This section outlines the research design of the dissertation titled “Are corporate accelerators springboards for startups: a performance analysis of Microsoft’s and Google’s accelerated startups.” The goal is to provide, as the title suggests, a structured approach to answering the research question: Are corporate accelerators springboards for startups or merely sand traps?
The study applies a quantitative, cross-sectional, and comparative research design. It analyses real-world performance data of startups that participated in corporate accelerator programs sponsored and influenced by Google and Microsoft, using statistical methods such as linear regression and ANOVA to test performance outcomes.
This post represents a series of articles related to a research and dissertation called “Are corporate accelerators springboards for startups: a performance analysis of the Microsoft’s and Google’s accelerated.
Purpose and Approach
The purpose of this study has a dichotomy:
- To assess whether each of the two corporate accelerator programs (Google and Microsoft) provides measurable performance advantages to the startups they accelerate.
- To compare both programs' outcomes against each other.
This design enables the dissertation to first evaluate each program individually, and then compare the two, aiming to determine if they serve as “springboards” for startup growth or merely create short-lived boosts with limited long-term impact — the so-called “sand traps.”
Research Type
The research is empirical, relying on structured numerical data and statistical models. It follows a quantitative, cross-sectional, and comparative research design:
- Quantitative: it means the study is based on measurable data and numerical indicators (objective), rather than interviews, narratives, or qualitative insights (subjective). It focuses on metrics like funding raised, survival rates, and IPO outcomes.
- Cross-sectional: it refers to the fact that the data represents a single point in time (or a defined snapshot of each startup’s first 3 years), rather than tracking changes over time (as in longitudinal studies).
- Comparative: it indicates that the study involves contrasting two distinct groups (those accelerated by Google and Microsoft) to determine which performs better across specific KPIs.
The research uses secondary data obtained entirely from Crunchbase, a platform that aggregates startup profiles, funding activity, and lifecycle events (e.g., IPO, closure). The database was curated manually and compiled into a spreadsheet. No other source of data has been used.
Research Strategy
It is designed to compare performance of startups accelerated by Google and Microsoft using structured data analysis. It involves three main components: the database, the unit of analysis, and the analytical tools employed to evaluate performance outcomes.
Database
The analysis is based on a curated database composed exclusively of startups that have participated in either the Google or Microsoft startup accelerator programs. The database, compiled from Crunchbase, ensures that only relevant cases tied to corporate acceleration are included, eliminating external noise, ambivalence or contradictions among databases.
Unit of Analysis
Each row in the database represents one startup, its properties, individual data and serves as a single unit of analysis. This unit-level approach allows for comparison of outcomes on a case-by-case basis, enabling a precise understanding of how each accelerator program correlates with startup performance. On the other hand, each column represents each of the variables involved in the collection of information of the database.
Analytical Tools
Linear Regression
It will be used to measure the predictive effect of accelerator participation on startup outcomes such as total funding, likelihood of IPO, and survival up to 3 years. It allows for quantifying the growth and direction of the relationship between acceleration and various success indicators.
ANOVA (Analysis of Variance)
It will be applied to compare average performance indicators — such as the number of funding rounds and the total amount of capital raised — between the two accelerator programs. This method helps determine whether there are statistically significant differences in outcomes between Google and Microsoft accelerator cohorts.
Justification for Quantitative Approach
A quantitative design was selected for several reasons:
- It allows for statistically robust comparisons between accelerator groups.
- It enables replicability and objectivity, supporting the generalizability of findings.
- It builds on established methods already applied in recent academic literature (Seitz et al., 2023; Canovas-Saiz et al., 2021).
Furthermore, quantitative methods are particularly suited to measuring KPIs such as:
- Number of funding rounds
- Amount of funding raised
- IPO timing
- Survival past three years
- Closure rates
Conclusion
This research design provides a structured, measurable, and comparative approach to evaluating the impact of Google and Microsoft’s startup accelerator programs. It ensures both programs are analyzed individually and against one another, using empirical data and rigorous statistical techniques.
References
- Canovas-Saiz, D., Martínez-Sánchez, Á., & Andreu-Andrés, M.Á. (2021). Incubators vs. Accelerators: A survival analysis of new ventures. Journal of Business Research, 125, 371–379. https://doi.org/10.1016/j.jbusres.2020.12.039
- Seitz, N., Krieger, B., Mauer, R., & Brettel, M. (2023). Corporate accelerators: Design and startup performance. Small Business Economics. https://doi.org/10.1007/s11187-023-00732-y
- Crunchbase. (n.d.). Crunchbase: Discover innovative companies and the people behind them. Retrieved from https://www.crunchbase.com/
- McLeod, S. (2022). Longitudinal study. Simply Psychology. https://www.simplypsychology.org/longitudinal-study.html
- McLeod, S. (2019). Qualitative vs quantitative research. Simply Psychology. https://www.simplypsychology.org/qualitative-quantitative.html
- Surbhi, S. (2016, October 3). Difference between primary and secondary data. Key Differences. https://keydifferences.com/difference-between-primary-and-secondary-data.html
