| 其他摘要 | When the outcome of a decision affects others, decision makers must explain their decision-making process and the results to others for support. This demand for explanation increases the need for justification. As most decisions in real life impact others, decision-making justification is not only a widespread phenomenon, but also one of many monitoring strategies used by organizations. Bettman et al. (1998) firstly proposed “maximizing justification” as one of the main goals in decision making. Unfortunately, not much has been known regarding decision-making justification to date. To fill in this gap, I conducted two studies to investigate the effect of justification on decision-making processes and strategies in this thesis. Additionally, most strategy identification approaches in previous studies focus only on decision outcomes. The lack of technical tools for integrating process and outcome data limits the precision and resolution of strategy identification. Furthermore, although some existing approaches do integrate multiple data types, they cannot classify strategies trial-by-trial. These limitations lead to the poor performance of extant classification approaches, and challenge the accuracy and validity of research findings. In this thesis, I attempted to combine eye-tracking process data with machine-learning algorithms to present an innovative method for distinguishing decision-making strategies in a more precise and accurate manner.
Study 1 focuses on the impact of different decision-making scenarios (risky vs. non-risky choices) and levels of justification demand (low: private decision; medium: the presence of relevant others; high: the need to explain to relevant others) on decision-making strategies. We classified strategies by the Multiple-Measured Maximum Likelihood (MMML) method based on behavioral data. The results show that in both scenarios, the majority of subjects were classified as using a weighting- and-adding compensatory strategy. For non-risky choices, different levels of justification had no effect on strategies used. For risky choices, demand of justification increased the proportion of individuals using compensatory strategies, but the effect was not statistically significant.
There are several limitations of the strategy classification method used in Study 1 that may explain why the experimental results were insignificant. Therefore, in Study 2, a new strategy classification method was used to explore the effect of justification levels in risky decisions. The new method is called Choice-Learning- Training-Classification (CLTC), and it integrates eye-tracking data and machine- learning algorithms in strategy classification. We compared CLTC with MMML in classification outcomes. The results show that the classification accuracy of CLTC was much higher than that of MMML for prescribed-strategy decision data, and the classification results of CLTC and MMML differed greatly for free-choice data. Moreover, because CLTC can classify subjects’ strategies trial-by-trial, its results show that increasing the demand for justification raised the rate of trials in which subjects used compensatory strategies, although the overall strategy remained largely unchanged. This is one of the possible reasons for why the effect of justification in Study 1 could not be observed based on changes in the overall strategy.
This thesis tries to bring the topic of justification in decision making back to research attention, and makes several contributions to decision-making research. First, it places the study of decision making in social contexts, improving its ecological validity; second, it shows preliminarily that the need for justification has an impact on the use of decision-making strategies and encourages decision-makers to integrate information in a more compensatory manner; third, it provides a theoretical basis for the proper use of justification supervision; and lastly, the strategy classification method applied in this thesis is innovative, has high accuracy and granularity, and can be highly useful for future decision research. The method can be used for investigating important but under-studied topics, such as adaptive decision-making. |
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