# A Modified Analytic Hierarchy Process Suitable for Online Survey Preference Elicitation
## 1 Introduction
The ease of implementing online surveys, the larger number of potential respondents they can reach, and the relatively low cost per response has made online surveys widely attractive for gaining preference information from the general community, as well as from special interest groups where individuals may be hard to identify a priori. A key advantage of the use of online surveys is that it allows access to relevant stakeholders who may be geographically dispersed, spatially space, or otherwise hard to reach, even if not large in absolute numbers. For example, [PERSON], et al. [35] employed an online AHP survey to assess satisfaction with the working environment within a large real estate firm with offices spread across the UK. The issue of inconsistencies and how they may be reduced, however, creates challenges for the use of online surveys to elicit preferences. The elicitation of preferences using the AHP has largely relied on interactions with stakeholders, either in small groups (e.g., workshops) or through the use of a survey instrument. In small group settings or where respondents are known, a common practice is to request respondents to re-consider inconsistent responses. This is less practical for online surveys. For example, the anonymity of respondents in online surveys makes following up with individual responses impossible. This lack of direct interaction with the respondents creates additional challenges for deriving preference weights through approaches such as the AHP. The consequence of these factors is that high levels of inconsistency are relatively common in online AHP surveys. For example, [PERSON] and [PERSON] [36] and [PERSON], et al. [37] respectively found 75% and 73% of respondents provided inconsistent responses. Excluding these observations substantially limits the benefits of the survey. Ideally, these inconsistencies would be identified, and respondents would be requested to reconsider their choices in the light of these identified inconsistencies. Some studies have built-in checks prompting respondents to reconsider their choices if inconsistency is observed [38; 39]. Feedback from respondents about these prompts was largely skeptical, as some felt that the researchers were trying to get them to choose a particular \"right\" answer [38]. Other online AHP systems have only employed \((n-1)\) comparisons (e.g., \(A\) against \(B\), \(B\) against \(C\), but not \(A\) against \(C\)) to avoid the potential for inconsistencies (e.g., [31; 40]). A wide variety of approaches has also been developed to ex post \"adjust\" the results to reduce inconsistencies and improve the reliability of the preference scores [41; 42; 43; 44; 45; 46; 47; 48; 49]. Given that a key factor contributing to these inconsistencies is the use of pairwise comparisons to elicit preferences, an alternative approach for preference data collection may be more beneficial for use in online surveys. [PERSON], et al. [50] developed an alternative approach to online data collection that is consistent with the AHP in terms of how the objectives are scored (i.e., 1-9 scale) and how the results are analyzed but does not rely on the use of pairwise comparisons. The method was developed to assess a large number (i.e., 22) of operational objectives identified by stakeholder representatives considering different fishery management options. The stakeholders to be consulted were geographically dispersed across over 3000 km of coastline, making an online survey the only practical option. The large number of objectives also made bivariate comparison approaches impractical, as the time required to complete the survey was expected to have resulted in low completion rates and the high cognitive burden likely to increase inconsistencies in responses. The approach, which we term the modified AHP (MAHP) for the purposes of this study, was developed and applied by [PERSON], et al. [50] without validation due to time constraints.