This meta-analysis investigates the value of algorithmic and human advice in decision-making processes. Specifically, it examines three core aspects: preference, adherence, and performance. First, we assess whether individuals exhibit a preference for advice originating from algorithms versus real people, exploring factors that might influence trust and reliance on one source over the other. Second, we evaluate the degree to which recipients follow or deviate from the advice provided by algorithms compared to human advisors, analyzing patterns in adherence. Lastly, we measure the impact of algorithmic advice on user performance, determining whether algorithmic guidance leads to improved decision outcomes relative to human advice. By synthesizing findings across studies, this analysis aims to provide a comprehensive understanding of the conditions under which algorithmic advice may be preferred, adhered to, and beneficial for enhancing performance in various contexts.

Behavioral strategy research aims to understand and improve strategic decision- making processes by analyzing managers’ psychological traits and cognitive activities. Our work establishes a bridge between behavioral strategy and cognitive neuroscience by performing an experimental design that uses eye-tracking analysis to understand the effect of visual attention on heuristics about managerial decisions. Our experimental approach allows researchers to show and quantify the effect of visual attention on repeated managerial decisions. Our analysis identify some attentional sources of decision-making efficiency, which are primarily driven by selectivity, as decision-makers learn to focus on fewer pieces of information to make a managerial decision. Moreover, each of these pieces of information is accessed fewer times as participants gain experience with the task. In addition, we show that incorporating attentional information into a heuristic improves the explanatory power of managerial decisions.

A signiffant gender gap in the labor market has been observed in almost ev- ery country (World Economic Forum, 2021). Existing studies have shown that gender differences in academic performance, career choices, and preferences can partly explain the gender gap (Blau and Kahn, 2017). Women and men per- form differently in several experimental tasks involving math, logic, and verbal skills (e.g., Gneezy et al., 2003; Niederle and Vesterlund, 2010, 2011; Shurchkov, 2012). Additionally, several studies document the gender differences in compet- itiveness, risk preferences, and social preferences (Croson and Gneezy, 2009). These results suggest that payment schemes affect men and women differently. Men are more responsive to competitive payment schemes (e.g., Gneezy et al., 2003) whereas women are more responsive to pro-social incentives (e.g., Tonin and Vlassopoulos, 2015). The goal of this project is to study whether payment schemes can be used to tackle the gender gap in the labor market and lead to more equality. We achieve this goal by answering three questions. Do payment schemes affect the performance of men and women differently in a given task? Do these payment schemes affect the beliefs about the genders’ performance as well? Finally, do these changes vary with the gender stereotype associated with the task? 1