Blockchain technology has increasingly attracted the attention of the financial service sector, customers, and investors because of its distinctive characteristics, such as transparency, security, reliability, and traceability. The paper is based on a Systematic Literature Review (SLR). The study comprehended the literature and the theories. It deployed the technology-organization-environment (TOE) model to consider technological, organizational, and environmental factors as antecedents of blockchain adoption intention. The paper contributes to blockchain literature by providing new insights into the factors that affect the intention to adopt blockchain technology. A theoretical model incorporates antecedents of blockchain adoption intention to direct an agenda for further investigations. Researchers can use the model proposed in this study to test the antecedents of blockchain adoption intention empirically.

This study aims to examine the mediating role of institutional trust (IT) between perceived corruption and subjective well-being (SWB) using data from 1566 households in a developing country. It deploys ordinary least square (OLS) and an ordered logit model within the generalized structural equation model. Results show that individuals who perceived no corruption in a country report more IT and higher levels of SWB. Furthermore, the direct effects of good governance, perceived IT, and the absence of corruption on SWB is also positive. Moreover, satisfaction with hospital services also improves happiness and life satisfaction levels. This study improves and validates how corruption is assessed to support future measures that reduce its harmful effects. Moreover, the masses must have widespread awareness about the critical nature of corruption and IT relative to well-being. This study also highlights the need to develop strong institutions to improve trust and minimize corruption.

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.

In this research, we employed multivariate statistical methods to investigate the perspectives of small and medium-sized enterprises (SMEs) concerning the Extended Producer Responsibility (EPR) regulation and their apprehensions related to EPR compliance. The EPR regulation, which places the responsibility of waste management on producers, has significant financial and administrative implications, particularly for SMEs. A sample of 114 businesses was randomly selected, and the collected data underwent comprehensive analysis. Our findings highlight that a notable proportion of businesses (44.7%) possess knowledge of the EPR regulation’s provisions, whereas only a marginal fraction (1.8%) lacks sufficient familiarity. We also explored the interplay between opinions on the EPR regulation and concerns regarding its financial and administrative implications. Our results establish a significant correlation between EPR regulation opinions and concerns, with adverse opinions prominently influencing concerns, particularly regarding financial burdens and administrative workloads. These outcomes, derived from the application of multivariate statistical techniques, provide valuable insights for enhancing the synergy between environmental regulations and business practices. EPR regulation significantly affects SMEs in terms of financial, administrative, and legal obligations, thus our study highlights that policymakers may need to consider additional support mechanisms to alleviate the regulatory burden on SMEs, fostering a more effective and sustainable implementation of the EPR regulation.

Over the course of many years, the Mekong Delta region has experienced relatively low and inconsistent levels of business attraction and low quality of the enterprise environment compared to other regions in Vietnam. To delve into whether this discrepancy reflects a negative perception of the business environment in the area, this study employs a dataset comprising the aggregate Provincial Competitiveness Index (PCI) and nine of its component scores, alongside other significant control variables, to analyze business attraction trends spanning from 2010 to 2020. It based on the modeling analysis for the panel data that includes Pool-OLS, FEM and REM models. The findings indicate that PCI serves as an important indicator influencing the quality of the business environment and plays a role in determining the location preferences of businesses. It is observed that public investment has exerted an impact on enticing new businesses to the region throughout this period. These research outcomes carry several policy implications, suggesting that public policy interventions can positively shape the business environment, consequently bolstering the appeal of business investments in the region.

The rising trend of tourists selecting agrotourism as a tourist destination has become an intriguing study issue. Seremban is a well-known tourist attraction that is popular among visitors. As a result, Seremban has been selected as the study site. However, river pollution may have an influence on Seremban’s natural environment and agrotourism potential. Furthermore, inadequate infrastructure, such as unauthorized parking, exacerbated the inhabitants’ problems. A growing number of young people leave Seremban to pursue employment or further education in other cities, with no desire to work as farmers. The labor scarcity has also made it difficult for farmers to grow their farms. Consequently, the study aims to examine how factors such as the natural environment, tourist infrastructure, perceived social advantages, and perceived barriers influence the attitudes of Seremban residents towards agrotourism, with a focus on its potential for driving economic growth. This study adopts quantitative research methods, employing descriptive and causal research designs. Primary data collection is conducted through questionnaires, supplemented by secondary data. Non-probability quota sampling is utilized due to the absence of a specific sampling frame, with a sample size of 385 respondents determined using G*Power software. Constructs are developed based on previous research, and the questionnaire comprises Likert-scale items to gauge attitudes and perceptions. A pilot study assesses the instrument’s reliability. Data analysis is performed using SPSS software, encompassing multiple linear regression and Pearson correlation analyses in addition to descriptive statistics. The findings provide valuable insights into the factors driving residents’ perceptions of agrotourism in Seremban, emphasizing the importance of the natural environment, tourism infrastructure, perceived social benefits, and perceived barriers in shaping attitudes. Additionally, the study highlights the resilience of residents’ positive attitudes toward agrotourism, despite potential challenges and barriers identified. Overall, these results offer implications for policymakers and stakeholders involved in tourism development in the region.