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The construction industry is facing an enormous number of challenges due to continuous advancements in construction technologies and techniques. Hence, construction management theories have to confront critical newly issues concerning market globalization and construction innovations. The key factor to address these challenges is to ameliorate the competitive abilities of the competing construction firms. In this context, measuring competitiveness of construction firms is an efficacious approach to amplify their competitive growth and profitability. To this end, the purpose of this research paper is to design a three-tier multi-criteria decision making model for competitiveness assessment and benchmarking of construction companies, meanwhile tackling a wide range of essential factors and attributes that covers broad aspects of the present competitive market.

In the first tier, four new pillars (4P) of competitiveness assessment are introduced for construction firms, namely, organization performance, project performance, environment and client and innovation and development. These pillars are able to aid in construction firms’ management on both long and short term basis. Hence, 21 key competitive factors and eighty key competitive criteria are identified, incorporated and analyzed in this research study. The second tier encapsulates carrying out a questionnaire survey in the Canadian and Vietnamese market to garner two main sets of information. The first set of information incorporates responses of the pairwise comparisons between competitiveness factors and criteria. The second set involves gathering utility scores pertinent to each competitiveness criteria. The developed model then leverages the use of analytical hierarchy process to scrutinize the relative importance priorities of competitiveness factors and criteria. The third tier of the developed model encompasses the use of multi-attribute utility theory to compute competitiveness scores for construction companies through blending criteria’ relative importance weights alongside their respective utility functions. In addition, the third tier comprises conducting a sensitivity analysis to derive the most important criteria influencing the overall competitiveness of construction companies. The developed model is tested and validated using three case studies; one construction company from Canada and two construction companies from Vietnam.

Results demonstrated that the developed model has a potential to render a synthesized and methodical performance evaluation for the competitive ability of a given construction company. Furthermore, it was found that Vietnamese companies are more considerate towards pillars pertaining to environment and client while Canadian companies are more attentive towards innovation and development. The outcome of sensitivity analysis revealed that effectiveness of cost management highly affects the competitive ability of Vietnamese companies while effectiveness of cost management exhibits the most significant influence on the competitive of Canadian companies.

The developed model can benefit construction companies to understand their competitiveness in their market and diagnose their strengths and weaknesses. It is also can be useful in efficient utilization of their limited resources and development of sustainable and long-term strategic plans strategic plans, which consequently leads to maintaining better position in their dynamic business markets.

Literature review manifests that reported competitiveness assessment models and practices are not able to address present challenges, technologies and developments in construction market.

This paper aims to comprehensively analyze renewable energy alternatives in Brazil, focusing on identifying the most suitable option for investment in the country’s sustainable development.

The study adopts the step-wise weight assessment ratio analysis-multiobjective optimization by ratio analysis −3NAG (a combination of three normalization methods) methodology, a multicriteria decision-making approach, to evaluate and rank renewable energy sources based on key criteria such as resource availability, cost-effectiveness, job creation potential and environmental impact.

The analysis reveals that solar energy emerges as the preferred choice for Brazil, offering significant advantages over other alternatives such as hydroelectric, wind and biomass energy. Solar energy’s distributed generation capability, cost reduction trends and positive environmental impact contribute to its favorable position in meeting Brazil’s energy needs.

While the study provides valuable insights into renewable energy selection, there are limitations regarding the criteria’ scope and the exclusion of specific renewable energy options. Future research could explore sensitivity analyses and incorporate additional criteria to enhance the study’s comprehensiveness.

This research contributes to the existing literature by thoroughly analyzing renewable energy alternatives in Brazil using a robust multicriteria decision-making methodology. The study’s findings provide actionable guidance for policymakers, businesses and stakeholders seeking to promote sustainable energy development in the country.

The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand.

It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites.

The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect.

This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

Innovation in financing processes, enabled by the advent of new technologies, has supported the development of alternative finance funding tools. In this context, the study analyses the growing importance of alternative finance instruments (such as equity crowdfunding, peer-to-peer (P2P) lending, venture capital, and others) in addressing the small and medioum enterprises' (SMEs) financing needs beyond traditional bank and market-based funding channels. By providing more flexible terms and faster approval times, these instruments are gradually reshaping the traditional bank-firm relationship.

To comprehensively understand this innovation shift in funding processes, the study employs a novel approach that merges three MCDA methods: Spherical Fuzzy Entropy, ARAS and TOPSIS. These methodologies allow for handling ambiguity and subjectivity in financial decision-making processes, examining the effects of multiple criteria, including interest rate, flexibility, accessibility, support, riskiness, and approval time, on the appeal of various financial alternatives.

The study’s results have significant theoretical and practical implications, supporting SMEs in carefully evaluate financing alternatives and enables banks to better identify the main “competitors” according to the “financial need” of the firm. Moreover, the rise of alternative finance, notably P2P lending, indicates a shift towards more efficient capital access, suggesting banks must innovate their funding channels to remain competitive, especially in offering flexible solutions for restructuring and high-risk scenarios.

The study advises top management that SMEs prefer traditional loans for their reliability and accessibility, necessitating banks to enhance transparency, innovate, and adopt digital solutions to meet evolving financing needs and improve customer satisfaction.

The study introduces a novel integration of Spherical Fuzzy TOPSIS, Entropy, and ARAS methodologies to face the complexities of financial decision-making for SME financing, addressing ambiguity and multiple criteria like interest rates, flexibility, and riskiness. It emphasizes the importance of traditional loans, the rising significance of alternative financing such as P2P lending, and the necessity for banks to innovate, thereby enriching the literature on bank-firm relationships and SME funding strategies.

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.

The cyber-physical system (CPS) is a well proven technology for improving system performance, resilience, and interconnectivity. In emerging nations like India, the logistics industry lacks practices connecting logistical equipment with cyberspace. This paper aims to bridge this gap by identifying and evaluating the performance metrics of connectivity solutions. Its goal is to establish an appropriate infrastructure that enables seamless connectivity within the CPS-enabled logistics ecosystem.

A novel integrated decision method is employed to classify the optimal connectivity solution for CPS. It integrates Regret Theory (RT) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-1) method in a Hesitant Fuzzy (HF) environment. This method considers the psychological traits of decision-makers and effectively incorporates their hesitancy for the classification.

The findings highlight security (c10) as the foremost critical performance metric, followed by cost (c6), scalability (c9), traceability (c2) and trustworthiness (c1) to build connective infrastructure for CPS. For extensive coverage scenarios, like freight transportation, cellular connectivity (a2) emerges as the most suitable connectivity solution.

This study provides a roadmap to logistics managers for selecting a suitable connectivity infrastructure to enhance seamless connectivity in logistics operations and processes. Technology providers can utilize the findings to develop the CPS infrastructure for effective freight logistics management.

This research introduces a novel decision-making tool for making choices related to advanced technology assessment. It holds significant value in facilitating well-informed decisions in the digital transformation era.

For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.

Metal laser-powder-bed-fusion using laser-beam parts are particularly susceptible to contamination due to particles attached to the surface. This may compromise so-called technical cleanliness (e.g. in NASA RPTSTD-8070, ASTM G93, ISO 14952 or ISO 16232), which is important for many 3D-printed components, such as implants or liquid rocket engines. The purpose of the presented comparative study is to show how cleanliness is improved by design and different surface treatment methods.

Convex and concave test parts were designed, built and surface-treated by combinations of media blasting, electroless nickel plating and electrochemical polishing. After cleaning and analysing the technical cleanliness according to ASTM and ISO standards, effects on particle contamination, appearance, mass and dimensional accuracy are presented.

Contamination reduction factors are introduced for different particle sizes and surface treatment methods. Surface treatments were more effective for concave design features, however, the initial and resulting absolute particle contamination was higher. Results further indicate that there are trade-offs between cleanliness and other objectives in design. Design guidelines are introduced to solve conflicts in design when requirements for cleanliness exist.

This paper recommends designing parts and corresponding process chains for manufacturing simultaneously. Incorporating post-processing characteristics into the design phase is both feasible and essential. In the experimental study, electroless nickel plating in combination with prior glass bead blasting resulted in the lowest total remaining particle contamination. This process applied for cleanliness is a novelty, as well as a comparison between the different surface treatment methods.

Awarding contracts based solely on the lowest price is unsuitable for every project. Consequently, most procurement systems in developed countries have progressed to the multicriteria selection practices (MSPs) for tender evaluation. MSPs consider a range of quality measures, such as completion time, life cycle cost, functional characteristics, environmental impact and innovation, alongside bid price. This study examines the prevailing MSPs in Sri Lankan public tender evaluations to enhance the effectiveness of the local tender evaluation process.

A desk study approach was employed to collect bidding documents, resulting in the identification of 66 documents. A systematic screening process was then applied to identify those bidding documents that incorporated MSPs. Subsequently, content analysis was conducted to determine the common features of the functions used in MSPs.

The study identified six primary functions related to MSPs incorporated in the bidding documents to procure building and substation projects. Three functions follow the price-to-quality method, while the remaining three follow the quality-to-price method. Among these identified functions, four functions employ objective evaluation criteria, such as thickness, capacity and operational loss. The other two functions utilize subjective evaluation criteria, such as the project’s design and technical specifications. Contract awarding will be based on either the highest score or the lowest bid, depending on the function type.

This study’s originality lies in exploring MSPs in the Sri Lankan public tender evaluation process and in disclosing their characteristics to promote the MSPs in Sri Lanka and developing countries.

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.