Adaptive Self-Explication of Multi-Attribute Preferences

Netzer, Oded; Srinivasan, V.

doi:10.2139/ssrn.1077434

Cited by 24 publications

(27 citation statements)

References 31 publications

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“…In the questionnaire, students were asked to distribute a total of 100 points across the six motivation variables, identifying the categories that might persuade them to participate. Constant-sum allocation was used to overcome the limitations of the rating approach and explicitly determine trade-offs among attributes which might not emerge clearly otherwise (as the respondents could easily indicate that every attribute is relevant to motivate them) (Netzer and Srinivasan, 2009). For each category the reduction from multiple to single-item scale was operated by asking separately to five PhD students and to five potential participants student which of the three items proposed by Fuller 7 better represented the category for them.…”

Section: -12mentioning

confidence: 99%

Motivation Orientations in Innovation Contests: Why People Participate

Pellizzoni

Buganza

Colombo

2015

Int. J. Innov. Mgt.

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Innovation contests allow companies to involve the general public or specific target groups in solving innovation problems. The increasing importance of innovation contests suggests the need for further research on why people become involved in non-compulsory innovation activities, such as innovation contests. The purpose of this paper was to examine which motivation orientations explain such participation and the quality of the ideas submitted. Drawing on motivation theories, this paper discusses the links between intrinsic and extrinsic motivation and problem-solver participation as well as solution quality. Our findings suggest that different levels of participation are driven by different motivation orientations. Low performers participated in contests based on a rational understanding of the extrinsic benefits and perceived effort required, as did non-participants. However, such extrinsic reasoning is insufficient for high-performer motivation; high performers participate in the task due to its intrinsic value. These findings have important implications for both researchers and practitioners.

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Section: -12mentioning

confidence: 99%

Motivation Orientations in Innovation Contests: Why People Participate

Pellizzoni

Buganza

Colombo

2015

Int. J. Innov. Mgt.

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“…Our adaptive approach to comparing models of decision making may remind some readers of related work in adaptive conjoint analysis (e.g., Netzer and Srinivasan 2007, Dzyabura and Hauser 2009). However, these approaches have a different set of goals than trying to find the best questions for comparing models.…”

Section: Discussionmentioning

confidence: 99%

“…For example, it has been applied to the detection of extrasolar planets (Loredo 2004), in clinical trials of experimental drugs (Haines et al 2003, Ding et al 2008), in neurophysiology experiments on spiking neurons (Lewi et al 2009), to the estimation of visual psychometric and psychophysical functions (Leek 2001, Kujala and Lukka 2006, Lesmes et al 2006, 2010), to the detection of banking fraud (Deng et al 2009), in web-based surveys to elicit multi-attribute decision heuristics (Netzer and Srinivasan 2007, Dzyabura and Hauser 2009), and even in modeling human causal inferences (Steyvers et al 2003, Kruschke 2008). …”

Section: Introductionmentioning

confidence: 99%

Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

et al. 2013

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Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models.

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“…To do so, we opted for the use of a constant-sum scale, because it allows a direct and comparative mapping of points from a contingent to individual objectives. Furthermore, Alwen/Krosnick [5], Chrzan/Golovashkina [6], Netzer/Srinivasan [7] and Orme [8] point out that this scale provides relatively good results in terms of high validity and a high degree of discrimination.…”

Section: Methodsmentioning

confidence: 99%

System of students' objectives: An analysis based on concepts of decision theory

2012

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Although a systematic approach for the measurement of academic teaching performances requires profound knowledge about the stakeholders' objectives, such knowledge hardly exists. In particular, a comprehensive system of objectives which reflects the choice of studies of the students, as the major stakeholder group is missing. Against this background, the paper presents a three step procedure to generate a system of students' objectives based on concepts of prescriptive decision theory. The procedure's benefit is illustrated, on the one hand, by deriving a structure of objectives which is independent of the students' choice of study and, on the other hand, by determining weights for the objectives of business students.

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Adaptive Self-Explication of Multi-Attribute Preferences

Cited by 24 publications

References 31 publications

Motivation Orientations in Innovation Contests: Why People Participate

Motivation Orientations in Innovation Contests: Why People Participate

Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

System of students' objectives: An analysis based on concepts of decision theory

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