Entrepreneurial activity in an environment of digital transformation: an analysis of relevant factors in the euro area

The paper addresses how the ongoing digital revolution—marked by rapid technological change, shifting consumer habits, and advances such as AI, robotics, big data, IoT, metaverse, and blockchain—affects entrepreneurial activity across the euro area. While digitalization offers benefits, it also risks digital exclusion and job displacement. Nonetheless, it creates substantial opportunities through new business models and enhanced consumer services. The study’s purpose is to analyze, at an aggregate level, which digital and technological factors are associated with entrepreneurial density, with special emphasis on self-employed entrepreneurs without employees, who represent a large share of new firms in Europe. The central hypothesis is that digital transformation generates entrepreneurial opportunities across all sectors of the economy, not just technology-intensive ones.

The review situates entrepreneurship within the digital transformation context. Key themes include: (1) Definitions and scope: Digital entrepreneurship differs from traditional forms as physical elements become digital; IT serves as facilitator, mediator, outcome, and ubiquitous business model (Hull et al., 2007; Steininger, 2019). (2) Ecosystems: Frameworks integrating digital and entrepreneurial ecosystems emphasize infrastructure governance, users, and markets (Sussan & Acs, 2017); digital entrepreneurship is defined as creating digital value through socio-technical enablers (Sahut et al., 2021). (3) Research streams: business models, entrepreneurial processes, platform strategies, ecosystems, education/training, and digital social entrepreneurship (Kraus et al., 2019; Baig et al., 2022). (4) Enablers and outcomes: Digitalization can enhance national welfare contingent on education, governance, and finance (Torres & Augusto, 2020); mobile payments increase entrepreneurship (Yin et al., 2019); rural digitalization supports survival and growth (Lekhanya, 2018). (5) Labor market and skills: Higher-skilled workers in ICT are more likely to become entrepreneurs amid digitization (Fossen & Sorgner, 2021); age and gender influence adoption of digital processes (Ferreira et al., 2019). (6) Robotics: Rapid increases in robot stocks in EU countries; mixed employment effects but growing sectoral importance (Carbonero et al., 2018; Klenert et al., 2023; Callarisa-Fiol et al., 2023). (7) R&D and innovation: R&D investment is central to digitalization; potential knowledge spillovers to entrepreneurship but context-dependent (Babina & Howell, 2018; Chen & Kim, 2023). (8) Customer experience: Digital entrepreneurship improves service experience and responsiveness, leveraging platforms and data (Hair et al., 2012; Leimeister et al., 2014; Srinivasan & Venkatraman, 2018; Baig et al., 2022). The review motivates hypotheses linking robotics, public and private R&D, and science/technology employment to entrepreneurial activity.

Design: Panel data analysis using euro area countries, 2009–2020. Countries with incomplete entrepreneurial density data (Malta, Greece, Ireland) were excluded. Final sample includes: Belgium, Bulgaria, Estonia, France, Germany, Italy, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Lithuania, Latvia, Austria, Romania. Rationale: Common monetary policy across Eurozone reduces macro-policy heterogeneity, facilitating analysis of digital/innovation factors. Dependent variables: Entrepreneurial density defined as (new enterprises in year t) / (labor force in year t), for (a) all new firms and (b) new firms without employees (self-employed). Independent variables: - Macroeconomic controls: year-on-year change in real GDP (log-difference), unemployment rate, bank credit to private sector (% GDP). - Digital/innovation factors: density of industrial robots (robots per active population), private sector R&D investment (% GDP), public sector R&D investment (% GDP), employment in science- and technology-intensive sectors (number of persons). Data sources: Eurostat, World Bank, International Federation of Robotics (IFR). Estimation approach: - Variables were log-transformed except relative change in GDP. - Fixed-effects panel models selected via Hausman tests (p=0.0036 for total firms; p=0.0856 for self-employed). - Robust standard errors used (HAC) to address autocorrelation and heteroscedasticity. - Diagnostics reported: Pesaran CD test p-values (0.9682; 0.9798), Wooldridge test p-values (0.1353; 0.1179). - Sample size: N=190 observations.

- Economic controls: Change in real GDP positively relates to entrepreneurial density: total firms coeff=0.1422 (p=0.0020); self-employed coeff=0.0617 (p=0.0916). Unemployment rate and bank credit/GDP are not statistically significant in either model. - Robotics: Robot density positively and significantly relates to entrepreneurship in both models: total firms coeff=1.8440 (p=0.0062); self-employed coeff=2.1258 (p=0.0078). Supports H1. - R&D investment: Private (business) R&D investment shows a significant negative association with entrepreneurship: total firms coeff=−3.1837 (p=0.0030); self-employed coeff=−2.5178 (p=0.0884). Rejects H2 (expected positive). Public R&D investment is not significant (total firms coeff=7.1157, p=0.1102; self-employed coeff=0.7467, p=0.1725), rejecting H3. - Science/technology employment: Employment in science- and technology-intensive sectors is not significantly related to entrepreneurial density (self-employed model coeff=4.3178, p=0.3260). Rejects H4. - Interpretation: Results suggest opportunity-driven entrepreneurship associated with digital transformation rather than necessity (unemployment non-significant). Business R&D may raise salaried opportunities and productivity within firms, reducing external venture formation despite knowledge creation. Robotization appears to expand entrepreneurial opportunities across sectors.

The findings address the central question of how digital transformation factors correlate with entrepreneurial activity. A positive link between robot density and entrepreneurship indicates that automation and related technologies open new market niches and complementary service/product opportunities beyond directly automated sectors. The absence of significance for unemployment and bank credit implies that, in a digital context, entrepreneurship is less driven by labor market distress or traditional bank finance and more by opportunity and alternative financing channels that digitalization enables. The negative association with private R&D investment and the non-significance of science/technology employment suggest that when firms invest heavily in R&D and hire highly skilled workers, these individuals may prefer secure, stimulating salaried positions, reducing external venture formation; firms may also internalize innovation through intrapreneurship and retention practices. Public R&D’s lack of direct impact on entrepreneurial density underscores the need for better translation mechanisms (public–private partnerships, commercialization pathways). Overall, the results reinforce the role of digitalization as an opportunity catalyst while highlighting institutional and organizational channels that mediate whether innovation yields new ventures or is absorbed within incumbent firms.

The study contributes by empirically linking key digital transformation indicators to entrepreneurial density in euro area countries, distinguishing effects for total new firms and self-employed ventures. Main contributions: (1) Robot density consistently and positively relates to entrepreneurship, indicating broad opportunity creation from automation. (2) Private R&D investment correlates negatively with entrepreneurial density, suggesting innovation may be internalized within firms, dampening external venture creation. (3) Public R&D and science/technology employment show no significant direct relationship with entrepreneurial density. Policy implications include: fostering environments for digital innovation and AI, enhancing entrepreneurship training and incentives, promoting public–private collaboration to align public R&D with entrepreneurial needs, creating regulatory sandboxes, encouraging university–industry linkages and spinouts, and supporting intrapreneurship to leverage internal talent and R&D. Future research should examine how specific digital technologies (e.g., blockchain, AI) directly affect venture creation, and how digital entrepreneurship impacts consumer service quality and customer experience across sectors.

- Limited measurement of specific digital technologies: The study lacks direct indicators for technologies such as blockchain or artificial intelligence and thus cannot isolate their distinct effects on entrepreneurial activity. - Aggregation and data coverage: The analysis uses country-level panel data for selected euro area countries (excluding Malta, Greece, Ireland due to incomplete data), potentially masking subnational heterogeneity and sector-specific dynamics. - Period effects: The sample spans major crises (Great Recession, COVID-19), which may introduce unobserved shocks despite fixed effects and robust errors. - Causality: The observational design and contemporaneous measures limit causal inference; relationships should be interpreted as associations. - Variable construction: Entrepreneurial density and robot density are aggregate measures that may not capture differences in firm quality, sectoral composition, or the nature (opportunity vs necessity) of entrepreneurship.