Search results

The purpose of this paper is to fill the momentous gap by explicitly investigating the asymmetric effects of monetary policy (MP) on non-bank financial intermediation (NBFI) assets.

The authors utilized panel data from 29 countries for the period of 2012–2020 and used the quantile regression estimation. In addition to simultaneous quantile regression (SQR), the authors also employ quantile regression with clustered data (Parente and Silva, 2016) and the generalized quantile regression (GQR) method (Powell, 2020).

The empirical results show a significant heterogeneous impact of MP. While there is a positive relationship between MP and NBFI assets (“waterbed effect”) at lower quantiles of NBFI assets, at middle and higher quantiles, MP has a negative impact on NBFI assets (“search for yield” effect). The authors further find that negative impact strengthens as the quantile levels of NBFI assets rise from mid to high. Findings also reveal that “procyclicality” (except higher quantile) and “institutional demand” hypotheses hold. However, regarding “regulatory arbitrage,” mixed results are observed indicating the impact of Basel III requirements.

Previous empirical studies have concentrated on either the Dynamic Stochastic General Equilibrium (DSGE) framework or conditional mean regression approaches and delivered mixed findings of the MP effects on NBFI. The current paper takes a step toward dealing with this issue by deploying quantile regression methodology, which shows the impact of MP on NBFI at different conditional distributions (quantiles) of NBFI assets instead of just NBFI's conditional mean distribution.

This study aims to examine the necessity effects of big data analytics capabilities (BDAC) on decision-making performance (DMP), particularly in the public sector.

The authors used the combined methods of partial least square structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to test the hypothesized relationships.

The findings show that the presence of all three BDAC (infrastructure, management and personnel) is significant and necessary to achieve higher levels of DMP. Specifically, the results revealed big data management capabilities to be of higher necessity to achieve the highest possible DMP. The findings provide public-sector practitioners with insights to support the development of their BDAC.

Time-sensitive domains such as the public sector require insight and quality decision-making to create public value and achieve competitive advantage. This study examined BDAC in light of the combined methods of (PLS-SEM) and NCA to test the hypothesized relationships in the public sector context.