Given the eclectic and localized nature of environmental risks, planning for sustainability requires solutions that integrate local knowledge and systems while acknowledging the need for continuous re-evaluation. Social-ecological complexity, increasing climate volatility and uncertainty, and rapid technological innovation underscore the need for flexible and adaptive planning. Thus, rules should not be universally applied but should instead be place-based and adaptive. To demonstrate these key concepts, we present a case study of water planning in Texas, whose rapid growth and extreme weather make it a bellwether example. We review historic use and compare the 2002, 2007, 2012, 2017 and 2022 Texas State Water Plans to examine how planning outcomes evolve across time and space. Though imperfect, water planning in Texas is a concrete example of place-based and adaptive sustainability. Urban regions throughout the state exhibit a diversity of strategies that, through the repeated 5-year cycles, are ever responding to evolving trends and emerging technologies. Regional planning institutions play a crucial role, constituting an important soft infrastructure that links state capacity and processes with local agents. As opposed to “top-down” or “bottom-up”, we frame this governance as “middle-out” and discuss how such a structure might extend beyond the water sector. 

Research on retailers’ behavioral intention and behavior of using the omnichannel ecommerce solution (OES) used the Unified Theory of Acceptance and Use of Technology (UTAUT2) model and supplemented the other factors such as seamless supply, omnichannel integration. Research concerns about behavioral intention and behavior of using OES as this is a global trend; OES has become one of the top priorities for businesses to thrive in the rapidly changing market and retain customers; increasingly high standards are being set for digital experiences. Therefore, retailers must quickly adapt to new trends for sustainable development to keep up with the transformation and increase the use of OES. The results show that effort expectation, social influence, hedonic motive, retailers’ capacity, seamlessly connecting have a positive impact on retailers’ behavioral intention and behavior of using OES. Behavioral intention and favorable conditions have a positive impact on behavior of using OES. Meantime, omnichannel integration have a negative impact on behavior of using OES in Vietnam. This research helps managers and OES providers to develop their skills and expertise, and the study results may prove diagnostically useful to the retailers’ behavioral intention and behavior of using OES.

This study examined the role of cryptocurrencies in tourism and their acceptance across EU regions, with particular attention to the digital transformation precipitated by the COVID-19 pandemic. The analysis focuses on the relationship between cryptocurrency acceptance points and the intensity of tourism, highlighting that the acceptance of cryptocurrencies is significantly correlated with tourism services. The literature review highlighted that Web 3.0, especially blockchain technology and decentralized applications, opens new possibilities in tourism, including secure and transparent transactions, and more personalized travel experiences. The research investigated cryptocurrency acceptance points and the intensity of tourism within the EU. The study illuminates that the acceptance of cryptocurrencies significantly correlates with tourism services. The data and methodology demonstrated the analysis methods for examining the relationship between cryptocurrency acceptance points and tourism intensity, including the use of clustering neural networks and Eurostat data utilization. The results showed a positive correlation between the number of cryptocurrency acceptance points and tourism intensity in the EU, affirming the research hypothesis. According to the regression analysis results, each additional cryptocurrency acceptance point is associated with an increase in tourism intensity. The significance of the research lies in highlighting the growing role of digital payment solutions, especially cryptocurrencies, in tourism, and their potential impacts on the EU economy. The analysis supports that the intertwining of tourism and digital financial technologies opens new opportunities in the sector for both providers and tourists.

The aviation industry is experiencing over and over again a technological revolution, nowadays with airports at the forefront of embracing smart technologies to enhance operational efficiency, security and passenger experience. This article comprehensively analyzes the benefits, challenges, and legal implications of adopting smart technologies in airport facilitation and security control. It examines the regulatory framework established by the International Civil Aviation Organization (ICAO) on an international level and by sovereign states on a national level. It explores using smart solutions such as automated systems, data and biometric verification, artificial intelligence (AI), and the Internet of Things (IoT) devices in airport operations. The authors’ purpose is to highlight the improvements in airport facilities and security measures brought about by these technologies, while addressing concerns over privacy, cost, technological limitations and human factors. By emphasizing the importance of a balanced approach and considering innovation alongside legal and operational imperatives, the article underscores the transformative potential of smart and integrated technologies in shaping the future of air travel.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

This study investigates the impact of human resource management (HRM) practices on employee retention and job satisfaction within Malaysia’s IT industry. The research centered on middle-management executives from the top 10 IT companies in the Greater Klang Valley and Penang. Using a self-administered questionnaire, the study gathered data on demographic characteristics, HRM practices, and employee retention, with the questionnaire design drawing from established literature and validated measuring scales. The study employed the PLS 4.0 method for analyzing structural relationships and tested various hypotheses regarding HRM practices and employee retention. Key findings revealed that work-life balance did not significantly impact employee retention. Conversely, job security positively influenced employee retention. Notably, rewards, recognition, and training and development were found to be insignificant in predicting employee retention. Additionally, the study explored the mediating role of job satisfaction but found it did not mediate the relationship between work-life balance and employee retention nor between job security and employee retention. The research highlighted that HRM practices have diverse effects on employee retention in Malaysia’s IT sector. Acknowledging limitations like sample size and research design, the study suggests the need for further research to deepen understanding in this area.