Fostering Digital Business Model Innovation: The Role of Government Venture Capital in High‐Tech SMEs
Jiaxin Liu et al.
Abstract
The advancement of digital technology plays a critical role in shaping business model transformation among high‐tech SMEs. Government venture capital (GVC) provides both financial and strategic resources for digital business model innovation (DBMI) and has attracted growing scholarly attention. Drawing on resource‐based theory, this study develops a theoretical framework to examine how GVC influences DBMI in high‐tech SMEs, with particular attention to the GVC certification hypothesis and resource mechanisms. We measure DBMI in Chinese listed high‐tech SMEs using a text analysis approach. This involves constructing and expanding a DBMI lexicon with word vectors based on natural language processing, preprocessing annual report texts, and calculating DBMI levels based on keyword frequencies. Investment data on GVC participation are collected to form an unbalanced panel dataset, and a multiperiod difference‐in‐differences (DID) model is employed to estimate the direct, mediating, and heterogeneous effects of GVC on DBMI. In addition, a PSM‐DID approach is applied to further test the robustness of the results. The empirical results indicate that GVC significantly enhances DBMI in high‐tech SMEs. This effect is particularly pronounced in firms characterized by high ownership concentration, nonstate ownership, and CEO nonduality. Furthermore, GVC promotes DBMI by alleviating financing constraints and enhancing firms' intellectual capital. This study contributes to resource‐based theory by elucidating the role of digital technology in business model innovation and highlighting the importance of GVC in fostering innovation and growth among high‐tech SMEs. The findings offer important theoretical and practical implications for policymakers and business managers, helping high‐tech SMEs leverage policy support to advance DBMI.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.