R&D investments in response to performance feedback: moderating effects of firm risk profile and business strategy

ABSTRACT

R&D investments in response to performance feedback have been extensively studied. We show that both firm risk profile (i.e. low vs high risk) and business strategy (i.e. Prospector vs Defender), two aspects understudied in this context, have incremental moderating effects (both separate and joint) on this R&D-performance feedback relationship. Using a sample of US listed firms from 2000 to 2019, we observe that, when performance relative to aspiration level decreases (increases), without controlling for moderating effects of risk and strategy, firms tend to increase (decrease) R&D investments. However, as risk profile changes from low to high risk, responses to performance feedback tend to change from decreasing R&D or maintaining status quo to increasing R&D investments. We also find that, in response to performance deviation from aspiration level (regardless of the direction), Defenders tend to decrease R&D investments, regardless of risk profile, whereas Prospectors tend to increase R&D investments (maintain status quo) when their performance relative to aspiration level decreases (increases).

KEYWORDS:

• Behavioral theory of the firm
• performance feedback
• R&D
• risk profile
• business strategy

JEL CLASSIFICATION:

• M21
• M41
• O32

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ Throughout this study, we use R&D intensity and R&D investments interchangeably.

² As R&D investments should be expensed based on the US GAAP, increasing R&D investments negatively affects the profit, while it ‘may’ generate profit later, after the product development and the launching phase being successfully accomplished (Bromiley, Rau, and Zhang 2017). However, decreasing R&D immediately improves the profit for the year (as it reduces operating expenses), with no immediate negative impact on the firm’s economy.

³ 20 is set to ensure the presence of at least 5 observations within each peer group. As previously discussed, on an annual basis, we assign firms within each industry to size quartiles to define peer groups.

⁴ Given that we use the ratio of R&D to sales revenue as our dependent variable, to avoid mechanical relationship we replace the ratio of R&D to sales revenue with the ratio of gross profit to sales revenue in our set of ratios to define firm business strategy. We believe it is a valid replacement as Prospectors’ involvement in greater R&D and their tendency to offer innovative and differentiated products entails asking higher prices and thus large ratio of gross profit to sales revenue. Nevertheless, our (untabulated) results based on the Bentley et al.’s original business strategy measure are not materially different.

⁵ Unlike the other individual variables, higher capital intensity corresponds to firms with Defender business strategy. We, therefore, reverse code capital intensity, so that observations in the lowest (highest) quintile are given a score of 5 (1).

⁶ Given that PERFCOMP_BELOW is a signed variable, and therefore has a negative mean value, a negative coefficient for this variable has a positive impact on the dependent variable.

⁷ This analysis is based on RISK calculated as the standard deviation of ROA. Untabulated results based on the standard deviation of stock return are not materially different.

⁸ Statements are based on an analysis of the R&D and net income figures from Tesla, Ford, GM and Toyota over the period 2010 till 2021.

⁹ Using natural logarithm of market value instead of this variable does not materially change our results.

¹⁰ We followed the formula proposed by Altman (1983) to calculate the Z-score: (1.2 × working capital divided by total assets) + (1.4 × retained earnings divided by total assets) + (3.3 × income before interest expense and taxes divided by total assets) + (0.6 × market value of equity divided by total liability) + (1.0 × sales divided by total assets).