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In the Global North, affordable housing has evolved and thrived, and it is now gaining traction in the Global South, where governments have been vocal supporters of the concept. Therefore, this paper aims to investigate the important criteria for selecting affordable housing units in Ghana.

A quantitative research approach was used, and a survey was administered to the residents. The data was analysed using both descriptive and inferential statistics. The relative importance index technique was used to rank the important criteria, and the EFA technique was used to create a taxonomy system for the criteria.

The hierarchical ranking of the most significant criteria for selecting affordable housing includes community safety, waste management and access to good-quality education. Furthermore, the important criteria for selecting affordable housing are classified into two groups, namely, “sustainability criteria” and “housing demand and supply and social service provision”.

This study has implications for the real estate industry and construction stakeholders, as this will inform decision-making in terms of the design of affordable housing and the suitability of the location for the development.

These findings provide a baseline to support potential homeowners and tenants in their quest to select affordable housing. Furthermore, these findings will aid future longitudinal research into the indicators or criteria for selecting suitable locations for the development of low- and middle-income housing.

Despite advancements in construction digitalisation and alternative building technologies, cost overrun is still a challenge in the construction industry. The inflation rate is increasing, especially in developing countries, and is critical in cost overrun matters. It can deviate construction built-up rate components. This may thwart improving construction-related Sustainable Development Goals (SDGs). Studies concerning the impact of the inflation rate on construction-related SDGs are scarce in developing countries, including Nigeria. The study investigated the impact of inflation on Nigeria’s construction projects and their outcome on SDGs and suggested possible ways to improve achievement of construction-related SDGs and their targets.

The researchers employed a qualitative research design. This is because of the study’s unexplored dimension. The researchers engaged 35 participants across major cities in Nigeria via semi-structured virtual and face-to-face interviews. The research utilised a thematic method for collated data and accomplished saturation.

Findings reveal that the impact of inflation on construction projects, if not checked, could hinder achieving construction-related SDGs in Nigeria. This is because of the past three years of hyperinflation that cut across major construction components. It shows that the upward inflation rate threatens achieving construction-related SDGs and proffered measures to mitigate inflation and, by extension, enhance achieving construction-related SDGs. This includes a downward review of the Monetary Policy Rate, control of exchange rate volatility and addressing insecurity to restore FDIs and FPIs confidence.

Besides suggesting possible solutions to mitigate hyperinflation on construction components to improve achieving construction-related SDGs, findings will stipulate government policymakers put measures in place through favourable fiscal and monetary policy implementation and encourage moving from a consumption to a production nation.

The purpose of this study is to provide a detailed demonstration of how artificial intelligence (AI) can be used to potentially generate valuable insights and recommendations regarding the role of accounting in addressing key sustainability-related issues.

The study offers a novel method for leveraging AI tools to augment traditional scoping study techniques. The method was used to show how the authors can produce recommendations for potentially enhancing organisational accountability pertaining to seasonal workers.

Through the use of AI and informed by the knowledge base that the authors created, the authors have developed prescriptions that have the potential to advance the interests of seasonal workers. In doing so, the authors have focussed on developing a useful and detailed guide to assist their colleagues to apply AI to various research questions.

This study demonstrates the ability of AI to assist researchers in efficiently finding solutions to social problems. By augmenting traditional scoping study techniques with AI tools, the authors present a framework to assist future research in such areas as accounting and accountability.

Studies showed that construction digitalisation could prevent or mitigate accidents rate on sites. Digitalisation applications may prevent or mitigate building project collapse (BPC) but with some encumbrances, especially in developing countries. There is a paucity of research on digital technologies application to prevent or mitigate BPC in Nigeria. Thus, the research aims to explore the perceived barriers that may hinder digital technologies from preventing or mitigating building collapse and recommend measures to improve technology applications during development.

The study is exploratory because of the unexplored approach. The researchers collected data from knowledgeable participants in digitalisation and building collapse in Nigeria. The research employed a phenomenology approach and analysed collected data via a thematic approach. The study achieved saturation at the 29th interviewee.

Findings show that lax construction digitalisation implementation, absence of regulatory framework, lax policy, unsafe fieldworkers' behaviours, absence of basic infrastructure, government attitude, hesitation to implement and high technology budget, especially in developing countries, are threats to curbing building collapse menace via digitalisation. The study identified technologies relevant to preventing or mitigating building collapse. Also, it proffered measures to prevent or mitigate building collapse via improved digital technology applications during development.

This research contributes to the construction digitalisation literature, especially in developing countries, and investigates the perceived barriers that may hinder digital technologies usage in preventing or mitigating building collapse in Nigeria.

Despite city authorities in Bangladesh being concerned about urban sustainability, they often face difficulties in addressing predominant urban challenges threatening urban sustainability, due to limited relevant literature. To reduce this gap, this study aims to address the predominant urban challenges and assess their severity levels in four city corporations of Bangladesh, e.g. Rajshahi, Sylhet, Barishal, and Gazipur.

Using a mixed-method approach, this study rigorously analyzed field-level data obtained from 1,200 residents across selected cities using diverse statistical techniques. The quantitative analysis included descriptive analysis, exploratory factor analysis, and chi-square tests, whereas qualitative insights were derived through thematic analysis.

The study uncovered nine predominant urban challenges under two crucial factors “Feeble Urban Management” and “Illicit Activities” that collectively explain 62.20% variance. “Feeble Urban Management” explains 44.17% variance, whereas “Illicit Activities” accounts for 18.13%. Within these challenges, uncontrolled urban sprawl, inadequate disaster management, congested roads, and shabby drainage and waste management pose significant threats to urban sustainability. Illicit activities, manifested by encroachment on water sources, grabbing roadside, destruction of natural properties, and activities undermining social security, compound the urban sustainability issue. Severity analysis reveals Sylhet (54.5%), Rajshahi (46.4%), and Barishal (31.2%) as highly impacted, whereas Gazipur exhibits moderate severity (66.7%).

The findings of this study reveal intrinsic insights into urban challenges in Bangladesh that will provide valuable guidance to city authorities, equipping them to implement integrated and effective initiatives and programs that overcome these predominant urban challenges, with a specific focus on Rajshahi, Sylhet, and Barishal city corporations.

This study aims to explore the impact of chief executive officers’ (CEOs) values on the socially responsible behaviors (SRBs) of top management teams. Drawing from the social learning framework, it examines the mechanisms through which CEOs’ values shape SRBs within organizational leadership.

Using the hierarchical regression model, this study assesses direct effects, while the Monte Carlo method is used to evaluate indirect effects. The analysis is based on time-lagged data collected from 122 CEOs and 287 corresponding top managers from small- and medium-sized enterprises in China.

The results indicate a positive correlation between CEOs’ self-transcendent values and their own SRBs (i.e. doing-good and avoiding harm behavior). This, in turn, promotes top managers’ SRBs.

By highlighting the micro-foundations of corporate social responsibility, this study enriches the understanding of SRBs enhancement in management teams. It reveals the significance of CEO self-transcendent values as a precursor to SRBs and elucidates the learning processes involved.

The United Nations' Sustainable Development Goal of ensuring healthy lives and promoting well-being for people of all ages underscores the vital role of public healthcare facilities (PHFs) in delivering essential healthcare services. However, these facilities often suffer from inadequate maintenance, exacerbated by the insufficient implementation of maintenance strategies. Recognizing the importance of PHFs in enhancing healthcare services, this paper investigates the Critical Success Factors (CSFs) in the maintenance strategies of PHFs in South Africa.

Through semi-structured interviews with nineteen purposively selected maintenance personnel from the Limpopo Department of Health (DoH), this study identified and analyzed the CSFs to enhance maintenance operations in PHFs. Thematic content analysis was employed to derive key insights from the collected data.

The study's findings highlight adequate maintenance planning and effective leadership as the two overarching CSFs in the maintenance of PHFs. These factors play a pivotal role in addressing challenges that hinder the current maintenance team from meeting maintenance requirements to the satisfaction of both staff and patients within PHFs.

The study offers valuable insights for policymakers to improve the effectiveness of maintenance operations in PHFs. By addressing the identified CSFs, policymakers can enhance maintenance operations in PHFs, positively impacting healthcare service delivery and the well-being of both staff and patients.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Abnormal behaviors such as rework, backlog, changes and claims generated by project organizations are unavoidable in complex projects. When abnormal behaviors emerge, the previously normal state of interactions between organizations will be altered to some extent. However, previous studies have ignored the associations and interactions between organizations in the context of abnormal organizational behaviors (AOBs), making this challenging to cope with AOBs. As a result, the objective of this paper is to explore how to reduce AOBs in complex projects at the organizational level from a network perspective.

To overcome the inherent limitations of a single case study, this research integrated two data collection methods: questionnaire survey and expert scoring method. The questionnaire survey captured the universal data on the influence possibility of AOBs between complex project organizations and the expert scoring method got the influence probability scores of AOBs between organizations in the case. Using these data, four organizational influence network models of AOBs based on a case were developed to demonstrate how to destroy AOBs networks in complex projects using network attack theory (NAT).

First, the findings show that controlling AOBs generated by key organizations preferentially and improving the ability of key organizations can weaken AOBs network, enabling more effective coping strategies. Second, the owners, government, material suppliers and designers are identified as key organizations across all four influence networks of AOBs. Third, change and claim behaviors are more manageable from the organizational level.

Project managers can target specific organizations for intervention, weaken the AOBs network by applying NAT and achieve better project outcomes through coping strategies. Additionally, by taking a network perspective, this research provides a novel approach to comprehending the associations and interactions between organizations in the context of complex projects.

This paper proposes a new approach to investigating AOBs in complex projects by simultaneously examining rework, backlog, change and claim. Leveraging NAT as a novel tool for managing the harmful effects of influence networks, this study extends the knowledge body in the field of organizational behavior (OB) management and complex project management.

The purpose of this paper is to examine the perception and experience of counter pharmaceuticals in Nigeria compared to the European Union.

The findings from this paper are based upon a survey of 362 Nigerians sourced in public places with the data and then compared results from a survey by the European Union.

The key findings from this research showed some areas of similarity between Nigerians and Europeans on their views on the acceptability of purchasing counterfeits. However, Nigerians were more open to purchasing counterfeits despite the risks. There was also more anxiety over the risk of purchasing counterfeit medicines compared to the European Union.

Due to security issues, survey data could only be sourced from Abuja, Lagos and the relatively peaceful southwestern region of Nigeria. The data is, therefore, not representative of Nigeria.

The purchasing experience of counterfeit medicines by Nigerians along with their anxieties highlights the need for more enforcement action to tackle this problem and reassure the public.

The paper highlights the challenges of securing genuine pharmaceutical products in the Global South and the need for more greater cross-border action to tackle the problem.

To the best of the authors’ knowledge, this is the first survey of Nigerian citizens on this issue.