Talk:Research quotient

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I developed Research Quotient, so have a conflict of interest with respect to this article. I am proposing changes (rather than editing directly) that rely more heavily on independent research and institutional sources, and separate the development, measurement, validation, and applications of RQ.

I would appreciate review and implementation by an uninvolved editor. The proposed replacement text is below.DrivenByData (talk) 10:33, 12 September 2026 (UTC)

Research quotient (RQ) is a firm-level measure of innovation introduced in a 2008 article in Management Science. RQ measures the percentage change in a firm's revenues associated with a one-percent change in its R&D investment—the output elasticity of R&D—after accounting for other production inputs and their elasticities.

Development

RQ originated in research intended to resolve a question about the relationship between R&D spending and firms' ability to benefit from external knowledge. The absorptive capacity literature, associated with Wesley Cohen and Daniel Levinthal, proposed that firms investing more in their own R&D became better able to recognize and exploit knowledge generated by other firms.

The 2008 study proposed an alternative explanation for the observed relationship. Rather than greater R&D spending causing firms to become more productive with their R&D because of spillovers, firms might differ in their underlying R&D productivity, with more productive firms investing more because of their higher returns. In this interpretation, causality runs from R&D productivity to R&D spending rather than from spending to productivity.

The article developed RQ, then called organizational IQ (innovation quotient), to control for differences in firms' R&D productivity. Once differences in RQ were taken into account, the absorptive-capacity term became insignificant.

Research involving the development and application of RQ subsequently received support through two National Science Foundation grants, Firm IQ: A Universal, Uniform and Reliable Measure of R&D Effectiveness and The Impact of R&D Practices on R&D Effectiveness.

Measurement

RQ is derived from a modified production function. A conventional production function relates firm output to inputs such as capital and labor:

$Y=K^{\alpha }L^{\beta }$

where Y is output, K is capital, L is labor, and the exponents represent the output elasticities of the corresponding inputs.

The RQ formulation extends the production function to include intangibles (R&D and advertising):

$Y=K^{\alpha }L^{\beta }R^{\gamma }A^{\delta }$

where R represents R&D and A represents advertising. The coefficient $\gamma$ is the firm's R&D output elasticity and is the underlying RQ measure. Thus, a value of $\gamma =0.10$ indicates that, holding the other inputs constant, a one-percent increase in R&D is associated with a 0.10-percent increase in output.

RQ differs from other measures of innovation, such as R&D intensity (R&D divided by sales), in that R&D intensity is an input measure. It also differs from patent-based measures in that it is not restricted to the 42% of R&D-active firms who file patents.

Validation and subsequent research

A validation of RQ in the finance literature compared it with existing measures of innovation inputs, outputs, and efficiency in a series of firm-value specifications. The authors found that RQ remained statistically significant in explaining firm value across all specifications examined, whereas the alternative innovation measures were not consistently robust.

RQ has subsequently been examined and used in research in management, finance, accounting, and corporate governance. Researchers have used RQ data from the Wharton Research Data Services (WRDS) Research Quotient dataset to study the impact of corporate boards, regulation, takeover markets, managerial ability, and corporate social responsibility on innovation.

Managerial and investor applications

RQ has also been presented as a managerial and investment measure of R&D productivity. In 2012, Harvard Business Review published “The Trillion-Dollar R&D Fix,” which used RQ to argue that firms were investing suboptimally in R&D—some overspending and some underspending—and estimated that the shareholder loss for the twenty largest R&D spenders was approximately $1 trillion.

In 2014, CNBC created the “R&D All-Stars: CNBC RQ 50,” a ranking of 50 U.S. public companies based on RQ. The project included CNBC television coverage, online analysis, company comparisons, and an R&D “Hall of Fame.” CNBC's editor-in-chief at the time described RQ as giving its investor audience a way to examine companies through a different lens.

A 2018 article in IEEE Engineering Management Review independently discussed RQ as an approach for connecting R&D spending with productivity and business outcomes.

See also

• Innovation
• Production function
• Research and development
• Total factor productivity

References

``` DrivenByData (talk) 00:18, 7 September 2026 (UTC)

Not done: your request appears to have been generated by a large language model. Please read WP:COI for instructions on COI edits and use the {{edit COI}} template. AlphaBetaDeltaLambda(αβδλ)<sup>talk</sup> 11:00, 12 September 2026 (UTC)

I developed Research Quotient and therefore have a conflict of interest with respect to this article. I am requesting the following specific changes rather than editing the article directly. The purpose is to address the article's promotional tone, reduce repetition and reliance on claims about the benefits of RQ, provide neutral context relative to other innovation measures, and incorporate independent research and institutional sources.

=== 1. Lead ===

Please replace the current lead:

"Research quotient (RQ) is a measure of companies' innovation capability introduced in the 2008 article, R&D Returns Causality: Absorptive Capacity or Organizational IQ. The measure was originally referred to as IQ (innovation quotient), but because IQ and innovation quotient were already in use commercially, it was referred to as RQ in subsequent work.

The motivating argument in the 2008 article was that the main prescription from absorptive capacity — that the more a company spends on R&D, the greater its ability to absorb spillovers from rivals' R&D, seemed implausible. This is because the greater the R&D, the closer a company gets to the knowledge frontier, and accordingly, the less likely it can use spillovers.

Instead, Knott proposed and found, it was not that spending more led to higher returns, it was that companies have inherently different returns (RQ), and those with higher RQs spend more."

with:

"Research quotient (RQ) is a firm-level measure of innovation introduced in the management literature in 2008. RQ is defined as the firm-specific output elasticity of R&D. Thus, it captures the percentage change in a firm's revenues associated with a one-percent change in its R&D investment, after controlling for other inputs and their elasticities."

Reason: The current lead mixes the definition of RQ with discussion of the 2008 study. The proposed text provides a concise definition, then moves the development of RQ to a separate section.

Source: Knott (2008), "R&D/Returns Causality: Absorptive Capacity or Organizational IQ," Management Science, 54(12), 2054–2067, doi:10.1287/mnsc.1080.0933.

=== 2. Replace "Origins of RQ" with "Development" ===

Please replace the existing "Origins of RQ" section with:

"== Development ==

RQ originated from an effort to solve an absorptive capacity puzzle. Cohen and Levinthal had proposed and found that a firm's ability to benefit from spillovers increases in its R&D. The puzzle is that as firms spend more, they approach the knowledge frontier, and therefore should have less need for spillovers. The 2008 study proposed that Cohen and Levinthal's positive relationship between R&D spending and returns reflected reverse causality: it is not that firms obtain higher returns by investing more in R&D; it is that some firms have higher returns to R&D, thus they invest more.

The article developed RQ, then called organizational IQ (innovation quotient), to control for differences in firms' R&D capability. Once differences in RQ were taken into account, the absorptive-capacity term became insignificant.

Research involving the development and application of RQ subsequently received support through two National Science Foundation grants, Firm IQ: A Universal, Uniform and Reliable Measure of R&D Effectiveness and The Impact of R&D Practices on R&D Effectiveness."

Reason: This section is redundant with the lead. The replacement separates the development of the measure from its definition and adds the subsequent NSF-supported research.

Sources:

• Cohen and Levinthal (1990), "Absorptive Capacity: A New Perspective on Learning and Innovation," Administrative Science Quarterly.
• Knott (2008), Management Science.
• National Science Foundation awards 0965147 and 1246893 / National Academies descriptions of those projects.

=== 3. Rename "Definition" to "Estimation" and revise the end of the section ===

Please rename the section "Definition" to "Estimation."

Please retain the existing production-function explanation and equations through the description of γ as the output elasticity of R&D. The existing section accurately explains the production-function basis of the measure.

Please delete:

"To support intuition, γ is rescaled to match the human IQ scale. An RQ of 100 is the average across all U.S. public firms engaged in R&D in 2010. The majority of firms (67%) have RQs which fall between 85 and 115."

and replace it with:

"RQ differs from other measures of innovation, such as R&D intensity (R&D divided by sales), in that R&D intensity is an input measure. It also differs from patent-based measures of innovation because it is not restricted to patenting firms; only around 42% of R&D-active firms obtain patents."

Reason: The IQ rescaling is not necessary to explain the underlying measure. The proposed replacement places RQ in neutral context relative to two commonly used innovation measures: R&D intensity and patents.

Sources:

• Knott (2008), Management Science, for the RQ construction.
• U.S. Census Bureau, CES-WP-22-09 (2022), for the proportion of R&D-active firms that patent.

=== 4. Delete "RQ and optimal R&D" ===

Please delete the entire section "RQ and optimal R&D"

Reason: This section presents a managerial prescription derived from RQ rather than defining, validating, or documenting the measure. Thus, it may have contributed to promotional-content concerns.

=== 5. Replace "RQ and firm Value" with "Validation and subsequent research" and "Managerial and investor applications" ===

Please replace the existing "RQ and firm Value" section, which currently begins:

"The goal of company innovation is typically to increase market value. Because RQ is derived from the production function, it is straightforward to show analytically that increasing RQ increases market value..."

and continues with claims concerning market-to-book value, stock returns, FCLT Global, MSCI, and CNBC,

with:

"== Validation and subsequent research ==

Validation of RQ for use in the finance literature compared it with existing measures of innovation inputs, outputs, and efficiency in a series of firm-value specifications. The authors found that RQ had low correlation with the existing innovation measures, and that RQ was the only measure consistently and positively related to contemporaneous and future firm value, as well as to future returns.

RQ has subsequently been examined and used in research in management, finance, accounting, and corporate governance. Researchers have used RQ data from the Wharton Research Data Services (WRDS) Research Quotient dataset to study the impact of corporate boards, regulation, takeover markets, managerial ability, and corporate social responsibility on innovation.

== Managerial and investor applications ==

While originally an academic measure, RQ has also been presented as a managerial and investment measure. In 2012, Harvard Business Review published “The Trillion-Dollar R&D Fix,” which used RQ to argue that firms were investing suboptimally in R&D—some overspending and some underspending—and estimated that the shareholder loss for the twenty largest R&D spenders was approximately $1 trillion.

In 2014, CNBC created the “R&D All-Stars: CNBC RQ 50,” a ranking of 50 U.S. public companies based on RQ. The project included CNBC television coverage, online analysis, company comparisons, and an R&D “Hall of Fame.”

A 2018 article in IEEE Engineering Management Review independently discussed RQ as an approach for connecting R&D spending with productivity and business outcomes."

Reason: The current section makes broad claims about the benefits of RQ, thus invoking promotional concerns. The replacement separates academic validation and independent subsequent use from practitioner applications.

Sources:

• Cooper, Knott, and Yang (2022), "RQ Innovative Efficiency and Firm Value," Journal of Financial and Quantitative Analysis.
• Harris, Glegg, and Buckley (2019), on boards and R&D productivity.
• Cianci et al. (2021), on regulation and R&D productivity.
• Ongsakul, Chatjuthamard, and Jiraporn (2022), on takeover markets and innovation efficiency.
• Subsequent independent papers on board independence, managerial ability, and corporate social responsibility using RQ.
• WRDS Research Quotient dataset.
• "The Trillion-Dollar R&D Fix," Harvard Business Review (2012).
• CNBC R&D All-Stars / RQ50 coverage (2014).
• Goldense (2018), "Improve R&D Spending and Productivity With the Research Quotient Model," IEEE Engineering Management Review.

=== 6. Delete "RQ and economic growth" ===

Please delete the entire "RQ and economic growth" section.

Reason: This section extends beyond description of RQ into a broader argument about the correlation between RQ and economic growth. Its removal should further reduce the promotional/advocacy tone.

DrivenByData (talk) 19:16, 12 September 2026 (UTC) DrivenByData (talk) 19:16, 12 September 2026 (UTC)