Purpose: This study aims to examine the present condition of blockchain technology (BT) applications in auditing by analyzing journal publications on the topic to acquire a better understanding of the field.Design/methodology/approach: This study makes use of the Bibliometric Analysis method and gathered 725 papers from the Web of Science and Scopus databases in the management and accounting, business, financial, economic and social science, as well as decision sciences fields from 2017 to 2021 using the R-Package Bibliometrix Analysis “biblioshiny”.Findings: The findings revealed that blockchain research in terms of auditing has already increased and started to spark a quick rise in popularity, but is still in its initial phases with important quality though less in quantity. Moreover, the Journal of Emerging Technologies in Accounting is the most prolific journal with 2019 as the highest publication year, with the United States and China as the most cited countries in this field. Furthermore, in this field, there are much research topics involving blockchain, audit and smart contracts; and there is less involving data analytics, governance, hyperledger, distributed ledger and financial reporting. Additionally, Sheldon (2019) and Smith and Castonguay (2020) are the most productive authors in the field in terms of the H-index.Research limitations/implications: This study has certain limitations such as the fact that it only looked at 105 papers in the domains of finance, business, economics, accounting, management as well as multidisciplinary science. Moreover, the research’s data and dates have an impact on the results dependability. As this is an original topic, fresh studies are anticipated to remain to shine a spotlight on and suggest answers to blockchain’s implications on auditing. Additionally, the period of time was limited to only the last five years, from 2017 to 2021. As a result, extensive study into the topic is required since there is currently a research deficit in the blockchain field in the setting of auditing. So, new research is required to offer new frameworks and understandings for describing the blockchain function in auditing, including processes, techniques, security, as well as timeliness. Investigations in unique circumstances and research employing innovative research methodologies for discovering the new issue would be valuable in acquiring a higher grasp of the complexities faced.Originality/value: This research contributed to the field by assessing the present state of the art of research on the usage and use of BT in finding research gaps, the audit profession and, most importantly, recommending a future direction for researchers in the subject.

The rapid proliferation of Internet of Things (IoT) devices in healthcare, from wearable sensors to implantable medical devices, has revolutionised patient monitoring, personalised treatment, and remote care delivery. However, the resource-constrained nature of IoT devices, coupled with the sensitivity of medical data, presents critical security challenges. Traditional encryption methods, while robust, are computationally intensive and unsuitable for IoT environments, leaving sensitive patient information vulnerable to cyber threats. Addressing this gap, lightweight encryption methods have emerged as a pivotal solution to balance security with the limited processing power, memory, and energy resources of IoT devices. This paper explores lightweight encryption methods tailored for IoT healthcare applications, evaluating their effectiveness in securing sensitive data while operating under resource constraints. A comparative analysis is conducted on encryption techniques such as AES-128, LEA, Ascon, GIFT, HIGHT, PRINCE, and RC5-32/12/16, based on key performance metrics including block size, key size, encryption and decryption speeds, throughput, and security levels. The findings highlight that AES-128, LEA, ASCON, and GIFT are best suited for high-sensitivity healthcare data due to their strong security features, while HIGHT and PRINCE provide balanced protection for medium-sensitivity applications. RC5-32/12/16, on the other hand, prioritises efficiency over comprehensive security, making it suitable for low-risk scenarios where computational overhead must be minimised. The paper underscores the significant trade-offs between efficiency, security, and resource consumption, emphasising the need for careful selection of encryption methods based on the specific requirements of IoT healthcare environments. Additionally, the paper highlights the growing demand for lightweight encryption methods that balance energy efficiency with robust protection against cyber threats. These insights offer valuable guidance for researchers and practitioners seeking to enhance the security of IoT-based healthcare systems while ensuring optimal performance in resource-constrained settings.

This study examines the evolution and impact of Big Data technologies across sectors, emphasizing key algorithms, emerging trends, and organizational challenges in their adoption. Special attention is given to ethical concerns related to data privacy, security, and scalability, underscoring the importance of responsible governance frameworks. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure transparency and methodological rigor. A comprehensive literature search identified 83 peer-reviewed articles from high-indexed journals, and a complementary bibliometric analysis of 1108 Scopus-sourced articles (2015–2024) was conducted using R Biblioshiny. This dual-method approach offers both qualitative depth and quantitative insights into major trends, influential sources, and leading countries in Big Data research. Key findings reveal that real-time data processing and AI integration have significantly enhanced data management capabilities, supporting faster and more informed organizational decision-making. This study concludes by highlighting the importance of ethical governance and recommending future research on sector-specific adoption patterns and strategic frameworks that maximize Big Data’s value while safeguarding privacy and trust.

Purpose This study aims to examine the relationship between internal and external factors and job satisfaction, and between job satisfaction and auditors’ performance. Design/methodology/approach This research used deductive approach. Data was gathered from 83 auditors in the Saudi Organisation for Certified Public Accountants (SOCPA) database. By implementing the partial least squares-structural equation modelling (PLS-SEM) technique, the suggested hypotheses were examined. Findings The results show that internal factors, i.e., achievement, advancement, recognition and growth, significantly impact job satisfaction. Subsequently, the external factors, i.e., company policies, relationship with a peer and relationship with supervisor, significantly impact job satisfaction. In contrast, work security has no relationship with job satisfaction. Furthermore, job satisfaction is a significant driver for auditors' performance. Research limitations/implications This research sheds light on the relationships between internal and external factors, job satisfaction and auditors' performance in the Saudi context. It would be interesting to investigate these relationships in a different setting, such as a different country, time or industry. Future studies should broaden the sample frame to include different types of employees to obtain more generalisable results. Practical implications This study may help managers of auditing departments formulate appropriate strategies and design effective programs to increase the level of job satisfaction between auditors by enhancing such factors, which will lead to improving the auditors' performance. Originality/value This research provide an empirical evidence to support the theoretical assumptions of Herzberg's which is much needed.

Objectives: This study investigates the impact of accounting information on stock prices in Saudi Arabia and examines whether the relationship is linear or nonlinear using a Markov-Switching Dynamic Regression (MSDR) model. Material and Methods: Annual data covering the period 1994–2021 were collected from the World Bank Development Indicators and Saudi capital market sources. The study employed descriptive statistics, unit root tests, linearity tests, and the MSDR model to analyse the relationship between stock prices and accounting information variables. Results: The findings indicate that the relationship between accounting information and stock prices is nonlinear. Stock market return, value of shares traded, and market capitalisation positively affect stock prices, whereas the number of shares traded exerts a negative effect. Stock market turnover ratio was not statistically significant. The MSDR model identified two market regimes: a more stable low-volatility regime and a high-volatility regime. The market remained in the low-volatility regime for most of the study period. Conclusion: Accounting information plays a significant role in explaining stock price movements in Saudi Arabia. The nonlinear nature of the relationship suggests that regime-dependent modelling provides more accurate insights than conventional linear approaches. The findings offer useful implications for investors, policymakers, and market regulators.