Interest in reverse innovation (RI) is increasing. According to the authors’ review, more than 350 reliable sources (scientific publications, academic books and working papers) examine or at least discuss the concept. As RI gains popularity among academic authors, some discrepancies have started to appear. This wealth of publications could impact prior advancements related to understanding of the phenomenon. The purpose of this paper is to decrease fragmentation and focus on identifying and understanding RI. A systematic review of RI was conducted. The review conformed to a rigorous set of core principles: it was systematic (organized according to a method designed to address the review questions), transparent (explicitly stated), reproducible and updatable, and synthesized (summarized the evidence relating to the review question). This systematic review provides an improved theoretical and practical framework for the concept of RI. In terms of theory, the authors have demonstrated that the idea behind the concept is not entirely new. A consensus on the definition of RI is not reached in the literature, and descriptions in organizational theory contexts are sometimes misleading. The authors analyzed all the various definitions provided in the literature. From a practical point of view, the authors have explained the academic interest in RI in relation to organizational strategy, in particular the context in which strategies are adopted. The concept of RI has significant managerial implications, and the authors have proposed a conceptual framework to help managers understand and grasp the implications of RI. Finally, the authors have provided suggestions for future research on RI. To the best of the authors’ knowledge, this is the first exhaustive literature review on RI.

Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine learning in the finance field. After collecting a large dataset comprised of 5053 documents, we conducted a computational systematic review of the academic finance literature intersected with neural network methodologies, with a limited focus on the documents’ metadata. The output is a meta-analysis of the two-decade evolution and the current state of academic inquiries into financial concepts. Researchers will benefit from a mapping resulting from computational-based methods such as graph theory and natural language processing.

This paper presents an empirical assessment of bilateral migration flows into the EU-15 countries. Using an extended gravity model, it identifies economic, welfare state, geospatial and linguistic variables as the principal determinants of migration flows into the EU-15 countries. As long as its effect is not offset by a high unemployment rate in the host country, the level of social protection expenditure influences migrants' choice of destination. However, albeit acting as a joint force with other economic, cultural and geospatial variables, the welfare state characteristics of the host country need to be reckoned with when studying European migration flows. Our empirical findings lend some support for a more unified or at least better coordinated social policy across the European Union.