Conjoint Measurement

Arning, Katrin

doi:10.1002/9781118901731.iecrm0040

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…As a result of the conjoint analysis, the relative importance of attributes shows how much each attribute influences the Fig. 6 Mean agreement to the barriers of a user-centred privacypreserving data market: " I would not use the data market because..." (n = 800) decision, and part-worth utilities reflect which attribute levels are most and least accepted (Arning 2017).…”

Section: The Research Process Of Study IIImentioning

confidence: 99%

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

2020

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Privacy-preserving data markets are one approach to restore users' online privacy and informational self-determination and to build reliable data markets for companies and research. We empirically analyze internet users' preferences for privacy in data sharing, combining qualitative and quantitative empirical methods. Study I aimed at uncovering users' mental models of privacy and preferences for data sharing. Study II quantified and confirmed motives, barriers, and conditions for privacy in data markets. Finally, in a conjoint study, trade-offs between decisive attributes that shape the decision to share data are analyzed. Additionally, differences between user groups with high and with low privacy concerns are observed. The results show that the anonymization level has the greatest impact on the willingness to share data, followed by the type of data. Users with higher privacy concerns are less willing to share data in data markets and want more privacy protection. The results contribute to an understanding of how privacy-preserving data markets could be designed to suit users' preferences.

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Section: The Research Process Of Study IIImentioning

confidence: 99%

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

2020

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“…Thus, conjoint studies simulate real-life decision situations, in which multiple decision criteria are involved, e.g., consumers selecting which product they want to buy and use. This is why conjoint analysis is a method that is frequently applied in the marketing sector to analyze preferences of consumers for products and brands, to identify the preferred product configurations, and to uncover possible trade-offs between evaluation criteria (Green and Srinivasan, 1978;Arning, 2017). Conjoint studies are an established research method in different disciplines [e.g., in the healthcare sector (Marshall et al, 2010) and environmental science (Alriksson and Öberg, 2008)].…”

Section: Conjoint Methodologymentioning

confidence: 99%

On the road to sustainable transport: Acceptance and preferences for renewable fuel production infrastructure

Linzenich

Engelmann²,

Arning³

et al. 2022

Front. Energy Res.

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To abate climate change and ameliorate the air quality in urban areas, innovative solutions are required to reduce CO2 and pollutant emissions from traffic. Alternative fuels made from biomass or CO2 and hydrogen can contribute to these goals by substituting fossil gasoline or diesel in combustion engines. Using a conjoint analysis approach, the current study investigates preferences of laypeople (n = 303) for fuel production facilities in terms of siting location, plant size, raw material used in the production, and raw material transport. The location was most decision-relevant, followed by raw material transport, whereas plant size and type of raw material played a less prominent role for the preference choice. The best-case scenario from the point of view of acceptance would be the installation of a rather small bio-hybrid fuel production plant in an industrial area (instead of an agricultural or pristine environment). No transport or transport via underground pipeline were preferred over truck/tank car or overground pipeline. The findings can be used as a basis for planning and decision-making for designing production networks for new fuel types.

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“…For example, Hainmueller et al. (2014) write, “…conjoint analysis is widely used by marketing researchers to measure consumer preferences, forecast demand, and develop products… these methods have been so successful that they are now widely used in business as well”; see also, e.g., Arning, 2017; Itsubo et al., 2004; Kuzmanovic & Martic, 2012; Moise et al., 2018.…”

Section: Traditional Utility Measurement‐based Methods: the Case Of Cmentioning

confidence: 99%

Predicting consumers’ choices in the age of the internet, AI, and almost perfect tracking: Some things change, the key challenges do not

Gal

Simonson

2020

Consumer Psychology Review

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Recent technology advances (e.g., tracking and “AI”) have led to claims and concerns regarding the ability of marketers to anticipate and predict consumer preferences with great accuracy. Here, we consider the capabilities of both traditional techniques (e.g., conjoint analysis) and more recent tools (e.g., advanced machine learning methods) for predicting consumer choices. Our main conclusion is that for most of the more interesting consumer decisions, those that are “new” and non‐habitual, prediction remains hard. In fact, in many cases, prediction has become harder due to the increasing influence of just‐in‐time information (user reviews, online recommendations, new options, etc.) at the point of decision that can neither be measured nor anticipated ex ante. Sophisticated methods and “big data” can in certain contexts improve predictions, but usually only slightly, and prediction remains very imprecise—so much so that it is often a waste of effort. We suggest marketers focus less on trying to predict consumer choices with great accuracy and more on how the information environment affects the choice of their products. We also discuss implications for consumers and policymakers.

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Conjoint Measurement

Cited by 9 publications

References 18 publications

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

On the road to sustainable transport: Acceptance and preferences for renewable fuel production infrastructure

Predicting consumers’ choices in the age of the internet, AI, and almost perfect tracking: Some things change, the key challenges do not

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