User acceptance of multi-criteria decision support systems: The impact of preference elicitation techniques

Aloysius, John; Davis, Fred D.; Wilson, Dianne; Taylor, A. Ross; Kottemann, Jeffrey E

doi:10.1016/j.ejor.2004.05.031

Cited by 46 publications

(39 citation statements)

References 38 publications

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“…Weights in most existing systems are entered by users on discrete scales by selecting a rating from a drop down list or using horizontally aligned radiobuttons and on continuous scales by using a slider. Aloysius and colleagues (Aloysius et al 2006) found an impact of the preference elicitation technique used on the user acceptance of dss. Their study comparing absolute measurement and pairwise comparison showed that forcing the user to make explicit trade-off judgments has a negative effect on user acceptance of the system.…”

Section: Decision Support Systemsmentioning

confidence: 99%

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Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Pommeranz

Broekens

Wiggers

et al. 2012

User Model User-Adap Inter

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Two problems may arise when an intelligent (recommender) system elicits users' preferences. First, there may be a mismatch between the quantitative preference representations in most preference models and the users' mental preference models. Giving exact numbers, e.g., such as "I like 30 days of vacation 2.5 times better than 28 days" is difficult for people. Second, the elicitation process can greatly influence the acquired model (e.g., people may prefer different options based on whether a choice is represented as a loss or gain). We explored these issues in three studies. In the first experiment we presented users with different preference elicitation methods and found that cognitively less demanding methods were perceived low in effort and high in liking. However, for methods enabling users to be more expressive, the perceived effort was not an indicator of how much the methods were liked. We thus hypothesized that users are willing to spend more effort if the feedback mechanism enables them to be more expressive. We examined this hypothesis in two follow-up studies. In the second experiment, we explored the trade-off between giving detailed preference feedback and effort. We found that familiarity with and opinion about an item are important factors mediating this trade-off. Additionally, affective feedback was preferred over a finer grained one-dimensional rating scale for giving additional detail. In the third study, we explored the influence of the interface on the elicitation process in a participatory set-up. People considered it helpful to be able to explore the link between their interests, preferences and the desirability of outcomes. We also confirmed that people do not want to spend additional effort in cases where it seemed unnecessary. Based on the findings, we propose four design guidelines to foster interface design of preference elicitation from a user view.

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Section: Decision Support Systemsmentioning

confidence: 99%

“…In order to construct utility functions the system needs to elicit values and weights for the given attributes of an item. The two most popular preference elicitation techniques are absolute measurement and pairwise comparison (active elicitation) (Aloysius et al 2006). Absolute measurement (e.g.…”

Section: Decision Support Systemsmentioning

confidence: 99%

Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Pommeranz

Broekens

Wiggers

et al. 2012

User Model User-Adap Inter

View full text Add to dashboard Cite

show abstract

“…In this respect, a multi-criteria analysis is often used (Aloysius et al, 2006). However, the downside of all multi-criteria analysis methods is their tendency to become complex due to the multitude of criteria and corresponding weights, which is especially problematic when the community is involved.…”

Section: Aggregation Modementioning

confidence: 99%

Subsurface activities and decision support systems

Herber

Scholtens

2016

Environmental Impact Assessment Review

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“…Further studies have demonstrated negative effects of trading-off such as decisional stress (Janis and Mann, 1977), decisional conflict and as a result, less accurate decision making (Aloysius et al, 2006), as well as too small numbers of iterations in interactive methods (Korhonen and Wallenius, 1996). The latter phenomenon observed by Gardiner and Vanderpooten (1997) can be attributed to anchoring (Miettinen, 1999;Buchanan and Corner, 1997), where the DM fixes one's thinking on some (possible irrelevant) information which hinders his/her willingness to move from the current Pareto optimal solution.…”

Section: Concepts and Consideration Of Pareto Optimalitymentioning

confidence: 99%

A new preference handling technique for interactive multiobjective optimization without trading-off

et al. 2015

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Luque, M. (2015). A new preference handling technique for interactive multiobjective optimization without trading-off. Journal of Global Optimization, 63 (4), 633-652. doi:10.1007/s10898-015- AbstractBecause the purpose of multiobjective optimization methods is to optimize conflicting objectives simultaneously, they mainly focus on Pareto optimal solutions, where improvement with respect to some objective is only possible by allowing some other objective(s) to impair. Bringing this idea into practice requires the decision maker to think in terms of trading-off, which may limit the ability of effective problem solving. We outline some drawbacks of this and exploit another idea emphasizing the possibility of simultaneous improvement of all objectives. Based on this idea, we propose a technique for handling decision maker's preferences, which eliminates the necessity to think in terms of trade-offs. We incorporate this technique into an interactive trade-off-free method for multiobjective optimization. We call the resulting method NAUTILUS 2, which is also suitable for negotiation support. We demonstrate the applicability of the new method with an example problem.

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User acceptance of multi-criteria decision support systems: The impact of preference elicitation techniques

Cited by 46 publications

References 38 publications

Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Subsurface activities and decision support systems

A new preference handling technique for interactive multiobjective optimization without trading-off

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