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The purpose of this study is to examine what factors affect the exit of small and medium-sized enterprises (SMEs) from tendering for public works in the Japanese construction industry using the Keiei Jikou Sinsa or Keisin (the database for evaluation of construction companies in Japan).

This study empirically analyzes SMEs’ exit using the binary logit model. For the empirical analysis, it uses the scores as well as financial and non-financial performance indicators of Keisin data.

The Keisin scores (the total score and W score), financial performance indicators (cash flow from operations and capital) and non-financial performance indicators (having unemployment insurance and operating years) significantly affect SME exits. Although the Keisin data are used for bid entry qualifications of public works, they can be applied to a factor analysis of the exit of SMEs in the construction industry.

As there exists little empirical analysis of the exit of SMEs globally, this study contributes to the research on this phenomenon.

The purpose of this paper is to examine the factors influencing defaults and exits from public works by prime contractors of small and medium-sized enterprises in the Japanese construction industry. By analysing the data for several years as panel data, this study determines the extent of influence of changes in company characteristics on the defaults and exits.

Using construction company evaluation (Keiei Jikou Sinsa or Keisin) data and by employing the panel binary logit random effect model, this study empirically analyses the construction industry.

This study shows that defaulting and exiting companies have different characteristics. The result shows financial performance indicators, non-financial performance indicators and Keisin scores to significantly affect defaults and exits. In particular, this study finds non-financial performance indicators, such as whether a firm draws insurance, to significantly affect its likelihood to default or exit and the influence varies on the basis of insurance type.

The feature of this study is that its analysis focuses not only on defaulting companies but also on exiting companies, defined as those that stop operating as prime contractors for public works but otherwise stay in business. In contrast to existing research, this study distinguishes between defaults and exits and analyses the factors that influence a firm following one of these two outcomes. Moreover, although Keisin data are usually used to determine whether companies qualify to enter bids for public works, they can be applied for an attribution analysis of corporate defaults and exits.

The main objective of this study was to establish a model of competitiveness factors to measure the value of construction companies adequately.

A theoretical model of four main factors was developed, and they include human capital, ethical values, process innovation and financing. Information was collected from 18 construction companies in the city of Metropolitan Lima, collecting information on each of these factors. Through an analysis of the principal components, the weighting of each factor about the competitiveness of a construction company was determined.

The cost of person-hours, the cost of equipment to execute work, and the cost of materials used are the strongest indicators to measure the competitiveness of a construction company. On the other hand, the number of employees holding university degrees and the number of master builders holding technical degrees in the human capital factor also have a moderate weight in determining the competitiveness value of a company. The indicator of work delivered within the deadline also moderately affects competitiveness. Finally, the monthly debt payment indicator has a very small effect on the company’s competitiveness.

In conclusion, this study provides evidence of a competitiveness model with highly related factors on human capital, process innovation and ethical values as the most important in measuring competitiveness.

El objetivo principal de la investigación fue establecer un modelo de factores de competitividad para medir adecuadamente el valor de las empresas constructoras.

Se desarrolló un modelo teórico de cuatro factores principales, que incluyen el capital humano, los valores éticos, la innovación de procesos y el financiamiento. Se recolectó información de 18 empresas constructoras de la ciudad de Lima Metropolitana, recolectando información de cada uno de estos factores. A través de un análisis de componentes principales se determinó la ponderación de cada uno de ellos sobre la competitividad de una empresa constructora.

El costo de horas hombre, el costo de los equipos para ejecutar obras y el costo de material utilizado son los indicadores más fuertes para medir la competitividad de una empresa constructora. Por otro lado, la cantidad de empleados con títulos universitarios y la cantidad de maestros de obra con título técnico del factor de capital humano también tienen un peso moderado en la determinación del valor de competitividad de una empresa. El indicador de obras entregadas dentro de plazo también tiene un efecto moderado sobre la competitividad. Finalmente, el indicador de pago mensual de la deuda tiene un efecto muy pequeño sobre la competitividad de la empresa.

En conclusión, el estudio proporciona evidencia de un modelo de competitividad con factores altamente relacionados sobre el capital humano, la innovación de procesos y los valores éticos como los más importantes a la hora de medir la competitividad.

The Indian hospitality and tourism industries, major economic growth drivers and employment generators, have been greatly affected by the outbreak of the COVID-19 pandemic. In FY 2020, the Indian tourism sector created 39 million jobs and contributed nearly US$194.3bn, or 6.8%, to India’s gross domestic product. The purpose of this study is to focus on ranking 22 listed hotels and 9 listed travel agencies in India based on their performance across 14 selected financial parameters in both the pre-COVID-19 year ending in March 2019 and the post-COVID-19 year ending in March 2021 to understand how the pandemic affected their businesses.

This research proposes to analyze the impact of the COVID-19 pandemic on the financial performance of 22 listed Indian hotels and 9 listed travel agencies evaluated over 14 financial parameters using a pipeline of two recently developed multicriteria decision-making techniques, method based on the removal effects of criteria (MEREC) and grey-based combined compromised solution (CoCoSo). First, the criteria weights are objectively determined using MEREC, and then the financial performances of the selected companies in both the hospitality and tourism industries are separately assessed using CoCoSo to get their overall performance score, based on which the companies are ranked in order of preference.

It was observed that Westlife Development, Lemon Tree Hotels, Indian Tourism Development Corporation, Royal Orchid and Country Club performed significantly poorer than their peers in the aftermath of the pandemic, whereas EIH, Advani Hotels and Resorts and TGB Banquets performed relatively better. Travel agencies Easy Trip and International Travel House performed particularly poorly because of the pandemic, but VMV Holidays performed relatively better in FY 2021.

The findings of the analysis will aid portfolio construction, corporate investment decisions, competition research, government policymaking and industrial analysis.

The proposed model is novel because it fills the research gap in the application of the integrated MEREC–CoCoSo method to study the impact of COVID-19 on the hospitality and tourism sectors in India.

Building information modeling (BIM) is vital in the performance improvement of contracting firms. Thus, there is a need to stimulate its implementation in the construction sector with a view to meeting the competitive demands of the industry. However, there have been very few studies on BIM implementation among contracting firms in Nigeria. Hence, this study aims to examine the current BIM uptake among Nigerian contracting firms, assess the barriers to BIM implementation and examine the ways of improving BIM implementation within these contracting firms.

A questionnaire survey was conducted on contracting firms (comprising small-, medium- and large-sized firms) in Lagos, Nigeria. The obtained data were analyzed using both descriptive and inferential statistics. These included percentage, mean score, standard deviation, the Kruskal–Wallis test and factor analysis.

This study revealed the current state of BIM implementation among contracting firms in Nigeria in terms of their knowledge of BIM, their usage of BIM as well as the BIM software adopted. Furthermore, the study identified 25 barriers to BIM implementation in contracting firms and identified 15 ways to improve BIM implementation in contracting firms. The relative importance of both the identified barriers and the ways for improving BIM implementation was gauged among contracting firms comprising small-, medium- and large-sized firms. The result of the Kruskal–Wallis test revealed that, except for two (out of 25) identified barriers, and one (out of 15) identified ways of improving BIM implementation in contracting firms, there is no significant statistical difference in the perceptions of the three respondents’ groups. The result of the factor analysis categorized the identified 25 barriers into seven main factors.

This study provides empirical evidence on the barriers to BIM implementation and the ways of improving its implementation among contracting firms, thereby providing a better insight of the Nigerian construction industry’s BIM environment.

This study’s findings can positively inform the decisions of construction stakeholders to formulate strategies capable of improving BIM implementation in the construction industry at large.

According to the construction literature, the number of projects applying integrated project delivery (IPD) principles is expected to increase in the new-normal era. However, given that the pandemic is not yet fully over, accurate and measurable data are not yet available. Also, there is a lack of empirical studies that could provide guidelines as to the application of IPD principles at the various stages of construction projects. Thus, the goal of this paper is to address this knowledge gap through case studies.

This paper follows a multi-step research methodology, namely, a literature review, case study and focus group discussions in the context of Canadian construction projects.

Based on the conducted literature review and focus group discussions, the authors identified: (1) new challenges in the various stages of the construction projects' life cycle, (2) their related proximity aspects (technological, organizational, geographical and cognitive) and (3) IPD principles that can address the identified challenges within their associated proximities. The results show that IPD relational principles can improve a project's organizational and cognitive proximities, while IPD digital integrative principles can enhance a project's geographical and technological proximities.

This study contributes to the theoretical checklists of challenges that the construction industry has experienced since the beginning of the pandemic, and to the practical guidelines of implementation of IPD principles to meet these challenges. The conducted case studies are timely and relevant, and their results provide new insights for key project stakeholders into the application of IPD to tackle new-normal challenges based on their proximity perspectives.

The impacts of COVID-19 on construction projects have attracted much attention in the construction management research community. Nevertheless, a systematic review of these studies is still lacking. The purpose of this paper is to systematically analyze the impacts of COVID-19 on the different stages of a project life-cycle, and comprehensively sort out the epidemic response measures adopted by project participants. In addition, the study also attempts to explore the challenges and opportunities faced by project management practitioners under the context of COVID-19.

This study comprehensively demonstrates the systematic review process of COVID-19 related research in the construction industry, systematically summarizes the research status of the impact of COVID-19 on construction projects, and defines the strategies to deal with COVID-19 in project management; and through the visualization research, determines the current key research topics and future research trends.

This study identifies 11 construction activities in the project management life cycle that are affected by COVID-19 and finds that the COVID-19 epidemic has the greatest impact on construction workers, construction standards, construction contracts and construction performance. The study further summarizes the six main epidemic countermeasures and mitigation measures taken within the construction industry following the arrival of the epidemic. In addition, the results of this study identify opportunities and future trends in intelligent construction technology, rapid manufacturing engineering and project management in the construction industry in the post-epidemic era through literature results, which also provide ideas for related research.

COVID-19 has brought severe challenges to society. It is of great significance for the future sustainable development of the construction industry to identify the impact of COVID-19 on all phases of the project and to promote the development of coping strategies by project stakeholders.

First of all, there is little study comprehensively reviewing the impacts of COVID-19 on the different stages of construction projects and the strategies to deal with the negative impacts. In addition, from a life cycle perspective, the used articles in this study were grouped into different categories based on project stages. This promotes an integrated and comprehensive understanding of historical studies. Moreover, on the basis of a comprehensive review, this paper puts forward future research directions to promote the sustainable development of the construction sector.

This study aims to propose a method known as the fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) for complex project selection in organizations. To fulfill study objectives, the factors responsible for making a project complex are collected through literature review, which is then analyzed by fuzzy TOPSIS, based on three decision-makers’ opinions.

The selection of complex projects is a multi-criteria decision-making (MCDM) process for global organizations. Traditional procedures for selecting complex projects are not adequate due to the limitations of linguistic assessment. To crossover such limitation, this study proposes the fuzzy MCDM method to select complex projects in organizations.

A large-scale engine manufacturing company, engaged in the energy business, is studied to validate the suitability of the fuzzy TOPSIS method and rank eight projects of the case company based on project complexity. Out of these eight projects, the closeness coefficient of the most complex project is found to be 0.817 and that of the least complex project is found to be 0.274. Finally, study outcomes are concluded in the conclusion section, along with study limitations and future works.

The outcomes from this research may not be generalized sufficiently due to the subjectivity of the interviewers. The study outcomes support project managers to optimize their project selection processes, especially to select complex projects. The presented methodology can be used extensively used by the project planners/managers to find the driving factors related to project complexity.

The presented study deliberately explained how complex projects in an organization could be select efficiently. This selection methodology supports top management to maintain their proposed projects with optimum resource allocations and maximum productivity.

This paper aims to explore the key risk factors affecting the Personnel Localization Management of international construction projects under the major public emergencies represented by the novel coronavirus pneumonia pandemic (hereinafter COVID-19) and how the public emergency affected the Personnel Localization Management from three levels: staff turnover rate, the number of different personnel, the salary and performance of workers. The paper also helps to enhance the construction enterprises' response capacity of major public emergencies and provides a comprehensive framework of optimization strategies for the Personnel Localization Management of international construction projects (hereinafter projects).

The main research method of this paper is the case study, and ten representative international construction projects are selected for case study in China construction enterprises (hereinafter CCE). And this study used the failure mode and effects analysis (FMEA) and comparative analysis to find out all potential risk factors under the COVID-19 and analyze how the epidemic affects the Personnel Localization Management of projects which based on the primary data from 10 projects obtained through in-depth interviews and the secondary data from China First Metallurgical Group and Central South Construction Group's Overseas Enterprise.

The findings show that the outbreak of the major public emergencies not only greatly increased eight risk factors but also directly led to an increase in staff turnover rate. Meanwhile, the numbers of Chinese and local managers and workers are all affected, and an increase in the number and the salary performance of local workers can be reduced, to a certain extent, to the cost-to-output ratio of the projects. The findings would help construction enterprises better cope with Personnel Localization Management and enhance the response capacity of major public emergencies.

This study will broaden researchers' horizons regarding “Personnel Localization Management under major public emergencies” and “risk factors of Personnel Localization Management in an international context.” Furthermore, construction enterprises looking for a better mechanism of Personnel Localization Management can benefit from research findings and lessons learned from the authors' case study during or before an outbreak of major public emergency. Lastly, the framework of optimization strategies for Personnel Localization Management can be used both for research purposes and practice issues in international construction projects.

The findings from the authors' case study offer the direction for international construction enterprises in China and other countries to formulate effective measures, strengthen overseas business and establish a crisis management mechanism for Personnel Localization Management under major public emergencies, and the findings provide emergency plans for projects to improve the public crisis handling capacity and respond to major public emergencies such as the COVID-19.

This study analyzes the impact of the COVID-19 on the Personnel Localization Management of international construction projects from the perspective of personnel. This study provides a theoretical reference for the international construction industry to actively respond to major public emergencies. Besides, the research is conducive to improving the emergency response mechanism in the construction industry, and further promoting the high-quality and globalized development of international construction.

This study provides other researchers with a comprehensive understanding of the risk factors affecting the Personnel Localization Management of projects under the COVID-19 and insight for further research on localization management, risk management, and project management.

The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a construction cost index (CCI) for Jordan’s construction industry using fuzzy analytic hierarchy process (FAHP) and predict future CCI values using traditional and machine learning (ML) techniques.

The most influential cost items were selected by conducting a literature review and confirmatory expert interviews. The cost items’ weights were calculated using FAHP to develop the CCI formula.

The results showed that the random forest model had the lowest mean absolute percentage error (MAPE) of 1.09%, followed by Extreme Gradient Boosting and K-nearest neighbours with MAPEs of 1.41% and 1.46%, respectively.

The novelty of this study lies within the use of FAHP to address the ambiguity of the impact of various cost items on CCI. The developed CCI equation and ML models are expected to significantly benefit construction managers, investors and policymakers in making informed decisions by enhancing their understanding of cost trends in the construction industry.