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This study seeks to fill a gap in the literature by examining the role of family firm succession in shaping the firm's approach to financialisation, which has received limited attention in the previous research. In addition, the study explores the influence of factors such as clan culture, concentration of control and generational differences on the relationship between succession and financialisation.

Data were based on a sample of 7,023 firm-year observations, compiled from the listed family firms in China's A-share. Several tobit models are used for analysing the data and testing the hypotheses.

Family firm succession is negatively related to the level of financialisation, and this relationship is influenced by clan culture, concentration of control and the stage of succession. Specifically, a higher clan culture, a greater concentration of ultimate control by the controlling family member and the dominance of the first generation in management strengthens the negative relationship between family firm succession and financialisation.

This study offers new insights into the consequence of family firm succession on a new area of the firm's strategy, i.e. financialisation. The study further advances the understanding of family firm succession by considering the role of clan culture, the concentration of control and the stage of the succession process.

The purpose of this study is to investigate the behaviour of M40 grade of self-compacting concrete (SCC) with high volume of ground granulated blast furnace slag (GGBS) (50%) and recycled concrete aggregate (RCA) content up to 100% to assess the mechanical properties of SCC. As per guidelines of IS: 383 – 2016, the RCA can be replaced up to 20% of natural coarse aggregate up to M25 grade of concrete. This study assesses the mechanical properties of SCC beyond 20% of RCA content. Based on the experimental investigations, the compressive strength of mixes decreases as the content of RCA increases. It is found that concrete mixes with 20% RCA and shows the maximum compressive strength at 56 days.

The fresh properties as per EFNARC and IS: 10262–2019 guidelines, ultrasonic pulse velocity testing, mechanical properties and microstructure analysis have been conducted to evaluate the performance of SCC with RCA for practical applications.

From the experimental investigations, it is found that up to 50% of recycled coarse aggregate can be used for structural applications.

The environmental pollution and dumping of waste on green land can be reduced by effective utilization of recycled coarse aggregate and GGBS in the production of SCC.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.