We examine the effect of internal employee coordination on customer trust, focusing specifically on employees’ responsiveness to each other as an important, quantifiable, and objective aspect of internal coordination. Using proprietary data from a company with exogenous assignment of employees to teams that serve individual customers, we study how inter‐employee responsiveness influences customer trust. Each customer is served via an app‐based group chat by a randomly assigned team of employees. Our data include more than 2 million group chat messages with over 16 thousand customers. We find that inter‐employee responsiveness serves as a credible signal that helps build customer trust, as evidenced by their subsequent contracting choices. The effect is more pronounced when the signal is (1) more frequent and (2) more intense. Our findings highlight the novel value of internal employee responsiveness as a credible signal that helps build trust with external stakeholders.

Jennifer Francis, Per Olsson, Dennis R. Oswald, Comparing the Accuracy and Explainability of Dividend, Free Cash Flow, and Abnormal Earnings Equity Value Estimates, Journal of Accounting Research, Vol. 38, No. 1 (Spring, 2000), pp. 45-70

Audit firms are investing billions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Although firms assume AI will enhance audit quality, a growing body of research documents that individuals often exhibit “algorithm aversion”—the tendency to discount computer‐based advice more heavily than human advice, although the advice is identical otherwise. Therefore, we conduct an experiment to examine how algorithm aversion manifests in auditor judgments. Consistent with theory, we find that auditors receiving contradictory evidence from their firm's AI system (instead of a human specialist) propose smaller adjustments to management's complex estimates, particularly when management develops their estimates using relatively objective (vs. subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.