# A Modified Analytic Hierarchy Process Suitable for Online Survey Preference Elicitation
## 4 Discussion
### Advantages of MAHP over Other Weighting Methods  
A range of methods other than the traditional AHP has been applied in the literature to assess preference weights. While these were not explicitly tested in this study, some general advantages of the MAHP can be inferred.  
A commonly applied approach is to use ranking- or rating-based approaches [2; 3; 4; 5; 6; 7]. These largely involve scoring each alternative usually from 0 to 100, with variants of the approach either awarding the most preferred a value of 100 and then scoring the other options relative to this, or awarding the least preferred a value of 10 and again scoring the other options relative to this [3]. In other approaches, points are allocated across alternatives such that they sum to a given value (e.g., 100) [4]. These approaches have the advantage that they avoid issues around inconsistency. However, comparative studies of these methods suggest in some cases that the objective weights may vary considerably between methods [7; 10], although others have found higher correlations between the results of the different methods [11].  
The MAHP approach has some benefits over these other rating-based approaches in that the resultant weights are not sensitive to the highest scores given. For example, a score of \(A=9\), \(B=7\) yields \(AB=2\), which is the same as if \(A=5\) and \(B=3\), and the same derived weight. In contrast, a rating-based approach would result in closer weights between the options in the first case (9, 7) than in the second case (5, 3).  
Other approaches have also been developed to reduce the number of comparisons required using the AHP. For example, [PERSON], et al. [62] developed a parsimonious AHP method that involved first rating each criterion (e.g., 0-100) and then using bivariate comparisons on a subset of alternatives using a set of reference criteria defined by the initial ratings with linear interpolation used to assess the weights for the excluded criteria. The approach was also applied by [PERSON] [63] to a transport development decision problem, who also validated the method against a traditional AHP approach, and by [PERSON] and [PERSON] [64] to identify the importance of different criteria to the choice of tourism destinations. While the parsimonious AHP method helps reduce the number of pairwise comparisons required, it requires interaction with the decision-makers to first assess the initial rankings in order to determine the appropriate sets of pairwise comparisons. It is therefore less amenable to online surveys, where direct interactions are infeasible and potentially the set of respondents may be large.  
Best-worst analysis has also been used in online surveys largely to rank alternatives or attributes (e.g., [65]). These generally require counts of responses over the survey sample and are less amenable to eliciting individual preferences. [PERSON] [66], [PERSON] [67], and [PERSON], et al. [68] developed variants of a best-worst multicriteria decision-making method where decision-makers first identified their best and worst alternatives, and pairwise comparisons were then developed based on comparing other alternatives to these best-worst alternatives. The decision-makers were then resurveyed to elicit their individual preferences. This additional step increases the complexity of the approach relative to the MAHP, which can be undertaken in a single step. Other best-worst scaling approaches have been developed in a form analogous to choice experiments for use in online surveys. While these have been successfully applied to elicit preferences (e.g., [69; 70; 71]), they are often limited to relatively few alternatives and require large samples to develop robust weightings.