How numeric advice precision affects advice taking

A meta-analysis of the weight of advice in decision-making

The degree to which people take advice, and the factors that influence advice-taking, are of broad interest to laypersons, professionals, and policy-makers. This meta-analysis on 346 effect sizes from 129 independent datasets (N = 17, 296) assessed the weight of advice in the judge-advisor system paradigm, as well as the influence of sample and task characteristics. Information about the advisor(s) that is suggestive of advice quality was the only unique predictor of the overall pooled weight of advice. Individuals adjusted estimates by 32%, 37%, and 48% in response to advisors described in ways that suggest low, neutral, or high quality advice, respectively. This indicates that the benefits of compromise and averaging may be lost if accurate advice is perceived to be low quality, or too much weight is given to inaccurate advice that is perceived to be high quality. When examining the three levels of perceived quality separately, advice-taking was greater for subjective and uncertain estimates, relative to objective estimates, when information about the advisor was neutral in terms of advice quality. Sample characteristics had no effect on advice-taking, thus providing no evidence that age, gender, or individualism influence the weight of advice. The findings contribute to current theoretical debates and provide direction for future research.

"My Goal Is to Lose 2.923 kg!"-Efficacy of Precise Versus Round Goals for Body Weight Reduction

Overweight individuals often struggle to lose weight. While previous studies established goal setting as an effective strategy for weight loss, little is known about the effects of numeric goal precision. The present research investigated whether and how the precision of weight loss goals—the number of trailing zeros—impacts a goal’s effectiveness. In two preregistered, longitudinal experiments, we contrasted competing predictions as to whether precise (e.g., 2.923 kg) or round (e.g., 3.000 kg) goals are more effective compared to a waiting control condition. In Experiment 1 (N = 121), participants in the two goal conditions lost more weight compared to the control condition—an effect that was mainly driven by precise (rather than round) goals. In Experiment 2 (N = 150), we sought to replicate this effect but found no significant weight loss differences. An individual participant data (IPD) meta-analysis across both experiments revealed that (a) the goal groups jointly lost more weight than the waiting control group and (b) the precise and round goal groups did not differ in weight loss success. An IPD-based multiple mediation analysis showed that healthier eating, but not physical exercise accounted for goal-setting-induced weight loss. We discuss possible explanations for the null findings in Experiment 2 and highlight directions for future research.

The Power and Peril of Precise vs. Round Health Message Interventions to Increase Stair Use

Taking the stairs vs. an elevator generate benefits for the individual by increasing overall physical activity, health, and wellbeing. In the present paper, we report two pre-registered field intervention studies that examine how health message interventions can motivate individuals to change their behavior. We empirically contrasted opposing predictions from the literature as to whether numerically round (60.00%) or precise (61.87%) health messages are more effective in causing people to use the stairs over taking the elevator. Both interventions were compared to a control condition (no-health message). Contrary to our hypotheses and extant findings, both intervention studies did not produce a significant positive effect of the interventions relative to the control condition. In recent years such null findings have received increasingly more appreciation, particularly in the light of evident downsides of file-drawered studies. We discuss a number of moderating factors that may determine when and why nudging interventions are (in-) effective (e.g., a priori behavioral prevalence, pre-established habits, ceiling effects, and building infrastructure), as well as limitations and avenues for future research.