The goodness of machine-part groups in cellular manufacturing systems is evaluated by different measures available in the literature. The commonly known grouping efficiency measures will be discussed in this paper. None of these measures has the ability to evaluate the efficiency of block -diagonal system and sub-system at the same time. Moreover, sparsity of individual cells was not taken into consideration in these measures. In this paper, a new grouping measure called Comprehensive Grouping Efficacy (CGE) is proposed to overcome the drawbacks of these measures. CGE is tested against some problems from the literature and the results demonstrate the ability of this measure to be used as comprehensive grouping measure since four of the well-known measures are included in the CGE formula. The superiority of CGE is that it can be used to find the efficiency of block-diagonal form, the efficiency of sub-system, sparsity index and efficacy index at the same time, which will give the designer the opportunity to control the cell size. Without knowing the efficiency of sub-systems (individual cells), the system designer will not be able to control the cell size.

The primary objective of this study was to examine the antecedents of digital transformation (DT) within multinational corporations (MNCs) in Malaysia, from the perspectives of corporate managers. Amidst limited research on DT within the MNC context, this paper examines the key drivers of DT in Malaysian MNCs. A quantitative method using non-probability sampling was used to gather the required data via the distribution of questionnaires among the MNCs’ managers. To evaluate the underlying theoretical model based on the collected data, we chose SmartPLS as the preferred method. Findings revealed that business value, digital leadership, inter-functional coordination and decision-making quality were significant drivers of DT, while DT exerted a positive influence on business performance. However, collaborative innovation did not have a significant relationship with digital transformation adoption in MNCs. The findings offer novel insights for both academics and international corporate managers, enhancing their understanding of the drivers behind DT adoption from the perspective of managers within MNCs in Malaysia.

This research aims to examine how data privacy concerns influence DOI in Digital Marketing and investigates how artificial intelligence trust mechanism integration modulates that effect. Primary data was collected by accessing the structured questionnaire targeting ZAIN Telecom employees as the chosen study site. The PLS-SEM technique was used in this research to analyze data privacy, digital marketing effectiveness, and AI-driven trust constructs. According to this study, digital marketing processes significantly focus on data privacy and, ultimately, AI-driven trust. Further, powerful AI trust systems will be required to reinforce data privacy and systematically configure and drive digital marketing enterprises. Thus, such systems could allow firms a sustainable competitive advantage in this new Battle for data security age. The findings discovered that data privacy concerns significantly impede AI-driven trust, diminishing digital marketing effectiveness. Therefore, they contribute to the literature by offering empirical support for AI-powered trust mediating between factors within a model and offering practical implications and extensions of the theoretical models. This literature helps industry practitioners and policymakers build trust in AI interventions to alleviate data privacy risks and improve support for digital marketing strategies.

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.

Potential international students use social media platforms to acquire important information about schools and their brand reputations while making international higher education decisions. By means of a cross-sectional data from 384 international students at three universities in North Cyprus, the study aims to explore the relationship between social media engagement, information credibility and enrolment intention. Furthermore, if information credibility works as a mediator between social media engagement and enrollment intention, and the moderating role of student type. The study makes several contributions to the literature review and its recommendation helps top management at universities and policy makers in North Cyprus to pay attention to the role of social media in recruiting international students. The findings of this study indicate that credibility fully mediates the relationship between social media engagement and enrollment intention, but student type doesn’t moderate the relationship between information credibility and enrollment intention. The limitations and future studies are discussed in this study.

This study investigates the mediating role of competitiveness in the relationship between FinTech adoption and banking performance in the Middle Eastern region. A quantitative research design is employed, utilizing survey data from banking professionals across multiple countries. The data is analyzed using PLS-SEM modelling. The results show a positive and statistically significant impact of FinTech integration on the competitiveness of financial institutions and the performance of banks. On the other hand, the mediation of competitiveness is involved in the process of FinTech adoption and bank efficiency, suggesting that banking institutions that can utilize FinTech advantageously have greater chances of translating the benefits of FinTech adoption into better performance. This study supports the literature by providing a practical example of the use of FinTech as a factor for competitiveness and improving the performance of banks in the Middle East. These findings have huge managerial and practical implications and can help large banks gain competitive advantage and effectively integrate FinTech platforms to achieve real improvements. The value of this research is that it fills the gap in the existing literature, i.e. the role of competition as a mediating factor. By combining studies on competitive strategy approaches with those on technological innovation theory, this study combines a complete worldview related to the performance of banks in the digital age.

With flexible learning, students are actively engaged in their own education and are held to high standards of performance. Online academic courses make it easier for students to receive personalized education because they provide students with more flexibility to concentrate on what is most important to them and give them greater control over their own education. This study’s objective was to investigate whether there is a correlation between how well students succeed in online classes and the extent to which they make use of the schedule and the geographical and resource flexibility offered by such programmes. This article uses a developing approach for predicting and classifying the flexibility in online learning of students who are at risk of failing due to academic and demographic variables. The K-nearest neighbours (KNN) method, the random forest (RF) method, and the logistic regression method were used to categorise the students participating in flexible online learning. The information for the dataset came from Kaggle, and it was gathered for use in testing machine learning. The dataset had a total of 1,875 instances representing 11 different features. Also, accuracy, precision, sensitivity and f-score metrics were applied to evaluate the system. The results show that the RF algorithm has a high accuracy percentage of 85%. The empirical findings demonstrate that students formed distinct patterns of learning time, location and access to knowledge. This suggests that flexibility was used to a significant degree. Patterns in learning time and the availability of learning materials were shown to have a substantial relationship with the accomplishments of the students. Understanding flexibility use habits may help adapt lessons and boost collaboration among similar students.

This study conducted a thorough analysis of a dataset from a video game to reveal the relationship between game content, interactive features, and classification ratings. The project attempted to convert raw data into meaningful insights by employing Python for data processing and machine learning. The analysis uncovered strong relationships between content descriptors and ESRB ratings, indicating a market that strategically customizes game material to different demographic groupings. Moreover, the inclusion of interactive features such as 'Users Interact' and 'In-Game Purchases' suggests a transition towards gaming experiences that are more immersive and financially interactive. The highlight of this project was the creation of a web-based tool that can accurately forecast game classifications, utilizing advanced models such as XGBoost. The application offers developers and rating organizations a vital tool to achieve accuracy in game classification. The study's conclusions provide a detailed comprehension of console market dynamics, clarifying the present patterns and possible future developments in the gaming industry.

Regulators and recent academics are now paying attention to digital marketing because it has become a complete marketing resource that might propel a firm toward success. As a result, the current study explores the impact of several digital advertising channels, including mobile, e-mail, and digital retargeting, on customer purchasing behavior in the Information Technology (IT) business in the UAE. The current research also examines the moderating effects of marketing tactics at the intersection of mobile advertising, digital retargeting, e-mail marketing, and customer purchasing choices in the IT sector of the UAE. To collect data for this study quantitatively, questionnaires were employed to solicit information from respondents, which was then analyzed using smart-PLS. The findings showed that digital retargeting, mobile, and e-mail marketing positively affect customer purchasing behavior in the UAE's IT sector. The results also demonstrated that marketing tactics drastically reduced the associations between mobile, e-mail, and consumer purchasing decisions in the UAE's IT sector. This study gave policymakers recommendations on better focusing on digital advertising, which could boost the organization's success.

This study provides an analytical view of the correlation between several factors that influence the credibility of information available through various sources on the Internet. The most critical factors include information quality, source credibility, argument strength, message credibility, and average credibility. Additionally, the study explores the impact of gender and years of experience as demographic variables on the nature and size of these relationships. The study relied on a critical review of previous related literature. In addition, it adopted an analytical approach using a sample of 300 Internet users through a questionnaire designed based on the study hypotheses. Most of these relationships were found to be at an average level, except for the relationship between source credibility and information credibility. No statistical indicators were observed. Consequently, the researchers acknowledge the need for caution when generalizing these results to society. The study also found that the gender of the recipient and the number of years of experience did not necessarily play a mediating role in the relationship between the tested factors and the credibility of information received via the Internet.