Mapping the Why: Geo-behavioral Drivers of Accelerator Effectiveness

Build a multi-region dataset that links accelerator outcomes (funding, survival, job growth) to geo-embedded social networks of founders, mentors, angels, and university actors. Use spatial social network (SSN) metrics—e.g., mentor embeddedness, cross-clique bridging, spatial proximity—to model outcome heterogeneity. Pair big-data SSN with short surveys/interviews that capture behavioral motives behind ties (e.g., trust, reciprocity). This study treats networks as spatial-behavioral infrastructure that conditions accelerator value-add, not just as countable connections. If mentor embeddedness or specific bridging patterns predict outcomes, accelerators can strategically engineer local networks (mentor recruitment, cross-institutional mixing) rather than copy-pasting curricula. It also introduces measurable “network-motive” variables to complement structural metrics. The impact is a generalizable, theory-driven explanation of regional deviations that gives policymakers and program managers levers (network architecture and behavioral norms) to improve effectiveness beyond program design alone.

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