Carina I. Hausladen , Martin Fochmann , Peter Mohr
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Predicting compliance: Leveraging chat data for supervised classification in experimental research
Behavioral and experimental economics have conventionally employed text data to facilitate the interpretation of decision-making processes. This paper introduces a novel methodology, leveraging text data for predictive analytics rather than mere explanation. We detail a supervised classification framework that interprets patterns in chat text to estimate the likelihood of associated numerical outcomes. Despite the unique advantages of experimental data in correlating textual and numerical information for predictive modeling, challenges such as limited sample sizes and potential data skewness persist. To address these, we propose a comprehensive methodological framework aimed at optimizing predictive modeling configurations, particularly in small experimental behavioral research datasets. We also present behavioral experimental data from a preregistered tax evasion game (n=324), demonstrating that chat behavior is not influenced by experimenter demand effects. This establishes chat text as an unbiased variable, enhancing its validity for prediction. Our findings further indicate that beliefs about others’ dishonesty, lying attitudes, and risk preferences significantly impact compliance decisions.
期刊介绍:
The Journal of Behavioral and Experimental Economics (formerly the Journal of Socio-Economics) welcomes submissions that deal with various economic topics but also involve issues that are related to other social sciences, especially psychology, or use experimental methods of inquiry. Thus, contributions in behavioral economics, experimental economics, economic psychology, and judgment and decision making are especially welcome. The journal is open to different research methodologies, as long as they are relevant to the topic and employed rigorously. Possible methodologies include, for example, experiments, surveys, empirical work, theoretical models, meta-analyses, case studies, and simulation-based analyses. Literature reviews that integrate findings from many studies are also welcome, but they should synthesize the literature in a useful manner and provide substantial contribution beyond what the reader could get by simply reading the abstracts of the cited papers. In empirical work, it is important that the results are not only statistically significant but also economically significant. A high contribution-to-length ratio is expected from published articles and therefore papers should not be unnecessarily long, and short articles are welcome. Articles should be written in a manner that is intelligible to our generalist readership. Book reviews are generally solicited but occasionally unsolicited reviews will also be published. Contact the Book Review Editor for related inquiries.