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Article

Linking Sustainable Project Management with Construction Project Success: Moderating Influence of Stakeholder Engagement

by
Shih-Wei Wu
1,
Yifan Yan
2,
Jialiang Pan
3,* and
Kun-Shan Wu
4,*
1
Department of Business Management, National Taipei University of Technology, Taipei 10608, Taiwan
2
College of Management, National Taipei University of Technology, Taipei 10608, Taiwan
3
School of Business and Management, Jiaxing Nanhu University, Jiaxing 314001, China
4
Department of Business Administration, Tamkang University, Taipei 251301, Taiwan
*
Authors to whom correspondence should be addressed.
Buildings 2023, 13(10), 2634; https://doi.org/10.3390/buildings13102634
Submission received: 17 August 2023 / Revised: 15 October 2023 / Accepted: 17 October 2023 / Published: 19 October 2023
(This article belongs to the Section Construction Management, and Computers & Digitization)
Browse Figure
Versions Notes

Abstract

:
Stakeholder engagement (SE) is an important factor in making a project successful. Steered by the resource-based value (RBV) and stakeholder theories, this paper aims to explore not only the correlation between sustainable project management (SPM) and sustainable project success (SPS), but also the moderating effect of SE on this connection. Data was gathered from 365 questionnaires distributed to construction project professionals in China. Structural equation modelling was employed to test the proposed hypotheses. The results confirm that both SPM and SE positively affect SPS, but the positive moderating effects of SE were found to be insignificant. This article provides the basis for the Chinese construction industry to determine how to achieve SPS through the enhanced implementation of SPM and effective SE.

1. Introduction

The construction industry is critical to global economic development [1,2]. Nevertheless, it has encountered significant obstacles, especially in terms of sustainability [3], which are also inevitable in China. Driven by large-scale urbanization and industrialization, China’s construction sector is growing rapidly, but in recent decades it has generated high emissions, huge energy consumption, and serious environmental problems [4]. It is therefore important for the construction industry to promote sustainable development, improve resource efficiency, and minimize negative influences on the natural environment. To address these challenges, the construction industry is gradually adopting a sustainable project management (SPM) approach. Accordingly, the growing importance of SPM as a new school of thought in project management has triggered a large number of studies related to the sustainable management of projects [5]. These studies focus on how construction companies understand (and respond to) the operational consequences of SPM, which is crucial to the construction industry’s overall transition to sustainability [6].
SPM essentially focuses on the simultaneous consideration of the triple bottom line aspects (economic, environmental, and social) of the management of projects. Nevertheless, SPM is a resource-intensive process, particularly in managing sizable construction projects. This has resulted in increasing resource constraints, requiring construction project managers to ensure the adoption of innovative solutions for sustainable practices to maintain their competitive advantage in the global construction industry [7]. As a result, it has become a challenge for construction companies all over the world to integrate SPM successfully into construction projects [8].
From the resource-based value (RBV) perspective, SPM practices can be regarded as tactical investments which may facilitate the creation of competitive heterogeneity in companies. Implementing SPM should lead to superior project success (PS). Therefore, SPM is increasingly recognized as a key factor in PS [9,10,11,12]. However, previous studies have predominantly examined the correlation between SPM and PS in developed nations [13,14,15,16,17,18], with limited empirical research exploring this nexus in developing countries [12,19,20]. Moreover, the focus of earlier research has mostly been on the assessment of the direct SPM–PS nexus, but the key mechanisms to strengthen this association are as yet unidentified. Accordingly, moderating variables should be included to explore the SPM–PS relationship, just as some scholars suggest [21]. Consequently, drawing on the Institute’s Individual Competency Benchmarking Framework of International Project Management Association [22], this study intends to examine the association between SPM and sustainable project success (SPS) to address a gap in the literature by identifying a key moderating variable: stakeholder engagement (SE). We have focused on SE as this construct is critical to “putting sustainability on the agenda” [23], because its significance cannot be ignored when it comes to managing projects effectively and achieving successful project outcomes.
The role of SE in successful projects (including construction projects) is crucial [24]. According to Freeman’s [25] definition, a stakeholder is any individual or group of individuals who can influence or be influenced by the performance of a project. In the context of construction projects, key stakeholders comprise clients, communities, consultants, contractors, subcontractors, and suppliers [12,26,27,28,29,30]. SE is extremely important as projects move forward. Even though minor decision-making and emergencies do not usually lend themselves to stakeholder involvement, it is important to include the aspect of stakeholders [31].
SE builds positive and productive relationships over a long period of time [32]. Engaging stakeholders can create mutually beneficial connections, allowing one to spot trends or new barriers affecting current or future projects [33]. Conversely, a lack of SE leads to immature technologies or more internal resistance [34].
The importance of SE in sustainability activities has been highlighted in research [35,36,37]. At present, effective SE is undoubtedly becoming part of professional practice in order to achieve positive project outcomes. Extensive literature has been produced on the direct relationship between SE and PS [38,39,40], but very few studies have examined the moderating effect of SE. The research advocated that future study should develop a thorough comprehension of the specific procedures of project stakeholders in complex project environments where firms can benefit from SE [41]. To address this gap, this paper utilized Structural Equation Modelling (SEM) to examine the influence of SPM and SE on enhancing SPS, with specific emphasis on the moderating effect of SE.
Steered by the resource-based value (RBV) and stakeholder theories, this study aims to explore not only the SPM–SPS and SE–SPS relationships but also the moderating impact of SE on SPM–SPS connection. To accomplish the objective, the research questions (RQs) listed below have been proposed:
RQ1. Does SPM contribute to SPS in construction?
RQ2. Does SE contribute to SPS in construction?
RQ3. Does SE positively moderate the SPM–SPS nexus in construction?
The construction industry in China constantly occupies an important position in the domestic economy. For instance, in 2022, its output value was approximately 8.3 trillion yuan, equating to 6.9% of China’s GDP. Over the last decade, the construction industry in China has achieved an added-value growth rate of at least 3.5% per year, surpassing the country’s average GDP growth rate [42]. On the other hand, China’s construction industry has suffered several setbacks like huge energy consumption, high emissions, and serious environmental problems. Hence, to obtain the answers to the RQs above, 365 construction project professionals working in the construction industry in Beijing, China, were recruited for a survey.
The findings reveal that incorporating SPM and SE will enhance SPS. SE does not moderate the relationship between SPM and SPS positively. Overall, this article contributes to research on SPM in several ways. Firstly, these documents are consistent with RBV theory and provide support for the SPM–SPS nexus. Secondly, most earlier articles utilized data from developed countries, but evidence from developing countries was scarce. Consequently, this paper fills a gap in the current literature by addressing the SPM–SPS nexus in the Chinese context. Thirdly, these findings are in line with stakeholder theory and provide support for SE-SPS nexus in the Chinese construction industry. Lastly, this article contributes to the knowledge base of mechanisms through exploration of the moderating role of SE on SPM and SPS.
Following the introduction, the rest of this study is organized as follows: Section 2 explores the existing literature and develops the hypotheses. Then, data collection instruments and study procedures are provided in Section 3. In the next two sections are the results and discussion. Finally, we draw conclusions, including the limitations and future directions of this article.

2. Literature Review and Hypotheses Development

2.1. Sustainable Project Success

The concept of project success (PS) has changed over the last twenty years, from the traditional notion of the “golden triangle” (cost, time, quality requirements) to the modern notion of post-construction outcome criteria such as stakeholder satisfaction. This concept is now moving towards sustainable project success (SPS) with more embedded sustainability criteria.
In recent years, there has been a surge of interest in project management sustainability, highlighting the long-term prospects for PS [40]. For the last few years, the construction industry has gradually shifted from conventional development to sustainable building. Construction-sustainable projects provide a balance between environmental, economic, and social factors [43,44]. In agreement with [20], this paper strives to evaluate SPS in the construction industry along six dimensions: business success, team, stakeholders, project efficiency, future readiness and sustainability.

2.2. Sustainable Project Management

Sustainable Project Management (SPM) is what many organizations will take into account when making business decisions and managing projects. This concept has evolved with the fulfilment of sustainable development in project management, aiming to ensure that sustainable project goals conform to economic, environmental, and social goals [45,46,47]. In addition, SPM in the construction industry is often related to reducing resource use [48], addressing influential project externalities [49], and protecting both human and environmental resources [50]. In line with [20], the focus of this study is to evaluate SPM in terms of economic, environmental, and social benefits of construction companies.
Furthermore, from the perspective of the RBV, SPM practices can be viewed as tactical investments for companies to highlight the creation of competitive differentiation. Consequently, implementing SPM should lead to superior SPS.

2.3. Stakeholder Engagement (SE)

Stakeholders are individuals or groups that can influence the development of a project, positively or negatively, due to a vested interest [15,40,51]. As per ISO 26000 guidelines [52], proactive stakeholder engagement (SE) is a fundamental principle of sustainability [53,54]. According to the Project Management Institute, SE refers to “the practice of consulting, communicating, compromising and building relationships to influence outcomes” [55]. SE is therefore the process of identifying stakeholders’ interests, involving them in the project, and managing the project to meet their needs. The successful implementation of a project is highly dependent on the engagement of stakeholders [56].
According to stakeholder management theory, a project can only be deemed successful when stakeholders’ needs and requirements are well considered during the stakeholder management process [57]. Research highlights that SE is the key element of PS in remediation projects, and that multiple-stakeholder engagement is a long-term success for any project [58]. Ineffective stakeholder engagement can lead to ineffective strategic decision-making, which in turn can lead to project failure [59].

2.4. Nexus between SPM and SPS

The connection between SPM and PS/SPS has been addressed in a number of studies, among which a positive SPM–PS (SPM–SPS) nexus has been identified. For example, scholars [60] examined the nexus between SPM and PS and stated that SPM had a significantly positive influence on PS.
In addition, research conducted in different regions like China [61], Pakistan [12,19], the United Arab Emirates (UAE) [18], and the UK [62] exposed that SPM positively affects the SPS of construction projects. In addition, researchers [20] exposed that SPM was found to have a significant and positive impact on SPS in the manufacturing industry in Malaysia. More recently, scholars [63] investigated the SPM–PS nexus with the moderating effect of effective communication regarding this association among project management professionals in Pakistan; their results from cross-sectional data also depicted a significantly positive influence of SPM on PS. Based on the literature to date, we propose the following hypothesis:
H1. 
SPM has a positive impact on SPS in construction.

2.5. Nexus between SE and SPS

There is widespread agreement that SE is of paramount importance in major infrastructure projects [64,65,66,67,68]. Previous research also suggested that SE positively affected PS. For instance, the results of the study by [38] revealed the positive impact of stakeholder management awareness on PS in the construction industry in Pakistan and confirmed the positive moderation of this awareness. Scholars [39] also exposed that SE had a significantly positive impact on the PS after examining their nexus in renewable energy sectors in Pakistan. Likewise, research on the relationships among SPM, SE, knowledge management, and SPS in virtual team environments [40] found that SE had a significantly positive influence on SPS. On the basis of the literature to date, we propose the hypothesis below:
H2. 
SE has a positive impact on SPS in construction.

2.6. Moderating Effect of SE

According to stakeholder theory [25], companies have a wide range of stakeholders that have legitimate interests in company performance and operations. Stakeholders show both interest and concern for a business in order to increase returns [69]; they can directly or indirectly influence the progress and success of the project. The sustainability, success, and performance of the project is ensured through the active participation of stakeholders. Therefore, it is concluded that stakeholder management plays a significant role in moderating the relationship between project governance and project performance [65].
Now that many studies have been criticized for viewing certain variables in isolation, some attention has recently been paid to the moderating effect of stakeholder-related factors on project performance from a contractor’s perspective. For example, with project management professionals in Pakistan as the target audience to study the moderating effect on the SPM–PS nexus, scholars [12] noted that the moderating effect of SE was not significant, just as academics [63] found the moderating effect of the effective communication insignificant. To confirm how the moderating effect of SE works in the Chinese context, we propose the hypothesis below:
H3. 
SE positively moderates on the nexus between SPM and SPS in construction.
These hypotheses are illustrated in Figure 1, which is based on a literature review and provides an initial conceptual framework for examining the direct effects of SPM and SE on SPS, and the moderating effect of SE on SPS in the Chinese construction industry.

3. Methodology

3.1. Measures and Scales

The nine items used for measuring SPM were derived from previous studies [20,60], which evaluated environmental, economic, and social dimensions, as stipulated in the Triple Bottom Line (TBL). Responses were obtained using a five-point Likert scale, with 1 indicating unimportant and 5 indicating very important. In addition, SPS constructs in the questionnaire comprised six dimensions: specifically, business success, team, stakeholders, project efficiency, preparation for the future, and sustainability; each of the dimensions was gauged through three items from [20]. All measurements were accomplished using a five-point Likert scale (1 = to a very little extent, 5 = to a great extent). Regarding the four items for SE, they were taken from [39], with all measurements conducted using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree).
To verify and improve the material to ensure that the questionnaire aligned with the examined theoretical model, a preliminary study was conducted within a reference group of 10 construction project managers.

3.2. Data Collection Procedures and Sample

In this study, a quantitative approach based on structured self-administered questionnaires was used to evaluate the conceptual model and test the proposed hypotheses. To draw a representative sample, Beijing, the capital of China where there are many large construction companies, was chosen as the sample location, and experienced construction project professionals working there were thus targeted.
In 2023, Beijing has 58 large construction firms with an annual revenue exceeding RMB 800 million based on the Measures for the Classification of Large and Medium-Sized Enterprises in Statistics by the National Bureau of Statistics of China. Thus, this study utilized these firms as the sample frame. The RAND function in Microsoft Excel 2019 was utilized for the random selection of three companies. As there was no list of employees available for the selected companies, we called the human resource managers from the selected construction firms and asked them whether they would join the survey. Finally, the human resource managers of three construction firms agreed to send a paper questionnaire to the construction project professionals of the company, and over 400 construction project professionals were willing to take part.
Data were collected from January to February in 2023 via self-administered paper questionnaires. Each questionnaire consisted of four sections. The first section contained questions relating to demographic information, including gender, age, education, etc. This was followed by sections asking questions about SPM, SPS, and SE.
The confidentiality and anonymity of the respondents were ensured for ethical reasons. Respondents were informed of the purpose of the study and their right to withdraw at any time.
A total of 400 questionnaires were sent out for data collection, and we got 365 valid responses, representing a response rate of 91.25%, which is significantly high [18,70]. Hence, the final sample consisted of 365 respondents participating in projects like constructing hospitals, manufacturing plants, residential & commercial buildings, and schools. Table 1 offers an overview of their demographic information.

3.3. Sample Size

Before performing PLS-SEM, this article employed G*POWER 3.1.9.7 version to ensure that the sample size (N = 365) had sufficient statistical power to support the recommendations of [71]. For a two-tailed test with an error probability of 0.05 and an effect size of (0.15), the power (1-beta error probability) is 0.824, well above the suggested limit of 0.80.

3.4. Data Analysis

Data for this study were analyzed using SmartPLS 4.0 software. SmartPLS 4.0 is suggested for analyzing data collected, especially models with moderator/mediator variables [72,73]. We performed a two-stage process where we tested the inner model in the first stage and then evaluated the outer model.

4. Results

4.1. Measurement Model

Cronbach’s α, composite reliability (CR), Average Variance Extracted (AVE), Fornell–Larcker Criterion, and cross-loadings are five criteria for validating the measurement model.
Table 2 verifies that all values of Cronbach’s α and CR surpassed the 0.7 threshold, indicating higher reliability [74]. In addition, all factor loadings shown in Table 2 were greater than the threshold values 0.7 [75]. Moreover, the AVE values for all constructs ranged from 0.641 to 0.749, exceeding the required minimum threshold of 0.50 [76]. Therefore, both criteria demonstrate the existence of good convergent validity in the measurement model.
Regarding formative structures, as suggested by scholars [77], weights derived from their respective contributions to the second-order structure were utilized to evaluate the convergent validity. A second-order formative model was constructed employing the repeated indicators approach within the PLS-SEM framework. To establish the validity of the higher order formative construct, we firstly evaluated multicollinearity using the variance inflation factor (VIF). VIF values for the second-order variables ranged between 2.552 and 4.415 (less than or equal to 5, as shown in Table 3), thus indicating that collinearity did not raise any concerns in our data [78]. Next, Table 3 shows that all first-order structure weights were significant, providing support for the second-order structure of SPM and SPS. Additionally, it can be observed that economics (SPME) made the most significant relative contribution to SPM. Similarly, the largest relative contribution was that of the SPSSUS (sustainability), suggesting that the SPSSUS had the most significant impact on SPS. Taken together, our findings imply that the reflective and formative structures do meet the requirement for convergent validity.
To confirm discriminant validity (DV), this paper employed the Fornell-Larcker [76] criterion and cross-loadings. The diagonal entries in Table 4 illustrate the square root of the AVE for each construct, while the off-diagonal entries show the correlations between constructs. Table 4 highlights that DV has been established, with evidence provided by the fact that the square root of each facet’s AVE is larger than the correlation coefficient between that facet and other facets [76]. Furthermore, Table 5 displays that the factor loadings of components on their respective construct are greater than their cross-loadings on other constructs, indicating good DV.

4.2. Multicollinearity and Common Method Variance

In order to assess whether there were multicollinearity problems in the constructs, we ensured the variance inflation factor (VIF) by employing the SmartPLS algorithm. The VIF values for first-order variables ranged from 1.545 to 2.215, indicating values lower than 3 [79]. VIF values ranging from 2.552 to 4.415 were observed for the second-order variables, indicating values below 5 [80]. Therefore, we say that there is not an overlapping problem in our data.
To assess the potential existence of CMV (common method variance), Harman’s single-factor test was performed using SPSS 26. All variable items were entered. The findings indicate that the first factor accounted for 45.37% of the variance, falling below the 50% threshold [81].
Additionally, the construct correlation matrix (Table 4) showed that the correlations between the constructs were less than 0.814; however, CMV was often supported by correlations exceeding 0.90 [82]. Therefore, it can be considered that the CMV problem in our article is not serious.

4.3. Outer Model (Structural Model)

After examining the reliability and validity of the constructs, we determined the path model via bootstrapping (5000 subsamples, bias-corrected and accelerated bootstrap), as advised by scholars [74]. A larger R2 value means that the constructs are more accurate and precise. The results showed that sustained project success (SPS) explained 86.9% of the variance.
We calculated the Q2 value of the model to evaluate the predictive relevance (PR). The results indicate that the model has a positive predictive power, as evidenced by a Q2 value greater than zero [83]. Effect size was assessed using Cohen’s f2, where 0.02, 0.15, and 0.35 infer small, medium, and large effects, respectively [84].
As shown in Table 6, SPM has a positive influence on SPS (β = 0.468, t = 9.771 and p < 0.01); therefore, H1 is supported. The value of f2 (0.418) demonstrates a substantial impact, indicating that SPM accounts for 41.8% of the variation in SPS.
SE has a positive influence on SPS (β = 0.352, t = 7.651 and p < 0.01), providing support for H2. The value of f2 (0.305) demonstrates a medium effect, representing that SE accounts for 30.5% of the variation in SPS.
The findings also indicate that SPM*SE negatively affects SPS (β = −0.0118, t = 5.233 and p < 0.01); therefore, H3 is not supported (Table 6). The value of f2 (0.099) demonstrates a medium effect, indicating that SPM*SE explains 9.9% of the variance in SPS.
Furthermore, according to scholars [85], an SRMR (standardized root mean square residual) value lower than 0.08 is considered a good fit. Hence, the results reveal an SRMR value of 0.071, signifying that the model achieves the criteria for goodness-of-fit.
Finally, following the examination of the entire sample, the conceptual model was evaluated in a construction-project-experience scenario. After excluding the sub-sample with less than five years’ experience, the results reveal that both SPM and SE positively affect SPS, but the positive moderating effects of SE were found insignificant. The findings of the experts’ sub-sample (over five years’ experience) closely aligned with those of the total sample (see Table 7).

4.4. Multiple-Group Analysis

This study employed the multiple-group analysis (MGA) technique to investigate the impact of control variables on the relationship between the independent and dependent variables. The Henseler MGA non-parametric approach [86] was used to analyze differences between two groups. The results of the MGA are presented in Table 8.
First of all, concerning the disparities between male and female participants regarding the effects of SPM, SE, and SPM*SE on SPS, it was observed that SPM had a considerably more significant impact on SPS among male respondents as opposed to female respondents. Moreover, a remarkable distinction was found between male and female respondents regarding the impact of SE on SPS, which showed that SE had a more robust influence on SPS among female participants than male ones. However, the differences in age, education level, construction project experience, and designation did not make a significant impact of SPM, SE, or SPM*SE on SPS.

5. Discussion

Following the above results, the main findings are discussed in detail here. In relation to Research Question 1 (Does SPM contribute to SPS in construction?), the results indicate that SPM positively affects SPS. Based on the RBV perspective, it can be claimed that SPM is one of the most important determinants of SPS, demonstrating that the ability to implement SPM enables construction industries to gain a competitive advantage through SPS [12,87]. This finding is in sync with other studies [12,14,18,19,20] which confirmed that SPM has a positive influence on SPS.
Concerning Research Question 2 (Does SE contribute to SPS in construction?), the results indicate that SE positively affects SPS. Drawing on the stakeholder theory, a project will be successful if it considers the requirements and needs of stakeholders through the stakeholder management process [57]. This finding is in sync with other studies [38,39,40].
With regard to Research Question 3 (Does SE positively moderate the SPM–SPS relationship in construction?), this study fails to confirm the positive moderating effect of SE, contrary to the Hypothesis 3. This finding does not concur with the propositions of [12,63,65]. This discrepancy may be because the design of construction projects is getting more and more complex, and many enterprises may not bother or fail to involve external stakeholder management in their projects.

6. Conclusions

In this study, we aimed to scrutinize the influence of SPM on SPS in the Chinese construction sector whilst also taking into account the moderating effect of SE. Structured questionnaires were distributed to gather data from construction project professionals operating projects like constructing hospitals, manufacturing plants, etc., in Beijing, China, with a total of 365 valid participants.
Briefly, the results obtained through PLS-SEM approach revealed the following information: (1) Both SPM and SE have a significantly positive influence on SPS; (2) SE’s positive moderating effect was found insignificant. That is to say, professionals in China’s construction industry generally agree that the implement of SPM and SE can enhance the success of sustainable projects. On the other hand, since the moderating effect of SE is not well recognized within the construction industry, action to improve SE is still high on the agenda in China.
This study makes both theoretical and practical contributions. Academically, the first contribution of this article is to advance understanding of SPM, SE, and their links to SPS. Secondly, the article’s findings align with the arguments put forth in [12,14,18,19,20] and are in line with the RBV’s perspective on the significance of integrating SPM practices to attain SPS. Thirdly, this article contributes to stakeholder theory by emphasizing the significance of SE in project management. Lastly, in contrast to previous researchers who considered SE to be a possible determinant of SPM [12,63,65], our study failed to confirm the positive moderating effect of SE. Nevertheless, existing research shows that SE is an important part of enabling organizations and project managers to successfully complete projects. We therefore need to look further at how other sectors and cultures can play a role.
Regarding the practical implications: Firstly, the results show that with the environmental, economic, and social dimensions, SPM is essential for the construction industry to improve SPS. Secondly, this research will help construction firms to improve SPS through effective SE in construction projects. Our research proposes that project managers in construction should carry out the effective SE to achieve maximum stakeholder satisfaction, leading to SPS. As a result, it is vital and important for project managers to acquire stakeholder management skills to demonstrate and fulfil stakeholder communication requirements. Finally, the results of this study could provide guidance for project management to achieve sustainable development in the construction industry.
This paper has some possible limitations. This study only examined a sample of large construction companies. Small and medium-sized enterprises construction companies were not included. Future research for scholars could expand data collection to include small and medium-sized enterprises companies. In addition, future research could be conducted using in-depth interviews with construction project professors with over five years’ experience to gain insight into the moderating effects of SE on SPM and SPS to better understand it’s impact on the construction industry. Furthermore, this paper focuses on the relationship between SPM and SPS, but it is crucial to consider factors like organizational culture and communication climate when implementing SPM [88]. Hence, future studies could further investigate the drivers that facilitate the implementation of SPM practices in construction firms.
Next, further research is required with a wider range of stakeholders encompassing various roles in sustainable construction. Moreover, in the construction industry, assessing the social impact of a construction project not only contributes to the sustainable development of society, but also is critical to the success of the construction project [89]. Future research could explore the role of social performance in the relationship between SPM and SPS.
Lastly, our paper is limited to construction project professionals working in Chinese construction industry. If future work can extend this model with more global samples, including other countries and cultures, it will be helpful to conduct comparative research.

Author Contributions

Conceptualization, Y.Y., J.P. and K.-S.W.; Methodology, J.P. and K.-S.W.; Software, J.P. and K.-S.W.; Validation, S.-W.W.; Formal analysis, J.P. and K.-S.W.; Investigation, Y.Y.; Resources, S.-W.W.; Data curation, Y.Y.; Writing—original draft, Y.Y., J.P. and K.-S.W.; Writing—review & editing, J.P. and K.-S.W.; Visualization, Y.Y.; Supervision, S.-W.W.; Project administration, S.-W.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Buildings 13 02634 g001
Figure 1. Conceptual model and hypotheses.
Figure 1. Conceptual model and hypotheses.
Buildings 13 02634 g001
Table 1. Respondents’ demographics.
Table 1. Respondents’ demographics.
ItemClassificationFrequencyPercentage (%)
GenderMale20556.2
Female16043.8
Age30 or below13537.0
31–408322.7
41–506517.8
51 or above8222.5
EducationAssociate degree14840.5
Bachelor10528.8
Graduate and above11230.7
Construction
Project Experience		1 years and less 10829.6
2–5 years10518.7
6–10 years7219.7
11 and above8021.9
DesignationSenior Engineer5114.0
Associate Senior Engineer5114.0
Intermediate Engineer6818.6
Assistant Engineer6417.5
Non engineer13135.9
Table 2. Reliability and convergent validity.
Table 2. Reliability and convergent validity.
Construct/VariableItemsFLVIFCronbach’s αCRAVE
Sustainable Project Management (SPM)
Economics (SPME)SPME10.8972.2150.8320.8360.749
SPME20.8481.868
SPME30.8501.826
Environmental (SPMEV)SPMEV10.8611.8670.8240.8240.739
SPMEV20.8581.847
SPMEV30.8601.854
Social (SPMS)SPMS10.8541.7810.820.820.735
SPMS20.8591.901
SPMS30.8591.817
Sustainable project success (SPS)
Business Success (SPSBS)SPSBS10.8491.7430.8030.8040.717
SPSBS20.8631.833
SPSBS30.8291.643
Impact on Team (SPSIMT)SPSIMT10.8561.7810.8010.8020.716
SPSIMT20.8401.67
SPSIMT30.8421.72
Impact on Stakeholder- Externa (SPSISE)SPSISE10.8631.8840.8190.820.734
SPSISE20.8651.846
SPSISE30.8431.757
Project Efficiency (SPSPE)SPSPE10.8581.8160.7920.7920.707
SPSPE20.8451.731
SPSPE30.8181.545
Preparation for the Future (SPSPPF)SPSPPF10.8611.7990.8140.8150.728
SPSPPF20.8411.74
SPSPPF30.8591.838
Sustainability (SPSSUS)SPSSUS10.8631.8990.8250.8250.741
SPSSUS20.8681.923
SPSSUS30.8521.792
Stakeholder Engagement (SE)SE10.8241.8360.8400.8410.676
SE20.8181.821
SE30.8321.881
SE40.8161.818
Note: FL = factor loading; VIF = Variance inflation factor.
Table 3. Weights of higher-order constructs.
Table 3. Weights of higher-order constructs.
High-Order ConstructsFormative IndicatorsVIFOuter Weightst-Valuesp-ValuesBCa Bootstrap Interval
5%95%
SPMSPME2.5520.36848.0780.0000.3560.381
SPMEV2.7680.36345.4660.0000.3510.377
SPMS2.9090.35848.5110.0000.3470.371
SPSSPSBS4.2630.18449.3160.0000.1780.19
SPSIMT4.4150.18346.4130.0000.1770.19
SPSISE3.8460.18643.0440.0000.1790.193
SPSPE2.8510.17741.3870.0000.170.184
SPSPPF3.7630.18340.9390.0000.1760.191
SPSSUS3.3620.18844.2360.0000.1810.195
Note: VIF = Variance inflation factor; BCa = The bias-corrected and accelerated.
Table 4. Fornell–Larker Discriminate validity.
Table 4. Fornell–Larker Discriminate validity.
VariableSESPMESPMEVSPMSSPSBSSPSIMTSPSISESPSPESPSPPFSPSSUS
SE0.822
SPME0.7580.865
SPMEV0.7260.7710.86
SPMS0.7780.7620.7630.857
SPSBS0.8050.7730.7410.7520.847
SPSIMT0.7820.7760.7330.7700.8100.846
SPSISE0.7990.7630.7560.7950.8020.7980.857
SPSPE0.7720.7680.7590.7610.7870.8140.7880.841
SPSPPF0.7690.7200.7150.7630.7880.7990.7650.7650.853
SPSSUS0.7960.7430.7180.7390.8120.7840.7870.7580.8030.861
Mean3.7523.8163.7973.8293.7893.8323.7713.8303.8623.781
SD0.8840.9910.9730.9610.9470.8890.9430.9250.9240.965
Note: SD = Standard Deviation. Correlations between constructs are shown below the diagonal. Values in bold are the square root of AVE.
Table 5. Cross-loadings.
Table 5. Cross-loadings.
SESPMESPMEVSPMSSPSBSSPSIMTSPSISESPSPESPSPPFSPSSUS
SE10.8240.6360.5980.6130.6730.6560.6420.6520.6490.659
SE20.8180.6180.5780.6210.6460.650.6450.6040.6010.668
SE30.8320.6570.6360.6840.6830.6530.6920.6460.6570.666
SE40.8160.580.5750.6410.6450.6140.6480.6360.6220.625
SPME10.6840.8940.690.6930.7220.7070.7010.7080.6560.691
SPME20.6430.8550.6870.6330.6480.6370.6170.6320.590.615
SPME30.6380.8470.6230.650.6350.670.6630.6530.6230.622
SPMEV10.6180.6770.8620.6510.6660.6020.6370.6610.6040.623
SPMEV20.6330.6660.8580.6410.620.6360.6580.660.5990.615
SPMEV30.6220.6460.860.6780.6250.6520.6540.6380.6420.614
SPMS10.690.7060.6810.8570.6580.6740.7040.6720.6420.639
SPMS20.6230.6310.6230.8620.6210.6130.6440.6250.6490.605
SPMS30.6870.6180.6570.8530.6550.6940.6940.6590.6720.657
SPSBS10.7050.6280.630.6420.8480.6970.6870.70.6630.665
SPSBS20.6890.6810.6440.6540.8610.7040.6750.6650.6770.699
SPSBS30.6520.6560.6090.6150.8310.6570.6770.6340.6630.699
SPSIMT10.6720.6620.6130.6730.6790.8540.6770.7090.6750.659
SPSIMT20.6610.6640.6460.6460.7090.8420.7040.6940.6990.651
SPSIMT30.6520.6430.60.6360.6680.8420.6450.6630.6530.679
SPSISE10.6990.650.6190.6720.6910.6890.8630.6510.6430.677
SPSISE20.7130.690.6690.7050.7310.7080.8640.7110.6620.687
SPSISE30.6390.6210.6540.6660.6380.6530.8440.6610.6620.659
SPSPE10.6380.6770.6440.6280.660.7010.6690.8580.6330.627
SPSPE20.6610.6290.6410.6420.6640.6770.6390.8450.6620.643
SPSPE30.6470.630.6290.650.660.6750.6780.8180.6330.64
SPSPPF10.6830.6370.6340.6770.6910.6670.6760.6480.8550.678
SPSPPF20.6260.5870.6040.6290.6550.7040.6460.6580.8470.689
SPSPPF30.660.620.5930.6480.6730.6740.6360.6520.8580.689
SPSSUS10.690.6370.6070.6190.7050.6680.6820.6550.6890.864
SPSSUS20.6920.640.6310.6380.7030.7050.7010.6590.7070.869
SPSSUS30.6720.6430.6160.6530.6880.650.6490.6420.6770.849
Table 6. Assessment of the structural model.
Table 6. Assessment of the structural model.
95% Confidence Interval
Betat-ValuesLower LimitUpper LimitRemarksEffect f2R2
H1SPM→SPS0.4689.7710.3890.548Supported0.4180.869
H2SE→SPS0.3527.6510.2710.424Supported0.305
H3SPM*SE→SPS−0.1185.233−0.157−0.083Not Supported0.099
Table 7. Assessment of the structural model: group with 5+ years of experience.
Table 7. Assessment of the structural model: group with 5+ years of experience.
95% Confidence Interval
Betat-ValuesLower LimitUpper LimitRemarksEffect f2R2
H1SPM→SPS0.4044.980.2730.536Supported0.3110.884
H2SE→SPS0.4015.2390.2600.518Supported0.378
H3SPM*SE→SPS−0.1223.812−0.179−0.074Not Supported0.138
Table 8. Multigroup analysis.
Table 8. Multigroup analysis.
RelationshipPath Coefficients Diff (Male–Female)p-Value Path Coefficients Diff
(Below 30–31 Above)		p-ValuePath Coefficients Diff (Associate Degree—Bachelor or Above)p-Value
SPM→SPS0.263 **0.008−0.0860.3990.060.58
SE→SPS−0.234 **0.0070.0040.947−0.0820.375
SPM*SE→SPS0.0330.5−0.0440.439−0.0290.576
RelationshipPath Coefficients Diff (Less Experienced–More Experienced)p-valuePath Coefficients Diff
(Engineer–Non Engineer)		p-Value
SPM→SPS0.0970.3470.0370.712
SE→SPS−0.070.466−0.1380.134
SPM*SE→SPS0.0050.908−0.0260.577
Note: ** p < 0.01. Less experienced = The construction project experience of respondents is less than five years; More experienced = The construction project experience of respondents is more than five years.
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Wu, S.-W.; Yan, Y.; Pan, J.; Wu, K.-S. Linking Sustainable Project Management with Construction Project Success: Moderating Influence of Stakeholder Engagement. Buildings 2023, 13, 2634. https://doi.org/10.3390/buildings13102634

AMA Style

Wu S-W, Yan Y, Pan J, Wu K-S. Linking Sustainable Project Management with Construction Project Success: Moderating Influence of Stakeholder Engagement. Buildings. 2023; 13(10):2634. https://doi.org/10.3390/buildings13102634

Chicago/Turabian Style

Wu, Shih-Wei, Yifan Yan, Jialiang Pan, and Kun-Shan Wu. 2023. "Linking Sustainable Project Management with Construction Project Success: Moderating Influence of Stakeholder Engagement" Buildings 13, no. 10: 2634. https://doi.org/10.3390/buildings13102634

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