Organizations are increasingly looking for ways to reap the benefits of cognitive diversity for problem solving. A major unanswered question concerns the implications of cognitive diversity for longer-term outcomes such as team learning, with its broader effects on organizational learning and productivity. We study how cognitive style diversity in teams—or diversity in the way that team members encode, organize and process information—indirectly influences team learning through collective intelligence, or the general ability of a team to work together across a wide (...) array of tasks. Synthesizing several perspectives, we predict and find that cognitive style diversity has a curvilinear—inverted U-shaped—relationship with collective intelligence. Collective intelligence is further positively related to the rate at which teams learn, and is a mechanism guiding the indirect relationship between cognitive style diversity and team learning. We test the predictions in 98 teams using ten rounds of the minimum-effort tacit coordination game. Overall, this research advances our understanding of the implications of cognitive diversity for organizations and why some teams demonstrate high levels of team learning in dynamic situations while others do not. (shrink)

The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression, k-dependence Bayesian, and Random Forest. The Bayesian inference and classification methods allow (...) an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts, performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing. (shrink)