Pooling stated and revealed preference data in the presence of RP endogeneity

Helveston, John Paul; Feit, Elea McDonnell; Michalek, Jeremy J.

doi:10.1016/j.trb.2018.01.010

Cited by 24 publications

(19 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combining hypothetical choice data and RP data to improve model fit and estimation accuracy is a common practice in choice modelling. Particularly in transport and travel behaviour studies this method and its various theoretical implications have been heavily discussed in a broad range of contexts and applications including commuter valuations of travel time savings and reliability, preferences for transport modes or route choice (Abildtrup et al, 2015;Ben-Akiva et al, 1994;Ortúzar, 2002, 2006;Duann and Shiaw, 2001;Fifer et al, 2011;Haghani and Sarvi, 2017, 2019Helveston et al, 2018;Hensher et al, 2008;Lavasani et al, 2017;Morikawa, 1994;Polydoropoulou and Ben-Akiva, 2001;Train and Wilson, 2008;van Essen et al, 2020;Wardman, 1988). This approach, however, has not been conventionally regarded as a mitigation method for HB.…”

Section: Pooled Estimation With Rpmentioning

confidence: 99%

Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods

Haghani

Bliemer

Rose

et al. 2021

Journal of Choice Modelling

View full text Add to dashboard Cite

Section: Pooled Estimation With Rpmentioning

confidence: 99%

Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods

Haghani

Bliemer

Rose

et al. 2021

Journal of Choice Modelling

View full text Add to dashboard Cite

“…The scanner type of data can be integrated to the stated choice experiment data, as was done in the seminal work of Adamowicz et al [17] and others [18][19][20][21] that combined the revealed and stated preference. Models' estimates based on pooled data may reduce the hypothetical bias and improve the goodness of fit of the models in preference analysis and predictions.…”

Section: Methodsmentioning

confidence: 99%

Consumers Preferences for Dairy-Alternative Beverage Using Home-Scan Data in Catalonia

Laassal¹,

Kallas

2019

Beverages

View full text Add to dashboard Cite

The changing lifestyles and the growing health concerns towards the negative impact of the saturated fatty acids originating from animals has increased consumers’ preferences for dairy-alternative products. These products belong to the food and beverage classification that is similar to certain types of dairy-based products in terms of texture and flavor, and has similar nutritional benefits. In this context, we seek to identify the willingness to pay (WTP) for the most important attributes that consumers take into account when purchasing the dairy-alternative drinks. A revealed preference discrete choice experiment was carried out using home-scan data belonging to ©Kantar Worldpanel (Barcelona, Spain) regarding the consumption of dairy-alternative drinks in Catalonia (Spain) in 343 households. Furthermore, factors that affect the purchasing frequency of this type of product were analyzed through the Poisson and negative binomial models. Results showed that price was the major driving factor, followed by the original non-dairy beverage flavor attribute. The original non-dairy beverage flavor compared to other added ingredients and tastes showed higher WTP when purchasing the non-dairy alternative. Marketing strategies should promote products by focusing on the “original” and “pure” version of the product without additional ingredients, or through reduction of the undesirable compounds if they exist in these kinds of beverages.

show abstract

“…Online surveys are the most often adopted approach for exploring consumers' heterogeneous engagement with DSR. Even though respondents' stated preferences could be different from their revealed preferences, this approach is seen as an efficient way to investigate consumers' possible behaviours (Helveston et al 2018). Moreover, unlike participants of DSR trials who opt in, respondents of an online survey are more likely to represent the general public as a whole, as they have not been screened by DSR recruitment mechanisms.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating UK consumers’ heterogeneous engagement in demand-side response

Keppo

Xenitidou

et al. 2020

Energy Efficiency

View full text Add to dashboard Cite

Demand-side response (DSR), the incentivised time-shifting of energy use by consumers away from peak times, is regarded as a potentially effective measure to balance electricity supply and demand. This will be even more important in the low-carbon energy system of the future, with a high share of nondispatchable power, such as variable renewable energy and nuclear power. Most DSR programmes require consumers' active engagement in shifting end-use activities. Previous studies have, however, rarely revealed sociodemographic factors influential for consumers' willingness-to-shift specific end-use activities. This study thus aims to fill this research gap and, using a multinomial logistic model to analyse a nationwide survey, identify factors influential for DSR-related decisions. The nationwide survey for 1004 respondents was carried out to collect data about consumers' willingness-to-shift their daily activities. We focused on the activities that constitute the major part of domestic energy consumption, i.e. cooking, dish-washing, entertainment, heating, laundry and showering. According to the results, consumers' original timing of the end-use activities, sociodemographic factors, ownership of specific appliances and level of concern for energy-saving are influential for their willingness-to-shift activities. These findings can not only help policymakers make more targeted DSR promotion plans but also help to improve broader modelling tools to better consider consumers' willingness-to-shift their demand.

show abstract

Pooling stated and revealed preference data in the presence of RP endogeneity

Cited by 24 publications

References 44 publications

Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods

Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods

Consumers Preferences for Dairy-Alternative Beverage Using Home-Scan Data in Catalonia

Investigating UK consumers’ heterogeneous engagement in demand-side response

Contact Info

Product

Resources

About