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We introduce, test, and compare two auction-based methods for eliciting discount rates. In these “patience auctions”, participants bid the smallest future sum they would prefer -or- the longest time they would wait for a reward, rather than receive a smaller, immediate payoff. The most patient bidder receives the delayed reward; all others receive the immediate payoff. These auctions allow us to compare discounting when participants’ attention is focused on the temporal versus monetary dimension of delayed rewards. We find that the estimated parameters in the three most commonly used discount functions (exponential, hyperbolic, and quasi-hyperbolic) differ across these two bidding methods (time-bids vs. money-bids). Specifically, our participants tend to show more impatience under time-bids. Furthermore, we find that people are more likely to exhibit exponential (as opposed to hyperbolic) discounting and exhibit less present bias under time-bids, compared to money-bids. To our knowledge, this paper is the first to directly compare time versus money preference elicitations, within the same subjects, using an incentive-compatible mechanism.  相似文献   
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Language is an integral part of marketing. Consumers share word of mouth, salespeople pitch services, and advertisements try to persuade. Further, small differences in wording can have a big impact. But while it is clear that language is both frequent and important, how can we extract insight from this new form of data? This paper provides an introduction to the main approaches to automated textual analysis and how researchers can use them to extract marketing insight. We provide a brief summary of dictionaries, topic modeling, and embeddings, some examples of how each approach can be used, and some advantages and limitations inherent to each method. Further, we outline how these approaches can be used both in empirical analysis of field data as well as experiments. Finally, an appendix provides links to relevant tools and readings to help interested readers learn more. By introducing more researchers to these valuable and accessible tools, we hope to encourage their adoption in a wide variety of areas of research.

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