This research aims to explore the impact of government policies to promote mass tourism in Bali. Qualitative method with the support of a phenomenological approach and in-depth interviews and FGD. The Butler tourism area life cycle model theory is used to evaluate the impact of tourism on land use and cultural conflict with six stages of destination development, namely exploration, involvement, development, consolidation, stagnation, and decline or rejuvenation. The findings reveal that Bali has experienced all stages of Butler’s model. From 1960–1970, Bali was in the exploration phase, offering tourists authentic experiences. At the beginning of 1970–2000, Bali had entered five phases marked by rapid tourism growth. Now, Bali reached a consolidation phase with a focus on managing tourism quality. Now, Bali is entering a phase of stagnation, facing challenges such as overcrowding and environmental degradation. Bali is at the crossroads between phases of decline and rejuvenation, with efforts to overcome environmental problems and diversify tourism products. This study concludes that mass tourism has significant positive and negative impacts on tourist destinations. Although it can improve the local economy and preserve culture, it can also cause environmental damage and cultural conflict. The Bali government’s policy strategy for the future is to overcome cultural conflicts including tourist education, sustainable tourism development, empowerment of local communities, enforcement of regulations, and intercultural dialogue. The implementation of this policy strategy can be carried out effectively to manage cultural conflicts towards a sustainable Bali tourism future.

Employees’ loyalty is essential for improving the organization’s performance, thus aiding sustainable economic growth. The study examines the relationship between employee loyalty, organizational performance, and economic sustainability in Malaysian organizations. The results indicate a robust positive correlation between organizational performance and employee loyalty, suggesting loyalty drives productivity, profitability, and operational efficiency. Additionally, the study highlights organizational performance as a mediator that connects loyalty to aggregate-level economic consequences, such as resilience and adaptability under volatile market conditions. The research emphasizes the role of leadership, company culture, and work environments that support cultivating loyalty. It also highlights how loyal employees can be a cornerstone of innovation and corporate social responsibility, which aligns with Malaysia’s sustainable development agenda. By addressing this, organizations are encouraged to adopt measures that can foster loyalty and ensure long-term economic sustainability, including employee engagement initiatives, talent management, and recognition systems. Research to come should investigate longitudinal dynamics, cross-cultural comparisons, and sector-specific factors to cement a better base of understanding about the impact of employee loyalty on organizational and economic outcomes.

The bubble milk tea industry in Malaysia which was thought to have slowed down in the recent years since its first appearance in 2010 has made a comeback. At the point of conducting this research, there are almost 100 brands of bubble milk tea in Malaysia and it is not surprising that some of these shops are selling more than a thousand cups a day. However, there has been limited research conducted on factors influencing brand equity on bubble milk tea brands in Johor Bahru. This study is to investigate whether brand loyalty, perceived quality, brand awareness and brand association influence brand equity on bubble milk tea brands in Johor Bahru through distribution of online questionnaires. This study novelty is at the examining the factors influencing brand equity in the context of bubble milk tea in Johor Bahru, Malaysia. Data derived from responses of 400 respondents through sampling were analysed using SPSS v29. Hypotheses testing performed through simple linear regression revealed that brand loyalty, perceived quality, brand awareness and brand association have significant effect on brand equity of bubble milk tea brands in Johor Bahru, Malaysia. It was also demonstrated that perceived quality has the most significance influence on brand equity. Organizations in the bubble milk tea industries are able to benefit from these findings by prioritizing their marketing strategies to gain competitive edge over their competitors. With findings that perceived quality having the most significance influence, marketers with limited resources can narrow down their options and focus on this specific dimension to increase their brand value.

This study analyzes the dynamic relationships between tourism, gross domestic product (GDP) per capita, exports, imports, and carbon dioxide (CO₂) emissions in five South Asian countries. A VAR-based Granger causality test is performed with time series data from Bangladesh, India, Nepal, Pakistan, and Sri Lanka. According to the results, both bidirectional and unidirectional relationships among tourism, economic growth, and carbon emissions are investigated. Specifically, tourism significantly impacts GDP per capita in Pakistan, Sri Lanka, and Nepal, yet it has no effect in Bangladesh or India. However, the GDP per capita shows a unidirectional relationship with tourism in Bangladesh and India. The unidirectional causal relationship from exports and imports to tourism in the context of India and a bidirectional relationship in the case of Nepal. In Pakistan, it is observed that exports have a one-way influence on tourism. The result of the panel Granger test shows a significant causal association between tourism, economic growth, and trade (import and export) in five South Asian economies. Particularly, there is a bidirectional causal relationship between GDP per capita and tourism, and a significant unidirectional causal relationship from CO₂ emissions, exports, and imports to tourism is explored. The findings of this study are helpful for tourism stakeholders and policymakers in the region to formulate more sustainable and effective tourism strategies.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.