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Some studies investigate the determinants of sustainability/integrated reporting in Higher Education Institutions (HEIs). However, empirical research is still very embryonic in the scope of sustainable development goals (SDGs). As far as the authors are aware, previous research related to reporting in HEIs has not considered the linkage with the SDGs. Thus, this paper aims to analyse the disclosure on the websites of the Portuguese HEIs regarding the SDGs and their determinant factors.

This study is based on content analysis of the information disclosed on the websites of all Portuguese HEIs. Through bivariate and multivariate statistics analysis, the authors also aim to identify the explanatory factors for the SDGs reporting (such as geographical location – coast/inland, HEIs’ size, educational system – Universities and polytechnics, institutional status – public and private).

The results indicate that 63.6% of Portuguese HEIs disclose information on SDGs in their websites. Findings of bivariate analysis revealed that public and larger HEIs are those that disclose more information about SDG on their websites. However, the logit regression result found that size is the only determinant factor of SDGs reporting.

To the best of the authors’ knowledge, this paper is the first Portuguese approach to SDGs reporting in the Portuguese higher education sector. The results will be of interest to policymakers and regulators who decide to implement and standardize SDGs reporting at higher education, as well as of HEIs’ managers who wish to follow these new trends in their reporting cycle.

The article investigates whether variety of democracy affects the probability to employ public subsidies for credit support by small- and medium-sized enterprises (SMEs) led by female entrepreneurs.

Building on the literature on democracy and on gender differences, it leverages a large firm- and country-level dataset (SAFE) of 31 democracies in Europe (EU and non-EU) over the 2009–2014 period by using probit models and instrumental variable approaches.

Results from the different econometric techniques and samples suggest that variety of democracy affects female-led SMEs in using public subsidies for credit support. The evidence is robust to endogeneity concerns.

The empirical evidence presents a time frame limitation. At the same time, SAFE is the only database that supplies information about the gender of firms and public subsidies for credit support, rendering it the only resource that allows the test of the hypothesis proposed. The article therefore offers insights for scholars to revisit our results in future studies that make use of datasets with a longer time span – when they will become available.

To the best of the authors' knowledge, the article is the first to study the effect of democracy on female entrepreneurial behavior in the use of public subsidies for credit support.

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.

The purpose of this paper is to study the experiences of middle-class working mothers from the ITES (Information Technology Enabled Service) sector in India during the COVID-19 pandemic. Their experiences of work from home are studied in the backdrop of the ideal worker model at work and the adult worker model at home. Further, the study aims to identify the need for sustainable, inclusive practices for working mothers in Indian organizations to break the male breadwinner model in middle-class households.

A qualitative approach to collect data from 39 middle-class mothers working in MNCs in four metro cities in India. The semi-structured, in-depth interviews focused on their experiences of motherhood, care and work before, during and after the COVID-19 pandemic.

The pandemic made it evident that the ideal worker model in organizations and the adult worker model at home were illusions for working mothers. The results indicate a continued obligation of the “ideal worker culture” at organizations, even during the health crisis. It made the working mothers realize that they were chasing both the (ideal worker and adult worker) norms but could never achieve them. Subsequently, the male breadwinner model was reinforced at home due to the matrix of motherhood, care and work during the pandemic. The study concludes by arguing the reconstruction of the ideal worker image to make workplaces more inclusive for working mothers.

The study is placed in the context of Indian middle-class motherhood during the pandemic, a demography less explored in the literature. The paper puts forth various myths constituting the gendered realities of Indian middle-class motherhood. It also discusses sustainable, inclusive workplace practices for mothers from their future workplaces' standpoint, especially in post-pandemic times.

The purpose of this paper is to investigate the research landscape of LSS 4.0 papers published in two well-known repositories, Scopus and Web of Science (WoS), in terms of publication trends, article distribution by author, journal, affiliations and country, and article clustering based on keywords, authors and countries. In addition, a literature review was carried out to build a conceptual framework of integrated Lean Six Sigma and Industry 4.0 (LSS 4.0) that encompasses operational, sustainability and human factors or ergonomics aspects.

The literature review of integrated Lean Six Sigma and I4.0 publications published in Scopus and WoS databases in the current decade was conducted for the present study. This study categorizes LSS, I4.0 and related research articles based on publication patterns, journals, authors and affiliations, country and continental-wise distribution and clustering the articles based on keywords and authors from the Scopus and WoS databases from 2011 to 2022 using the search strings “Lean”, “Six Sigma”, “Lean Six Sigma” and “Industry 4.0” in the Title, Abstract and Keywords using Biblioshiny, VOS viewer and Microsoft Excel.

In the recent three years, from 2020 to 2022, LSS 4.0 has been substantially increasing and is seen as an emerging and trending area. This research identifies the most influential authors, most relevant affiliations, most prolific countries and most productive journals and clusters based on keywords, authors and countries. Further, a conceptual framework was developed that includes the impact of operational, sustainability and ergonomic or human factors in LSS 4.0.

This article assists in comprehending the trends and patterns of LSS 4.0. Further, the conceptual framework helps professionals and researchers understand the significance and impact of integrating LSS and Industry 4.0 in the aspects of human factors/ergonomic, sustainability and operations. Also, the research induce professionals to incorporate all these factors while designing and implementing LSS 4.0 in their organization.

This conceptual framework and bibliometric analysis would aid in identifying potential areas of research and providing future directions in the domain of LSS 4.0. It will be beneficial for academicians, professionals and researchers who are planning to apply and integrate techniques of LSS and technologies of I4.0 in their organizations and research.