Small and medium-sized enterprises (SMEs) play a critical role in achieving environmental sustainability, particularly in developing economies where regulatory enforcement and resource constraints remain significant challenges. Drawing on Institutional Theory, this study examines how green leadership influences environmental performance in Ghanaian SMEs, with digital innovation as a mediating variable and environmental culture as a moderating variable. Institutional Theory provides the conceptual foundation for explaining how normative pressures embedded in leadership values and organizational culture, alongside mimetic pressures associated with digital innovation adoption, shape firms’ environmental outcomes. Using survey data collected from SMEs in Ghana and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results revealed that green leadership has a significant positive effect on both digital innovation and environmental performance. Digital innovation also significantly enhances environmental performance and partially mediates the relationship between green leadership and environmental performance. Notably, the findings demonstrated that environmental culture significantly moderates the relationship between digital innovation and environmental performance, with the effect stronger in organizations with a well-developed environmental culture. This indicates that internalized environmental values amplify the effectiveness of digital innovation initiatives. The study contributes to the sustainability and organizational literature by extending Institutional Theory to the SME context in a developing economy and by clarifying the conditional role of environmental culture in translating digital innovation into superior environmental performance. Practically, the findings suggest that SME leaders and policymakers should promote environmentally oriented leadership, invest in digital innovation, and cultivate strong environmental cultures to enhance sustainability outcomes.

The article highlights Malaysia’s multicultural history, the advancement of Internet technology, and the worldwide appeal of Chinese food, all of which serve as a good basis for the project. This study focuses on Malaysian Chinese takeout systems. The research’s primary goals include developing new business options for the Chinese food sector, as well as enhancing customer happiness and efficiency of takeout systems. As a result, the project intended to create a Web-based system for managing several tasks associated with meal ordering by users. For the system development, an Object-Oriented System Development (OOSD) methodology was used, mostly with the Java programming language. Model-View-Control (MVC) framework was employed throughout development to improve system administration. Redis and HTTP session technologies were included for user login to increase system security. For database operations, MyBatis and MyBatis Plus were also employed to enhance ease and security. The system adheres to design principles and leverages technologies like ElementUI and jQuery to further fulfill this criterion to provide a user-friendly interface. The results of this study demonstrate significant improvements in the overall efficiency of the takeout process, leading to enhanced user experiences and greater customer satisfaction. In addition to streamlining operations, the system opens new avenues for the Malaysian Chinese food industry to capitalize on the growing demand for online food ordering. This research provides a solid foundation for future innovations in takeout systems and serves as a reference point for enhancing the Chinese gastronomy sector in a rapidly digitizing world.

The process of digitalization within the realm of tourism is not merely a trend but rather a significant catalyst that is rapidly propelling the comprehensive transformation of the tourism industry into a new era of technological advancement. This intricate process fundamentally involves the seamless integration and application of cutting-edge digital technologies across various tourism-related activities and services. The advent of innovative solutions that harness the immense capabilities of artificial intelligence, the analytical power of big data, the security features of blockchain, and the interconnectedness provided by the Internet of Things primarily serves to enhance the overall quality of services offered, optimize pricing strategies to align with market demands, and improve risk management protocols within the industry. This paper methods uses 100 Scopus indexed papers about Smart Tourism Development in Kazakhstan. It is imperative to underscore the fact that the ongoing digitalization process, while offering numerous advantages, simultaneously imposes rigorous new requirements concerning the qualifications and competencies of staff members, as well as the paramount importance of data security measures and the protection of consumer rights in the digital environment. The effective management of this digital transformation necessitates a holistic and integrated approach that encompasses not only the development of robust infrastructure but also the enhancement of digital literacy among employees and the establishment of a dynamic and innovative ecosystem that encourages creativity and adaptability.

This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

Soil salinization is a difficult challenge for agricultural productivity and environmental sustainability, particularly in arid and semi-arid coastal regions. This study investigates the spatial variability of soil electrical conductivity (EC) and its relationship with key cations and anions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, CO₃²⁻, HCO₃⁻, SO₄²⁻) along the southeastern coast of the Caspian Sea in Iran. Using a combination of field-based soil sampling, laboratory analyses, and Landsat 8 spectral data, linear Multiple Linear Regression and Partial Least Squares Regression (MLR, PLSR) and nonlinear Artifician Neural Network and Support Vector Machine (ANN, SVM) modeling approaches were employed to estimate and map soil EC. Results identified Na⁺ and Cl⁻ as the primary contributors to salinity (r = 0.78 and r = 0.88, respectively), with NaCl salts dominating the region’s soil salinity dynamics. Secondary contributions from Potassium Chloride KCl and Magnesium Chloride MgCl₂ were also observed. Coastal landforms such as lagoon relicts and coastal plains exhibited the highest salinity levels, attributed to geomorphic processes and anthropogenic activities. Among the predictive models, the SVM algorithm outperformed others, achieving higher R² values and lower RMSE (RMSE_Test = 27.35 and RMSE_Train = 24.62, respectively), underscoring its effectiveness in capturing complex soil-environment interactions. This study highlights the utility of digital soil mapping (DSM) for assessing soil salinity and provides actionable insights for sustainable land management, particularly in mitigating salinity and enhancing agricultural practices in vulnerable coastal systems.

The main purpose of this paper was to examine the impact of generative artificial intelligence (AI) on employee well-being and work dynamics. Using qualitative methodology, three semi-structured interviews were conducted to investigate the implications of generative AI on employee outcomes such as efficiency, job satisfaction, ethical considerations, and work-life balance. The findings highlighted the potential benefits and risks associated with generative AI implementation in the workplace. The study contributed to the literature by adopting a qualitative approach, allowing in-depth exploration of individual experiences with generative AI in the workplace. The study discussed the implications for employers, employees, and society.