368

18. Wolters, C., (2004). Advancing achievementgoal theory: Using goal structures and goal orientations to predict students' motivation, co g n i t i o n , a n d a c h i e ve m e n t . J o u r n a l o f Educational Psychology, 96, 236-250

19. Urdan, T., Midgley, C.,(2003). Changes in theperceived classroom goal structure and pattern of adaptive learning during early adolescence. Contemporary Educational Psychology, 28, 524-551

20. Skaalvik, EM, Federici, RA., Klassen, RM.,(2015), 'Mathematics achievement and self-e ffi c a c y : Re l a t i o n s w i t h m o t i v a t i o n f o r m a t h e m a t i c s ' v o l 7 2 , p p . 1 2 9 - 1 3 6 . , 10.1016/j.ijer.2015.06.008

21. Sakiz, G., Pape, S., Hoy, A.,(2012). Doesperceived teacher affective support matter for m i d d le s c h o o l st u d e n t s i n m a t h e m a t i c s classrooms? Journal of School Psychology, 50 (2), 235-255

22. Akin, A., Kurbanoglu, I. N., (2011). Therelationship between math anxiety, math attitudes, and self-efficacy: a structural equation model. Studia Psychologica, 53(3), 263–274.23Smith, S., (1997). Early childhood mathematics. Boston, MA: Allyn & Bacon.

24. Beilock, S. L., Gunderson, E. A., Ramirez, G.,Levine, S. C., (2010). Female teachers' math anxiety affects girls' math achievement. Proceedings of the National Academy of Sciences of the United States of America, 107, 1860-1863.

25. Shields, S. A., (2005). The politics of emotionin everyday life: "Appropriate" emotion and claims on identity. Review of General Psychology, 9(1), 3-15.

26. Harper, N. W., Daane, C. J.,(1998). Causes andreduction of math anxiety in preservice elementary teachers. Action in Teacher Education, 19, 29-38.

27. Ruffell, M., Mason, J., Allen, B.,(1998).Studying attitude to mathematics, Educational Studies in Mathematics, 35(1), 1-18.

28. Stevenson, H. W., Stigler, J. W., (1992). Thelearning gap. New York, NY: Summit Books.

29. Philipp, R. A., (2007). Mathematics teachers'beliefs and affect. In F. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 257-315). Reston, VA: National Council of Teachers of Mathematics.

30. Ambrose, R., (2004). Initiating change inprospective elementary school teachers' orientations to mathematics teaching building on bel iefs . Journal of Mathematics Teacher Education, 7(2), 91-119.

31. Hackett, G.,(1985). The role of mathematicsself-efficacy in the choice of math-related majors of college women and men: A path analysis. Journal of Counseling Psychology, 32, 47-56.

32. Skaalvik, E., Skaalvik, S.,(2013). School goalstructure: Associations with students' perceptions of their teachers as emotionally supportive, self-concept, intrinsic motivation, effort, and help-seeking behaviour. International Journal of Educational Research, 61, 5-14

Pg.12

Pg.13

2,Abiodun Awosina1, Ruben Ndihokubwayo and 3Julius Fapohunda

1,2,3 Department of Construction Management and Quantity Surveying, Faculty of Engineering, Cape Peninsula University of Technology, South Africa

1 Corresponding Author: Abiodun Awosina Email: abiodunawosina@nispdsl.co.za

IMPLEMENTING DESIGN REQUIREMENTS: THE MITIGATING FACTOR ON CONSTRUCTION COST UNDERESTIMATION

ABSTRACT

Purpose

The construction project cost estimate has been viewed as a mere budgetary allocation, in which a c c u r a c y r e m a i n s u n c e r t a i n . C o s t underestimation have negative contribution to construction projects and have not been adequately explored with the objective of providing mitigating mechanisms. Hence, the aim of this paper is to examine the implementation of design requirements as a mitigating factors of cost underestimation.

Methodology

The literature relating to cost estimation was reviewed, and the empirical data is obtained through a structured questionnaire. Convenient purposive sampling of respondents was used to select contractors and quantity surveyors who were involved in construction activities within and around Cape Town. The quantitative data gathered were analysed using central tendency descriptive statistics.

Limitations

In view of the confidentially nature of some project costs in the private construction sector from competitors, the research has limitations to gather statistics on the actual extent of projects that are underestimated or overestimated for comparative studies.

Findings

The study draw attention of construction project stakeholders to the factors influencing cost e s t i m a t i o n a n d t h e e ff e c t s o f c o s t underestimation in order to proffer mitigating mechanism. The findings based on empirical data revealed that implementing design requirements is the most significant mitigating mechanism of cost underestimation on construction projects.

Also, a significant relationship was found between self-efficacy and student achievement with slight differences between male and female students.

Value of the paper

The paper will contribute to the body of knowledge in the construction industry, by drawing attention to cost underestimation and its mitigating mechanisms.

Keywords:

Accuracy, construction project, cost estimate, project stakeholders, underestimation.

1. INTRODUCTION

Construction project cost estimate has been viewed as a mere budgetary allocation that its accuracy remains uncertain, and this often causes gap between the initial project cost and completion cost. Unfortunately, little attention has been given to the shortcomings that emanates from cost underestimation, including factors contributing to underestimation, and the mitigating mechanisms. In making emphasis on the importance of cost estimate reveals that “without an accurate cost estimate, nothing short of an act of God can be done to prevent a loss, regardless of management competence, financial

1strength of the contractor, or know how” .

The studies on transport infrastructure projects across twenty countries around five different continents reveal that there has been precisely no

Pg.14

improvement in accuracy of cost estimates over seventy years, thus cost underestimation still

2persists in the construction industry .

3According to Project Management Institute , there is an emphasis on the process of monitoring the performance of cost to detect any variance, ensuring adequate record of cost due to changes, preventing inappropriate changes from being recorded, informing stakeholder of changes and managing such changes. Inconsistencies are

4identified in the estimating techniques of estimators, as well as a lack of clear definition of purpose, scope of works and contents during the process of cost estimation. The factors that relates to accuracy of cost estimating practice are complexity of project, scale and scope of construction, market condition, and method of construction, site constraints, client's financial position, buildability and the location of the

1project . There is a need for capacity improvement for the Highways Agency in order to deliver adequate road programme, and avoid factors that contribute to cost increase such as inflation, inaccurate estimating, project definition, risk,

4time, land, third party cost and preliminaries .

The purpose of this paper is to appraise the implementation of design requirements with the objective of proffering a mitigating mechanism to cost underestimation on construction projects. While empirical data have been collected, the literature relating to level accuracy of cost estimation, effects of design changes, technical planning and techniques for cost estimation, including mit igating mechanism of cost underestimation was reviewed.

LITERATURE REVIEW2.

Mitigation of cost underestimationForecasting of cost that engage the use of cost planning tend to be more accurate than price

5forecasts without cost planning . Hence, cost planning is considered a mitigating factor in cost underestimation. Risk factor are significantly related to construction items such as frequent

6changes in design . Figure 1, illustrate risks like the “waterfall effect” that spread across three parties: the project owner, prime contractor and sub-contractors. Understanding the risk involved and managing the risk through mitigation and application of contingencies are vital for project success.

Figure 1: Waterfall effects share responsibility for risk amongst different parties

Factors influencing cost estimates

A cost estimate is a prediction of expected project cost for a defined scope of work with a degree of assumption, uncertainty and error. Hence, the level of assumption, uncertainty and error can

7produce cost underestimation . The forecasting of building price is an uncertain business, while an

8estimator's price is highly subjective . The judgement of the estimator takes as a starting point the base price (cost data) and adjusts for the particular requirements of the new design to be estimated. In arriving at a cost estimate, the project estimator should not only consider the scope of work or design which they are estimating, but also additional information relating to external factors, which are different from the

9scope of works and design .

Project cost planning

3Project Management Institute proposes the following processes as being core to project planning: scope planning, scope definition, activity definition, activity sequencing, activity duration estimation, schedule development, resource planning, cost estimating, cost budgeting and project planning development. The level of information available to the estimator

10increases as the design progresses . Hence, the accuracy level of estimates varies at different stages of project development; estimates prepared at the early stage are less accurate, and estimates prepared at a later stage of project development are more accurate as more

11information becomes available . Cost estimation during successive stages of the project does increase in accuracy, as detai led design

2information becomes available . The effect of

Pg.15

increases in information can be assessed by comparing the accuracy of estimates made at the conceptual design stage to estimates made at the detail design stage. As more information is made available through different stages of work, estimators will need to proceed to different forms of estimating technique. However, this will often entail the reworking of estimates from scratch, due to non-compatibility of different estimating techniques. The more detailed the project information made available, the more sensitive

12the estimating techniques becomes .

Causes of inaccurate cost estimation

The increase in price, changes in design, and incompleteness of cost estimation and omission of key elements at the project planning stage are

13attributed to causes of cost underestimation . In addition, the causes of inaccurate cost estimate are insufficient time for estimating, inadequate specifications, incomplete drawings, quality of project management, lack of historical cost data, and lack of confidence in structure of site

14feedback . A study conducted on the accuracy of train ridership projections that were available to project decision makers in the United States of America identify the bias of the forecaster as the causes of difference between the forecast and

15actual performance of the project . The bias arising due to the economically preferred transit mode that higher ridership is spread over lower capital cost and operating expensive. Hence, the mental state, psyche, and bias appraisal of the project stakeholders due to the optimistic plans of the promoters about the successful outcome

2of the project which includes over-estimating the likelihood of positive event and under-

16estimating the likelihood of negative event . Uncertainties are inherent in the selection, application and interpretation of estimating

17method, thus affecting the estimation result . Currency exchange, inflationary pressure and project financing are significant factors which

6influence project cost estimate . The credibility, reputation and survival of project stakeholders especially forecaster, contractor's estimator and promoters, are put in doubt in the event of

18project cost underestimation .

Effects of cost underestimation

The effects of cost underestimation occur as

exposure to risk, financial loss, loss of reputation and credibility of estimator, rise to claims and

2, 6dispute . There are similarities between cost

1 6underestimation and cost overrun , cost underestimation is the act of assessing (planning) cost of a project lower than what the actual cost turned out to be after implementation, while cost overrun is the excess of actual cost over budget. Cost overrun have negative effects on project stakeholders, especially client, contractor and consultants with adversarial relation, creating cash flow problems, mistrust, arbitration, litigation, and a general feeling of apprehension amongst project

6stakeholders .

Effects of design changes

Cost underestimation from the point of view of most contractors is a result of inaccuracy of material take-off, increases in material costs and

19cost increase due to external factors . The study conducted in the Free State region of South Africa reveal that inadequate planning and incomplete design at the time of tender are the

20main causes of cost underestimation . The long periods between design and time of tendering are significant factor in the process of estimating

6construction cost , while the primary objective of p r o j e c t p r o m o t e r s p r o c u r i n g a n e w infrastructure project is to ensure the delivery on

21time and within budget . The period between design and tendering, if not adequately managed creates additional risk and burden on project teams, planning and project execution. A study conducted on public–private partnership(PPP) projects in UK indicates that PPP projects entail longer-term in elements of design, financing and operation of new projects. Hence, this longer term leads to complexity and u n c e r t a i n t y o f p r o j e c t s . T h e l e n g t h y procurement periods of construction project

21translate to higher transaction cost .

Inadequacy at planning stage

The production of accurate cost estimates at the early stages of the project remains a challenge for most organisat ions involved in the development and delivery of infrastructure

18 22projects . Construction User Round Table suggest that the cost and time may exceed forecast if other factors are not properly

Pg.16

managed. They further suggest shifting bulk analysis, design and decision-making of the project to the inception in order to maximise good opportunity in decision-making processes. The following causes of inaccurate cost estimates are identified as being: insufficient time for estimating, inadequate specifications, incomplete drawings, quality of project management, lack of historical cost data, and lack of confidence in the structure of site

14feedback . A study conducted in the Free State region of South Afr ica concluded, that inadequate planning and incomplete design at the time of tender are the main causes of cost

20underestimation , while other issues relating to cost underestimation are inadequate cost checks, incompleteness or mistakes byestimators assigned the responsibility of estimating the cost of the construction project.

Effective techniques to mitigate project underestimation

In order to derive a more accurate cost estimate, more sophisticated estimating techniques should be developed and used in estimating

14project cost . In addition, continuous training, acquisition of knowledge, skills and improving estimating technique by estimators, utilising more modern technology, will fine-tune the cost

23performance of construction projects . The utilisation of IT advancement and modern business behaviours, which integrate innovation is recommended as a means of improving

24accuracy in estimating project cost . However, using IT-based estimation such as computer-aided design (CAD) or Building information modeling (BIM) will not entirely eliminate human subjective judgements and assumptions, such as those involved in the calculation or planning of contingencies, preliminaries, provisional sums and prime cost sums, which generally rely heavily on the judgement and experience of the estimators. Hence, the estimator must exercise great caution when estimating works that are

5out of their area of expertise or regular activities , knowledge and care are essential components of positive cost estimation. The knowledge and experience of the estimator, together with a good performance record, have substantial effects on

5the validity and reliability of cost estimates . In t h e r e v i e w o f t h e a d v a n t a g e s o f c o s t overestimation, revealing that, in the view of project facilitators, who comprise part of client

organisation, it is better to be on the safe side with cost overestimation rather than risk the uncertainty associated with cost

25 underestimation . However, it is not a legitimate practice to overestimate in order to mitigate the r i s k s o f u n d e r e s t i m a t i o n . B o t h c o s t underestimation and cost overestimation are classified as errors in cost estimating, though the conservatives tend to prefer overestimation despite a diversity of opinion in the industry as a

25whole . In generally, it acceptable that cost estimating is the process of developing approximate cost required to complete project

3activities .

Design requirements

Detailed design information, manufacturing, assembling, testing, and delivery are

26requirements for detailed estimate , including 23the adopting of the outlined various ways to

lessen project cost underestimation such as:putting in place proper design and documentation; carrying out effectivecommunication and coordination between engineers; receiving sufficient design information from the designer; ascertaining clarity onassumptions from designer and clientestablishing formal feedback for design andestimating activities; adopting realistic times for estimating; utilising more rigorous estimating techniques; incorporating market sentiment and economic conditions into estimate; use of tender document as estimate; measuring of design and construction risk; and applying cost planning and cost control during the design stage.

3. METHODOLOGY

Research approach

In conducting of social and human science research, there are two primary approaches, which are the quantitative and qualitative

27approaches . A quantitative research method was adopted where data were collected using a structured questionnaire through online survey monkey. The questionnaire was developed and divided into different sections. The first section re l a t e d t o t h e g e n e r a l i n fo r m a t i o n o f respondents and the second part relate to

Pg.17

research data collection on the effects of cost underestimation. The questions focussed on the following areas; loss of reputation and credibility of project stakeholders, exposure to risk, and financial loss as effects of inaccurate cost estimation.

Population and sampling

Purposive sampling is a type of non-probability sampling in which the researcher selects the units to be observed based on judgment about which sample will be the most useful or

28representative . Due to the different range of attributes, behaviours and experience of respondents, the researcher used purposive sampling technique. A total of one hundred and forty two (142) emails were sent via the survey website to the selected respondents. The respondents were selected from the available register of the South African Council for Quantity Surveying Professions (SACQSP) and the general b u i l d i n g c o n t r a c t o r s r e g i s t e r e d w i t h Construction Industry Development Board (CIBD), construction professionals working in quantity surveying practice and construction firms with close proximity to Cape Town CBD. A high response rate of 52% was recorded, which accounts for seventy four (74) respondents that completed and returned the questionnaire. The survey was open for one month, during this period reminder emails were sent out online through the web link: https://www.surveymonkey.com/r/X7S3FW8. The online survey and reminders accounted for the high response rate.

Data processing and analysis

The questionnaire for the study was designed to generate statistics for quantitative data. In designing the rating scale for the questions, the 5 points Likert scaling was used to rate the response. Analysis of data involves closely-related operations conducted in a manner that will summarise and organise the collected data in o rd e r to y i e l d a n s we r s to t h e re s e a rc h

29questions . Data were inputted and analysed on the computer using Statistical Package for Social Sciences (SPSS) version 23. The quantitative data gathered were analysed using descriptive statistics. In analysing the research data, the measures of central tendency using mean value were used to analyse the data.

Reliability of research instrument

Cronbach's alpha coefficient was used to present the test on reliability of the scale questions using SPSS version 23. Table 1 presents the overall Cronbach's alpha coefficient value for the total questions was 0.90, which indicates excellent in relation to the following rules on degree of

30reliability : > 0.9 to be excellent, > 0.8 to be good, > 0.7 to be acceptable, > 0.6 to be questionable, > 0.5 to be poor, and < 0.5 to be unacceptable. Test on reliability of the research instrument were co n d u c te d b a s e d o n C ro n b a c h ' s a l p h a coefficient value, the result shown a coefficient value 0.90 which indicate that the reliability of the research instrument is found to be satisfactory.

Research variables N

Cronbach’s

alpha

coefficient

values

Degree of

reliability

Design

requirements 9 0.84 Good

Effective

techniques and

skills

7 0.93 Excellent

Planning 7 0.94 Excellent

All questions

combined 29 0.90 Excellent

Table 1 Consistence reliability for scale items

Pg.18

3. FINDINGS AND DISCUSSIONS

Profile of respondents

The selected respondents work in both private and public sector as indicated in Table 2. 73% of the respondents work in private sector, 10.8% work in public sector, while 16.2% of the respondents work in both private and public sectors. These data suggest that majority of the respondents are working in the private sector compared to the public sector.

The respondents acquired adequate educational qualification to understand and respond to the questions listed in the survey, 97% of the respondent acquired a minimum of bachelors' degree as indicated in Table 3.

Design requirements

Respondents were asked to rate the importance of the design requirements as mitigating mechanisms on cost underestimation; where 1=not important, 2=slightly important, 3=fairly important, 4=important, 5=very important, and U=Unsure. Table 4 shows that the production of well-prepared bills of quantities had the highest ranking with MV=4.89. The provision of clear scope of work had the second highest ranking with MV=4.80. Loss of income to financing institution had the third highest ranking with MV=4.64. The design requirements as mitigating mechanisms on cost underestimation had AMV=4.55.

31Construction Industry Development Board define bills of quantities as a document for tendering, usually prepared in standard form, comprising both a descriptive list of quantities of works and a description of the materials, workmanship and other matters required for construction works. Hence, describing works items provides adequate information for the estimator to accurately and confidently price the works.

Table 2 Working sector of respondents

Respondents’ working

sectorN Percentage (%)

Private 54

73.0

Public 8 10.8

Both 12 16.2

Total 74 100.0

Table 3 Respondents’ formal qualification

Respondents’ formal

qualificationN Percentage (%)

Diploma

1 1.4

BTech degree 5 6.8

BSc degree 9 12.2

BSc Honours 21 28.4

Masters 27 36.5

Doctoral 10 13.5

Others 1 1.4

Total 74 100.0

Table 4 Design requirements as mitigating mechanisms on cost underestimation

Pg.19

The Average Mean Value (AMV) of 4.55 is in between average to high of all variables measured for design requirements as mitigating m e c h a n i s m s o n c o n s t r u c t i o n c o s t underestimation.

According to Project Management Institute (1996), where the scope of a project is poorly defined, the final cost of project is expected to be high. Further close relationships can be posited between describing work items and defining scope of work; work activity description normally describes the scope of work activity to be executed. The use of bills of quantities during tender stage reduces the cost of tendering by contractors and results in savings on the cost of

32the project .

They further reveal that without bills of quantities, the tenderer is exposed to the risk of underestimating the project cost, thereby making it difficult to complete the works or encouraging the cutting of corners in an attempt to recover the consequent loss.

Effective techniques

Respondents were asked to rate the importance of effective techniques and skills as mitigating mechanisms on cost underestimation; where 1=not important, 2=slightly important, 3=fairly important, 4=important, 5=very important, and U=Unsure. Table 5 shows that estimating the works within area of professional expertise had the highest ranking with MV=4.14. Making a l l o w a n ce fo r co n t i n g e n c i e s b a s e d o n requirements had the second highest ranking w i t h M V = 4 . 8 0 . M a k i n g a l l o w a n c e f o r contingencies based on experience had the third highest ranking with MV=3.91. The effective techniques and skills as mitigating mechanisms on cost underestimation had AMV=3.67.

The accuracy of cost estimate depends on the technique applied by the cost professional

33during the production process of cost estimate . Despite the availability of modern technology and advance methods of computation, realistic estimation still relies heavily on human insight, experience and expertise. The human insight and expertise is referred as the human factor in

34estimation .

Ta ble 5 Effective techniques as mitigating mechanisms on cost under estimation

Effective techniques N

Mea

n

Val

ue

S.D

Rank

Estimating the works within

area of professional expertise74 4.14 0.81 1

Making allowance for

contingencies based on

requirements 74 3.96 0.78 2

Making allowance for

contingencies based on 74 3.91 0.86 3

The process of exercising

continuous skill development74 3.64 1.23 4

The practice of IT based

estimating technique

69 3.45 1.09 5

The usage of modern

technology to implement 69 3.45 1.15 6

The facilitation of on-job

training to acquire skills in 69

3.16

1.33

7

Average mean value

3.67

Pg.20

The Average Mean Value (AMV) of 3.67 is in between average to high of all variables measured for effective techniques as mitigating m e c h a n i s m s o n c o n s t r u c t i o n c o s t u n d e re s t i m a t i o n . E x p e r t i s e c a n n o t b e generalised across different generic contract

5types . The expertise required of cost estimator varies across different generic building contracts, such as educational, medical, industrial, retail, offices, housing and other engineering contracts; hence such expertise is perceived to be t ightly bounded and is suggestive of a difference in forecasting processes of different types of projects. The expertise of the cost estimator does not necessarily translate well across a wide range of

5project types .

Planning

Respondents were asked to rate the importance of the planning as mitigating mechanisms on cost underestimation; where 1=not important, 2=slightly important, 3=fairly important, 4=important, 5=very important, and U=Unsure. Table 6 shows that preparing cost plan during successive stages of work had the highest ranking with MV=4.45. The planning of project scope of work had the second highest ranking with MV=4.43. Planning the procurement requirements for the project had the third highest ranking with MV=4.32. Planning as mitigating mechanisms on cost underestimation had AMV=4.22.

Table 6 Planning as mitigating mechanisms on cost underestimation

Planning N M

ean

Va

lue

S.D

Ra

nk

Preparing cost plan during

successive stages of work74 4.45 0.62 1

The planning of project scope of

work

74 4.43 0.49 2

Planning the procurement

requirements for the project

74

4.32 0.66 3

Planning the work schedule for

construction activities

74 4.26 0.79 4

Planning the resource schedule

for construction activities

74

4.14 0.74 5

Planning for unforeseen risk in

construction works

74 3.99 0.89 6

Taking out adequate risk cover

to mitigate negative effect74

3.96 0.91 7

Average mean value

4.22

Pg.21

The Average Mean Value (AMV) of 4.22 is in between average to high of all variables m e a s u r e d f o r p l a n n i n g a s m i t i g a t i n g m e c h a n i s m s o n c o n s t r u c t i o n c o s t underestimation. The importance of the cost plan as planning technique that provides designers with continuous cost evaluation feedback during successive stages of design, thereby guiding them toward accurate cost

5estimates . The cost plan is updated at various stages of the project, when detailed or new information is made available.

Design requirements as mitigating factor on

cost underestimationThe contractor's adequate understanding of design and site constructability issues enhances the possibility of achieving positive cost

35performance . The probability of achieving positive project performance in relation to cost is reliant on the contractors' understanding of the project scope at inception, as well as an u n d e r s t a n d i n g o f h o w t o e xe c u t e t h e construction works in accordance with the design.

It is shown in Table 7, from the ranking of average mean values of subsets of mitigating mechanisms o n c o s t u n d e r e s t i m a t i o n , t h a t d e s i g nrequirements is the most significant mechanism in mitigating cost underestimation on construction projects.

Table 7 Mitigating construction cost underestimation

Mitigating construction cost

underestimation

Ave

rag

e

Mea

n

Va

lue

Ra

nk

Design requirements as mitigating

mechanisms on cost underestimation4.55 1

Planning as mitigating mechanisms on cost

underestimation4.22 2

Effective techniques and skills as mitigating

mechanisms on cost underestimation3.67 3

4 CONCLUSIONS AND RECOMMENDATIONS

The paper set out to explore the factors which proffer mitigating mechanisms on construction cost underestimation based on empirical study of three variables; design requirements as mitigating mechanisms on cost underestimation, effective techniques as mitigating mechanisms on cost underestimation, and planning as mitigating mechanisms on cost underestimation. The findings based on empirical data revealed that design requirements is the most significant factor i n m i t i g a t i n g c o s t u n d e r e s t i m a t i o n o n construction projects with such requirements as production of well-prepared bills of quantity, adequate project specification, clear scope of work, usage of appropriate condition of contract and tender requirements, adequate s ite information, complete drawings, and the avoidance of design changes during execution of works.

The underestimation of construction cost have detrimental consequences in the construction industry and bears damaging effects on construction projects and project stakeholders. The paper recommends that contractors and cost professionals should exert adequate attention towards the events leading to the production process of cost estimates; from competency, techniques, the definition and understanding of the bills of quantity, reviewing the cost plan, risks and scope changes, and adequate planning to avoid design and material changes.

5. REFERENCES

1. Akintoye, A. 2000. Analysis of factorsinfluencing project cost estimating practice, Construction Management and Economics, 18(1): 77-89.

2. Flyvbjerg, B., Holm, M. S. and Buhl, S.2002. Underestimating costs in public works projects: Error or Lie?, Journal of the American Planning Association, 68(3): 279-295.

3. Project Management Institute. 2013. Aguide to the project management body of knowledge, 5th edition, PMI Standards Committee, Pennsylvania.

4. Nichols, M. 2007. Review of highwaysagency's major programme. Department for Transport (DfT). UK, London. Available: hp://assets.d.gov.uk/publicaons/pgr-roads-

nicholsreport/nicholsreport.pdf [07 December 2013].

5. Skitmore, M., Stradling, S. and Tuohy, A.1994. Human effects in early stage construction contract price forecasting. Engineering Management, 42(1): 29-40.

6. Mahamid, I. and Dmaidi, N. 2013. RisksLeading to Cost Overrun in Building Construction from Consultants' Perspective, Organization, Technology & Management in Construction: An International Journal, 5(2): 860-873.

7. Seeletse, S. and Ladzani, W. 2012. Projectcost estimation techniques used by most emerging building contractors of South Africa, Journal for the physical and development sciences, 19(1): 106-125.

8. Gunner, J. and Skitmore, M. 1998. Buildingcontract price forecasting: Price intensity theory, Engineering, Construction and Architectural Management, 6(3): 267-275.

9. Ponte, D.M. 2009. Minimizing risksassociated with construction cost estimates. Available: http://asceinsurance.com/Portals/dnn.asceinsurance.com/forms/pl/articles/ ASCE-PL-EstimateRisks-Article-F.pdf [20 June 2014].

10. Skitmore, M. 1988. Factors affecting thethaccuracy of engineers' estimates. In 10

International Cost Engineering Congress, the American Association of Cost Engineers, 10-12 July, 1988, New York. 1988 Available: http://eprints.qut.edu.au/ 59700/ [13 August 2014].

11. Hendrickson, C. and Au, T. 2000. Projectmanagement for construction: Fundamental concepts for owners, engineers, architects,

nd and builders. 2 edition World Wide Web Publication.

12. Skitmore, M. and Gilmore, J. 1989. A newapproach to early stage estimating. The Chartered Quantity Surveyor, 11(9): 36-38.

13. Nijkamp, P. and Ubbels, B. 1999. Howreliable are estimates of infrastructure costs? A comparative analysis, Research Memorandum, Department of Spatial Economics, Free University Amsterdam. Available: http://degree.ubvu.vu.nl/RePEc/vua/wpaper/ pdf/19980029.pdf [26 November 2013].

14. Al-Hasan, M., Ross, A., and Kirkham, R.2006. An investigation into current cost estimating practice of specialist trade contractors. In 2006 Liverpool Built Environment & Natural Environment Conference. London, UK: Liverpool John Moore University. http://www.ljmu.ac.uk/BLT/BUE_Docs/alhassan.pdf.

15. Pickrell, D.H. 1990. Urban rail transportproject: Forecast versus actual ridership and cost report to office of grants management. Urban Mass Transportation Administration, Washington DC. Available: http://www.debunkingport land.com/docs/Pickrell(no_text).pdf [27 November 2013].

16. Gupta, K.P. 2009. Cost Management:Measuring, Monitoring and Motivating Performance, Global India Publications, New Delhi.

17. Pfleeger, S.L, Wu, F. and Lewis, R. 2005.Software Cost Estimation and Sizing Methods: Issues and Guidelines, Rand Corporation, California.

Pg.22

18. MacDonald, R. 2011. Can Cost Overrunsbe avoided in Capital Works Projects? If so how? International Public Works Conference, 21-24 August 2011, Canberra, Australia.

19. Kaming, P.F., Olomolaiye, P.O., Holt, G.D.and Harris, F.C. 1997. Factors influencing construction time and cost overruns on high-rise projects in Indonesia. Construction Management Economics, 15(1): 83-94.

20. Monyane, T.G. 2013. Identifying causes of cost overrun and effective cost control measures of public project in the Free State Province. A Thesis submitted in partial fulfilment of the requirements of Tshwane University of Technology for the Degree of Magister of Technology: Quantity Survey (Structured). Pretoria: University of Technology.

21. Reeves, E., Flannery, D. and Palcic, D.2013. Are We There Yet? The Length of Tendering Periods for PPPs in Ireland, Paper presented to the CBS-Sauder-Monash Public-Private Partnership Conference Series, June 13 – 14. 2013. Sauder School of Business, University of British Columbia, and Vancouver, Canada.

22. Construction User Round Table. 2004.Collaboration, Integrated Information, and the Project Lifecycle in Building Design, Construction and Operation. Available: http://codebim.com/wp-content/uploads/2013/06/CurtCollaboration.pdf [28 November 2013].

23. Azman, M.A. and Samad, Z.A. 2011.Perspective of quantity surveyor towards the accuracy of preliminary cost estimates in public of Malaysia. The Quantity Surveying International Convention, 11-12 October 2011, Penang, Malaysia.

24. Olatunji, O.A. 2012. The impact ofbuilding information modelling information modelling on estimating practice: analysis

of perspectives from four organizational business models. The University of Newcastle's Digital Repository [Online]. Available: http://novaprd.newcastle.edu.au/vital/access/manager/Repository/uon:12337.

25. Cheung, F. K. T., Wong, M. W. L. andSkitmore, M. 2008. A study of clients' and estimators' tolerance towards estimating errors, Journal for the construction management and economics, 26(4): 342-362.

26. Stewart, R.D. 1991. Cost Estimating, NewndDimension in Engineering, 2 edition, John

Wiley and Sons Inc. Press, New Jersey.

27. Kothari, C. R. 2004. Research Methodology:ndMethods and Techniques, 2 edition, New

Age International, New Delhi.

28. Babbie, E. 2013. The Basics of SocialResearch. 6th edition, Cengage Learning, California.

29. Kumar, C.R. 2008. Research methodology,APH Publishing, New Delhi.

30. George, D. and Mallery, P., 2003, SPSS forWindows step by step: A simple guide and

threference. 11.0 update, 4 edition. Allyn & Bacon, Boston.

31. Construction Industry DevelopmentBoard. 2010. Practice Note # 21: Bills of

stquantities, 1 edition, Construction Industry Development Board, Pretoria.

32. Davis, P. R., and Baccarini, D. 2004. Theuse of bills of quantities in construction projects-an Australian survey. In Proceedings of the COBRA 2004 International Construction Research Conference of the Royal Institution of Chartered Surveyors, 7 - 8 September. Leeds Metropolitan University, Leeds: RICS Foundation.

Pg.23

33. Akinyede, I.J. and Fapohunda, J.A., 2014.The impact of design changes on budgeted cost of building projects in South Africa. In Proceedings of the 8th Construction Industry Development Board (CIDB) Postgraduate Conference, 10 – 11 February. University of the Witwatersrand, Johannesburg, South Africa.

34. Page, J.S. 1996. Conceptual CostndEstimating Manual, 2 edition, Gulf

Publishing Company, Houston.

35. Doloi, H. 2013. Cost overruns and failurein project management: Understanding the roles of key stakeholders in construction projects, Journal of construction engineering and management, 139(3): 267-269.

Pg.24

APPLICATIONS AND ASSESSMENT OF A QUALITY ASSURANCE

PROCESS MEASUREMENT MODEL IN CONSTRUCTION PROJECTS

Kgashane Stephen Nyakala1 & Thomas Munyai1, Andre Vermeulen2 & Jan-

Harm, C Pretorius2

1Department of Operations Management, Tshwane University of

Technology, South Africa

2Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa

*Corresponding author: KgashaneNyakala

Email: nyakalaks@tut.ac.za

PURPOSE:

The study developed a comprehensive model d emonstrating v arious relationships between the level of the skill a cquisition p rocess p roject construction design, project planning and c ontrol t echniques, a nd organisational structures.

ABSTRACT

Purpose

This paper establishes a comprehensive model demonstrating various relationships between the level of the skill acquisition p rocess a nd p roject construction design, project planning and control techniques, and organisational structures.

Methodology

The study was descriptive and exploratory by nature and was quantitative. Quantitative data was gathered through structured questionnaires using 160 randomly selected project managers, architects, quantity surveyors, South African local government administrators and managers working in construction projects in the Mopani area in Limpopo. Data were analysed using descriptive statistics such as frequencies and percentages.

Findings

The results indicate that the level of skill acquisition process has strong and positive effects on project construction design and organisational structure after controlling for the effects of developing and measuring the quality assurance (QA) process. The results showed that project evaluations were important key enablers for defining and measuring the quality assurance process in construction projects. Furthermore, t he r esults indicated t hat a ll w orkers w ere a ccountable for p ursuing q uality i mprovement a ctivities

for road construction projects.

What is original/value of the paper

The application o f t he q uality m easurement model c ould b e i mplemented b y c hecking for road quality assurance in construction projects.

Keywords: Small and medium-sized enterprises (SMEs), construction projects, quality a ssurance, d esign a nd implementation.

Pg.25

1. INTRODUCTION

The Small and medium-sized enterprises (SMEs) construction industry is a key driver of job creation in relation to other industries1. SMEs contributeapproximately 10% of South Africa’s Gross Domestic Product (GDP), and the construction industry establishes more than half of the total national capital

investment in most countries2. The SME road construction organisations function to address the increasing unemployment, mostly because large businesses’ demand for labour does not rise according to their growth3. Thedevelopment and establishment of SME construction organisations are a means of providing economic strength including a better delivery of economic activities4.

It is therefore critical for any SME road construction organisation to provide a current construction quality assurance process, which is highly competitive and challenging and delivers the infrastructure projects within the approved cost5.The built environment is mostly constructed, designed,operated , maintained anddecommissioned by constructionorganisations, which are involved directly in construction and buildings design and

civil engineering infrastructure6.

Project managers in construction organisations need to practically improve the construction industry through skills development, sufficient knowledge and subsequently to create employment opportunities7.One of the utmost prominent and extensively deliberated developments has been the expansion of quality assurance practices in the organisation of community services8. Notwithstanding these achievements, South Africa’s construction industry faces a number of serious problems amongst SME construction organisations, i.e.,

efficiency and quality of road infrastructure 1. For SMEs functioning in the construction industry, it is necessary to apply appropriate quality reassurance methods and project management techniques to measure construction road quality4. The current study therefore addresses these issues in relation to SMEs’ road construction projects. We believe that SME contractors for construction projects must utilise all available tools and practices to improve methods, which comprise of all processes within the organisation. With the anticipated “model” the study establishes criteria which should be incorporated in a measurement model or rural road building tool to measure a critical quality reassurance system in SME construction projects.

This research therefore seeks to explore the application and assessment of a QA process model for SME-led projects, and proposes to integrate the processes of QA with the current SME data structure to improve the existing quality construction process.

2. LITERATURE REVIEW

Quality of construction projects and sustainability

Quality of service is essential in the construction industry, as it is a key factor of competitiveness9. Quality refers to meeting the necessities of the project in terms of delivery of a well-defined scope of work and meeting the requirements of the constructor in the construction process10. Quality involves products, defects, processes, customers and systems11. Various studies have endeavoured to describe the quality of construction projects by exploring numerous methods implemented by

Pg.26

researchers5, 12, 13 . Reviewing the existing literature provides a start in defining quality in construction projects . According to the Institution of Civil Engineers, quality management is critically required for construction projects, remains a concept held by senior managers but is not fully disseminated to their subordinates14. Alongside quality, cost, sustainability, delivery and customer satisfaction is added competitive primacy to adjust appropriate process strategies proactively in building construction sites 15, 16, 17 . This marks the starting point of our paper which asks to what degree the quality assurance process has been successfully measured in SME road construction organisations.

The South African road network system is the heartbeat of progress, and it offers access and flexibility for the execution of economic and social activities 18. This helps both national and local government in achieving their objective of promoting the sustainable use of resources for economic growth to benefit the community 19. Modern quality management for construction projects is classified as the following: (1) quality planning and development, (2) quality assurance, (3) quality inspection, (4) quality control, (5) total quality management, and (6) engineering systems. Hence it sets responsibilities for the Quality Engineer to promote compliance through awareness of the need and opportunity for improvement, provide recognition for training and skills development, systematise achievement of goals, carry out projects to solve difficulties, report progress , and communicate outcomes whilst managing efficient use of available materials, both human and technolog ical 20 . Tools such as cause and effect diagrams, checklists, flow charts, Pareto charts , histograms , scatter diagrams and control charts are the quality tools designed for ease of interpretation. They offer the means for achieving quality management decisions,

and some of them are developed by quality engineers15.

SME c onstruction o perations and performance measures

SME construction operations is an important sector in the South African economy which needs to position itself for greater global participation. Yet, in many cases it lacks technical skills, specifically a lack of experience in order to make the substantial leap necessary for global competitiveness. Research has identified the construction manager as the most important point of responsibility within SME construction organisations which are executing building and civil engineering work on site 16.Previous research on construction project control in the United Kingdom (UK) has focused on current practices, existing problems and recommendations for future improvement 29. The principle factors contributing toconstruction delays was investigated in the State of Qatar21.

The key challenges facing the South African construction industry in relation to its performance, development and growth was also examined21. Some studies have investigated government quality and the economic returns of transport infrastructure

investment in European regions 13.

The construction operations function is also identified as site preparation, the building installation and building completion22. The importance of measuring the use of project management techniques during this construction project delivery by SME contractors has been recognised. Many applications of building and civil engineering work on site have been reported, which have been tested and used to measure the quality of construction projects in various contexts 9,

23, 24. For example, Erasmus and Doeben-Henisch proposed a theory for system engineering management to assess perceived facility quality for variety of sectors25. Some researchers have

Pg.27

developed alternate concepts for strategies to explain the advantages of effective project management systems 24.For instance, Nkomo explains assessing the environments of rural road networks in South Africa by applying visual observations and – field based measurements.

George has suggested the benefit from the application of the financial challenges gap-theory using the critical incident technique for emerging contractors in developing countries 7. The results of the study suggested that the quality of SME construction project measurement instrument is a useful tool in assessing and monitoring building projects in rural communities and empowering the project team members to pinpoint where quality of work process improvements are required from the customers’ perspectives.

Quality assurance in construction project delivery

According to Statistics South Africa, because South Africa has a very high accident and fatality rate and damage to roads, government has ring- fenced funding for the maintenance and rehabilitation of provincial roads 18 . There has concurrently been a considerable decline in study’s done on construction projects in developed countries such as Sub-Saharan Africa and South Africa 6 .Related to this, a study conducted by Dallasega found a lack of life-cycle assessment, inadequate construction design, inadequacies of materials, material and labour scarcities in construction project delivery26. The quality assurance (QA) process assures improvements in quality, product design, processes, services, concurrent engineering, experimental design, management and design team formation 15.

Assuring quality in construction projects includes not only inhibiting quality complications or faults through planned and systematic activities but also through reporting internal and external audits to

stakeholders10, 27. Quality assurance in construction projects should be ongoing. This process should include inputs, tools, and techniques, and outputs 20, 26.Recognising this situation, construction equipment and machinery are critical components for modern construction and gain a direct influence on the project progress. Furthermore, the empirical evidence of the application of project control systems and performance of rural roads construction projects is extremely recent, and debate is continuing as to whether it can be implemented at all in the public-sector environment 28. Quality assurance strategy emphasises taking action to improve performance, more efficient use of resources, providing extra aids to stakeholders considering change requests, and taking corrective actions29. With this proposed “framework” the study attempted to establish criteria that could be incorporated in a measurement tool or rural road building model to measure critical quality reassurance system in construction projects.

Research Objective

The objective of the study was to determine, by means of a comprehensive literature review, what critical aspects or operations ensured road quality in the SME construction project delivery. A literature review was conducted to identify critical points of knowledge of this study’s subject, with the intention of providing an overview of current literature on the topic. This would enable SME road construction organisations to measure the effectiveness of road projects and implement QA processes4. The primary objective of the study was to develop a road construction QA process measurement model. The study aimed to enrich the importance and understanding of successful road construction QA effectiveness and its implementation processes.

To facilitate the study’s objectives, the subsequent study questions sought to determine:

Pg.28

a. What must be improved in relatingto a discrepancies analysis ofexisting competence of acontractor to implement a QAmeasurement model in theconstruction projects?

b. What is the role of localgovernment in theimplementation of QA processesby SME construction projects?

c. How can the implementation ofQA processes be developed?

3. RESEARCH METHODOLOGY

The sample of this study was comprised of 160 SME road project contractors operating in local government in South

Africa. A multistage sampling methodwas used to select the 160 road project

contractors to participate in thestudy13. The questionnaire design took the objectives of the research into consideration with the aim of answering the study questions. Quantitative data was gathered through the administration of structured questionnaires comprised of two (2)

sections. Section A recorded the biographic profile of the respondents, namely, age, highest educational level obtained, number of years’ experience and type of ownership in the business. Section B focused on the project characterisation in relation to performance variables, namely, road quality construction projects, QA process implementation, engineering management strategies, SME-ledprojects, quality management techniques, and tools. Data was coded, captured and analysed using Windows version 22 of the Statistical Package for the Social Sciences (SPSS). Factor analysis was adopted to uncover potential variables measuring aspects of the same underlying dimensions33. Exploratory factor analysis is usually performed when exploring patterns of correlations between observed measures. The SPSS guidelines manual indicates that the Kaiser-Meyer-Olkin (KMO) and the Bartlett’s Test of Sphericity are used to determine whether the correlation matrix is an identity matrix showing whether the factor model is appropriate 31.

4. FINDINGS AND DISCUSSION

In this study, KMO and Bartlett’s Test were used to determine the correlations between variables as this would guide the study as to whether to proceed with the factor analysis of data. Eight critical aspects contributing to the QA process implementation were identified and a measurement model was designed to characterise a n a ll i nclusive

‘interpretation’ of the Road Quality Construction Implementation Process. The measuring model allowed for an incorporated methodology relating to implementation of QA in the road construction project. The detailed application and assessment of the QA process measurement model as introduced is shown in Figure 1.

Pg.25

Figure 1: Road quality assurance measurement model

Figure1: Road QA measurement model

We describe the QA process measurement model for SME-led projects and its application in Figure 1. Each of the eight (8) critical aspects was identified distinctly from the item explanations, and in that way, an overall assessment on both the construction sector and SME project-based organisation model was developed. The developed model of SME construction organisation emphasises that project managers need to continuously and effectively implement project control systems during the project to improve rates of project success. The core resources and capability needed for the QA process measurement instrument

through interaction among the workforce, is technological know-how and evaluating quality control of construction work in SME construction project. The results showed that the study’s participants were well qualified and experienced as displayed in Table 1. Among the responding employees, 39.4 % (n=63) indicated that they were employed for more than 9-12 years, 26.3% (n=42) reported working for between 3-6 years, 23.1% (n=37) indicated working in the business between 6-9 years, 8.1% (n=13) had worked for more than 12 years, whilst 3.1 % (n=5) had worked for less than 3 years.

Causes of

quality of

work failure

Impacts of

processes for

QA

development

Impacts of

people

involvement

Idenficaon

Finalisaon of

programme &

road project

efficiency

Quality standard

& codes

adopon

Effecve

communicaon

system implemented

Project

construcon

design

EvaluaonCommitment &

support

Project planning &

control techniques

Process

implementaon

Organisaonal

structures

To identify key dimensions of process implementation in the SME construction industry, item statistics were initially calculated. In order to establish the consistency of data, we commonly use the Cronbach’s Alpha (coefficient alpha). Table 2 shows the total reliability value for each item. Overall, the Quality of Implementation-Cronbach Alpha for the entire scale was 0.82332. A Cronbach’s alpha coefficient of more than 0.7 signifies reliability and internal consistency. The KMO measure of

sampling adequacy is an index used to examine the appropriateness of applying factor analysis11. A value of 0.5 or above indicates that use of factor analysis is appropriate or values below 0.5 imply that factor analysis may not be appropriate. The Chi-Square application was used in the KMO, and Bartlett’s Test was calculated. The KMO measure of sampling (MSM) was considered to have a strong positive relationship, according to the rule of thumb on the strengths of correlation coefficients9.

Table 2: Reliability value for each item

Correlated item total correlation

Cronbach’s Alpha if item deleted

Up-to-date training is provided for employees .845 .945 Management is committed to providing QA/QC training

.906 .934

Employees have a high level of satisfaction .909 .933 Employees are given the opportunity to develop skills

.895 .933

Management facilitates employees’ learning of new skills

.811 .954

Every stakeholder becomes involved during the planning process

.889 .971

Stakeholder approval of the work package is facilitated

.932 .966

All stakeholders receive the project document .945 .965 Community provides input on costs and resources .946 .965 Project manager provide s work breakdown details using software

.906 .969

Formal system of record-keeping is used for projects .858 .974 Project scope is designed to adopt technology .901 .970 Scope of work or specification .949 .956 Unforeseen and/ or different geotechnical conditions

.956 .954

Design of road construction reviewed .956 .968 Implementation of QA processes .936 .969

Table 1: Respondents number of years working in road construction

Less than 3 years 3-6 years 6-9 years 9-12 years 12 or more years Total

5 42 37 63 13 160

3.1 26.3 23.1 39.4 8.1 100.0

3.1 26.3 23.1 39..4 8.1 100.0

3.1 29.4 52.5 91.9 100.0

Frequency Percent Valid Percent

Cumulative Percent

Practical implementation of the process .936 .984 Payments or processing time for tax exemption .841 .915 Contractor’s establishment costs .884 .906 Costs are re-estimated and or-incorporated .880 .907 All pricing/incentives of services rendered .781 .926 Financial difficulties faced by the contractor .732 .935 Standard set of guidelines on survey implementation

.905 .974

QA procedures are applied to describe monitoring of survey implementation in actual settings

.953 .967

Evaluation of the QA process .957 .966 Quality control of construction work .945 .968 Defective work is reworked or improved .889 .976 Organisation improves execution of strategies and plans

.946 .980

Organisational structure is aligned with QA processes

.970 .973

Planning, leading and control are facilitated .982 .970 Quality of the road is defined, established and controlled

.926 .985

Customer feedback systems are in place .610 .957 Workforce has been given the schedules for projects .874 .870 Project objectives are shared with all role players .914 .857 There is cooperation between senior management, workforce and community members

.859 .875

Table 3 shows that the factor analysis was appropriate, and displayed values that were above 0.6, which showed that the KMO, Bartlett’s degree of freedom (df) and significance (sig) were also considered satisfactory for factor analysis. Exploratory factors analysis with

principal components was conducted on the level of skill acquisition process scale items. All the QA processes related to the effectiveness of road construction projects had 35 items and each was rated on a five-point scale, ranging from ‘1’ (very poor) to ‘5’ (excellent).

Table 3: KMO and Bartlett’s Test

KMO and Bartlett’s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.767

Bartlett’s Test of Sphericity Approx. Chi-Square

834.118

Df 21 Sig. .000

The various implementation and effectiveness indicators were all loaded on one factor, which was labelled level of skill acquisition process. This one factor solution had an eigenvalue of 4.633 and

explained 69.679 per cent of the variance. The rotated factor loadings varied between 0.745 and 0.914 for this factor and are displayed in Table 4.

Factor Factor loading

Eigenvalue % of Variance explained

% of Cumulative

1-Factor Up-to-date training is provided for employees

0.827 4.633 69.679 69.679

Management is committed to providing QA/QC training

0.914

Employees have a high level of satisfaction

0.895

Management facilitates employees’ learning of new skills

0.840

Every stakeholder becomes involved during the planning process

0.745

4. CONCLUSIONS AND RECOMMENDATIONS

The presented findings contribute to the understanding of the use of a holistic approach for construction projects’ QA measurement, and fill an existing gap in the knowledge on the use of the QA process measurement tool for construction quality management. This paper explored the implementation of the QA process in road construction project s, and proposed integrated solutions to improve the current quality of road works through the development of distinct measures for enhancing road quality building. A good understanding of the impact of road construction SME-led projects in implementing successful QA processes and aligning organisational structures with QA processes in the local government construction in South Africa was clearly identified and debated in this paper. The main findings of this study evidently indicated that the SME project-based organisations are faced with an unsettled construction project industry and intimidating economic conditions. Concerns have reemphasised the need for a building projec t QA measurement model for road construction projects. Improper process implementation and poor construction project s may increase accident rates, in addition to destorying roadways and vehicles. It is thus essential for SME construction organisations to implement the QA process effectively,

and monitor, inspect and evaluate this at both strategic and operational levels. A QA process measurement model has been proposed to combine quality/work processes effectiveness, skills acquisition process, project planning and project control techniques, construction design of projects, implementation process, quality standards, organisational structures and involvement of people. The key benefits of the road quality construction project model proposed in this study lie in the following aspects: first, measuring the efficiency of project control ensures adequacy of the road construction system and facilitates the quality construction process. Second, the fully structured and standardised quality of construction codes are integrated in the model to provide clear project task necessities for preparation and authorisation. Typical quality of work failures caused by inaccuracy of cross-reference codes can be avoided. Third, adopting a program of product quality for construction projects ensures the timely inspection and implements effective communication systems in order to better understand the stakeholders’ needs.

Based on the aspects addressed which positively influence quality of work process of road building projects, the study recommends that if SME-based projects are measured through skills

Table 4: Exploratory factor analysis

acquisition, project planning and control techniques, project construction design, organisational structure, quality standard and codes adoption, process implementation and impacts of people involvement. Quality control in SME construction projects should mean performing check-up and analysis and taking corrective actions so as to obtain customer’s satisfaction that would bring long term effectiveness and business reality for the organisations.

It can be concluded that the QA process measurement model based construction quality application is helpful and suitable in SME- led projects. First, due to project management tools and techniques, it is possible and feasible to apply a QA process measurement model for quality of construction work and to fully establish a team from inside or outside the organisation for training and education of employees in order to deal with the necessities of design adjustments. Second, a QA process measurement model can be appropriate for the modern industry standard practices in quality control and validated through a case study. Further to this, the study should create a much greater awareness of the benefits of improved rural road networks to enhance urban-rural migration in developed countries, which could result in attracting new development opportunities, hence decreasing the socioeconomic cost of rural life. SME construction organisations are perceived as a means to address the difficulties in creation of employment amongst local citizens of South Africa. They are frequently concerned about penetrating new markets, and captivating and increasing economies in progressive and resourceful ways. This study showed that the SME construction organisations projects lack strategic planning and

experience poor quality of construction project delivery. Inadequate development and quality control has been identified as a key source of such failure. It is evident that competitive pressures in an ever-changing worldwide economy increasingly confront South African SME construction organisations. The study developed a measurement model enabling SME contractors to implement a QA process effectively. It would enable SME contractors in road construction project delivery to identify work processes strengths and failures at strategic levels within their operation and or project activities. Android applications and software could be developed in Matlab, MS Excel and other languages for commercialisation of the developed model. It also needs to be observed that the developed tool could easily be implemented in many building projects. The study reinforces the application and understanding by all stakeholders of QA processes. It forces SME contractors to concentrate on quality control in relation to construction process concepts and the approval of the processes of QA implementation at all levels within the organisation. The research also identifies critical core aspects contributing to measuring and improving organisational structures, implementation process, plans, systems, perfor mance measurements and organisational performance. It also sets the groundwork for future developments guiding the SME contractors concerning the processes of QA implementation. Finally, the study serves as an assessment model determining whether there are significant relationships between aspects contributing to the success of QA processes.

[2] Republic of South Africa (RSA) National Development Plan (NDP) 2030. (2016) Our Future-make it work. National Planning Commission National Development Plan 2030.

[3] Department of Public Works. (2003) “White Paper on “Creating an enabling environment for reconstruction growth and development in the construction industry”. Pretoria. Republic of South Africa.

[4] Nyakala, K.S., Vermeulen, A., Pretorius, J.H.C. & Munyai, T.T. (2017) “Implementation of quality assurance practices and effectiveness of road construction industry: A case of South African local municipalities”. Proceedings of the 2017 Global Business and Technology Association Conference, 636-650.

[5] Burke, R. (2010) “Fundamentals of project management: tools and techniques”. Ringwood: Burke Pub.

[6] Kululanga, G. (2012)”Capacity building of construction industries in Sub-Saharan developing countries: a case for Malawi”. Engineering, Construction and Architectural Management, Vol, 19, no.1, 86-100.

[7] George, H. (2016)”Eliciting the financial challenges facing emerging contractors in developing countries using the critical incident technique: A case of South African Construction Industry”. Business and

Management Horizons, Vol.4, no.2, 23-33.

[8] Vermeulen, A. Jan- Harm, C, Pretorius & Nyakala, K.S. (2018) “Development of a road quality assurance measurement tool in construction projects”. International Association for Management on Technology (IAMOT) 2018 Conference Proceedings, 217-225.

[9] Nel, H. & Pretorius, J.H.C. (2014) “Design on a prevention, appraisal and failure quality cost model”. International Association for Management of Technology (IAMOT) 2014 Conference Proceedings, May 2014.

[10] Arditi, D. & Gunaydin, H.M. (1997) “Total quality management in the construction process”. International Journal of Project Management, Vol,15 no.4, 235-243.

[11] American Society for Quality. (2007) Quality basics. [Online]Available at: www.asq.org. [Access date 12-10-2016].

[12] Chen, L. & Luo, H. (2014) “A BIM-based construction quality management model and its application”. Automation in Construction, Vol.46, 64-73.

[13] Crescenzi, R., Cataldo, M.D. & Rodriguez-Pose, A. (2016) “Government quality and the economic returns of transport infrastructure investment in European regions”. Journal of Regional Science, Vol.56 no.4, 555-582.

[14] Institution of Civil Engineers. (1996) Civil Engineering Procedure. London: Thomas Telford Ltd.

[15] Rumane, A.R. ( 2011) Quality Management in Construction

5. REFERENCES

[1] Van Wyk, L. (2003) A review of the South African construction industry: Economic, regulatory and public sector capacity influences on the construction industry. Pretoria: CSIR.

Projects. CRC Press Taylor & Francis Group, LCC.

[16] Bowen, P.A., Edwards, P.J. & Cattel, K. (2012) “Corruption in the South African construction industry: a thematic analysis of verbatim comments from survey participants”. Construction Management and Economics, Vol.30 no.10, 885-901.

[17] Dallasega, P., Marengo, E., Nutt, W., Rescic, L., Matt, D.T. & Rauch, E. (2015) “Design of a framework for supporting the execution management of small and medium sized projects in the AEC-industry”. In the 4th International Workshop on Design in Civil and Environmental Engineering.

[18] Statistics South Africa. (2015) Producer Price Index (PPI), April 2015. Statistical release P0142.1.

[19] Nkomo, S.L., Desai, S. & Peerbhay, K. (2016) “Assessing the conditions of rural road networks in South Africa using visual observations and field-based manual measurements: A case study of four rural communities in Kwa-Zulu Natal Province”, Review of Social Sciences, Vol, 1, no.2,42-55.

[20] Honnakker, P., Carayon, P. & Loushine, T. (2010) “Barriers and benefits of quality management in the construction industry: An empirical study”. Total Quality Management, Vol.2, no.2, 953-969.

[21] Mofokeng, G. & Thwala, W.D. (2012) “Mentorship programmes within the Small and Medium Sized Contractor Development Programme: A case study of the Free State Province, South Africa”. Journal of Economics and

Behavioural Studies, Vol.4, no.12, 712-722.

[22] Aigbavboa, C.O. & Thwala, W.D. (2014) “Challenges faced blacked owned small and medium construction companies: A case study of Nelspruit- Mbombela Municipality, South Africa”. Journal of Economics and Behavioural Studies, Vol.6, no.10, 771-778.

[23] Kujala, J., Ahola, T. & Huikuri, S. (2013) “Use of services to support the business of a project-based firm” International Journal of Project Management, Vol.31, 177-189.

[24] Marthur, G., Jugdev, K. & Fung, T.S. (2013) “Project management assets and project management performance outcomes: exploratory factor analysis”. Management Research

[25] Erasmus, L.D. & Doeben- Henisch, G. (2011a) “A Theory for System Engineering Management. In ISEM 2011 Proceedings”. Presented at the Industrial, Systems and Engineering Management Conference. Stellenbosch, South Africa.

[26] Tshivhase, L. & Worku, Z. (2012). “Barriers toward the development of emerging contractors in the Limpopo Province Afr ican Journal of Science, Technology, Innovation and Development, Vol.4, no.1, 41-62.

[27] Thorpe, B., Summer, P. & Duncan, J. (1996) Quality Assurance in Construction. Surrey, UK: Gower Publishing Ltd.

[28] Obare, J.O., Kyalo, D.N., Mulwa, A.S. & Mbugua, J. (2016) “Implementation process of project control systems and performance of rural roads construction projects in Kenya:

Role of project team experience diversity”. European Scientific Journal, Vol.12, no.29, 408-422.

[29] Kruger, D., Ramphal, R. & Maritz, M. (2014) Operations Management, 3rd Ed. Cape Town: Oxford University Press Southern Africa.

[30] Welman, C., Kruger, F. & Mitchell, B. (2012) Research Methodology, 3rd Ed. Cape Town: Oxford Southern Africa.

[31] Tabachnick, B.G. & Fidell, L.S. (2007) Using multivariate statistics. 5th Ed. Hillside, NS: Erlbaum.

[32] Kaiser, H. (1974) “An index of factorial simplicity”. Psychometrika, Vol. 35, 401-415.

[33] Field, A. (2013) “Discovering statistics using IBM SPSS statistics”.4th Ed. Thousand Oaks. CA. SAGE Publication.

1. INTRODUCTION

Rapid urbanization has brought numerous positive effects in South Africa such as the creation of numerous economic, cultural and political interaction. However, there are some negative effects that have come with it, and the most severe of them is that of certain parts of the country turning into slums. Slums form as a result of homeless people embarking on a pursuit to create shelter in pockets of land which are closer to suburban activity. To curb the surge of the formation of slums, there have been numerous building models which have made entry into the low-cost housing arena. Some of these are Moladi, Khaya ReadyKit and Fischer Housing. An additional and very unusual form of housing that has also entered into the low-cost housing arena are EPS Dome houses [1]. EPS dome houses exude qualities such as ultra-thermal insulation, gale resistance and earthquake resistance. The success of commercially produced EPS dome houses is premised in the achievement of expanding standard grade EPS by 20% to create an extremely strong ‘plastic ’ [2]. Although EPS dome houses exude such excellent performance qualities, there are two main disadvantages that continue to taint its success. These are that EPS dome house suppliers have numerous minimum order quantity requirements that make the purchase of such a model highly inaccessible. Secondly, Manufacture of the individual EPS pieces requires complex machinery. This research is premised on finding out how far unmodified standard grade EPS in the form of a dome facility can be taken.

Unmodified standard grade EPS that is 200mm-thick with a density of 20kg/m3 represents the same amount of energy as a 17mm-thick layer of pine wood [3]. This strength quality of EPS is motivation enough to see if an EPS dome facility constructed using unmodified EPS, can successfully shelter human beings or at least be used as some form of environmentally friendly storage facility.

In this research, the individual components used in the construction of three low-cost housing models (Moladi, Khaya ReadyKit and Fischer Housing) as well as their complexity of assembly will be used to comparatively formulate opinions as well as motivations for the possible usage of EPS dwellings. However,

THE STRUCTURAL SUITABILITY OF EXPANDED POLYSTYRENE (EPS)

USING COMPARATIVE ANALYSIS

Bonke Mncwango and Dhiren Allopi Department of Civil Engineering and

Surveying Durban University of Technology

P O Box 1334, Durban, 4000, South Africa

Corresponding Author: Bonke Mncwango, Email: bmncwango@gmail.com

ABSTRACT:

Purpose The r es earch e xamines t he p otential o f carving standard grade Expanded Polystyrene (EPS) into a form of a dwelling/facility using a hot-wire system to re-create a commercially produced EPS dome facility.

Design The adopted research approach is that of both qualitative and quantitative research.. Existing industry models such as Moladi, Khaya ReadyKit and Fischer Housing were used to frame the exploratory perspective of this investigation.

Findings It was found that although it takes time to carve the individual pieces using a hot-wire system, but the change in construction method can make it possible to increase the accessibility of such a model. Due to the low compressive strength of standard grade EPS, it was found that the most appropriate uses for such a model would be that of being a temporary facility.

Value This research will be of value to design professionals in alleviating the environmental impacts of commonly used conventional materials through the inclusion of EPS where low compressive loads are permissible.

Key words: Construction methods, Low-cost housing, Polystyrene, Material innovation

Pg.38

it is necessary to first highlight some of the challenges that are confronting South Africa in order to build a greater appreciation for such a research endeavor.

2. THE PLIGHT OF IN ADEQUATE HOUSING

Slums differ in material composition according to the type of available resources to the ‘builder’ at the time of starting the building. Most of these structures are Hyper -permeable. “Hyper-permeability is an extreme form of indefensible housing. It encapsulates housing that presents multiple forms of permeability, where apertures lack glazing, where construction materials cannot withstand force, walls and roofing are water -permeable, and basic construction elements such as joists, beams, insulation, and structural integrity are often not present, meaning elements of housing are poorly secured, and liable to easy removal. Housing with these material qualities eases criminal access.” [4]

The last time a global survey was attempted in counting the number of homeless people in the world was in 2005 by the United Nations[5]. They estimated that there were 100 million people worldwide who were homeless in the year 2005.

South Africa is a beautiful country with a wealth of heritage and warmth since it is also the birth-place of a number of some of the world’s greatest activists. However, great as it is, it still faces some tough challenges even after 23 years of democracy. South Africa’s biggest challenges are as follows [6]:

• There are many South Africans thatare not employed;

• The quality of free education for blackpeople is sub-standard;

• Public services are poorly located;• The poor are continually marginalized

by the existing various spatialchallenges;

• The disease burden of South Africa iscurrently as a result of thedeteriorating public health system;

• Public service performance isinefficient;

• Extremely high levels of corruption;• South Africa continues to be a divided

society.

Most of the above eight challenges are as a result of South Africa’s history. The ills of the apartheid era have left South Africans with a terrible legacy of inequality. This inequality amongst citizens often times causes multiple service delivery riots and protests across all provinces. This is similar to what was experienced in the early 1940’s where “the urban working class was growing rapidly, and small-scale riots and strikes bore witness to mounting African frustration over inadequate wages, housing and transport.” [7]

South Africa is an upper-middle-income country [8]. Though it is an ‘upper-middle-income country’, most of South African households encounter poverty and vulnerability to diseases on a daily basis. Apartheid took away people’s assets [9]. It ensured distortion in the economic and social arenas through racial discrimination, and resulted in a destabilized state. This led to the ultimate collapse of South Africa due to an agonizing phase of life for all its citizens. The poor and negative attitude of government workers at the time, along with “the absence of information concerning rights, roles and responsibilities” [9], and the lack of accountability by all tiers of government, are the very same catalysts which have contoured the nature of poverty in South Africa today.

Although South Africa is one of the many countries where poverty amongst its citizens is prevalent, it also remains true that poverty is a huge global crisis that affects multiple other regions in the world. “Poverty is a global phenomenon and has proven difficult to resolve. Strategies to address it need to be focused on factors associated with poverty through local research as problems differ from region to region.” [10]

A primary indicator of an impoverished country/community is the lack of adequate housing. There is an amazing psychological phenomenon that is evident when one looks at the lack of confidence of families who reside in informal structures as opposed to those that reside in formal structures. This ‘lack of confidence’ can be experienced when interacting and conversing with different individuals who are of informal structure residence. It is indeed true that “the type of dwelling a family resides in, that is, a formal structure versus an informal one, contributes significantly to improved quality of life and poverty status.” [11]

Pg.39

People seem to be more aware of their lack of financial resources if even where they reside is unsatisfactory. It is therefore apparent that one of the first key issues that should be dealt with when attempting to bring aid to impoverished communities is resolving their lack and need for adequate housing.

ALTERNATIVE BUILDING TECHNOLOGIES

‘Alternative Building Technologies‘ can be defined as follows [12]: “Innovative housing techniques refer to any deviation from traditional construction methods that are specified within the limitations of SANS 10400- it does not necessarily have to be a material that has never been used before” There are systems which have been introduced in South Africa that are seeking to contribute positively to the issue of lack of housing. The most notable of these are:

a) Moladib) Khaya ReadyKitc) Fischer Housing

a) Moladi

Moladi is a construction system founded in 1986 in South Africa. The objective of the Moladi system is to achieve quick house construction by means of recyclable, re-usable and removable formwork moulds. Mortar fills the formwork to form the wall structures of the house. The formwork panels can be re-used up to 50 times therefore making the system cost effective due to repetitive application [13].

Moladi system includes [13]: • Raft foundation• Superstructure• Moladi bonding agent• Windows• Doors• Roof • Ceilings

Figure 1 Moladi formwork moulds [13]

b) Khaya ReadyKit

The system was developed in 1993. The Khaya Readykit constitutes timber panels that are a standard width of 1m. 1.5m and 2m are also made if required. These panels are “made of dried and cured 114mm x 38mm SAP to stand between 2500mm and 3000mm high and either 1000mm or 1500mm wide. An acrylic resin cured fibreglass mesh is attached to each face of the frame. When plastered the timber performs merely a structural role, with a lime/cement plaster on each face providing the insulation and finish” [14].

Figure 2 Khaya ReadyKit timber panels [14]

c) Fischer Housing Fischer Profile South Africa is a company that was founded in 1979. Fischer Profile South designs and manufactures miscellaneous automotive components. Fischer Profile South Africa has recently ventured into the low-cost housing sector through the start of its housing division. There are two designs that are currently available by Fischer housing [15]:

• Design No.1 is for a 21m2 house:

Type 21, as it is called, is a one bedroomed house with a kitchen/lounge area as well as a bathroom. The standard width of the house is 2600mm and it has a height of 2850mm [15].

• Design No. 2 for 40m2 house:

Type 40, as it is called, is a two bedroomed house with a kitchen/lounge area as well as a bathroom. All the units of the house are supplied in knock -down condition, ready for assembly by the appropriate professionals on-site [15].

Pg.40

Figure 3 Complete fischer housing unit [15]

3. RESEARCH METHODOLOGY

The research study employed the dual approaches of qualitative and quantitative research. The study begins by using the narrative perspective of qualitative research since the main source of data is from existing documentation and reports.

The perspective of correlational research under the quantitative approach was used to form the framework of comparative analysis. This was done by considering the various construction materials used in each low-cost housing model and relating these to predetermined criteria. The varying levels of performance of the considered low-cost housing models; along with the statistics of EPS as sourced from existing data and from observation of the carved live- model helped to generate the results needed in order to test the probability of the following hypothesis:

“If a dome facility is constructed using standard grade EPS, it will be able to adequately shelter humans or stock from the elements”

Quantifying environmental sustainability and wastage

The protection of the environment is a major key in prolonging human life. The adverse impact of the environment on health is one of the major motivators for choosing construction methods and materials that are environmentally friendly. It is therefore important to consider the various components that are required in the make-up of each individual construction model that seeks to contribute to the housing deficit.

a) MoladiMoladi requires “one cubic metre of Moladi mortar (which consists of 1800kg or 0.720m3 of local decomposed granite/river sand), 250kg of 42,5N Ordinary Portland Cement (OPC), 5 litres of moladiCHEM, a non-toxic, water based chemical cocktail as well as 200 litres of water” [13].

b) ReadyKitThe ReadyKit system requires polystyrene insulation boards and strengthening wire mesh, plaster sand as well as timber for roof trusses [14].

c) Fischer building systemThe Fischer building system consists of sections that are manufactured from coiled steel and zincal produced on Fischer Roll forming Lines [15].

From the above it can be seen that there are some environmental concerns with the three above considered models. For the Moladi system, the use of 200 litres of water in the midst of an already drought-stricken country will prove very problematic during mass production of the units. Timber used for roof trusses in the Readykit system does not assist in efforts of tree preservation. With regards to the Fischer building system, the process of mining iron ore from the ground before it is smelted is a costly process which is also very energy intensive and also further depletes the earth’s natural resources.

In contrast to the above models, the envisioned major components in a live -size EPS dome house would be as follows:

• Two lengths of 23m of R8 rebar• 125m3 of EPS

An environmentally sustainable material is quantified by considering the following 3 factors [16]:

• Carbon footprint• Resource depletion• Waste generation

EPS is 100% recyclable [17]. Typical municipal solid waste streams will have “pape r and paper board: 37.1%, glass: 9.7%, metals: 9.6%, plastics: 6.9%, polystyrene foam: 0.26% (Expanded Polystyrene), rubber and leather: 2.5%, textiles: 2.1%, wood: 3.8%, food wastes: 8.1%, garden refuge: 17.9% and miscellaneous organic waste: 1.8%.” [18] The above values indicate that the contributions of EPS towards filling up of landfills is very marginal. Expanded Polystyrene dome facilities would have minimal impact on the environment due to their use of EPS.

Pg.41

6

Aspects of EPS as a building model

Expanded polystyrene is generally available in 3 types of densities, namely; 15, 20 and 30 kg/m3. With the use of materials such as EPS, the different shapes of a house can be moulded to form various inter-locking pieces that will be easy to assemble on-site. A live miniature model of 1200mm diameter X 600mm carved dome was created using a hot-wire system as shown in figure 5. Carving of each individual piece took 4 hours amounting to 3 days in total to complete carving all the pieces necessary to produce such a model. Due to a large portion of a dome house being pre-fabricated, it has less impact on the environment than conventionally built houses.

Figure 5 The complete EPS model

Figure 4 Hot-wire system used to carve each The EPS piece

The live model was placed exposed for six months from February 2018 to July 2018. it has been exposed to 3 out of the four seasons of the year (1 month of Summer, 3 months of Autumn and 2 months of Winter). Although the test case model would be plastered both inside and outside when constructed for the purposes of inhabitancy; it was a point of interest to study the material in its most natural state. This brings rise to the question of what would then happen to the test case model in the remaining 6 months (1 month of winter, 3 months of Spring and 2 months of Summer). An aspect which can be extrapolated in order to better understand what would be happening in the internal structure of an EPS body shell in the next 6 months is its discoloration potential. The sun emits ultraviolet or UV light, and any change of the surface color of any material indicates that a chemical reaction is taking place. The discoloration of the test case model was assessed over the 6-month period and extrapolated over the remaining 6 months. Figure 6 shows that from the first month through to the 6th month there is a continuous trend of discoloration in the test case model. This indicates that there will definitely be a continuous process where the test case model will absorb water during rain and dry during sunny days. The EPS test case model throughout the 6-month period having undergone constant discoloration and varying levels of wetting and drying has not shown any signs of deformation in shape. This emphasizes the resilience of a dome shape in response to natures varying levels of heat and rain. The discoloration of the EPS and indicative wetting and drying will all be minimized through appropriate cladding material such as a waterproofing coat, plaster coat and a paint coat.

Figure 6 Discoloration potential of EPS

Pg.42

Using the 7 principles of design [19] as a basis for assessment of the four different systems, on a scale of 0 to 10 for each criterion, the systems can be rated as follows:

Figure 7 Performance of building systems in terms of the 7 principles of design

0 2 4 6 8 10 12

Moladi

Khaya ReadyKit

Fischer Housing

EPS Dome House

Performance of building systems in terms of the 7 principles of universal predicon

Size & space for approach and use Low physical effort Tolerances for error

Percepble informaon Simple & intuive use Flexibility in use

Equitable use

The researcher concurs with the ideals of the paradigm shift [20] as depicted in figure 8. Too often it has been thought that achieving sustainability in construction means being aware of only cost, quality and time. This is a very limited approach to sustainability since construction sustainability should also consider; minimal negative environmental impact, minimal consumption of matter/energy as well as human satisfaction.

Figure 8 [20] New paradigm

Feasibility Matrix

A feasibility matrix was compiled based on four compelling criteria which are the preliminary gateway to the consideration of any model. The scales are based on 0 to 100% for each criterion. The score given for each model has been decided upon through an assessment of the construction method statement, historical portfolio and technical data sheets provided by each implementing company for the different systems.

Pg.43

Table 1 Feasibility Matrix comparing EPS Dome, Moladi, Khaya Readykit and Fischer housing

Characteristics Wt. Candidate 1: EPS Dome House

Candidate 2: Moladi

Candidate 3: Khaya Readykit

Candidate 4: Fischer housing

Construction feasibility An assessment of the ease to construct each solution based on whether any training is needed prior to assembly

30%

No prior training required due to toothed joints of EPS dome pieces.

Score: 100

Training on the use of Moladi formwork is required.

Score: 85

Training on the assembly

of the

timber panels is required.

Score: 85

No prior training is required since the Fischer unit is prefabricated and is delivered in an assembled state.

Score: 100

Political Feasibility

An assessment of how well the solution will be accepted by both opposition and ruling political organizations

20%

Solution will be new to most governing authorities in South Africa, however, Solution has been fully endorsed by governing authorities in Japan. Kaga Province in Japan has a community of 480 residences constructed solely of EPS

[1]. This shows that such a model has great potential of acceptance Score: 70

Solution has been in existence for 32 years in South Africa but it has not made significant impact in the low-cost backlog, meaning that governing authorities have not bought into it.

Score: 60

Solution has been in existence for 25 years in South Africa

but it has not made significant impact in the low-cost backlog, meaning that governing authorities have not bought into it.

Score: 60

Fischer housing has been developing prefabricated units for 39 years in South Africa but it has not made significant impact in the low-cost backlog, meaning that governing authorities have not bought into it.

Score: 60

Technical viability

An assessment of the practicality of the solution and the availability of resources and experts to implement and maintain each solution

40%

Expanded Polystyrene is chemically

manufactured and is therefore available in abundance. Impact on the environment is negligible.

Score: 90

Moladi still makes use of reinforcing steel and the system is therefore not entirely environmentally friendly.

Score: 80

Khaya Readykit requires numerous panels of dried and cured timber and timber is a scarce resource and the system is therefore not entirely environmentally friendly.

Score: 80

Fischer housing low cost units are made of stainless steel and stainless steel is a green product since it is recyclable. Impact on the environment is negligible.

Score: 90

Legal Feasibility

An assessment of how well the solution can be implemented within existing legal and contractual obligations

10%

Concept is compliant with all legal requirements.

Score: 100

Concept is compliant with all legal requirements.

Score: 100

Concept is compliant with all legal requirements.

Score: 100

Concept is compliant with all legal requirements.

Score: 100

Ranking

100%

90%

79.5%

79.5%

88%

Pg.44

4. CONCLUSION

It is easier to paint a picture that will reflect the true extremities of the housing situation in South Africa by considering both the past and the present. A laboratory test done on

standard grade EPS of a density of 15kg/m3 during this research showed that it has a mean modulus of 1,14226mPa. The modulus of conventional material is significantly higher than 1,14226mPa and hence as a permanent housing unit such a model is not advised. However, the research has shown that it is possible to increase the accessibility of such a model through a change in construction method. Where low compressive strengths are permissible, unmodified standard grade EPS is suitable for temporary shelter in disaster alleviation , for student shelters on campus, for workers on construction sites seeking shelter for lunch or for overnight lodging and for temporary storage of material. This is because it was observed that the dome shape is structurally stable and highly resistant to contamination since no trace of contamination was observed during the 6-month observation.

Due to the cumbersome framework of the delivery of low-cost housing in many countries as well as the excessive number of homeless people as illustrated by the United Nations [5], an intermediate solution in the form of EPS dome houses is mandatory in the efforts of eradicating penury.

Conventional building methods have gained popularity through their lengthy existence in the built environment, however, for a sustainable future; it is important that a heightened level of environmental sensitivity is demonstrated in the choice of materials for all infrastructure projects.

5. REFERENCES

[1] Japan Dome House Co. Ltd (2018) Superior Features of Dome House, Available at: http://www.i-domehouse.com/page02.html [Accessed 16 March 2018]

[2] Japan Real Estate (2018) Dome Houses of Japan: Made of Earthquake- Resistant Styrofoam, Available at: https://resources.realestate.co.jp/living/dome-houses-of-japan-made-of-earthquake-resistant-styrofoam/, [20 May 2018]

[3] Expanded Polystyrene Association of Southern Africa (2018) Behaviour of EPS in case of fire, Available at: http://www.epsasa.co.za/Images/Publications/Fire.pdf [09 April 2018]

[4] Meth, P. (2016) ‘Informal Housing, Gender, Crime and Violence: The Role of Design in Urban South Africa’ The British Journal of Criminology, Volume 57, Issue 2, 1 March 2017, pg. 402–421

[5] Homeless World Cup (2017) Global Homelessness Statistics Available at: https://www.homelessworldcup.org/homelessness-statistics/ [Accessed 02 May 2017]

[6] Leader (2011) 9 major problems facing South Africa - and how to fix them Available at: http://www.leader.co.za/article.aspx?s=1&f=1&a=2893 [Accessed 01 April 2017]

[7] Gerhart, G.M (1979) Black Power in South Africa: The Evolution of an Ideology, London: University of California Press

[8] Brand South Africa (2013) South Africa: economy overview Available at: https://www.brandsouthafrica.com/investments immigration/business/economy/econoverview [Accessed 01 April 2017]

[9] May, J. (1998) ‘Poverty and inequality in South Africa’ Indicator South Africa, Volume 15, Issue 2, Jan 1998, pg. 53 – 58

[10] Meyer, D.F (2016) ‘Predictors of Poverty: A comparative analysis of low income communities in the northern Free State region, South Africa’ International Journal of Social Sciences and Humanity Studies September (18) pp. 132-149

[11] Achia, T.N.O., Wangombe, A. & Khadioli, N. (2010) ‘A logistic regression model to identify key determinants of poverty using demographic and health survey data’ European Journal of Social Sciences October (8) pp. 38-45

[12] Engineering News (2012) South Africa begins to embrace new building techniques in bid to beat homes backlog Available at: http://www.engineeringnews.co.za/article/building-on-bricks-2012-07-27 [Accessed 01 April 2017]

[13] Moladi (2018) Moladi Low-cost housing, Available at: http://www.moladi.net/technology_about_us.htm [Accessed 15 march 2018]

[14] ReadyKit Construction (2018) A Unique, Patented Construction System, Available at: https://www.readykit.co.za/construction.htm l [Accessed 27 February 2018]

Pg.45

[15] Fischer Housing (2018) Innovative, High Quality, Low Cost Accommodation Units, Available at: https://www.fischerhousing.co.za/ [Accessed 12 February 2018]

[16] Tonini, D., Albizzati, P.F., Astrup, T.F. (2018) ‘Environmental impacts of food waste: Learnings and challenges from a case study on UK’ Waste Management June (76) pg. 744-766

[17] Isowall Group (2018) Recycling of EPS, Available at: http://www.isowall.co.za/recycling-of-eps/ [Accessed 01 May 2018]

[18] Expanded Polystyrene Association of South Africa (2006) The polystyrene industry cares about you and the environment, Available at: https://www.aaamsa.co.za/page/associations/EPSASA/Overview _EPSASA.pdf [Accessed 03 May 2017]

[19] Mace, R.L. (1998) ‘Universal Design in Housing’ Assistive Technology, Volume 10, Issue 1, pg. 21-28

[20] Hussin, J., Rahman, I.A., & Memon, A.H. (2013) ‘The Way Forward in Sustainable Construction: Issues and Challenges’ International Journal of Advances in Applied Sciences, Volume 2, Issue 1, March 2013, pg. 31-42

Pg.46

INSTRUCTIONS FOR AUTHORS

JOURNAL OF CONSTRUCTION

1. Submission of manuscripts

Authors should submit their papers electronically to The Editor at joc@asocsa.org.

Provided that the paper is aached as a separate file using the recommended MS Word soware format. All electronic submissions containing viruses will be deleted without opening them.

Manuscripts must be submied in English and must be original, unpublished work not under consideraon for publicaon elsewhere. It will be assumed that authors will keep a copy of their manuscript. Manuscripts are not returned to the author(s).

Manuscripts are blind peer reviewed by acknowledged experts. Revisions may be required before a decision is made to accept or reject the paper. If an author is uncertain about whether a paper is suitable for publicaon in JOC, it is acceptable to submit a synopsis first.

2. Effecve communicaon

The paper should be wrien and arranged in a style that is succinct and easily followed. An informave but short tle, a concise abstract and keywords and a well-wrien introducon will help achieve this. Simple language, short sentences and a good use of headings all help to communicate informaon more effecvely. Discursive treatments of the subject maer are discouraged. Figures should be used to aid the clarity of the paper. The reader should be carefully guided through the paper.

3. Publicaon Fees

The Journal of Construcon is an Open Access Journal, and all accepted arcles carry a publicaon fee of Ten Thousand Rands (R 10,000).

4. Preparaon of the manuscript

Length: Although there is no length limitaon, papers should preferably be between 3,000 and 6,000 words in length (8 to 12 pages). Longer papers will only be accepted in exceponal cases and might be subject to serializaon at the discreon of the editor.

Layout: The manuscript must be in English, typed and 1.5 line-spaced 10-pt Arial font type on one side of A4 paper only, with a 3cm margin on the le -hand side. All other margins are to be 2 cm. All text should be linked to the le and right margins i.e. paragraphs should not be indented and text should be jusfied. One-line spacing should be le between paragraphs and double line spacing before a new heading. Leave one line space between a heading and the following paragraphs. All headings should be in 12pt bold capitals. Paragraphs and sub-paragraphs should not be numbered.

The pages should be numbered consecuvely. There should be no loose addenda or notes or other explanatory material. The manuscript should be arranged under headings and sub-headings.

Title page (page 1): The first page of the manuscript must contain a concise and informave tle, a secondary running tle of not more than 75 characters and spaces, the name(s), the affiliaon(s) and address(es) of the author(s) and the name, address, telephone, fax and email of the author who will be responsible for correspondence and correcons. The tle should be in 12pt bold capitals, the name(s) of the author(s) in 10pt bold upper and lower case and the affiliaon(s) and address(es) in 10pt upper and lower case with a single line space between each.

Abstract and keywords (page 2): To produce a structured abstract, complete the following fields about the paper. There are four fields which are obligatory (Purpose, Design, Findings and Value); the other two (Research limitaons/implicaons and Praccal implicaons) may be omied if they are not applicable to the paper. Abstracts should contain no more than 150 words. Write

concisely and clearly. The abstract should reflect only what appears in the original paper. Provide no more than 5 keywords.

Purpose of this paper What are the reason(s) for wring the paper or the aims of the research? Design/methodology/approach How are the objecves achieved? Include the main method(s) used for the research. What is the approach to the topic and what is the theorecal or subject scope of the paper? Findings What was found in the course of the work? This will refer to analysis, discussion, or results. Research limitaons/implicaons (if applicable) If research is reported on in the paper this secon must be completed and should include suggesons for future research and any idenfied limitaons in the research process. Praccal implicaons (if applicable) What outcomes and implicaons for pracce, applicaons and consequences are idenfied? Not all papers will have praccal implicaons but most will. What changes to pracce should be made as a result of this research/paper?

What is original/value of paper? What is new in the paper? State the value of the paper and to whom. All headings and sub- headings should be in 10 pt bold capitals and the keywords themselves should be in 10 pt bold upper and lower case.

Introducon (page 3): The introducon should clearly state the purpose (aims and objecves) of the paper. It should include key references to appropriate work, but is NOT the place for a comprehensive historical or literature review.

Discussion: The discussion should emphasize the implicaons and praccal significance of research findings, their limitaons, and relevance to previous studies.

Acknowledgements: A short acknowledgement secon of one paragraph is permissible at the end of the text.

Conclusions: Conclusions should state concisely the most important proposions of the paper, as well as the recommendaons of the authors based on the proposions.

Illustraons: Illustraons must accompany the manuscript and should be included in the text. Photographs, standard forms and charts must be referred to as Figure 1, Figure 2, etc. They should be numbered in the order in which they are referred to in the text. The figure idenficaon and accompanying descripon and any reference should be one line space immediately below the figure and linked to the le margin.

Illustraons should be submied in a form ready for reproducon, preferably as high-resoluon .jpg files. Diagrams and drawings should be drawn in black ink on white paper. Alternavely they should be high quality laser computer printouts from reputable computer soware drawing packages.

Drawings and diagrams must not exceed 140mm in width and all dimensions must be in mm. Annotaon must be in upper and lower case leering, the capital of which should be 3 mm high.

Figures will normally be reduced in size on reproducon and authors should draw with this in mind. With a reducon of 2:1 in mind the authors should use lines not less than 0.2Smm thick and upper and lower case leering, the capitals of which should be 4mm high. Typewrien annotaons are not acceptable.

Tables: Tables must be located close to the first reference to them in the text and must be referred to as Table 1, Table 2, etc. and be numbered in the order in which they are referred to in the text. The table idenficaon and accompanying informave descripon and any reference should be one line space immediately

Pg.474g.45

above the table and linked to the le margin. The table idenficaon should be in bold. Idenfy all stastical methods and sources of data.

Tables should only have horizontal lines, the heading and boom lines being in bold. All words should be in upper and lower case leering. The headings should be aligned to the le of their column, start with an inial capital and be in bold. Units should be included in the heading. Any explanaons should be given at the foot of the table, not within the table itself.

Table 1 Components of expenditure

Component Expenditure (%)

Cleaning works 40,9 Mechanical services 37,7 Building works 13,6 Civil works 7,8 Total 100,0

Source1

Symbols, abbreviaons and convenons: Symbols, abbreviaons and convenons in papers must follow the recommended SI units. Where non-standard abbreviaons are used, the word(s) to be abbreviated should be wrien out in full on the first menon in the text, followed by the abbreviaon in parentheses.

References: The numbered superscript reference system must be used. References in the text should be numbered consecuvely [1] , etc. References should be collected at the end of the paper as they appeared in the manuscript. The style should follow the examples below:

[1] Bon, R. (1997) “The future of internaonal construcon.” Building Research andInformaon 25, 137-41.

[2] Stone, P.A. (1980) Building Design Evaluaon: Costs -in-use. E & FN Spon,London.

[3] Barre, S. (1981) “Implementaon of public policy.” In Policy and Acon, Barre, S. and Fudge, C. (eds), Chapman & Hall, London, 1-33.

If no person is named as the author the body should be used (for example: Royal Instuon of Chartered Surveyors (1980) Report on Urban Planning Methods, London.

Endnotes: A limited number of explanatory notes is permissible. These should be numbered 1, 2, 3, consecuvely in the text and denoted by superscripts. They should be typed on a separate sheet of paper at the end of the text. Endnotes should not be used for academic or project citaons.

Copyright: Submission of a paper to JOC is taken to imply that it represents original, unpublished work, not under consideraon for publicaon elsewhere. The Journal of Construcon is commied to open access for academic work and is, therefore, an open access journal, which means that all arcles are available on the internet to all users immediately from the date of publicaon. This allows for the reproducon of arcles, free of charge, for noncommercial use only and with the appropriate citaon informaon. All authors publishing in the Journal of Construcon accept these as the terms of publicaon.

Permission to publish illustraons must be obtained by the author before submission and any acknowledgements should be included in the figure capons. Should the author wish to have the paper published elsewhere, such as in an anthology, the author must write and seek consent from the Publisher which will normally be given provided acknowledgement of the original source is provided.

Copyright of the content of all arcles and reviews remains with the designated author of the arcle or review. Copyright of the layout and design of Journal of Construcon arcles and reviews remains with the Journal of Construcon and cannot be used in other publicaons.

Benefits of open access for authors, include: - Free access for all users worldwide- Authors retain copyright to their work- Increased visibility and readership- Rapid publicaon - No spaal constraints

Pg.48