Entrepreneurship-enabling factors and innovation diffusion data

Description

The dataset consists of processed data derived from publicly available global data collected through the Global Entrepreneurship Monitor's (GEM) National Expert Survey (NES), specifically the GEM 2021 NES Global Individual-Level Nations Dataset. The 2021 data captures expert perceptions of entrepreneurial environments during a historically significant period marked by the COVID-19 pandemic and its widespread economic disruptions. As a result, the data provide a valuable snapshot of how entrepreneurial ecosystems functioned under crisis conditions, including expert evaluations of factors such as resilience, adaptability, institutional support, and policy effectiveness. Examining these conditions enables researchers to better understand how entrepreneurship systems respond to external shocks and periods of uncertainty. Furthermore, the dataset serves as an important historical benchmark for assessing long-term trends and changes in entrepreneurial ecosystems, providing critical context for evaluating the evolution of entrepreneurship-related conditions over time.

Steps to reproduce

To facilitate replication and transparency, the following information outlines the variable selection, analytical methods used to create this dataset, and potential uses. The data are at the individual level, across all variables, with no nested or aggregated data. The variables on which data is processed and presented are based on the following original GEM variables: • NES_F1 is the expert's assessment of the primary condition that supports entrepreneurship, chosen from a range of factors such as policy frameworks, economic environment, digital infrastructure, and related ecosystem characteristics. This variable is processed to represent entrepreneurship-enabling factors. • NES_COUNTRY is a country identifier. • Standardized average scores are available for the R&D level of transference. According to GEM methodology, entrepreneurship-related indicators were originally measured using a 1-to-9 rating scale. GEM subsequently applied principal component analysis (PCA) to identify underlying dimensions of the entrepreneurial ecosystem. R&D transference emerged as one of these dimensions, representing the extent to which research and development activities are translated and disseminated within the entrepreneurial environment. Corresponding standardized scores were generated by GEM to facilitate comparison across observations. This variable is processed to represent innovation diffusion. The dataset processed these original data by calculating counts, descriptive statistics and by employing factorial analysis of variance. The factorial model was deployed to generate effect size estimations of the country and entrepreneurship-enabling factors on innovation diffusion. The dataset includes: • The raw, unadjusted means, standard deviations, and sample sizes (count) for every possible combination of entrepreneurship-enabling factors and country variables. • Effect size as estimators and associated statistical test, showing how much each specific group or condition changes the predicted value of innovation diffusion. The data juxtaposes entrepreneurship-enabling factors and innovation diffusion. The data can be used to evaluate if conditions that promote entrepreneurship also contribute to the propagation of innovation. By adopting a cross-country approach, the data can be used to develop a more refined understanding of the conditions under which innovation can be effectively translated into entrepreneurial opportunity. The data enable researchers to assess whether national context or specific entrepreneurship-supportive factors plays a more significant role in promoting innovation.

Institutions

• Plymouth State University

  New Hampshire, Plymouth

Categories

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