Pathology

Business

Computer network

Operations research

Multiple-criteria decision analysis

Operating system

Engineering

Artificial intelligence

Alternative medicine

Resource (disambiguation)

Computer science

Subconscious

Process (computing)

Medicine

Selection (genetic algorithm)

IEEE Transactions on Engineering Management

Competitive accelerators can help startups overcome resource constraints and the liability of newness, but they are also highly selective when choosing startups. However, little is known about how a top accelerator selects startups and what the decision criteria are in that process. Here, we analyze a unique dataset of the real profiles of the startups that applied to the first seed accelerator in Southeast Asia and the accelerator's decisions to uncover the selection process and latent criteria. We used a scoreboard of 30 criteria based on the real-win-worth (Is it real? Can it win? Is it worth doing?) framework to compare the profiles of the selected versus the rejected startups. Our analyses revealed that accelerator managers' implicit decision criteria shifted from eight real or win criteria in the initial screening of many startups to another four win or worth criteria in the final selection of a small number of startups. These critical criteria were further used to build regression models that predict screening and selection results. Understanding the shifting decision criteria may inform accelerator managers of their own subconscious preferences to improve the decision process, and as a result also increase entrepreneurs' empathy toward accelerator managers to sharpen their applications.

How Do Accelerators Select Startups? Shifting Decision Criteria Across Stages