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This study aims to analyze the impact of digital transformation on firm performance within the banking sector, specifically focusing on the Amman Stock Exchange (ASE)-listed banks from 2015 to 2022. Additionally, it explores the influence of gender dynamics on the implementation and outcomes of these digital transformation initiatives.

The study adopts a robust empirical approach, using manual content analysis of annual reports from ASE-listed banks. The Digital Transformation Disclosure Index (DTDI) is used to assess the extent and nature of digital transformation initiatives within these banks. The methodology is designed to provide a comprehensive evaluation of the correlation between digital transformation efforts, firm performance and gender dynamics.

The research reveals that digital transformation initiatives have a significant positive impact on the performance of ASE-listed banks. It also uncovers nuanced insights into the role of gender dynamics, indicating that gender diversity within firms influences the adoption and success of digital transformation strategies in complex ways.

The findings of this study contribute to the understanding of digital transformation in the banking sector, offering empirical evidence on its benefits for firm performance. Additionally, the study illuminates the intricate role of gender dynamics in digital transformation, providing a new perspective on organizational diversity within the context of technological change.

This research pioneers in academically linking digital transformation and gender dynamics within the banking sector, addressing a notable gap and introducing a fresh academic perspective. Practically, it equips banking executives and policymakers with actionable insights for gender-inclusive digital strategies, crucial for enhanced firm performance. Methodologically, the study sets a benchmark in research innovation, using the DTDI to offer a replicable model for future investigations in this evolving field.

This paper aims to explore the intention to adopt the Metaverse in Islamic banks, with a particular focus on evaluating perceived usefulness, ease of use, user satisfaction and the influence of religiosity. Integrating the technology adoption model (TAM) and religiosity intention model, this study will dissect the multidimensional aspects influencing the acceptance of Metaverse technologies.

Surveying Islamic bank professionals in Jordan, this study used a structured questionnaire and data augmentation to analyze Metaverse adoption factors. Using partial least squares-structural equation modeling, the relationships between ease of use, usefulness, religiosity and satisfaction were explored.

The study identifies pivotal relationships among perceived usefulness, ease of use, user satisfaction and religiosity in the context of adopting metaverse technologies in Islamic banks in Jordan. Evidence highlights the dominant role of perceived usefulness and ease in influencing both intention to use and satisfaction levels. Religiosity, while not a direct influencer, plays a collaborative role, underscoring a balanced mix of technological and religious elements that will potentially shape the adoption trajectory of metaverse technologies within this specific banking sector.

Integrating metaverse technologies in Islamic banks necessitates balancing technological advances with Sharia compliance. The study underscores the importance of aligning user experience with religious values and fostering innovation within Islamic ethical guidelines.

This study uniquely integrates the TAM and religiosity-intention model to explore metaverse adoption in Islamic banks, unveiling a nuanced interplay between technology and religious values. It offers practical insights for tailoring innovations in the Islamic financial ecosystem.

This study investigates the impact of corporate governance on FinTech disclosure levels in Jordanian conventional and Islamic banks. It aims to determine whether governance mechanisms affect disclosure practices in the FinTech sector, exploring the interplay between governance and transparency in financial innovations.

The research methodology entails a thorough analysis of data from all 15 Jordanian conventional and Islamic banks listed on the Amman Stock Exchange, covering the period from 2015 to 2022. This study uses manual content analysis using a custom FinTech Disclosure Index (FDI) and quantitative analysis with a two-way clustered error regression model.

The findings show that corporate governance mechanisms, particularly board size, board meetings and “Big4” audit firms, are crucial in enhancing FinTech disclosure across conventional and Islamic banks. However, Islamic banks consistently show higher disclosure levels than their conventional counterparts, attributed to their distinct governance structures that emphasize ethical governance and transparency. These results indicate an awareness among decision-makers about the importance of business model transformation toward FinTech.

This study pioneers the introduction of FDI, using it for a novel comparative analysis of FinTech disclosure levels between Islamic and conventional banks. By exploring how various governance structures influence FinTech disclosure, this research provides fresh insights into the interplay between corporate governance and financial technologies in the banking sector.

This research examines the impact of the COVID-19 pandemic and governance structure on corporate narrative reporting (CNR) concerning Industry 4.0 (I4.0) technologies in Jordanian commercial banks. The study aims to explore how these factors influence the extent and nature of disclosures in annual reports.

The study uses a comprehensive manual content analysis method to investigate the annual reports from all 15 Jordanian commercial banks from 2010 to 2022. This approach allows for the detailed examination of I4.0 disclosures, using a specially developed index to measure various disclosure dimensions. An ordinary least squares model is used to assess the determinants of CNR on I4.0, considering factors such as the pandemic’s impact and various governance attributes.

The findings indicate that both the COVID-19 pandemic and specific governance factors (e.g. board size and audit committee size) significantly enhance the disclosure of I4.0 technologies. The study reveals that during the pandemic, banks significantly increased their level of detailed disclosures about I4.0 strategies, challenges and benefits, reflecting a strategic response to the pandemic’s disruption.

This study introduces a novel I4.0 Reporting Index for banks, measuring disclosures across strategy implementation, business model transformation, challenges and benefits. It adds to the existing literature by offering insights into narrative reporting practices concerning I4.0 technologies within the banking sector and illuminates the impact of the COVID-19 pandemic on these practices.

The price clustering behavior is becoming a core part of the market efficiency theory especially with the development of trading strategies and the occurrence of major and unprecedented shocks which have led to severe inquiry regarding asset price dynamics and their distribution. However, research on emerging stock market is scant. The study contributes to the literature on price clustering by investigating an active emerging stock market, the Muscat stock market one of the Arabian Gulf Markets.

This research adopts the artificial intelligence technique and other statistical estimation procedure in understanding the price clustering patterns in Muscat stock market and their main determinants.

The findings reveal that stock prices are marked by clustering behavior as commonly highlighted in the previous studies. However, we found strong evidence of price preferences to cluster on numbers closer to zero than to one. We also show that the nature of firm’s activity matters for price clustering behavior. In addition, firms with traded bonds in Oman market experienced a substantial less stock price clustering than other firms. Clustered stock prices are more likely to have higher prices and higher volatility of price. Finally, clustering raised when the market became highly uncertain during the Covid-19 crisis especially for the financial firms.

This study provides novel results on price clustering literature especially for an active emerging market and during the Covid-19 pandemic crisis.

This study aims to examine the degree of consciousness of forensic accounting (FA) in Jordan. This study surveys practitioners and academicians about their views and thoughts toward the expected role of using FA techniques to detecting and preventing fraud practices and shedding more light on advantages and obstacles of using the FA techniques.

To collect the data, a questionnaire was constructed and distributed to the study population which consists of accounting academics, students and accounting practitioners.

The results of this study show evidence that both students and professionals have a lower level of awareness on the FA concept and its importance. The results also confirm there is a significant correlation between, fraud prevention and detection, advantages of the application of FA, the training courses toward the application of FA and the application of FA in the context of Jordan. It has also been confirmed that there is a number of significant factors hinders this implementation in Jordan.

The findings of this study offer many policy implications for regulators and policymakers on the needed relevant information to address and implement FA in education and practice, thereby activating the FA concept in Jordan.

The primary motivation of this study is driven by the limited and inconclusive research on the FA as a monitoring tool, notably there is a high possibility of fraud and misstatement practices due to the agency conflict. This study is the first of its kind to discuss this topic in the context of Jordan. The need to integrating the accounting education within accounting profession regarding FA becomes an urgent need to develop the awareness level of practitioners when it comes to practice of FA.

This study aims to examine the relationships between various components of digital financial literacy, namely, awareness, subjective knowledge, experience, the digital legal framework and skills, and their influence on the adoption of cashless payment systems among university affiliates in Jordan. It also explores the mediating role of gender in this relationship. The study integrates the Theory of Planned Behavior (TPB) and social role theory (SRT).

This study uses a cross-sectional survey across 34 Jordanian universities. Data from 418 participants were analyzed, focusing on factor analysis to assess the constructs' reliability and validity and to explore the moderating effects.

The findings illuminate that digital financial awareness, experience and skills are significant catalysts for using cashless payments among the targeted demographic. In contrast, the digital legal framework and subjective financial knowledge did not significantly influence cashless payment use. Additionally, gender differences emerged, highlighting a stronger association between digital financial experience and cashless payment usage for women.

The study's uniqueness stems from its detailed analysis of digital financial literacy's effect on cashless payment adoption in Jordan's academia, incorporating aspects like legal frameworks, awareness, and skills. It innovatively considers gender's moderating role, adding fresh insights into digital finance practices. Using the TPB and SRT, the research connects theory with Jordan's empirical data, suggesting strategies for education and policy. This work advances understanding of digital financial literacy in fostering a more inclusive digital financial system, contributing significantly to digital finance and behavioral economics literature.

In an era where sustainability and digital transformation are becoming indispensable pillars of successful business operations, this chapter explores the potent synergy between these two paradigms. As businesses strive to align their operations with Environmental, Social, and Governance (ESG) goals, digital transformation emerges as a powerful enabler. This chapter delves into how digital technologies are not only revolutionizing traditional business models but are also paving the way toward more sustainable practices. From data-driven decision-making to improved resource management, this chapter discusses the diverse ways in which digital transformation contributes to sustainability. It also offers an in-depth analysis of real-world case studies, illustrating how businesses have successfully integrated digital transformation in their pursuit of sustainability. Recognizing the potential roadblocks, this chapter also addresses the challenges businesses may face in this journey, including cybersecurity risks, data privacy issues, and the need for technological literacy. It further presents strategies to navigate these challenges and underscores the importance of preparedness in managing potential risks. Finally, this chapter ventures into the future of digital transformation, evaluating current trends and predictions, and their potential impact on sustainable business practices.

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.

Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.

The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.

The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.