DFV Engine Adoption Impact Analysis Report (v3)

Analysis Period: 1958-1975 Subject: Formula 1 Constructor-Season Panel Data Observations: 108 (Constructors: 25, Years: 18) Report Date: December 16, 2025

Executive Summary

This study empirically analyzes the adoption and learning effects of modular technology (Cosworth DFV engine) using Formula 1 panel data from 1950-1975.

Key Findings

1. Research Background

1.1 Research Question

"What is the impact of modular technology adoption on organizational performance?"

The introduction of the Cosworth DFV engine in 1967 was a pivotal moment in F1 history: - Standardized engine available for purchase by anyone - Shift from vertical integration to modular approach - Dominated F1 for approximately 25 years

1.2 Theoretical Background

Modularity Theory - Modular technology lowers entry barriers - However, technology access ≠ performance - Learning and integration capabilities are key

2. Data and Variables

2.1 Data Structure

Panel Data Configuration: - Unit: Constructor (team) × Season - Period: 1958-1975 (constructor championship era) - Observations: 108

2.2 Key Variables

Dependent Variable

• PointsShare: Team points / Total season points (adjusts for rule changes)

Independent Variables

• DFV_Adopt: DFV engine usage indicator (0/1)
• DFV_Persist: Cumulative years of DFV usage (0-9)
• BaselineCap: Prior capability (average PointsShare 1960-1966)

2.3 Descriptive Statistics

Variable        Mean      Std      Min      25%      50%      75%      Max
PointsShare    0.167    0.169    0.000    0.042    0.123    0.260    1.000
Season_Points  24.46    20.97    0.000    7.000   18.500   36.000   92.000
DFV_Adopt       0.42     0.50    0.000    0.000    0.000    1.000    1.000
DFV_Persist     1.57     2.38    0.000    0.000    0.000    2.000    9.000
BaselineCap     0.15     0.16    0.000    0.000    0.132    0.345    0.381

3. Hypothesis Testing Results

3.1 H1': Direct Effect of DFV Adoption

Hypothesis: "DFV adoption itself does not guarantee performance improvement"

Model:

PointsShare = β₁·DFV_Adopt + γᵢ + δₜ + ε

Results: - Coefficient (β₁): 0.094391 - p-value: 0.2174 - Decision: Not significant → H1 supported

Interpretation: Adopting the DFV engine does not immediately improve performance. This suggests technology access ≠ performance gain.

3.2 H2': Learning Effect from DFV

Hypothesis: "Performance effects arise from persistent DFV usage and learning"

Model:

PointsShare = β₁·DFV_Persist + β₂·DFV_Adopt + γᵢ + δₜ + ε

Results: - Coefficient (β₁): 0.030845 - p-value: 0.0456 - Decision: Positive and significant → H2 supported

Interpretation: Each additional year of DFV usage increases PointsShare by approximately 0.0308. This demonstrates the importance of learning effects and experience accumulation.

Practical Implications: - Teams using DFV for 3 years have PointsShare approximately 0.062 higher than 1-year users - Continuous optimization is key

3.3 H3': Capability Amplification Effect

Hypothesis: "Modular technology amplifies performance of teams with strong prior capabilities"

Model:

PointsShare = β₁·DFV_Persist + β₂·BaselineCap + β₃·(DFV_Persist × BaselineCap) + γᵢ + δₜ + ε

Results: - Interaction Coefficient (β₃): -0.019164 - p-value: 0.9189 - Decision: H3 rejected (not significant)

Interpretation: No significant interaction between prior capability and DFV learning effect was found. This suggests DFV learning effects are uniform across all teams.

Implications: - DFV has a "democratization" effect - Both strong and weak teams gain similar benefits through learning

4. Overall Conclusions

4.1 Key Findings

1. Access ≠ Performance: DFV adoption does not guarantee immediate performance gains
2. Learning is Key: Performance improves through persistent usage and optimization
3. Uniform Learning Effects: Both strong and weak teams follow similar learning curves

4.2 Theoretical Contributions

Implications for Modularity Theory: - Value of modular technology comes from utilization capability, not technology itself - "Plug-and-Play" illusion: Technology access alone is insufficient - Reaffirms importance of learning curves

4.3 Practical Implications

Technology Adoption Strategy: 1. Focus on long-term learning plans rather than simple adoption 2. Initial investment and patience required 3. Establish continuous improvement and optimization processes

5. Limitations and Future Research

5.1 Limitations

1. Limited Period: Restricted to 1958-1975
2. Single Technology: Focus only on DFV engine
3. Team Heterogeneity: Difficult to fully control for team resources and budgets

5.2 Future Research Directions

• Extend to other modular technologies (turbo engines, aerodynamics, etc.)
• Add team size and budget data
• Analyze interaction with driver capabilities

Appendix: Methodology

Analysis Model

Two-Way Fixed Effects (TWFE)

Y_it = β·X_it + γᵢ + δₜ + ε_it

• γᵢ: Team fixed effects (time-invariant team characteristics)
• δₜ: Year fixed effects (regulation changes, common shocks)
• Standard Errors: Clustered at constructor level

Software

• Python 3.12
• statsmodels (TWFE estimation)
• pandas (data processing)

End of Report

This report is based on panel_data_1950_1975_v3.csv