How Process Explanations Impact Assessments of Predictions of Uncertain Events

When making predictions, experts can choose how much explanation to provide-either very little or a lot-about the process used to make inferences. While literature on consumer research suggests that providing more information generally improves evaluations (Calder, Insko, and Yandell 1974; Chaiken 1980; Stiff 1986), we show that consumers report preferences for more explanation in choices among predictions, but, in the context of single probability prediction evaluations, we demonstrate that providing process information can lower evaluations. This occurs because explanation sometimes leads to the inference that the prediction-making process is less complicated, and, therefore, the expert's analysis was not as in-depth, affecting inferences about both the prediction (accuracy) and the expert (trustworthiness). Thus, consumers may believe a prediction to be less accurate and trust the expert less when she offers more explanation. We draw from the area of inference-making to develop our theory. This research stream suggests that when little information is present (e.g., other than the overall prediction), consumers use prior knowledge to make inferences. Because an expert provided this estimate, they infer that the process of arriving at the overall prediction was complicated. However, when an explanation is presented, consumers can generate new information from this content. Hence, when a brief explanation is presented, it is likely to lack details and look superficial, leading to inferences that the expert did not do an in-depth analysis, which should be less likely to occur with detailed explanations. In study 1, participants (N=121) from Amazon's Mechanical Turk participated in this study for monetary compensation. Participants were asked to choose one of two analysts' predictions to share with others. They were told that the analysts had made predictions about job prospects in various fields, one of which related to finding administrative jobs in the hoteling and tourism (HT) industry. Participants chose between an expert who did not provide any explanation (Analyst A) and one who did (Analyst B). In the brief explanation condition, Analyst B said he looked at two factors, the chances of finding an administrative job and finding a job in the HT industry, and combined them. In the detailed explanation condition, Analyst B described four steps used to arrive at the same two factors and in the fifth step combined them. Participants overwhelmingly (87%) cho