ABSTRACT
This study investigates the complex interplay among innovation, research and development (R&D), and entrepreneurship within the context of European nations. The focus of the study is also on the contributory role of tertiary educational institutions in nurturing entrepreneurial activities. To deepen the understanding of these multifaceted relationships and their subsequent impact on regional economies, the research introduces a novel metric termed the Innovation Readiness Environment (IRE) index. This index combines various indicators such as R&D expenditure, patenting rates, firm size, and educational levels, thereby providing a framework for evaluating firms' innovative capabilities and entrepreneurial success in a given region. Utilization of this index offers policymakers and stakeholders a nuanced understanding of the regional innovation ecosystem, facilitating the identification of strengths and deficiencies. This, in turn, enables the formulation of targeted policy interventions to enhance innovation and entrepreneurship. One relevant conclusion drawn from this study is the pivotal role of tertiary education in catalyzing entrepreneurial ventures. The findings posit that higher levels of entrepreneurial education significantly supplement an individual's likelihood of entrepreneurial success by imparting the requisite skills and knowledge indispensable in a competitive business milieu. By fostering an environment conducive to innovation, higher education institutions emerge as critical agents in cultivating entrepreneurial acumen and stimulating economic expansion. The study further incorporates a spatial analytical framework to elucidate the regional specificities of innovation at the pan-European scale.
ABSTRACT
This article proposes the Shiny app 'CLC Estimator' -Congeneric Latent Construct Estimator- to address the problem of estimating latent unidimensional constructs via congeneric approaches. While congeneric approaches provide more rigorous results than suboptimal parallel-based scoring methods, most statistical packages do not provide easy access to congeneric approaches. To address this issue, the CLC Estimator allows social scientists to use congeneric approaches to estimate latent unidimensional constructs smoothly. The present app provides a novel solution to the challenge of limited access to congeneric estimation methods in survey research.
Subject(s)
Research Design , Monte Carlo MethodABSTRACT
Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007-2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.
