Global Economic Revolutions: Big Data Governance and Business Analytics for Sustainability

This study explores the critical factors affecting the construction and operation of the co-location data center in the Kuwaiti market. The study adopted the union model to collect and process data. The current research used a qualitative method, constructivism philosophy paradigm, inductive approach, and exploratory research type. Semi-structured interviews were used to collect and process data from nine research experts using purposeful sampling. The collected data were analyzed using the six thematic steps: becoming familiar with the data, generating initial codes, searching for themes, reviewing themes, defining themes, and writing up). This study reviews and discusses the thematic analysis of the data of the critical factors affecting the feasibility of establishing and operating co-location data centers in the Kuwaiti market. The results of constructing and operating a co-location data center showed two main themes, internal and external themes; under each theme, sub-themes clarify and explain the main themes—the internal theme, which includes the design of the location to ensure business continuity, operations of facilities, security, staff, and service level agreement operations, financial management, and company experience. At the same time, the external theme includes government regulations and standard certification.

This paper aims to examine the role of socio-demographics in forming attitudes toward AI-based digital voice assistants. The study employs the quantitative approach to analyze the socio-demographic data of the study, descriptive statistical methods were applied. The differences in the attitudes towards AI-based digital voice assistants by various demographics were examined using one-way ANOVA statistics. The paper enhances understanding of AI-based digital voice assistants by highlighting the differences in consumer attitudes with regard to gender, age education, income, and occupation levels. Interestingly, the study also confirms a positive significant correlation between usage time and attitude towards the use of digital voice assistants. Utilizing this will surely allow businesses to carry out appropriate customized services/products for reaching the targeted consumers and formulates appropriate business strategies.

To standardise rural development initiatives, many technological platforms are used. Financial technology [fintech] is a broad umbrella term for a variety of initiatives that provide this kind of vital data and support to the public. Indian farmers are yet to get benefits from agricultural technological development and fintech.

Most agricultural technology and fintech if used efficiently will significantly cut down on farming expenses. The promise of fintech in agriculture could not be realised since development agencies could not adequately deploy agricultural technology. Therefore, it is important to research how financial technology may enhance life in rural areas. Fintech paved the way for the expansion of banking services to rural areas. More than 20 million farmers who live in rural regions who have used this fintech banking system have benefitted. Farmers are increasingly making use of this service. Banking technology has contributed to reduced operating costs and improved corporate transparency, and fintech has aided in removing poverty from community frames. This research was conducted to assist institutional lenders, and more specifically banks, in determining the optimal scale at which to use fintech-enabled lending solutions in rural regions. Over 600 responses were gathered from 60 different villages in Tamil Nadu. The connection between fintech-enabled technology was studied using a conjoint analysis. The results of this research show that people in rural areas want access to financial services, including loans, that are based on their individual needs. This means that rural population must be the basis of the current system. It’s estimated that 62% of people are resistant towards technology. To get rid of that amount of resistance, we need the 2.5x gearing effect. The purpose of developing the Rural Expertise Accomplishment System [REAS] was to bridge the gap between the financial technology sector and agricultural networks. Rural networking driven by demand may overcome the limitations of fintech-enabled financial services.

Log files are a great way to find out what's wrong with a system and how secure it is. They can be very large and have a complicated structure, which is why they are so useful. We use Machine Learning (ML) to find network anomalies and build different models that are driven by data to find DDoS attacks. The main goal of this article is to reduce the number of times that DDoS detection is wrongly labeled. In this paper, we describe a method for security analysis that uses Deep Learning techniques like simple LSTM, LSTM with embedding, and Seq-to-Seq LSTM on several systems log files to find and extract data that may be related to distributed denial of service (DDoS) attacks made by malicious users who want to break into a system. Through a process of learning, these data will help to find attacks, predict attacks, or find intrusions. In this study, we looked at how different optimizers, the size of the hidden state, and the number of layers affected the same architecture to find the best way to set it up. When compared to other models, the proposed model was able to correctly identify DoS/DDoS packets that had never been seen before with a 98.95% level of accuracy.

The aim of the paper was to explore the effectiveness of NB-IOT Narrowband Physical Downlink Shared Channel (NPDSCH), which the 3GPP released in version 13 to serve as a mobile communications feature. When compared to previous cellular technologies, NB-expanded IOT's coverage, data rate, latency, and battery lifetime are its key characteristics. These NB-IOT capabilities make it extremely useful for IOT manufacturing and enable the upcoming technology that may be applied in a variety of situations of deployments, including Agriculture, smart cities, and health, and WSNs. The primary goal of this survey is to evaluate the execution of various NB-IOT grid characteristics with acceptable rates of error in uplink and downlink connections. The effectiveness of the distinct approaches was estimated based on how well they met the requirements of IOT sector. In order to evaluate the various criterion sets and determine which options offer the optimal cost-efficiency trade-offs for building an NB-IOT grid, software simulations were employed. According to the findings, data that is sent in a reduced Transport Block Size (TBS) units experiences less errors than data that is sent in a bigger size units. The outcomes also reveal that, in the propagation channel model, the ER rises as the Doppler frequency rises. The findings also demonstrate that as the subject of modulation and coding scheme (IMCS) increases, the ER rises; finally, the results indicate that an increasing number of antennas is nearly optimal for all parameters.

In this research paper we are proposing a sorting visualizer with sound is a tool that can assist users in comprehending and learning about different sorting algorithms. It enables them to see in real-time the process of sorting a set of data, such as a list of numbers. The visualizer animates the movement of the data as the algorithm sorts it, making it easy to see how the algorithm works and how the order of the data changes. Sound effects, such as beeps or chimes, can also help the user understand the sorting process by providing an auditory cue for the algorithm’s various actions. When the algorithm compares two elements, for example, a beep may be heard, or a chime may be heard when elements are swapped. Sorting visualizers with sound can be used for educational purposes, such as teaching students about different sorting algorithms and how they work. They can also be used by programmers to better understand how specific sorting algorithms work, and to compare the efficiency of different algorithms. Additionally, sorting visualizers can be used to debug sorting code, and to ensure that the algorithm is sorting the data correctly. Overall, sorting visualizers with sound is a powerful tool for understanding, learning, teaching and debugging sorting algorithms. It offers a visual and auditory representation of the algorithm’s behavior, making it easy for users to understand the process and identify any errors. This tool can be used to improve understanding of sorting algorithms and to help developers write efficient code.

Artificial intelligence (AI) has been used as an innovative and developing big data analytics technique, and AI has been a significant consideration in hospitality and tourism, as a result of the recent world which is wealth in data and implies the need to consider big data analysis to achieve some insights about the tourism and hospitality business, mainly to know their customer, in terms of preferences and what contributes to their satisfaction. This paper aims to present a holistic view of Machine Learning in tourism and hospitality research and discover the challenges and gaps that need to be focused on by forthcoming investigation. This research found that the holistic literature reviews on this subject are yet limited, restricting our understanding of the historical advancement of Machine learning investigation and its potential prospects. Future research can be extended to a systematic literature review to offer a more comprehensive review of how Machine learning has high light on tourism and hospitality research and suggest the newest growth in this subject.

In many developing countries, such as Jordan, the use of blockchain technology is in its infancy. In this study, the decision-making performance of audit firms in Jordan is examined together with the factors impacting the intention to adopt blockchain technology. This article offers a model for the adoption of blockchain technology that combines essential factors from prior research with additional, unstudied factors. 104 decision-makers from different Jordanian audit firms provided data that was used to evaluate the model of blockchain technology adoption and identify the factors linked to blockchain technology adoption. The factor, intention to use blockchain technology, was significantly influenced by infrastructure, system quality, and trust. The factor, intention to adopt blockchain technology, has been found to have a positive link with decision-making performance. These findings can be utilized by decision-makers to expand the adoption of blockchain technology systems in Jordan.

The evolution of Blockchain has revolutionized supply chain activities and has impacted numerous organizational functions. This research investigates the applications of Blockchain in the supply chain management of Indian cement industry. A sample of 285 retail industry suppliers (Delhi/NCR) was taken. The information is gathered using a structured questionnaire based on a five-point Likert scale and convenience sampling. Eight constructs with 34 manifests were discovered from previous studies, hypotheses are evaluated using factor analysis, and the model fit is achieved using confirmatory factor analysis (CFA) and structural equation modelling (SEM). Applications of blockchain technology will have a favorable impact on the supply chain of the cement industry because it offers better transparency in the supply chain, flexibility, sustainable process & transportation, effective utilization, and various relative benefits. The proposed model is the first to empirically substantiate and evaluate the multimodal framework in which applications of Blockchain act as a mediator to achieve operational performance. As a result, blockchain models should be implemented throughout the supply chain, which will benefit all stakeholders.

The rapid technological development in our lives has created a new form of currency called digital currency. It can be defined as a form of virtual currency that is electronically created and stored. This study investigates issues that affect an individual’s behavioral intention to use digital currency. Factors influencing behavioral intention by individuals in the Kingdom of Bahrain, such as perceived usefulness, perceived ease of use, trust, social influence, and security, are investigated. A questionnaire was distributed to citizens and residents to collect primary data, and 396 responses were acknowledged, bringing the study’s sample size to the required level. The study’s conceptual framework and primary hypothesis were developed using a quantitative research methodology. The data was checked for errors and validity, and a regression analysis was performed to check the study’s hypothesis. The findings of the study proved a significant level of behavioral intention toward using digital currency in the Kingdom of Bahrain, where perceived usefulness, perceived ease of use, social influence, and trust were found to significantly affect behavioral intention to use digital currency, while security, on the contrary, did not significantly affect behavior.

Cloud computing, the Internet of Things (IoT), big data, and general-purpose machine learning algorithms (GPML) can manage a number of data sources, including audio, video, and text, to increase the accuracy of product demand projections. Enterprise sales are distinct from other technology sales categories. It refers to the acquisition of large contracts with lengthy sales cycles, several decision-makers, and a higher amount of risk than typical sales. It has an unique decision-making method. Nearly every firm in the current Internet age has included or improved Artificial Intelligence (AI) technologies. In a similar vein, contemporary technology is used to business sales to simplify the work of sales representatives. The jobs of the personnel have not changed, but they are much simplified. The idea of selling things by fusing online and physical venues is known as the “new retail format.” Business-to-business (B2B) product sales management is another aspect of enterprise sales management. This research shows how effective marketing strategies and current retail AI integration are significantly impacted.

The purpose of this study is to discuss the role of Industry 4 (I4) technologies in Knowledge Management (KM). Industry 4 has the ability to enhance business processes and industrial operations. This study recognizes and discusses the opportunities for enterprises using I4 technology. Data was gathered from peer-reviewed academic publications found in databases like Scopus and Google Scholar. Moreover, the study highlights the roles of artificial intelligence, internet of things, cloud computing, and blockchain technology in KM. These technologies have the potential to enhance KM activities such as data collection and gathering, storage, knowledge exchange, and application. Third, this paper presents an overview of KMin the UAE and the potential use of industry 4 technology in this area. Future research should concentrate on different implementation issues and techniques to overcoming them. Evaluating case studies of Industry 4 technology can assist us in better understanding the problems.

The Covid 19 pandemic broke down the barriers between classroom teaching and online teaching, enabling people to study at any time and from any location. The next evaluation in online is VR and AR which is more technology oriented. The future of the internet may be the metaverse, which would expand the virtual universe. The present research is intended to serve as a resource for the changes in medical education pedagogy in the coming digital era. A survey was conducted to determine the level of awareness and acceptance of the new technology-driven education. Further, there was an attempt made to know the student’s opinion about the difficulties they may face when they get into the virtual education world. The finding of the research was that 48% of the students are early adopters of the new education mode, and they are happy to accept and intend to use the technology. The study found that students were aware of the metaverse while learning but were not experienced in metaverse learning, although most of them were interested in metaverse learning.

Transportation contributes 24% of worldwide fossil fuel combustion carbon (IEA 2020). Electric vehicles (EVs) will help in decarbonizing the transportation industry to promote climate action globally (Coignard et al 2018, Taljegard 2019, Muratori 2021, Dioha 2022). Battery EVs (BEVs) led the rise from practically zero in 2010 to over 16 million in 2021. (IEA 2022). The IEA's 2021 Global EV Outlook estimates that by 2030, 145 million EVs will make up 7% of the global vehicle pool (IEA 2021) As part of the EV30@30 Campaign, India has set a goal of reaching a 30 percent share of electric vehicles (EVs) by the year 2030. EVs would have the following effects: a) a decrease in the amount of petroleum fuel used for road transportation; b) a change in consumer demand away from automobiles with internal combustion engines and toward EVs; and c) a need for an increased amount of energy and a charging infrastructure. These changes will impact many stakeholders. This study attempts to encapsulate India's EV transition and its players. Over the past 5–10 years, EVs have grown in popularity and market share. Pure electric vehicle firms are increasing. This review article also examines the significance of the factor and the expected return rate for the AEV industry based on the study of selected EV businesses and found that EV stock prices are significantly affected.

In order to effectively communicate, keep records, make decisions, and evaluate data, businesses require access to information. Recently, jobs in data science domain have skyrocketed in popularity thanks to abundant data and cheap processing power. Degree programmes in Information Technology strive to keep up with the industry's rising demand. The authors looked at a large number of job listings from different sites to determine what skills and experiences are necessary for positions involving artificial intelligence and machine learning. The author also analyses the necessary abilities for each vocation and provides an evaluation. Furthermore, they compared the locations of ML and AI head-to-head. Data engineering, exploratory data analysis, programming, statistics, the internet of things, applied mathematics, neural network architectures, language, multimedia processing, and big data were all found to be more highly valued by employers in roles requiring ML and AI. Instead, communication skills and a generalist mindset are valued more highly in AI positions. Having these clearly defined abilities could also aid in the job search and in adapting existing course material to match the growing needs of the market.

Measuring volatility is used in many important financial and economic models. This paper proposes a new semiparametric score driven exponentially weighted moving average (SP SD-EWMA) model to improve the efficiency of the parametric score driven exponentially weighted moving average (SD-EWMA) model when residuals are fat-tailed and possibly skewed. We experimented the efficiency performance of our model on S&P 500 sample daily data. Implementing in-sample and out-sample analysis it is shown that the proposed model outperforms the other competing models (SD-EWMA and Standard EWMA models) as it renders higher log-likelihood function and smaller Akaike Information Criterion as well as smaller prediction errors. To validate further the outperformance of the proposed model we checked the forecast error of one, two and three days-ahead of value-at-risk (VaR) measure over the out-of-sample period to ensure that proposed model renders the smallest rate of violations and close to the expected level. This paper highly recommends using the proposed model to reduce the statistical efficiency loss of the classical/score-driven parametric volatility models and that could help investors and asset managers better adjusting their trading strategies.

Recently, the social media phenomenon has spread widely across the globe, resulting in the creation of new opportunities to influence individuals in a variety of fields. In addition, social media have become forcefully imposing themselves on our reality, as they are an integral part of a lifestyle, especially regarding SMEs. Small and Medium Enterprises (SMEs) performance is an essential and powerful factor determining the business venture position in the competitive environment. Effective management is associated with the SMEs active and competitive performance. This paper aims to develop a framework that shows the social media impact on SMEs performance with a mediating variable that has effective business management. Also, this paper is interested to understand the link between innovation and social media to the performance of SMEs. This study develops a conceptual framework relying on reviewed recent literature. The study revealed that innovation, social media marketing, and effective management predicts SMEs performance. Besides of that, the recommendations and suggestions are provided in this paper to help decision-makers in regulating and taking corrective actions.

Abstract Entrepreneurship provides economic growth for the nation in various ways. Entrepreneurs are contributing new ideas to the world and presently with help of digitalization introducing some innovation and technology-driven startups. Educational Institutions plays a significant role to develop entrepreneurial skill among students. There are various factors affecting the students for their intention towards Digital Entrepreneurship. Family background, Environment, Society needs are the important factors affecting the students to move towards entrepreneurs and since the present world coping with technology and digitalization, entrepreneurship also moving towards digitalization. This research aims to study the student’s intention towards digitalized entrepreneurship and its effect.