How Do Venture Capitalists Make Decisions? by Paul Gompers, William Gornall, Steven Kaplan, and Ilya A. Strebulaev: An In-Depth Review

Source & Citation: NBER Working Paper No. 22587 (2016); later published in Journal of Financial Economics (2019).National Bureau of Economic Research (NBER)

Link: https://www.nber.org/papers/w22587

Problem Statement: The venture capital industry plays a vital role in funding innovation, yet its decision-making process is largely opaque. This study investigates how VCs make decisions across four key functions: sourcing, evaluating, selecting, and post-investment value addition.

Research Design & Methodology:

This is an empirical, descriptive survey-based study without a stated conceptual or theoretical model. The study does not define independent or dependent variables (IVs/DVs) in a formal statistical framework. Instead, it surveys VCs on the perceived importance of 14 decision factors across stages (sourcing, selection, and post-investment). No regression models or outcome-based causal frameworks are applied.

Key Factors Evaluated by VCs:

VCs were asked to rank and rate the importance of the following factors:

• Management Team – most frequently rated as the top factor (95% marked important; 47% most important)

• Business Model (83%)

• Product (74%)

• Market Size & Competitive Landscape (68%)

• Industry (31%)

• Valuation & Terms (not given a % but discussed in context)

• Exit Potential (mentioned in post-investment focus)

• Governance (role emphasized in value-add phase)

  
  

Methodology:

Design: Structured, cross-sectional survey-based study intended to descriptively map VC decision-making behaviors across four functional domains: deal sourcing, selection, valuation, and post-investment support.

Sample Size: 885 venture capitalists from 681 unique VC firms, primarily in the United States, collectively managing over $200 billion in AUM.

Data Aggregation: For firms with multiple respondents, responses were averaged to produce a single firm-level entry.

Procedure:

• The survey instrument contained more than 60 closed-ended questions, covering sourcing methods, deal evaluation factors, selection strategies, valuation techniques, syndication behavior, return expectations, and post-investment involvement.

• Respondents rated the importance of 14 selection factors using 5-point and 7-point Likert-type scales, depending on the section.

• The survey also included frequency-based questions (e.g., frequency of strategy use, level of engagement in post-investment activities).

• Data was analyzed through mean ratings, frequency distributions, and comparison across stages (e.g., how importance shifts from sourcing to selection).

• No inferential statistics (e.g., regression) or causal models were employed; the design is entirely descriptive.

Responses were anonymized, and results are reported at the aggregate level to protect firm-specific strategic information.

Key Findings

1. Importance of the Founding Team

   • The management team was ranked as the most important factor by 47% of VCs, and listed as “important” by 95%.

   • In post-hoc investment attribution, 96% of respondents credited team as a key driver of successful outcomes, and 92% cited poor teams in failed deals.

   • VCs consistently emphasized team over product, market, or valuation across all investment stages.

   • Table 7 and narrative sections affirm that “VCs place the greatest importance on the management/founding team.”

2. Deal Flow Sources

   • On average, VCs screen ~200 companies per year.

   • Deal sourcing channels are distributed as follows:

     • 30% proactively self-generated

     • 30% from professional networks

     • 20% referred by other investors

     • 8% referred by portfolio companies

     • 10% inbound directly from company management

3. Evaluation of Business Factors

   • Business model rated important by 83%

   • Product characteristics by 74%

   • Market size and competitiveness by 68%

   • Industry by 31%

   • However, only a minority ranked these above the founding team in relative importance.

4. Post-Investment Involvement

   • VCs frequently play an active strategic role post-investment:

     • 87% provide strategic guidance

     • 72% connect founders to investors

     • 69% assist with customer introductions

     • 65% offer operational guidance

     • 58% help hire board members

     • 46% help recruit employees

   • These findings show the breadth of VC engagement beyond capital, especially in high-growth phases.

5. Use of Quantitative Methods

   • 31% of early-stage VCs reported they do not forecast cash flows when evaluating deals.

   • This signals a heavy reliance on qualitative judgment in early-stage funding decisions.

6. Investment Outcome Dispersion

   • Approximately 25% of VC investments result in a loss.

   • A small subset of deals yield outsized returns (≥10x), but exact percentage for “10x” exits is not confirmed with precision.

7. Implications for Future Research

   • The study is descriptive and does not apply regression or causal models.

   • No analysis is conducted on how decision criteria vary based on founder gender, race, or socio-demographics.

   • Opens clear opportunities to extend the findings using behavioral experiments, NLP-driven pitch analysis, or gender-differentiated funding models, leaving meaningful opportunities for future researchers to explore these dimensions further.

     
     

Limitations and Biases:

The authors explicitly acknowledge multiple sources of bias:

• Sampling bias, due to high response rates from elite networks (e.g., Kauffman Fellows, top-tier business schools), likely skewing the sample toward more successful VC firms.

• Self-selection bias, since those who responded may differ systematically from those who did not, particularly in their firm success levels and decision philosophies.

• Consequently, the findings may reflect “best practices” of top-performing VCs, rather than the average behavior of the broader VC industry.

• Segment-specific response rates varied: 37% for elite-affiliated groups vs. 19% for VentureSource-sourced firms.

• The authors also warn of positivity bias, common in surveys, where respondents may overreport strategic involvement or understate failures.

  
  

Conclusion and Perspective:

This paper represents a seminal and comprehensive contribution to understanding how venture capitalists make decisions. Its breadth of data and methodological rigor provide an invaluable benchmark for both practitioners and scholars.

That said, several avenues remain open for further exploration:

• Empirical causal pathways: The study is descriptive in nature and does not model causal relationships between decision criteria and investment outcomes. There is significant scope to extend this work through predictive modeling or causal inference frameworks using observational data, offering scholars fertile ground for future inquiry.

• Founder identity dimensions: While the paper captures what VCs value, it does not explore how these values may be shaped by founder-level characteristics (e.g., gender, race, or socio-economic background). This presents a timely opportunity to examine the role of implicit bias and representation gaps in early-stage funding decisions - an important area for further research.

• Behavioral-experimental validation: Given the reliance on self-reported responses, future research might incorporate behavioral experiments or real-world decision simulations to bridge the gap between stated beliefs and actual investment behavior, inviting more empirical validation.

• Dynamic heuristics across market cycles: The analysis captures decision shifts across investment stages, but does not explore how heuristics evolve longitudinally with changing market dynamics, fund lifecycles, or macroeconomic trends. This leaves an important avenue open for longitudinal studies.

• Institutional and structural influences: LP expectations, fund size, syndicate dynamics, and other structural variables may also shape VC decisions - offering further dimensions for exploration and model refinement.

  
  

These extensions could meaningfully expand the authors’ foundational work and offer new insight into how VC decision-making evolves in increasingly diverse and data-driven environments.

This study stands as a cornerstone in venture capital research, notable for both its exceptional scale surveying 885 VCs representing ~$200 billion in AUM and its structured capture of decision-making across all major investment stages. What stands out is how clearly it connects academic structure with real-world VC behavior. It’s become a key reference point in the field. I see strong potential to build on this - especially by studying how these decisions may shift when factors like founder identity, AI-driven evaluation tools, or market cycles are introduced. This paper has given me a strong foundation to think more critically about where gaps still exist - especially around bias, representation, and the role of technology in funding decisions.

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