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In the current era of high environmental uncertainty, the advancement of green technologies has led to innovative practices in the manufacturing sector, becoming the preferred approach for achieving sustainable development in today’s business markets. Manufacturing firms require green innovation to improve their environmental performance and monitor operations effectively, but the adoption and implementation of these innovations is still low among manufacturing industries. To bridge this gap, a study was conducted using resource-based view (RBV) theory and the technology–organization–environment (TOE) framework to develop and validate a model that encourages firms to adopt green innovation. A survey was administered to 179 respondents from manufacturing firms, and the data were analyzed using structural equation modeling (PLS-SEM). The integrated constructs of the model—perceived benefits, top management support, coercive pressure, normative pressure, and mimetic pressure—all predicted green management accounting practices. Additionally, the study found that green management accounting practices directly and significantly impacted green environmental performance. The developed model provides clear implications for decision makers, highlighting the importance of adopting green practices and innovative technologies in order to enhance environmental performance. Advanced green technologies have shown a significant connection between green management accounting practices and environmental performance, particularly in developing economies.

Effective security support remains a challenge, even for mobile banking applications; this is leading to the loss of many customers due to limited protection of customer data and privacy. Cyber threats include everything from identity theft to malware threats and email and online fraud. Thus, businesses and individuals should use risk assessment methods and countermeasures to protect their m-banking apps. With this in mind, a new model using the Technology Acceptance Model (TAM) has been proposed. The model has been broken down into six main countermeasure categories, namely: perceived risk, perceived security, perceived trust, ease of use, usefulness and service quality. To test this model, structural equation modelling (SEM) was used. Our findings reveal that perceived security, perceived trust and service quality play key roles in improving the adoption of mobile banking apps. In addition, the findings indicate that perceived risk had a negative impact on both clients’ trust and their attitudes toward the use of mobile banking services. The proposed model could increase the adoption of m-banking apps by enhancing their defenses against security risk issues. The model enhances the risk reduction (63.0%), data protection (75.0%), trust (32.1%), quality of service (74.0%), ease of use (44.0%) and usefulness (45.3%) ratios.

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

The domain name system (DNS) plays a vital role in network services for name resolution. By default, this service is seldom blocked by security solutions. Thus, it has been exploited for security breaches using the DNS covert channel (tunnel). One of the greatest current data leakage techniques is DNS tunneling, which uses DNS packets to exfiltrate sensitive and confidential data. Data protection against stealthy exfiltration attacks is critical for human beings and organizations. As a result, many security techniques have been proposed to address exfiltration attacks starting with building security policies and ending with designing security solutions, such as firewalls, intrusion detection or prevention, and others. In this paper, a hybrid DNS tunneling detection system has been proposed based on the packet length and selected features for the network traffic. The proposed system takes advantage of the outcome results conducted using the testbed and Tabu-PIO feature selection algorithm. The evolution of the proposed system has already been completed using three distinct datasets. The experimental outcome results show that the proposed hybrid approach achieved 98.3% accuracy and a 97.6% F-score in the DNS tunneling datasets, which outperforms the other related works’ techniques using the same datasets. Moreover, when the packet length was added into the hybrid approach, the run-time shows better results than when Tabu-PIO was used when the size of the data increases.

Acquiring knowledge, either individually or in a team, is known as learning. The mode of learning may be either supervised or unsupervised. Language learning develops communication ability in a second foreign language, such as the Malay language... Malaysia is among the countries that students around the globe visit for undergraduate and postgraduate studies. But, the international students faced communication challenges with both the local students and the academic staff due to language variations. The local students lack confidence in English, while the international students don’t understand the Malay language. The communication The Gap was pointed out as the major problem for the international students to carry out their academic activities as most lecturers deliver their classes using Bahasa Melayu (Malay language). It is expected for the international students to learn the Malay language, but the local students are communicatively shy due to their lack of confidence in English. The present study investigates the Malay language learning difficulties of the international students at Universiti Sultan Zainal Abidin and Universiti Malaysia Terengganu. The researcher adopted quantative analysis with a structured questionnaire as the research tool. The experiment was carried out with the IBM SPSS version tool, and hardware components such as the Intel Celeron quad core processor (2M cache, 2.0GHz), 4GHz RAM, and 500GHz HDD. One hundred and seventy (170) with eighty-five (85) of equal size between the two universities were considered as the sample size. Findings reveal that most of the respondents disagreed that it was easy to learn Malay with the traditional system that presented a frequency of 41.8%. In addition, 43.5% of the respondents believed that learning Malay with the traditional system was time-consuming. Finally, respondents disagreed that traditional learning systems are easy to read. As such, most of the respondents suggest a mobile application as a substitute to the traditional mode of Malay language learning that will enhance learning anywhere at any time, regardless of the location.

This study aimed to identify financial and cash flow risks associated with SMEs and investigated how managers perceived these risks using the analytical hierarchical process (AHP). Accordingly, a three-level decision model was structured using two criteria, probability and consequences, and a list of six different types of risks as decision alternatives. Data were collected by a survey questionnaire from SME managers/owners and analyzed in accordance with the AHP method. The results show that the priority weight for risk criteria was 52% for probability and 48% for consequences. Further, with an average weight of 18.8%, the risk of an increase in bank charges ranked as the highest type of risk faced by SMEs. However, the risk of low or no profits was ranked as the lowest with an average weight of 13.4%. This study is one of the few, if not the first, to investigate SME managers’ perceptions using an AHP method and to provide insightful information on how SME managers/owners perceived various financial and cash flow risks. The study results may support the use of the AHP method in understanding managers’ perceptions and attitudes toward various types of risks associated with SMEs.

The rapid advancement of smartphones, as well as their widespread use, has resulted in a significant increase in new security concerns. Malware’s covert techniques make signature-based anti-virus/anti-malware solutions difficult to detect. The features used in such solutions are extracted from static or dynamic analysis. In this paper, an Android malware detection system has been proposed. It consists of two main subsystems that work in parallel, one has been trained for benign labeled apps while the second one has been trained on malware labeled apps. Each subsystem is based on an ensemble approach that consists of OC-SVM, LOF, and modified isolation forest (M-iForest) classifiers. Each subsystem used three one-class classifiers to take the decision in each subsystem independently. Moreover, each subsystem used both features that are extracted from static and dynamic malware analysis. The evaluation has been conducted based on two An-droid malware benchmark datasets which are DREBIN and CICAndMal2017. The proposed system achieved the highest accuracy compared to other related techniques; The accuracy for the DREBIN dataset was 98.7%, and 95.67% F-Score, while the accuracy for CICAndMal2017 was 98.99% and F-Score of 96.82%.

In the last decade, information systems (ISs) have made dynamic developments in light of their ability to enhance the performances of businesses. In relation to this, an organization that is effectively and efficiently managed often displays optimum performance using financial systems such as electronic accounting (e-accounting). Thus, essentially, e-accounting is utilized for the automation of operational processes and for improving business efficiency and performance. More currently, e-accounting dynamic development has laid credence to the performance of businesses in a way that the influence cannot be exaggerated. Nevertheless, past studies evidenced that successful e-accounting depends on critical success factors, and hence this study primarily aims to conduct an evaluation of e-accounting using DeLone and McLean’s information system model (DM ISM) among firms in Jordan. More specifically, this study determines the influence of information quality, system quality, service quality, system usage, and user satisfaction on business performance. The current study adopted a quantitative method, applying a self-administered survey questionnaire for the purpose of data collection from 104 e-accounting users. This study employed partial least squares structural equation modeling (PLS-SEM) to validate the data, and based on the findings, system quality and information quality affect system use; service quality of e-accounting had no significant impact on use, but e-accounting use had a significant influence on the satisfaction of users. Moreover, e-accounting system use and user satisfaction positively influence business performance. This study is an extension of the current IS literature, particularly of those focused on determining the effects of e-accounting benefits. This study validated the proposed model in the context of Jordanian firms and contributes to both the literature on and practice of e-accounting. This study provided implications, limitations, and recommendations for future research.

In the literature, studies have evidenced the efforts adopted by firms to develop digital technology with the hope of achieving sustainable decisions and competitive performance. However, studies have yet to provide an extensive explanation of the mechanisms used by firms in their digital technology adoption to impact and enhance value, particularly among small and medium enterprises (SMEs). In this regard, accounting information has served as a fundamental basis for business decision-making and the extensive use of digital technology has paved the way for the efficiency and effectiveness of accounting functions in modifying information relating to such functions. More specifically, a digital accounting system (DAS) enables the reporting and processing of large transaction amounts and generates the data required for analysis. However, despite these advantages, SMEs have been slow in their adoption and usage of DASs. Accordingly, this study drew upon resource-based view theory and the technology-organization-environment framework to propose an integrated model for examining the determinants and impact of using DAS among SMEs. The proposed model encapsulates the use and performance aspect of DAS. The study utilized a self-administered survey questionnaire as the primary data collection instrument. Data from 183 SMEs in Jordan were analyzed using partial least squares-structural equation modeling. The findings reveal that compatibility, organizational readiness, top management support and government support all had significant effects on DAS usage, which, in turn, had a positive and significant effect on DAS performance. With regard to the moderating effects, COVID-19 was found to have a moderating role on the DAS usage–DAS performance relationship. The study findings explain the way firms can enhance their DAS use to obtain optimum performance, thereby contributing to the literature on the antecedents and effects of using current information technology/information systems. The study recommends that the government of Jordan prepare and carry out a campaign concerning the importance of DASs for SMEs.

Online transactions have been reported to be hindered by privacy concerns. Although information privacy presents a threat to e-commerce adoption, cultural differences between nations can additionally impede this trend while raising people’s concerns about the privacy of their personal information. By removing geographic and time restrictions, the rise of e-commerce has completely changed how businesses interact with their clients. As a result, this research looked into how national culture affected the relationship between e-commerce adoption and information privacy in the United Arab Emirates. We suggested that, regardless of a society’s technological and economic infrastructure, privacy concerns and e-commerce adoption are influenced by cultural values. Our research model, which was validated using online survey questionnaires, was created employing Hofstede’s cultural dimensions. Self-administered questionnaires were used in a quantitative strategy. A sample of 249 consumers was chosen, and partial least squares structural equation modeling (PLS-SEM) was used to analyze the data. Our analysis revealed that key factors in people’s intention to transact online include their degree of perceptions of Internet safety, acceptance of e-commerce, privacy concerns, and personal interests. Additionally, the results show that gender has a positive effect as a mediator between the factors: “Privacy Concerns, Personal Interest, Safety Perceptions, and Transaction Willingness”. These results show how culture affects the adoption of e-commerce. Nevertheless, surprisingly, according to the research’s findings, privacy concerns were not indicative of cultural values, indicating that the idea of information privacy is more sophisticated than what a society’s culture represents.

The most current highly infectious disease, which has become a global health challenge permeating entire sectors of society, is COVID-19. In the education sector, the transmission of COVID-19 has been curbed through the closure of institutions and the facilitation of online learning. The main objective of this study was to propose an integrated model of the unified theory of acceptance and use of technology combined with the DeLone and McLean model, to examine the influence of quality features, namely, performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and social influence (SI), on the intentions and satisfaction of users toward mobile learning (m-learning) use in the context of Saudi learning institutions. The study obtained m-learning user data using an online questionnaire, after which the data were exposed to partial least squares structural equation modeling to test the proposed research model. The findings supported the influence of PE, EE, and FC on intention toward m-learning use but did not support the significant influence of SI. Moreover, system, intention, and user satisfaction were found to positively and significantly influence m-learning-system usage, with system, information, and service quality being top drivers of such user intention and satisfaction. The results reflect the required information concerning the strategies of higher institutions to enhance m-learning-system acceptance among students, with general implications for learning acceptance and usage.

Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of distributed data, where DDM discovers patterns and implements prediction based on multiple distributed data sources. However, DDM faces several problems in terms of autonomy, privacy, performance and implementation. DDM requires homogeneity regarding environment, control, administration and the classification algorithm(s), and such that requirements are too strict and inflexible in many applications. In this paper, we propose the employment of a Multi-Agent System (MAS) to be combined with DDM (MAS-DDM). MAS is a mechanism for creating goal-oriented autonomous agents within shared environments with communication and coordination facilities. We shall show that MAS-DDM is both desirable and beneficial. In MAS-DDM, agents could communicate their beliefs (calculated classification) by covering private and non-sharable data, and other agents decide whether the use of such beliefs in classifying instances and adjusting their prior assumptions about each class of data. In MAS-DDM, we will develop and use a modified Naive Bayesian algorithm because (1) Naive Bayesian has been shown to be the most used algorithm to deal with uncertain data, and (2) to show that even if all agents in MAS-DDM use the same algorithm, MAS-DDM preforms better than DDM approaches with non-communicating processes. Point (2) provide an evidence that the exchange of information between agents helps in increasing the accuracy of the classification task significantly.

Small and medium enterprises (SMEs) are the pillars on which most businesses worldwide rest. Thus, without the support of qualified information systems, it can be very challenging for them to improve their performance and difficult for them to reach sustainability goals. Despite the essentiality of economic sustenance for a competitive advantage in the postmodern industrial era, Jordanian SMEs are hampered with multiple challenges, such as accounting information quality, which supports various organizational decisions. The prevalence of information technology (IT) optimizes accounting operations through accounting-based information. A computerized accounting system (accounting information system, or AIS) facilitates accurate reporting, processes large-scale transactions, and generates meaningful reporting for subsequent evaluation. Given the lack of AIS implementation in SMEs, despite its notable advantages, this study aims to investigate the AIS-implementation antecedents and their implications towards sustainable business performance among Jordanian SMEs. An integrated model was recommended based on the technology–organization–environment (TOE) framework and resource-dependency theory (RDT) for the incorporation of AIS-implementation elements and sustainable business performance into one model. A self-administered questionnaire was disseminated among 194 respondents within the context of Jordanian SMEs for data collection and evaluation using structural equation modelling (SEM). Based on the study outcomes, external pressure, compatibility, financial support, top management support (TMS), and external assistance significantly impacted AIS implementation, which subsequently catalyzed sustainable business performance. Such results could offer useful insights into how organizations could optimize AIS implementation for sustainable business performance and expand the current body of literature on IS- or IT-implementation antecedents and impacts. The implications of this study are that SMEs should develop effective AIS implementation in order to reach sustainability goals. Therefore, we recommend and encourage SMEs decision makers to utilize AIS for their businesses.

Photovoltaic (PV) power production systems throughout the world struggle with inconsistency in the distribution of PV generation. Accurate PV power forecasting is essential for grid-connected PV systems in case the surrounding environmental conditions experience unfavourable shifts. PV power production forecasting requires the consideration of critical elements, such as grid energy management, grid operation and scheduling. In the present investigation, multilayer perceptron and adaptive network-based fuzzy inference system models were used to forecast PV power production. The developed forecasting model was educated using historical data from October 2011 to February 2022. The outputs of the proposed model were checked for accuracy and compared by considering the dataset from a PV power-producing station. Three different error measurements were used—mean square error, root-mean-square error, and Pearson’s correlation coefficient—to determine the robustness of the suggested method. The suggested method was found to provide better results than the most recent and cutting-edge models. The MLP and ANFIS models achieved the highest performance (R = 100%), with less prediction errors (MSE = 1.1116 × 10−8) and (MSE = 1.3521 × 10−8) with respect to MLP and ANFIS models. The study also predicts future PV power generation values using previously collected PV power production data. The ultimate goal of this work is to produce a model predictive control technique to achieve a balance between the supply and demand of energy.

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.

Recently, the emergence of the COVID-19 has caused a high acceleration towards the use of mobile learning applications in learning and education. Investigation of the adoption of mobile learning still needs more research. Therefore, this study seeks to understand the influencing factors of mobile learning adoption in higher education by employing the Information System Success Model (ISS). The proposed model is evaluated through an SEM approach. Subsequently, the findings show that the proposed research model of this study could explain 63.9% of the variance in the actual use of mobile learning systems, which offers important insight for understanding the impact of educational, environmental, and quality factors on mobile learning system actual use. The findings also indicate that institutional policy, change management, and top management support have positive effects on the actual use of mobile learning systems, mediated by quality factors. Furthermore, the results indicate that factors of functionality, design quality, and usability have positive effects on the actual use of mobile learning systems, mediated by student satisfaction. The findings of this study provide practical suggestions, for designers, developers, and decision makers in universities, on how to enhance the use of mobile learning applications and thus derive greater benefits from mobile learning systems.

Resource limited networks have various applications in our daily life. However, a challenging issue associated with these networks is a uniform load balancing strategy to prolong their lifespan. In literature, various schemes try to improve the scalability and reliability of the networks, but majority of these approaches assume homogeneous networks. Moreover, most of the technique uses distance, residual energy and hop count values to balance the energy consumption of participating nodes and prolong the network lifetime. Therefore, an energy efficient load balancing scheme for heterogeneous wireless sensor networks (WSNs) need to be developed. In this article, an energy gauge node (EGN) based communication infrastructure is presented to develop a uniform load balancing strategy for resource-limited networks. EGN measures the residual energy of the participating nodes i.e., Ci ∈ Network. Moreover, EGN nodes advertise hop selection information in the network which is used by ordinary nodes to update their routing tables. Likewise, ordinary nodes use this information to uni-cast its collected data to the destination. EGN nodes work on built-in configuration to categorize their neighboring nodes such as powerful, normal and critical energy categories. EGN uses the strength of packet reply (SPR) and round trip time (RTT) values to measure the neighboring node's residual energy (Er) and those node(s) which have a maximum Er values are advertised as reliable paths for communication. Furthermore, EGN transmits a route request (RREQ) in the network and receives route reply (RREP) from every node reside in its closed proximity which is used to compute the Er energy values of the neighboring node(s). If Er value of a neighboring node is less than the defined category threshold value then this node is advertised as non-available for communication as a relaying node. The simulation results show that our proposed scheme surpasses the existing schemes in terms of lifespan of individual nodes, throughput, packet loss ratio (PLR), latency, communication costs and computation costs, etc,. Moreover, our proposed scheme prolongs the lifespan of WSNs and as well as an individual node against exiting schemes in the operational environment.

Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as “The Internet of Things”. WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network life-time, but also affects the availability of these resources for other tasks. To increase life-time and provide an energy-efficient WSN, here we have proposed a new scheme called “A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs”. CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network life-time by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.

For the development effective and successful mobile learning applications, it is important to understand the users' requirements in different stages of usage. In this paper, we developed a new model to study the effect of different factors on mobile learning applications development at the three main stages of usage (static stage, interaction stage and transaction stage). The results of this study showed that each stage of the three stages, static, interaction, and transaction has different requirements in terms of system compatibility, security, information quality, awareness, perceived functional benefit, self-efficacy, perceived image, perceived uncertainty, availability of resources, and perceived trust. In addition, the results demonstrated that the requirements and perceptions of users towards the adoption and use of mobile learning application in the three stages significantly differ. The novelty of this research will be an added value to the body of knowledge and its implications will be vital for researchers and designers who are developing mobile learning applications.

This study aimed to alleviate the gap between the literature regarding the Social Networking Applications (SNAs) use for active collaboration and engagement as sustainability in higher education and task-technology-fit (TTF) and compatibility on their consequence on students' satisfaction and their performance impact its sustainability used in higher education. Although researchers have examined (SNAs) usage within multiple situations, the roles of (TTF) and compatibility as mediating variables have not been investigated through TAM model and constructivism theory on measuring education sustainability. Overall 602 students and researchers took part in this study, which were selected from public university. Using the method of structural equation modeling (SEM), we surveyed to discover the perception of students toward the (SNAs). Based on the results, the (SNAs) use for collaboration and engagement as sustainability in higher education, and TTF and compatibility positively impacted the student's learning performance on measuring education sustainability, and they were found to be completely pleased with the perceived ease of use and perceived usefulness. In conclusion, the role of TTF and compatibility presents positive influences performances related to sustainability for education; and both factors mediates associations among collaboration and engagement as sustainability in higher education, students' satisfaction on (SNAs) usage and students' performance related to sustainability for education. Therefore, their impact should be encouraged in learning processes in higher education institutions.

This piece theorises the limitations of transitioning reflection from individualistic to participatory practice. It addresses the question: what are the challenges of introducing crowd-refl...

This study aims to investigate the perceptions of near-field communication (NFC) usage for mobile payments in Saudi Arabia. In order to develop a mathematical framework for the acceptance of NFC quality of information for mobile payments, researchers have combined the technological acceptance model (TAM) and the idea of perceived risk. An online and physical study of 1217 NFC portable credit card holders in Saudi Arabia was conducted. Exploratory and confirmatory analyses were utilized to analyze the factor structure of the measurement items, and Smart PLS 2.0 from structural equation modeling (SEM) was used to assess the theories and hypotheses that had been put forth. The results show that (1) social influence, perceived element of risk, and subjective norms each have a negative influence on preconceptions of trust in online payment methods using NFC; (2) social influence, perceived element of risk, and social norms all have a positive effect on satisfaction with the security of electronic payment using NFC; (3) perceived ease of use has a negative effect on perceived confidence in digital payment using NFC; and (4) perceived ease of use has a negative effect on perceived trust in online payment using NFC. As a consequence of these findings, users’ attitudes regarding the use of NFC and behavioral intentions to utilize NFC mobile payment can be revealed. This study created a unique approach for assessing perceptions, perceived trust, and NFC information quality in mobile payment uptake in Saudi Arabia. As a consequence, banks may find this research useful as they implement new strategies to attract more customers, such as perceived security, brand trust, and NFC information quality in mobile payment adaption.

The adoption of big data analytics (BDA) is increasing pace both in practice and in theory, owing to the prospects and its potential advantages. Numerous researchers believe that BDA could provide significant advantages, despite constant battles with the constraints that limit its implementation. Here, we suggest an incorporated model to investigate the drivers and impacts of BDA adoption in the Jordanian hotel industry based on the technology–organisation–environment framework and the resource-based view theory. The suggested model incorporates both the adoption and performance components of BDA into a single model. For data collection, in this study, we used an online questionnaire survey. The research model was verified based on responses from 119 Jordanian hotels. This study yielded two significant findings. First, we discovered that relative advantage, organizational readiness, top management support, and government regulations have a major impact on BDA adoption. The study results also reveal a strong and favourable association between BDA adoption and firm performance. Finally, information sharing was found to have a moderating effect on the association between BDA adoption and firm performance. The data revealed how businesses might increase their BDA adoption for improved firm performance. The present study adds to the limited but growing body of literature investigating the drivers and consequences of technology acceptance. The findings of this study can serve as a resource for scholars and practitioners interested in big data adoption in emerging nations.

The learning environment usually raises various types of anxiety based on the student’s abilities to use technology and their abilities to overcome the negative feelings of an individual being watched all the time and criticized. Hence, learners still feel anxious while using computers and socializing in an e-learning environment. Learners who are faced with computer and AI tools are confused and frustrated. The uneasiness stems from anxiety or uneasiness, which is highly evident in daily interaction with computers and artificial intelligence tools or devices in e-learning contexts. The uneasiness stems from anxiety or uneasiness, which is highly evident in the daily interaction with computers and artificial intelligence tools or devices in e-learning contexts. To investigate this phenomenon empirically, a questionnaire was distributed among a group of undergraduate students who are studying different majors. This study aims to investigate the role of social anxiety and computer anxiety in an e-learning environment at the university level. Universities in the Gulf area are among those implementing e-learning systems. In spite of this, recent studies have shown that most students at Gulf universities are still resistant to using online systems; hence, it is necessary to determine the type of anxiety that creates such resistance and their relationship with other external variables such as motivation, satisfaction and self-efficacy. Students would be more likely to use e-learning tools and participate more effectively in their courses using the accessible electronic channels when the degree of anxiety is low. In this study, we have proposed a theoretical framework to investigate the role of social anxiety and computer anxiety in e-learning environments in the Gulf region. We examined how different variables such as satisfaction, motivation and self-efficacy can negatively or positively affect these two types of anxiety.

The growing number of security threats has prompted the use of a variety of security techniques. The most common security tools for identifying and tracking intruders across diverse network domains are intrusion detection systems. Machine Learning classifiers have begun to be used in the detection of threats, thus increasing the intrusion detection systems’ performance. In this paper, the investigation model for an intrusion detection systems model based on the Principal Component Analysis feature selection technique and a different Support Vector Machine kernels classifier is present. The impact of various kernel functions used in Support Vector Machines, namely linear, polynomial, Gaussian radial basis function, and Sigmoid, is investigated. The performance of the investigation model is measured in terms of detection accuracy, True Positive, True Negative, Precision, Sensitivity, and F-measure to choose an appropriate kernel function for the Support Vector Machine. The investigation model was examined and evaluated using the KDD Cup’99 and UNSW-NB15 datasets. The obtained results prove that the Gaussian radial basis function kernel is superior to the linear, polynomial, and sigmoid kernels in both used datasets. Obtained accuracy, Sensitivity, and, F-measure of the Gaussian radial basis function kernel for KDD CUP’99 were 99.11%, 98.97%, and 99.03%. for UNSW-NB15 datasets were 93.94%, 93.23%, and 94.44%.

In this study, we present the results of an assessment of an initiative that seeks to transcend the application of digital information in the higher education sector by recommending an integrative approach that quantifies both the flow of digital information and tutors’ quality impacts concerning technology acceptance model (TAM) constructs and the perceived experience of digital information in education (DIE). There is a mounting evidence that the educational institutions that prioritize the perceived experience and the quality of the tutors do not, generally, take into account the limited exposure to digital information and technologies. Data gathered from a survey of 485 college students were used to evaluate the model and hypotheses. The findings show that users’ perceptions of the value of DIE may depend on several extrinsic conditions that improve their experiences of learning and teaching. The user’s traits, such as technological preparedness, are vital in determining perceived ease of use. In some cultures, the superior quality of the tutor may further increase perceptions of the technology’s perceived usefulness. The intention to adopt technology may also be highly influenced by other variables such as information flow. Therefore, academic institutions must reevaluate the usefulness of digital information technology as a tool for improving educational sections. This research limited its focus to educational environments in which DIE has a significant impact on the teaching and learning setting. Future works may concentrate on health or monetary organizations.

The Internet of Things (IoT) interconnects physical and virtual objects embedded with sensors, software, and other technologies, which exchange data using the Internet. This technology allows billions of devices and people to communicate, share data, and personalize services to make our lives easier. Despite the multiple benefits offered by IoT, it may also represent a critical issue due its lack of information security. Since the number of IoT devices has been rapidly increasing all over the world, they have become a target for many attackers, who try to steal sensitive information and compromise people’s privacy. As part of the IoT environment, data and services should be protected with features such as confidentiality, accuracy, comprehensiveness, authentication, access control, availability, and privacy. Cybersecurity threats are unique to the Internet of Things, which has unique characteristics and limitations. In consideration of this, a variety of threats and attacks are being launched daily against IoT. Therefore, it is important to identify these types of threats and find solutions to mitigate their risks. Therefore, in this paper, we reviewed and identified the most common threats in the IoT environment, and we classified these threats based on three layers of IoT architecture. In addition, we discussed the most common countermeasures to control the IoT threats and mitigation techniques that can be used to mitigate these threats by reviewing the related publications, as well as analyzing the popular application-layer protocols employed in IoT environments and their security risks and challenges.

Artificial intelligence applications (AIA) increase innovative interaction, allowing for a more interactive environment in governmental institutions. Artificial intelligence is user-friendly and embraces an effective number of features among the different services it offers. This study aims to investigate users’ experiences with AIA for governmental purposes in the Gulf area. The conceptual model comprises the adoption properties (namely trialability, observability, compatibility, and complexity), relative advantage, ease of doing business, and technology export. The novelty of the paper lies in its conceptual model that correlates with both personal characteristics and technology-based features. The results show that the variables of diffusion theory have a positive impact on the two variables of ease of doing business and technology export. The practical implications of the current study are significant. We urge the concerned authorities in the governmental sector to understand the significance of each factor and encourage them to make plans, according to the order of significance of the factors. The managerial implications provide insights into the implementation of AIA in governmental systems to enhance the development of the services they offer and to facilitate their use by all users.

Network security risks are increasing at an exponential rate as Internet technology advances. Keeping the network protected is one of the most challenging of network security. Many security mechanisms were implemented to detect and identify any malicious activity on the network. Intrusion Detection System (IDS) one of the most used mechanisms to reduce the effects of these risks. Machine Learning (ML) classifiers are widely begin used to classify the network traffic as normal or abnormal. In this paper a comparative evaluation of the following ML classifiers: LogisticRegression, Multinomial Naive Bayesian, Gaussian Naive Bayesian, Bernoulli Naive Bayesian, k-Nearest Neighbors, Decision Tree, Adaptive Boosting, Random Forest, Multilayer Perceptron, and GradientBoosting is performed to specify the best classifier in identifying intrusion detection. The used evaluation metrics are accuracy, precision, and F-measure. The UNSW-NB15 dataset is used to assess ML classifiers. The experimental results show that the RandomForest classifier outperforms the other classifiers in terms of accuracy at 87%, precision 98%, and F-measure 84%.

The paper gives an overview of the ICS security and focuses on Control Systems. Use of internet had security challenges which led to the development of ICS which is designed to be dependable and safe. PCS, DCS and SCADA all are subsets of ICS. The paper gives a description of the developments in the ICS security and covers the most interesting work done by researchers. The paper also provides research information about the parameters on which a remotely executed cyber-attack depends.

In recent years, data has been expanding every second in terms of velocity, volume, and variety. This has resulted in enormous and complex big data, raising challenges in the storage, management, analysis of these big data and security thereof. Many organizations tend to use cloud systems in order to facilitate the operation in big data without being fully aware of the security and privacy challenges that the utilization of these systems pose and consequently ignoring the important practices and techniques that should be implemented when using cloud systems. These security threats therefore require more research in order to produce solutions to the cloud system environment. The goal of this paper is collection of most common cyber security threats in the cloud system environment and most common used mitigation techniques by reviewing the published papers in the period from 2019 to 2020 followed by cloud risk assessment case study in an organization in Saudi Arabia.

With the greater number of individuals using smartphones and apps increasing gradually, and with the easily of use. The users may perform more and more sensitive and critical tasks, making them a very lucrative target for attackers. Due to the flaws of existing smartphones and applications and multiple attacks on them, Researchers and academics have had to come up with full security measures challenge. Our result in this paper shows that most of all selected studies in this review targeted major cybersecurity threats in smart phones and apps such as malware attack, phishing attack, software failure, Dos attack, sniffing and spoofing attacks, physical attack, etc. This paper is a literature analysis providing a review of twenty previous studies related to cybersecurity in smartphones and apps. Also, identify major cybersecurity threats that could compromise the system based on four assets of smart-phone (device, data, application and network connectivity). Moreover, present and discuss some techniques to help improve security in smartphones and apps. Finally classify and prioritize the most significant threats that could affect the system.

As the increase of the demand on digital financial transactions, the higher the risks are associated with it. This paper examines the largest financial security incidents and then explains in detail some of the major incidents happened as reported by many sources. Then, the paper identified the risk mitigation for credit card companies’ solutions by examining a research and some steps written by experts. Credit card security incidents are increasing, and its methods are rapidly evolving. Therefore, it is important for all credit card companies to consider these solutions and in order to mitigate risks and losses.