110 6.4 TARGETED POPULATION
Kothari (2004) states that all items in any field of inquiry constitute a “universe” or “population”. For purposes of sampling, a population does not refer to the population of a country, but to objects, subjects, phenomena, cases, and events which the researcher wishes to research in order to establish new knowledge (Brynard et al., 2014). A population refers to a group in the universe which possess specific characteristics – the universe being all subjects who possess the attribute in which the researcher is interested e.g. the total number of number of inhabitants in the country who possess a post-graduate qualification.
The targeted respondents that made up the population that was sampled for this study were architects, buyers, civil engineers, construction managers, construction project managers, logistics managers, quantity surveyors and other professionals who are involved in construction projects in Gauteng Province, South Africa. Swan and Khalfan (2007) advise that in a study of this magnitude, the inclusion of all construction professionals is essential; therefore a list of construction professionals who work within the province of Gauteng was obtained from the various professional councils and the Council of the Built Environment.
6.5 SAMPLING
It often happens in research that the population being studied for a particular research study is of such a magnitude that it could take the researcher years to complete the research (Brynard et al., 2014). Therefore, the researcher has to select from the population being studied a small group which is still representative of the general population being studied. This small group that represents the larger population is referred to as a sample.
Sampling is a technique of selecting willing member of the population and involving them in a study so that the results are representative of the entire population. It is a technique employed to select a small group (the sample) with a view to determine the characteristics of a large group (the population) (Ashraf & Brewer, 2004; Brynard et al., 2014). If selected critically, the sample will present the same characteristics as the large group.
Sampling is used to simplify the research study being conducted as it makes it easier to study a representative sample of a population than to study the entire population. Sampling also saves time
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as studying the entire population can be time-consuming, especially if the population is very large or spread over a large geographical area. Furthermore, sampling is regarded as a convenient and cost-saving approach to a research study as collecting data from every single element of the population can be very costly, especially if one also considers the magnitude of the population and how distributed over a large geographical area it is. Leedy (1989) states that when observed with insight, a well chosen few (a judicious sample) from the mass of humanity will tell you more than all the multitudes together.
In research terms, particularly for the purpose of sampling, ‘population’ does not refer to the total number of citizens in a country. It refers to all objects, activities, phenomena, events, or cases the researcher wishes to study in order to establish new knowledge (Brynard et al., 2014). Therefore it is important to determine which objects, activities, phenomena, events, or cases are to be considered as a population from which the sample will be selected for the purpose of the study. It is also important to bear in mind that a larger sample is more representative of the population, making the conclusions to be drawn more accurate. The more heterogeneous a population is, the larger the sample should be (Bless & Higson-Smith, 1995). There are two categories of statistical sampling in social sciences, according to Teddlie and Yu (2007) and Latham (2007), which are indicated as follows:
Examples of probability samples: o Simple random samples o Stratified random samples o Systematic samples o Cluster samples
Examples of non-probability samples: o Accidental or incidental samples o Purposive samples
o Quota samples o Snowball samples
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In the case of probability sampling, we can determine the probability that any element or member of the population will be included in the sample. The advantages of probability sampling is that it enables us to indicate the probability with which sample results deviate in differing degrees from the corresponding population values. This study therefore used a random sampling technique as it gave all the participants an equal chance to be selected and they were all selected on the same criterion which was that the respondent had to be a construction professional or a relevant stakeholder within the construction industry and practising in the Gauteng Province, South Africa.
6.6 DATA COLLECTION
The most frequently used techniques of data collection within the two basic research methods are the following:
Review of relevant literature
Interviews
Questionnaires
Observation
For purposes of successfully carrying out of this research study, a questionnaire was chosen as the preferred data collection instrument. The questionnaire was designed based on information gathered from the reviewed literature. After it had been approved for data collection, it was then distributed by hand to various respondents around the targeted area. After the respondents had completed the filling in of the questionnaire, the researcher did most of the fieldwork with regard to their recollection. However, some respondents delivered the questionnaire to a place agreed to by the researcher.
6.7 INSTRUMENTS OF DATA COLLECTION
A questionnaire was chosen for this research as the preferred data collection instrument. Questionnaires are considered a valuable method of collecting a wide range of information from a large number of respondents. Questionnaires are described as a series of questions asked to individuals to obtain information which is statistically useful about a particular topic (Wikipedia, n.d). Burns and Grove (1993) define a questionnaire as a printed self-report form designed to draw information that can be obtained through the written responses of the subject. When properly
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constructed and administered, questionnaires become a vital instrument by which statements can be made about specific groups, people or the entire population (Mofokeng, 2012).
Questionnaires are considered advantageous based on the reason that they give the respondents time to think about the responses to the questions posed to them in the questionnaire. They are easily analysed and are in most cases familiar to the participants; a number of people have had some sort of experience with completing questionnaires, therefore this reduces the participants’ uneasiness in participating. Furthermore, questionnaires make it possible for a larger geographical area to be reached.
Questionnaires can either be closed-ended or open-ended. Respondents in the open-ended questionnaires are expected to respond to the questions in writing in their own words. The respondents can provide more details if they wish. However, in closed-ended questionnaires, according to Burns and Grove (1993), the respondents are given options related to the research topic which are determined by the researcher. For reasons of easier administration and analysis, a closed-ended questionnaire was adopted for this research.
The questionnaire was assembled through the design of seven sections: A, B, C, D, E, and F. The questionnaire was also designed in a manner that does not compromise the anonymity of the participants, as they were assured that their identities will remain in confidence; they will not be published with findings of the research. As part of the questionnaire, an estimated time to complete the questionnaire, which was determined through a pilot survey, was stipulated along with the researcher’s contact details in case the respondents needed clarity regarding anything in the questionnaire.
Section A was aimed at extracting demographic data. This is concerned with data such as gender, age, level of education, profession, and so forth, which would assist the researcher with the interpretation of the results. Section B of the questionnaire focused on gaining the industry perception of ERP systems. Section C aimed to assess the critical factors for the successful implementation of ERP systems. Section D was meant to evaluating the possible benefits of implementing ERP systems in a construction firm.
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Section E assessed the key performance indicators for successful ERP system implementation in a construction firm. Lastly, Section F assessed the potential improvements in project performance that can be gained through the implementation of ERP systems in the construction industry.
Validity and reliability of data-measuring instruments are crucial to scientific research (Brynard et al., 2014). Thus content, criterion-related, construction, and external validity were carefully observed. Furthermore, attached to the questionnaire were instructions and guidelines for the purpose of guiding the respondents on how to answer the questionnaires.
Out of the two hundred copies of the questionnaires sent out, one hundred and seventy-one were recollected, representing a response rate of eighty-six per cent. However, only one hundred and sixty-seven out of one hundred and seventy-one questionnaires were usable, which results in a usable response rate of eighty-four per cent. These results formed the basis of this study as summarised in Table 3.2 below:
11Table 6.1: Questionnaire survey
Survey Responses Respondents
Questionnaire sent out 200
Questionnaires collected 171
Usable questionnaires 167
Usable response rate (%) 84%
Before analysis could be initiated, the collected data from the respondents had to be cleaned and screened. Frequency analysis of the raw data was then done using the Statistical Package for Social Sciences (SPSS).
6.8 PERIOD OF COLLECTION
The visitation and distribution of these questionnaires to the randomly selected companies stretched over a time period of approximately three months, ranging from early November 2016 until the end of January 2017
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6.9 DATA ANALYSIS: MEAN ITEM SCORE (MIS)
Measurement instruments are meant to measure specific characteristics of respondents and not the individuals themselves; therefore the instruments for measuring should be valid and reliable (Huysamen, 1994). It is important that the research questions and research problems that have been formulated be tested during the research. The measuring instruments should be applied to test or measure the reliability of the methods of data collection (Brynard et al., 2014)
Attitude scales are designed to measure varying degrees of attitudes towards a particular issue (Brynard et al., 2014). The respondent selects an appropriate response on a scale of, for example, five rank-order points, with the two extremes of the scale being “Strongly agree” and “Strongly disagree”. Each point is represented on the scale by the assignment of scores, from 1 to 5. By the summing and averaging out the scale points indicated by the respondents, the researcher can assess and determine the respondents’ attitude towards a particular subject matter.
For this current study, a five-point Likert scale was used to determine the perception of ERP systems in the construction industry, the critical factors for successful implementation of ERP, the benefits of implementing ERP systems in construction firms, key performance indicators for successful ERP systems in construction firms, and the possible improvement regarding project performance through the implementation of ERP systems in construction firms in the Gauteng Province, South Africa. The adopted scales are as follows:
1. = Strongly disagree (SD)
2. = Disagree (D)
3. = Neutral (N)
4. = Agree (A)
5. = Strongly agree (SA)
The five-point scale was transformed to a mean item score (MIS) for each of the perceptions of ERP systems in the construction industry, the critical factors for successful implementation of ERP, the benefits of implementing ERP systems in construction firms, key performance indicators for successful ERP systems in construction firms, and the possible improvement regarding project performance through the implementation of ERP systems in construction firms. The indices were then used to determine the rank of each item. The ranking made it possible to cross-reference the
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relative importance of the items as perceived by the respondents. This method was used to analyse the data collected from the questionnaire survey.
The computation of the relative mean item score (MIS) was calculated from the total of all weighted responses and then it was related to the total responses on a particular aspect. This was based on the principle that respondents’ scores on all the selected criteria, considered together, are the empirically determined indices of relative importance. The index of MIS of a particular factor is the sum of the respondents’ actual scores (on the five-point scale) given by all the respondents as a proportion of the sum of all maximum possible scores on the five-point scale that all the respondents could give to that criterion. A weighting was assigned to each response, ranging from one to five for the responses of ‘Strongly disagree’ to ‘Strongly agree’. This is expressed mathematically below. The mean item score (MIS) was calculated for each item as follows:
Where:
MIS = 1n1+ 2n2 + 3n3 +4n4+5n5 ……… Equation 1.0 ƩN
n1= Number of respondents for ‘extremely unlikely’ or ‘strongly disagree’; n2 = Number of respondents for ‘unlikely’ or ‘disagree’;
n3 = Number of respondents for ‘neutral’;
n4 = Number of respondents for ‘likely’ or ‘agree’;
n5 = Number of respondents for ‘extremely likely’ or ‘strongly agree’; N = Total number of respondents
After mathematical computations, the criteria were then ranked in descending order of their mean item score (from the highest to the lowest).
6.10 CONSISTENCY
The Cronbach’s alpha was adopted for the purpose of conducting a measure of the internal consistency to determine the reliability of the measuring instrument. Cronbach’s alpha describes the extent to which all items in a test measure the same concept (Takakol & Dennick, 2011): the less variation an instrument produces in repeated measurements of an attribute, the more reliable the instrument is. The Cronbach’s alpha aims to determine the degree of correlation of items in a
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set (Humaidi & Said, 2011). The Cronbach’s alpha was calculated for each section and subsection of the questionnaire. The results of each category of questions in this study are shown in table 6.2 below:
12Table 6.2: Values of Cronbach’s alpha
Section Category Cronbach
Alpha Section B Perceptions of ERP systems
1 Financial Management Solution 0.820
2 Human Resource Management Solution 0.873
3 Procurement and Logistical Management Solution 0.840
4 Project Management Solution 0.874
Section C Critical success factor for ERP system implementation 0.932 Section D Benefits of implementing ERP systems 0.946 Section E Key performance indicators for successful ERP implementation 0.937 Section F Improvements in project performance through ERP
systems
1 Time-related factors 0.891
2 Cost-related factors 0.913
3 Quality-related factors 0.912
Humaidi and Said (2011) state that the closer the Crobanch’s alpha is to the value of 1.0, the better the reliability. Furthermore George and Mallery (2003) and Gliem and Gliem (2003) also state that a value of 0.7 is still acceptable: however, a value of 0.8 and above should be the goal. Since, the Cronbach’s alpha of all variables in this study ranged from 0.820 to 0.912, the indication was that the internal consistency of the direct constructs was within the acceptable values.
6.11 LIMITATION OF THE STUDY
This research study was only based on the relevant construction project stakeholders in Gauteng Province, South Africa. The relevant respondents are architects, buyers, civil engineers, construction managers, construction project managers, logistics managers, quantity surveyors and
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other professionals who are involved in construction projects in the Gauteng Province, South Africa. This study only determined the perception of ERP systems in the construction industry, the critical factors for successful implementation of ERP, the benefits of implementing ERP systems in construction firms, key performance indicators for successful ERP systems in construction firms, and the possible improvement regarding project performance through the implementation of ERP systems in construction firms in the Gauteng Province, South Africa.
6.12 SCOPE OF THE STUDY
This research study concentrated on ERP systems in the Gauteng Province construction industry; therefore the distribution of the questionnaire was mainly focused on construction professionals. ERP systems can be examined from various spectrums: however, the scope of this research margins the study between the establishment of perceptions of construction professionals regarding ERP systems, the benefits of implementing such a system in a construction organisation, the critical factors that facilitate a successful implementation of such a system, the key performance indicators of a successful implementation and lastly, the establishment of whether construction project performance can be improved through the implementation of ERP systems.
6.13 ETHICAL CONSIDERATION
This research study did not encounter any ethical issues. Considerations that were made with regard to ethics in this study were in the form of acknowledging, through proper citation, the professionals in the industry whose works contributed to the literature in this study. The obligation of the researcher included transparency regarding the research inquiry that was being conducted and also what the data was to be used for. The researcher had to reassure participants of the duration of the exercise, and that their input would remain in confidence with the intention of being used only in the academia. The respondents had the right not to answer questions that they felt were not appropriate, without any coercion. Anonymity and confidentiality were maintained throughout the progress of the study. A written cover letter of permission to carry out this research study was obtained from the University of Johannesburg, Department of Construction Management and Quantity Surveying, Doornfontein Campus and was affixed to the questionnaires sent out.
119 6.14 CONCLUSION
This chapter described and justified the research methodology that was used in carrying out this study. Furthermore, this chapter described the population, research approach and design, the geographical location, sample and sampling technique, data collection instrument, measuring instrument, period of collection, limitations, methods that were placed in position to ensure the validity and reliability of the data, as well as strategies used to ensure ethical standards during the carrying out of this study. The next chapter of this research study presents the data analysis and discussion.
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CHAPTER SEVEN
DATA ANALYSIS OF THE QUESTIONNAIRE SURVEY RESULTS
7.0 INTRODUCTION
This chapter presents the results of the data gathered for this study through the distribution of a closed-ended structured questionnaire. The questionnaire was distributed among the randomly selected target of construction professionals in the Gauteng Province, South Africa, who included architects, quantity surveyors, construction managers, civil engineers, and construction project managers, among others. The analysis of the data and interpretation of the results were obtained from the hundred and sixty-seven usable questionnaires that were recollected from the two hundred that were handed out, reflecting an eighty-four per cent response rate.
The questionnaires analysed comprised six sections each, of which all were answered. Section A was aimed at extracting demographic data of the respondents. Section B of the questionnaire focused on gaining the industry perception of ERP systems. Section C aimed to assess the critical factors for the successful implementation of ERP systems. Section D was meant to evaluate the possible benefits of implementing ERP systems in a construction firm. Section E assessed the key performance indicators for successful ERP system implementation in a construction firm. Lastly, Section F assessed the potential improvements in project performance that can be gained through the implementation of ERP systems in the construction industry in South Africa.
7.1 DATA ANALYSIS
7.1.1 SECTION A: BACKGROUND INFORMATION OF RESPONDENTS
Figure 7.1 shows the gender orientation of the 167 respondents, reflecting sixty-three per cent of the respondents to have been male, while thirty-seven per cent of them were female.
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Figure 7.1: Respondents’ gender
Findings relating to the respondents’ ethnic group are shown in figure 7.2. The results reflect that sixty-one per cent of the sample assessed was African (blacks), twenty-three per cent were white, seven per cent were coloured, and nine per cent were either Indian or Asian.
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Figure 7.2: Respondent’s ethnicity
Figure 7.3 relates to the respondents’ age group. The results reflects that 15 per cent of the respondents were in the age group of 21 – 25 years old, 33.5 per cent of the respondents were in