Unstructured Direct Elicitation of Decision Rules
We investigate the feasibility of unstructured direct-elicitation (UDE) of decision rules consumers use to form consideration sets. With incentives to think hard and answer truthfully, tested formats ask respondents to state non-compensatory, compensatory, or mixed rules for agents who will select a...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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American Marketing Association
2013
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| Online Access: | http://hdl.handle.net/1721.1/80727 https://orcid.org/0000-0001-8510-8640 |
| _version_ | 1826213201192157184 |
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| author | Ding, Min Hauser, John R. Dong, Songting Dzyabura, Daria Yang, Zhilin Su, Chenting Gaskin, Steven |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Ding, Min Hauser, John R. Dong, Songting Dzyabura, Daria Yang, Zhilin Su, Chenting Gaskin, Steven |
| author_sort | Ding, Min |
| collection | MIT |
| description | We investigate the feasibility of unstructured direct-elicitation (UDE) of decision rules consumers use to form consideration sets. With incentives to think hard and answer truthfully, tested formats ask respondents to state non-compensatory, compensatory, or mixed rules for agents who will select a product for the respondents. In a mobile-phone study two validation tasks (one delayed 3 weeks) ask respondents to indicate which of 32 mobile phones they would consider from a fractional 4[superscript 5]x2[superscript 2] design of features and levels. UDE predicts consideration sets better, across profiles and across respondents, than a structured direct-elicitation method (SDE). It predicts comparably to established incentive-aligned compensatory, non-compensatory, and mixed decompositional methods. In a more-complex (20x7x5[superscript 2]x4x3[superscript 4]x2[superscript 2]) automobile study, non-compensatory decomposition is not feasible and additive-utility decomposition is strained, but UDE scales well. Incentives are aligned for all methods using prize indemnity insurance to award a chance at $40,000 for an automobile plus cash. UDE predicts consideration sets better than either additive decomposition or an established SDE method (Casemap). We discuss the strengths and weaknesses of UDE relative to established methods. |
| first_indexed | 2024-09-23T15:44:55Z |
| format | Article |
| id | mit-1721.1/80727 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:44:55Z |
| publishDate | 2013 |
| publisher | American Marketing Association |
| record_format | dspace |
| spelling | mit-1721.1/807272022-10-02T03:50:29Z Unstructured Direct Elicitation of Decision Rules Ding, Min Hauser, John R. Dong, Songting Dzyabura, Daria Yang, Zhilin Su, Chenting Gaskin, Steven Sloan School of Management Hauser, John R. Hauser, John R. Dzyabura, Daria We investigate the feasibility of unstructured direct-elicitation (UDE) of decision rules consumers use to form consideration sets. With incentives to think hard and answer truthfully, tested formats ask respondents to state non-compensatory, compensatory, or mixed rules for agents who will select a product for the respondents. In a mobile-phone study two validation tasks (one delayed 3 weeks) ask respondents to indicate which of 32 mobile phones they would consider from a fractional 4[superscript 5]x2[superscript 2] design of features and levels. UDE predicts consideration sets better, across profiles and across respondents, than a structured direct-elicitation method (SDE). It predicts comparably to established incentive-aligned compensatory, non-compensatory, and mixed decompositional methods. In a more-complex (20x7x5[superscript 2]x4x3[superscript 4]x2[superscript 2]) automobile study, non-compensatory decomposition is not feasible and additive-utility decomposition is strained, but UDE scales well. Incentives are aligned for all methods using prize indemnity insurance to award a chance at $40,000 for an automobile plus cash. UDE predicts consideration sets better than either additive decomposition or an established SDE method (Casemap). We discuss the strengths and weaknesses of UDE relative to established methods. Research Grants Council (Hong Kong, China) (SAR (9041182, CityU 1454/06H)) Pennsylvania State University (Smeal Small Research Grant) 2013-09-13T16:44:26Z 2013-09-13T16:44:26Z 2011-02 2010-02 Article http://purl.org/eprint/type/JournalArticle 0022-2437 1547-7193 http://hdl.handle.net/1721.1/80727 Ding, Min, John R. Hauser, Songting Dong, Daria Dzyabura, Zhilin Yang, Chenting Su, and Steven P. Gaskin. "Unstructured Direct Elicitation of Decision Rules." Journal of Marketing Research (2011) 48(1): p. 116-127. https://orcid.org/0000-0001-8510-8640 en_US http://www.marketingpower.com/AboutAMA/Pages/AMA%20Publications/AMA%20Journals/Journal%20of%20Marketing%20Research/TOCs/SUM_2011.1/unstructured_direct_elicitation.aspx Journal of Marketing Research Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf American Marketing Association MIT web domain |
| spellingShingle | Ding, Min Hauser, John R. Dong, Songting Dzyabura, Daria Yang, Zhilin Su, Chenting Gaskin, Steven Unstructured Direct Elicitation of Decision Rules |
| title | Unstructured Direct Elicitation of Decision Rules |
| title_full | Unstructured Direct Elicitation of Decision Rules |
| title_fullStr | Unstructured Direct Elicitation of Decision Rules |
| title_full_unstemmed | Unstructured Direct Elicitation of Decision Rules |
| title_short | Unstructured Direct Elicitation of Decision Rules |
| title_sort | unstructured direct elicitation of decision rules |
| url | http://hdl.handle.net/1721.1/80727 https://orcid.org/0000-0001-8510-8640 |
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