Studying the factors in influencing the users’ intentions to adopt Big Data technology in Malaysia is crucial. This study adopted three grand theories which consisted of Dissemination of Innovation (DOI) theory, Technology Acceptance Model (TAM), and Technology-Organization Environment (TOE) framework. The model specifies technological, organizational, and environmental factors as determinants of the users’ intentions to adopt big data technology. This study aims to review the technology, organizational and environmental factors that can determine the intentions towards big data technology adoption. A total of 224 questionnaires were obtained and screened. Data was analyzed using the Partial Least Square Modelling of Structural Equations due to one of the best software for verifying structured data on structural equations modelling (SEM) Smart PLS 3.0 as analytical tools. This study finds that the predictor variables of compatibility, security are significant and critically direct to the users’ intentions to adopt big data technology. The results indicate that the model is suited for studying users’ intentions to adopt Big Data technology in educational organizations. This study can help the Malaysian ministry of education to emphasize the important factors in further developing the use of Big Data technology in organizations. The findings provide important recommendations and implications for BDA technology practitioners and application developers, which could coincide with successful BDA technology deployment. This study provides practitioners with practical recommendations for guidance in incorporating and endorsing BDA activities in their organizations in order to maximize the benefits of revolutionary technology, particularly in government agencies.

Web based and/or existing e-commerce have changed the traditional view of CRM to the eCRM. Recent studies on eCRM has investigated success factors of e-CRM in different contexts and fields. This paper aims to investigate the effect of customer satisfaction, customer trust and commitment, customer profitability on eCRM in Saudi Arab. This study was designed quantitative cross-sectional questionnaire survey. This quantitative study used questionnaire survey design and 319 respondents were taken into account for analysis procedure. Structural Equation Modelling was applied and SmartPLS tools was used to test the hypotheses. Customer satisfaction, customer profitability, trust and commitment have shown to have positive significant effects on eCRM. Empirical evidence of this study confirmed that from the customer point of view customers satisfaction, customer profitability, customer trust and commitment were the strong predictors of successful eCRM.

The study aims to explore supply chain innovation in retail trading of Small and Medium Enterprises (SMEs) in different regions of Abu Dhabi by considering the contribution of supply chain integration, supply chain orientation, and competition capability, along with the mediating role of intelligent information systems. Online questionnaires were used to collect the relevant information from managerial employees at target firms. A total of 357 responses were considered suitable for the purpose of the study. All the constructs were internally and externally estimated to be reliable and valid based on factor loading values, Cronbach’s alpha, composite reliability, average variance extracted, and discriminant validity. Supply chain integration, orientation, and competition capability were considered as the key contributors to supply chain innovation. However, in the case of supply chain orientation towards intelligent information systems and of intelligent information systems towards supply chain innovation, the association was weak. Intelligent information systems had a weak relationship with competition capability, supply chain integration, orientation, and innovation. The results have real-world implications for management and policymakers of retail trade SMEs in Abu Dhabi. Using the results and conclusions, upper management can shape corporate policy to improve supply chain integration, supply chain orientation, and competition capability, resulting in supply chain innovation in retail trading SMEs.

This study examines the extent of adoption of electronic payment methods in Jordanian banks and compares them with traditional alternatives. Focusing on banking customer behavior, we conducted a comprehensive survey among banking customers to uncover insights on trust, perceived benefits, financial literacy, ease of use, security and cost considerations. By analyzing the data collected using the questionnaire study tool, we reveal the motivations behind customers’ electronic payment preferences. By highlighting these factors, our research contributes to the understanding of the evolving payment landscape and provides actionable insights to enhance the adoption of electronic payment in Jordan’s financial system, in line with global technological trends. The results of the study revealed that all study variables had a significant impact (trust, ease of use, perceived security, and cost of service) on the use of electronic payment methods. In addition, gender, number of years of dealing with the bank, and type of electronic payment used more than once by customers in the banking sector did not have a significant impact.

The increasing amount of IoT devices increases the size of network traffic data, causing an increase in the incidence of security breaches in IoT networks. Cybercriminals have developed malware to compromise the security of sensitive data, among other cyber threats. In the presence of inadequate and robust security mechanisms, sensitive data is prone to vulnerability. Hence, protecting data in the IoT environment is becoming a mandatory task. Various approaches have addressed malware detection using network data features. However, there is still room for improvement in developing superior techniques and utilizing more comprehensive datasets. This paper presents a novel lightweight ensemble voting classifier to detect malware traffic by deploying the best possible network data. The merits of the correlation coefficient and Opposition-Based Crow Search Algorithm (OCS) have been leveraged to compute the best possible features. Another advantage of this proposed experiment is its focus on a dataset tailored to malware traffic features. This focus enables highly accurate malware detection. After feature selection using OCS, the proposed malware classifier is trained and validated with both 5-fold and 10-fold cross-validation techniques. The tested results confirm that the presented malware classifier performs best using a minimal feature set, which is highly advantageous for IoT networks due to resource constraints.

Advancements in technology have had a profound impact on the way we learn, teach, and access knowledge. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a dynamic, engaging, and inclusive space for education. One of the promising advancements in the field of artificial intelligence technology is ChatGPT which offers personalized and effective learning experiences by providing students with customized feedback and explanation. The effect of ChatGPT must be compared with the effect of Google at the educational level since both present a source of information and explanation. Thus, this study aims at investigating the differences between these two learning sources to measure their effectiveness from different perspectives. The model proposed in this study was evaluated using the PLS-SEM approach, utilizing data collected from 153 university students in the UAE. The results of this evaluation indicate that the GPT (Generative Pre-trained Transformer) has a significant impact on user acceptance, mediated by information quality, system quality, perceived learning value, and perceived satisfaction. These factors play a crucial role in determining users' acceptance of the GPT. However, it is important to note that some aspects of the model were not supported, suggesting that they do not have a significant predictive effect on the use of ChatGPT. Nonetheless, the findings of this study contribute to the existing literature on AI and environmental sustainability, providing valuable insights for practitioners, policymakers, and AI product developers. These insights can help guide the development and implementation of AI technologies in a way that aligns with users' needs and preferences while considering the larger environmental context.

The purpose of this study is to explore how various online marketing tools, including Content Marketing, Email Marketing, Social Media Marketing, and SEO, affect customers' decisions to purchase Internet services. The study utilized a survey distributed through simple random sampling, with 410 questionnaires given to Zain Telecom customers in Zarqa city. After excluding incomplete questionnaires, 385 were analyzed using multiple regressions, descriptive analysis, and reliability tests. Results showed that all independent variables had a significant impact on customers' purchasing decisions of Internet services, with Email Marketing having the strongest relationship. The study recommends further investment in Email Marketing strategies to improve customer retention and acquisition.

This research aims to analyze the sustainability challenges in small and medium-scale enterprises in Indonesia, focusing on marketing orientation, social media marketing, and three domains of business performance, namely economic, social and environmental aspects. Data from 238 respondents in Pekalongan, Central Java Province, Indonesia, were analyzed using the SEM-PLS method. These findings support six hypotheses revealing the impact of marketing orientation on various aspects of business performance (economic/social/environmental) and the influence of social media marketing on economic, environmental, and social dimensions. The results have significant implications for SMEs in Indonesia. The findings highlight the need for improvement in sustainability practices, such as increasing alignment between marketing orientation and social media marketing strategies.

In the ever-evolving landscape of supply chain management, the quest for efficiency has become paramount. This abstract explores a groundbreaking solution that combines the power of permissioned blockchain technology with innovative data reduction strategies to redefine how supply chains operate. Traditional supply chain systems often grapple with data overload, causing delays, inaccuracies, and operational inefficiencies. However, this abstract presents a promising approach that unleashes efficiency by harnessing the capabilities of a permissioned blockchain. Through data reduction techniques tailored to the needs of supply chain management, this approach streamlines the flow of information while maintaining security and trust among participants. This paper seeks into the technical foundations of permissioned blockchains, highlighting their suitability for supply applications where confidentiality and controlled access are imperative. Furthermore, it examines various data reduction methodologies, emphasizing their role in minimizing redundant data, optimizing communication, and enabling real-time decision-making. The impact of this innovative approach on supply chain stakeholders is profound. It reduces data related bottlenecks, enhances transparencies, and fosters collaboration among participants. Additionally, it provides a scalable framework adaptable to diverse supply chain ecosystems. As supply chain efficiency becomes increasingly important in our interconnected world, this permissioned blockchain-driven data reduction strategy offers a compelling vision for the future. It promises to unlock a new era of streamlined operations, cost savings, and improved customer satisfaction, ultimately shaping the next generation of supply chain management.

Social Networking Sites (SNS) play an important role in human psychological well-being. This research explores the effects of perceived overload on the passive usage intention of SNS among Malaysian users through SNS fatigue and anxiety by employing a Cognition-Affect-Conation (C-A-C) model. Cross-sectional survey research was carried out and the research acquired responses from 383 SNS users. Data was analyzed through PLS-SEM. The outcomes noted that perceived cognitive overload, perceived information overload, SNS fatigue, and anxiety are positive antecedents of SNS passive usage intention. The research brings valuable insights into the formation of passive usage intention of SNS and provides implications for service providers and users.