Get a clear understanding of core definitions about the conceptual framework for startup accelerators.
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.
Introduction to Conceptual Framework for Startup Accelerators
It provides theoretical foundation to understand how participation in an acceleration program influences startup development and long-term outcomes. Drawing from the literature on entrepreneurship, innovation ecosystems, open innovation, and startup financing (look further in the references), this section outlines the key concepts, variables, and relationships that shape the impact of accelerators for the rest of the dissertation; in fact, the whole chapter of literature review aimed for that. These constructs serve as an analytical lens through which accelerator performance will later be empirically assessed.
Defining Key Concepts
The conceptual framework for startup accelerators evaluates functioning core elements defined here:
Startup Accelerators (boosted by CAs)
Fixed-term, cohort-based programs that provide startups with mentoring, resources, and exposure, typically in exchange for equity or strategic alignment (Hallen et al., 2022).
Startup Performance
Measurable outcomes that indicate a startup’s success or failure, such as funding rounds, survival rates, acquisition/IPO exits, and patent activity (Assenova & Amit, 2024).
Springboard vs. Sand Trap Effects
Springboards refer to accelerators that launch startups toward scalable growth. Sand traps refer to programs that stall progress or create dependency (Hallen et al., 2022).
Status Spillover
The prestige or reputation a startup gains from being associated with a high-status entity, such as a corporate sponsor or elite accelerator, which additionally grants legitimacy (Seitz et al., 2023).
Human Capital and Mentorship
The skills, knowledge, and networks startups gain from mentors, peers, and advisors during the program (Woolley & MacGregor, 2022).
Open Innovation
A process in which startups share and receive knowledge from mentors, investors, peers, and external stakeholders to co-create value, also known as external innovation (Perrone, G., (n.d.)).
Funding Model (Grant vs. Equity)
Grants offer non-dilutive funding, while equity implies investor involvement and ownership. Each impacts the startup differently in terms of control, accountability, and growth potential (Perrone, G., (n.d.)).
Accelerator Design Dimensions and Their Impact
Different accelerators operate with varying structures, resources, and philosophies. These design choices significantly influence startup trajectories. Below is a comparative table of typical accelerator design factors and their potential effects on startup performance:
| Design Dimension | Grant-Based Accelerators | Equity-Based Accelerators |
|---|---|---|
| Funding Type | Non-dilutive; often public or university-based | Dilutive; venture-capital-driven |
| Startup Motivation | Access to support without giving up equity | Willingness to trade equity for high growth potential |
| Mentorship Intensity | Moderate; often government/institution-linked | High-intensity; includes investors and serial entrepreneurs |
| Pressure for Performance | Lower; tied to learning and ecosystem growth | Higher; driven by ROI expectations |
| Post-Program Support | Alumni networks, weak follow-up | Stronger ties if investor returns are at stake |
This table draws on observations from lecture slides “Static Games”, Hallen et al. (2022), and Woolley & MacGregor (2022), emphasising that the type of support model shapes the degree and nature of startup evolution during and after acceleration.
Mediating and Moderating Variables
The relationship between accelerator participation and startup performance is rarely direct. It is shaped by mediating variables and moderating variables. Understanding how they influence the outcome is essential for designing accurate performance evaluations.
Mediating Variables: Explaining the Mechanism
These variables act as the link between the accelerator and startup outcomes, making the effects visible and traceable, which explain how or why an effect happens
Mentorship and Coaching
Structured mentorship connects startups with experienced founders, experts, or investors. This exchange of knowledge enhances the quality of strategic decisions, which may in turn affect innovation capacity and funding access (Sarto et al., 2022).
Status Spillover and Signaling
Startups gain symbolic value and visibility from being associated with elite accelerators or high-status partners. This perceived credibility increases trust from investors, leading to higher chance of fundraising or partnerships (Hallen et al., 2022).
Peer Interaction and Open Innovation
Accelerators function as temporary innovation ecosystems where founders learn from each other. These peer-driven collaborations can lead to knowledge spillovers (similar to the status spillover), prototype feedback, and even joint ventures (Perrone, G., (n.d.)).
Moderating Variables: Influencing the Context
These factors determine how strong or weak the accelerator’s influence may be, depending on startup or ecosystem conditions, which influence when or under what conditions the effect is stronger or weaker.
Ecosystem Maturity
Startups embedded in strong innovation ecosystems (like Silicon Valley) may already have access to funding, networks, and talent, making the marginal benefit of acceleration smaller. (Fehder, 2023).
Startup Stage or Timing
Early-stage startups are more likely to benefit from foundational mentoring and business model validation, whereas more mature ventures may need highly specialised or specific-case support. The fit between stage and accelerator design is critical (Woolley & MacGregor, 2022).
Strategic and Sectoral Fit
A startup whose product closely aligns with the accelerator’s industry focus or mentorship network is more likely to convert participation into commercial growth. Misalignment can lead to distractions, pivots, or underperformance (Hallen et al., 2022). However, it other cases, this could also lead into new business opportunities previously untapped by the corporation.
What This Framework Enables
By clarifying how startup accelerators function within broader entrepreneurial systems, this conceptual framework:
- Guides the selection of variables for empirical testing.
- Helps interpret whether improved startup performance is attributable to acceleration or external factors.
- Provides a structure for comparing different accelerator models and understanding their strategic fit.
- Supports a critical evaluation of whether these programs enable long-term growth (springboards) or create dependency and underperformance (sand traps).
- give the context, above all, to understand the next upcoming chapters of the dissertation.
References
- Assenova, V., & Amit, R. (2024). The impact of accelerator participation on startup performance: An empirical assessment. Entrepreneurship Development Program.
- Fehder, D. C. (2023). Coming from a good pond: The influence of a new venture’s founding ecosystem on accelerator performance. Administrative Science Quarterly. https://doi.org/10.1177/00018392231204839
- Hallen, B. L., Cohen, S. L., & Park, S. H. (2022). Are seed accelerators status springboards for startups? Or sand traps? Strategic Management Journal, 1–37. https://doi.org/10.1002/smj.3484
- 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
- Woolley, J. L., & Macgregor, N. (2022). The influence of incubator and accelerator participation on nanotechnology venture success. Entrepreneurship Theory and Practice, 46(6), 1717–1755. https://doi.org/10.1177/10422587211024510
- Perrone, G. (n.d.). Static games [Lecture slides]. Master Degree Program in Management Engineering, Università degli Studi di Palermo.
