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This study aims to show the relationship between competition, financial stability and ownership structure of banks in the Middle East and North African (MENA) countries.

This study uses the generalized method of moments (GMM) estimators to generate research results. This study uses an unbalanced panel dynamic data set. It covers the period 2011 to 2017 in MENA banks.

This study implies that there is a significant and positive relationship between market power and the financial stability of banks in MENA countries. It explains a competitive market focus on credit risk, which turns them risky. From the bank’s ownership view, Islamic banks are in a less risky position which means Islamic banks are more stable than other ownership structures. On the other hand, government specialized institute displays their poor financial stability and risky from other ownership structures. Unfortunately, there is no significant impact of ownership structure on competition unless Islamic banks prove that they (Islamic banks) perform better in market power.

The empirical findings of this study suggest that MENA banks should improve the process of managing and monitoring the non-performing loan (loan segment business). It reduces the level of credit risk, which leads to achieving more profit. It also recommends that loan quality should improve immediately in this region for declining financial disruption. Based on the ownership structure, policymakers and stakeholders should adjust their risk and financial stability. Notably, the stakeholders can focus on Islamic banks in this region as this type of ownership structure showing superiority over other ownership structures.

This study is based on the latest data set and produced outcomes by using a GMM estimator. It also uses multiple measures of competition and risk variables to get robust results. Moreover, to the best of the knowledge, this study is the pioneer to examine the competition, risk (financial stability) and ownership structure of banks in the MENA countries.

Given the serious question raised by the subprime of the 2008 global financial crisis over the rising practices of excessive rewarding of executives in the USA and European firms, the executive pay-performance nexus has emerged as a popular topic of debate in the contemporary corporate finance research. Conducted mostly on the Anglo-Saxon contexts, research outcomes have been inconclusive and dichotomous. Considering this backdrop, this study aims to investigate the endogenous relationship between executive compensation and risk taking in the context of the USA.

Using a large sample of non-financial firms from 2010 to 2020 based on panel data and two-stage least square regression. In this study, the riskier corporate decision is measured as book leverage and ratio of R&D expense to total assets. Chief executive officers’ (CEO) experience and age are used as instrumental variables, and these are expected to influence compensation incentives and, hence, affect firm riskiness indirectly. Firm size, return on assets and CEO turnover are reported to affect compensation and corporate decisions, therefore, included as control variables. Given that higher executive compensation is related to riskier corporate decision in firms, this study incorporates total wealth (i.e. accumulated equity related compensation) as an additional proxy of compensation, and this selection is justifiable by the perfect contracting notion of the agency theory.

The results of this study show a significant positive and increasing nexus among compensation and riskier corporate decisions. Besides, the compensation level proxied through the percentage of each form of compensation in total compensation is very important as greater equity and greater salary diminishes risk taking.

The outcomes of this study have useful implications for firm stakeholders and policymakers.

The level of pay measured by the percentage of each type of compensation in total compensation is of utmost importance as it can increase or decrease risk taking in corporate decisions.

The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.

The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.

The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.

Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.

Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.

Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.

This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.

This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.

This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

Increased fraudulent practices have heightened the need for innovation in anti-fraud programs, necessitating the development of analytics techniques for detecting and preventing fraud. The subject of fraud analytics will continue to expand in the future for public-sector organizations; therefore, this research examined the progress of fraud analytics in public-sector transactions and offers suggestions for its future development.

This study systematically reviewed research on fraud analytics development in public-sector transactions. The review was conducted from June 2021 to June 2023 by identifying research objectives and questions, performing literature quality assessment and extraction, data synthesis and research reporting. The research mainly identified 43 relevant articles that were used as references.

This research examined fraud analytics development related to public-sector financial transactions. The results revealed that fraud analytics expansion has not spread equally, as most programs have been implemented by governments and healthcare organizations in developed countries. This research also exposed that the analytics optimization in fraud prevention is higher than for fraud detection. Such analytics help organizations detect fraud, improve business effectiveness and efficiency, and refine administrative systems and work standards.

This research offers comprehensive insights for researchers and public-sector professionals regarding current fraud analytics development in public-sector financial transactions and future trends.

This study presents the first systematic literature review to investigate the development of fraud analytics in public-sector transactions. The findings can aid scholars' and practitioners' future fraud analytics development.