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Article

Post-COVID-19 Recovery: An Integrated Framework of Construction Project Performance Evaluation in China

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Department of Civil and System Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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School of Civil Engineering, Chang’an University, Xi’an 710064, China
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Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong 999997, China
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School of Design, The Hong Kong Polytechnic University, Hong Kong 999997, China
5
Department of Management, Lincoln International Business School, University of Lincoln, Lincoln LN6 7TS, UK
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Department of Land Economy, University of Cambridge, Cambridge CB3 9EU, UK
*
Author to whom correspondence should be addressed.
Systems 2023, 11(7), 359; https://doi.org/10.3390/systems11070359
Submission received: 14 May 2023 / Revised: 12 June 2023 / Accepted: 4 July 2023 / Published: 14 July 2023
(This article belongs to the Section Systems Engineering)

Abstract

:
With the lifting of the COVID-19 lockdown, the construction industry is gradually moving towards a new normality. This study aims to evaluate the construction project performance in the post-COVID-19 pandemic context and proposes a roadmap framework to achieve project recovery in China. This paper follows a sequential mixed methodology with three core steps. First, the critical success factors (CSFs) and key performance indicators (KPIs) are derived from literature reviews and expert interviews. Second, the study conducts a questionnaire survey with 150 experts. Third, the research implements factor analysis and analytic hierarchy process (AHP) analysis for CSFs and characteristics and comparative analysis for KPIs. Based on the results, the study employs structural equational modelling (SEM) to connect the CSFs and KPIs and develop a roadmap towards the post-COVID-19 pandemic recovery of the construction projects. The study identifies 32 CSFs and 25 KPIs and categorises them into five clusters, respectively. The SEM analysis suggests that management and technological innovation significantly contribute to achieving enterprise strategic goals and advancing industrial development. The consistency of project goals and external expectations also positively affect the satisfaction level of stakeholders and social impact. In addition, the AHP clarifies that the stability of the external environment, the internal support, and the adequacy of resources are critical drivers to the post-COVID-19 recovery of construction projects. This research proffers a roadmap towards the project recovery of the construction industry in the post-COVID-19 era by connecting the performance indicators and their critical success drivers. The findings would guide comprehensive design and construction, project life cycle management, and assist in dealing with public health emergencies in construction project management to maximise the organisation’s profits and positive social impact.

1. Introduction

The COVID-19 pandemic has resulted in dramatic global social changes and economic fluctuations. Due to the dynamic nature of the COVID-19 [1] and the compound frame of construction projects [2], the impacts of the pandemic manifested across several dimensions of the construction sector [3,4,5,6]. The growing health risk and worldwide city lockdowns swept the industry in early pandemic stages, where only approximately 35% of the construction organisations fully operated [5] and the averaged unemployment rate of construction workers increased by 95% in the US [7]. Most organisations developed and implemented pandemic response measures to mitigate the impacts on the operation of construction projects, such as remote working, social distancing, and regular health checks. Some contractors, however, blocked labours from outside contact at construction sites to reduce the risk of exposure to the virus. Even so, a large number of construction sites still experienced supply chain crises, professional labour shortages, financial and safety issues, and reduced productivity [4,7,8,9]. These unforeseen risks deeply affect the performance of construction projects and have a profound influence on the entire industry.
As the pandemic is brought under control, the global economy has gradually climbed out from the depths since 2022. The urban environments began rescinding temporary policy changes resuming pre-COVID activities and contributed to the recovery process of construction project performance [8]. The term project performance recovery refers to the process from a project being negatively affected by an external intervention to gradually achieving the project objectives, including time, expense, safety, quality, and sustainability [10]. In the aftermath of the COVID-19, however, there remains a significant number of uncertainties in the construction project performance recovery process. For example, contractors have to recruit a sufficient professional workforce in an environment of worker shortage and rebuild the supply chains in a short period to ensure the supply of construction materials. In addition, higher sustainability requirements pose additional challenges to construction project management in the post-pandemic era [8]. Additionally, other uncertainties, such as the uptake of remote working, the risk of multi-wave infection [11], and potential structural economic and social changes [12], could prove critical challenges to construction activity recovery.
While the academia has discussed the short-term impact of COVID-19 on the development and construction industry [13], it is also important to draw a clear guideline to project performance recovery for the practitioners. However, there is limited research on comprehensive recovery strategy to address the specific challenges in the post-COVID era available to the construction organisations. To bridge this research gap, this study aims to develop an integrated roadmap to construction project performance recovery based on information from both the academia and the construction industry. The research explores important paths towards post-COVID performance recovery of construction projects through identification and analysis of the critical success factors. The study also points out the drivers to post-COVID recovery of construction projects through a sequential and mixed method analysis of key performance indicators. The outcomes of this research not only provide an action plan to manage the impacts of COVID-19 pandemic but also provide insights into project management strategies for fast project performance recovery in the post-COVID era.
The AHP is a critical component of this study as it provides a structured technique for organizing and analysing complex decisions, based on mathematics and psychology. AHP has been extensively used in complex decision-making scenarios where both qualitative and quantitative aspects need to be considered. In the context of this study, AHP helps in determining the relative importance of the CSFs in the post-COVID-19 recovery of construction projects. It allows for a comprehensive and rational analysis of the various factors that contribute to the success of construction projects in the post-COVID stage.
The remainder of this paper is organised as follows. Section 2 reviews the literature and identifies the KPIs and CSFs. Section 3 describes the sequential mixed methodology employed in this research. Section 4 introduces the survey data and analysis results. Section 5 further discusses the analysis results and proposes the roadmap based on the analysis. Section 6 concludes the paper.

2. Literature Review

This study conducts a literature review on three specific focuses: (1) the impact of COVID-19 pandemic on construction project performance, (2) critical success factors (CSFs), and (3) key performance indicators (KPIs) of construction projects. The criteria for selecting information sources included assessing the impact of the COVID epidemic on construction projects and reflecting the evaluation process of engineering projects under normal situations using CSFs and KPIs inherent to construction projects. Peer-reviewed journal articles, government guidelines, and relevant agency guidelines, published between January 2020 and March 2022, were searched and indexed for the impact of COVID on construction projects. The research only includes sources that meet all of these criteria.
A search of published information was used to identify potentially relevant literature. The same keywords were used in the same information source for each topic: (1) COVID, Coronavirus, Pandemic, Construction Project; (2) Construction Project, critical success factors, CSF; (3) Construction Project, key performance indicators, KPI. Following this strategy, relevant documents from January 2020 to March 2022 were generated for topic 1 (N1 = 74), while topics 2 and 3 were generated from the year 2000 to the present (N2 = 576, N3 = 632). The eligibility of identified documents was first assessed against the title and abstract (removing 633 documents) and was then against full text (removing 597 documents). Some documents were removed because they did not directly relate to the specific topics of interest, namely the impact of COVID-19 on construction projects, critical success factors (CSFs) in construction projects, and key performance indicators (KPIs) in construction projects. Others were excluded because they did not provide empirical data or were not peer-reviewed, which was a requirement for this study to ensure the reliability and validity of the information used. Others were based on contexts that were not relevant to this study, such as construction projects in countries with significantly different industry standards and practices than China. Some documents were also removed because they were duplicates or had overlapping data with other selected studies. After this rigorous selection process, the remaining 52 documents were deemed to meet all the criteria and were included in the review.

2.1. Impact of COVID-19 on Construction Project Performance

COVID-19 is the most impactful global infectious disease that humanity has faced in recent years, and it can be spread through direct contact with the mouth, nose, or eyes of infectious respiratory droplets, as well as direct contact with an infected person or indirect contact with an infected surface. Based on the above characteristics, most governments around the world have implemented strict lockdown measures, restricted the movement of people and gatherings, and required personal protective measures to reduce the spread of the virus, as recommended by epidemiologists [14,15,16]. Following the COVID-19 pandemic, these lockdowns have become stricter or more liberal depending on the country as the epidemic situation changes, but social distancing to prevent the spread of the disease will continue, which led to a significant impact on construction projects that are of a labor-intensive nature [17,18].
The pandemic has disrupted day-to-day practices in the construction industry. Construction projects are usually multiple objective process [19]. There are several reports on the impacts of COVID-19 on construction engineering, including site health and safety, economic costs, legal risks, manpower availability, supply chain instability, and uncertainty due to the unpredictable evolution of the pandemic [20,21,22]. The magnitude of these impacts varies based on project size, contractor characteristics, local government policies, etc. [4]. Pamidimukkala and Kermanshachi showed that the main challenges facing construction workers during the COVID-19 pandemic are organizational, economic, psychological, personal, and adjustment factors [21]. Some studies also suggest workforce protection, project performance protection, and project continuity protection as important steps to help construction workers overcome health and safety challenges modeled the spread of COVID-19 among construction workers and concluded that the workforce could be reduced by 30% to 90%, where construction managers should maximize low-risk activities related to the spread of the virus [4,23,24].

2.2. Critical Success Factors of Construction Project Performance

Critical success factor (CSF) is a widely employed management term defined as a necessary element for a construction project to achieve its mission. In the past few decades, there has been a growing emphasis to comprehensively deconstruct the complex factors contributing to the success or failure of construction projects. For example, important success factors for research and development (R&D) projects, PPP projects, and safe construction projects have been studied by previous researchers [25]. Chua et al. clearly suggested that for construction projects, the socio-political environment, the relationship between stakeholders (e.g., government, clients), and the capacity of project managers, designers, and contractors are important [26]. Chan et al. proposed that to minimize the potential costs of green building projects, creative technological approaches and the excitement of the project team are important [27]. Kog and Loh distinguished the value of influences from various stakeholder perspectives and pointed out that stakeholder capacity, project team commitment, socio-political climate, and project scale have an impact on project execution [28]. It is evident, through analyzing the literature, that the most significant CSF differs from angles. This project extracted 32 primary success factors for the construction in China based on the literature review. These are summarized in Table 1.

2.3. Key Performance Indicator (KPI) of Construction Project Performance

Phua argued that the performance of a multi-organizational project can be specified and calculated, at least at an operational level, based on the degree to which the project meets budget, timeline, and technical requirements [74]. There are valid start and finish dates, and they will be completed in compliance with the stated requirements within the specified time span. The traditional view that it is effective to complete the construction project on time and within the budget according to requirements and stakeholder satisfaction is also supported by previous studies [75].
To illustrate how project management contributes to the success of a construction project, there is a notional model which divides project management into two domains: process and performance [76]. The process domain designs an effective project management framework and produces goods in the stages of input, process and outcome in order to address project priorities. Conversely, the performance domain concentrates on development of performance goals, improvement measures, and performance evaluation. Researchers also argued that by developing KPIs, a more objective metric for assessing project performance assessment can be achieved [77].
The evaluation of success in construction projects has always been dominated by traditional time, cost, and quality indicators, which were collectively referred as the iron triangle [77,78]. However, in successive years, some more comprehensive measures to evaluate project efficiency have been developed and implemented. For instance, Pheung and Chuan [74] argued that conventional metrics such as time, cost, and quality are no longer confined to the measurement criteria of project performance, instead recommending applying metrics of performance to the success of project management or the success of the product or both [74].
Others have also proposed that customer contentment and stakeholder satisfaction should also be included in performance appraisal standards, apart from the iron triangle [79,80]. An early study found that the top five widely applied metrics for assessing project success include technological performance, implementation quality, management and organizational effect, personal development, manufacturing capabilities, and business performance [81]. Recent works noted the following requirements for evaluating the project performance: the facility was produced on schedule and according to budget specifications; the project provided the owner with satisfactory benefits; the project met its business objectives; the project met its predetermined goal of manufacturing facilities; the project met the needs of the project team and the successor; the project addresses the needs of stakeholders [82,83]. In addition to traditional cost, time, quality, and scope metrics, the main performance indicators are as follows: appreciation of customers; appreciation of project staff; appreciation of users; appreciation of contracting parties; appreciation of stakeholders were highlighted by [84].
Based on a detailed literature review and preliminary interviews with academic researchers and industry specialists, this project compiled a list of 25 KPIs and divided it into five categories, as shown in Table 2.

3. Methodology

The research methodology contains three key steps. First, CSFs and KPIs were derived from literature reviews and expert interviews. Second, the study conducted a questionnaire survey with 150 experts and adopted validity, reliability checks, and other checks to determine whether the questionnaire results are suitable for subsequent analysis. Third, a comprehensive framework connecting CSFs and KPIs was established. Factor Analysis and Analytic Hierarchy Process (AHP) analysis were carried out for CSFs and Characteristics and Comparative Analysis for KPIs. Structural Equational Modelling (SEM) was employed to connect the CSFs and KPIs and develop a roadmap towards the post-COVID-19 pandemic recovery of the construction projects. Figure 1 illustrates an overview of this mixed research methodology.

3.1. Prospective Research and Questionnaire Survey

This research was conducted through a questionnaire. The second part of the questionnaire was designed to determine the impacts of the COVID pandemic on different KPIs of Chinese construction projects during the post-COVID pandemic period. All 25 variables included in the questionnaire were set on a five-point Likert Scale (5 for highly positive impact and 1 for very highly negative impact). The third part of the questionnaire was designed to determine the CSFs of Chinese construction projects during the post-COVID pandemic period. The 32 variables (CSFs) are from the previous literature review, which are shown in Table 1. All 32 variables included in the questionnaire are set on a five-point Likert Scale (5 for very important and 1 for very unimportant), and these scales were used to conduct the factor analysis.

3.2. Factor Analysis

The aim of evaluating a construction project’s critical success factor is to avoid accidents that could go wrong and cause key factors to cause safety hazards. Risk is a factor in a project that can lead to cost overruns, time overruns, and inadequate requirements, thus jeopardizing the project’s successful completion. There are risks associated with all projects, and the degree of presence of risk factors in a specific field is negatively linked to the likelihood of project success. Previous research has asserted that the use of CSF during the construction process for constructive risk management strategies and actions helps customers achieve negotiable projects on schedule and on budget [85,86].
In recent years, in different risk management areas, factor analysis techniques have been used, such as assessing the risk classification of mortgage loans and calculating the organization’s downside risk. The primary objective of this analysis is to acquire coherent and reliable CSFs rankings from the crucial success factors. Factor analysis is an effective tool for the detection of a crucial success factor. Factor analysis will classify CSF into particular classes by analyzing the association between variables and CSFs will rank the weight of each factor.

3.3. Analytic Hierarchy Process (AHP)

AHP is a Multi-Attribute Decision Making (MADM) system presenting a wide range of applications in organizational decision-making and is commonly used in various fields around the world [87]. The method is composed of effective instruments to prioritize key management issues [88]. This approach focuses on prioritizing selection criteria and on separating more important criteria from less important criteria [89]. The AHP also employs actual indicators such as price, quantity, or subjective opinions as inputs to the matrix. Output includes the ratio of the ratio and the consistency index obtained by measuring the main feature vector and the value of the feature. Since human decisions frequently tend to be arbitrary, and the AHP allows for some contradictory interventions [87,90,91].

3.4. Structural Equation Modeling (SEM)

Structural equation modeling (SEM) is a statistical technique that can quantify the structural correlation between one or more continuous or discrete independent variables (IV) and one or more continuous or discrete dependent variables. This research method has been widely adopted by construction management and social science research worldwide, including risk path identification in international construction projects [92], safety behavior analysis [93], and pro-environmental behavior [1]. This article uses the SEM model to establish a CSF-KPI evaluation framework with SmartPLS 3.3.3.

4. Results

4.1. Respondent Profile

The researchers distributed the uniform questionnaire to 150 experts in the Chinese construction industry and received 135 responses, 94 of which were valid. Figure 2 illustrates the respondent profile, including their roles in the project, position levels, and experience in the industry. The figure also presents the general information of the current projects of the respondents: the project scale, source of investment, and the nature of the project. Figure 2 illustrates the respondent profile of the respondent. About 45% of the participants were contractors and over 50% of the respondents held over five years of industrial experience. The preliminary study shows that these factors have insignificant effects on the results. The questionnaire was designed to capture a broad range of perspectives within the Chinese construction industry, and as such, it is robust to variations in respondent demographics. The predominance of contractors in the sample, for instance, reflects the reality of the industry where contractors play a significant role. Their responses, therefore, provide valuable insights into the practical aspects of project recovery in the post-COVID-19 context. Furthermore, the study’s methodology, which includes the use of factor analysis and the Analytic Hierarchy Process (AHP), ensures that the identified KPIs and CSFs are not unduly influenced by the characteristics of the respondents but are reflective of the broader trends and realities of the construction industry in the post-COVID-19 era.

4.2. Questionnaire Result Assessment

To ensure that the result is suitable for further analysis, validity analysis was used to study the rationality of the design of quantitative data [94,95]. This project used the Kaiser–Mayer–Olkin (KMO) test and the Bartlett’s test of sphericity to determine validity. Reliability analysis was used to research the quality of the test results of various items, i.e., the reliability and accuracy of the answers to quantitative data [96]. The test results are shown in the following Appendix A. The split-half reliability test was used to verify the accuracy of the reliability analysis. The split-half reliability test divided the questionnaire items into two halves, calculated the Cronbach α coefficient and the correlation coefficient of the scores of the two halves, and then estimated the reliability of the entire scale suitability of the questionnaire items [97,98].
The relevant test results are shown in Table 3, which indicates that the questionnaire survey results fit the research framework very well and can be followed up for analysis.

4.3. Factor Analysis of CSFs

The research conducted factor analysis of CSFs to explore that quantitative data can be condensed into several factors and the corresponding relationship between each factor and the questionnaire. The results are shown in Table A7. CSF factor analysis and Table A8. CSF factor loading is summarized in Table 4.
For factor extraction, the principal component analysis method was used to group the listed CSFs. According to the results of the principal component analysis after the maximum variance, the 32 independent variables were divided into five meaningful groups, of which 11 variables belong to the first group, eight variables belong to the second group, five variables belong to the third group, and five variables belong to the fourth group. Four variables belong to the fifth group. Grouping extracted five potential factors with feature values greater than 1.4, which explained 52.671% of the variance. This shows that for these five components, the largest percentage (>50%) difference is explained by CSF. The eigenvalues of the remaining potential factors are less than 1.4 and the variance contribution is less than 4.35%. This shows that the model with five extracted components can fully display the characteristics of the data. To facilitate further discussion, this article renamed the five extracted groups based on the analysis results. The five potential grouping barriers can be renamed as follows.
  • F1. Strength of participating parties and macro support
  • F2. Innovation and project control
  • F3. Project organization management
  • F4. Consistency of goals and external expectations
  • F5. Project flexibility and risk management

4.4. CSFs Importance Index Analysis by Analytic Hierarchy Process

This questionnaire research involves importance scale questions. The characteristic of this type of question is that the higher the score, the more important or the more recognized. It can be understood that the higher the importance, the higher the weight [88]. Therefore, this information can be used to calculate weights, using the AHP hierarchy process and the optimal sequence diagram method, respectively. Both of these research methods use relative importance for weight calculation. The AHP hierarchy process itself is an expert scoring and weighting method, that is, the relative importance is described through expert scoring, and then the weight is calculated [99].
The 32-level judgment matrix (see Figure A3. AHP Judgment Matrix) was constructed for 32 items based on the AHP hierarchy process (the calculation method is the sum product method), and the eigenvector of each item was analyzed. Combining the eigenvectors can calculate the maximum eigenvalue of 32.000, using the maximum eigenvalue to calculate the CI value (0.000). The CI value was used in the following Consistency check. The CI value calculated for the 32-order judgment matrix is 0.000 (Table A9, CSF AHP analysis), and the look-up table (Table A10. RI Value) for the RI value is 1.677, so the calculated CR value is 0.000 < 0.1, which means that the judgment matrix of this study meets the consistency test, and the calculated weights are consistent. The test result was as follows: maximum eigenvalue is 32, CI is 0, RI is 1.677, CR is 0, CSF consistency test, and the weight ranking outcome is shown in Figure 3.

4.5. Summary of the KPI Result and Ranking Using Descriptive Statistics

Table A11, Stakeholders’ perception of COVID-19 pandemic’s impact on KPIs, summarizes the results and rankings using descriptive statistics. The ranking is based on the average, standard deviation (SD), and total number of respondents for a given indicator. These results are visualized in Figure 4.
Table 5, Stakeholders’ perception of COVID-19 pandemic’s impact on KPIs, gives different attitudes of different participants to COVID-19 pandemic’s impact on KPIs of construction projects. It can be seen that, relatively speaking, the owner has a relatively conservative attitude towards the completion of the project’s KPIs, and the scores of all items are lower than the average. The designer showed a relatively more optimistic attitude towards KPI 1-15, while the constructor was relatively more optimistic towards KPI 16–25.
In addition, the owners hold a relatively positive attitude towards the positive social impact caused by the organization or project, the growth of market share, and the promotion of innovation and development of construction industry, while they hold a relatively conservative attitude towards the completion of the project and the achievement of some objectives. The designers hold a relatively positive attitude towards the positive social impact caused by the organization or project and the achievement of some objectives (such as the satisfaction of the government), while they hold a relatively conservative attitude towards the application of innovative technologies and the achievement of some objectives (such as the achievement of the iron triangle of the project). The contractor has a relatively positive attitude towards the positive social impact and construction compliance caused by the organization or project, but a relatively conservative attitude towards the achievement of the strategic objectives of the enterprise (organization), the satisfaction of the participants, and the completion of the iron triangle of the project. In general, the completion of the inherent goals of the project may be relatively neutrally affected, while the social goals of the project and the organization social value may be relatively positively affected.

4.6. Hypothetical Explanation Linking CSF and KPI

There is evidence showing that the ability to participate parties to a large extent guarantees that the project can proceed smoothly and better adapt to the external environment, therefore continuing to promote the goal [63,100]. Therefore, Hypothesis 1 is that the strength of the participating parties and the supported macro environment have a positive effect on the project implementation efficiency and effect (F1 → K1). Ebekozien et al. pointed out the importance of digital innovative technologies (including BIM, digital platforms, etc.) in the recovery of the construction industry after the COVID-19 epidemic to achieve sustainable development and stakeholder satisfaction [8]. Hypotheses 2 and 3 are that innovation and project control have a positive correlation with the satisfaction of key stakeholders and achievement of the enterprise (organization) strategic goals. Some research pointed out that focusing on the communication and coordination between different participants and strong project organization and management can enable the opinions of stakeholders to be collected and adopted, which is essential for achieving stakeholder satisfaction [101]. Hypothesis 4 is based on this and indicates the positive correlation from F3 to K2. Another research reviewed the different expectations of external stakeholders in the development of construction projects and the actual management steps were taken by the project manager [102]. This implies that the consistency of project goals and external expectations is conducive to the progress of the project, the satisfaction of key stakeholders, and creates a positive social impact (Hypothesis 5–7: F4 → K1, F4 → K2, F4 → K5). In addition, some studies also pointed out the interaction between project flexibility and risk management and the possibility of new risk management models [103]. The updated risk management model can promote the innovative development of the industry. Better risk management can reduce the negative impact of the project on society, thereby increasing the positive impact. This provided support for Hypothesis 8–9 (F5 → K4, F5 → K5).
Based on extensive literature support, this article makes hypotheses (H1-9) about the significant positive correlation of KPI groups by CSF factors, as shown in Table 6.

4.7. Structural Equation Modeling (SEM)

SEM requires a statistically significant sample size in order to generate accurate results as it is linked to the stability of parameter estimations [115]. According to the recent literature, a sample size of 100 to 400 is appropriate and suitable for SEM analysis [116]. In this paper, SmartPLS is used for PLS-SEM method for analysis based on the above CFS factor analysis and KPI classification. This article establishes SEM based on the internal logical relationship between the KPI that has been classified in the design and the CFS that has been factored.
SEM includes a measurement model used to quantify the correlation between each exogenous variable and its respective latent variables, as well as a structural model of the correlation between structures [117]. Research cited in the literature proves that these thresholds are reasonable. Based on the above analysis and the coefficient table in the Appendix A, the value of the standard quantity exceeds the respective threshold recommended by the reference study. The results show that there is no obvious correlation between the two structures, which confirms the validity and reliability of the measurement model to further construct the SEM [117,118]. This article evaluated 5000 boot samples based on the recommendations [119]. Table 7 shows that, except for the path from F4 to K1 (p value = 0.11), the P value of all other paths was below 0.05. This means that the relevant results are within a 95% confidence interval. Therefore, this article assumes that all paths of the model are supported [119]. The visualization result of SEM is shown in Figure 5.

5. Interpretation and Discussion

5.1. Strength of Participating Parties and Macro Support

According to the results of the SEM, the correlation between F1 and K1 is 0.24, which indicates that the ability of the participating parties and external macro support have a significant impact on the construction project implementation efficiency and effect on the post-COVID-19 pandemic period. On the one hand, it can be seen that in the post-COVID-19 pandemic period, social and economic stability under the leadership of the government, as well as the leadership, planning, and efforts made by the government to deal with the virus, provide solid macro support in the recovery of construction projects. In the COVID-19 period, the Chinese government required personnel in outbreak areas to stop the movement, ensuring the health of relevant personnel while objectively increasing the uncertainty and cost of the project. In the post-COVID-19 pandemic period, the Chinese government actively undertook coordination and guidance for project participants to resume construction works instantly, leading to the fast project recovery in the construction industry. Therefore, solid macro support from the government is essential to lead project participants to overcome the project changes and uncertainties due to the COVID-19 outbreak.
On the other hand, the strength of the participating construction parties has played an essential role in the project recovery of the post-COVID pandemic period. The strength of project participants enhances the project’s adaptability to the changeable external environment of COVID-19. Particularly, the strength of resource resilience among participating parties can effectively deal with the negative impact of COVID-19. Resource resilience includes good financial support to deal with long-term economic risks, the reserve of talents, machines, and materials to deal with market fluctuations, etc. In addition, the previous research also suggests that the continuous support of senior management is crucial to ensure resource resilience among project participants, which is helpful to boost the performance recovery of construction projects in the post-COVID pandemic period [39].

5.2. Innovative Applications

The result of the SEM shows that the correlation between F2 and K3 is 0.66 and the correlation with K4 is 0.49. These significant correlations indicate that innovation and strict project management are essential for the construction industry to respond to the post-COVID-19 environment. The innovative applications boost the construction project performance by achieving the enterprise strategic goals for industry innovation and development.
Modular industrialization construction (MiC) technology is a strategic goal for many construction enterprises to improve construction project performance. The modularization, industrialization, automated off-site production, and on-site assembly automation brought by MiC technology will realize the product-based construction method [120]. The COVID-19 pandemic provided a unique opportunity for construction enterprises to widely use this innovative method, as MiC technology is valuable to reach efficiently controllable working conditions in building emergent healthcare facilities. In the post-COVID-19 pandemic period, MiC technology will take a leading role in project recovery, which facilitates the construction enterprises to push the construction project to the integrated automated production system with efficient off-site manufacturing.
The second innovative application is digitalization in construction, which will stimulate industry innovation and development. The COVID-19 pandemic hastened the adoption of digital tools [9]. Instead of finishing the design while construction is underway, companies can increase efficiency and integrate the design phase with the help of “digital twins” to add more levels of information such as schedule and cost by using building information modeling (BIM) to create complete 3D models at the early stage of the project [121]. This drastically altered the risk management and decision-making process in construction projects during the COVID-19 pandemic crisis. The digitalization of management and production processes will continue to help project participants to achieve efficient project recovery in the post-COVID-19 pandemic. The application of digital management methods (such as DingTalk) and digital building technology can achieve better collaboration, enabling better system control, coordination, and integration mechanisms, and shifts to more data-driven decision-making. These innovations will change construction projects’ design, operation, contract, and construction management, and how these interact with project participants.

5.3. Project Organization Management

SEM pointed out that the correlation between F3 and K2 is 0.21, which implies that the project organization itself has a positive impact on the satisfaction of the project-related key stakeholders. To realize the project recovery, efficient project organization management is highlighted to strengthen stakeholder relationships dealing with challenges in the post-COVID-19 pandemic.
The COVID-19 pandemic poses many project management problems for the construction industry, such as the tight schedule, bad weather, fragile supply chain, and intensive working interfaces. Therefore, project organization management, which focuses on communication and coordination between different participants and strong organizational support, is crucial. Top management must create regulations to strengthen oversight of the strategy and minimize risks and strengthen contact with the various stakeholders. In the post-COVID-19 pandemic era, competent stakeholder management is waiting to be established based on the knowledge and lessons gained from the construction projects in the COVID-19 pandemic period.

5.4. Consistency of Goals and External Expectations

The correlations between F4 and K1, K2, and K5 are 0.14, 0.30, and 0.35, respectively. This indicates that the consistency of goals and external expectations of the project are crucial to the externally defined success of the project. However, F4 is not very relevant to the K1 project implementation efficiency and effect, as the p value of this path is above 0.1. Therefore, this hypothetical path was rejected.
The COVID-19 pandemic reinforces the importance of external expectations, which means that social operations are systematically evaluated. CSF 32 is the most important factor in this group. It also ranks first in the AHP analysis and is significantly ahead of other CSFs. The stability of the social, economic, and political environment is regarded as the most important factor in the post-COVID-19 pandemic. Correspondingly, external environmental conditions and external stakeholders also play an important role in the success of the project. According to Chan and Oppong [29] and Cleland [101], external stakeholders are divided into three main groups for discussion: government authorities, the public (consumers, environment, society, politics, and “interventionists” Groups as representatives), and affected local communities. Stakeholders use their power and intentions to influence project results according to their interests and expectations [122]. Especially in the post-COVID-19 period, stakeholders’ attitudes towards construction projects and this labor-intensive work have largely affected the normal operation of the project. Therefore, project managers encourage stakeholders to participate in project delivery to ensure that the different expectations of stakeholders are systematically and formally captured and incorporated into project plans and policies [123].
At the same time, during the post-COVID-19 period, the external environment changes frequently, and the update and implementation of relevant policies may have an impact on the project itself. In the long run, the management of external environmental conditions and external stakeholders enhances the feasibility of the project and ensures the company’s interests to external stakeholders. PM needs to identify and manage activities that have a significant impact on stakeholder satisfaction in the construction process [124]. Effective communication between the project manager and external stakeholders is essential to maintain a good relationship. Good communication allows the project manager to understand and understand the expectations of its stakeholders, and stakeholders can also obtain important information related to the project, which is extremely important for the advancement of the project during the post-COVID-19 period.

5.5. Project Flexibility and Risk Management

The correlation between F5 and K4, K5 is 0.17 and 0.27, respectively. This highlights that good project flexibility and risk management can positively impact industry and social post-COVID-19 project recovery.
Learning from the experience in the COVID-19 pandemic, a benign people-oriented organization and project culture construction (especially flexibility and dedication during the pandemic), can significantly reduce the social risks within the organization during the project process, including the loss of labor and the heavy working pressure. As more complex construction projects resume building in the post-COVID-19 period, flexible risk management with good scope management, effective risk control, reasonable risk-sharing mechanism, and collaborative working culture is helpful to deal with the social–technical challenges of construction projects.

5.6. Project Performance Recovery Roadmap

Based on the analysis on the identified CSFs and KPIs, this research develops a roadmap for the performance recovery of construction projects in the post-COVID pandemic era (see Figure 6). This multi-level roadmap connects four different scales: drivers, path, direction, and aim. A driver refers to a fundamental driving force to the project performance recovery and the driver level includes 32 CSFs in this research. A path refers to a common way that similar drivers work together and leads to a specific direction. A direction refers to a critical dimension of construction project performance and points to the destination. In this roadmap, there are five paths derived from the CSF grouping that contribute to five directions derived from the KPI grouping. The study employed SEM to connect the paths and directions. The destination is the final goal/perception that requires the contribution of all directions. The destination in the proposed roadmap is post-pandemic project performance recovery and advancement. With sufficient data and model support for each connection of every two levels, this process from driver to aim constitutes a comprehensive roadmap.

6. Conclusions

This research aimed to propose an integrated framework connecting KPIs in the post-COVID-19 recovery of construction projects and the CSFs to achieve them. This study first developed a hypothetical model under the respective structures of CSFs that contribute to KPIs. Based on the professionals’ views in the Chinese construction industry, the research employed AHP to classify the importance of CSFs and SEM to reveal the quantitative relationship between CSF and KPI groups. The study developed a theoretical roadmap towards the construction project performance recovery and advancement in the post-COVID-19 period based on the analysis results.
Theoretically, this study contributes to building a roadmap framework to identify the interrelationships among construction project performance promotors in four levels: driver, path, direction, and destination. Practically, the roadmap can guide comprehensive project life cycle management and deal with public health emergencies such as COVID-19 in construction and infrastructure management to maximize the organization’s profits and positive social impact. There are two limitations to this study. First, the sample size used in this article is limited. Second, this research focuses on the opinions of Chinese construction professionals. Thus, the results that are applicable to other countries/regions may require further studies.

Author Contributions

Conceptualization, H.-S.G.; Software, H.-S.G.; Formal analysis, H.-S.G.; Investigation, J.X. and I.Y.J.; Resources, H.-S.G., M.-X.L. and Q.-C.W.; Data curation, M.-X.L.; Writing—original draft, H.-S.G.; Writing—review & editing, M.-X.L., Q.X. and J.X.; Visualization, I.Y.J. and Q.X.; Supervision, J.X.; Project administration, J.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Research Grants Council of the Hong Kong Special Administrative Region, China grant number PolyU/RGC Project No. PolyU15225822.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Acknowledgments

The authors would like to express their sincere gratitude to all respondents and experts involved in the project. Besides, the authors would like to thank Chi Hung-Lin and Wang Ting from the Hong Kong Polytechnic University and Tongji University. The authors also thank the Cambridge Commonwealth, European and International Trust, and Johns Hopkins University for their partial financial support.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. CSF validity analysis.
Table A1. CSF validity analysis.
ItemsFactor LoadingsCommunalities
Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7Factor 8Factor 9
10.201−0.1910.110.540.4160.0710.390.010.0110.711
20.2060.2840.3540.16−0.003−0.1750.4470.3330.0930.624
30.1110.160.0790.4040.2440.1910.590.0050.1060.662
40.22−0.0240.3880.2580.1370.120.0180.1790.5650.651
5−0.072−0.0010.1810.0550.0260.1070.2170.6040.4340.654
60.1970.1930.1180.066−0.0820.0850.6710.1230.2890.658
7−0.0330.6350.1750.10.040.2640.435−0.069−0.0090.71
80.1930.2420.1830.570.054−0.0640.120.2610.1480.565
90.0770.1780.5540.1360.0290.30.5020.031−0.0650.711
100.1750.3050.304−0.0970.164−0.0260.3080.0780.5810.692
110.1990.2120.70.180.058−0.0050.1610.1670.0930.673
120.2010.477−0.0250.2750.0610.2380.10.0410.4860.652
130.3150.092−0.054−0.2350.3780.530.2840.371−0.0330.81
14−0.1050.2050.0640.528−0.0230.2820.2450.1350.190.529
150.0960.0750.1720.2010.1050.157−0.0280.8750.0140.887
160.3370.220.6010.05−0.0170.3210.090.2020.1120.691
17−0.0170.2140.1590.3370.0020.687−0.0080.0730.0990.672
180.2650.1030.190.7280.1520.11700.057−0.0070.687
190.256−0.0560.0240.1340.1150.590.217−0.020.4640.711
200.1290.0990.6430.1730.1530.1620.0080.1130.3310.642
210.1640.1630.231−0.0320.2210.5930.1630.143−0.0150.556
220.7230.2050.2490.0770.1510.2020.1430.0070.020.718
230.778−0.0160.1350.0860.099−0.0520.2020.0870.2040.733
240.6170.0550.3660.0830.310.1250.088−0.080.030.652
250.6510.248−0.0380.327−0.1480.223−0.0490.1580.1540.716
260.366−0.0110.1340.1450.3630.418−0.3020.0020.0760.577
270.1670.5210.170.2120.359−0.0010.1240.41−0.0730.691
280.1140.7980.1910.0680.170.0630.1290.0340.1460.763
290.0570.0890.1360.0310.8570.1240.030.031−0.0260.783
300.3830.5270.2680.199−0.0070.239−0.0260.1910.0510.633
310.3330.3020.140.2840.3610.0070.0860.0360.2650.512
320.0990.17−0.0580.1790.7340.115−0.0060.1380.2990.735
Eigenvalues (Initial)10.2762.0761.6241.4711.4071.3811.2961.1231.004-
% of Variance (Initial)32.114%6.488%5.073%4.598%4.398%4.315%4.051%3.509%3.137%-
% of Cum. Variance (Initial)32.114%38.602%43.675%48.274%52.671%56.986%61.037%64.546%67.684%-
Eigenvalues (Rotated)3.1322.662.6342.4452.4062.3992.2111.9041.868-
% of Variance (Rotated)9.788%8.311%8.231%7.641%7.519%7.496%6.909%5.951%5.838%-
% of Cum. Variance (Rotated)9.788%18.099%26.330%33.970%41.489%48.986%55.895%61.845%67.684%-
KMO0.784-
Bartlett’s Test of Sphericity (Chi-Square)1472.165-
df496-
p value0-
Table A2. KPI validity analysis.
Table A2. KPI validity analysis.
ItemsFactor LoadingsCommunalities
Factor 1Factor 2Factor 3
KPI 10.440.260.7430.813
KPI 20.1780.1730.7050.559
KPI 30.2590.0940.8470.794
KPI 40.1680.2980.7680.707
KPI 50.650.440.3380.73
KPI 60.690.2030.3630.65
KPI 70.6640.3260.4350.736
KPI 80.5080.4240.3890.588
KPI 90.8070.30.2460.801
KPI 100.7820.2880.2290.746
KPI 110.3320.5130.2970.462
KPI 120.7270.2440.3290.696
KPI 130.6080.5310.1610.677
KPI 140.3530.6320.1570.549
KPI 150.3480.7270.2260.7
KPI 160.4010.5830.1440.521
KPI 170.2910.670.2430.592
KPI 180.1020.7820.1270.638
KPI 190.5850.490.210.627
KPI 200.2250.7280.2360.636
KPI 210.5820.5140.2450.663
KPI 220.5020.5230.3590.653
KPI 230.5160.5980.2410.682
KPI 240.5380.6260.1050.693
KPI 250.6440.4940.10.669
Eigenvalues (Initial)13.7941.6931.097-
% of Variance (Initial)55.174%6.772%4.386%-
% of Cum. Variance (Initial)55.174%61.946%66.333%-
Eigenvalues (Rotated)6.6456.1213.817-
% of Variance (Rotated)26.578%24.486%15.268%-
% of Cum. Variance (Rotated)26.578%51.064%66.333%-
KMO0.932-
Bartlett’s Test of Sphericity (Chi-Square)1881.479-
df300-
p value0-
Table A3. CSF reliability test.
Table A3. CSF reliability test.
Reliability Statistics (Cronbach Alpha)
ItemsCorrected Item—Total Correlation (CITC)Cronbach Alpha if Item DeletedCronbach α
10.4680.9280.93
20.5260.928
30.5730.927
40.5690.927
50.4020.929
60.4950.928
70.4870.928
80.5310.928
90.5610.927
100.5470.928
110.5860.927
120.5740.927
130.4810.928
140.4350.929
150.4660.929
160.6460.926
170.4730.928
180.5270.928
190.5140.928
200.5830.927
210.5030.928
220.6310.926
230.5150.928
240.5570.927
250.520.928
260.3940.929
270.5890.927
280.5520.928
290.3820.93
300.6160.927
310.5780.927
320.4750.928
Cronbach α (Standardized): 0.931
Table A4. KPI reliability test.
Table A4. KPI reliability test.
Reliability Statistics (Cronbach Alpha)
ItemsCorrected Item—Total Correlation (CITC)Cronbach Alpha if Item DeletedCronbach α
KPI10.7560.9630.965
KPI20.5020.966
KPI30.5760.965
KPI40.6040.964
KPI50.8290.962
KPI60.7120.963
KPI70.8080.962
KPI80.740.963
KPI 90.8030.963
KPI 100.7690.963
KPI 110.630.964
KPI 120.750.963
KPI 130.7680.963
KPI 140.6560.964
KPI 150.7430.963
KPI 160.6530.964
KPI 170.6790.964
KPI 180.5670.965
KPI 190.7530.963
KPI 200.6670.964
KPI 210.7810.963
KPI 220.7820.963
KPI 230.790.963
KPI 240.7590.963
KPI 250.7420.963
Cronbach α (Standardized): 0.965
Figure A1. CSF reliability test (split-half).
Figure A1. CSF reliability test (split-half).
Systems 11 00359 g0a1
Figure A2. KPI reliability test (split-half).
Figure A2. KPI reliability test (split-half).
Systems 11 00359 g0a2
Table A5. CSF item—analysis.
Table A5. CSF item—analysis.
Group (M ± SD)t(CR)p
Low Grouping (n = 25)High Grouping (n = 25)
13.36 ± 0.644.12 ± 0.53−4.5970.000 **
23.44 ± 0.964.60 ± 0.50−5.3540.000 **
33.44 ± 0.654.56 ± 0.58−6.410.000 **
43.56 ± 0.874.56 ± 0.51−4.9670.000 **
53.44 ± 0.824.20 ± 0.65−3.640.001 **
63.48 ± 0.824.48 ± 0.65−4.760.000 **
73.40 ± 0.714.40 ± 0.58−5.4770.000 **
83.52 ± 0.874.60 ± 0.50−5.3730.000 **
93.44 ± 0.774.60 ± 0.50−6.3280.000 **
103.60 ± 0.824.48 ± 0.59−4.3780.000 **
113.16 ± 0.754.36 ± 0.49−6.7220.000 **
123.56 ± 0.774.68 ± 0.56−5.9030.000 **
133.52 ± 0.774.52 ± 0.51−5.4130.000 **
143.84 ± 0.804.68 ± 0.56−4.3090.000 **
153.28 ± 0.684.40 ± 0.71−5.7150.000 **
163.24 ± 0.724.56 ± 0.51−7.4730.000 **
173.72 ± 0.614.56 ± 0.51−5.2780.000 **
183.28 ± 0.464.28 ± 0.61−6.5280.000 **
193.44 ± 0.824.60 ± 0.58−5.7810.000 **
203.44 ± 0.824.56 ± 0.51−5.8070.000 **
213.48 ± 0.714.48 ± 0.51−5.6980.000 **
222.96 ± 0.684.48 ± 0.59−8.4970.000 **
232.80 ± 1.004.24 ± 0.72−5.8340.000 **
242.84 ± 0.904.48 ± 0.65−7.3840.000 **
253.20 ± 0.764.52 ± 0.59−6.8560.000 **
263.40 ± 0.824.24 ± 0.60−4.1520.000 **
273.52 ± 0.824.68 ± 0.48−6.1020.000 **
283.32 ± 1.074.68 ± 0.56−5.6410.000 **
293.64 ± 0.914.44 ± 0.65−3.5820.001 **
303.28 ± 0.684.64 ± 0.49−8.1280.000 **
313.36 ± 0.864.48 ± 0.59−5.380.000 **
323.96 ± 0.794.68 ± 0.48−3.9050.000 **
** p < 0.01.
Table A6. KPI item—analysis.
Table A6. KPI item—analysis.
Group (M ± SD)t(CR)p
Low Grouping (n = 25)High Grouping (n = 25)
KPI 12.00 ± 0.584.12 ± 0.65−12.240.000 **
KPI 22.44 ± 0.964.35 ± 0.94−7.1780.000 **
KPI 33.04 ± 0.544.50 ± 0.86−7.2940.000 **
KPI 42.96 ± 0.684.23 ± 0.91−5.6510.000 **
KPI 52.48 ± 0.654.69 ± 0.47−13.830.000 **
KPI 62.40 ± 0.824.46 ± 0.58−10.4160.000 **
KPI 72.20 ± 0.764.46 ± 0.58−11.9250.000 **
KPI 82.64 ± 0.704.62 ± 0.57−11.0620.000 **
KPI 92.24 ± 0.724.42 ± 0.64−11.3990.000 **
KPI 102.48 ± 0.774.42 ± 0.58−10.2170.000 **
KPI 112.80 ± 0.654.15 ± 0.73−6.9960.000 **
KPI 121.96 ± 0.614.23 ± 0.71−12.2170.000 **
KPI 132.40 ± 0.964.73 ± 0.53−10.6820.000 **
KPI 142.96 ± 0.794.27 ± 0.67−6.4070.000 **
KPI 153.00 ± 0.824.58 ± 0.50−8.3360.000 **
KPI 162.80 ± 0.824.31 ± 0.68−7.180.000 **
KPI 172.96 ± 0.734.38 ± 0.64−7.4050.000 **
KPI 183.08 ± 0.764.42 ± 0.76−6.3220.000 **
KPI 192.52 ± 0.654.54 ± 0.58−11.6640.000 **
KPI 202.88 ± 0.784.54 ± 0.51−9.0220.000 **
KPI 212.28 ± 0.844.35 ± 0.56−10.3420.000 **
KPI 222.60 ± 0.654.65 ± 0.49−12.8780.000 **
KPI 232.72 ± 0.744.54 ± 0.71−8.9990.000 **
KPI 242.64 ± 0.864.35 ± 0.63−8.1090.000 **
KPI 252.36 ± 0.914.42 ± 0.58−9.6430.000 **
** p < 0.01.
Table A7. CSF factor analysis.
Table A7. CSF factor analysis.
Total Variance Explained
FactorEigen Values% of Variance (Initial)% of Variance (Rotated)
Eigen% of VarianceCum. % of VarianceEigen% of VarianceCum. % of VarianceEigen% of VarianceCum. % of Variance
110.27632.11432.11410.27632.11432.1143.1329.7889.788
22.0766.48838.6022.0766.48838.6022.668.31118.099
31.6245.07343.6751.6245.07343.6752.6348.23126.33
41.4714.59848.2741.4714.59848.2742.4457.64133.97
51.4074.39852.6711.4074.39852.6712.4067.51941.489
61.3814.31556.986------
71.2964.05161.037------
81.1233.50964.546------
91.0043.13767.684------
100.9643.01270.696------
110.9072.83573.53------
120.872.7276.25------
130.7912.4778.72------
140.6872.14880.869------
150.6572.05282.92------
160.6492.02884.948------
170.5911.84786.795------
180.5011.56788.362------
190.4521.41289.775------
200.4331.35291.127------
210.41.2592.377------
220.371.15693.533------
230.3511.09794.63------
240.2870.89695.526------
250.2680.83696.362------
260.2420.75697.118------
270.2220.69497.812------
280.1950.6198.422------
290.1560.48698.909------
300.1460.45799.366------
310.1130.35499.719------
320.090.281100------
Table A8. CSF factor loading.
Table A8. CSF factor loading.
Factor Loading (Rotated)
ItemsFactor LoadingCommunalities
Factor 1Factor 2Factor 3Factor 4Factor 5
10.1330.2230.2580.588−0.0060.479
20.5860.2710.3250.069−0.1440.547
30.5110.0940.2180.4060.1180.497
40.1410.420.5030.1640.1380.495
50.212−0.0070.6370.0010.1690.479
60.620.1710.202−0.0060.0710.459
70.773−0.033−0.0060.0960.2420.667
80.2990.2830.5090.276−0.1290.522
90.6080.2020.1710.0170.2470.501
100.5320.2810.1710.1150.0930.413
110.460.4340.320.0450.0110.505
120.4130.2210.2810.1960.2370.393
130.1810.1390.0180.240.6870.582
140.338−0.0470.4910.1740.1420.408
150.0510.1060.7080.1120.2360.584
160.3850.5050.282−0.0830.3520.613
170.1960.0530.3590.0630.5820.512
180.1150.3690.4190.402−0.0130.487
190.130.2610.2370.1320.5610.474
200.2830.3950.3830.1040.1870.429
210.2630.1630.0980.1260.6370.527
220.2780.718−0.0380.1720.2640.693
230.1430.7550.0580.1590.0190.62
240.1760.683−0.0640.2890.1960.624
250.1190.6080.2540.0260.1910.486
26−0.1880.4110.0720.3250.4710.537
270.450.1580.2560.4150.1070.476
280.690.150.0250.2090.1480.565
290.0750.062−0.0880.7580.2890.675
300.4130.4370.2210.0570.2650.484
310.2990.3830.1630.4560.0670.474
320.0760.0830.1470.740.2580.648
Figure A3. AHP judgment matrix.
Figure A3. AHP judgment matrix.
Systems 11 00359 g0a3
Table A9. CSF AHP analysis.
Table A9. CSF AHP analysis.
ItemsEigenvectorsWeightMaximum EigenvalueCI
10.9452.952%320
21.0163.176%
31.0033.135%
41.0163.176%
50.9763.051%
61.0463.268%
70.993.093%
81.0193.185%
91.0323.226%
101.0243.201%
111.0033.135%
121.0433.259%
131.0083.151%
141.043.251%
150.9532.977%
161.0193.185%
171.0353.234%
180.9613.002%
1913.126%
200.9983.118%
210.9823.068%
220.9552.985%
230.9182.869%
240.952.968%
250.9713.035%
260.9452.952%
271.0483.276%
281.0223.193%
291.0223.193%
301.0033.135%
310.9873.085%
321.073.342%
Table A10. RI value.
Table A10. RI value.
RI Table
Order345678910111213141516
RI0.520.891.121.261.361.411.461.491.521.541.561.581.591.5943
Order1718192021222324252627282930
RI1.60641.61331.62071.62921.63581.64031.64621.64971.65561.65871.66311.6671.66931.6724
Order3132333435363738394041424344
RI1.67551.67731.681.68281.68371.68641.68831.69031.69211.69291.69471.69581.69851.6991
Order4546474849505152535455565758
RI1.70061.70151.70231.70451.70561.70651.70661.70711.7091.711.71091.71131.71231.7127
Table A11. Stakeholders’ perception of the COVID-19 pandemic’s impact on KPIs.
Table A11. Stakeholders’ perception of the COVID-19 pandemic’s impact on KPIs.
DimensionsIndicatorsAssessment Outcome
A. Owner (N = 34)B. Designer N = 21)C. Constructor (N = 39)Overall
MinMaxMeanStd. DeviationMinMaxMeanStd. DeviationMinMaxMeanStd. DeviationMinMaxMeanStd. Deviation
Efficiency (Project management success)KPI 1153.0291.193143.2380.995153.2381.185153.1491.136
KPI 2153.1181.365253.8570.964153.6671.162153.4891.233
KPI 3153.4411.078153.9050.995253.8330.824153.7020.971
Satisfaction of key stakeholdersKPI 4153.5591.021253.810.75153.6430.932153.6380.926
KPI 5153.6181.28253.8570.91153.810.943153.7231.062
KPI 6153.5291.212253.7140.956153.6431.186153.6061.147
KPI 7153.2651.163253.810.873153.5481.173153.4891.124
KPI 8253.51.052254.0950.831153.7861.025153.7231.01
KPI 9153.3531.228253.810.981153.691.07153.5741.122
KPI 10253.4710.896153.810.928153.5711.107153.5850.999
Enterprise (organization) strategic goalsKPI 11253.3240.912253.6190.74153.5950.885153.50.877
KPI 12152.9411.324253.3811.117153.3571.265153.2131.26
KPI 13153.51.261253.810.928153.811.174153.6911.173
KPI 14253.6470.812253.810.75153.6430.932153.670.847
KPI 15253.8240.834153.811.03153.9520.854153.8620.875
KPI 16253.4120.821153.5710.978153.7620.958153.5960.931
Industry innovation and developmentKPI 17253.7350.828253.811.03153.810.833153.7660.873
KPI 18253.7350.963253.7620.889153.810.862153.7770.894
KPI 19253.5291.022253.810.873153.7621.078153.6811.018
KPI 20253.6760.976353.810.68153.9050.932153.7980.899
Comprehensive social impactKPI 21153.3241.199153.6191.117153.6430.932153.51.075
KPI 22253.5591.133253.8571.014153.8331.08153.7231.092
KPI 23253.5590.894253.7620.889153.8811.041153.7450.961
KPI 24253.6181.045253.9520.921153.7380.885153.7340.952
KPI 25153.3241.093153.7141.056153.9521.058153.6491.095

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Figure 1. Research methodology.
Figure 1. Research methodology.
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Figure 2. The respondent profile and project general information.
Figure 2. The respondent profile and project general information.
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Figure 3. CSF weight ranking.
Figure 3. CSF weight ranking.
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Figure 4. KPI box plot.
Figure 4. KPI box plot.
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Figure 5. Structural equation modeling results. Note: * p < 0.05, ** p < 0.01; *** p < 0.0001.
Figure 5. Structural equation modeling results. Note: * p < 0.05, ** p < 0.01; *** p < 0.0001.
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Figure 6. Project performance recovery roadmap.
Figure 6. Project performance recovery roadmap.
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Table 1. Construction project critical success factors.
Table 1. Construction project critical success factors.
ItemCSFsReferences
1Organizational strategy[5,29,30,31,32,33,34]
2Determination of project goals and scope (to ensure that the project can continue to advance, including target identification, quantitative control index formulation, process monitoring, etc.)[29,30,31,32,33,35]
3Effective strategy and goal planning[5,29,30,31,32,33]
4Organizational design and structure of the project[36,37]
5Good relationship with key stakeholders[18,29,32,38]
6Adequate communication and coordination of the participating parties[18,29,32,38]
7Trust between stakeholders (for example, sticking to ethics and fulfilling promises during the project)[29,32,39]
8Competency and leadership level of the owner (including strategic ability, financial ability and governance ability)[39,40,41]
9The competency and leadership level of the project manager (including technical skills and communication skills)[39,42,43]
10The competency level of the contractor (including the construction ability and delivery ability)[34,44,45]
11The working ability of construction personnel[30,34,46,47]
12Strong support from within the organization (such as stability, unity and collaboration within the team)[18,39,40,41,48]
13Healthy organization and project culture (especially flexibility and dedication during the pandemic)[34,49,50,51]
14Adequacy of resources (including manpower, machinery, materials and construction funds)[30,31,34,47,50,52,53,54]
15Effective incentive and restraint mechanism (Positive human dynamics)[29,32,48]
16Project system control, coordination and integration mechanism[29,32,38]
17Effective risk control, reasonable risk sharing mechanism[34,38,39,42,48]
18Effective complexity degradation and control[30,31,49]
19Good scope management[55,56]
20Effective and detailed contract management (such as contract specification documents with equal rights and responsibilities)[53,55,56,57]
21Appropriate contracting model and project delivery system[34,58,59]
22Guide and focus on innovation management (including system innovation, technological innovation, construction management model innovation, investment and financing model innovation, etc.)[49,57]
23Preliminary scientific research and necessary personnel training (such as integrating industry-university-research innovation institutions, and organizing scientific research projects)[47,60,61]
24Past experience accumulation and talent reserve of similar projects (scientific research includes the accumulation of past practice of participating units, the technology developed and mastered by relevant research institutes, and the technology and experience imported from abroad)[30,47,60]
25Adopt or innovatively absorb advanced technologies and methods (such as BIM, modular building technology, etc.)[61,62,63]
26Application of advanced management methods (such as Dingding)[61,64]
27Direct or strong leadership of the country/government (so as to give full play to the advantages of the system, carry out necessary coordination, and be able to concentrate on major tasks)[34,41,65,66]
28Strong support from the government and related institutions (such as policies and guidelines, scientifically planned resumption plans, nucleic acid testing, etc.)[41,65,66,67]
29Public acceptance and support of construction projects[68,69]
30Effective external management and supervision (for example, supervision departments at all levels carry out follow-up supervision and audit of the legality and compliance of the project construction process, and relevant pandemic prevention departments supervise pandemic prevention measures, etc.)[70,71,72]
31Fully understand the restrictions on project implementation by external environmental conditions [34,70,71,72]
32Stability of the social, economic and political environment[34,47,51,73]
Table 2. Construction project key performance indicators.
Table 2. Construction project key performance indicators.
ItemsKPIs
K1Project implementation efficiency and effect
KPI 1Project management triangle (time, quality, cost) target realization
KPI 2Occupational health, safety and environment (HSE) goals achieved
KPI 3Meet relevant regulations and requirements of design, technology, environmental protection, etc.
KPI 4Meet the designed function, and delivery publicly needed value/service
K2Satisfaction of key stakeholders
KPI 5Government satisfaction
KPI 6Owner’s satisfaction
KPI 7Satisfaction of participating parties (including consulting units, design units and construction units, etc.)
KPI 8Public satisfaction
KPI 9Satisfaction of other key stakeholders
KPI 10Establish good cooperation and relationship
K3Organizational Process Assets (OPA)
KPI 11New technologies
KPI 12Profits/benefits realization
KPI 13Opening new markets or increasing market share/competitiveness
KPI 14New organizational capacity and competency
KPI 15Improve brand/reputation
KPI 16Train professionals for companies or projects
K4Enterprise Environmental Factors (EEF)
KPI 17Has industry benchmarking or demonstration effects, certain management systems or technical standards can be promoted to similar or similar projects
KPI 18Effectively promote the innovation and coordinated development of the construction industry and related industries
KPI 19Competitiveness of the industry in the international market
KPI 20Contribute to theoretical and practical innovation in engineering technology and management
K5Comprehensive social impacts
KPI 21Delivery social-economic benefits to the community
KPI 22Sustainability in environment, society and economy
KPI 23Maintain social cohesion/society harmony
KPI 24Enhance people’s pride and self-confidence
KPI 25Job creation
Table 3. Questionnaire result assessment.
Table 3. Questionnaire result assessment.
TestCSF/KPIAppendix AIndicatorValueEvaluation
Validity analysisCSFTable A1KMO0,784Good
Bartlett’s test of sphericity1472.165
(p value = 0.000)
Very Good
KPITable A2KMO0.932Very Good
Bartlett’s test of sphericity1881.479
(p value = 0.000)
Very Good
Reliability testCSFTable A3Cronbach α (Standardized)0.931Very Good
KPITable A4Cronbach α (Standardized)0.965Very Good
Reliability test (split-half)CSFFigure A1Spearman–Brown split-half reliability coefficient0.877Very Good
KPIFigure A2Spearman–Brown split-half reliability coefficient0.908Very Good
Item analysisCSFTable A5pp ≤ 0.01All significant
KPITable A6pp = 0All significant
Table 4. CSF factor analysis and loading.
Table 4. CSF factor analysis and loading.
Factor 1Factor 2Factor 3Factor 4Factor 5
CSFFactor LoadingCSFFactor LoadingCSFFactor LoadingCSFFactor LoadingCSFFactor Loading
20.586160.50540.50310.588130.687
30.511200.39550.637290.758170.582
60.62220.71880.509310.456190.561
70.773230.755150.708320.74210.637
90.608240.683180.419
100.532250.608
110.46260.411
120.413300.437
140.338
270.45
280.69
Table 5. Ranking of stakeholders’ perception of the COVID-19 pandemic’s impact on KPIs.
Table 5. Ranking of stakeholders’ perception of the COVID-19 pandemic’s impact on KPIs.
OwnerDesignerContractorOverall
KPI 153.824KPI 84.095KPI 253.952KPI 153.862
KPI 183.735KPI 243.952KPI 153.952KPI 203.798
KPI 173.735KPI 33.905KPI 203.905KPI 183.777
KPI 203.676KPI 53.857KPI 233.881KPI 173.766
KPI 143.647KPI 223.857KPI 33.833KPI 233.745
KPI 53.618KPI 23.857KPI 223.833KPI 243.734
KPI 243.618KPI 93.81KPI 53.81KPI 53.723
KPI 43.559KPI 73.81KPI 183.81KPI 83.723
KPI 233.559KPI 43.81KPI 173.81KPI 223.723
KPI 223.559KPI 203.81KPI 133.81KPI 33.702
KPI 63.529KPI 193.81KPI 83.786KPI 133.691
KPI 193.529KPI 173.81KPI 193.762KPI 193.681
KPI 83.5KPI 153.81KPI 163.762KPI 143.67
KPI 133.5KPI 143.81KPI 243.738KPI 253.649
KPI 103.471KPI 133.81KPI 93.69KPI 43.638
KPI 33.441KPI 103.81KPI 23.667KPI 63.606
KPI 163.412KPI 233.762KPI 63.643KPI 163.596
KPI 93.353KPI 183.762KPI 43.643KPI 103.585
KPI 253.324KPI 63.714KPI 213.643KPI 93.574
KPI 213.324KPI 253.714KPI 143.643KPI 113.5
KPI 113.324KPI 213.619KPI 113.595KPI 213.5
KPI 73.265KPI 113.619KPI 103.571KPI 23.489
KPI 23.118KPI 163.571KPI 73.548KPI 73.489
KPI 13.029KPI 123.381KPI 123.357KPI 123.213
KPI 122.941KPI 13.238KPI 13.238KPI 13.149
Table 6. Hypotheses.
Table 6. Hypotheses.
HypothesisPathLiteratures
1F1 → K1 (+)[104]
2F2 → K3 (+)[105,106]
3F2 → K4 (+)[107,108]
4F3 → K2 (+)[109,110,111]
5F4 → K1 (+)[70,102]
6F4 → K2 (+)[70,102]
7F4 → K5 (+)[70,102]
8F5 → K4 (+)[103,110,112,113]
9F5 → K5 (+)[51,111,114]
Note: → indicates positive correlation.
Table 7. Structural equation modeling assessment.
Table 7. Structural equation modeling assessment.
PathCoefficientp-Value
F1 → K10.240.04 *
F2 → K30.660.00 **
F2 → K40.490.00 **
F3 → K20.210.02 *
F4 → K10.140.11
F4 → K20.300.00 **
F4 → K50.350.00 **
F5 → K40.170.03 *
F5 → K50.270.01 **
* p < 0.05 ** p < 0.01.
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Guo, H.-S.; Liu, M.-X.; Xue, J.; Jian, I.Y.; Xu, Q.; Wang, Q.-C. Post-COVID-19 Recovery: An Integrated Framework of Construction Project Performance Evaluation in China. Systems 2023, 11, 359. https://doi.org/10.3390/systems11070359

AMA Style

Guo H-S, Liu M-X, Xue J, Jian IY, Xu Q, Wang Q-C. Post-COVID-19 Recovery: An Integrated Framework of Construction Project Performance Evaluation in China. Systems. 2023; 11(7):359. https://doi.org/10.3390/systems11070359

Chicago/Turabian Style

Guo, Han-Sen, Ming-Xin Liu, Jin Xue, Izzy Yi Jian, Qian Xu, and Qian-Cheng Wang. 2023. "Post-COVID-19 Recovery: An Integrated Framework of Construction Project Performance Evaluation in China" Systems 11, no. 7: 359. https://doi.org/10.3390/systems11070359

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