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This study aims to provide insights into gender diversity in Pakistani boardrooms, particularly for the dominant family business type, which is strongly guided by (non-financial) family-related objectives when making business decisions, such as the appointment of board members. Pakistani companies operate within the framework of weak legal institutions and a traditionally highly patriarchal environment. This study examines how corporate decisions regarding the appointment of female board members play out in this socio-political and cultural environment.

Board composition and board characteristics were examined using hand-collected data from 213 listed family firms and non-family firms on the Pakistan Stock Exchange from 2003 to 2017. Univariate analyses, probit regressions and robustness tests were performed.

Pakistani family firms have a significantly higher proportion of women on their boards than do non-family firms. They are also significantly more likely to appoint women to top positions, such as CEO or chairs.

Evidently, women are allowed to enter boards through family affiliations. Gender quotas appear an ineffective instrument for breaking through the “glass ceiling” in this socio-cultural environment. Thus, gender parity must entail the comprehensive promotion of women and the enforcement of legal reforms for structural and cultural change.

The analysis focuses on a Muslim-majority emerging Asian market that has been scarcely researched, thus offering new perspectives and insights into board composition and corporate governance that go beyond the well-studied Western countries.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.