This research explores the impact of social capital on the adoption of digital applications within Micro, Small, and Medium Enterprises (MSMEs). In the current competitive environment, embracing digitalization is essential for improving the competitiveness of MSMEs; however, challenges such as weak social networks can impede technology uptake. Utilizing a quantitative approach, the study distributed questionnaires to 160 MSME participants, with data analysed through path analysis using SmartPLS. The findings indicate that social capital—which includes relationships, networks, and trust among business stakeholders—plays a critical role in facilitating access to and adoption of digital applications. Results suggest that MSMEs with strong social networks are more adept at integrating digital technologies, fostering innovation, and improving overall business performance. The study recommends initiatives to enhance collaboration and strengthen social networks among MSMEs to support their digitalization efforts. Ultimately, this research deepens the understanding of social capital's influence on MSME digitalization and offers practical strategies for stakeholders to increase the utilization of digital applications in this sector.

In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancemenThe purpose of this study is to: (1) evaluate the technological skills of the final-year undergraduate students, (2) how such abilities influence their likelihood of getting employed and (3) student opinions on whether a computer lab-based specialization in software training can boost employability. This study is a survey-based methodology. The sample size encompasses 140 final year students in the College of Business Administration at Prince Sattam bin Abdulaziz University during the academic year 2023-2024. Descriptive statistical analyses indicate that most students believe they have sufficient technical skills but not enough for securing better jobs. Moreover, it is clear among them that expertise in specialized software packages enhances career prospects significantly. The research results also show a huge gap between technological competencies learners have now and what employers demand currently. In response, this study suggests that PSAU should establish software laboratories in their colleges for specialized training on software as required by the job market and workplace. The Vice Rectorate for Academic and Educational Affairs launches a program called “Graduate and Professional Skills Development Program (PSAU-GPSDP)”, which emphasizes student employability as it develops, implements, and evaluates mechanisms to enhance students’ chances of getting jobs upon graduation. The results of this study are in line with SDG No. 8 (Decent Work and Economic Growth), SDG No. 4 (Quality Education), and Saudi Vision 2030. Therefore, this study has practical implications for decision-makers at the Ministry of Education and university levels, university professors, researchers on how employability skills of students could be enhanced in the higher education institutions.

In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. This study presents a sophisticated simulation for NIDS tailored to intelligent vehicle environments, utilizing the extensive capabilities of Scapy—a robust network manipulation tool—to generate a highly accurate dataset of network traffic reflective of real-world scenarios. We created a diverse dataset involving 100,000 network flows, covering a wide array of benign, malicious, and anomalous traffic patterns, to thoroughly evaluate the detection capabilities of our proposed system. This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. These metrics showcase the system's enhanced capability to precisely classify various network flows, emphasizing the importance of realistic synthetic data in boosting the training and accuracy of NIDS in intelligent vehicles. The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.

The growth in Information and Communication Technology has brought dynamic changes to financial payment systems through smart devices, and mobile payment systems are significant among different smart payment systems. Further, the Technology Acceptance Model's components affect the smart payment user's behavior. Moreover, students are a substantial part of society and are more inclined to use mobile payments. Therefore, the present research examines the influence of technology adoption factors on the perception of students using mobile payments. The study adopted TAM components as influencing factors, such as Perceived usefulness, perceived ease of use, Perceived cost, and Perceived trust. The data was collected from 100 respondents consisting of male and female students. The study employed simple regression analysis to report the results. The results show that the perception of students towards the use of m-payment is strong, with a mean of 1.52. The result is similar to the explanatory variables, ranging from 1.77 to 1.97. The study found that the technology adoption factors, such as Perceived usefulness, perceived ease of use, and Perceived cost, positively influenced the students' perception of using mobile payments with p-values ranging from 0.001 to 0.049. The results of Perceived trust were positive but insignificant. Therefore, the present research observed a significant influence of technology adoption factors on the perception of students using mobile payment.

The purpose of the study was to examine the effectiveness of the Mobile Food Ordering Application (MFOA) in a collectivist country like Pakistan. Data was gathered using an online survey-based approach from 354 MFOA users and was analyzed using the structural equation modeling technique through Smart PLS 3.0. The results show that consumers’ online reviews strongly influence customer satisfaction and continued intention. Similarly, price value and online tracking of food services are strongly associated with customer satisfaction. Consumer habits and facilitation conditions are significantly associated with consumer continued intention. Habit is also found to partially mediate consumer satisfaction and continued intention. The study did not find any support for performance expectancy, effort expectancy, social influence, price value, hedonic motivation, or online review with continued intention. Similarly, performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation were not associated with consumer satisfaction. The present work is the first of its kind that has empirically examined the effectiveness of MFOAs in Pakistan. It lays down useful practical implications for practitioners, policymakers, and academia.

Mobile learning application now is considered a powerful application for learning and was adopted in universities in the period of Covid-19. After Covid-19 pandemic, university students have been allowed to use mobile learning systems, it is needed to ensure students’ intention to continuously use mobile learning for their learning activities or not. Thus, the purpose of this paper is to understand the main determinants that encourage the continuous use of mobile learning. To achieve that, we used the UTAUT-2 model to predict the main determinants of mobile learning acceptance. In our study, a quantitative technique was employed to collect the data. A hybrid approach SEM-ANN was applied to validate the research model. The findings indicated that performance expectancy and effort expectancy had a strong effect on students' attitudes towards mobile learning platforms. In addition, the results showed that performance expectancy and effort expectancy have a significant impact on students' continuous intention to use mobile learning platforms after Covid-19. In addition, hedonic motivation and habit had a positive effect on both students' attitudes and continuous intention to use mobile learning platforms. Moreover, Social influence factor and facilitating conditions had a significant effect on students' continuous intention to use mobile learning platforms after Covid-19.

The rapid digitalization in recent years has opened up many technological possibilities, gradually transforming various sectors and society as a whole. This digital shift has enabled advancements in a number of fields, leading to improved resource efficiency, systems and processes. The Internet of Things (IoT) refers to a system of interconnected devices that share information that exchange information with one another via the internet. IoT devices are now everywhere, found in applications ranging from unmanned aerial vehicles to smart home environments, from the Industrial Internet of Things to the Internet of Medical Things. The core concept of IoT revolves around establishing a seamless and intelligent communication ecosystem, facilitating interactions between devices over the internet. This is anticipated to create new opportunities for enhancing services in various societal sectors, such as transportation, farming and smart cities. However, IoT-based networks face limitations and challenges that hinder the realization of their full potential. This paper outlines these challenges and proposes solutions, emphasizing the importance of collaboration and innovation. The paper also anticipates future trends in IoT, particularly the integration of 5G connectivity, cloud computing and AI, and identifies areas for future research to address current challenges and explore new applications.

Eco-friendly business practices range from energy efficiency to sustainable management; therefore, the implementation of these practices not only has the potential to mitigate environmental impacts, but also to improve the long-term competitiveness and sustainability of companies. In this study we analyze eco-friendly business practices and their impact on environmental sustainability in companies in a region of the Peruvian Amazon. A quantitative, non-experimental approach was considered, with an explanatory design; for which, a sample size of 200 companies was considered, which were randomly selected, to which two instruments were applied, the first with 7 indicators and the second with 3, valid, with an alpha of 0.720 and 0.670 respectively, assessing structural equation modeling. The results show that eco-friendly business practices are oriented towards sustainability (C.R. = 19.280), as well as preventive and eco-efficient practices (C.R. = 5.023, C.R.= 14.185); these findings can serve as a basis for the creation or improvement of public policies that promote eco-friendly business practices, encouraging companies to adopt measures that favor environmental sustainability. Therefore, eco-friendly business practices have a positive impact on environmental sustainability in companies in a region of the Peruvian Amazon; in addition, preventive practices and eco-efficient practices have a positive impact on environmental sustainability in companies in a region of the Peruvian Amazon.

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 paper presents RIFD-DLT, an advanced near-lossless image compression algorithm that combines Delta Encoding with the original rounding the intensity followed by division (RIFD) method. The RIFD method first minimizes the image intensities, which makes the next compression stages more efficient. Subsequently, Delta Encoding subtracts neighboring rows in each of the image's three-color matrices, using the proximity of pixel values in adjacent rows to further reduce the image intensity. Extensive investigations show that RIFD-DLT outperforms the state-of-the-art algorithms and benchmarks with respect to compression ratio and processing time. More specifically, RIFD-DLT compresses data from 11520 KBs to 2131 KBs with an 81.93% reduction and a 43.7% improvement over original RIFD-Huffman when compressing Kodak Image set. When comparing the RIFD-DLT with LICA algorithm, the total file size is reduced by 71.2%, representing a 10.73% improvement for RIFD-DLT. Also, RIFD-DLT shows notable speed gains over RIFD-Huffman, requiring only 34.62 seconds to compress and decompress all images from three datasets (Waterloo, Kodak and EPFL), as compared to 50.99 seconds for RIFD-Huffman. As for the image quality, the proposed algorithm achieved an average PSNR values of 58.51 dB, 51.3 dB, and 52.22 dB for the EPFL, Kodak, and Waterloo image sets, respectively, demonstrate the excellent image quality that persists after decompression, with a minimal distortion that is imperceptible to the human visual system and identically to the RIFD-Huffman PSNR. These findings show that, while preserving excellent image quality, RIFD-DLT provides an incredibly efficient and fast method of image compression.