Sustainable Leadership for Environmental Risk

Inhaltsverzeichnis

1. Frontmatter

2. Analysis of Errors in Domain of Algebraic Functions

   Ahmad A. S. Tabieh

   Abstract

   Study objectives include identifying and analyzing the most common mistakes in finding the domain of algebraic functions committed by first-year students in the Civil Engineering program when studying the Pre-Calculus course. The study sample consisted of 25 male and female students enrolled in the first year of the Civil Engineering program, taking the Pre-Calculus course at the Middle East University in Jordan. The researcher employed a qualitative approach based on a case study, collecting data from students’ responses to open-ended mathematical questions presented to them as exercises. The study utilized the APOS Theory, consisting of four stages: Action, Processes, Objects, and Schemas, with the goal of categorizing observed errors in students’ solutions to the exercises into themes. The results revealed that the errors fell into four themes: conceptual errors, procedural errors, interpretation errors, and extrapolation errors. Most conceptual errors occurred when the domain of a function represented by a graph was expressed in terms of independent variables. Procedural errors occurred when determining the domain of an algebraic function written in algebraic notation. Interpretation errors arose when students found the domain of an algebraic function through the simplified representation of the original function (equivalent function). Extrapolation errors occurred when students used the rule \(D\left( {f + g} \right) = D\left( f \right) \cap D\left( g \right)\) to find the domain \(D\left( {f*g} \right) = D\left( f \right) \cap D\left( g \right)\), where (*) represents any arithmetic operation. The results also indicated that the participants struggled with performing operations on sets, both closed and open intervals, as well as inequalities. The study recommended implementing strategies to reduce the observed errors and improve students’ understanding of finding the domain of algebraic functions through educational activities that involve investigating and analyzing errors through class discussions.

3. Artificial Intelligence and Its Relationship with the Future of Psychotherapy

   A Critical Reference Study Mohammed Ahmad Mahmoud Khattab, Marwan Ibrahim Salameh, Heba Fathi Farag Selim

   Abstract

   The aim of this study, through reviewing the research and academic heritage of recent studies and research on artificial intelligence in the field of human sciences in general, and in psychological therapy in particular, is to explore the aspects covered by these studies. What are the most important findings that they have reached? A critical perspective is also provided on this research heritage to help avoid the shortcomings and gaps in such research, which focuses on the phenomenon of artificial intelligence in the future. What are the potential challenges facing this profession, especially in light of the advancement and development of artificial intelligence programs, their ability to assess and diagnose psychologically, and to provide psychiatric and psychological treatment plans? The results showed that among the reasons that drive these students to seek treatment with artificial intelligence is the fear of social stigma at 94% of the total sample, at 95% among girls, and at 91% among boys, and the fear of dealing with strangers at 98% among patients with social phobia and at a higher rate among girls at 100%, and depression depended on taking antidepressant medications without consulting a psychiatrist at 79% due to their belief in the futility of humans.

4. Content Analysis of the Book “Arabic: My Language” for Fourth Grade in Light of the Characteristics of a Good Textbook in Jordan

   Hussein Hikmat Mistareehi, Abdelraouf Hameed Alyamani

   Abstract

   The study aimed to analyze the content of the book “Arabic: My Language” for fourth grade in light of the characteristics of a good textbook, applicable in Jordanian schools for the academic year 2023–2024. The researchers employed the descriptive-analytical method. The study population consisted of the “Arabic: My Language” textbook for fourth grade. To achieve the study’s objectives, a content analysis form was constructed to measure the book’s conformity with the characteristics of a good textbook. The form’s validity and reliability were verified and applied to the study population. The results showed that the degree of suitability of the “Arabic: My Language” book for fourth-grade students, according to the overall characteristics of a good textbook, was very appropriate. The suitability percentages for each domain of good textbook characteristics were as follows: book design and layout (93.75%), book language (83.33%), language activities (81.22%), and general outcomes and linguistic content (75%). The results also indicated that the book’s description and analysis revealed its suitability for the age group in the following areas: general information, design, and presentation style. The study recommended focusing on linguistic content by linking the curriculum to the educational environment and conducting studies on other samples that include additional variables.

5. Evaluating Factors Shaping Bitcoin Closing Prices with Fintech Strategies

   Jamil J. Jaber, Younis Ahmed Ghulam, Anwar Al-Gasaymeh, Rania Al Omari, Nawaf N. Hamadneh

   Abstract

   This study seeks to evaluate the factors shaping cryptocurrency market data forecasting by utilizing 2451 daily observations of Bitcoin (BTC) closing prices from November 2017 to July 2024. Employing a hybrid financial technology (Fintech) approach, the model combines a nonlinear spectral method involving the Maximum Overlapping Discrete Wavelet Transform (MODWT) with a Genetic Fuzzy System inspired by Thrift’s technique (GFS.Thrift). Input variables, such as the logarithm volume of Bitcoin (LVL) and logarithm Ethereum closing price (LET), were chosen based on factors like correlation, tolerance, Variance Inflation Factor (VIF), and multiple regressions, sourced from the cryptocurrency market. The adaptive GFS.THRIFT model underwent training on 80% of the dataset, reserving the remaining 20% for performance testing. A comparative analysis between the proposed model (MODWT-GFS.Thrift) against the traditional model, GFS.Thrift, demonstrated that the performance of MODWT-GFS.Thrift was found to be less effective than that of the traditional model.

6. Ethereum and Bitcoin Price Prediction Models in the Post-COVID-19 Era: A FinTech Approach

   Jamil J. Jaber, Asma S. Alzwi, Anwar S. Al-Gasaymeh, Mahmoud Barakat Alnawaiseh

   Abstract

   This study analyzes daily BTC/USD and ETH/USD prices over a 1419-day period from February 1, 2021, to November 20, 2024. Statistical methods, including Partial Autocorrelation Function (PACF), Autocorrelation Function (ACF), Unit Root Test, and optimal ARIMA models, are employed to investigate the stationarity and prediction of the cryptocurrency prices. The results suggest that BTC/USD series is stationary after first differencing, while the stationarity of ETH/USD series remains uncertain due to observed characteristics in the PACF and ACF plots. The Augmented Dickey–Fuller (ADF) test supports the stationarity of both series. Predictions for the subsequent 360 days indicate a consistent upward trend in BTC/USD and ETH/USD prices from December 2024 to November 2025. These findings offer valuable insights for investors and stakeholders in the cryptocurrency market to anticipate potential trends and make informed decisions based on the projected price movements of BTC/USD and ETH/USD.

7. Innovative Approach to Forecasting Jordanian Commercial Bank Stock Market Using a Hybrid Genetic Algorithm-Fintech Model

   Jamil J. Jaber, Younis Ahmed Ghulam, Anwar Al-Gasaymeh, Amro S. Alamaren, Mheel AL-Smaihyeen

   Abstract

   This study aims to enhance the prediction accuracy of the daily ASE’s weighted price index of the banking sector (SCB) through the application of a nonlinear spectral model known as maximum overlapping discrete wavelet transform (MODWT). The model incorporates five mathematical functions: Haar, Daubechies (Db4), least square (LA8), best localization (BL14), and Coiflet (C6). Employing a genetic fuzzy system based on Thrift’s methodology (GFS.Thrift), the study leverages a dataset provided by the Amman Stock Exchange (ASE) consisting of 4,423 observations spanning from January 2, 2006, to January 7, 2024. The adaptive GFS.THRIFT model undergoes training with 90% of the dataset, while the remaining 10% is reserved for testing its predictive capabilities. The selection of input variables, including standardized gross domestic product (SGDP) and inflation (SI), is conducted through multiple regressions and multicollinearity tests. Findings from the study reveal a negative relationship between SGDP and SCB, while a positive association is observed between SI and SCB. Notably, the proposed model (GFS. Thrift + C6) demonstrates superior performance compared to other existing models, including the original GFS.Thrift model.

8. Exploring the Speed of Adjustment in Gold Price Volatility Risk with Brent Oil, Silver, and Interest Rates

   Jamil J. Jaber, Mohammad H. Saleh, Anwar Al-Gasaymeh, Adnan M. Rawashdeh, Omar Alsinglawi, Asma S. Alzwi

   Abstract

   This study delves into the nuanced exploration of the velocity of adjustment in gold price volatility risk (GPs), interwoven with Brent oil (Oil), silver prices (SPs), and interest rates (InT). The dataset, encompassing 4789 daily entries spanning from March 1, 2006, to October 22, 2024, was meticulously curated from reputable sources like the World Bank. Through rigorous statistical analyses, key independent variables were sieved out using tests for multicollinearity such as Variance Inflation Factor (VIF) and Tolerance, alongside multiple regression frameworks including Ordinary Least Squares (OLS), Fixed Effect, and Random Effect models. The study’s revelations stand firm: a conspicuous absence of multicollinearity, substantiated by VIF values comfortably below 10 and Tolerance nearing one. Moreover, the input variables exhibit a significant positive effect on the output variable, GPs. Notably, the Error Correction Model (ECM) unveils that the pace of adjustment in gold price volatility risk demands a patient span of 811 days (equivalent to 2.25 years) to revert to its long-term trajectory post shock. These discoveries underscore the efficacy of the proposed forecasting model, heralding it as a promising tool for applications within the gold market.

9. Estimating Volatility Risk in the OPEC Basket Price

   Amro S. Alamaren, Baker I. Albadareen, Anwar Al-Gasaymeh, Firas Al-Rawashdeh, Jamil J. Jaber

   Abstract

   This study examines daily prices, returns, and volatility risk of the OPEC Basket over a 5640-day period from March 1, 2003, to November 13, 2024. Various statistical models, including Haar, D4, La8, Bl14, and C6, are utilized to predict prices, returns, and volatility risk. The results show that the Haar model outperforms the other models in predicting OPEC Basket Prices, based on error metrics such as RMSE, MAE, MPE, MAPE, MASE, and ACF1. Additionally, the Haar model also demonstrates superior performance in predicting volatility risk, with lower error values across these same metrics except ACF1. For returns, both the Haar and D4 models outperform the other models based on RMSE, MAE, MASE, and ACF1. These findings are significant as they provide critical insights for investors and stakeholders in the oil market. Accurate price prediction is essential for strategic decision-making, and volatility risk assessment helps stakeholders manage risk exposure. The superior performance of the Haar model for both price and volatility predictions indicates its potential use as a reliable tool for forecasting in the oil market. The consistent results from the D4 model for return predictions further enhance the robustness of the findings, offering an additional method for risk management. By improving predictive accuracy, these models can help investors make better-informed decisions, mitigate potential losses, and optimize investments in a highly volatile market like oil.

10. The Impact of Digital Marketing on Event and City Recommendations: Insights from Santa Tecla Festival in Tarragona, Spain

    Mohammed Riyad Al-Dweik, Narcís Bassols i Gardella, Jamil J. Jaber

    Abstract

    Tourism and events are transforming through creative techniques that enhance visibility, engagement, and participant experiences, with digital marketing playing a pivotal role in this evolution. This study analyzes the impact of digital marketing on event and city recommendations, emphasizing their interdependent link. Data were gathered from 400 attendees and participants of the Santa Tecla event in Tarragona, Spain, employing in-person and online surveys. Multiple regression, simple regression, and ANOVA analyses were utilized to evaluate 12 hypotheses. The results indicate that digital marketing substantially affects event and city recommendations, with both elements exerting mutual influence on each other. The study also emphasizes how demographic factors, including income levels and travel patterns, influence these effects. Income levels substantially impact digital marketing, event, and city recommendations, whereas visiting patterns affect event and city recommendations but do not influence digital marketing. These data provide practical implications for event organizers and tourism marketers, highlighting the necessity for targeted methods to engage various demographic segments. This research enhances the theoretical comprehension of digital marketing’s significance in tourism and offers practical recommendations for improving event promotion and destination branding.

11. The Impact of Investment in Financial Technology on the Financial Performance of Banks

    Anwar Al-Gasaymeh, Linda A. Al-Gasaymeh, John Kasem, Jamil J. Jaber, Ahmad A. Al-Naimi, Eman M. Alnimer, Jassim Al-Gasawneh

    Abstract

    The current study aimed to reveal the influence of investment in financial technology in its dimensions represented in (investment in ATMs, investment in software and systems, investment in credit cards) on the financial performance represented in (return on assets rate, return on equity rate) of Jordanian commercial banks. According the study’s participant community comprised of Jordanian commercial banks, the descriptive analytical approach was utilized in order to accomplish the goal of the research. The number of banks in the study community was (12), as shown by the bulletins that were distributed through the Jordanian Banks Association and Central Bank of Jordan during the duration of 2023, where the data from the year (2013–2023) were analyzed using the software (E-VEIWS), and the most important results reached were to the fact it is a considerable and positive impact of financial technology in terms of its dimensions represented in (investment in ATMs, investment in credit cards, investment in software and systems) on the financial performance in its dimensions (return on assets rate, return on equity rate) in Jordanian commercial banks. The study recommended the necessity of encouraging Jordanian commercial banks to establish alliances with financial technology companies to develop innovative solutions, such as payment applications. Digital and mobile banking services, and Jordanian commercial banks adopt strategies to enhance digital transformation by developing digital banking platforms that meet customer needs and applying artificial intelligence technologies to improve financial performance.

12. Jordanian Commercial Banks’ Readiness to Adopt Blockchain Technology

    Anwar Al-Gasaymeh, Linda A. Al-Gasaymeh, Shawkat Salameh Gasaymeh, Ghazi Qasaimeh, John Kasem, Jamil J. Jaber, Ahmad A. Al-Naimi

    Abstract

    This chapter achieves the administrative, technical, and human readiness of Jordanian commercial banks to adopt blockchain technology and provide high-quality financial services to all customers. This study described and analyzed the phenomena using descriptive analysis. A questionnaire was created to assess study variables, validate their correlations, and interpret the data to get the intended outcomes. The study included all 12 Amman Stock Exchange-listed commercial banks till 2023. These banks’ managers of branches, leaders of departments, financial department personnel, computer systems, and programs, including customer service were the research sample. The 300 electronic surveys were sent to specified banks, and 269 were collected. (11) questionnaires were removed owing to identical replies, leaving (258) for statistical analysis using SPSS. Jordanian commercial banks’ administrative, technical, and human preparedness to adopt blockchain had a statistically significant influence. The research suggested rising interest in the technology’s blockchain due to its potential benefits to the bank and its obligation to enhance human preparedness.

13. The Impact of Digital Transformation on Customer Satisfaction in Jordanian Commercial Banks

    Anwar Al-Gasaymeh, Linda A. Al-Gasaymeh, John Kasem, Jamil J. Jaber, Ahmad A. Al-Naimi, Eman M. Alnimer, Jassim Al-Gasawneh

    Abstract

    This study examined how digital transformation (monitoring and detection, biometric identification, and secure payment) affects Jordanian commercial bank customer satisfaction. The research community comprised of 12 Jordanian commercial banks; hence, the descriptive analytical technique was chosen to attain its goals. The study sample comprised customer service, public relations, and consumers. (180) questionnaires were provided to participants, and (156) were recovered. SPSS software was used to analyze the data, making it valid for statistical analysis. A statistically significant impact caused by digital transformation in its different aspects on customer satisfaction in Jordanian commercial banks was found, along with an increase in digital transformation application and customer satisfaction. Human capital must be increased to accelerate financial institution digitization, according to the report. Despite sophisticated technologies, human capabilities are essential to success in this profession.

14. The Impact of Cybersecurity Risks on the Use of Bank Cards at Jordanian Commercial Banks

    Anwar Al-Gasaymeh, Linda A. Al-Gasaymeh, Shawkat Salameh Gasaymeh, Ghazi Qasaimeh, John Kasem, Jamil J. Jaber, Jassim Al-Gasawneh, Ahmad A. Al-Naimi

    Abstract

    This research evaluates the effect of cybersecurity risks, which mean information security risks, mobile application security risks, and cyberattack risks, on the use of bank cards in Jordanian commercial banks. The approach adopted to attain the aims of this paper is descriptive–analytical. The study sample covers IT managers, cybersecurity specialists, and financial analysts of 12 Jordanian commercial banks. An electronic structured questionnaire was used whereby 300 questionnaires were sent out, and it resulted in 278 responses, which were statistically analyzed. SPSS was used for data analysis. The results showed that cybersecurity risks significantly influenced the use of bank cards, pointing to the need for inclusive security measures in online banking. Therefore, the study recommends that Jordanian commercial banks increase the level of investment in cybersecurity to match the ever-increasing threat landscape for secure and efficient banking transactions.

15. The Impact of Adopting Financial Technology on the Red Ocean Strategy in Jordanian Commercial Banks

    Anwar Al-Gasaymeh, Wlla Fareed Algharibeh, Linda A. Al-Gasaymeh, Shawkat Salameh Gasaymeh, Ghazi Qasaimeh, John Kasem, Jamil J. Jaber, Ahmad A. Al-Naimi

    Abstract

    This research has been undertaken to carry out a study regarding the impact of FinTech adoption upon the competitive red ocean strategy, with a focus on blockchain technology, cloud computing, and big data within Jordanian commercial banks. The nature of the approach adopted to achieve the objectives under this scope has been descriptive–analytical. All 12 Jordanian commercial banks were studied, and financial management, IT specialists, and compliance officers were targeted. There were 180 electronic surveys, and 165 responders received them. The SPSS research shows that FinTech adoption has a statistically significant influence on the red ocean strategy in Jordanian commercial banks, demonstrating the significance of technology in boosting competitive posture. This research study does display an area where the Jordanian banks will have to invest more into FinTech and become prepared for the adaptation to technological advancement in order to remain competitive in this dynamic financial sector.

16. The Impact of Electronic Readiness on the Adoption of Financial Technology in Jordanian Commercial Banks

    Anwar Al-Gasaymeh, Linda A. Al-Gasaymeh, John Kasem, Jamil J. Jaber, Ahmad A. Al-Naimi, Jassim Al-Gasawneh

    Abstract

    This study’s goal is to ascertain the degree to which electronic readiness, through dimensions, such as administrative readiness, infrastructure readiness, and human readiness, ensures the adoption of financial technology in Jordanian commercial banks. This description is attained through the descriptive–analytical approach. A population of 12 Jordanian commercial banks was selected, and accountants, IT specialists, auditors, and senior auditors were selected as a sample. A total of 250 questionnaires were distributed by means of electronic, out of which 225 were retrieved and valid for statistical analysis. For data analysis, SPSS software was used, and tests include descriptive statistics, correlation, and regression tests. The results indicated that electronic readiness and its dimensions have a significant effect on the adoption of FinTech. The study recommended further investment by Jordanian commercial banks in human capital and digital infrastructure to enhance readiness for the rapidly advancing technological landscape.

17. The Impact of Artificial Intelligence Using Machine Learning and Deep Learning on Fraud Detection in Jordanian Commercial Banks

    Ahmad A. Al-Naimi, Laith Al-Shouha, Shafiq Al Abed, Mohammad Ahmad Alnaimat, John Kasem, Anwar Al-Gasaymeh

    Abstract

    Machine learning and deep learning were used to explore how they affect fraud detection in Jordanian commercial banks. The study surveys numerous banking professions using descriptive analysis. The research community included 12 Jordanian commercial banks. IT, audit, risk management, banking directors, and audit and compliance professionals from Jordanian commercial banks form the research sample. SPSS analyzed 233 of 250 electronic questionnaires distributed in the surveys. The results suggest that AI technology improved fraud detection statistically. These studies show that sophisticated technology improve financial institution security and efficiency. The research shows that AI investment must be constant to keep up with rapid technological advancements to help banks battle increasingly complex fraud. It also requires continuing AI training for staff to identify fraud effectively.

18. The Impact of Blockchain Technology on the Efficiency of Internal Financial Systems in Jordanian Commercial Banks

    Ahmad A. Al-Naimi, Laith Al-Shouha, Shafiq Al Abed, Mohammad Ahmad Alnaimat, John Kasem, Anwar Al-Gasaymeh

    Abstract

    This research was performed aiming for investigating the relation between blockchain technology and its dimensions with regard to block, hash, information, and timestamp vis-à-vis the efficiency of internal financial systems in Jordanian commercial banks. Using a descriptive analytical approach, the study considered all 12 major Jordanian banks as the community. It distributed 300 questionnaires to a wide range of banking professionals, such as financial system analysts, IT specialists, and senior executives. Most responses were great, with 265 questionnaires retrieved for analysis. The statistical tests are done using SPSS software. The result indicated that due to the effective use of blockchain technology, the operational efficiency improved significantly. These findings imply that every single aspect of blockchain technology contributes uniquely to enhancing the efficiency, safety, and integrity of all financial transactions. Given this tremendous growth in the development of finance technologies, the study goes on to recommend that Jordanian commercial banks should keep investing in blockchain technology so as not to be at a disadvantage in competing with other banks, and also to take full advantage of improved security in transactions to effectively respond to customers and regulatory demands.