Purpose: This study aims to identify the primary determinants of consumer behavior influencing customer satisfaction in the context of online mobile application (App) purchases of perishable products. Utilizing the well-established SERVQUAL (Service Quality) model, which has been extensively studied in various service-oriented settings, the research seeks to determine the factors with the greatest impact on customer satisfaction during online transactions of perishable products. Design: The investigation focuses on analyzing the five core dimensions of the SERVQUAL model: tangibles, reliability, responsiveness, assurance, and empathy. The study employs a survey methodology administered through Google Forms, targeting the population residing in the Klang Valley of Malaysia. A total of 400 samples were successfully collected using a snowball sampling technique. Methodology: The study employs the SERVQUAL model as the theoretical framework to examine the dimensions of tangibles, reliability, responsiveness, assurance, and empathy. The survey, conducted through Google Forms, targeted the population in the Klang Valley of Malaysia, with a sample size of 400 collected through snowball sampling. Findings: The study’s outcomes reveal the robust predictive capability of the overarching SERVQUAL model in the realm of online perishable product procurement. Notably, the assurance dimension emerges as the most influential factor, emphasizing its pivotal role in shaping and defining customer satisfaction for online retailers of perishable goods in the Malaysian market. Novelty: This research contributes to the understanding of consumer behavior in online perishable product purchases, by identifying determinants of consumer behavior; the study promotes sustainable production and responsible consumption within the perishable products category, offering insights beneficial for online retailers in the Malaysian market. This study aligns with United Nations sustainable development goals especially industry innovation, food security and responsible consumption.

This comprehensive review examines recent innovations in green technology and their impact on environmental sustainability. The study analyzes advancements in renewable energy, sustainable transportation, waste management, and green building practices. To accomplish the specific objectives of the current study, the exploration was conducted using the PRISMA guidelines in major academic databases, such as Web of Science, Scopus, IEEE Xplore, and ScienceDirect. Through a systematic literature review with a research influence mapping technique, we identified key trends, challenges, and future directions in green technology. Our aggregate findings suggest that while significant progress has been made in reducing environmental impact, barriers such as high initial costs and technological limitations persist. Hence, for the well-being of societal communities, green technology innovations and practices should be adopted more widely. By investing in sustainable practices, communities can reduce environmental degradation, improve public health, and create resilient infrastructures that support both ecological and economic stability. Green technologies, such as renewable energy sources, eco-friendly construction, efficient waste management systems, and sustainable agriculture, not only mitigate pollution but also lower greenhouse gas emissions, thereby combating climate change. Finally, the paper concludes with recommendations for policymakers and industry leaders to foster the widespread adoption of green technologies.

The proposed research work encompasses implications for infrastructure particularly the cybersecurity as an essential in soft infrastructure, and policy making particularly on secure access management of infrastructure governance. In this study, we introduce a novel parameter focusing on the timestamp duration of password entry, enhancing the algorithm titled EPSBalgorithmv01 with seven parameters. The proposed parameter incorporates an analysis of the historical time spent by users entering their passwords, employing ARIMA for processing. To assess the efficacy of the updated algorithm, we developed a simulator and employed a multi-experimental approach. The evaluation utilized a test dataset comprising 617 authentic records from 111 individuals within a selected company spanning from 2017 to 2022. Our findings reveal significant advancements in EPSBalgorithmv01 compared to its predecessor namely EPSBalgorithmv00. While EPSBalgorithmv00 struggled with a recognition rate of 28.00% and a precision of 71.171, EPSBalgorithmv01 exhibited a recognition rate of 17% with a precision of 82.882%. Despite a decrease in recognition rate, EPSBalgorithmv01 demonstrates a notable improvement of approximately 14% over EPSBalgorithmv00.

Green Human Resource Management (HRM) is considered an emerging field of management that evaluates and ensures green performance and outcomes in organizations. In today’s dynamic business environment, work-life balance has become one of the key issues faced by many employees all over the world. Maintaining work-life balance is an issue increasingly recognized as of strategic importance to the organization and significance to employees. In doing so, the present study introduced independent and dependent variables to explain the underlying mechanisms of green HRM and work-life balance and its impact on employee performance. A total of 90 employees of the calibration services company have completed a set of questionnaires through Google Forms to provide data for the analysis. This study is using census method as one of the best probability sampling techniques to be used it’s a systematic method that collects and records the data about the members of the population and is suitable when the case-intensive study is required or the area is limited. This study has adopted the quantitative method in this research as the method allows the researcher to focus on the research. The data were analyzed through SPSS which facilitates descriptive statistics, correlation, and multiple regressions. Multiple regression analysis was used to test the hypotheses in this research. The findings showed that green HRM and work-life balance were the significant variables influencing employee performance in the study. In addition, the significance of the study included providing new knowledge from the theoretical perspective, obtaining a better understanding of the importance of green HRM and work-life balance from the perspective of employee performance, and contributing to the efforts made by the government to improve the probability of green culture in organizational and balancing professional life and family life employment of employees through policies from the perspective of the government. Lastly, recommendations for employers, employees, government, and future research are made to improve employee performance.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

This study employed a deductive approach to examine external HRM factors influencing job satisfaction in the post-pandemic hybrid work environment. Explores the intermediary functions of age, gender, and work experience in this particular environment. The data-gathering procedure consisted of conducting semi-structured interviews with carefully chosen 50 managers representing various sectors, industries, organizations, and professions. The applied approach was adopted to allow a systematic and unbiased investigation of the mediating variables. The study used SPSS 25 and Smart PLS 4 to analyze the model, enhancing understanding of HRM challenges in a constantly evolving workplace. The findings offer valuable insights for HR experts and businesses, highlighting the value of comprehending what methods HRM components influence job satisfaction to optimize employee well-being and productivity. The study provides applied recommendations designed for enhancing employee contentment in the AI-evolving professional atmosphere, shedding light on the importance of supportive leadership strategies, particularly during AI-triggered downsizing. Additionally, we welcome a new era to push forward in integrating and managing AI tools and technologies to automate decision-making and data processing. Results propose that Exogenous influences of human resource management (HRM) influence manager job satisfaction considerably. Specifically, downsizing caused by AI was found to have negative consequences, whereas diversity and restructuring have favorable effects. Gender was recognized as a crucial factor that influences outcomes, then age and years of experience have the most visible effect.