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Purpose The purpose of this paper is to link valuation of different accounting items to research and development (R&D) investment decisions and investigate how suboptimal R&D choices during initial public offering (IPO) are linked to future operating and market underperformance. Design/methodology/approach For firms with substantial growth opportunities, accounting net income is a poor measure of the firm’s performance (Smith and Watts, 1992). Therefore, other metrics such as R&D intensity are used by investors to evaluate firms’ performance. This leads to a coexistence of two strategies: if earnings are the main value driver, firms tend to underinvest in R&D; and if R&D expenditures are the main value driver, firms tend to overinvest in R&D. Findings The authors show that the R&D investment decision varies systematically with cross-sectional characteristics: firms that are at the growth stage, unprofitable or belong to science-driven industries are more likely to overinvest, while firms that are able to avoid losses by decreasing R&D expenditure are more likely to underinvest. Finally, they find that R&D overinvestment leads to future underperformance as evidenced by poor operating return on assets, lower product market share, higher frequency of delisting due to poor performance and negative abnormal stock returns. Originality/value While prior literature concentrates on R&D underinvestment as a tool of reporting higher net income, the authors demonstrate the existence of an alternative strategy used by many IPO firms – R&D overinvestment.

How does artificial intelligence (AI) impact audit quality and efficiency? We explore this question by leveraging a unique dataset of more than 310,000 detailed individual resumes for the 36 largest audit firms to identify audit firms’ employment of AI workers. We provide a first look into the AI workforce within the auditing sector. AI workers tend to be male and relatively young and hold mostly but not exclusively technical degrees. Importantly, AI is a centralized function within the firm, with workers concentrating in a handful of teams and geographic locations. Our results show that investing in AI helps improve audit quality, reduces fees, and ultimately displaces human auditors, although the effect on labor takes several years to materialize. Specifically, a one-standard-deviation change in recent AI investments is associated with a 5.0% reduction in the likelihood of an audit restatement, a 0.9% drop in audit fees, and a reduction in the number of accounting employees that reaches 3.6% after three years and 7.1% after four years. Our empirical analyses are supported by in-depth interviews with 17 audit partners representing the eight largest U.S. public accounting firms, which show that (1) AI is developed centrally; (2) AI is widely used in audit; and (3) the primary goal for using AI in audit is improved quality, followed by efficiency.

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