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  • 标题:Effects of alternative elicitation formats in discrete choice experiments
  • 本地全文:下载
  • 作者:Scheufele, Gabriela ; Bennett, Jeffrey W.
  • 期刊名称:Journal of Agribusiness
  • 印刷版ISSN:0738-8950
  • 出版年度:2010
  • 出版社:Journal of Agribusiness
  • 摘要:An elicitation format prevalently applied in discrete choice experiments (DCEs) offers each respondent a sequence of choice tasks. Each choice task contains more than two choice options. Empirical evidence shows, however, that repeated choice tasks influence choice behaviour through institutional learning, fatigue, value learning and strategic response. The study reported in this paper uses a split sample approach. This approach was based on field surveys using a single binary elicitation format. To expand the research on effects of sequential binary DCE formats, a majority vote baseline was used. We present evidence for effects caused by institutional learning, and by either strategic behaviour or value learning, after respondents answered repeated choice questions. However, we did not find any indications for strategic behaviour in respondents caused by their awareness of having multiple choices. The decision to use a sequential or a single elicitation format may therefore imply a trade-off between decreased choice accuracy and potentially increased strategic behaviour in respondents. This trade-off is due to an incentive incompatible mechanism. Further research is needed to explore strategic behaviour induced by incentive incompatible elicitation formats, using alternative approaches that are not compromised by a confounded baseline, that facilitate the differentiation between value learning and strategic behaviour, and that allow the use of less restrictive model specifications. Such research should also investigate the effects of varying incentives induced by the order in which choice questions are presented to respondents.
  • 关键词:discrete choice experiments;split sample approach;elicitation format;incentive compatibility;strategic behaviour;learning effects;panel mixed logit models
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