Cyclically, the debate on Keynes’ economic policies reemerge. The economic impact of the pandemic caused by COVID-19 has relaunched the discussion about the importance of Keynesian policies, the multipliers effects, and their impact on stimulating economies. This paper aims to analyze the importance and relevance of the Keynesian multiplier before the pandemic, in a period without experiencing exceptional aggregate shocks. The main focus of the research is to examine the shortcomings of the public investment multiplier, which plays a central role in Keynesian theory. Despite the undeniable relevance of the concept, the issue is to understand the extent to which the multiplier is still relevant in specific contexts. The research presents empirical evidence which suggests that the effects of public investment depend on structural characteristics of economies specifically trade liberalization, the dimension of internal markets, the question of countries having the freedom to issue their currency, and the issue of currencies being accepted as an international reserve. A sample of 35 OECD countries was used for the period 2010–2018. The Keynesian public investment multiplier was calculated for several countries and the obtained values were related to various correlations carried out to assess the relationship between public investment, national income, and specific characteristics of the economies to which the multipliers are sensitive. The results obtained contrast in terms of short-term and long-term impacts so, is at least dubious, that one can rely on Keynesian public policies to boost economies at least in the absence of substantial shocks to aggregate demand.

The state delivery of affordable and sustainable housing continues to be a complicated challenge in Africa, and there is a need to encourage private sector participation. As a result, this study examines the risks associated with private sector participation in affordable housing and supporting infrastructure investment and the strategies towards mitigating the risks from an Afrocentric perspective. The evidence from a systematic literature review was coupled with the opinion of an international expert panel to address the paper’s aim and provide recommendations for developing improved housing and supporting infrastructure in Sub-Saharan Africa. The review outcomes and the qualitative data from the panel discussion were analysed using thematic analysis. The results revealed that market dynamics, land supply and acquisition constraints, cost of construction materials, unsupportive policies, and technical and financial factors constitute risks to affordable housing in the region. Mitigation strategies include leveraging joint efforts, strengths, and resource bases, increasing access to land and finance for private sector participation, developing a supportive government framework to promote an enabling environment for easy access to land acquisition and development finance, local production of building materials, research and technology adoption. In line with the United Nations (UN) Agenda 2030 targets and principles, reforms are required across the housing value chain, involving the private sector and community. Application of the study’s recommendations could minimise the risks of affordable housing delivery and enhance private sector participation.

The present study, developed under a quantitative approach, explanatory scope and causal correlational design, aims to determine the influence of invisible learning on the research competence of high school students in two private schools in the city of Lima, Peru, whose educational models seek to develop autonomous learning and research through discovery learning and experimentation. Two questionnaires were applied to 120 students of the VII cycle of basic education, one to measure the perception regarding invisible learning with 20 items and the other to measure investigative competencies with 21 items; both instruments underwent the corresponding validity and reliability tests before their application. Among the main findings, descriptive results were obtained at a medium level for both variables, the correlations found were significant and moderate, and as for influence, the coefficient of determination R² yielded a value of 0.13, suggesting that 13% of investigative competence is predicted by invisible learning. These results show that autonomy, the use of digital technologies, metacognition and other aspects that are part of invisible learning prepare students to solve problems of varying complexity, allowing them to face the challenges of contemporary knowledge in an innovative and effective manner.

This study aims to elucidate the impact of marketing investment dimensions (MTS, MTOE, ROMI) on profitability indicators (ROA, ROE, GPM, OPM) and sustainable growth indicators (SGR, ARG) for service companies. The study population consisted of 135 service companies listed on the Amman Stock Exchange. A purposive sample of 55 companies was selected from this population. Financial reports and statements from 2018–2022 for these companies were analyzed to achieve the study objectives, employing appropriate statistical methods like multiple regression to test hypotheses. Previous literature shows conflicting results regarding the relationship between marketing investment dimensions and profitability/sustainable growth. Some studies found positive impacts, while others did not. This study contributes to this debate by providing statistical evidence. The results show that higher MTS, MTOE, and ROMI have a positive impact on SGR, OPM and ROA but a negative impact on GPM, ARG, and ROE. This underscores that marketing investments should be viewed in conjunction with overall operating expenses. Companies that control other expenses and increase the marketing investment proportion of total operating expenses may achieve better financial performance. Marketing investment metrics can serve as useful diagnostics and measures of effectiveness for improving marketing profitability, financial performance, and growth. In summary, this study statistically demonstrates the nuanced impacts of marketing investments on service company profitability and sustainable growth indicators. The results emphasize analyzing marketing spends in context of broader expenses and overall company financial health.

This study investigates the optimization of ride-sharing services (RSS) on the ride-hailing service (RHS) providers in Bangladesh. This study employed an explanatory sequential mixed method research design- a qualitative study followed by a quantitative one. Qualitative data were collected through focus group discussions and in-depth interviews with twenty (20) riders and drivers in Bangladesh, and quantitative data were collected from 300 respondents consisting of riders and drivers using a convenience sampling technique. Factor analysis and hierarchical cluster analysis were applied to the data analysis. The qualitative analysis reveals several significant factors associated with RSS and RHS, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, business growth, and security. The quantitative results indicate that using RSS is associated with more significant benefits than RHS in various aspects, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, and expansion of the automobile industry. The findings may assist policymakers in understanding how RSS can yield more incredible economic, environmental, and social benefits than RHS by analyzing fare sharing among passengers, carbon emissions, fuel consumption, and the expansion of the vehicle markets etc. Therefore, the government can formulate distinct policies for RSS holders due to their contributions to economic, social, and environmental concerns. While RHS services are available in many cities in Bangladesh, this study considered only Dhaka and Sylhet cities. Thus, future studies can consider more respondents from other cities for a holistic understanding.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.