This study aims to examine how knowledge management strategy (KMS) and entrepreneurs’ perceptions of accelerator-program benefits in transferring knowledge capabilities (KC) influence firm revenue and return on investment (ROI) over a four-year period.
Drawing on data from 402 ventures in the global accelerator learning initiative, the authors use Tobit regression to address censored outcomes and apply machine-learning models to uncover complex, nonlinear relationships. Robustness is assessed via two-stage least squares analysis.
KMS – particularly intellectual property patent-based – exerts a persistently negative effect on both revenue and ROI, likely reflecting high commercialization costs. Entrepreneurs’ perceptions of accelerator benefits in transferring KC produce mixed results: Financial support (investor access and direct funding) significantly boosts revenue. Mentorship consistently predicts stronger performance over time. Business-skills development yields variable effects and occasionally correlates negatively with ROI. Social support (networking and enhanced credibility) gains importance over time, underpinning sustained growth. Interaction analyses reveal that robust mentorship and networking can mitigate the adverse impact of KMS activities.
Accelerators should tailor their support for ventures with established KMS by emphasizing mentorship and investor-access programs, ensuring that KMS investments translate into long-term firm success.
By integrating econometric techniques with machine-learning approaches, this research offers novel insights into which accelerator components most effectively leverage knowledge-management assets to drive venture performance.
