Sampling size

Different questionnaires were circulated to different companies around Gauteng. 119 were sent to ERP specialists via email, and clothing and textile employees, while some were delivered during site visits. Employees were given enough time to respond, especially those who received questionnaires via email.

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7.5 Data collection

The researcher also used questionnaires to collect important information related to the research area or topic, and both primary and secondary data were used. Some of questionnaires were sent via emails, to enable ERP users enough time to complete them. The use of questionnaires was an advantage because data collection and analysis was time consuming, even though they were analysed using SPSS. The majority of the respondents were aware of and understood the ERP system and implementation that is why the feedback was likely to be very rich. However, the absence of the researcher restricted the opportunity to get more feedback from other companies.

7.5.1 Primary data

The primary data for the study was questionnaires. They were designed (open &closed ended), by the researcher following assistance from UJ‘s Director of STATKON. The closed ended questionnaire was well designed, allowing respondents to provide feedback easily, and also for consultants to analyse using SPSS. As indicated in Table 7.1 below the ERP specialist questionnaires focusses on different companies in South Africa. 59.4% of questionnaires were distributed in large enterprises, 28.7% in medium enterprises, 7.9% in small enterprises, and 1.0% in micro enterprises.

Table 7.1: Business type

Frequency Percent Cumulative

Percent Valid Micro enterprise: 5

workers or less 1 1.0 1.0 Small enterprise: 6 – 49 workers 8 7.9 8.9 Medium enterprise: 50 – 200 workers 29 28.7 37.6 Large enterprise: 200

and above workers 60 59.4 97.0

Other 3 3.0 100.0

Total 101 100.0

Data was collected in different business types that are using an ERP system, as indicated in Table 7.2 below: Manufacturing (42.6%), Clients (17.8%), Finance (17.8), Marketing (7.9%) and other (13.9 %).

Page | 76 Table 7.2: Business function

Frequency Percent Cumulative

Percent Valid Clients 18 17.8 17.8 Finance 18 17.8 35.6 Manufacturing 43 42.6 78.2 Marketing 8 7.9 86.1 Other 14 13.9 100.0 Total 101 100.0

As indicated in Table 7.3 below, data was collected in companies that are implementing an ERP system, 39.6% of companies are using SAP, 17.8% are using Oracle, 16.8% are using SYSPRO, 12.9 % Baan, 5.9% Micro dynamics, 3.0% JD Edwards, and 4.0%, other.

Page | 77 Table 7.3: Types of ERP system.

Frequency Percent Cumulative

Percent Valid JD Edwards 3 3.0 3.0 LN/ Baan 13 12.9 15.8 Microsoft Dynamics 6 5.9 21.8 Oracle 18 17.8 39.6 SAP 40 39.6 79.2 SYSPRO 17 16.8 96.0 Other 4 4.0 100.0 Total 101 100.0 7.5.2 Secondary data

Secondary data mainly consisted of available database (published journals, articles, and books), literature review, together with companies’ magazines/websites regarding ERP systems.

7.6 Data Analysis

SPSS from the University of Johannesburg was considered. The analysis was taken on with the idea of arranging data collection to get feedback from research questions .Part of data analysis focused on: Descriptive Analysis: Biographic Information (ERP specialists); Descriptive Statistics; Factor Analysis; Exploratory reliability analysis, Tests of Normality; Results from Correlation; Results from open ended responses; Descriptive Analysis: Biographic Information (Clothing and Textile Industries); and Results from open ended responses, Which were considered to give more responses from questionnaires. Reason for using factor analysis was making sure that results are analysed in various sections, e.g. Section 2 of ERP critical success factors; and 3 of measuring quality. Test for normality and correlation were also used based on critical success factors, and measuring quality of ERP implementation.

7.6.1 Descriptive Analysis

The descriptive statistics have the total number of uses which includes the description of the characteristics of sample being researched. It also assists in checking all variables for any violation of the assumption of underlying the techniques of stats that can be used to address research questions, and addresses research objectives. (SPSS guide 6, 2007) The study uses descriptive statistics for all CSFs of ERP.

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Assessing normality: Test for normality indicates the Kolmogorov-Smirnov statistics, it assist in assessing the distribution of the score, this uses a non-significant value of more than 0.5 , which indicates a normal distribution, that also suggests the violation of the assumptions of normality, which is common if scales are larger. The study focused on tests for normality for all 9 CSF, and Measuring quality of implementation and outcome. Test for normality used Kolmogrov-Smirnov tests, because all factors’ degree of freedom (df) values were more than fifty, the total degree of freedom (df.) was 101.

7.6.2 Factor Analysis

Factors analysis focuses on a big set of variables, it also considers a method that might reduce the data, focusing on a smaller set of factors or components. Factors analysis prepares this by focusing on groups amongst interrelation of a set of variables. For factor of correlation matrix to be considered as an appropriate factor, correlation matrix must indicate an appropriate correlation of r=0.3 or more than that; Bartlett’s test of Sphericity must be statistically significant at p < 0.5,and Keiser-Meyer-Olkin (KMO) value must be .6 or above. And to determine how many factors to extract, using Kaiser’s criterion, a researcher should only focus on components that have eigenvalue of one or more. (SPSS guide, 2007).The researcher used a sample size of 101, which the ratio of 5 to 7 cases of each of 9 critical success factors, and focuses on eigenvalue of 1 or more, and also focused on the total variance explained to determine the total number of components that meet the criterion . The researcher also focuses on components matrix, which indicates the unrotated loadings of each items.

7.6.3 Exploratory reliability analysis

It is vital for a researcher to check scales that are reliable when selecting scale that is supposed to be included in a study. Cronbach's Alpha coefficient is the most commonly used indicator of internal consistency, and its scale should be above .7. If there are low values of 0.5, it is very important for a researcher to discuss the mean inter-item reliability for correlation for each item. Depending on the sample that it is used, reliability of those items might differ (SPSS guide, 2007).

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The study uses reliability scales of Cronbach's Alpha Based on Standardized Items, for all critical success factors. Vision and planning of Project 0.884, Choice of an ERP system 0.758, Support from top management 0.896, Project management 0.833, Project Champion 0.762, Business Process Re-engineering 0.857, Communication 0.902, User training and education 0.875, and Organisational resistance 0.864, Measuring the quality before implementation 0.795, Measuring the quality after implementation 0.869; Rating the quality of factors relevant to a company, before implementation 0.923, and Rating quality of factors relevant to a company, after 0.910. The researcher also discusses Reliability Statistics and Summary Item Statistics.

7.6.4 Correlation

The study focused on correlations tests for all nine CSF, Measuring quality of ERP implementation and outcome, and rating factors. The researcher uses tests for non- parametric correlation, which considered the Spearman Rank Order Correlation (rho), significance [sig. (2-tailed)], and the large sample sizes (N), for best results. A strong relationship between most variables within organizations was identified with most correlation being more than 0.5, N= 101; and p< .05, which shows that the relationship between these factors was very solid. It is very important for a researcher to check the size of correlation coefficients. Values that ranges from -1.00 to 1.00 shows the relationship between variables, with 0 shows that there is no relationship, 1.0 perfect positive correlation, whereas -1.0 is perfect negative correlation.

Table: 7.4: Correlation Interpretation

Small r= .10 to.29

Medium r=.30 to .49

Large r=.50 to 1.0