Innovative online survey about sustainable wastewater management: What young Swiss citizens know and value

Aubert, Alice H.; Schmid, Sara; Beutler, Philipp; Lienert, Judit

doi:10.1016/j.envsci.2022.08.018

Cited by 4 publications

(3 citation statements)

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“…Therefore, we designed our study to measure more facets of users' survey experience. In addition, this study complemented previous ones [27][28][29] with an improved experimental design, interfaces designed newly in response to feedback we had collected about previous interfaces, and a full-scale decision problem. One major improvement in the experimental design reported here is that we can differentiate the effects of gamification from those of learning loops.…”

Section: General Motivationmentioning

confidence: 92%

“…The elicitation part of the survey used state-of the art methods to focus on how people make trade-offs. The nongamified interface resulted from iterative development: Two prototypes had been developed and tested before [29,54]. The nongamified interface of the present study had a simple design displaying a progress bar.…”

Section: Gamification: With Vs Without (Control)mentioning

confidence: 99%

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Gamified online surveys: Assessing experience with self-determination theory

Aubert,

Scheidegger,

Schmid

2023

PLoS ONE

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We developed four online interfaces supporting citizen participation in decision-making. We included (1) learning loops (LLs), good practice in decision analysis, and (2) gamification, to enliven an otherwise long and tedious survey. We investigated the effects of these features on drop-out rate, perceived experience, and basic psychological needs (BPNs): autonomy, competence, and relatedness, all from self-determination theory. We also investigated how BPNs and individual causality orientation influence experience of the four interfaces. Answers from 785 respondents, representative of the Swiss German-speaking population in age and gender, provided insightful results. LLs and gamification increased drop-out rate. Experience was better explained by the BPN satisfaction than by the interface, and this was moderated by respondents’ causality orientations. LLs increased the challenge, and gamification enhanced the social experience and playfulness. LLs frustrated all three needs, and gamification satisfied relatedness. Autonomy and relatedness both positively influenced the social experience, but competence was negatively correlated with challenge. All observed effects were small. Hence, using gamification for decision-making is questionable, and understanding individual variability is a prerequisite; this study has helped disentangle the diversity of responses to survey design options.

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Section: General Motivationmentioning

confidence: 92%

Section: Gamification: With Vs Without (Control)mentioning

confidence: 99%

Gamified online surveys: Assessing experience with self-determination theory

Aubert,

Scheidegger,

Schmid

2023

PLoS ONE

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“…For meaningful analysis, identifying key and fundamental criteria instead of having a large set of criteria was emphasized in [35], and the characteristics of good criteria including, e.g., completeness, conciseness, non-redundancy, understandability, measurability and preferential independence, were listed. Moreover, some recent studies used online surveys to elicit criteria from a large number of individuals without the assistance of an analyst (or facilitator) (see, e.g., [2,22]).…”

Section: Brief Literature Reviewmentioning

confidence: 99%

A Systematic Way of Structuring Real-World Multiobjective Optimization Problems

Afşar¹,

Silvennoinen²,

Miettinen³

2023

Lecture Notes in Computer Science

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In recent decades, the benefits of applying multiobjective optimization (MOO) methods in real-world applications have rapidly increased. The MOO literature mostly focuses on problem-solving, typically assuming the problem has already been correctly formulated. The necessity of verifying the MOO problem and the potential impacts of having an incorrect problem formulation on the optimization results are not emphasized enough in the literature. However, verification is crucial since the optimization results will not be meaningful without an accurate problem formulation, not to mention the resources spent in the optimization process being wasted. In this paper, we focus on the MOO problem structuring, which we believe deserves more attention. The novel contribution is the proposed systematic way of structuring MOO problems that leverages problem structuring approaches from the literature on multiple criteria decision analysis (MCDA). They are not directly applicable to the formulation of MOO problems since the objective functions in the MOO problem depend on decision variables and constraint functions, whereas MCDA problems have a given set of solution alternatives characterized by criterion values. Therefore, we propose to elicit expert knowledge to identify decision variables and constraint functions, in addition to the objective functions, to construct a MOO problem appropriately. Our approach also enables the verification and validation of the problem before the actual decision making process.

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