The Trans Sumatra Toll Road (TSTR) is a mega toll road project with an assignment State-Owned Enterprise (SOE) scheme in Indonesia. In its development, TSTR has several limitations, including funding, low investment feasibility and the un-optimum implementation of land value capture (LVC). This has the impact of delaying the completion of project development, decreasing the performance of toll road developer companies and even causing bankruptcy. LVC is an alternative funding scheme proven successful in other countries such as Hongkong, England and Vietnam. Several transportation projects based on transit-oriented development have successfully achieved profits using the LVC method. With a low project feasibility, the implementation of the Road Plus Property Developer (RPPD) business model is expected to be a solution to improve investment performance in the TSTR project. RPPD is defined as an assignment scheme toll road business model based on LVC implementation. This research aims to develop policies for implementing the RPPD business model on toll road SOE-assigned schemes. The data was collected by in-depth interviews with experts in two stages. The data analysis method used is Soft System Methodology (SSM). This research produces two recommended actions: ratification of the Presidential Regulation regarding the implementation of LVC and institutional transformation of regionally owned business entities in the property sector. It is hoped that implementing the RPPD policy will become a priority in completing the TSTR project.

Introduction: The digital era has ushered in transformative changes across industries, with the real estate sector being a pivotal focus. In Guangdong Province, China, real estate enterprises are at the forefront of this digital revolution, navigating the complexities of technological integration and market adaptation. This study delves into the intricacies of digital transformation and its profound implications for the financial performance of these enterprises. The rapid evolution of digital technologies necessitates examining how such advancements redefine operational strategies and financial outcomes within the real estate landscape. The inclusion of government support as a variable in our study is deliberate and stems from its profound influence on shaping the digital landscape. Government policies and initiatives provide a regulatory framework and offer strategic direction and financial incentives that catalyze digital adoption and integration within the real estate sector. By examining the moderating effect of government support, this study aims to uncover the nuanced interplay between policy-driven environments and the financial performance of enterprises undergoing digital transformation. This exploration is essential to understanding the broader implications of public policy on private-sector innovation and growth. Objectives: The primary objective of this research is to evaluate the impact of digital transformation on the financial performance of Guangdong’s real estate enterprises, with a specific focus on return on equity (ROE) and return on assets (ROA). Additionally, this study aims to scrutinize the role of government support as a potential moderator in the relationship between digital transformation and financial success. The research seeks to provide actionable insights for policymakers and industry players by understanding these dynamics. The digital transformation of Guangdong’s real estate sector presents a complex landscape of challenges and opportunities that shape the industry’s evolution. On one hand, the integration of innovative digital technologies into established operational frameworks poses significant challenges. These include the need for substantial investment in new infrastructure, the imperative for a cultural shift towards digital literacy across the workforce, and the continuous demand for upskilling to remain agile in an increasingly digital market. On the other hand, digital transformation affords manifold opportunities. For instance, enhanced operational efficiencies through automation and data analytics offer substantial benefits in terms of cost savings and process optimization. Furthermore, leveraging data-driven insights enables more informed strategic decision-making, which is critical in a competitive real estate market. The capacity to innovate service offerings by tapping into digital platforms and customer relationship management systems also presents a significant opportunity for real estate enterprises to differentiate themselves and capture new market segments. Methods: This study explores the digital transformation of real estate firms in Guangdong, highlighting government support as a critical moderator. Findings show that digital initiatives improve company performance, with government backing amplifying these benefits. Regional disparities in support suggest a need for tailored strategies, indicating the importance of policy in driving digital adoption and innovation in the sector. The study advises firms to leverage local policies and policymakers to address regional imbalances for equitable digital transformation. This study uses a sample of 28 real estate enterprises in Guangdong Province from 2012 to 2022. Panel data analysis with a fixed effects model tests the hypotheses. The study also conducts robustness checks by replacing the key variables. Results: The findings indicate that digital transfo

Although infrastructure is widely recognized as a key ingredient in a country’s economic success, many issues surrounding infrastructurespending are not well understood. This paper explores six themes: the returns to infrastructure; the role of the private sector; the evaluation and delivery of infrastructure in practice; the nature of network industries, pricing and regulation; political economy considerations of infrastructure provision; and infrastructure in developing countries. This paper aims to provide insights into many of these questions, drawing on the existing literature.

The study’s goal was to investigate the impact of e-learning determinants on student satisfaction and intention to use e-learning tools. The dependent and independent variables in this study were based on the technological acceptance model. The study examines three determinants, including usefulness, ease of use, and facilitating conditions, as independent variables, while student satisfaction and intention to use were used as dependent variables. Additionally, this study is unique by adding student satisfaction as a dependent variable and a mediator to examine the relationship between e-learning determinants and intention to use. A questionnaire was prepared and distributed to 324 undergraduate students from Jordan’s private universities on the basis of a convenience sample. The proposed hypotheses were investigated using the quantitative techniques of regression in SPSS and SEM in AMOS. The findings of this study revealed that student satisfaction and intention to use e-learning were positively impacted by e-learning determinants. It found that intention to use was positively impacted by student satisfaction. Furthermore, e-learning intention to use was found to be positively impacted by e-learning determinants via student satisfaction. Universities and other educational institutions are advised to identify the appropriate e-learning determinants that satisfy students’ demands and motivate them to use e-learning tools in light of the study’s findings. Private universities can accomplish their goals, stay ahead of the competition, and obtain a competitive advantage by properly understanding e-learning determinants, student satisfaction, and the application of successful e-learning solutions.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

The present work conducts a comprehensive thermodynamic analysis of a 150 MW_e Integrated Gasification Combined Cycle (IGCC) using Indian coal as the fuel source. The plant layout is modelled and simulated using the “Cycle-Tempo” software. In this study, an innovative approach is employed where the gasifier's bed material is heated by circulating hot water through pipes submerged within the bed. The analysis reveals that increasing the external heat supplied to the gasifier enhances the hydrogen (H₂) content in the syngas, improving both its heating value and cold gas efficiency. Additionally, this increase in external heat favourably impacts the Steam-Methane reforming reaction, boosting the H₂/CH₄ ratio. The thermodynamic results show that the plant achieves an energy efficiency of 44.17% and an exergy efficiency of 40.43%. The study also identifies the condenser as the primary source of energy loss, while the combustor experiences the greatest exergy loss.