Public-Private Partnerships (PPPs) can be an effective way of delivering infrastructure. However, achieving value for money can be difficult if government agencies are not equipped to manage them effectively. Experience from OECD countries shows that the availability of finance is not the main obstacle in delivering infrastructure. Governance—effective decision-making—is the most influential aspect on the quality of an investment, including PPP investments. In 2012, the OECD together with its member countries developed principles to ensure that PPPs deliver value for money transparently and prudently, supported by the right institutional capacities and processes to harness the upside of PPPs without jeopardizing fiscal sustainability. Survey results from OECD countries show that some dimensions of the recommended practices are well applied and past and ongoing reforms show progress. However, other principles have not been well implemented, reflecting the continuing need for improving public governance of PPPs across countries.

Inequity in infrastructure distribution and social injustice’s effects on Ethiopia’s efforts to build a democratic society are examined in this essay. By ensuring fair access to infrastructure, justice, and economic opportunity, those who strive for social justice aim to redistribute resources in order to increase the well-being of individuals, communities, and the nine regional states. The effects that social inequity and injustice of access to infrastructure have on Ethiopia’s efforts to develop a democratic society were the focus of the study. Time series analysis using principal component analysis (PCA) and composite infrastructure index (CII), as well as structural equation modeling–partial least squares (SEM-PLS), were necessary to investigate this issue scientifically. This study also used in-depth interviews and focus group discussions to support the quantitative approach. The research study finds that public infrastructure investments have failed or have been disrupted, negatively impacting state- and nation-building processes of Ethiopia. The findings of this research also offer theories of coordination, equity, and infrastructure equity that would enable equitable infrastructure access as a just and significant component of nation-building processes using democratic federalism. Furthermore, this contributes to both knowledge and methodology. As a result, indigenous state capability is required to assure infrastructure equity and social justice, as well as to implement the state-nation nested set of policies that should almost always be a precondition for effective state- and nation-building processes across Ethiopia’s regional states.

This paper analyzed the equitable allocation of infrastructure across regional states in Ethiopia. In general, in the past years, there has been a good start in the infrastructure sector in Ethiopia. However, the governance and equity system of infrastructure in Ethiopia is not flexible, not technology-oriented, not fair, and not easily solved. The results of in-depth interviews and focus group discussions (FGDs) showed that there is a lack of institutional capacity, infrastructure governance, and equity, which has negatively impacted the state- and nation-building processes in Ethiopia. According to the interviewees, so long as the unmet demand for infrastructure exists, it remains a key restrain on doing business in most Ethiopian regional states. This is due to the lack of integrated frameworks, as there are coordination failures (lack of proper government intervention, including a lack of proper understanding and implementation of the constitution and the federal system). In Ethiopia, to reduce these bottlenecks arising from the lack of institutional capacity, infrastructure governance, and equity and their effects on nation-building, first of all, the government has to critically hear the people, deeply assess the problems, and come to the point and then discuss the problems and the way forward with the society at large.

Recently, there has been a lot of buzz on social media, particularly in the form of vlogs, about newly launched semi-high speed trains in India popularly known as Vande Bharat Express. However, no information is available about the extent to which people trust the vlogs promoting the trains and the trains themselves. Therefore, this research aims to investigate the impact of watching vlogs about semi-high speed trains on the trust and attitude towards them, and how they perceive the risks associated. This study is guided by the trust transfer theory to investigate how trust transference can lead to a traveler’s intent to use semi-high speed trains. This study involved 338 participants. The relationship between variables was examined using SmartPLS 4 software. The findings indicate that trust in semi-high speed trains can be established through vlogs leading to intention to use. On the theoretical side, it provides insight into how trust, attitude, and perceived risk can affect the adoption of new technology, while on the practical side, it helps to understand how vlog coverage can be used as a tool to increase trust and ultimately drive adoption. Vlog coverage, trust in vlog content, trust in semi-high speed trains and behavioural intention altogether are not well understood in current literature despite the important implication for managers, academicians and consumers alike. This study contributes to the field of transportation and railways, social media and communication, and hospitality and tourism research. The study helps policy makers to understand users’ characteristics regarding the latest social media tools and adopt them accordingly to provide a better governance policy.

This paper investigates the transformative role of Artificial Intelligence (AI) in enhancing infrastructure governance and economic outcomes. Through a bibliometric analysis spanning more than two decades of research from 2000 to 2024, the study examines global trends in AI applications within infrastructure projects. The analysis reveals significant research themes across diverse sectors, including urban development, healthcare, and environmental management, highlighting the broad relevance of AI technologies. In urban development, the integration of AI and Internet of Things (IoT) technologies is advancing smart city initiatives by improving infrastructure systems through enhanced data-driven decision-making. In healthcare, AI is revolutionizing patient care, improving diagnostic accuracy, and optimizing treatment strategies. Environmental management is benefiting from AI’s potential to monitor and conserve natural resources, contributing to sustainability and crisis management efforts. The study also explores the synergy between AI and blockchain technology, emphasizing its role in ensuring data security, transparency, and efficiency in various applications. The findings underscore the importance of a multidisciplinary approach in AI research and implementation, advocating for ethical considerations and strong governance frameworks to harness AI’s full potential responsibly.

The proposed research work encompasses implications for infrastructure particularly the cybersecurity as an essential in soft infrastructure, and policy making particularly on secure access management of infrastructure governance. In this study, we introduce a novel parameter focusing on the timestamp duration of password entry, enhancing the algorithm titled EPSBalgorithmv01 with seven parameters. The proposed parameter incorporates an analysis of the historical time spent by users entering their passwords, employing ARIMA for processing. To assess the efficacy of the updated algorithm, we developed a simulator and employed a multi-experimental approach. The evaluation utilized a test dataset comprising 617 authentic records from 111 individuals within a selected company spanning from 2017 to 2022. Our findings reveal significant advancements in EPSBalgorithmv01 compared to its predecessor namely EPSBalgorithmv00. While EPSBalgorithmv00 struggled with a recognition rate of 28.00% and a precision of 71.171, EPSBalgorithmv01 exhibited a recognition rate of 17% with a precision of 82.882%. Despite a decrease in recognition rate, EPSBalgorithmv01 demonstrates a notable improvement of approximately 14% over EPSBalgorithmv00.