JOURNAL OF ADMINISTRATIVE AND ECONOMIC SCIENCES

This study aimed to investigate the views of relevant stakeholders regarding  practices of applying International Financial Reporting Standard 3 Business Combinations and International Accounting Standards 36 impairment of assets - goodwill – in Saudi joint stock companies,  and to sort the difficulties and recommend solutions related to the application and ways to improvement. A random sample size of 94 participated in the questionnaire for this study. The results revealed that there is difficulties in applying the measurement and disclosure requirements of the impairment test for goodwill, in particular inputs relating to value in use and discount measurement. The results also showed keenness regarding improving disclosure requirements for these two accounting standards, while also calling the IASB to revisit allowing amortization of goodwill in line with accounting information relevance.

The study highlighted the impact of the characteristics of Professional Skepticism in the quality of the audit process in the Jordanian environment, where the study community is one of all 438 Jordanian external auditors engaged in the audit profession, where the study used the analytical descriptive approach ,300 questionnaires were distributed to the study community, of which 230 were retrieved, and after statistical analysis and the use of appropriate statistical methods, the study found a positive impact on the characteristics of Professional Skepticism in the quality of the audit process, The results showed that the knowledge search feature is the most influential factor in the quality of the audit, followed by the questioning mind. The study also found that there is an impact on the importance of the auditor's awareness that the audit process should be carried out with quality because it supports his reputation and fame in his field of work. The study recommended many recommendations, the most important of which is the need to increase Professional Skepticism practices by Jordanian professional auditors, obtain all the necessary information and make decisions in its light.

This study is set up to find out the impact of transitioning to accrual accounting in government units on the quality of financial reports in the Kingdom of Saudi Arabia. It also mis toims to  statistically significant differences between the perspectives of the study sample members with reference to the variables of years of experience and educational qualification regarding the effect of transitioning to applying accrual accounting. A questionnaire was designed and distributed to a judgmental sample (Judgmental or Snow Ball Sampling) of (321) Accountants at the Ministry of Finance. The study sample members (respondents) are allowed to distribute the questionnaire to other qualified individuals regarding the subject under study.  In light of this, data was collected and analyzed and hypotheses were tested using the statistical analysis program (SPSS). The study proved that transitioning to applying accrual accounting in government units has a positive impact on the quality and accuracy of financial reports in the Kingdom of Saudi Arabia. It also provides the possibility of comparing the financial reports of government units and improving the economic and financial capabilities of the Kingdom of Saudi Arabia.The study also demonstrated that there are statistically significant differences between the responses of the study sample individuals about the impact of transitioning to accrual accounting in government units on the quality of financial reports in the Kingdom of Saudi Arabia with reference to the variables of years of experience and educational qualification.With respect to these findings, the study recommended the need to complete the project of transition to accrual accounting in all government units. Adequate resources for training support for media programs, professional institutions and auditing, and support from the legislative authority and higher institutions should be encouraged.

Purpose – This study's purpose is to examine the effect of ownership structure, characteristics of the board, and CEO characteristics on integrated reports (IR) disclosure. Design/methodology/approach – The study relied on the content analysis approach in examining the annual reports of a sample of 67 non-financial companies listed on the Saudi Stock Exchange from 2016 to 2020, with a total of 335 observations. Findings – The study findings indicate that there is a significant positive effect of institutional ownership, board size, board independence, board gender diversity, CEO duality, and CEO expertise on IR disclosure. Moreover, the results also concluded that there is a significant negative effect of family ownership, board meetings, and the tenure of the CEO on IR disclosure. Implications – This paper contributes to activating the supervisory role of the Saudi Capital Market Authority to verify companies’ commitment to disclosing ownership structure information and the extent of compliance with governance mechanisms, which leads to improving the level and quality of IR disclosure and reducing information asymmetry. Originality/value – The study helps corroborate the literature and the theoretical explanation of the relationship between corporate governance and IR disclosure in the Saudi context.

The study aimed to identify the impact of adopting digital transformation in enhancing institutional excellence according to the Unified theory of acceptance and use of technology(UTAUT). The study adopted the analytical descriptive approach using the questionnaire as a tool for data collection . The study relied on a simple random sample to select the study items, which amounted to  (220) employees. The study came out with a number of important results: The dimensions of digital transformation represented by performance expectancy,  effort expectancy, facilitating conditions and social influence  exist in private universities  with a relative weight of (85.32%). There is a high level of institutional excellence between the sample members in private universities, with a relative weight of (84.6%). In addition, there is a statistically significant effect between the dimensions of digital transformation represented by facilitating conditions and social influences in promoting institutional excellence in private universities, while there is no effect for the dimensions performance expectancy,  effort expectancy in promoting Institutional excellence in private universities . the study  found that there were statistically significant differences in the averages of the responses of the two subjects towards the effect of adopting digital transformation in promoting institutional excellence due to (gender, qualification, experience), and there are no differences due to the job title.

This study aimed to examine the reality of King Saud University's diversity of human resources in terms of identifying its characteristics and dimensions, the management techniques employed, and the obstacles and difficulties faced by academic leaders in managing it. The research population (deans and agents of faculties; heads and deputies of academic departments) was surveyed using the survey method using a questionnaire. The returned (281) questionnaire was put to use in a statistical evaluation. The study revealed that cultural diversity exists at the university to a high degree in terms of academic specialization and social status, while it exists (to some extent) in terms of nationality, gender, language, and race, but very low degree in terms of religion. The study also showed that academic leaders employ a variety of tactics in this area, such as evaluating the conditions of female employees by granting them appropriate leaves, promoting employees solely based on performance, allowing employees the freedom to express their opinions and ideas, respecting different cultures, equally allocating resources and benefits to all employees, respecting employees' national and religious holidays, and training. The difficulties facing diversity management at the university are as follows: the absence of a precise mechanism for measuring the level of diversity, the lack of awareness of cultural diversity and its importance, the lack of financial resources for training in diversity management, and inadequate academic leaders' ability to manage diversity.

The purpose of this research was to investigate visitors' intention to visit the Kingdom of Saudi Arabia specifically for concert events. A quantitative research approach was employed to collect and analyze data in order to gain insights into this phenomenon. The study collected a sample of 384 individuals from both local and international tourists in Saudi Arabia using a convenience-sampling technique. The collected data was analyzed using SPSS (Statistical Package for the Social Sciences) and PLS-SEM (Partial Least Squares Structural Equation Modeling) with the assistance of Smart-PLS 3.3.9 software. The findings of the study indicate that visitors' attitude, subjective norms, e-word of mouth, and destination image have direct and positive impacts on their intention to attend concert events in Saudi Arabia. Moreover, the research revealed that past travel experience plays a significant moderating role in the relationships between e-word of mouth and visitors' intention, as well as between destination image and visitors' intention. However, past travel experience was not found to have a significant moderating effect on the relationship between attitude and visitors' intention, nor on the relationship between subjective norms and visitors' intention. The results of this study have implications for policy makers, the Saudi Arabian government, the Saudi tourism industry, researchers, and other stakeholders. They can utilize these findings to enhance their understanding of visitors' intentions regarding concert events in Saudi Arabia and to develop effective strategies to promote such events. It is important to acknowledge certain limitations of this study. Firstly, the research relied solely on quantitative methodology, and cross-sectional data was analyzed, limiting the depth of insights that could be obtained. Future research could benefit from adopting a mixed methods approach and conducting longitudinal studies to gain a more comprehensive understanding of the subject.

The aim of this research is twofold. First, investigating the effects of both corporate management and corporate structure on corporate entrepreneurship. Second, examining the mediating part of HRM practices in the effects of corporate management and corporate structure on corporate entrepreneurship. Gathering data via a reliable and valid questionnaire developed based on previous related works form a sample of managers selected form service companies, the results, which were obtained by SmartPLS 3.0 software, according to structural equation modeling (SEM) to test the hypotheses. revealed that HRM practices is significantly and positively related to corporate entrepreneurship, both corporate management and corporate structure were significantly and positively related to HRM practices. Also, the results indicated that corporate management had no significant effect on corporate entrepreneurship while the effect of corporate structure on the same dependent construct was negative. Finally, it was found that HRM practices had a full mediation role between corporate management and corporate entrepreneurship whereas HRM practices had no mediating role between corporate structure and corporate entrepreneurship. Based on these results, it was concluded that a key determinant of corporate entrepreneurship, which is corporate management, does not work without effective practices of HRM.

The research aims to study the reality of the mining sector's strategy and its role in supporting the global economy with the current changes by highlighting the most important decisions and measures taken to develop it through achieving sustainable development and supporting the global economy, which was recently affected by the Corona pandemic (Covid-19), In addition to the war between Russia and Ukraine, which caused an economic crisis, the following problem arises: What is the reality of the mining sector’s strategy in determining its role in supporting the global economy, especially with the current changes? In the study, I relied on the descriptive and analytical approach through the theoretical aspect related to the mining sector’s strategy and its support for the global economy, and the applied aspect through an analytical study. The study ultimately concluded that the mining sector contributes significantly to the gross domestic product, which leads to encouraging manufacturing industries and attracting large foreign direct investisments and advanced technology.

This study deals with the application of the investment-accelerator model to the economy of the Kingdom of Saudi Arabia. Quarterly data used from the first quarter 2010 to the fourth quarter 2021. The study aims at identifying the effect of change in income on the investment. It uses a descriptive-analytical approach, applying the econometrics technique to build, estimate, and test the model. Furthermore, the study applies the unite root test and cointegration technique to analyze the data. The results revealed that the change in income has a significant effect on investment in the Kingdom of Saudi Arabia. The magnitude of the investment accelerator is 0.57. The increase in the income change by one Riyal causes an increase in investment by 57 Halalas. Thus, the adjusted determination factor is only 16%; only 16% of the change in investment is accounted for by income changes. Moreover, the results of the study show the weakness of the explanatory power of the investment accelerator model in the Kingdom of Saudi Arabia. The study recommends the reduction of the value-added tax in order to stimulate investment.

With the great development and accelerated of business sector, business schools are facing growing gap between their educational outputs and requirements of the business sector.  This is due to the limitations of traditional education to keep up with the evolution of the business and its needs. This study presents a proposal of a radical shift in educational process of business education to be a problem-based learning through practice and solving dilemmas and case studies in the business sector instead of lecture and teaching system.  Problem based learning is considered to be the most innovative instructional method conceived in the history of education. Since business management activities depend on resolving issues and problems faced by business daily work such planning, financing, producing, resourcing, and marketing, the use of problem-based learning and case studies style represents a quantum leap in the style of business education that will link theoretical learning in practice.  This can be supported through the establishment of university services business that holds operational business services in the best interest of universities and their various business schools, departments and students.

This study investigates the relationship between informal employment and the shadow economy in India. Utilizing time series data spanning from 1991 to 2016, this study employs regression analysis to analyze the dynamics between these variables. While its primary objective is to examine the association between informal employment and the shadow economy, it also explore the relationship between self-employment and the shadow economy. Initial analyses focus on the computation of statistical measures such as mean, variance, and distribution characteristics. To address concerns of potential spurious regression outcomes, the study rigorously applies the Augmented Dickey-Fuller (ADF) test for stationarity verification. Findings of this study reveal significant correlations between informal employment, self-employment, and the shadow economy, shedding light on the complex interplay among these factors within India's economic landscape. This study contributes valuable insights to the existing literature, identifying key factors influencing the scale of informal employment and its impact on the shadow economy. This, in turn, offers significant implications for both policymakers and scholars in the field.

This research aims to study the effect of bank stock returns on the returns and money flows of equity mutual funds in Saudi Arabia. This study included all equity funds managed by investment companies owned by Saudi banks (both traditional and Islamic). Multivariant regressions are implemented using monthly data starting from 2006 to 2017. The results show that there is a positive correlation between the bank returns and the equity mutual funds returns, which illustrate the bias of fund managers through investing in the stocks of banks that own by their mother companies. As a result, it is possible to predict the variations funds returns by monitoring the stocks of the banks. Furthermore, the results show that there is a significant relationship between bank stock returns and the mutual funds flow. The outcomes exhibit a positive impact of the previous month bank returns on current equity fund flow which means that investors are motivated by the performance of bank stocks. In conclusion, the findings demonstrate that bank performance is a key driver for fund return and flow. This study reveals important implications for academic researchers, investors and fund managers to better understand the behaviour of the Saudi mutual fund market.

This scientific paper examines the role of Venture Capital (VC) and Angel Investor (AI) networks in promoting entrepreneurship as a catalyst for sustainable economic development in Saudi Arabia. By using a mixed-methods approach of interviews and surveys, the paper seeks to identify key challenges facing VC and AI networks in the country, and offer insights and recommendations to stakeholders looking to further promote and support entrepreneurship. Data was collected from 250 entrepreneurs and 10 investors (Venture Capitalists and Angel Investors from different Saudi regions). Through a data descriptive analysis, the paper will explore the key challenges faced by entrepreneurs in securing funding and mentorship. It will then examine the benefits of VC and AI networks in providing early-stage funding and support, as well as their impact on the broader economy. Finally, it will discuss recommendations for policymakers, investors, and entrepreneurs looking to promote and support entrepreneurship in Saudi Arabia, such as measures to increase access to finance for startups, initiatives to promote entrepreneurship education and training, and efforts to attract more foreign investment and talent. The paper will ultimately provide valuable insights for stakeholders looking to support and promote sustainable economic and social development in Saudi Arabia.

This study delves into the intricate dynamics of the Saudi stock market, specifically the Tadawul All Share Index (TASI), during the COVID-19 pandemic. It explores the multifaceted relationship between investor sentiment and market responses, considering various influential factors such as local market confidence, regional stability, and government policies. Through a comprehensive analysis of TASI's performance in comparison to major global indices like the Dow Jones, NASDAQ, and S&P 500, the study reveals unique market behaviors and the relative resilience of the Saudi market amidst global economic upheaval. Key findings include the TASI's ability to maintain stability despite global volatility, with a low to moderate positive correlation with these global indices and a 15% increase in trading volume during key intervention periods . The significant impact of the Saudi government’s economic and public health measures on stabilizing the market is also highlighted. The study's timeframe spans from January 2020 to November 2023, offering a timely insight into the market's adaptation to an unprecedented global crisis. The research contributes to a deeper understanding of the Saudi stock market's dynamics during the pandemic, providing valuable insights for investors, policymakers, and the broader Saudi economy.

Stock market volatility is a key factor in economic stability, especially in emerging and oil-dependent economies like those of the Gulf Cooperation Council (GCC). This study investigates the volatility patterns in the GCC stock markets—Saudi Arabia, UAE, Oman, Qatar, and Bahrain—using data from January 2010 to December 2020. Employing advanced quantitative methods such as volatility modeling and time series analysis, the study explores the relationship between economic indicators, geopolitical events, and global market trends in influencing market volatility. The primary objectives are to identify the drivers of instability, detect underlying volatility patterns, and assess their significance over time. ARCH/GARCH models are used to examine the persistence and clustering of volatility, while also analyzing market dynamics in the context of external shocks and structural breaks. Key findings show that higher GDP growth correlates with reduced volatility, whereas inflation and geopolitical tensions increase market fluctuations. The study offers essential insights for investors, policymakers, and financial institutions in the GCC to better understand market behavior, manage risk, and promote financial stability.

The organizational development process is considered a fundamental process for achieving success in higher education institutions, and following strategies for organizational development that do not achieve the satisfaction of its employees lead to the failure of administrative development. The current study adopted the use of programming by objectives to differentiate between organizational developments strategies (Reengineering-Total Quality - Restructuring- Automation). The study relied on the experimental descriptive approach. Applying it to the Islamic University of Madinah based on a scale consisting of five criteria for differentiation (job satisfaction - human resources - nature of business - material costs - awareness and culture within the institution), and in order to trust the proposed scale, the extent of honesty has been verified. Then the scale was applied to a random stratified sample. Descriptive statistical analyzes of the proposed scale criteria were made in order to explore the nature of the responses to the study sample, and the programming by objectives model was applied to choose the strategy Ideal, whose results showed that the automation strategy and the rightsizing strategy are the two best strategies for administrative development.

This scientific paper examines crowdsensing security and economic issues. The study focuses on Artificial intelligence (AI), blockchain, and cryptography. By analyzing their effects, this paper shows how these technologies increase crowdsensing security and economic sustainability. AI-enabled algorithms improve crowdsensing data processing and decision-making. AI methods help stakeholders make educated decisions by extracting key insights from crowdsourcing data. Data integrity, trustworthiness, and privacy in crowdsensing platforms depend on blockchain technology. By securing conversations and sensitive data, encryption improves security. This article emphasizes crowdsensing system economics, including cost-effectiveness, scalability, and revenue creation. AI in data analysis helps generate market insights, improve service quality, and drive innovation. Blockchain technology enables transparent, auditable transactions, providing new revenue streams. In conclusion, integrating AI, blockchain, and encryption, is critical for tackling crowdsensing security and economic concerns. The findings offer recommendations for stakeholders, researchers, and policymakers on how to properly harness these technologies in order to ensure secure and economically sustainable crowdsensing ecosystems.

The widespread adoption of social media platforms has provided a valuable source of data for understanding public sentiment and engagement towards various topics, including healthcare and vaccination. In this paper, we analyze the impact of sentiment in tweets related to COVID-19 vaccination in Saudi Arabia on user engagement metrics, such as likes, replies, and retweets. We t using web crawling techniques and applied sentiment analysis using BERT (Bidirectional Encoder Representations from Transformers) transformer models. Descriptive analysis of the data revealed the distribution of sentiment labels and user engagement metrics. We found that positive sentiment was associated with higher average likes and replies, while neutral sentiment showed a lower engagement level. Furthermore, we conducted statistical tests, including ANOVA (Analysis of Variance), to determine significant differences in user engagement based on sentiment labels. The results indicated a significant difference in likes count among different sentiment categories. This study provides insights into tailoring messaging strategies, monitoring sentiment trends, and collaborating with influencers to enhance the success of vaccination campaigns. The results emphasize the need for incorporating sentiment analysis into public health strategies to effectively engage the population and promote vaccination acceptance.

Objectives: Heart disease is a significant and widespread cause of illness and death around the world. Traditionally, heart disease risk assessment relies on clinical variables. The integration of deep learning techniques in this domain is a challenging, yet promising, solution. Methods: In this study, we employed a Deep Convolutional Neural Network (DCNN) to predict the risk of heart disease using clinical cardiology data. The dataset used in the analysis was obtained from the publicly available UCI Heart Diseases Repository consisting of 920 patients’ records comprising 14 attributes. The model was trained and fine-tuned using a grid search approach to optimize the best hyperparameters. Findings: The performance of the model was rigorously assessed through cross-validation techniques. We found that the proposed technique demonstrated remarkable performance, achieving an impressive testing accuracy of 83%. Precision, recall, and F1-score were equally notable at 84%, 83%, and 83%, respectively; showcasing the model's well-balanced classification capabilities. Our research produced promising results while highlighting the potential of the proposed DCNN model as a robust tool for heart disease prediction.