Chung-Hua Institution for Economic Research

Big Data Analytics and Artificial Intelligence (BDA-AI) integration revolutionizes Human-Machine Interaction (HMI) in E-commerce, enhancing user experience, operational efficiency, and decision-making. This study develops a conceptual framework examining technological and environmental factors influencing AI adoption through Flow Experience and Technology Acceptance Model (TAM) integration. It explores how Perceived Vividness (PV), Perceived Enjoyment (PE), and Feedback contribute to Flow Experience, influencing Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Satisfaction, and Continuous Intention. Using systematic literature review, this research identifies theoretical gaps and proposes a model for empirical validation. Findings provide insights for E-commerce developers, businesses, and policymakers to optimize AI-driven interfaces, enhance user engagement, and improve customer retention in Taiwan's digital commerce sector.

Financial studies on the herding effect have been very popular for decades, as detecting herding behavior helps to explain price deviations and market inefficiencies. However, studying the herding effect as a single influencing factor is believed to be insufficient to explain the changes in investment behavior, as the herding effect itself may be caused by other influencing factors. In other words, the issue must be studied alongside other factors. In this study, we adopt the quantile regression model to comprehensively understand the herding effect’s influence on household investment in China, and the empirical results indicate that herding behavior leads to different investment outcomes for households in different scenarios. In this analysis, we consider a variety of household characteristics, such as income level and risk tolerance, to provide a nuanced understanding of investment behavior. Additionally, in this study, we explore the interaction between herding behavior and macroeconomic variables. Nevertheless, the results suggest that, if herding behavior can be reduced by the head of the household, profitability can be increased, or at the very least, losses can be reduced.

This study evaluates the circular economy performance of global chemical companies from 2013 to 2022, focusing on production efficiency and circular efficiency. Dynamic network Data Envelopment Analysis model is employed to assess the overall, production and circular performance of firms. Additionally, management decision matrix is presented, offering firms a clearer understanding of their efficiency levels and a detailed projection analysis on the optimization adjustments needed for both resource inputs and outputs. The findings reveal varying levels of overall efficiency among the observed firms. While some companies have managed to maintain or improve their efficiency over the study period, others exhibit significant inefficiencies. Opportunities for operational streamlining and resource optimization are identified, particularly in reducing resource consumption without compromising production capacity. The analysis of circular efficiency further highlights substantial potential for improving sustainability practices, particularly through enhanced waste reuse and a reduction in environmental impact. This study provides practical implication of circular economy performance in the chemical industry, offering a new way to measure and improve resource efficiency and sustainability practices.

This study evaluates the economic benefits of meteorological information services (MISs) provided by the Central Weather Administration (CWA) in Taiwan, specifically for Taiwan’s capture fishery industry. Using the contingent valuation method (CVM) and conducting in-person interviews, we collected questionnaires from capture fishery practitioners nationwide to gather their subjective evaluations of the meteorological information services provided by the CWA. Based on these evaluations, we further investigated the respondents’ willingness to pay (WTP) for the CWA’s meteorological information services. An empirical analysis of the bid function was conducted to identify the key factors influencing the respondents’ bidding behavior. The empirical findings indicate that the primary factors affecting bids include subjective perceptions (such as forecast accuracy ratings and the ratings of the impacts on fishing production), working location, fishing vessel tonnage, and fishing methods. The median WTP for Taiwanese capture fishery practitioners is Taiwan dollar (TWD) 2111.12 per person per year. Based on the number of capture fishery practitioners in Taiwan in 2019, the total annual economic value of applying MIS in coastal and offshore fisheries is estimated to be between TWD 376 million and TWD 496 million per year.

Abstract Since its emergence in 2020, COVID‐19 has profoundly influenced the Taiwan economy and employment landscape, leading to differential impacts across sectors. In response to this unprecedented crisis, the Taiwanese government promptly enacted the “Special Regulations on the Prevention and Rescue of Severe Specific Infectious Pneumonia” on February 25, 2020, which came into force on March 13. This study examines the pandemic's effects through various stages, with a particular focus on the service industry, the sector most affected owing to diminished domestic demand. Using a computable general equilibrium model by integrating monthly changes, the study assess the effectiveness of the Taiwan government's interventions in mitigating job losses and stabilising the economy. Our findings indicate that without the government's relief and revitalisation measures, unemployment rates would have surged significantly. The study underscores the critical role of policy interventions in mitigating the economic impacts of the pandemic.