Summary of studies examined in this review paper
| Author (Year) | Journal | Innovation |
|---|---|---|
| Regret theory | ||
| Chorus et al. (2008) | Transportation Research Part B: Methodological | Regret theory in discrete choice |
| Thiene et al. (2012) | Environmental and Resource Economics | RRM vs. RUM comparison in environmental economics |
| Hess and Stathopoulos (2013) | Journal of Choice Modeling | Mixing RRM and RUM models |
| Boeri et al. (2013) | Conference-paper at the International Choice Modeling Conference | Monte Carlo comparison of RRM and RUM |
| Boeri et al. (2014) | Transportation Research Part A: Policy and Practice | Probabilistic segmentation of respondents into utility maximizers and regret minimizers |
| Chorus et al. (2014) | Journal of Business Research | Literature review comparing RRM and RUM estimates |
| Incentive compatibility | ||
| Carson and Groves (2007) | Environmental and Resource Economics | Theoretical treatment of incentive compatibility in stated preference discrete choice |
| Vossler et al. (2012) | American Economic Journal: Microeconomics | Consequentiality as a main factor in incentive compatibility |
| Hensher (2010a) | Transportation Research Part B: Methodological | Review of sources of hypothetical bias in stated preference studies |
| Opt-out and don't know | ||
| Boxall et al. (2009) | Australian Journal of Agricultural and Resource Economics | Increasing complexity results in increasing opt-outs |
| Meyerhoff and Liebe (2009) | Land Economics | Attitudinal and socio-demographic influences on opt-out behavior |
| Lanz and Provins (2012) | CEPE Working Paper Series, ETH Zurich | Socio-demographic influences on opt-out behavior and serial nonparticipation |
| Von Haefen et al. (2005) | American Journal of Agricultural Economics | Hurdle model for serial nonparticipation |
| Balcombe and Fraser (2011) | European Review of Agricultural Economics | General model for “do not know” responses in choice experiments |
| Attribute processing and ANA | ||
| Hensher (2007) | Chapter in Kanninen (2007) | Theoretical exposition on different attribute processing strategies, influence of complexity on attribute processing |
| Hensher (2010b) | Chapter in Proceedings of the International Choice Modeling Conference 2010 | Dempster–Shafer belief functions to assess processing strategy, attribute non-attendance, attribute aggregation |
| Mariel et al. (2012) | Conference Paper at the European Association of Environmental Resource Economists | Compare stated and inferred methods to detect attribute non-attendance |
| Alemu et al. (2013) | Environmental and Resource Economics | Investigate reasons for attribute non-attendance |
| Colombo and Glenk (2013) | Journal of Environmental Planning and Management | Consider attribute non-attendance and alternative non-attendance due to unacceptable attributes |
| Scarpa et al. (2009) | European Review of Agricultural Economics | Develop a latent class and a Bayesian approach to account for attribute non-attendance |
| Hensher et al. (2012) | Transportation | Develop a latent class approach to attribute non-attendance with constrained parameters across classes |
| Puckett and Hensher (2008) | Transportation Research Part E: Logistics and Transportation Review | Adapt estimation for rationally adaptive behavior including adding-up and ignoring attributes using follow-up questions |
| Kravchenko (2014) | Journal of Choice Modeling | Monte Carlo investigation of effects of attribute non-attendance on parameter estimates |
| Quan et al. (2018) | Agribusiness | Compared attribute non-attendance and full set of choices for WTP for food safety |
| Order effects | ||
| Day et al. (2012) | Journal of Environmental Economics and Management | Empirically testing for various types of order effects |
| Scheufele and Bennett (2012) | Environmental and Resource Economics | Strategic responses and changes in cost sensitivity along a series of choice sets |
| McNair et al. (2011) | Resource and Energy Economics | Difference in WTP between single and multiple choice sets |
| Meyerhoff and Glenk (2013) | Working Papers on Management in Environmental Planning, TU Berlin | Instruction choice sets may induce starting point bias |
| Choice set design and attribute selection | ||
| Coast et al. (2012) | Health Economics | Use of qualitative methods in attribute selection, recommendations for reporting the design process |
| Abiiro et al. (2014) | BMC Health Services Research | Detailed description of attribute selection process |
| Michaels-Igbokwe et al. (2014) | Social Science and Medicine | Use of decision mapping processes for attribute selection |
| Kløjgaard et al. (2012) | Journal of Choice Modeling | Description of qualitative process for attribute selection, including observational fieldwork and key informant interviews |
| Experimental design | ||
| Sándor and Wedel (2001) | Journal of Marketing Research | Bayesian design procedure incorporating managers' beliefs about future market shares into priors |
| Bliemer et al. (2009) | Transportation Research Part B: Methodological | Efficient experimental design for nested logit models |
| Bliemer and Rose (2010) | Transportation Research Part B: Methodological | Efficient experimental design for random parameters logit models |
| Ferrini and Scarpa (2007) | Journal of Environmental Economics and Management | Monte Carlo investigation of parameter estimates using designs with vs. without prior information |
| Gao et al. (2010) | Agricultural Economics | Monte Carlo investigations of parameter estimates using different design types and various numbers of attributes and levels |
| Rose et al. (2008) | Transportation Research Part B: Methodological | Pivot designs in computer-aided discrete choice experiments |
| Survey mode, sampling | ||
| Olsen (2009) | Environmental and Resource Economics | Comparison between mail and Internet survey in choice experiment |
| Estimation strategy, endogeneity | ||
| Walker et al. (2011) | Transportation Research Part A: Policy and Practice | BLP approach to treat endogeneity in transportation choice model |
| Petrin and Train (2010) | Journal of Marketing Research | Control function approach to revealed preference data |
| Guevara and Ben-Akiva (2010) | Chapter in proceedings of the International Choice Modeling Conference 2010 | Use control function method and show link between control functions and latent variables |
| Guevara and Polanco (2013) | Paper presented at the International Choice Modeling Conference 2013 | Use of a multiple indicator solution to correct for endogeneity |
| Guevara and Ben-Akiva (2012) | Transportation Science | Scale factor correction for models estimated by the control function method |
| Author (Year) | Journal | Innovation |
|---|---|---|
| Transportation Research Part B: Methodological | Regret theory in discrete choice | |
| Environmental and Resource Economics | RRM vs. RUM comparison in environmental economics | |
| Journal of Choice Modeling | Mixing RRM and RUM models | |
| Conference-paper at the International Choice Modeling Conference | Monte Carlo comparison of RRM and RUM | |
| Transportation Research Part A: Policy and Practice | Probabilistic segmentation of respondents into utility maximizers and regret minimizers | |
| Journal of Business Research | Literature review comparing RRM and RUM estimates | |
| Environmental and Resource Economics | Theoretical treatment of incentive compatibility in stated preference discrete choice | |
| American Economic Journal: Microeconomics | Consequentiality as a main factor in incentive compatibility | |
| Transportation Research Part B: Methodological | Review of sources of hypothetical bias in stated preference studies | |
| Australian Journal of Agricultural and Resource Economics | Increasing complexity results in increasing opt-outs | |
| Land Economics | Attitudinal and socio-demographic influences on opt-out behavior | |
| CEPE Working Paper Series, ETH Zurich | Socio-demographic influences on opt-out behavior and serial nonparticipation | |
| American Journal of Agricultural Economics | Hurdle model for serial nonparticipation | |
| European Review of Agricultural Economics | General model for “do not know” responses in choice experiments | |
| Chapter in | Theoretical exposition on different attribute processing strategies, influence of complexity on attribute processing | |
| Chapter in Proceedings of the International Choice Modeling Conference 2010 | Dempster–Shafer belief functions to assess processing strategy, attribute non-attendance, attribute aggregation | |
| Conference Paper at the European Association of Environmental Resource Economists | Compare stated and inferred methods to detect attribute non-attendance | |
| Environmental and Resource Economics | Investigate reasons for attribute non-attendance | |
| Journal of Environmental Planning and Management | Consider attribute non-attendance and alternative non-attendance due to unacceptable attributes | |
| European Review of Agricultural Economics | Develop a latent class and a Bayesian approach to account for attribute non-attendance | |
| Transportation | Develop a latent class approach to attribute non-attendance with constrained parameters across classes | |
| Transportation Research Part E: Logistics and Transportation Review | Adapt estimation for rationally adaptive behavior including adding-up and ignoring attributes using follow-up questions | |
| Journal of Choice Modeling | Monte Carlo investigation of effects of attribute non-attendance on parameter estimates | |
| Agribusiness | Compared attribute non-attendance and full set of choices for WTP for food safety | |
| Journal of Environmental Economics and Management | Empirically testing for various types of order effects | |
| Environmental and Resource Economics | Strategic responses and changes in cost sensitivity along a series of choice sets | |
| Resource and Energy Economics | Difference in WTP between single and multiple choice sets | |
| Working Papers on Management in Environmental Planning, TU Berlin | Instruction choice sets may induce starting point bias | |
| Health Economics | Use of qualitative methods in attribute selection, recommendations for reporting the design process | |
| BMC Health Services Research | Detailed description of attribute selection process | |
| Social Science and Medicine | Use of decision mapping processes for attribute selection | |
| Journal of Choice Modeling | Description of qualitative process for attribute selection, including observational fieldwork and key informant interviews | |
| Journal of Marketing Research | Bayesian design procedure incorporating managers' beliefs about future market shares into priors | |
| Transportation Research Part B: Methodological | Efficient experimental design for nested logit models | |
| Transportation Research Part B: Methodological | Efficient experimental design for random parameters logit models | |
| Journal of Environmental Economics and Management | Monte Carlo investigation of parameter estimates using designs with vs. without prior information | |
| Agricultural Economics | Monte Carlo investigations of parameter estimates using different design types and various numbers of attributes and levels | |
| Transportation Research Part B: Methodological | Pivot designs in computer-aided discrete choice experiments | |
| Environmental and Resource Economics | Comparison between mail and Internet survey in choice experiment | |
| Transportation Research Part A: Policy and Practice | BLP approach to treat endogeneity in transportation choice model | |
| Journal of Marketing Research | Control function approach to revealed preference data | |
| Chapter in proceedings of the International Choice Modeling Conference 2010 | Use control function method and show link between control functions and latent variables | |
| Paper presented at the International Choice Modeling Conference 2013 | Use of a multiple indicator solution to correct for endogeneity | |
| Transportation Science | Scale factor correction for models estimated by the control function method | |
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