In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.

The arrival of the era of robots and autonomous machines is undisputable. It is anticipated that the future business environment will be characterized by a variety of intelligent systems and autonomous robots. In 2017, the International Federation of Robotics reported that momentum gained by robotic technologies is strong and that the sales volumes of both service and industrial robots is expected to grow. Building on this projection, the present study proposes a set of prerequisites or key determinants for the adoption of autonomous robots in the supply chain and logistics industry: technological context (i.e., relative advantage, complexity, and cost), organizational context (i.e., management support, financial support and employee competence) and environmental context (i.e., competitive pressure, customer pressure and vendor support). The study adapts a quantitative research design and uses an online survey to collect the needed data to test the conceptual framework and hypotheses proposed. Part of the study results confirms the association between the cost of digital technologies and the adoption of autonomous robots. However, the study found no evidence that the perceived relative advantage positively impacts supply chain and logistics firms’ adoption of autonomous robots. The study offers some managerial advices to supply chain mangers and marketers of the digital technologies and tools that can be applied in the supply chain setting.

Artificial Intelligence (AI) techs help businesses turn internal and external data into real gold by analyzing customer data, extracting valuable insights, and offering individual solutions. The current study aimed to identify the mediating effect of artificial intelligence and digital experience on the relationship between neuromarketing (emotional appeal and memory encoding) and consumer behavior. The current study was applied to consumers within the MENA region by using an online questionnaire self-administered by (837) individuals. The study hypotheses were all accepted, and results confirmed that there is a mediating effect of AI and digital experience (DX) on the relationship between neuromarketing (emotional appeal and memory encoding) and consumer behavior. The degree to which digital experiences can modulate the association between emotional appeal, memory encoding, and consumer behavior, is not absolute; other factors and external cues that may be integral parts of a seller's marketing campaign remain relevant as well. The marketing strategy, quality of the product, prices, and effect of the enclosing environment influence consumer behavior from the digital dominion. Consequently, the comprehensive study of the digital situation is the premise of comprehending as well as using the mediating influence of digital experience on a consumer's mind. The study supports the idea that it is crucial for digital experience to bring up good feelings and provide opportunities for better information memory. Building visualizations that are appealing, interactive, and immersive to the users is intended to keep them interested and promote remembering. Through this approach, information on the brand can be associated with, recalled, and can become a significant decisive factor in future purchase decision-making.

Digital transformation is a challenge that also impacts the selection of tools for implementing projects. Which tools are suitable for handling complex digital twins? Project management must respond to this with suitable approaches. The challenge for decision-makers is to choose the right one. Based on literature research and a case study, influencing factors are derived and practice-relevant project management approaches are collected. Furthermore, a decision model is developed that, on the one hand, supports the decision-maker in selecting tools before and during the project, and on the other hand makes empirical values from past projects usable for future decisions. The results show that the number of influencing factors is large, and the approaches are di-verse. In complex projects, this can lead to complex decision-making situations that require appropriate decision models. The developed “Supervised Decision Model – L5” is based on five levels (L): (L1) Building a database; (L2) Derivation of algorithms; (L3) Initial approach selection; (L4) Review of the initial selection; (L5) Using experiences for future decisions. In practice it turns out that complex projects – like Digital Twins - often fail. Modified decision models for selecting suitable approaches should therefore take the following as-pects into account: (a) decision-makers are actively supported in the initial decision phase; (b) initial decisions once made are checked in the early phase of the project and corrected if necessary; (c) the lessons learned are recorded in the database as empirical value and used for future decisions.

The usage of Artificial Intelligence (AI) technique under retail industry will bring glorious outcome and flourishing benefits for both the retailers and the distinguished customers. The multiple platforms of AI usage in retail arena are discussed under two different cluster classified as online and offline, based on the terms of execution of retail activity. The present research was conducted with the objectives of evaluating the contribution of quality, customer relationship management and big data in designing futuristic retail model and analyzing the intention of retailers and shoppers in experiencing the emergence of AI. Disproportionate multistage judgement sampling method was em-ployed. A sample of 610 shoppers from four different capital regions of southern part of states in India was considered for the statistical analysis. Data was collected during the first quarter period of 2018. Descriptive research design was used to describe the intention of shoppers towards the emer-gence of AI in the Indian retail sector. The usages of AI technologies in online and offline retail are grouped separately and its effect on building the quality, customer relationship management and big data was evolved. Finally, its impact on the retailers intention and customers delight was studied through Structural Equation Modeling with testing of appropriate hypothesis.

Despite certain advancements, the incorporation of artificial intelligence in universities is still inadequate. The requirement for students will continue for a while, although the development of artificial intelligence-based chatbots in schools has limited the role of students. The research aimed to assess the willingness of Jordanian learners in higher education to use artificial intelligence-powered chatbots for instructional purposes. The present research suggests nine hypotheses derived from the UTAUT2 model to assess students' desire to use artificial intelligence-based chatbots in learning. The pupils' information was gathered and examined using PLS-SEM. The research results showed that nine hypotheses were confirmed. The outcomes indicate that learners are interested in adopting artificial intelligence-based chatbots into their studies. The research's findings will supply administrators at higher education with valuable insights into the effectiveness of artificial intelligence-based chatbots in learning. Moreover, the findings will help developers of artificial intelligence-based chatbots, higher learning administrators, and legislators execute artificial intelligence-based chatbots that fulfil modern educational requirements.

This bibliographical review gives us a clear and summarized analysis of the management tools for a water infrastructure construction project and, a tool that allows the management of water resources through the application of everything analyzed and compiled in scientific articles obtained from the Scopus database and after that it was analyzed using the VOSviewer tool, which has the complexity of analyzing a large amount of data. This analysis was carried out from the appearance of the first related investigations until the year 2023, analysis graphs were obtained from representative levels of the words “artificial intelligence”, “project management” of “construction” and “water” resource with greater interest in the analysis. The results obtained allowed us to understand the great variety of technological tools that are available today to be able to manage the construction of a project through artificial intelligence and its components that work together, likewise the application of these tools is carried out by countries as well as the United States. The United States and China are the ones that represent the greatest interest in these investigations, however this contribution is minimal to be able to generate effective solutions since each project presents its particular characteristics that technology has to adapt to. The future of these projects was also analyzed, such as the management of water resources through intelligent technologies that allow the preservation, care and maintenance of water resources, in addition to this, it is emphasized that worldwide there are already problems of droughts, lack of water resources and shortages of water in some countries. This research has the purpose of an overview for decision-making in the execution of the project at the water level and after the management of the water resource, it is important to apply these tools for their different advantages and carry it out to large-scale works in Peru subsidized by the Peruvian state since they are the most responsible for ensuring the care, maintenance and preservation of water resources.

Reputation management is of more significant consideration for organizations across the globe. However, for relevant purposes, Public Relations accompanied by Artificial Intelligence further play a significant role. Mainly supported by the social exchange theory, this study also focuses on Public Relations practices and Artificial Intelligence as facilitating the pathway to reputation management for online retail organizations in the United Arab Emirates. The researchers selected a sample of n= 330 individuals and analyzed it using Structural Equation Modelling to test the proposed conceptual model. Results revealed that Public Relations practices significantly affect Competitive Value, Online Communication, and Behavior Change. Besides, the effect of Artificial Intelligence on Competitive Value, Online Communication, and Behavior Change also remained significant. Further, the three relevant factors (Online Communication and Behavior Change) significantly affect reputation management, indicating the overall effect of PR practices and AI on reputation management in online retail organizations in the UAE. Hence, it is concluded that PR practices and AI technology have a substantial role in ensuring reputation management. Besides, factors including Competitive Value, Online Communication, and Behavioral Change have a positive role in reputation management, further ensuring attaining the organizational goals. Finally, the researchers discussed the results and highlighted the theoretical implications accordingly.

Currently, school sustainability is the focus of attention of all parties, including education quality management experts, which is related to schools' weaknesses in using big data and artificial intelligence (AI). The aim of this research is to analyze the role of using big data and AI in improving the quality of education quality management in Indonesia and its impact on school sustainability. The research design uses a quantitative approach, especially correlational, verification or hypothesis testing based on empirical data in the field. The research population was all teachers, principals and high school supervisors in Palangkaraya City, Central Kalimanan, totaling around 5,423 people. The sample size used the Hair formula and obtained a sample size of 178 people. Data was collected using a questionnaire which was distributed to selected samples using a Google form. Primary data was analyzed using SMART PLS. The results shows that the use of big data had an impact on the quality education management, it also has an impact on school sustainability, while artificial intelligence had an impact on the quality education management, it also had an impact on school sustainability, In addition, quality education management had an impact on school sustainability and it mediated the relationship between the use of big data and school sustainability, and finally, quality education management mediated the relationship between artificial intelligence and school sustainability.

The growing significance of Artificial Intelligence (AI) across different fields highlights the essential role of user acceptance, as the success of this technology largely depends on its adoption and practical use by individuals. This research aims to examine how perceived cybersecurity, novelty value, and perceived trust affect students' willingness to accept AI in educational settings. The study's theoretical basis is the AI Device Use Acceptance (AIDUA) model. Using structural equation modeling, the study tested hypothesized relationships using data from 526 students at Jordanian universities. The results showed that social influence is positively associated with performance expectancy, while perceived cybersecurity is positively related to both performance and effort expectancy. Novelty value is positively associated with performance expectancy but a negative one with effort expectancy. Additionally, effort and performance expectancy significantly influence perceived trust and the willingness to accept AI. Moreover, perceived trust has a notable positive effect on the willingness to accept AI in education. These findings provide valuable guidance for the creation and improvement of AI-driven educational systems in universities, contributing to the broader understanding of AI technology acceptance in the educational field.