In the rapidly-developed construction industry, labor productivity has improved to a great extent, still, it is low compared with many other industries. The enhancement of labor productivity has become important that attracts much attention and focus from researchers in Vietnam and around the world.
This paper focuses on key factors affecting labor productivity of construction sites in Vietnam by introducing a regression model to evaluate the extent of each factor’s impact on the labor productivity of construction workers.
Ten groups of impacting factors were identified as factors relevant to construction worker, factors relevant to site operation and management, factors relevant to motivation, factors relevant to working time, factors relevant to labor working tools, factors relevant to labor working conditions, factors relevant to working safety, factors relevant to project informations, factors relevant to natural environment, and factors relevant to socio-economic conditions.
By referring to research results, Vietnamese construction contractors will be able to come up with workable solutions towards a better performance of construction workers.
On that basis, the productivity of construction firms and the workers will be improved correspondingly.
Open Peer Review Details | |||
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Manuscript submitted on 04-08-2019 |
Original Manuscript | Application of the Regression Model for Evaluating Factors Affecting Construction Workers’ Labor Productivity in Vietnam |
Labour productivity is one of the most important factors affecting the economic growth and competitive capacity of each enterprise as well as the country [1G.S. Becker, Human capital: A Theoretical and Empirical Analysis, with Special Reference to Education., University of Chicago Press: Chicago, .-3T.W. Shultz, "Investment in human capital", Am. Econ. Rev., vol. 161, pp. 1-17.]. Although Vietnamese labor productivity has recently been improved, it is still lower than in other countries in ASEAN. According to Seminars “Boosting productivity in the context of industriali zation”, Vietnam’s labor productivity is approximately equivalent to 7% to that of Singapore, 17.6% of Malaysia 36.5% of Thailand, 42.3% of Indonesia [4General Statistics Office of Vietnam, Vietnamese productivity is lower than Laos, by 7% Singapore, VnEconomy, .], 56.7% of Phillippines, and especially 87.4% of Laos [5N.V. Tuan, Improving Productivity in State-owned Enterprises, Seminar on Improving productivity in industrialized context, Vietnam Economic Forum II: Towards a more rapid and sustainable growth of Vietnam’s economy, 11 January 2018, Hanoi., .]. In recent years, although there have been great scientific and technological developments, these have not yet been exploited by Viet- namese construction firms. Specifically, labor productivity in construction is only equal to 85% of those obtained in other industries. The growth rate of productivity is about 10%, which is lower than the average annual growth rate of about 16% [6V. Anh, Raising productivity in construction sector., Bidding Newspaper, .]. One of the causes is the lack of applying advanced technology, heavy equipment, and machinery. Construction labor productivity, therefore, is one of the most important factors in the productivity of a business, which has a direct effect on labor productivity in the construction field and national economy. This research conducts regression analysis to evaluate the extent of the impact of each factor on the labor productivity of construction workers in Vietnam. The result helps construction enterprises in finding the solutions for improving labor productivity of construction workers in particular and the economy of Vietnam in general.
Productivity enhancement plays an important role in any profit-oriented organization, as representative of the effective and efficient conversion of resources into marketable products and determines the profitability of a business [7S. Wilcox, B. Stringfellow, R. Harris, and B. Martin, "Management and productivity", Transportation research board, committee on management and productivity., Washington, USA, .]. Therefore, considerable efforts have been made to understand the concept of productivity, with researchers’ different approaches leading to a variety of productivity definitions [8N.M. Lema, Construction of labor productivity modeling., University of Dar Elsalaam, .-10C.H. Oglesby, and H.W. Parker, Productivity improvement in construction., McGraw-Hill: USA, .]. Productivity has been generally defined as the ratio of outputs to inputs. Output and input are vastly different from one industry to another. The definition of productivity also varies when applied to different fields of the same industry. Labor is one of the basic requirements in the construction sector. Labor productivity is simply defined as the amount of work done by craft workers within a certain period of time [11I. Mahamid, "Contractors perspective toward factors affecting labor productivity in building construction", J. Eng. Constr. Archit. Manage., vol. 20, no. 5, pp. 446-460.
[http://dx.doi.org/10.1108/ECAM-08-2011-0074] , 12D. Arditi, and K. Mochtar, "Trends in productivity improvement in the US construction industry", J. Constr Manage Econ., vol. 18, no. 1, pp. 15-27.
[http://dx.doi.org/10.1080/014461900370915] ]. In other words, the definition of labor productivity is the number of goods and services produced by a productive factor (manpower) in the unit of time [13F.J. Drewin, Construction Productivity: Measurement and Improvement through Work Study., Elsevier Science Ltd: NewYork, .]. On the other hand, construction projects are carried out on-site and thus, in each project, the working conditions and project participants change from area to region [14H. Ottosson, Practical project management for building and construction., Taylor & Francis Group: New York, .]. Accordingly, labor productivity also varies depending on the region in which the project is being implemented. As a result, most studies have been conducted for specific regions by considering managers’ perspective and different results were obtained [15A. Kazaz, and T. Acıkara, "Comparison of labor productivity perspectives of project managers and craft workers in turkish construction industry", Conf. Enterp. Inf. Sys., .
[http://dx.doi.org/10.1016/j.procs.2015.08.548] ].
The factors that affect the productivity of the construction has been studied by a number of researchers, but there are still many productivity problems that remain unknown and need to be further investigated even in developed countries [16Makulsawatudom and Emsley, "Critical factors influencing construction productivity in Thailand", Proceeding of CIB 10th International Symposium Construction Innovation and Global Competitiveness, .Cincinnati, Ohio, USA]. Moreover, policies for increasing productivity are not necessarily the same in every nation and the critical factors in developing countries are different from those in developed countries [17G. Polat, and P. Arditi, "The JIT management system in developing countries", J. Cons. Manage. Econ., vol. 23, no. 7, pp. 697-712.
[http://dx.doi.org/10.1080/01446190500041388] ]. Herbsman et al., (1990) [18Z. Herbsman, "Research of factors influencing construction productivity", J. Constr. Manage Econ., vol. 32, no. 8, pp. 49-61.
[http://dx.doi.org/10.1080/01446199000000005] ] classified factors that affect construction productivity in two main groups: technological factors and administrative factors. Technological factors mainly include the ones related to the design of the project; and the administrative group factors relate to the management and construction of the project. Technological factors include subgroups such as design factors, material factors and location factors. Administrative factors comprise sub-groups, such as construction methods and procedural factors, equipment factors, work factors and social factors.
Abdulaziz et al., (2012) used a questionnaire to investigate constructors in Kuwait with 45 productivity factors, which were grouped into the following four main groups: management; technological; human/labor; and external. The results of this study showed that the following 10 are perceived as the most significant impact factors on labor productivity: clarity of technical specifications; the extent of variation/ change orders during execution; level of coordination between the disciplines of design; lack of supervision of work; proportion of subcontracted work; level of complexity of the project; lack of an incentive scheme; lack of construction manager’s leadership; stringent inspection by the engineer; and hesitation to respond to requests for information [19A.M. Jarkas, and C.G. Bitar, "Factors affecting construction labor productivity in kuwait", J. Constr. Manage. Econ., vol. 138, no. 7, pp. 811-820.
[http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000501] ]. Olomolaiye et al., (1998) stated that the factors that influence building productivity are rarely constant, and may vary from one country to another, from one project to another and even within the same project, depending on circumstances. This study has classified the factors that influence the productivity of the constructions in 2 categories: external and internal, representing those outside the control of the company’s management, and those that originate within the company [20P. Olomolaiye, A. Jayawardane, and S.F. Harri, Construction productivity management., Chartered Institute of Building: UK, .]. Heizer et al., (1990) classified the factors that influence the productivity of the construction site in 3 groups: characteristic factors of work; factors of the working conditions of the project; and non-productive activities [21J. Heizer, and B. Render, Production and operations management “strategic and tactical decisions”., Prentice Hall: New Jersey, .]. Jiukun et al. (2009) identified 83 factors that affect productivity and assessed the levels of influence of these factors through a survey of almost two thousand artisan workers at construction sites located throughout the United States. They stated that factors involving tools and consumables, materials, management of engineering drawings and construction equipment were identified as having the greatest impact on productivity from the craft workers’ perspective [22J. Dai, P.M. Goodrum, and W.F. Maloney, "Construction craft workers’ perceptions of the factors affecting their productivity", J. Constr. Eng. Manage., vol. 135, no. 3, pp. 217-226.
[http://dx.doi.org/10.1061/(ASCE)0733-9364(2009)135:3(217)] ].
Due to the unique characteristics of construction projects, the executive process is often time-consuming and goes through several periods with many components participating in. Therefore, the labor productivity of workers is affected by several factors [23N.H. Thanh, Lecture Organizing the implementation of construction investment projects., National University of Civil Engineering: Hanoi, .]. By referencing literature and considering practices on the construction site in Vietnam, the authors utilized 49 factors and divided them into 10 groups as shown in Table 1.
In the previous research [58D.T. Hai, and N. Van Tam, "Analysis of affected factors on construction productivity in Vietnam", Int. J. Civ. Eng. Tech. (India), vol. 10, no. 2, pp. 854-864.], the authors used the method of the Relative Importance Index (RII) [59A. Soekiman, K.S. Pribadi, B.W. Soemardi, and R.D. Wirahadikusumah, "Factors relating to labor productivity affecting the project schedule performance in indonesia", The Twelfth East Asia-Pacific Conference on Structural Engineering and Construction, pp. 865-873.
[http://dx.doi.org/10.1016/j.proeng.2011.07.110] ] to rank the extent of the impact of factors on labor productivity of construction workers in Vietnam. In this research, the authors used regression analysis method [60N.D. Tho, Methods of scientific research in business., Labor and Social Publishing House: Hanoi, .] to evaluate the extent of the impact of 10 groups on labor productivity of construction workers in Vietnam.
This research used regression analysis method to evaluate the extent of the impact of factors on labor productivity of construction workers in Vietnam in 5-step order as follow:
Step 1 - Determining the Research Model: Researching model which focusses on the interrelated connection between general factors such as (dependent variable) labor productivity of construction workers and effect factors (independent variables) [60N.D. Tho, Methods of scientific research in business., Labor and Social Publishing House: Hanoi, .] has the following form as shown in eq. (1):
(1) |
Where: - β_{0}: free coefficient
- β_{1}, β_{2}, β_{3}, …, β_{k}: recurrent coefficients
- X_{1,} X_{2,} X_{3, …,} X_{k}: independent variables (factors which effect components)
- Y: dependent variables (labour productivity of construction workers)
Step 2 - Design of survey form and collection of figures: A survey form was designed to evaluate the impact level of the above factors. One of the most popular forms that measure abstract concepts in studying socioeconomics is Rennis Likert scale [61H. Trong, and C.N.M. Ngoc, Analyzing research data with SPSS, Hanoi, .Statistical Publishing House., 62R. Likert, "A technique for the measurement of attitudes", Arch. Psychol., p. 140.]. It is necessary to determine the size of samples in conducting quantitative analysis. According to Hair et al. (1998) [63J. F. Jr, Multivariate Data Analysis, New York: Macmillan Publisher, .], experience formula which is often used to calculate the size of samples for regression analysis is as follows: n ≥ 50 + 10*p (n is the size of samples, p is the number of independent variables in the model). Therefore, the indispensable size of samples is: n ≥ 50+ 10*p = 50 + 10*10 = 150.
Step 3 - Test the Reliability of Scale: Cronbach’s Alpha failed-safety of effect factors were tested by ρc composite reliability, ρvc variance extracted, and Cronbach’s Alpha α. According to Hai et al. (1998) [63J. F. Jr, Multivariate Data Analysis, New York: Macmillan Publisher, .], the standard for evaluating the level of relevance of the model, which is expressed by failed-safety of scale, is ρc > 0,5 or ρvc > 0,5; or α ≥ 0,6 [61H. Trong, and C.N.M. Ngoc, Analyzing research data with SPSS, Hanoi, .Statistical Publishing House., 62R. Likert, "A technique for the measurement of attitudes", Arch. Psychol., p. 140.].
Item-total correlation is a coefficient showing the association level between observed variables and others. The standard to evaluate whether a coefficient actually contributes, is that the item-total correlation must be higher than 0.3. If observed variables have item-total correlation smaller than 0.3, they are weed out of the evaluated factors [64J.L. Cronbach, "Coefficient alpha and the internet structure of test", Psychometrika, vol. 16, no. 3, pp. 297-334.
[http://dx.doi.org/10.1007/BF02310555] ].
Exploratory Factor Analysis (EFA): The criteria for applying and choosing variables in EFA include:
Bartlett's Test of Sphericity and Kaiser-Meyer-Olkin Measure of Sampling Adequacy (Kaiser-Mayer-Olkin) are used to evaluate the suitability of EFA. Thereby, the hypothesis that variables are not interrelated in general is rejected. As a result, the EFA is called appropriate if 0.5≤ KMO ≤1 and sig< 0.05. If KMO < 0.5, showing that the analyzed factors are not suitable for data [61H. Trong, and C.N.M. Ngoc, Analyzing research data with SPSS, Hanoi, .Statistical Publishing House.].
Standard extracting factors consist of index eigenvalue (represents the amount of fluctuation explained by factors) and index cumulative (ρvc variance extracted shows to what percent analyzing factors could be explained and what percentage is lost). Factors having eigenvalue < 1 do not have a better function in summarizing information than original variables (hidden variables in scales before EFA). Therefore, factors are only extracted if eigenvalue > 1 and are accepted if ρvc variance extracted ≥ 50% [61H. Trong, and C.N.M. Ngoc, Analyzing research data with SPSS, Hanoi, .Statistical Publishing House.].
Factor loading denotes the correlation between variables and factors, which is used to evaluate the extent of EFA. According to Hair et al., (1998) [63J. F. Jr, Multivariate Data Analysis, New York: Macmillan Publisher, .], factor loading > 0.3 is considered to be the minimum; factor loading > 0.4 is considered important; factor loading > 0.5 is considered to have practical meanings [64J.L. Cronbach, "Coefficient alpha and the internet structure of test", Psychometrika, vol. 16, no. 3, pp. 297-334.
[http://dx.doi.org/10.1007/BF02310555] ].
Step 4 - Analysis of regression is aimed at determining the influence level of each factor to the overall factor through coefficient β. The higher coefficient β shows the significant effect on the overall factors of that factor. Coefficient β has a valuation within -1 and +1 and can be defined as:
The assumption about multi-collinearity phenomenon is tested through tolerance value or variance inflation factor coefficient. If coefficient VIF < 2, the multi-collinearity phenomenon of independent variables is trivial. The recurrent equation is only accepted if there is no multi-collinearity phenomenon or independent variables do not have a coherent relationship [60N.D. Tho, Methods of scientific research in business., Labor and Social Publishing House: Hanoi, ., 64J.L. Cronbach, "Coefficient alpha and the internet structure of test", Psychometrika, vol. 16, no. 3, pp. 297-334.
[http://dx.doi.org/10.1007/BF02310555] ].
Step 5 - Testing the Suitability of the Multiple Regression model: The suitability of the model is tested by adjusting target R^{2} and testing ANOVA [63J. F. Jr, Multivariate Data Analysis, New York: Macmillan Publisher, .].
The formal research model showing the correlation relationship between the overall effect factor (dependent variable) [60N.D. Tho, Methods of scientific research in business., Labor and Social Publishing House: Hanoi, .] in labor productivity of construction workers in Vietnam and particular effect factor (independent variable) has a form shown in eq. (2) below:
(2) |
Where: - Y: dependent variable (labour productivity of construction workers)
- β_{0}: free coefficient
- β_{1}, β_{2}, β_{3},…, β_{10}: recurrent coefficients
- X_{1,} X_{2,} X_{3, …,} X_{10}: independent variables (effected factors):
X_{1}: The factor relevant to the construction worker
X_{2}: The factor relevant to operation and management on construction site
X_{3}: The factor relevant to motivation
X_{4}: The factor relevant to working time
X_{5}: The factor relevant to tools and subject of labor
X_{6}: The factor relevant to labor condition
X_{7}: The factor relevant to labor safety
X_{8}: The factor relevant to project information
X_{9}: The factor relevant to the natural environment
X_{10}: The factor relevant to socio-economic
One of the most important stages was to collect accurate data. Survey objects are construction workers, project mana- gers, site managers, site engineers, supervisors, etc. The total number of questionnaires is 300; the questionnaires received and validated were 267, which is higher than the necessary number, so the collected data was approved as it fulfilled the requirement. Data is shown in the following Table 2.
Testing Cronbach’s Alpha faild-safety of effect factors:
The Cronbach Alpha test is used to determine whether the factor included in the quantitative study has had an effect on the synthetic variable. Cronbach’s Alpha = 0.844 > 0.7, therefore reliability is acceptable. Influenced factors have Cronbach's Alpha if the item deleted > 0.3, so being closely related to other factors in the model, influence factors should be retained in the research model. The influencing factors after meeting Cronbach’s Alpha requirements at a significant level will be subjected to exploratory factor analysis to obtain a component matrix (Table 3).
Exploratory Factor Analysis EFA: Variables after being tested for reliability by Cronbach’s Alpha coefficient and item-total correlations are further checked for their correlation by a variable group. Factor analysis is used when the KMO coefficient has a value greater than 0.5. Factors whose values were less than 0.4 will continuously be excluded from the variable group to ensure convergence between variables in a factor; when Initial Eigenvalue is greater than 1 and the Total Variance Explained is greater than 0.5. In this study, the principal component method with Varimax rotation was used for factor analysis. All of the original 10 observed variables after the reliability test with Cronbach’s Alpha coefficients were satisfied and included in the exploratory factor analysis. EFA’s results are shown in Table 4 as:
After conducting EFA analysis to determine convergence value and differentiate the value of the scale, results obtained are as follows:
The following rotated component matrix to study of the number of samples required 267 samples, and the load factor loading was found to be 0.4. At the component matrix, the observed variables with factor loading were less than 0.4, therefore, the observed variables were uploaded to two groups of factors and the coefficient difference at less than 0.3 was removed (Table 5).
Regression analysis is used to determine the specific weight of each factor to the labor productivity of construction workers in Vietnam. Regression analysis was performed with 8 independent variables: X_{1}, X_{2}, X_{3}, X_{4}, X_{5}, X_{6}, X_{7}, X_{9}, and a dependent variable Y. The values of the elements used to run the regression were the normalized values of the observed variables. Regression analysis was conducted with the support of the SPSS.20 software. Regression results are expressed in Table 6 as follows:
Results of regression analysis with multiple values of β > 0 show that all the independent variables are correlated with the dependent variable. Also collinearity statistics with Tolerance> 0.1 and variance inflation factor VIF < 2, thereby confirming that the influencing factors are independent of each other. The phenomenon of multicollinearity between the independent variables has no significant influence in the regression model. The results of multiple regression analysis were applied to the regression equation to ensure statistical significance. The value of the independent variable in the Sig model was less than 0.05. It was observed that variables in the code were statistically significant at 5 % significance. So the independent variables in the model are related to dependencies. The regression results show that both having 8 independent variables that affect the dependent variable coefficients by Sig's 8 turns were below 0.05.
Based on the standardized regression coefficient, regression equations determine the extent of the influence of each factor on the labor productivity of construction workers in Vietnam which is identified in eq. (3) as:
(3) |
Where: - Y: dependent variable (labour productivity of construction workers)
- β_{0}: free coefficient
- β_{1}, β_{2}, β_{3},…, β_{10}: recurrent coefficients
- X_{1}, X_{2}, X_{3}, X_{4}, X_{5}, X_{6}, X_{7}, X_{9}: independent variables (effect factors):
X_{1}: Factors relevant to construction worker β_{1} = 0.284
X_{2}: Factors relevant to operation and management on construction site β_{2} = 0.219
X_{3}: Factors relevant to motivation β_{3} = 0.203
X_{4}: Factors relevant to working time β_{4} = 0.183
X_{5}: Factors relevant to tools and subject of labor β_{5} = 0.185
X_{6}: Factors relevant to labor condition β_{6} = 0.209
X_{7}: Factors relevant to labor safety β_{7} = 0.178
X_{9}: Factors relevant to natural environment β_{9} = 0.181
By testing the suitability of the model by target R^{2} and con- ducting ANOVA test, regression results are shown in Table 7:
Adjustment coefficient R2 in this model, which is 0.625 > 0.5, affirms that impact factors determined by the model of the research are appropriate. This shows that there is a 62.5% variation in labor productivity of construction workers in Vietnam (Y) which is explained, in general, by the above defined 8 variables. This analysis ANOVA in Table 8, shows that parameter F has Sig. = 0, which proved recurrent construction model to be appropriate for the collected data.
Recurrent analysis results prove that independent variables in the model are appropriate and have statistical signification with meaning level 5% (Table 9).
“Factors relevant to construction worker” have the most significant impact on labour productivity of construction workers in Vietnam with coefficient β = 0.284.
“Factors relevant to operation and management on construction site” with coefficient β = 0.219 were 2^{nd} in terms of their impact on labour productivity of construction workers in Vietnam.
“Factors relevant to labor condition” and “Factors relevant to motivation” have a positive effect on labour productivity of construction workers in Vietnam with almost the same coefficients β which are 0.209 and 0.203.
“Factors relevant to tools and subject of labor”, “Factors relevant to working time”, “Factors relevant to the natural environment” and “Factors relevant to labor safety” have a medium level of impact on labour productivity of construction workers in Vietnam.
“Factors relevant to project information” and “Factors relevant to socio-economic” were not considered because these factors were excluded from the formal research model. In other words, these factors have an inappreciable impact on labour productivity of construction workers in Vietnam.
This research was carried out to evaluate influence factors on labor productivity of construction industry workers in Vietnam. Throughout the literature review, 10-factor groups impacting the labor productivity of construction workers in Vietnam were identified and listed. They are factors relevant to construction workers, factors relevant to site operation and management, factors relevant to motivation, factors relevant to working time, factors relevant to labor working tools, factors relevant to labor working conditions, factors relevant to working safety, factors relevant to project informations, factors relevant to natural environment, and factors relevant to socio-economic conditions. 267 questionnaires were collected for analysis. From data collected through the survey, the authors used regression analysis method to evaluate and rank the impact levels of these factor groups. To rely on the researching results, the authors highlighted the role of executive entrepreneurs in finding solutions in order to develop human resources, raise the quality of construction workers staff, manage missions and targets in building site, and at the same time improve working conditions to create motivation for workers. The authors also petition the government for promulgating policies which support enterprises to step up applying science, technology and modern technical methods of construction.
This research scope is limited to the construction industry in Vietnam and objects are factors impacting the labor productivity of construction workers. Further research should be carried out on other aspects such as civil projects, industrial projects, traffic projects, irrigation projects or technical lower-layer projects. One similar research is extremely indispensable for determining levels of impacted factors for the success of construction investment projects in the whole.
Not applicable.
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None.
The authors declare no conflict of interest, financial or otherwise.
Declared none.
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