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Our paper analyses the characteristics of the boards of directors of the best performing listed Italian banks during the last global financial crisis (from 2008 to 2015). Through a fuzzy-set qualitative comparative analysis (fsQCA), the study identifies three different board configurations (archetypes) that these banks adopted: a committee-based board, a gender diversity-based board, and a large size-based board. Although different in some of their characteristics, these configurations share the rationale of favouring the advisory services that the board can provide to managers in their decision-making activities. These services, in turn, are particularly valuable in a time of high risk and uncertainty for banks. With its results, the paper contributes to the literature on the relationship between board characteristics and organisational performance and to a better understanding of the configurations that can best serve when firms' decision-making, for various reasons, becomes particularly complex and can benefit from board support. Our results also contribute to further detail the governance characteristics of banks in the Italian context.

Although digital transformation in the financial industry is gaining momentum in recent years, the literature exploring FinTech business models is still scant. We fill this gap by analysing, through a case study, the business model of a big bang disruptor. Specifically, we select the case of Alpha that is considered one of the most important players in the provision of digital lending services in Europe. We use a business model canvas, deemed as the most suitable analytical framework to this end. We find that, in the FinTech industry, the business model of a big bang disruptor is customer-oriented and its services are provided through a digital platform, in order to ensure a better user experience. Additionally, a big bang disruptor leverages partnerships with different actors to co-create value. Finally, we claim that the business model of a big bang disruptor is characterised by agility, flexibility and adaptability. We contend that these features are crucial to remain competitive in an ever-changing environment.

This study employs a panel data model that uses trade credit's own recent history to predict trade credit levels. A predictive model of trade credit is developed to predict the levels of trade payables and receivables. Previous forecasting techniques do not incorporate the targeting aspect and long period historical data. A target ratio should be set for trade payables and trade receivables to total assets. Trade credit is debt finance which is maintained at a certain ratio to total assets. In this paper, we make use of panel data from 230 non-financial South African listed firms from 2001 to 2013. Firms use trade credit targeting to pursue growth opportunities and their size affects their access to capital. Trade credit's recent history can be used to predict target trade credit levels. The paper makes an original contribution by developing a model to predict the level of trade credit.