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A validation Study of House of Quality key performance indicators Services ICT Insurance Transportation Construction Healthcare Retail Wholesale Finance Government Education Manufacturing Other 0 10 20 30 40 Table 4 – Sector of the organization Table 5 – Function of members As represented in Table 4 most respondents are employed in the financial sector and the services sector. The number of respondents working in these sectors can be explained by the fact that in the first year of the online union employees of two financial organizations where faced with reorganizations and mergers. There was no collective bargaining agreement for their specific sector. To represent the interests of these employees, De Unie started a campaign and became the employee’s representative organization. This also explains the results of Table 5 from which we can conclude that most members have a financial related or management function. Daily 0.9% A few times a week 2,6% Once a week 4,3% A few times a month 12,1% Once a month 24,1% Less than once a month 56,0% Figure - 9 Frequency site visits Figure - 10 Reason to visit website The frequency of visits to the website of the online union is presented in Figure 9. Overall we can conclude that members do not visit the site of the online union very often. The majority of the 56 Sales ICT Human resources Government Research and Development Marketing-PR-Advertising Legal Procurement and Logistics Healthcare Finance - Administration General management Consultancy Other 0 10 20 30 Information 87,1% Recreation 10,3% Interaction 2,6%
A validation Study of House of Quality key performance indicators sample visits the site less than once a month (56%) or once a month (24.1%). As showed in Figure 10, the main reason for members to visit the site is to gather information (87.1%). 4.2.2 Factor analyse In our model quality (Q) measured by one item is the sum of an expectation rating (E) extracted from the corresponding perception rating (P). A gap-score is defined as Q = P – E. The computed gap- variables are used for further scale analysis. Further analysis of the gap scores will be discussed in section 4.3. After qualitative research in phase one, the explored items were structured into five dimensions (Table 2). To test the structure of the modified dimensions factor analysis was conducted. Factor analysis is a multivariate statistical technique which aim is to reduce a large number of variables to a smaller number of underlying dimensions, known as factors. Therefore factor analysis will try to explain a maximum amount of variance in our data by means of a small number of factors. Before conducting factor analysis, we should test if factor analysis is an appropriate method for detecting structures in our data. Therefore we will first inspect the correlation matrix, the Bartlett’s test of sphericity, the Kaiser-Meyer-Olkin (KMO) and the measure of sampling adequacy (MSA). When the results of the tests conclude that factor analysis is allowed, the items will be structured into factors. The Bartlett’s test of sphericity will test the inequality between the correlation matrix and the identity matrix. The test values detected a significant result (Chi 1164.774; p:0.00) from which we can conclude that there are differences between the correlation matrix and the identity matrix. The second test, the Kaiser-Meyer-Olkin test, measures the sampling adequacy. A high value indicates sufficient intercorrelations between the variables which mean that the correlations between pairs of variables can be explained by other variables.. The test value in our analysis is 0.875. A value of >0.8 is meritorious. The last test for sufficient intercorrelations is the Measure of Sampling Adequacy (MSA). Looking at the anti-image matrix in SPSS, low MSA values of
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