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Africa's business environment (BE) is characteristically unfriendly and poses severe development challenges. This study evaluates the impact of business climate on productivity in sub-Saharan Africa (SSA).

Macroeconomic data for 51 sub-Saharan African economies from 1990 to 2018 are employed for the analysis. The seemingly unrelated regression model is used to address inter-sectorial linkages.

The study uncovers several findings. First, a high start-up cost substantially leads to productivity losses by limiting the funds available for investment in productivity-enhancing labour and technology and limiting the number of businesses that see the light of day. The productivity impacts of start-up costs are most enormous for industry, followed by services and agriculture. Second, economies with favourable financing environments tend to be more productive economy wide and sector wise. Third, high taxes and tax inefficiency lower productivity by reducing the resource envelope of firms, thus lowering investment amounts. Fourth, poor business infrastructure inflicts the most damage on productivity. Lastly, business administration and macroeconomic environments impact sectoral and economy-wide productivity.

SSA economies must strive to lower the cost of starting a business as high start-up costs injure productivity. One way of reducing start-up costs is to create a one-stop shop for registering and formalising a business. Another way is to automate business registration and administrative processes to reduce red tape and corruption.

The authors extend the body of knowledge by analysing sectoral and economy-wide productivity effects of various business climate indicators while accounting for inter-sectoral linkages, cross-sectional dependence and endogeneity.

The construction industry has long been criticized for unethical conduct. The owner usually manages the contractor's opportunistic behaviors by employing a professional supervisor, but there is a risk of covert collusion between the supervisor and contractor. Based on the principal–agent theory and collusion theory, this paper aims to investigate optimal collusion-proof incentive contracts.

This paper presents a game-theoretic framework comprising an owner, supervisor and contractor, who interact and pursue maximized self-profits. Built upon the fixed-price incentive contract, cost-reimbursement contract, and revenue-sharing contract, different collusion-proof incentive contracts are investigated. A real project case is used to validate the developed model and derived results.

This paper shows that the presence of unethical collusion undermines the owner's interests. Especially, the possibility of agent collusion may induce the owner to abandon extracting quality information from the supervisor. Furthermore, information asymmetry significantly affects the construction contract selection, and the application conditions for different incentive contracts are provided.

This study still has some limitations that deserve further exploration. First, this study explores contractor–supervisor collusion but ignores the possibility of the supervisor abusing authority to extort the contractor. Second, to focus on collusion, this paper ignores the supervision costs. What's the optimal supervision effort that the owner should induce the supervisor to exert? Finally, this paper assumes that the colluders involved always keep their promises. However, what if the colluders may break their promises?

Several collusion-proof incentive contracts are explored in a project management setting. The proposed incentive contracts can provide the project owner with effective and practical tools to inhibit covert collusion in construction management and thus safeguard construction project quality.

This study expands the organization collusion theory to the field of construction management and investigates the optimal collusion-proof incentive contracts. In addition, this study is the first to investigate the effects of information asymmetry on contract selection.

The purpose of this study is to examine households’ behavior towards dirty cooking energy utilisation in an environment where relatively higher accessibility to clean energy is noted. Although the low utilisation rate of clean energy can partly be attributed to utility gains anticipated in dirty energy mixes (DEMs) arising out of accessibility constraints, affordances and enablers, it is still unclear on the extend at which each of these contributes towards DEMs manifestation among the seemingly well-to-do households with higher levels of clean energy mixes (CEM) access. This study, therefore, hinges on scrutinising on this lower utilisation patterns despite a seemingly higher accessibility of CEMs, specifically liquified petroleum gases (LPG).

The study is based on a household’s survey that was carried out in 2018, reaching a sample of 393 households using questionnaires in four wards of the Kigamboni district in Tanzania. Subsequent analyses were descriptive as well as inferential based on binary logistic regression analysis where utilisation of DEMs was predicted for both the high and low social economic status (SES) households by incorporating accessibility constraints, affordances and enablers.

The results show, first, if one assumes energy stacking is not an issue, as households become more constrained towards CEMs utilisation, they shift towards DEMs suggesting that the overall effect is a substitution, and second, the complementarity effect ultimately outweighs the substitution effect as households do not shift from DEMs to CEMs rather stack multiple energy. DEMs flourish in this case study area because those with high income are among those in the lowest SES, and some of those with the highest SES are from among the lowest income category, and all of them end up with more DEMs because shifting towards CEMs require income to complement SES.

Policy-wise, removing hurdles in accessing CEMs such as LPG subsidy programme, gas stove provision to the poor, and enhanced LPG awareness will most likely benefits only those who do not stack energy in cooking while strategies targeting those at the lowest SES such as higher education attainment, empower women as a family decision maker, encourage co-occupancy to enlarge the household size and contain urban growth within certain perimeter will have a significant impact only if they raise both incomes and SES.

Despite of the dominance of DEMs for cooking such as charcoal and firewood in Tanzania, CEMs such as LPG, have emerged as complements or alternatives in the household energy basket. The utilisation of such CEMs is, however, still very low despite the accessibility, cost, environmental and health advantages they offer. Accessibility is not the only factor fuelling CEMs; a complementarity must exist between SES and income for the positive transition towards CEMs to be realised.

This paper develops a decision-analysis model to prioritize and select the site to establish a new hospital over different indicators such as cost, market conditions, environmental factors, government factors, locations and demographics. In this way, an integrated model is proposed under the intuitionistic fuzzy information (IFI), the standard deviation (SD), the rank-sum (RS) and the measurement of alternatives and ranking using the compromise solution (MARCOS) approach for ranking hospital sites (HSs).

The IF-SD-RS model is presented to obtain the combined weight with the objective and subjective weights of diverse sub-criteria and indicators for ranking sites to establish the hospital. The IF-MARCOS model is discussed to prioritize the various sites to establish the hospital over several crucial indicators and sub-criteria.

The authors implement the developed model on a case study of HSs assessment for the construction of new hospital. In this regard, inclusive set of 6 key indicators and 18 sub-criteria are considered for the evaluation of HSs. This study distinguished that HS (h₂) with combined utility function 0.737 achieves highest rank compared to the other three sites for the given information. Sensitivity analysis is discussed with different parameter values of sub-criteria to examine how changes in weight parameter ratings of the sub-criteria affect the prioritization of the options. Finally, comparative discussion is made with the diverse extant models to show the reasonability of the developed method.

This study aims to develop an original hybrid weighting tool called the IF-SD-RS model with the integration of IF-SD and IF-RS approaches to find the indicators' weights for prioritizing HSs. The developed integrated weighting model provides objective weight by IF-SD and subjective weight with the IF-RS model. The model presented in the paper deals with a consistent multi-attribute decision analysis (MADA) concerning the relations between indicators and sub-criteria for choosing the appropriate options using the developed IF-SD-RS-MARCOS model.

This study aims to present an innovative sandbox platform that implements a decision support system (DSS) to assess the sustainable development goals (SDGs) addressed at the municipal level. It intends to determine the relative importance of each SDG in municipalities and explore the synergies that can be discovered among them.

Participatory action research is used to develop a DSS and an algorithm designated as discrete heavy fuzzy was also developed, which extends the Apriori algorithm to include discrete quantitative assessments of the level of SDG compliance by each project. A scenario consisting of three municipalities in Portugal (i.e. Porto, Loulé and Castelo de Vide) was chosen to demonstrate the implementation of the sandbox platform and to interpret the observed results.

The results reveal significant differences in the typology of SDGs addressed by each municipality. It was found that municipal sustainable projects are strongly influenced by the contextual factors of each municipality. Porto has projects that address the first five SDGs. Loulé appears projects that promote innovation, the fight against climate change and the development of sustainable cities. Castelo de Vida has initiatives related to innovation and infrastructure and decent work and economic growth.

This study provides knowledge about the relative importance of the SDGs in Portuguese municipalities and explores the synergies among them. The proposed sandbox platform fills the gaps of the ODSlocal Webtool by proposing a dynamic and interactive approach for the exploration of quantitative indicators regarding the implementation status of the SDGs established in the 2030 Agenda.

This study provides knowledge about the relative importance of the SDGs and the various synergies that exist between them considering the Portuguese municipalities. The sandbox platform presented and developed within this study allows filling the gaps of the ODSlocal Webtool that gathers essentially qualitative information about each project and offers a dynamic and interactive exploration with quantitative indicators of the implementation status of the SDGs established in the 2030 Agenda.

Achieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.

An integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.

The results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.

A new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.

The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection.

“Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review.

Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news.

The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.

This study examines the effect of social media marketing on voting intention applying a combination of fuzzy logic methodology and a multidimensional panel data model.

The study adopts a multidimensional panel data method that includes several fixed effects. The dependent variable is a multifaceted construct that measures the participants’ intention to vote. The independent variables are electronic word of mouth (eWOM), customisation (CUS), entertainment (ENT), interaction (INT), trendiness (TRD), candidate’s perceived image (CPI), religious beliefs (RB), gender and age. The grouping variables that signify fixed effects are employment status, level of education, mostly used social media and religion. First, the significance of said fixed effects was tested through an ANOVA process. Then, the main model was estimated, including the significant grouping variables as fixed effects.

Employment status and level of education were significant fixed effects. Also, eWOM, ENT, INT, CPI, RB and gender significantly affected participants’ voting intention.

Being based on a questionnaire that asked participants about how they perceive different aspects of social media, the present study is limited to their perceptions. Therefore, further studies covering the voters’ behaviour in action could be efficient complements to the present study.

The findings could guide the political parties into prioritizing the aspects of social media in forming an effective campaign resulting in being elected.

The findings have the potential to help the public in making better informed decisions when voting. Furthermore, the results of this study indicate applications for social media which are beyond leisure time fillers.

Fuzzy logic and multidimensional panel data estimates are this study’s novelty and originality. Structural equation modelling and crisp linguistic values have been used in previous studies on social media’s effect on voting intent. The former refines the data gathered from a questionnaire, and the latter considers the possibility of including different grouping factors to achieve a more efficient and less biased estimation.

Individuals generate plenty of waste that is affecting the life while consumption of air and water at the base. The increasing industrialization, population and waste generation without proper measures of waste management are leading to major challenges to environmental sustainability. Considering these challenges, the present study focuses on the types and sources of waste generation and waste reduction by encouraging the reduction, recycling and reuse of waste products. The study aims to provide a well-functioning sustainable waste management system, that incorporates feedback loops, focuses on processes, embodies adaptability and diverts waste from disposal.

The university under study is situated at the central location of Pune City in India. The university has diverse units like academic and admin buildings, canteens and mess, hostels, a clinic, workshops and gardens. To fulfil the objective of this study a qualitative case study approach of research was adopted. A total of thirty-three representatives and waste management personnel from various units of the university were interviewed. The interviews were semi-structured and the duration of it was around 25–55 min. The interview transcripts were coded, and qualitative analysis was conducted.

This study proposes a strategic sustainable waste management model for environmental sustainability that brings circularity by closing the loops and focusing on sustainable development goals.

The findings of this research can guide universities to manage the waste generated through various sources and attain sustainable development goals and environmental sustainability at large by closing the loops. The study provides insights into waste management and environmental sustainability. The universities can make their resources more circular by following the strategies of reducing, reusing and recycling (3R). This study recommends customization according to the needs of specific universities and institutions. Researchers can take this study further by testing and customizing it as per requirement. Also, an effort can be extended to implement the model in other related areas.

This research is a unique attempt to advance knowledge of waste management practices for sustainable development by exploring different techniques opted by for individual entities from the university campus to understand the environmental impact.

This study aims to explore the critical application areas of radio frequency identification (RFID) technology for sustainable buildings.

The quantitative research approach was adopted through a structured questionnaire administered to relevant stakeholders of construction projects. The data collected were analysed with the exploratory factor analysis, relative importance index (RII) and fuzzy synthetic evaluation (FSE).

The study’s results have categorised the crucial areas of application where construction industry stakeholders should focus their attention. These areas are divided into four categories: management technologies, production technologies, sensing technologies and monitoring technologies. The findings from the FSE indicate that monitoring technologies represent the most significant category, whereas management technologies rank as the least significant. Moreover, the RII analysis highlights that tools management stands out as the most important application of RFID, while dispute resolution emerges as the least significant RFID application.

The study establishes the core areas of RFID application and their benefits to sustainable buildings. Consequently, it helps stakeholders (consultants, clients and contractors) to examine the RFID application areas and make informed decision on sustainable construction. Furthermore, it provides systematic proof that can aid the implementation of RFID in developing countries.

The study provides an insight into the possible application areas and benefits of RFID technology in the construction industry of developing countries. It also developed a conceptual frame for the critical application areas of RFID technology in the construction industry of developing countries.