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The purpose of this study is to investigate how the implementation of digital banking services (mobile applications) by globally systemically important banks (G-SIBs) affects banks’ performance in the USA and Europe from 2005 to 2022.

The study employs advanced econometric methods to analyze the link between deposits and banking performance, utilizing linear regressions and multivariate Bayesian regressions.

Our results indicate that customer deposits positively impact a bank’s performance after the introduction of the mobile application feature of check deposits, whereas social risk negatively impacts banking financial performance. These findings support the hypothesis that technology implementation improves the profitability and growth of traditional banks.

While findings are robust econometrically in linear and Bayesian regressions, variables reflecting the digitalization of banks remain limited. For instance, the number of mobile users or the volume of digital transactions per bank since the implementation of the mobile app is not available.

In a rapidly growing technology and constantly changing customers behaviors, this research has practical implications from bankers’ perspective to continue the technological innovation efforts and from regulators’ perspective to strengthen requirements for the digital banking services.

We provide empirical evidence that including a banking app for smartphones’ users for remote banking services benefit the financial performance of banks. However, the social risk remains significant for banks in terms of customers' satisfaction, data privacy and cybersecurity.

This paper employs an innovative approach to create a mobile app “discriminatory” factor and examine the relationship between deposits and banks’ performance before and after the introduction of a mobile app for too-big-to-fail banks in Europe and the USA. Additionally, we consider the social risk component of the ESG score, as a bank’s decision to implement mobile applications and technology for its customers potentially affects social risks associated with customer satisfaction and technology usability.

The purpose of this study is to evaluate the global developments in the area of fintech solutions by analyzing Islamic and Conventional banks core accounting and market analysis IFIs and their impact on financial inclusion within its core markets.

The authors collect and analyze annual accounting and market Data of the top ten largest Islamic banks and the top ten US Conventional banks, in terms of Total Asset and Market Capitalization, from Bloomberg Data.

The analysis of Bloomberg data shows higher risk-return for Islamic banks–except ROE Market measure that we suggest-than US conventional banks. Nonetheless, Islamic banking grew faster than conventional banking over the period 2006–2021. As a business model, we find that Islamic banks take more credit with more than seventy percent of their profit from loans, while US conventional banks struggle to reach seventy percent interest rate ratio. The authors’ research documents that Fintech and digitalization are driving Islamic finance growth during financial and economic downturns.

FinTech data is not available for banks, further insights of analysis on FinTech and Innovations in the banking sectors.

Islamic banks continuously innovate to satisfy the users of their services and Fintech is opportune to innovation. This study could be interesting for both practitioners and academics wishing to understand and compare Islamic and conventional banking futures.

The authors compared two banking systems, the US and Islamic Banks, which could be useful for users to differentiate between those banking operations.

The authors collected accounting and market data from Bloomberg of top 10 Islamic and top 10 US Conventional banks from 2006 to 2021 to examine Risk-Return, Growth and Business Model of those banks. The authors propose a new Risk-Return measure ROE-Market and its volatility.

Recent advances in stochastic frontier analysis (SFA) suggest two alternative approaches to account for unobserved heterogeneity and to distinguish between persistent and transient inefficiency. The first approach is the generalized true random effects (GTRE) model, and the second approach is an autoregressive inefficiency (ARI) model. This study compares them to highlight whether they capture similar inefficiency aspects.

Using recent methodological advances in SFA, the authors estimate the GTRE and the ARI models using a Monte Carlo experiment and two real datasets from two industries (banking and agriculture).

The authors find that the two models provide quite different results in terms of inefficiency persistence and overall inefficiency (combination of transient and persistent inefficiency), regardless of the dataset considered.

The study findings suggest that researchers should be careful when referring to these two models because they do not capture the same inefficiency aspects, even though they have the same conceptual basis. This work is a warning about the empirical aspects of the persistent and transient efficiency framework, in order to convey a consistent story to the reader on firms' performance.

Even though they are used in a large number of studies, the present paper contributes to the productivity and efficiency literature by providing the first comparison of the GTRE and the ARI models.

Research and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.

The authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.

The estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.

This paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.

This paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.

This study aims to develop a unique methodology to construct a bank’s financial technology (Fintech) score, which captures the degree of digitalization of a bank’s operations. Using the Fintech score as the proxy, this study investigates the effect of Fintech on the operating performance of the top largest Islamic bank.

The methodology used measures the link between the degree of digitization of a bank and its operational performance. This study applies the three-degree polynomial of regression to the largest Islamic bank in which the explanatory variable is the natural logarithm of Fintech score, and the response variable is common operating performance measure. To check the sensitivity of the estimates to the sample size and assumptions’ violation, this study has applied Bootstrapping and Bayesian processes to the three-degree polynomial regressions.

The study estimates from 2007 to 2021 show that the relationship between the operating performance of the Islamic banks and the Fintech is nonlinear and strongly significant: operating returns increase with the increasing level of Fintech, whereas the operating returns decrease with the increasing Fintech variance. At an aggregate level, this study attributes a significant rise in internet coverage to the emergence of Fintech in the Middle East region.

This study constructs an implicit measure of Fintech that measures the adoption of Fintech by the bank and, consequently, offers the technology to their customers for higher use satisfaction. This study finds that Fintech is linked to the operating performance in a nonlinear fashion, in which Fintech and Fintech variance have the opposite effect on operating performance: Fintech increases the operating profitability, whereas Fintech variance decreases the operating profitability of a bank.

This paper aims to find the effects of regulatory and supervisory policies on bank risk-taking. The same regulation and supervision have different effects on bank risk-taking depending on influence factors. These factors were considered and a sample of the largest European banks from France, Germany, UK, Italy, Spain and Greece was used over the period 2005-2011.

In this paper, the author analyses the effects of regulation and supervision on risk-taking. The author uses a sample of the biggest banks from six European countries (France, UK, Germany, Italy, Spain and Greece) over the period 2005-2011. Because the applicable entry of IFRS was in 2005, thus data of European banks are not available before this date. For each country in the sample, the 10 largest banks (defined by total assets) that lend money to firms were identified. The author does not include central banks or postal banks, which generally do not lend money to firms and are described as non-banking institutions (La Porta et al., 2002).

It was found that restrictions on bank activities, supervisors’ power and capital adequacy decrease risk-taking. Thus, regulation and supervision enhance bank’s stability. While, deposit insurance increases the risk due to its association to moral hazard. Finally, it was found that strengthening regulatory and supervisory framework raises the risk-taking and weakens the stability of European banks.

The author contributes to existing empirical analyses in three ways. First, the existing literature has drawn a lot of attention on US banks. However, the purpose of this paper is to examine the biggest banks of three European leaders (France, Germany and UK) and three more European countries influenced by the recent crisis (Spain, Italy and Greece) over the period 2005-2011. Second, most studies focus mainly on the relationship between regulation and profitability, yet seldom on the relationship between regulation, supervision and risk-taking. The author focuses on this relationship. Third, this study applies the two-step dynamic panel data approach suggested by Blundell and Bond (1998) and also uses dynamic panel generalized method of moments (GMM) method to address potential problems. The two-step GMM estimator that the author uses is generally the most efficient.

This paper aims to present an analysis of how regulatory and supervisory policies affect risk-performance nexus.

Empirically, on a sample of 60 large European banks over the period 2005-2011, the authors explore this relationship by using the panel smooth transition regression (PSTR) modeling because the nexus between risk and performance is nonlinear and it depends on specific national factors especially regulatory and supervisory policies.

The major finding of this study is that the effect of risk on banking performance is conditional by the improvement of banking governance in Europe.

The PSTR helps to account for a change of regime in the effects of risk on performance.

This paper explains the use of PSTR modeling.

The objective of this research is to examine empirically the effects of digital maturity (DM) on the firm's financial performance as measured by return on assets (ROA), return on equity (ROE) and Tobin's Q.

The authors use a panel data sample of 92 observations collected from 23 listed firms on Sweden's stock exchange over four years, 2015–2018. The authors hand collect DM from the digital leader's reports and collect financial data from DataStream. Using both static and dynamic panel (generalized method of moments (GMM) estimation) regression models to perform endogeneity problem, the authors explore the impact of the DM index on ROA, ROE and Q of Tobin.

The results show that DM has a negative effect on ROA and ROE but a positive effect on Q of Tobin. This negative relationship can be explained, by the fact that information technology (IT) investment and the DM could take years to be materialized and to be captured by performance indicators. Company investment in IT will increase and basically the ROA will be negatively affected because the higher value of IT assets is not amortized. Nevertheless, in the long term, company can maximize its performance. The positive effect on Q of Tobin captures the long-run effect of digital transformation.

This research can be helpful for firms in their process of digital transformation to succeed with the change, create value and to understand the challenges they have to face. In the short term, firms undertaking digital transformation will face some financial difficulties which affect negatively their ROA and ROE, but in the long term they can maximize their performance (captured by Tobin’s Q) and improve their market value.

In previous research, the impact of digital transformation on performance has been measured in terms of revenue growth, profit margins and in terms of earnings before interest and taxes (EBIT). Even if the authors have sufficient evidence of the positive effect of digital transformation on organizational performance, there is no support of the positive effect on financial performance. So, the authors try to fill this gap. This research has also the merit of examining this relationship empirically through a dynamic panel data estimation two-step system GMM, while the majority of previous studies are qualitative in nature based on interviews and questionnaires or simple correlations.

The paper analyzes downside and upside risk spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.

By using VAR-ADCC models and conditional value at risk (CoVaR) techniques, downside and upside risk spillovers between stock markets of G7 countries and China are analyzed before and during the COVID-19 pandemic.

The results suggested existence of a significant and asymmetrical two-way risk transmission between majority of pair markets, but the degree of asymmetry differs according to the use of the entire cumulative distributions or distribution tails. Downside and upside risk spillovers are significantly larger before the COVID-19 pandemic in all cases except between CAC 40/DAX and S&P/SSE pairs.

The paper used CoVaR and delta-CoVaR to investigate the downside and upside spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.

Cryptocurrencies such as bitcoins represent a novel method of conducting financial transactions and exchanging money. However, their adoption by the general public remains low. Within countries facing financial distress and characterized by a high level of risk, cryptocurrency adoption might offer opportunities for countering crises. The purpose of this study is to explore the factors that influence individuals' adoption of cryptocurrencies for financial transactions within a high-risk context.

To do so, it presents a behavioral model, which is tested using data collected from a survey of 255 respondents residing in Lebanon. The causal relationships between the different factors and individuals' willingness to use cryptocurrencies were then analyzed through Structural Equation Modeling.

Findings show that financial technology awareness and social influence contribute to reducing perceived risk and increasing individuals' willingness to use cryptocurrencies, while individuals' risk aversion and the presence of regulatory support increase the perceived risk of cryptocurrencies.

The study is among the first to use a human-centered approach to understanding cryptocurrency adoption and takes place within a country that is facing a deep financial crisis. Its outcomes contribute to existing theories of cryptocurrency adoption and provide policymakers with insight into how adoption is unfolding namely in developing countries.