The growth of financial literacy is significant nowadays. Because of this, more people are becoming increasingly responsible in their financial planning, investments, and living expenses. In developing new technology, it is necessary to know the technological acceptance of the prospective users of the technology itself. This study aims to identify the primary factors influencing the technology acceptance levels of lower-middle socio-economic users for a digital financial literacy application. The proposed model in this research was developed based on UTAUT, TAM, and Usability model, and it consists of six primary constructs: (1) Performance Expectancy; (2) Effort Expectancy; (3) Social Influence; (4) Resources and Cost; (5) Satisfaction; (6) Behavior Intention. All the hypotheses used in this study were statistically measured using SmartPLS tools. This study found that because many lower-middle socio-economic users lack sufficient understanding of technology to properly utilize it, that a digital platform is not the right tool to teach them financial literacy.

The challenge for enterprises to enter the market in the bottom of the pyramid (BOP) segment is to revise their supply chain design. This article compiles the cases that demonstrate how enterprises can successfully enter BOP markets. Factors are extracted and analyzed according to the stages of the value chain and the supply chain. Furthermore, the Importance—Performance Analysis (IPA) is used to explore the gap and the opportunities for improvement. Also, the multinational enterprises (MNEs) in Taiwan and local manufacturers in Indonesia are compared for their suitability to the different business strategies. Results of these analyses could serve as a gap analysis tool for decision-making and business strategies entering the BOP market.

Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country.

Factors influencing blockchain adoption intention in Philippine small and medium enterprises

Checkout service is crucial in ensuring customer satisfaction and enhancing retail efficiency. In recent years, self-checkout has become increasingly popular in modern retail operations. However, despite its growing adoption, there is limited quantitative evidence on its effectiveness in reducing operational costs and improving overall efficiency. In this study, a discrete-event simulation model based on real-world scenarios of a retail store in Taoyuan City, Taiwan, was developed using ARENA (version 16) simulation software. Four checkout scenarios were modeled and compared through statistical tests to evaluate checkout performance. The results showed that the proposed self-checkout model with improved service time enhanced operational efficiency and contributed to reducing operational costs. These findings suggest that retail managers should implement strategic measures to optimize self-checkout operations to achieve efficient and cost-effective store performance. Finally, practical and managerial implications are discussed at the end of the study.