From knowledge creation to commercialization performance: Non-linear effects on green and digital energy start-ups

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Type of the article: Research Article

The conversion of knowledge into organizational and market performance is a central challenge for innovation-driven economies. This study examines how innovation activity and its commercialization shape the entrepreneurial performance of knowledge ecosystems, using green and digital energy start-ups as an empirical context. The aim is to investigate the non-linear relationships among knowledge creation, knowledge commercialization, and start-up development, with particular attention to whether mismatches between innovation activity and market uptake generate diminishing returns or imitation-driven entrepreneurial dynamics. The analysis is based on a balanced panel of 37 countries over the period 2018–2023, comprising 222 country-year observations. The methodology applies TWFE with DK standard errors, complemented by quadratic specifications, turning-point analysis, and lagged models. The findings reveal an inverted U-shaped relationship between innovation commercialization and start-up activity, confirmed by negative, significant squared terms for sales of new-to-market and new-to-firm innovations in green start-ups (β2 = −0.1588) and digital start-ups (β2 = −0.1462), with turning points at 0.1428 and 0.0021, respectively. Funding dynamics demonstrate threshold effects: product innovation has a U-shaped relationship with early-stage digital funding (β2 = 2.0630), while process innovation strengthens later-stage funding for green (β2 = 1.8100) and digital start-ups (β2 = 1.8434). The knowledge commercialization gap positively affects digital start-ups (β = 0.1111), suggesting that uncommercialized knowledge may stimulate market entry. Lagged results confirm temporal effects, with past innovation commercialization reducing digital start-up activity (β = −0.2483). Knowledge creation alone does not ensure performance growth; sustainable start-up development requires effective knowledge-to-market conversion.

 
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    • Table 1. Non-linear effects of innovation indicators on start-ups and funding (Driscoll-Kraay standard errors)
    • Table 2. Turning points of non-linear relationships between innovation indicators and entrepreneurial outcomes
    • Table 3. Effects of the knowledge commercialization gap on start-ups and funding (Driscoll–Kraay standard errors)
    • Table 4. Effects of the knowledge commercialization gap ratio on start-ups and funding (Driscoll–Kraay standard errors)
    • Table 5. Lagged non-linear effects of innovation indicators on start-ups and funding (Driscoll–Kraay standard errors)
    • Table 6. Lagged effects of the knowledge commercialization gap and knowledge commercialization gap ratio on green start-ups (Driscoll–Kraay standard errors)
    • Table A1. Descriptive statistics of variables used in the analysis
    • Table B1. Variables used in the empirical analysis
    • Conceptualization
      Milena Kirilova Filipova, Olha Рrokopenko, Petra Krišková, Dmytro Halynskyi
    • Funding acquisition
      Milena Kirilova Filipova
    • Writing – original draft
      Milena Kirilova Filipova, Olha Рrokopenko, Petra Krišková, Dmytro Halynskyi
    • Writing – review & editing
      Milena Kirilova Filipova, Olha Рrokopenko, Petra Krišková, Dmytro Halynskyi
    • Data curation
      Olha Рrokopenko
    • Formal Analysis
      Olha Рrokopenko
    • Investigation
      Olha Рrokopenko
    • Methodology
      Olha Рrokopenko
    • Project administration
      Olha Рrokopenko
    • Software
      Olha Рrokopenko
    • Validation
      Olha Рrokopenko
    • Visualization
      Olha Рrokopenko
    • Resources
      Petra Krišková
    • Supervision
      Dmytro Halynskyi