The COVID-19 pandemic has significantly restricted household resilience, particularly in developing countries. The study investigates the correlation between livelihood capital and household resilience amid uncertainties due to the COVID-19 pandemic, specifically in Bekasi Regency, West Java Province, Indonesia. Livelihood capital encompasses social, human, natural, physical, and financial, which are crucial in shaping household resilience. This study used the SEM-PLS method and utilized a survey of 120 respondents (household heads) from four villages in two districts (Muaragembong and South Tambun) in Bekasi Regency to identify critical factors that either enhance or impede rural household resilience during and after the pandemic. Findings reveal that households possessing human capital, financial capital, and empowerment are more adept at navigating socioeconomic difficulties during and after the pandemic. However, this research stated that trust and social networks enhance household resilience during the pandemic, whereas social norms are crucial for rebuilding household resilience in the post-pandemic phase. The finding revealed that social cohesion adversely affected household resilience during and after the pandemic, while trust diminished household resilience in the post-pandemic COVID-19 phase. These findings offer insight to policymakers, scholars, and other stakeholders aiming to foster household resilience during and in recovery efforts after the pandemic.

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.

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.

The objective of this research paper is to investigate potential avenues for value creation in the refined sugar market for domestic use, a market currently facing a critical juncture. The growing concerns about the health impacts of sugar have resulted in a notable decline in demand. Furthermore, changes in European Union regulations have introduced additional operators into the Spanish market, increasing competition and amplifying the need for innovation. This study examines how brands can respond to these challenges by enhancing their value proposition through market segmentation, targeted marketing strategies, and adaptive packaging solutions. To achieve this objective, we have conducted market research, which involved an in-depth interview, and a questionnaire distributed to 402 individuals responsible for household purchases. The findings suggest potential approaches for addressing the needs of consumers with a focus on health and well-being, while simultaneously enhancing the durability of products, thus facilitating greater brand differentiation. Furthermore, the study underscores the pivotal role of public policies and regulatory frameworks in influencing consumer behavior and market dynamics. Policies promoting sugar alternatives, labelling requirements, and packaging innovations have been demonstrated to impact brand strategies and consumer preferences. By aligning with these policy-driven shifts, companies can enhance their positioning in a mature and competitive market. This research contributes to the existing literature on brand value in commodity markets by integrating insights into the impact of regulation and consumer segmentation. Our recommendations emphasize the importance of marketing strategies that are informed by an understanding of the policy context, which not only enhances brand equity but also promotes sustainable growth in the retail sugar industry.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

Usually in the study of limit problems, will encounter more complex problems, in this paper, we discuss how to use the concept of equivalent infinitesimal better limit operation. At the same time, in the process of research, we re-explore the proof of Taylor's formula, and find that some functions have a similar expansion form to Taylor's formula, that is, 'fractional expansion'. It is also found that after the linear combination of Taylor expansion and fractional expansion, the obtained expansion is more accurate, which helps us to have a better understanding of the approximation of function expansion.