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Obviously, the task of addressing a large number of pairwise questions required in this study would be enormous requiring intensive efforts and extended time. Hence, in order to economise efforts and establish a more rational approach to collect judgements from qualified telecommunication experts, it was decided to design two types of questionnaires that contain the same material but differ in layout, which were web-based and text-type questionnaires. Appendix G presents both questionnaire types that were used to collect answers to pairwise questions. A particular focus is given to the web-based approach as it brought greater value to group decision making by allowing users to give critical input from anywhere in the world.

1. Web-based questionnaires: In today’s world, the internet has become an integral part of our daily life; it offers a valuable opportunity for collecting experts’ opinions. Hence, the author created the required online questionnaires by using a subscription-based service to an online survey provider which allowed for many extra benefits including more responses are being received from around the world. To minimise experts’ time and efforts, the questionnaires were divided into several independent links, so that any respondent can choose and answer the questions that are relevant to his/her expertise. The survey links were then e-mailed to a group of telecoms experts as well as academics who were identified from online forums as described in subsection 5.2.2 and published in three online forums. They were sent to staff-members attached to several telecoms companies such as, British Telecoms (BT), Siemens, Alcatel, NEC, etc. They were also circulated within several schools of Electrical and Electronic Engineering in Libya and the UK. The results were collected, stored automatically by the service provider and appeared in the account instantly. Thus, one can view the completed surveys individually or download them as a spreadsheet. In addition, the individual results were e-mailed to the author as soon as they were collected which proved to be practical in this study. This approach was found to be an efficient data collecting technique as it constituted a group decision support system that permitted experts from all over the world to express their preferences through the internet. It allowed for greater and more effective participation in the decision making

process by bringing about greater consensus. It also eliminated the need for face-to-face meetings, which usually dominated by one person to promote his/her own views.

2. Text-type questionnaire: It was designed using Microsoft Word and sent to reachable persons who prefer the portability and convenience that the paper format allows in providing judgements in this form. They are mainly academics with a busy-schedule from Electrical and Electronic Engineering schools at nearby universities within the city of Manchester. Several completed questionnaires were collected using this method.

Several case studies in the literature applied AHP/ANP indicated the use of three to seven respondents (Saaty, 1994). Hence, in order to minimise experts’ biases when answering pairwise comparison questions, four judgements were obtained for each particular question as shown in Appendix H. All judgements obtained from individual experts using either of the two methods described above were tabulated and then aggregated into a representative group judgement by calculating the geometric mean for each pairwise question (see, section 4.5) as shown in Appendix H. The aggregated group judgements were then arranged in corresponding consensus pairwise judgement matrices and finally entered into

SuperDecisions to perform necessary computations to synthesise the results. Figure 6.4 illustrates how the judgements are entered into the SuperDecisions. It shows an example of pairwise comparisons between alternatives, with respect to ‘Reliability’.

Figure 6.4 An example of SuperDecisions pairwise comparison process

While a score of 1 indicates equality between the two technologies, the blue scores represent the dominance of the row element in the matrix (e.g., G1) over the column element (e.g., G2) and the red scores are vice versa. The question being asked is “With respect to reliability, which technology is more reliable: Fibre optic technology or power line communication?” The group judgement was that G1 ‘is between ‘very strongly’ and ‘extremely’ more reliable than G2. Thus, a ‘blue’ score of 8 corresponding to the group judgement regarding this question is clicked to highlight the technology providing more

reliability relative to the technology providing less reliability. A reciprocal value is automatically assigned in the opposite position in the matrix.

6.3.2.2 Determining the normalised weights

The next stage of the process includes the computations of the relative importance of the elements. For each comparison matrix a local priority vector is computed, by applying the eigenvector approach described in chapter 4, subsection 4.3.2.3.1. Appendix I presents all pairwise comparisons matrices including their eigenvectors and consistency ratios in the last column. An example of such aggregated comparison matrix is shown in Table 6.13. It compares the alternatives with respect to the reliability factor. Its corresponding eigenvector is also shown in the rightmost column.

Table 6.13 Comparison matrix of alternatives with respect to reliability

Alternatives G1 G2 G3 G4 Eigenvector G1 1.000 8.240 3.350 5.730 0.580 G2 0.121 1.000 0.178 0.217 0.044 G3 0.299 5.630 1.000 3.830 0.262 G4 0.175 4.610 0.261 1.000 0.114 CR=0.0958

From the above table one can see that in terms of reliability, the Fibre optic cable technology has the highest priority (0.580) followed by Microwave links and Satellite with (0.262) and (0.144), respectively. The less reliable technology is the Power line communication (0.044). Since the value of CR is less than 0.1, this matrix is considered of acceptable consistency. SuperDecisions can deal with the issue of improving matrices’ consistency, by identifying the most inconsistent judgements. The matrix consistency can then be improved by providing more consistent judgements by the decision makers so

that CR≤0.1. For further explanation of consistency and how to calculate it, one can refer

to chapter 4, subsection 4.3.2.3.1.