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This study investigates the influence of organizational social capital (OSC) on the social and economic performance of social enterprises (SEs) in Greece and the mediating role of social entrepreneurship orientation (SEO) in these relationships.

A theoretical framework was developed integrating resource-based theory, OSC theory and behavioral entrepreneurship theory. The data were collected from 345 Greek SEs and structural equation modeling (SEM) with bootstrap analysis was employed to estimate path coefficients.

This study shows that OSC positively impacts SEs’ social and economic performance, while SEO mediates only the relationship between OSC and SEs’ social performance. This research offers insights for scholars, practitioners and policymakers in social entrepreneurship by highlighting the significance of OSC and SEO.

This study contributes to the literature on SEs by integrating resource-based theory, OSC theory and behavioral entrepreneurship theory, presenting a novel comprehensive theoretical framework for understanding SEs’ performances. Additionally, the study advances the understanding of SEO as a mediator in the relationship between OSC and SEs’ social and economic performance. The unique focus on the Greek context provides a valuable setting for examining the relationships among OSC, SEO and SEs’ performances.

Deep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?

This paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000–2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.

The findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.

The findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.

Very few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.