This study investigated the use of digital story strategy in teaching Islamic education on achievement and how it affects the development of moral thinking. The quasi-experimental design was implemented as a methodology and the sample included of (60) students from the fourth grade from Abdul Rahman bin Awf School in Abha. The results showed that there are statistically significant differences at the significance level (α ≤ 0.05) between the average responses of students in the two groups in the test. The experimental group performed better than the control group. The findings also showed that there are statistically significant differences at the significance level (α ≤ 0.05) between the average responses of students in the two groups (experimental and control) in the moral thinking scale and favour of the experimental group.

Most countries have adopted a more liberal policy to socialize public relations under the influence of neoliberalism and lobbying by economic elites to strengthen the role of market mechanisms and citizens’ entrepreneurial activity. The nature, scale, sequence, and strategy of economic and social reforms in each country have their specifics. Today multi-vector and large-scale changes are taking place in social and labor policy, and they do not always have an internal logic. The study assesses prospects for the development of the labor market in the context of global transformations. Within the framework of this study, the collected information was processed gradually. Data processing was modified during the study phase. At the first stage, data processing results were used to determine total and non-farm self-employment for two groups of countries with developing economies and estimate the scale of vulnerable employment. At the second stage, indicators were identified that characterize various categories of economically active population that belong to the precariat. At the third stage, the authors analyzed data on non-standard forms of employment. The authors assumed that these forms have a right to exist and will be implemented more often. There is an imbalance between standard and non-standard forms of employment. Further research should consider the transformation of labor from material and intangible dominants to creativity.

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.

This study investigates the willingness of Indonesian consumers, particularly in West Java, to pay for green products by applying and expanding the Theory of Planned Behavior (TPB). It examines how perceived green product value and willingness to pay premiums influence consumer intentions and behavior toward green purchases. The research highlights the gap between consumers’ willingness to pay for environmentally friendly products and the actual sales of such products. By incorporating perceived value and willingness to pay into the TPB framework, the study aims to find what factors that can address the gap particularly in a developing country context to contribute to shaping a pro-environmental socio-cultural community in Indonesia and mitigates country’s significant environmental challenges. In the context of 251 young consumers in Indonesia, this study finds that subjective norms do not significantly influence purchase intentions. However, attitudes and behavioral controls do effectively encourage green behavior, suggesting that societal norms for green behavior may not be fully established. In addition, while willingness to pay a premium and perceived value of green purchases can influence green behavior, consumers are generally reluctant to pay higher prices for environmentally friendly products.

This study investigates the significance of data analytics in digital marketing for sustainable business growth. Data analytics has become an indispensable instrument in the world of digital marketing, offering organisations the means to achieve sustainable growth while minimising their environmental impact. We gathered data from 273 marketing and business consultants, chosen for their expertise in digital channels and data analytics, using a survey research design. The questionnaire, which was validated through expert review and pilot testing, assessed the relationship between data analytics utilization and its impact on competitive advantage and business optimization. We conducted statistical analyses, including descriptive and inferential statistics, using SPSS version 25.0. Findings reveal a significant correlation between data analytics adoption in digital marketing and sustainable business competitive advantage, as well as a notable impact on business optimization. Recommendations emphasise the strategic importance of customer segmentation and predictive analytics in leveraging data analytics for targeted marketing campaigns and proactive adjustments to market trends. This study underscores the indispensability of data analytics in the evolving digital marketing landscape, offering actionable insights for businesses seeking sustainable growth and competitive advantage.

The paper demonstrates the importance of subnational data on housing to be systematically reported and added to country typologies. We asked which national and local level characteristics of housing regimes can serve as benchmarks for reasonable country groupings. The aim of this paper is to (1) develop a methodological tool enabling the comparison of conditions for housing policy implementation on national and subnational levels and (2) identify the group of countries where conditions for housing policy implementation on national and subnational levels tend to be comparable. This country classification can be used as a practical instrument for comparative analyses and policy learning. As a conceptual framework, we used the international comparative Housing research 2.0 launched by Hoekstra in 2020. For our analysis, we selected 15 basic factors that were tested in 24 European countries. We have identified three key factors having an impact on housing policy implementation: decentralisation level in housing, local budget housing expenditure and the information on which governance level has core competencies within housing. The numeric database has been run through a k-means cluster analysis. Five distinct types of countries with similarities in conditions for housing policy implementation on national and subnational level have been identified and described.