In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

In the realm of contemporary business, Business Intelligence (BI) offers significant potential for informed decision-making, particularly among executives. However, despite its global popularity, BI adoption in Malaysia’s service sector remains relatively low, even in the face of extensive data generation. This study explores the factors influencing BI adoption in this sector, employing the Technology Acceptance Model (TAM) as its conceptual framework. Drawing on relevant BI literature, the study identifies key TAM factors that impact BI adoption. Using SEM modelling, it analyses quantitative data collected from 45 individuals in managerial roles within Malaysia’s service sector, particularly in the Klang Valley. The findings highlight the crucial role of Perceived Usefulness in influencing the Behavioral Intention to adopt BI, serving as a mediating factor between Computer Self-efficacy and BI adoption. In contrast, Perceived Ease of Use does not have a direct impact on BI adoption and does not mediate the relationship between Computer Self-efficacy and Behavioral Intention. These insights demonstrate the complex nature of BI adoption, emphasizing the importance of Perceived Usefulness in shaping Behavioral Intentions. The outcomes of the study aim to guide executives in Malaysia’s service sector, outlining key considerations for successful BI adoption.

The Primary and secondary shadow education refers to a kind of unofficial education that exists outside the traditional mainstream primary and secondary education system in China, with both commercial and educational attributes. As the primary and secondary school stage is an important key stage for further education, existing research mainly focuses on the spatial distribution of primary and secondary school basic education facilities and non-subject training, with fewer studies targeting primary and secondary school subject tutoring shadow education. With the changes in China’s education industry and the introduction of the Double Reduction Policy, there is an urgent need to conduct in-depth research on the spatial aggregation characteristics and influencing factors of Shadow Education Enterprises for primary and secondary school students. This paper takes the main urban area of Zhengzhou City as the study area, and takes primary and secondary school Shadow Education Enterprises as the research object, and applies spatial analysis methods such as kernel density, nearest-neighbor index, and geographic detector to quantitatively analyze the spatial distribution characteristics of primary and secondary school shadow education tutoring enterprises in Zhengzhou City and the factors affecting them The results show that: 1) The overall spatial pattern of primary and secondary school tutoring Shadow Education Enterprises in the main urban area of Zhengzhou City has largely formed a core-edge structural feature that spreads from the urban center to the periphery, and presents the spatial agglomeration feature of “double nuclei many times” distributed along both sides of the Beijing-Guangzhou Line. 2) The distribution of mentoring Shadow Education Enterprises in the main urban area of Zhengzhou City in relation to provincial model primary and secondary schools is significant and there is a significant difference between the distribution around secondary schools and primary schools. 3) The spatial distribution of Shadow Education Enterprises in the main urban area of Zhengzhou City is mainly influenced by factors such as the size of the school-age population, the level of commercial development, the location of school buildings and the accessibility of transport.

This paper focuses on examining the relationship among organizational factor, work-related factor, psychological factor, personal factor and the commitment of oil palm smallholders toward Malaysian Sustainable Palm Oil (MSPO) certification. The study employed a descriptive research methodology and a structured survey instrument to gather data from oil palm smallholders (n = 441) through simple random sampling technique. Data analysis was conducted using SPSS and partial least square structural equation modeling (PLS-SEM) to test the proposed relationship. The findings reveal that organizational factors significantly impact the affective (β = 0.345, p < 0.05), normative (β = 0.424, p < 0.05), and continuance commitment (β = 0.339, p < 0.05) of oil palm smallholders. Additionally, work-related factors show a substantial effect on these same dimensions of commitment; affective (β = 0.277, p < 0.05), normative (β = 0.263, p < 0.05), and continuance (β = 0.413, p < 0.05). Psychological factors significantly impact the affective (β = 0.216, p < 0.05) and normative commitment (β = 0.146, p < 0.05), with no statistically significant influence on continuance commitment. Conversely, personal factors exhibit limited influence, affecting only continuance commitment (β = 0.104, p < 0.05) to a minor degree, with no statistically significant impact on affective and normative commitment. The present research is among the few empirical findings that have examined the oil palm smallholders’ commitment towards MSPO certification. By emphasizing the role of organizational and work-related factors, the study offers valuable insights for stakeholders within the oil palm sector, highlighting areas to enhance smallholder commitment toward sustainability standards. Consequently, this study contributes a unique perspective to the existing body of literature on sustainable practices in the oil palm industry.

This study explores the complex dynamics of handling augmented reality (AR) data in higher education in the United Arab Emirates (UAE). Although there is a growing interest in incorporating augmented reality (AR) to improve learning experiences, there are still issues in efficiently managing the data produced by these apps. This study attempts to understand the elements that affect AR data management by examining the relationship between the investigated variables: faculty readiness, technological limits, financial constraint, and student engagement on data management in higher education institutions in the UAE, building on earlier research that has identified these problems. The research analyzes financial constraints, technological infrastructure, and faculty preparation to understand their impact on AR data management. The study collected detailed empirical data on AR data management in UAE higher education environments using a quantitative research methods approach, surveys. The reasons for choosing this research method include cost-effectiveness, flexibility in questionnaire design, anonymity and confidentiality involved in the chosen methods. The results of this study are expected to enhance academic discourse by highlighting the obstacles and remedies to improving the efficiency of AR technology data management at higher education institutions. The findings are expected to enlighten decision-making in higher education institutions on maximizing AR technology’s benefits for improved learning outcomes.