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In developing countries such as India, start-ups play an essential role in “industrial output,” “Gross Domestic Product ” and “employment creation.” Evidence suggests that pandemics have risen over the last century due to rising global travel and assimilation, urbanization, alterations in land use, and significantly larger exploitation of the natural environment. These trends are likely to continue and intensify. These pandemic episodes affect businesses, especially start-ups. Supplier selection is among the vital critical elements that start-ups must include in start-ups' strategy procedures during the pandemic episodes. This study's focus is to “identify,” “analyze,” and “categorize” the factors affecting supplier selection in start-ups during frequent pandemic episodes like coronavirus disease 2019 (COVID-19).

Through “literature review” and “experts' opinion” from various start-ups in India, ten affecting factors were identified. Total Interpretative Structural Modeling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) were employed to analyze the interrelationship among the factors affecting the supplier selection on start-ups during frequent pandemic episodes, and these factors were ranked as “autonomous,” “independent,” “linkage,” and “dependent” factors.

The findings show that “performance history,” “service levels,” “technical capability,” and “financial stability” are the most critical factors affecting the supplier selection on start-ups during frequent pandemic episodes. The next importance should be safety and environmental concern” and “quality.”

The factors affecting supplier selection on start-ups during frequent pandemic episodes are the current focus of this study. This study is mainly performed on Indian start-ups and can be extended to other countries.

The start-ups can rely on this study to clearly understand the factors affecting the supplier selection on start-ups during frequent pandemic episodes.

There is no research regarding factors affecting supplier selection on start-ups during the COVID-19 emergencies. This research gap is filled by analyzing aspects linked to supplier selection in start-ups. This gap inspired the present study, which employs the “Total Interpretive Structural Modeling (TISM)” technique to uncover supplier selection determinants and investigate hierarchical interconnections among factors influencing/affecting supplier selection in start-ups during frequent pandemic episodes.

The global construction industry is significant to economic development, whereas the sector, particularly its small and medium sized enterprises (SMEs) have continued to suffer from low labour productivity for decades. This has given rise to the concern of relevant construction stakeholders on the need to address the challenges undermining labour productivity growth in construction. Hence, this study aims to conduct a meta-data analysis of factors that hamper productivity growth of construction SMEs in developing countries.

A systematic review of existing studies relative to factors affecting construction labour productivity (CLP) is presented. Thereafter, eight developing countries-based studies that are specific to SMEs were selected for meta-data analysis using relative importance index values from the studies.

The essential productivity influencing factors were identified and quantitative data of the selected studies were synthesised. The effect summaries derived from the meta-data analysis revealed that the most significant factors that negatively affect CLP amongst SMEs include: workers’ skills, inadequate training, rework, management style and incentive to labour.

The study is limited to scientifically analysed secondary data relative to SME contractors in developing countries.

The findings of the study can be adopted by construction stakeholders to evolve productivity growth policies for construction SMEs in developing countries.

Synthesis of quantitative data of different studies has lent deeper insight into a more realistic and scientific precision of factors affecting labour productivity of construction SMEs.

Contractors of different scales, operating in different construction industries of varying institutional and economic settings, have different considerations when making bid or no-bid and mark-up decisions. Focusing on the large and medium-sized contractors in the Jilin province, China, the purpose of this study is to examine important factors affecting their decision to bid (d2b) and mark-up decisions and investigate differences between large and medium-sized contractors in evaluating the importance of the various factors affecting their d2b and mark-up decisions.

This study used a survey design for timely data collection from a large population. Contractors’ bidding attitudes was collected using an online survey questionnaire with a list of 40 key factors. Statistical analytical methods were applied for comparing the two groups of contractors.

The results of this study indicate that factors related to client conditions are most critical for both large and medium-sized contractors in their d2b and mark-up decisions. The results also show statistically significant differences between the two groups of contractors on a subset of factors affecting their d2b and mark-up decisions. The large contractors have placed more emphasis on projects' potential financial and strategic benefits. Another notable finding is that both groups of contractors have placed great emphasise on “government legislations” in their d2b and mark-up decisions.

These findings should be interpreted in consideration of several limitations. Firstly, the sample size is relatively small, and the focus was on a single province in the China construction industry. Next, this study only explores differences between large and medium-sized contractors in evaluating the importance of the various factors affecting their d2b and mark-up decisions.

Contractors could refer list of critical factors in competing for jobs in Jilin province or other provinces of similar institutional and economic settings. Construction clients, on the other hand, should consider the list of critical factors in the formulation of their competitive tendering procedures, thus enhancing the efficiency in their procurement of construction services.

Research on contractors’ bidding decision-making in the context of Chinese construction industry remains scarce; the research findings have implications for the industry stakeholders.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

With the aim to provide a global view of factors affecting mark-up size on construction projects, this study performs a meta-analytical review of the relevant studies over the past 20 years.

The analytical process involved the identification and evaluation of the importance of critical factors affecting mark-up size on construction projects, and the assessment of the generalisability of findings of the meta-analysis. A random-effects model was adopted in the statistical meta-analysis.

The results show that there are 23 critical factors, and the top five factors are: (1) competitiveness of other bidders; (2) number of bidders; (3) relationship and past experience with client; (4) experience on similar project; and (5) project size. A heterogeneity test further shows that there is no statistically significant heterogeneity across the studies, reinforcing the generalisability of the findings to a global context.

The list of critical factors from a global perspective should form a good basis for future efforts in bidding model development.

The research findings have practical implications to both construction clients and contractors in formulating their contracting practices and strategies.

This is the first meta-analysis of a sizeable collection of replicated studies on factors affecting mark-up size on construction projects in the literature.

Increasing environmental pollution and destruction have damaged sustainability in polluting societies and organizations. For this reason, various environmental management programs have recently been developed by governments and the environmental protection agencies (EPAs) to control and reduce environmental pollution. One of these programs is environmental audit (EA), which has not been fully implemented yet in developing countries and it has many disadvantages. It seems that one of the reasons for the lack of perfect implementation of EA in these countries is the lack of adequate review of the factors affecting its implementation. The purpose of this paper is to identify the factors affecting EA implementation and then analysis of causal relationships between them.

Decision-making trial and evaluation laboratory (DEMATEL) technique was used to identify causal relationships of factors affecting EA implementation. Based on the literature review and interviewing nine experts in EA, 17 factors were identified to influence EA implementation. Six experts were selected for the evaluation using snowball sampling method. Using the DEMATEL approach, a cause and effect relationship diagram was generated through which the effect of factors was analyzed.

Seventeen factors were categorized in terms of cause and effect, and the interrelationships of factors were also analyzed. “Reducing environmental impacts and improving environmental performance,” “obtaining environmental management system certificates” and “contributing to the environmental goals of investment projects” are the most prominent factors on the basis of their prominent score.

Analysis in the research is highly dependent on expert judgments and opinions may be biased. However, the initial matrix obtained from the experts is hindered by the ambiguity about some relationships. But this can be improved by using fuzzy and gray set theories. The factors used for the analysis may not be comprehensive and other researchers may find other factors.

Identifying the factors affecting EA implementation and analyzing causal relationships between them can be a guide and help governments to improve the implementation of EA or even develop this policy by being aware of the effect of the factors analyzed.

To the best of the authors’ knowledge, no comprehensive study of factors affecting EA implementation has been undertaken; this study is the first to identify these factors and analyze them using DEMATEL. Therefore, it is suggested that governments and EPAs in the policies focus on the significant factors.

This study aims to empirically analyze the symmetries and asymmetries among the critical factors affecting building information modeling (BIM) implementation between countries with different income levels. To achieve that aim, the study objectives are to identify: critical factors affecting BIM implementation in low-, lower-middle-, upper-middle- and high-income countries; overlapping critical factors between countries with different income levels; and agreements on the critical factors between countries with different income levels.

This study identified potential BIM implementation factors using a systematic literature review and semi-structured interviews with architectural, engineering and construction (AEC) professionals. Then, the factors were inserted into a questionnaire survey and sent to AEC professionals in Afghanistan, India, Malaysia and Saudi Arabia. The collected data was analyzed using the following techniques and tests: mean, standard deviation, normalized value, Kruskal–Wallis, Dunn and Mann–Whitney.

Five critical factors overlap between all countries: “availability of guidelines for implementing BIM,” “cost-benefit of implementing BIM,” “stakeholders’ willingness to learn the BIM method,” “consistent views on BIM between stakeholders” and “existence of standard contracts on liability and risk allocation.” Also, the criticality of the factors often differs between income levels, especially between low- and high-income countries, suggesting a significant gap between low- and high-income countries in BIM implementation.

This study differs from prior works by empirically analyzing the symmetries and asymmetries in BIM implementation factors between countries with different income levels (i.e. low-, lower-middle-, upper-middle- and high-income countries).

Although there are many efforts within organisations to improve the financial performance of business processes, the results of studies on the impact of internal factors on the financial performance of business processes in an organisation are inconsistent, even contradictory. Therefore, this paper aims to examine the extent and trends of the impact of factors inside companies on the financial performance of business processes and discover lessons learned to improve the financial performance of business processes.

This analysis was done through a quantitative study of listed companies in Vietnam. Pooled OLS regression, REM, FEM and robust regression were performed on 566 companies.

The results provide four main findings. First, firm size and operational efficiency strongly correlate with financial performance. Second, financial leverage has a negative, significant connection with financial performance. Third, net working capital has a positive and meaningful relationship with EPS and a negative association with ROE. Fourth, liquidity does not have any significant association with financial performance.

This study only restricts the internal factors affecting the financial performance of business processes without mentioning the external factors. Furthermore, this study is limited to one emerging country and has not been compared with companies in different countries.

The findings of this study may help inform users inside and outside the organisation to understand the factors that affect the financial performance of business processes. As a result, information users will focus more on aspects that can improve their financial performance to make informed decisions.

This study has many differences compared to previous studies. First, it focuses on the internal factors affecting the financial performance of business processes in non-financial listed companies in Vietnam, which has an emerging economy. First, it focuses on the internal factors affecting the financial performance of business processes in non-financial listed companies in Vietnam, which has an emerging economy. Second, this study analyses data in companies' financial statements for the ten years from 2012 to 2021, when the Vietnamese economy, in particular, and the world economy experienced many fluctuations due to the impact of the post-financial crisis 2007–2008 and the COVID-19 pandemic. Third, this study provides empirical evidence to support RBV, RDT theories and the trade-off theory of capital structure.

Small and medium contractors (SMCs) play a significant role in socioeconomic development. Their strong links with other sectors of the economy have a multiplier effect on any country’s growth. However, the construction business, especially for SMCs, is not an easy business as several roadblocks affect their sustenance. This study aims to examine the factors affecting the sustainable competency of SMCs emerging from the business environment in which the Ethiopian construction industry (CI) operates.

A literature review was conducted to identify 39 factors arising from five core sources (i.e. government policies, regulatory frameworks, industry networks, competitive bidding culture and construction technology and innovation). A questionnaire survey was conducted to gather industry stakeholders’ perceptions of the identified factors, and the results were analyzed using descriptive statistics.

Findings indicate 37 significant factors affecting sustainable competency arising from five sources, and the top factors from their respective sources were unfavorable financial policy; unfavorable economic regulatory framework; lack of trust between parties in the industry; inability of SMCs to compete with bigger construction companies; and poor linkages between CI and research and development institutions. Furthermore, factor analysis identified 12 components, and the top ones were competition and uncertainties in the supply chain; unsuitable bidding environment; and ineffective industry networks.

The findings will contribute to the body of knowledge on the factors affecting the sustainable competency of SMCs in the Ethiopian CI. They also indicate priority areas of competitiveness improvement and have implications for decision-makers.

This research intends to develop a model for predicting employees at a high-risk attrition and identify the most important factors affecting them.

In this study, using the triangulation technique of a mixed research method, the employee attrition problem is investigated by identifying its affecting factors. For that matter, data related to the human resources department of a pharmaceutical company in Iran are used. And to achieve the intended goal, advanced data mining algorithms and interviews with human resource managers are applied.

A model for predicting employees at a high-risk attrition is presented based on the gradient boosting machine algorithm with 89% accuracy. The use of the mixed research approach shows that qualitative and quantitative methods can be more effective in identifying the factors affecting employee churn or loss of staff. The results also contain a new situation arising out of the COVID-19 pandemic and remote working scenarios having impact on employee attrition. Finally, human resource policies are presented based on variables related to each of the identified factors.

The novel contributions of this study include real data related to a leading pharmaceutical company as well as a combination of two quantitative and qualitative methods. The hybrid approach can identify the reasons for attrition and, consequently, retention policies to benefit from the advantage of both approaches. Data mining can be useful to identify the factors, which are usually not mentioned in termination interviews, such as direct managers. On the other hand, the results obtained from termination interviews can also include features that the authors cannot identify through data mining, which are specifically related to the characteristics of the pharmaceutical industry such as building a more professional career path. From a practical perspective, since this company specializes in pharmaceutical marketing in a new way and is primarily comprised graduates, it is important to note that the churn of specialized people disperses organizational and technological know-how. On the other hand, the pharmacist community in Iran is small, and their attrition might adversely affect not only the reputation of an organization but the employer's brand as well. So, this research would help other similar firms in retaining their valuable human capital.