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Abstract

Construction labors play critical roles in executing the project. Therefore, the purpose of this study is to provide and review using partial least square structural equation modeling (PLS-SEM) approach that the skilled and unskilled labor force impact on project performance which has been overlooked in the previous literature in the context of the public construction industry in developing countries, like Pakistan. To achieve the objective of this study, a hypothetical model was developed and empirically examined by using Structural Equation Modeling. Data were gathered through a questionnaire survey method. In total, 400 construction practitioners responded to the questionnaire on behalf of their organization. The results revealed that unskilled labor has a significant negative impact on project performance during the construction phase, whereas the results confirmed that skilled labors have a significant positive impact on project performance in enhancing the success rate of the project in the public construction industry. These results could be used by construction experts to elaborate a broader and rooted view of the labor skills affecting the project performance. The results provide adequate information to policy and decision makers concerning labor skills being a compulsory part of the operational strategy in accelerating the better execution and success of construction projects. The current study adds to the construction project management literature by examining the effect of labor skills on project performance positively or negatively, and the hypothesized model was developed that should be adopted by practitioners to ascertain labor skills for the successful execution of the project.

Keywords

skilled labor unskilled labor project performance construction industry

Introduction

Improvements in project performance due to enhancements in labor skills have been highly demanding in the construction sector for a long time. It is widely acknowledged that project performance is based on the skills of the labors during the execution of the project, whose skills and abilities can affect the project’s progress to a greater or lesser extent. This is mostly the case for construction projects. Normally the project is managed and supervised by the project manager, assistant project manager, construction engineer, project architect, contractors, or subcontractors and tasks are performed by involving physical labor at a construction site. Construction is a labor-intensive industry (Hossein et al., 2018). This study, therefore, proposes a different technique to account for the impact of skilled and unskilled labor (workers) on project performance during project execution. For this purpose, we present a conceptual model in which workers of different skills affect project performance. Performance in any industry is important in achieving measures to make sure sustainability and competitiveness (Muthuveloo et al., 2017). Performance is the key indicator of all activities in any industry as it ascertains the survival of industry (Wang et al., 2015), the success of a project, and triple constraint (time, cost, quality) (Cserháti & Szabó, 2014; Xiao & Proverbs, 2002).

Project performance is measured and accessed by the labors’ skills during project execution, as labors are the most significant resource in the construction industry (Wong et al., 2006). Therefore, the impact of skilled and unskilled labors on project performance should be well understood so that the project is built and constructed without any delay or postpone. Labors or workers play an indispensable role during the execution of the project. Labor costs include a large portion (30%–50%) of the total actual cost of projects (Karimi et al., 2017). Hence, the skills of labor and efficiency are a critical factor in project performance (Hanna et al., 2005). Execution of construction project is initiated at the availability of experienced and skillful labor force. When a project supervisor and/or contractor cannot find the needed quality standard of skilled labors, the project is constructed or executed with unskillful labors. When skilled labor quantity issues arise, a project cannot meet its basic labor demands, which has a negative impact on the project performance. As labors are the key players in executing the primary plans and activities in project construction, they have a substantial impact on productivity (Maloney, 1983). Labor productivity is a difficult and challenging job of numerous dynamics, which may have positive and negative impact on project performance.

Different challenges confronted in the construction industry happen through a prerequisite to maintain skilled and experienced labor (Wong et al., 2006). Paul (2016) found that the Hong Kong construction industry suffered from skilled labor shortages. Moreover, the author identified workable strategies for solving these problems, measured the effectiveness of these identified strategies, and conclusively developed a conceptual labor supply model. In the context of the North American construction industry, Karimi et al. (2017) assessed the relationship between skilled labor availability and project performance, as quantified and evaluated by project productivity and timeline. It is recognized in labor economics theory that labor supply is ascertained by some major aspects, comprising of the demographic trend, labor force involvement rate, educational qualification, occupational preference of labors, and immigration (Boswell et al., 2004).

Project productivity can be substantially influenced by a scarcity of skilled labors (Hossein et al., 2018). For example, a study conducted by Dai et al. (2009) categorized 10 groups of factors that show the fundamental form of productivity. The authors found training, craft worker, superintendent competency, qualification, and foreman competency, which were related to labor concerns. Moreover, they found that labors’ qualification is the key factor that has the utmost chance for project productivity enhancement. The effectiveness of labors’ better training has a significant effect on higher project productivity (Wang et al., 2008). Karimi et al. (2016) found that unavailability or lack of experienced skilled labors directly influence the decline in productivity of construction projects. Furthermore, the authors found that there is a substantial relationship between the lack of recruiting skilled labor and the decrease in project productivity. Projects facing skilled labor scarcities usually suffer from higher material and labor costs, exceeding project time, scheduling, and lack of quality to meet the project timeline.

Furthermore, less experienced or unskilled labors have less knowledge about safety incidents due to the lack of information on appropriate construction practices and processes (Karimi et al., 2016). Glazner et al. (2005) conducted a study and concluded that inappropriate acts, lack of experience, and lack of follow-up safety instructions were the most common causes for damages during the construction of the project. Moreover, the study observed that 54.5 % of damages were because of a lack of safety measures during construction.

During project designing and planning, the key problems related to labor skills must be anticipated or identified promptly so that these problems can be dealt with in time and effectively. Failure to ascertain these problems will affect in uncertain and unmanageable measures that delay the completion of construction projects. Studies conducted by Gündüz et al. (2013) and Hussain et al. (2018b) on causes of construction delays found the unqualified/inexperienced labors as a factor causing construction delays. Odeh and Battaineh (2002) investigated the causes of construction projects delay and stated according to the contractors’ point of view that labor productivity is one of the topmost causes of project delays. Skill shortages arise when there is an inadequate number of labors with the needed abilities, experiences, practices, capabilities, or proficiencies required to perform a specific task (Ho, 2016).

Having skilled and productive labor has been pivotal to the construction industries’ growth and performance (Chang-Richards et al., 2017). In the construction industry, skilled workers comprise of masons, carpenters, steel fitters, plumbers, plasterers, and painters where their services are most compulsory in completing the construction projects (Sherekar et al., 2016). Neugart and Schömann (2002) emphasized that an effective policy has to consider that education, training, and lifelong learning policies should act to change in need for skills and knowledge flexibly and at an appropriate time. Therefore, it is indispensable for the construction industry to comprehend the complexity of the labor market to make sure the availability of stable labor skills. Experienced labor provides the opportunity to enhance project performance and productivity (Heravi & Eslamdoost, 2015).

All primary stakeholders working in the construction industry should comprehend and recognize the value of labor force problems and need to make better planning and policies for labor force conditions and requisites, thus letting them support to improve the labor force skills be able to meet the current and future demands of the construction industry (Wong et al., 2006). The possession of better labor skills and intellectual abilities leads to increased productivity, reduced labor costs, and better quality products (Jarkas & Bitar, 2012). According to Husseini (1992), vocational training centers, educational institutions, workshops, and on construction job-sites are three possible training opportunities to labors that can manage the effective output of every construction project. Nevertheless, skills and potentials can be achieved through learning, pre-employment training, knowledge, and experience; ultimately, the project performance will be improved as well as this will increase the success rate of the construction project. According to Bheemaiah and Smith (2015), the skilled worker is a part of the industry human resources with a great skill level that generates substantial economic benefit through the job performed. Skills improvement in labors’ performance through a different source of motivational features support as motivating and revitalizing labor force that encourages and supports skilled labors to quality output that will bring about monetary savings and economic development (El-Gohary & Aziz, 2014; Heravi & Eslamdoost, 2015).

Project safety performance can be drastically better if the construction project is constructed with highly experienced skilled labors. Experience and better knowledge play a substantial role in the unsafe behavior of skilled labors (Choudhry & Fang, 2008). Jannadi (1996) performed a study on factors affecting the safety of the construction industry, and on the basis of a survey, the author concluded that safety managers and labors (workers) are in the same opinion on the topmost six dynamics, out of which the top first three are maintaining safe working conditions, establishing safety training, and educating workers and supervisors. As project execution is risky and precarious due to outdoor tasks, work-at dangerous places, difficult job-site plants, and machinery operation, skilled workers play a significant role in all conditions. To avoid errors and improve the project performance, Petersen (1971) emphasized the need to enhance inspection procedures and train the labor force, give tasks according to the labor skills, and pre-task planning by project supervisors.

Summarizing the above discussion, the previous literature presents a variety of facts and information about the relationships; no research has quantitatively examined using partial least square structural equation modeling (PLS-SEM) technique the impact of skilled and unskilled labor on project performance. Relationships between these constructs are greatly dependent on the subjective and objective nature of how project performance is influenced. Therefore, this study aims to provide empirical evidence to support by validating a suitable model to assess these constructs and examining the impact of skilled and unskilled labor on project performance by employing the data collected from the Pakistani public construction industry. Thus, this study fills this gap by applying the PLS-SEM approach to analyze the impact of skilled and unskilled labors on project performance. Structural equation modeling (SEM) has become an extensively used approach when examining the relationship of such models, such as those that reveal the influence of human resource management (HRM) practices on organizational performance and behavioral human resource consequences (Ringle et al., 2018). This approach provides a variety of advantages for researchers using causal models to predict or justify a distinct construct, for example, expatriation behavior (Schlägel & Sarstedt, 2016) and job satisfaction (Buonocore & Russo, 2013). In this study, we attempt to provide an empirical model that will benefit not only researchers but also practitioners to reveal how project performance can be influenced by skilled and unskilled labors. The key contribution of the current study is to support construction practitioners to implement recommended measures for different skills of labor and take advantage from the key strategies for skilled labors to increase the chance of project performance.

Particularly, this study pursues to answer the question of how project performance is influenced by skilled and unskilled labors. The subsequent research questions are investigated:

Research Question 1: Is there a significant relationship between skilled labor and project performance?

Research Question 2: How does skilled labor influence project performance?

Research Question 3: Is there a significant relationship between unskilled labor and project performance?

Research Question 4: How does unskilled labor influence project performance?

This article is organized as follows. The next section describes the research objectives. The development of the hypothesis is followed by the research model description. The study describes the methodology used in this empirical study, and the data analysis and findings. It is followed by the “Discussion” section. The final section discusses the conclusion of the study.

Research Objectives and Scope

The primary objectives of the current study are to (a) analyze the relationships between skilled and unskilled labor and their impacts on project performance using the PLS-SEM approach and to propose a model, (b) identify whether there is a significant (positive) relationship between skilled labor and project performance, and (c) identify whether there is a significant (negative) relationship between unskilled labor and project performance. In this regard, the contribution of this study depends on linking the quantitative constructs of project performance such that key influences can be revealed. To do so, we carried out a literature review to support the conceptual model, and conducted a survey for empirical justification, employing PLS-SEM. Moreover, the key objective of this study is maximizing the explained variance of the dependent variable and, thus, supports prediction concerned goals (Hair et al., 2016).

Hypothesis Development

Studies have unveiled that the role of labors or workers in construction projects is important to project performance. Although, literature has mainly overlooked the effects of skilled and unskilled labors on project performance of infrastructure projects. To present a complete analysis to identify the influence of skilled and unskilled labor on project performance, the following section will construct relevant hypotheses. Understanding the existing phases of project life cycles, this study proposed a project life cycle for project performance with respect to construction labor, as shown in Figure 1(A) that will fit construction industries labor management, and a conceptual model is shown in Figure 1(B).

Figure 1.

(A) Proposed project performance model (with respect to labor) for construction projects and (B) conceptual/research model.

Unskilled Labor and Its Relation to Project Performance

The construction industry is seriously concerned with the lack of unskilled labors’ productivity because of social, cost-effective, and physical-related issues affecting the performance of the labors (Naoum, 2016). There are many challenges confronting the construction industry; unskilled labors are one of the key challenges faced by the construction industry, as the skilled labors during construction play a pivotal role in the enhancement of project performance. However, unskilled labors are one of the most unproductive labor types in the construction industry that influence the project performance.

Labor force shortage and lack of skilled labor present major challenges for long-term economic viability and project performance. Projects facing a shortage of skilled labors generally have a tight schedule to achieve project targets (Hossein et al., 2018). Mahamid (2011) concluded that poor labor productivity is one of the major significant parts influencing project performance in the Palestinian construction industry. Inappropriate management of resources in construction projects can substantially effect in time, cost, quality, and safety. Therefore, it is vital for construction managers and contractors or service providers to be aware of the approaches and techniques leading to assess the productivity of the workers in different skills (Shehata & El-Gohary, 2012). Construction projects usually experience lack of performance regarding time delays, cost overruns, and quality defects (Meng, 2012); one of the major cause of poor performance in a construction project is unskilled labor. The key defect of project performance is identified as human errors and lack of skilled labors in the industry (Atkinson, 1999; Love & Li, 2000).

Skilled labor issues on a project can be affected by both labor quantity and quality matters. When a project supervisor or contractor cannot engage the needed quality levels of skilled labors, the project is executed or constructed by unskilled and lack of experienced labors. Certainly, lack of skilled labor and inexperienced labor result in late delivery of project completion, cost and schedule overrun, and lack of quality in construction projects. Au-Yong et al. (2013) concluded that lack of maintenance staff, lack of inexperienced labors, lack of capability, and lack of knowledge were determined as the key features participating in a poor outcome. Therefore, the first hypothesis is developed as follows:

Hypothesis 1 (H1): Unskilled labor has a negative effect on project performance.

Skilled Labor and Its Relation to Project Performance

Skills, either conceptual, technical, or physical, are important to the success of construction projects in the construction industry (Slattery & Sumner, 2011). It is necessary for the contractor or service provider to be aware of the basic standard in the selection of competent and professional skilled labors that can lead to improving the efficiency and performance of construction projects (Liepman, 1960). Arshad and Ab Malik (2015) emphasized that productivity enhancement can be accomplished when construction labors with better and suitable skills and knowledge, as well as good mental and physical strength, achieve the milestone with efficiency and effectiveness. Better skills in labor raise labor productivity and improve the performance of the project.

Labors with high skills and expertise will achieve better productivity and also have sound physical and psychological health that can execute the tasks with efficiency and effectiveness (Bong, 2009). M. J. Wong (2006) emphasized the reality that labor force predicting is an indispensable strategic managerial practice for the construction industry. Such a perspective understands HRM as an important part of improving the performance of construction. Jarkas (2017) concluded that skilled labor forces are one of the key productive human resources in the construction industry, and for that reason, construction performance mostly depends on skilled workforces’ performance. Skilled labors’ efficiency is one of the critical features of labor productivity that needs to pay more attention to effective project execution. The amount of skilled labors’ high performance has been considered to be a key element that supports efficient project performance. Skilled labors are directly concerned with the performance of the construction project. Therefore, the second hypothesis is formulated in this way:

Hypothesis 2 (H2): Skilled labor has a positive effect on project performance.

Research Methodology

Methodological Approach

This study followed the methodology recommended by Saunders et al. (2015). In this study, post-positivism was used as an epistemological stance. A deductive approach was selected for a strong design that comprises both prevailing theory and novel empirical support. A questionnaire survey design was chosen to collect quantitative data in a cross-sectional approach from a wide range of respondents from the construction industry to achieve the widest coverage of the extracted theory.

Questionnaire Development

A quantitative method was adopted for this study, depending on a questionnaire containing question-statements from a thorough search of the previous literature. The literature review helps to identify the selection of variables used in this study and presented hypotheses for the model, as shown in Figure 1(B). All items (except the demographic variables) were measured using a 1 (strongly disagree) to 5 (strongly agree) Likert-type scale and were derived from established and previously validated scales. The existing scales were adopted, modified, and extended. The questionnaire comprises of two main sections: (a) the first section consists of background information of the respondents (i.e., age, education background, working experience, job position, and industry type) and (b) the second section focuses on the key questions and is categorized into three parts (as shown in Table B1, see Appendix B) such as unskilled labor, skilled labor, and project performance.

Sample, Data Collection, and Analysis Techniques

To accomplish the objective of the current study, the key individuals from the Pakistani public construction industry from Public Works Development (PWD), Defense Housing Authority (DHA), Capital Development Authority (CDA), Water and Power Development Authority (WAPDA), Water and Sanitation Agency (WASA), and National Logistic Cell (NLC) were identified as the target population. Respondents were Chief Engineers, Superintendent Engineers, Executive Engineers, Assistant Executive Engineers, Managerial Personnels, Assistant Engineers, Designer/Architect Engineers, Site Engineers, Quantity Surveying, Contractors, and Subcontractors. The characteristics of the 400 samples are presented in Table B2 (see Appendix B).

Prior to the data collection, a pilot study was conducted. For this purpose, in-person interviews by distributing the questionnaire were organized with 18 construction professionals and 12 university professors, who reviewed the survey questionnaire for structure, readability, clarity, completeness, and provided constructive feedback. We conducted a pilot study with professionals to measure the construct validity and reliability of the questionnaire. After the pilot study, the amendment was made accordingly to ensure the validity and reliability of the questionnaire. To evaluate the internal consistency or reliability of the questionnaire, Cronbach’s alpha test was employed, where the outcomes verified a high reliability coefficient greater than .7.

Following the finalized pilot study, data for further analysis were collected from individuals who have sufficient knowledge and practical experience in engaging projects in the construction industry. A cross-sectional research design was used to gather quantitative data for generalizable findings; snowball sampling was performed with the aim of getting a large number of completed questionnaires. For data collection, this study used a self-administered and email survey questionnaire. A total of 750 questionnaires were distributed through in-person handed and via email to target participants involved, particularly, in project management activities from construction industries in Pakistan. The participated respondents were construction industry professionals and contractors as well as subcontractors. The individuals were asked to answer the survey questionnaires based on their expertise and professional knowledge using a 5-point Likert-type scale.

As shown in Table B2 (see Appendix B), the respondent profile consists of the Chief Engineers (3.5%), Superintendent Engineer (3%), Executive Engineer (5.25%), Assistant Executive Engineer (22.75%), Managerial Personnel (13.25%), Assistant Engineer (17%), Designer/Architect-Engineer (5.25%), Site Engineer (10.75%), Quantity Surveying (3.75%), Contractor (7.5%), and Subcontractor (8%). The collected data comprise PWD (23.25%), NLC, (19.5%), DHA (18%), CDA (18.5%), WAPDA (11%), and WASA (9.75%).

In the end, 415 questionnaires were collected, of which 400 were usable for final analysis. The collected data were analyzed through the latest statistical software, such as SmartPLS v3.2.8, to verify and validate the model. SEM is a multivariate analysis method that mixes outlook of factor analysis, causal analysis, causal modeling, multiple regression, covariance structures, or path analysis (Demirkesen & Ozorhon, 2017) to assess the number of related dependency relationships altogether. The key purpose of why SEM was used for the current study is that the SEM recommends numerous benefits about reliability, validity, complexity, and verification of the model. This method is presently considered as the reliable and dominant approach to traditionally employed covariance-based structural equation modeling (CB-SEM) method (Richter et al., 2015; Rigdon, 2016). PLS-SEM method has presently obtained greater consideration, particularly, in strategic management, management information systems (Ringle et al., 2012), project management (Hussain et al., 2019), marketing management (Hair et al., 2012), hospitality management (Ali et al., 2018), tourism (Sarstedt et al., 2019) as well as in family business (Sarstedt et al., 2014). Similarly, HRM researchers should account for the latest developments in PLS-SEM-related methodological research (Hair et al., 2019).

Data Analysis and Findings

The collected data were checked for any possible statistical error of normality, and demographic characteristics were verified to examine the data prior to the main analysis. To check the data normality, the data met the skewness and kurtosis (Appendix A, Table A1) measures, and the values of all variables were observed between +2 and −2 range, which is satisfactory to run SEM.

In the current study, the authors used a multivariate analysis technique employed to compute path models with latent variables. Particularly, in this study, SmartPLS Version 3.2.8 was employed to evaluate the study research model. PLS-SEM has been extensively agreed by the academician and researcher (such as authors, reviewers, editors, etc.; Ali et al., 2018; Frank & Sarstedt, 2019; Hair et al., 2019; Khan et al., 2018; Richter et al., 2015; Ringle et al., 2018). SEM computes a structural correlation among independent constructs (Hair et al., 2016). The PLS-SEM allows calculating multifaceted models with numerous constructs, variables, and structural paths exclusive of imposing distributional suppositions on the collected data (Hair et al., 2019). This method is usually perceived as an alternative to Jöreskog (1970) CB-SEM (Hair et al., 2011).

Model Assessment Using PLS-SEM

The PLS approach of SEM using SmartPLS Version 3.2.8 was employed to investigate the research hypotheses. This approach was chosen due to its ability to examine a complex model (Hair et al., 2013). Hair et al. (2016) recommended that the model consists of a two-step technique to analyze the data analysis, such as a measurement model and a structural model. The first step comprises evaluating the observed variables and can ascertain fundamental hypothesized constructs, whereas the second step contains measuring the relationship between the unobserved variables.

Measurement model assessment

The internal consistency or reliability of a construct aims to evaluate to what extent the manifest variables are determining the latent constructs in the measurement model (Götz et al., 2010; Hair et al., 2016; Hussain et al., 2018a) For that reason, two key methods are generally used by the researchers in previous research, such as the Cronbach’s alpha and the composite reliability (CR; Götz et al., 2010). Table B3 (see Appendix B) reveals that the Cronbach’s alpha and CR values were above the threshold of .7 in the measurement model. These findings show that the model has acceptable consistency or reliability.

Furthermore, the average variance extracted (AVE) is used as a standard to validate the convergent validity of each latent construct in the model (Hair et al., 2013), and the latent variable must explain not less than 50% (0.50) of every indicator variance (Hair et al., 2019). Therefore, in this study, Table B3 (see Appendix B) reports that the AVE values for all of the indicators were greater than 0.5. Hence, a higher degree of convergent validity was found, and the convergent validity of the measurement model is verified.

Discriminant validity is the extent to which a latent construct is different from other constructs by empirical criteria in the model (Hair et al., 2013, 2019). Two different techniques are used to find out discriminant validity. These are the cross-loadings and Heterotrait–Monotrait Ratio (HTMT). With respect to the cross-loadings of the indicators exhibited in Table B4 (see Appendix B), all the indicators had values smaller than the other (opposite) construct (Hair et al., 2012). Second, HTMT is a trustworthy tool for measuring discriminant validity (Frank & Sarstedt, 2019). A general criterion, HTMT values must be less than 0.85 (Sarstedt et al., 2017), if the values are greater than 0.85, it indicates a possible problem of discriminant validity (Hair et al., 2016). For the current study, as shown in Table B5 (see Appendix B), HTMT values were less than the least limit of 0.85, which demonstrates that there is no problem of discriminant validity. Moreover, by employing bootstrapping procedure, the HTMT values are substantially smaller than one (Sarstedt et al., 2017).

The above analyses proved the discriminant validity of all the constructs. In general, these calculations assure the entire conditions for ascertaining the validity and reliability of the measurement model.

Assessment of the structural model

The measurement model had adequate findings; therefore, the authors continued with the assessment of the structural model.

The complete effect size and variance explained in the dependent (project performance) construct for the structural model is measured through the coefficient of determination (R²), which measured the explanatory power of the model. The result showed that the R² value was .36 in the current study, as illustrated in Figure 2. This implies that the two exogenous latent constructs such as skilled and unskilled labor considerably explain 36.0% of the variance in the project performance, implying that approximately 36.0% of the variation in the endogenous construct (project performance) was due to skilled and unskilled labor force latent constructs in the model. As suggested by Chin (1998), R ² values are .19 (weak), .33 (moderate), and .67 (substantial) in PLS path model. Therefore, the R² value in the current study was moderate.

Figure 2.

Path coefficient, R², and Q².

The path coefficients in a PLS model are the same as the standardized beta coefficients in regression analysis (Hair et al., 2011; Hussain et al., 2018a), and path coefficients (β) are employed to test the results of the hypothesis. The T statistics and 95% confidence intervals (CIs) are used to test the significance of the hypothesis in the PLS path model at the .05 level. Hypothesis tests employing CI are useful because they present more evidence about the parameter evaluation (Henseler et al., 2009). Figure 2 exhibits the hypothesized relationships with path coefficients. Findings of SEM analysis providing support to H1 show a negative direct relationship between unskilled labor and project performance (β = –.561; T = 11.805; p = .000). Hence, the result of the current study supported the hypothesized relationship for H1. Similarly, it is revealed that the relationship between skilled labor and project performance (β =.574; T = 12.002; p = .000) revealed that the hypothesis is statistically significant. Hence, the second hypothesis that skilled labor force directly influences project performance was supported and accepted. The CIs of the path coefficients are based on a bootstrapping of 5,000 (as suggested by Hair et al., 2011) samples that permit the generalization of the results, and also the relationships are significant at 95% CIs. The bootstrapping results also reveal that the path coefficients are statistically significant (Sarstedt et al., 2019). Table 1 shows a compiled summary of these findings.

Table 1.

Result of Hypotheses Testing and Confidence Interval.

Paths	Hypotheses	β	95% bootstrapping CI		Decision
Paths	Hypotheses	β	2.5%	97.5%	Decision
Unskilled labor → Project performance	H1	–.561	–.573	–.408	Supported
Skilled labor → Project performance	H2	.574	.418	.582	Supported

Note. CI = confidence interval.

p < .05.

As the R² value of the model was .36, it signifies that the suggested model has sufficient explanatory significance but justifying a model based on only the R² value is not an effective technique (Hair et al., 2016). Therefore, Q² (i.e., Stone, 1974) test is employed to measure the predictive relevance of the model, which can be calculated using blindfolding procedures, and the Q² value should be more than zero (Hair et al., 2016). The current study model Q² value was calculated as .20 as shown in Figure 2, which provides confirmations of the primary assumptions of the study, that the dependent variable (such as project performance) contained in the current study has a robust predictive relevance. Therefore, the current study proposed model predictive relevance was accomplished.

Furthermore, the study examined PLSpredict. PLSpredict employs the values for the independent constructs’ indicators of cases in the holdout sample, and this method helps the training sample to create and measure predictions of the dependent constructs’ indicators from PLS path model estimations (Shmueli et al., 2016). A training sample is a part of the complete dataset employed to evaluate the model constraints such as the loadings, path coefficients, and indicator weights (Shmueli et al., 2019). This study’s findings indicate that in HRM studies, there is an opportunity for refining the estimation of models’ predictive quality. Whereas scholars and investigators usually think about in-sample predictive power, they pay little consideration to out-of-sample predictive power valuation. PLSpredict suggests a means to evaluate a model’s out-of-sample predictive power (Shmueli et al., 2019). Provided that scholars generalize their findings outside the sample, this fact, just like the Shmueli et al. (2016) PLSpredict method, is supposed to become a component of all HRM scholar’s toolkit when performing with PLS-SEM. First, we analyzed that all the dependent constructs’ indicators outperform the maximum naïve standard as Q²predict value is a benchmark, which uses the mean value of the variables in a training sample as predictions of the variables in the holdout sample (Shmueli et al., 2019). Next, in light of the measures recommended by Shmueli et al. (2016), the existing PLS predict algorithm application in the SmartPLS software permits to achieve k-fold cross-validated prediction errors and prediction error summing up statistics, for instance, the root mean squared error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) to assess the predictive performance of their PLS path model for the manifest variables and the latent variables. The values of RMSE in the linear model (LM) should be greater than the values of RMSE in PLS. In the current study, as shown in Table B6 (see Appendix B), the LM values in RMSE are greater than the values of RMSE in PLS. The prediction error of the PLS results is smaller than the linear model that would support actually to have the predictive power of this study model.

Finally, the correlation between the variables is shown in Table B7 (see Appendix B). Table B7 shows that there was a robust correlation between the independent variables (i.e., skilled labor, unskilled labor) and the dependent variable (project performance). The relationship between unskilled labor and project performance exhibited negative correlations (−.308), whereas the correlation between skilled labor and project performance was positive (.326).

By complete analysis of measurement and structural model, the current study provides a useful insight into the effect of skilled and unskilled labor on project performance and can help in forming a couple of strategies to be formulated to improve skills of the workforce in the construction industry. Both the measurement model and the structural model were confirmed. Furthermore, the above findings prove that the proposed hypothetical model of the current study has substantial predictive relevance and explanatory power.

Discussion

In the contemporary competitive environment, construction industries try to hire and engage labors to improve the chance of project success in the construction environment through the supervisory approach. Therefore, this study endeavored to investigate the constructs influencing the project performance in the construction industry. According to the proposed model, these influencing constructs were skilled labor and unskilled labor. In what seems to be one of the first research of its type in the construction industry, and in the context of labor skills, this study evaluated the influences of these constructs on project performance.

The findings from the current study revealed that unskilled labors negatively and significantly affect the project performance (β = –.561, T = 11.805, p = .000); hence, H1 is validated. This finding is consistent with Hossein et al. (2018), who identified that the lack of skilled labors in the construction industry experienced a higher degree of cost overruns and posed a threat to cost performance. Lack of expertise, lack of knowledge, poor workmanship, and lack of construction experience were determined as the key factors that cause poor project performance.

The involvement of unskilled construction labor force cannot deliver an instant solution for the contemporary issues that may have occurred during execution or construction of the project, because they have lack of knowledge and expertise to solve such problems. The findings from this study suggest that the project supervisor had to pay attention to all requirements of the labor skills when hiring the labor force for the execution of the project. Sometimes because of increased profit margin, contractor or service providers hire unskilled labors with low wages that may cause time delays, cost escalation, quality defects, schedule overrun, increase in the amount of rework or defects, inappropriate use of materials, improper construction methods, and increase in safety incidents. This finding is in accordance with Tabassi and Bakar (2009) which revealed that the quality of construction projects is affected by unskilled labors. Lack of management and supervision of construction resources such as labor force results in poor performance in a construction project. Hence, it is vital for service providers or contractors and project managers to be well aware of the techniques used to measure and improve the performance of construction projects. The findings from the current study suggest that project performance will be enhanced when there was a matching skill related to the construction work. Otherwise, there will be flaws and errors in construction work. The findings indicated that construction project manager should view and make better policies for project labors (who construct the project during execution such as masons, marble workers, woodworkers, plaster and construction workers, carpenters, steel bender, painters, electricians, etc.).

It is found that greater involvement of skilled labors in a construction project during the execution was statistically related to improved project performance. The results from this study revealed H2 (i.e., skilled labor) had a positive and significant influence on project performance (β = .574, T = 12.002, p = .000). This result is consistent with some previous findings that stated construction productivity mostly relies on skilled labors’ endeavor and effort so that the project performance will be enhanced (El-Gohary & Aziz, 2014; Jarkas, 2017). In spite of this, the findings are supported and validated by Abdul-Rahman et al. (2006), who raised attention to the significance of having experienced, trained, and skilled workers to enable the construction industry to increase the success rate. Therefore, the labors who have good skills and knowledge, as well as better expertise in construction or building of infrastructure projects were able to execute the day-to-day task effectively and efficiently.

The qualification of the workforce in a construction field has the greatest chance for project performance improvement (Dai et al., 2009). Skilled labor force with higher experience, qualification, training, knowledge, and expertise is measured as a vital step toward accomplishing sustained durable productivity and projects’ performance. Project planning has a substantial impact on project performance measurement, efficiency improvement, and successful completion through on-site efforts. The findings from this study suggest that the parameters such as knowledge, experience, expertise, and so on are noteworthy for every labor who participates in construction work. This inference is greatly important because construction project managers generally are likely to execute the project within time, cost, quality, scope, and in a better safety environment. The findings also indicated that project performance is improved by a trained labor force and should be considered as an opportunity for the construction industry. These findings are consistent with the earlier studies in this area such as Hwang et al. (2013) and Zou et al. (2007) which suggested to arrange vocational and professional development training programs to improve the competency, expertise, and skills of human resource. As training and development are essential for executives, managers, and supervisors to enhance the skills, the skills development and training programs also help construction labors to polish, upgrade, and improve their skills, expertise, and capabilities to enhance the project performance.

In terms of the theoretical contribution, to our best knowledge, the current study is the first to empirically examine the influence of skilled labor and unskilled labor on project performance. Therefore, the current study filled this gap by providing empirical evidence (used PLS-SEM approach) and contemporary insights of the methods in which the project performance can directly influence the skills of skilled and unskilled labor.

The project management practitioners will get the advantage from the findings of the current study. The study findings found that project performance can be improved through the hiring of skilled labors, and project management or owner should make better policies for hiring labor force to construct any infrastructure project. The findings indicated to project management practitioners a collection of supports related to the skills of labor and project performance. The findings are also useful for project practitioners with new insights into effective and efficient human resource hiring and employing them in a better manner to get better outcomes from the project without any defects or errors during project execution.

Furthermore, this study has several managerial implications for project management practitioners for the alignment of this study findings. First, these findings provide project management practitioners with a clearer snapshot of the effects of skilled labor on project performance. The findings from the current study revealed that project practitioners could see the extent to which skilled labors enhance the performance of the project in the construction industry. Therefore, project management practitioners need to consider the fact that skilled labors’ performance has a positive and significant influence on project performance. This is because the skilled labor practices ease and enhance the construction industry as the hiring of the skilled workforce works better. Through productivity and efficiency of the skilled labor, construction industry may be more cost-efficient, improved quality products, timely completion, and implement safety protocols. Considering the fact that better-skilled labors might become a particular competitive advantage for the construction industry, decision and policy makers can ascertain these suitable tactics to construct project practices and to effectively better explain strategic planning and decision making to support these notions. Moreover, the service provider or contractor will follow and implement the labor policies as these practices are directly related to project performance, and the contractor should apply these policies in the execution phase of the project as well as the project owner should monitor labor force–related policies during the execution of the project.

Second, it gives direction and support for service providers or contractors and project managers to make suitable policies for the hiring and involvement of labors in construction. Although hiring unskilled labors will have to pay the low wages as compared with skilled labors, but they have a lack of knowledge and expertise to construct the project as planned; consequently, unskilled labors may negatively affect project performance. Labor force should enhance the overall performance of the project; the project owner should seek to offer more training for the construction labors and improve the skills of unskilled labor. Moreover, the government should allocate budget for training programs for unskilled labors.

Conclusion

The current study examined labor skills and project performance framework to test the research hypothesis in the conceptual model. The study found that two kinds of labor force involved in the construction industry to execute the project, such as skilled and unskilled. The study findings provided empirical support from 400 samples in the Pakistani construction industry. On the basis of a review of the existing literature, a hypothesized model was developed. The questionnaire survey was then conducted with construction professionals from public construction industries, and a hypothesized model was empirically confirmed. Both hypotheses were supported. Therefore, the study concluded that unskilled labor could have a significant negative impact on project performance, whereas skilled labor has a significant positive impact on project performance.

In the construction industry, human resource (labor force) is the most indispensable and significant asset to execute a construction project. The key purpose of this study was to quantitatively demonstrate the empirical impact of labor force skills on construction project performance. Based on the findings of this study, unskilled labors have a lack of knowledge, lack of construction skills, lack of expertise, and poor workmanship that cause a significant negative impact on project performance. Consequently, time overrun, cost overrun, quality defects (insufficient quality) occur, which directly affects the sustainability of any project, and safety problems persist during the execution of the project. Therefore, this situation can be better handled by skilled labors through better experience, knowledge, and expertise. Understanding the skilled labor impact on project performance, which was examined in the current study, is indispensable for construction projects. The current study findings revealed that skilled labor has a significant positive impact on project performance during the execution of the project.

These findings provided support to project practitioners regarding labor skills being an important asset of the execution phase in easing and enhancing the performance of projects. Moreover, this study provided researchers and scholars a basis for future research in labor skills and project performance. In addition, the findings from the current study were of great significance to everyone involved in the construction industry, especially public construction industries policy and decision makers to devise better policies and conditions for hiring a skilled, experienced, and competitive labor force that is able to achieve better future requirements of the construction industry and which is also very essential for project sustainability.

The current study has some limitations that propose a plan for future research. The primary limitation of this study is the validity threats of unobserved heterogeneity. In this research, the model explained the observed measurements for project performance, while the remaining unobserved heterogeneity values for dependent variables are not focused on this research. Future research may focus on a larger sample size and more relevant respondents to increase the response rate and measurement ratio. According to literature, such types of studies are already validated, and there is more chance of measurement, which can be measured by focusing on bigger sample size and target population.

In addition, in this study, a cross-sectional research design is used, which restricts inferences about causal direction. Hence, the authors suggest that future longitudinal studies be managed on the impact of skilled and unskilled labor on project performance during the project execution.

Another limitation of this study is its usage of a snowball sampling. Snowball sampling depends on referrals from initial participants to generate further participants. However, future research should employ any other sampling technique, such as probability sampling methods to take representative samples.

Moreover, the analysis was based solely on public construction projects which are initiated, planned, executed/constructed, and monitored by government organizations in Pakistan. So, we did not include private organization officials and practitioners. In the future, research can be done to collect data from both public and private organizations to more generalize the findings and make comparisons across public and private contexts. Furthermore, the authors support case studies to assess project performance from diverse informants, such as project managers, contractors, and other stakeholders. This method would help to document in-depth familiarity of growing and challenging problems related to labor force in construction industry.

Finally, the sample size used in this study was reasonably little as compared with the target population of public construction industry. So, future studies from other construction organizations are also required to investigate more factors/elements of skilled and unskilled labor by making comparative analysis of diverse nations.

Footnotes

Appendix A

Table A1.

Descriptive statistics.

Variables code	Mean	Standard deviation	Kurtosis	Skewness
UnSKLD_1	3.36	1.20	–0.90	–0.20
UnSKLD_2	3.38	0.91	–0.27	–0.03
UnSKLD_3	3.11	1.23	–1.06	0.02
UnSKLD_4	3.58	0.82	0.13	–0.34
UnSKLD_5	3.67	0.90	–0.53	–0.23
UnSKLD_6	3.50	0.99	–0.36	–0.37
SKLD_1	3.57	1.10	–0.66	–0.32
SKLD_2	3.29	0.77	0.23	–0.22
SKLD_3	3.03	1.25	–1.01	0.06
SKLD_4	3.17	1.09	–0.69	–0.10
SKLD_5	3.10	1.22	–0.96	0.04
SKLD_6	2.74	0.90	–0.31	0.13
SKLD_7	3.36	1.18	–0.82	–0.21
PP_1	3.30	0.93	–0.34	–0.17
PP_2	3.13	1.25	–1.02	–0.05
PP_3	2.87	1.18	–0.87	0.12
PP_4	2.63	0.95	–0.46	0.13
PP_5	3.79	0.83	–0.08	–0.35

Appendix B

Table B7.

Latent Variable Correlations.

Primary constructs	Project performance	Skilled labor	Unskilled labor
Project performance	1.000	.326	–.308
Skilled labor	.326	1.000	.441
Unskilled labor	–.308	.441	1.000

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by Humanities and Social Sciences Projects of Ministry of Education (No. 18YJA630113) and The Humanities and Social Sciences Projects of Guangdong General Universities (No. 2017WTSCX097).

ORCID iD

Shahid Hussain

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Impact of Skilled and Unskilled Labor on Project Performance Using Structural Equation Modeling Approach

Abstract

Keywords

Introduction

Research Objectives and Scope

Hypothesis Development

Unskilled Labor and Its Relation to Project Performance

Skilled Labor and Its Relation to Project Performance

Research Methodology

Methodological Approach

Questionnaire Development

Sample, Data Collection, and Analysis Techniques

Data Analysis and Findings

Model Assessment Using PLS-SEM

Measurement model assessment

Assessment of the structural model

Discussion

Conclusion

Footnotes

Appendix A

Appendix B

Declaration of Conflicting Interests

Funding

ORCID iD

References