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The main purpose of this study is to examine the effect of corporate digital transformation on bond credit spreads. Additionally, it also explores the two potential channels, information asymmetry and default risk, through which digital transformation can influence bond credit spreads.

We use the bond issuance data of Chinese listed companies over the period 2008–2020. Corporate digital transformation of these companies is measured with textual analysis of the management discussion and analysis part of annual reports. We employ a panel regression model to estimate the effect of digital transformation on bond credit spreads.

We find robust evidence that companies with higher digital transformation experience lower bond credit spreads. We further observe that credit spread reduction is higher for firms that are smaller, non-state-owned, have lower credit ratings and have less analyst coverage. We also find evidence that digital transformation reduces credit spreads by reducing the information asymmetry between firms and investors with enhanced information transformation mechanisms and lowering corporate default risk by strengthening operating efficiency.

To the best of our knowledge, this study is the first attempt to understand the impact of corporate digital transformation on bond credit spreads. Our findings help to understand the effect of digital transformation on firms’ credit worthiness and access to capital.

The purpose of this paper is to examine the impact of legal protection of bank minority shareholders (noncontrolling shareholders) and bank creditors (e.g. depositors or debt-holders) on bank dividend payout policies using a panel data set of 5,918 banks from 52 countries over the period 1998-2007, after controlling for country-level deposit insurance coverage and bank- and country-level regulatory pressures.

Tobit panel regression models are used to examine the impact of legal protection of shareholders and creditors on bank dividend payout amounts. And, logit panel regression models are used to examine the impact of legal protection of shareholders and creditors on banks’ likelihood to pay dividends.

The authors support the outcome hypothesis by finding that banks pay higher amount of dividends and, are more likely to pay dividends in strong minority shareholder protection countries. However, the authors reject the substitute hypothesis by finding that banks pay higher dividends and are more likely to pay dividends in weak creditor rights countries, and banks do not substitute weak creditor rights with lower dividend payout amounts. Contrary, the authors support the literature which argues the importance of creditor rights for capital market development because one possible reason for low dividend payouts in strong creditor rights countries could be that the banks retain more profits for extending more loans.

By finding that creditor rights index has a negative relation with bank dividend policies in contrast to its positive relation with nonfinancial firms’ dividend policies, the authors support the literature which argues that managers of banks give less importance to factors such as current degree of financial leverage, the contractual constraints such as dividend restrictions in debt contracts, and the financing considerations such as the cost of raising external funds, while deciding about the dividend payments. The authors also suggest to keep financial and nonfinancial firms separate, to better understand the dividend puzzle.

Extant literature recognizes that legal institutions such as shareholder protection and creditor rights affect corporate firms’ dividend policies significantly but largely excludes banking sector. This paper, by examining the relations between legal protection of shareholders and creditors and bank dividend policies, fills this research gap.

Following the job demands-resources theory, this study investigates the role of female managers in enhancing employee well-being in terms of psychological health via workplace resources.

To accomplish this objective, we conducted a comprehensive literature review to identify key IPS. Subsequently, a fuzzy-based algorithm was employed to prioritize these skills. Following this, we developed an algorithm based on Extreme Gradient Boosting (XGBoost) to predict the quality of workers’ IC. The efficacy of the XGBoost model was assessed by applying it to three real-life construction projects.

Upon application of the model to the case studies, we made the following conclusions: (1) “Leadership Style,” “Listening,” “Team Building” and “Clarifying Expectations” emerged as significant skills and (2) the model accurately predicted workers’ IC quality in over 78% of the cases. This algorithm has the potential to preempt interpersonal conflicts, enhancing job-site productivity, team development and human resources management. Furthermore, it can guide construction managers in designing IPS training programs.

This study contributes to the existing knowledge by addressing the crucial connection between IPS and IC quality in construction projects. Additionally, our novel approach, integrating fuzzy logic and XGBoost, provides a valuable tool for IC prediction. By identifying significant IPS and offering predictive insights, this research facilitates improved communication and collaboration in the construction industry, ultimately enhancing project outcomes.