IDEAS home Printed from https://ideas.repec.org/a/ibn/ijmsjn/v11y2020i2p1.html
   My bibliography  Save this article

Dynamic Attribute-Level Best Worst Discrete Choice Experiments

Author

Listed:
  • Amanda Working
  • Mohammed Alqawba
  • Norou Diawara

Abstract

Dynamic modelling of decision maker choice behavior of best and worst in discrete choice experiments (DCEs) has numerous applications. Such models are proposed under utility function of decision maker and are used in many areas including social sciences, health economics, transportation research, and health systems research. After reviewing references on the study of such experiments, we present example in DCE with emphasis on time dependent best-worst choice and discrimination between choice attributes. Numerical examples of the dynamic DCEs are simulated, and the associated expected utilities over time of the choice models are derived using Markov decision processes. The estimates are computationally consistent with decision choices over time.

Suggested Citation

  • Amanda Working & Mohammed Alqawba & Norou Diawara, 2020. "Dynamic Attribute-Level Best Worst Discrete Choice Experiments," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 11(2), pages 1-1, March.
    • Handle: RePEc:ibn:ijmsjn:v:11:y:2020:i:2:p:1
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ijms/article/download/0/0/39524/40405
    Download Restriction: no
    File URL: http://www.ccsenet.org/journal/index.php/ijms/article/view/0/39524
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Potoglou, Dimitris & Burge, Peter & Flynn, Terry & Netten, Ann & Malley, Juliette & Forder, Julien & Brazier, John E., 2011. "Best-worst scaling vs. discrete choice experiments: An empirical comparison using social care data," Social Science & Medicine, Elsevier, vol. 72(10), pages 1717-1727, May.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, September.
    3. Marley, A.A.J. & Islam, T. & Hawkins, G.E., 2016. "A formal and empirical comparison of two score measures for best–worst scaling," Journal of choice modelling, Elsevier, vol. 21(C), pages 15-24.
    4. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    5. Anas, Alex, 1983. "Discrete choice theory, information theory and the multinomial logit and gravity models," Transportation Research Part B: Methodological, Elsevier, vol. 17(1), pages 13-23, February.
    6. Peter Arcidiacono & Paul B. Ellickson, 2011. "Practical Methods for Estimation of Dynamic Discrete Choice Models," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 363-394, September.
    7. Lipovetsky, Stan & Conklin, Michael, 2014. "Best-Worst Scaling in analytical closed-form solution," Journal of choice modelling, Elsevier, vol. 10(C), pages 60-68.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lipovetsky, Stan, 2018. "Quantum paradigm of probability amplitude and complex utility in entangled discrete choice modeling," Journal of choice modelling, Elsevier, vol. 27(C), pages 62-73.
    2. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    3. Yoo, Hong Il & Doiron, Denise, 2013. "The use of alternative preference elicitation methods in complex discrete choice experiments," Journal of Health Economics, Elsevier, vol. 32(6), pages 1166-1179.
    4. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    5. David Hensher & John Rose & Zheng Li, 2012. "Does the choice model method and/or the data matter?," Transportation, Springer, vol. 39(2), pages 351-385, March.
    6. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    7. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    8. Miren Lafourcade & Jacques-François Thisse, 2011. "New Economic Geography: The Role of Transport Costs," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 4, Edward Elgar Publishing.
    9. Dhakal, Bhubaneswor & Yao, Richard T. & Turner, James A. & Barnard, Tim, 2012. "Recreational users' willingness to pay and preferences for changes in planted forest features," Forest Policy and Economics, Elsevier, vol. 17(C), pages 34-44.
    10. Kanchanaroek, Yingluk & Termansen, Mette & Quinn, Claire, 2013. "Property rights regimes in complex fishery management systems: A choice experiment application," Ecological Economics, Elsevier, vol. 93(C), pages 363-373.
    11. Domanski, Adam, 2009. "Estimating Mixed Logit Recreation Demand Models With Large Choice Sets," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49413, Agricultural and Applied Economics Association.
    12. Reinhard A. Weisser, 2020. "How Personality Shapes Study Location Choices," Research in Higher Education, Springer;Association for Institutional Research, vol. 61(1), pages 88-116, February.
    13. Lu, Hui & Hess, Stephane & Daly, Andrew & Rohr, Charlene & Patruni, Bhanu & Vuk, Goran, 2021. "Using state-of-the-art models in applied work: Travellers willingness to pay for a toll tunnel in Copenhagen," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 37-52.
    14. Gevrek, Z.Eylem & Uyduranoglu, Ayse, 2015. "Public preferences for carbon tax attributes," Ecological Economics, Elsevier, vol. 118(C), pages 186-197.
    15. Ji, Yongjie & Rabotyagov, Sergey & Kling, Catherine L., 2014. "Crop Choice and Rotational Effects: A Dynamic Model of Land Use in Iowa in Recent Years," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170366, Agricultural and Applied Economics Association.
    16. Daniele Pacifico, 2012. "Fitting nonparametric mixed logit models via expectation-maximization algorithm," Stata Journal, StataCorp LP, vol. 12(2), pages 284-298, June.
    17. Tang, Wei & Mokhtarian, Patricia L, 2009. "Accounting for Taste Heterogeneity in Purchase Channel Intention Modeling: An Example from Northern California for Book Purchases," Institute of Transportation Studies, Working Paper Series qt9mg5s5g8, Institute of Transportation Studies, UC Davis.
    18. Daniel R. Petrolia & Matthew G. Interis & Joonghyun Hwang, 2018. "Single-Choice, Repeated-Choice, and Best-Worst Scaling Elicitation Formats: Do Results Differ and by How Much?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 365-393, February.
    19. Stefania Troiano & Daniel Vecchiato & Francesco Marangon & Tiziano Tempesta & Federico Nassivera, 2019. "Households’ Preferences for a New ‘Climate-Friendly’ Heating System: Does Contribution to Reducing Greenhouse Gases Matter?," Energies, MDPI, vol. 12(13), pages 1-19, July.
    20. Richartz, P. Christoph & Abdulai, Awudu & Kornher, Lukas, 2020. "Attribute Non Attendance and Consumer Preferences for Online Food Products in Germany," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 69(1), March.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ijmsjn:v:11:y:2020:i:2:p:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.