The Malaysian government’s efforts to promote solar photovoltaic (PV) usage among households face a challenge due to its low adoption rate. This study delves into the factors influencing the exponential adoption of solar PV electricity generation among landed residential property owners in Malaysia. The research aims to comprehensively examine the predictors influencing the adoption of solar PV systems among Malaysian households. Hence, the study employs an enhanced Theory of Planned Behavior framework, integrating sustainable energy security dimensions such as availability, affordability, efficiency, acceptability, regulation, and governance. The sample comprised 556 Malaysian residents who owned and resided in the landed properties. The home locations where at least one solar PV installation existed within a residential street. Snowball sampling was employed through referrals, leveraging social and community networks. Collected data was analyzed using the partial least squares structural equation modeling. Attitude, affordability, and acceptability emerged as pivotal factors significantly impacting the intention to use solar PV systems among Malaysian households. This research not only enriches academic discourse but also offers practical implications for policymakers, guiding the formulation of targeted strategies to promote sustainable energy practices and facilitate the widespread adoption of solar PV systems in Malaysia.

This study investigates the dynamic landscape of agritourism in Thailand, emphasizing innovations, challenges, and policy implications in the post-COVID-19 era. Employing a qualitative approach, including a comprehensive literature review and semi-structured interviews with stakeholders, the research identifies key agritourism models, such as immersive learning experiences, technology-driven agritourism, and unconventional practices like salt and coconut plantations. Findings reveal that agritourism has adapted to shifting market demands through diversification, technological integration, and a heightened focus on sustainability. Notably, technology adoption in precision farming and hydroponics enhances resource efficiency and visitor engagement, while initiatives like rice paddy field tourism and highland agritourism showcase the cultural and ecological richness of rural landscapes. The study underscores the critical role of policy frameworks, infrastructure development, and community empowerment in fostering sustainable agritourism practices. Key policy recommendations include targeted subsidies, capacity-building programs, and harmonized regulatory frameworks to address challenges such as financial constraints, regulatory ambiguities, and inadequate infrastructure. This research contributes to the broader discourse on sustainable tourism and rural development, aligning agritourism with the United Nations Sustainable Development Goals (SDGs). By synthesizing insights on innovation, resilience, and sustainability, this study offers a comprehensive roadmap for policymakers, practitioners, and academics to leverage agritourism as a vehicle for rural revitalization and global sustainability. Future research directions are proposed to explore the long-term impacts of technological integration, community empowerment, and resilience strategies in agritourism.

Electronic Word of Mouth (eWOM) has become a pivotal factor influencing consumers’ decisions, particularly in the context of hotel services. With the advent of social media, it provides individuals with powerful tools to share its experiences and opinions about hotels. In this digital age, customers increasingly rely on online reviews and recommendations from their peers when selecting accommodations. eWOM on social media platforms has a substantial impact on customers’ perceptions and decision-making processes. This study aims to better understand the influence of eWOM by social media platforms on purchase intention of hotel services. To understand the influence of eWOM, this study uses the information adoption model as the model has been widely used in previous eWOM studies. The information quantity construct has been added to strengthen the model. The online questionnaire was distributed to social media users by using Google forms via social media platforms and only 210 of them were responded. The SmartPLS 4.0 software is used to analyze the data as the Partial Least Square-Structural Equation Modelling (PLS-SEM) is a method to confirm the structural equation models and to test the link between inert developments. Based on results, the information quantity and information quality of hotel services on eWOM positively influences the information usefulness and the information usefulness of hotel services on eWOM positively influences the purchase intention. The results lead to increase sales of hotel services and contribute to economic growth.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.