QUALITY IN HOUSING PROJECT: EXAMINING THE
RELATIONSHIP TOWARDS LABOR PRODUCTIVITY, REWORK AND CUSTOMER SATISFACTION USING PLS-SEM APPROACH
Tanzil Budinata
1, Budi Susetyo
21,2
Master of Civil Engineering Department Mercu Buana University, Jakarta Indonesia
1
[email protected],
2[email protected],
ABSTRACT
Housing market shows positive growth in Indonesia due to high demand for occupancy over the last few years.
However, it has been well known that housing projects are prone facing various quality problems that bring adverse consequences for project performances, i.e., cost, schedule, productivity, and satisfaction of housing customers.
The objective of this study is to investigate further relationship of quality performance against labor productivity, rework, and customer satisfaction. The hypothetical model was analyzed by using Structural Equation Model approach based on collected data through survey questionnaires of 45 respondents derived from site personnel of PT. Bumi Parama Wisesa which consist of several working divisions. The final model consists of 20 indicators in total reflecting all tested variables. From the results of the data analysis using PLS-SEM, it was revealed that quality performance has a strong relationship among the tested variables. Especially on customer satisfaction and rework variables. Customer satisfaction and rework have path coefficient value (β)0.325 and -0.558 respectively.
With this result, it can be concluded that better-quality performance will improve customer satisfaction and rework reduction. The findings of this study provided important contribution for construction practitioners especially those who involved in the development of housing project to pay more attention in the implementation of quality management by developing effective and efficient project quality program. thus, labor productivity, reduction of rework and customer satisfaction can be significantly improved. Considering, housing projects vulnerable facing quality failure due to low-quality management on site were performed by mostly housing contractors in Indonesia.
Keywords: Housing projects, Productivity, Rework, Customer satisfaction, PLS-SEM
1. INTRODUCTION
Housing projects are a promising sector with significant growth over time in Indonesia.
Bank of Indonesia has revealed, property sales growth showed positive enhancement by 23.77% in the first quarter in 2019 compared to the fourth quarter in 2018 [1]. Other growth indicators can be seen from the realization of housing financing facilities provided by the financing institutions. Nationally, Bank of Indonesia revealed the realization of housing financing significantly improved by 163% in the second quarter in 2019 compared to the previous quarter, namely Rp. 4,352, 010, 866, 517 with the number of houses in all provinces in Indonesia were 45,289 units of houses [1].
Besides those positive trends, it has been realized that housing projects prone to facing
various quality problems. This was triggered by the reality that housing projects were commonly carried out by the contractors with a small to medium qualification who do not have an adequate quality management system on site.
Thus, the possibility of error in quality relatively frequent. In line with that reality, the previous study had also confirmed that small- scale contractors having less awareness of the importance of quality management systems [2]
Based on the study of relevant literatures, quality failures have become an endemic problem in almost all construction projects over the past few decades [3] This problem has brought adverse consequences in terms of productivity, cost, construction schedule, and customer’s perception [3]–[7]. This is because the impact of quality failure required a rework
process. Previous studies have proved that rework is the key contributor of cost overrun and delay in construction schedules. As presented in table 1, the cost amount incurred by rework was in the range of 2 – 16.5% of the overall project cost. It certainly makes sense, since the rework cost component includes many items, i.e. materials, plant or equipment, suppliers or subcontractors, re-designs, procurement of repair work, destruction, waste disposal, time delays, supervision, inspection, and retesting [3].
Table 1. Percentage of Rework Cost from Various Literature
Researcher Percentage from Contract Value Josephson and
Hammarlund 1999
2-6%
Fayek et al (2003) 2-12%
Love dan Li (2000) 3.15% (residential) and 2.4% (industrial)
Josephson (2002) 4.4%
Love (2002) 6.4% and 5.6%
Oyewobi et al (2011) 5.06% (new building) and 3.23% (renovation)
Burati (1992) 12.4%
Mills et al (2009) 4%
Love et al (2010) 10.29% (civil infrastructure project) Forcada et al (2014b) 16.5% (civil
infrastructure project) Abdul Rahman
(1993)
5%
2. REVIEW OF RELATED LITERATURE
2.1 Definition of Quality
Quality will have different meaning for different people since the definition of quality were based on personal perspective [8]. Some may say “zero defect”, “meeting customer expectation”, “fitness for use”, etc. However, ISO 9000 :2015 provided a detailed definition i.e. “the degree to which a set of inherent characteristics of an object to fulfils requirements” [9]. Meanwhile, Juran et al (2019) gave a concise definition of quality i.e.
“Compliance with requirements or specification”. Furthermore, Juran provided extensive thinking and concept on how to manage quality accordingly by implementing 3(three) managerial approach, namely quality
planning, quality control, and quality improvement known as “Juran’s trilogy”[10]
2.2 Labor Productivity
Productivity refers to the ability to improve the value and quality of services or products [5].
In other words, productivity refers to the quantitative relationship between input and output. Construction projects are labor- intensive industries. Labor is the most important resource, so the productivity is generally depending on human performance. In construction studies, researchers generally use hourly output to measure labor productivity.
They used labor hours as input and the physical amount of work completed as output [11]
2.3 Rework
Many literatures have proved that rework is an endemic problem in almost all construction projects over the past few decades and become one of the the main contributor in negative performance in terms of cost, schedule and customer satisfaction [4]. Previous literature has provided clear understanding for construction practitioners about definition of the rework, i.e. “unnecessary effort of re-doing process or activity that was not done accordingly in the first opportunity” [3].
2.4 Customer Satisfaction
Customer satisfaction is the customer's perception of the extent to which customer expectations have been met [9]. Customer satisfaction has been realized as the key purpose in the development of construction project [7].
Construction team should build good relationship and considered the customer as parties that can give significant contribution on the successful of a project. Because, customer can assist project team to complete the project successfully within planned budget and quality [7].
3. RESEARCH METHOD
This research used a quantitative approach with a causal research design because it aims to measure the relationship among variables or to analyze the influence of variables against another one. For research object and location, it was carried out on the Nava Park housing project, a prestigious housing project developed by PT. Bumi Parama Wisesa which is located on a premium commercial area of Bumi
Serpong Damai in South Tangerang City, Banten province, Indonesia.
The main stage of this research consists of several stages, starting from formulating research problems that will be used as research topics, as well as creating research design concepts. The idea of research problems was generated from empirical problems that were occurred in the construction projects and were strengthened by the study of relevant literatures.
The next step is to create and develop instruments of data collection, selecting samples and perform data collection. The instruments of data collection in this study were designed using questionnaires. Once the required data is collected, data processing and analysis is then carried out using SmartPLS software.
Data for this research were collected through survey questionnaires derived from site personnel of PT. Bumi Parama Wisesa as the developer company which consist of several working divisions, i.e Construction, Engineering/Planning, and Estate as well as collected from contractors and suppliers who were incorporated in the development of Nava park housing project with total 45 respondents who participated in the survey. Among the total of 45 responses, 80% of participated respondents were male and 86.67% of them have bachelor degree education. Moreover, 93.34% of respondents have more than 5 years of professional experience in managing housing project. Based on the given demographic informations presented in table 2, it can be assumed that all respondents have sufficient knowledge and experience to understand and respond the survey properly.
Table 2. Respondent Demographic Information Characteristic Frequency
(N=45)
Percentage
Age (Years old)
< 20 1 2.22%
21 - 30 9 20.00%
31 - 40 18 40.00%
41 - 50 14 31.11%
> 50 3 6.67%
Gender Male 36 80.00%
Female 9 20.00%
Education
High school
3 6.67%
Diploma 2 4.44%
Characteristic Frequency (N=45)
Percentage Bachelor
degree
39 86.67%
Master degree
1 2.22%
Professional Experience
< 5 years 3 6.67%
5 - 10 years
17 37.78%
10 - 15 years
12 26.67%
15 - 20 years
6 13.33%
> 20 years
7 15.56%
Position
Senior manager
1 2,22%
Project manager
7 15,55%
Site manager
3 6,66%
Engineer 25 55,55%
Safety officer
1 2,22%
Quantity surveyor
5 11,11%
Quality Control
3 6,66%
4. RESULT AND DISCUSSION 4.1 Assessment Measurement Model
(Outer Model)
The outer model assessment aims to see how well the indicator reflects its construct.
Construct validity consist of convergent validity and discriminant validity test. Those validity tests are done to convince validity and reliability instruments of data collection [12]
Convergent validity at the indicator level is known as item reliability. It is reflected by the loading factor value. Loading values are considered sufficient if its value ≥ 0.7 (Fornell et al, 1992). Previously, 39 indicators were identified in the proposed model. However, 19 indicators were invalid and should be deleted from the path model due to the loading factor below 0.7. Valid indicators are presented in table 3. After all the indicators in the proposed model already have a loading factor above 0.7, the next step is to evaluate the AVE value on each construct. The acceptable AVE value is >
0.5 [13]. As presented in table 3, it can be seen that all AVE values are greater than 0.5
Subsequently, convergent validity at the construct level is known as internal consistency or composite reliability (ρc) [12]. Another way to see composite reliability is by looking at Cronbach alpha value. Sarstedt et al (2014) sorted Cronbach alpha value, where 0.6 – 0.7 is considered acceptable, 0.7 – 0.95 is considered satisfactory to good [13]. As presented in table 3, all Cronbach alpha values on each construct denoted satisfactory validity
Table 3. Construct Reliability and Validity Construct Indicator Loading
Factor
AVE Value
Cronbach Alpha
Quality Performance
QP1 0.779
0.563 0.845
QP2 0.708
QP4 0.797
QP7 0.774
QP9 0.726
QP10 0.711 Labor
Productivity
PROD1 0.932
0.805 0.939 PROD2 0.896
PROD6 0.960 PROD8 0.865 PROD10 0.826
Rework
RW3 0.927
0.895 0.971
RW5 0.973
RW7 0.922
RW9 0.957
RW10 0.951 Customer
Satisfaction
CS1 0.839
0.716 0.868
CS2 0.855
CS3 0.889
CS5 0.800
Secondly, the validity test in the outer model assessment is discriminant validity. The discriminant validity at the construct level was assessed by comparing the AVE square root value of a construct with all other constructs.
The discriminant validity is considered acceptable if the root value of an AVE squared construct is greater than the correlation value with the other construct [12]. As seen in Table 4, all the AVE square root value on each construct meet discriminant validity.
Table 4. Relationship Between Construct and AVE Square Root Value
Construct Quality Perfor mance
Labor Product ivity
Rework Cust.
Satisfac tion Quality
Performance
0.750
Labor Productivity
0.496 0.897
Rework -0.558 -0.462 0.946
Customer Satisfaction
0.656 0.645 -0.622 0.846
Furthermore, a cross-loading value was tested to see if each indicator sufficiently represents its latent variables. The cross-loading value of each explained indicator must be greater than the cross-loading value of the indicator on another construct [14]. The results of this cross-loading value can be seen in table 5.
From the cross-loading value of each of the above indicators, it can be seen that the cross- loading value of each indicator had already greater than each explained construct.
Therefore, this research model can be declared have met the measurement model and no more indicators need to be removed.
Table 5. Cross-Loading Value
Indicator Quality Perfor mance
Labor Produ ctivity
Rework Customer Satisfaction
QP1 0.779 0.275 -0.513 0.609
QP2 0.708 0.308 -0.365 0.325
QP4 0.797 0.431 -0.519 0.520
QP7 0.774 0.307 -0.473 0.510
QP9 0.726 0.532 -0.361 0.458
QP10 0.711 0.371 -0.228 0.488 PROD1 0.445 0.932 -0.365 0.659 PROD2 0.323 0.896 -0.358 0.450 PROD6 0.462 0.960 -0.475 0.647 PROD8 0.528 0.865 -0.375 0.547 PROD10 0.433 0.826 -0.490 0.550 RW3 -0.461 -0.444 0.927 -0.522 RW5 -0.560 -0.482 0.973 -0.635 RW7 -0.455 -0.380 0.922 -0.522 RW9 -0.612 -0.454 0.957 -0.605 RW10 -0.529 -0.417 0.951 -0.638
CS1 0.508 0.611 -0.449 0.839
CS2 0.505 0.438 -0.451 0.855
CS3 0.572 0.501 -0.586 0.889
CS5 0.614 0.608 -0.592 0.800
4.2 Assessment Structural Model (Inner Model)
The key point of the inner model assessment is to describe the relationship between exogenous constructs and endogenous construct within the path model [13]. The following techniques were used to assess the result of the structural model, i.e. determination coefficient value (R2), path coefficient value (β), t-statistics value and structural model equation.
4.2.1 Determination Coefficient (R
2)
The cutoff value for determination coefficient is between 0 – 1, with higher-level indicated better level of predictive accuracy. As a general guide, the R2 values are 0.75, 0.50, 0.25 where are classified as substantial, moderate, and weak respectively [13]. As presented in table 6, the R2 calculation result on each construct was measured. Labour productivity is considered weak since its value below 0.25 and rework is considered moderate since its value below 0.5, while customer satisfaction is considered substantial since its value below 0.75. The meaning of the value is that the exogenous construct that affects labour productivity in the tested model represents 24.6% the possibility of labour productivity. For rework, exogenous construct in the tested model represents 31.1%the possibility of rework. While customer satisfaction, it represents 61.5% the possibility of customer satisfaction.
Table 6. R2 Value
Endogenous Variable R Square Labor Productivity (LP) 0.246
Rework (RW) 0.311
Customer Satisfaction (CS) 0.615
4.2.2 Path Coefficient (β) and T – Statistics
The model assessment was carried out by comparing the path coefficient value between the construct within the tested model. The path coefficient value (β) is standardized between -1 and +1. Coefficients closer to +1 indicated a strong positive relationship. While the coefficient is close to -1, showed a strong negative relationship [13]. Moreover, the significance level of the relationship between construct can be seen from T- statistics value which is computed through bootstrapping
process. To determine the significance level of hypothesis, T – statistics value must be greater than at t-table value which can be found in the distribution table. The hypothesis of this study is an alternative hypothesis with one-tail test.
The degree of freedom (df) is 44, and the desired level of significance (α) = 0.05. Based on the distribution table, it can be found that the t – table value is 1,680.
Based on path coefficient value and T- statistics that is presented in table 7, it can be seen that the result supports all the hypotheses.
Exogenous construct has a strong influence on all endogenous constructs. Therefore, all the hypotheses in the model are accepted and significant.
Besides, Sarstedt et al., (2014) suggested that assessment of structural model relationship was not limited to direct effect only, however, the total effect should be considered to be assessed to obtain extensive view in the structural model relationship. The total effect is number of direct effects and indirect effect between exogenous and endogenous construct in the structural model [13]. Based on given definition, computation of total effect is 0.325 + (0.496*0.357) + (-0.558*- 0.276) = 0.65608.
The value clearly confirms that quality performance has a strong influence on all endogenous constructs.
Table 7. Path Coefficient Value and T – Statistics
Hypot hesis
Path β T
Statist ics
P- Value
Conclusion
H1 QP >> LP 0.496 5.479 0.000 Significant H2 QP >>CS 0.325 2.054 0.041 Significant H3 QP>>RW -0.558 6.032 0.000 Significant H4 LP>>CS 0.357 2.733 0.006 Significant H5 RW>> CS -0.276 2.017 0.044 Significant
4.2.3 The goodness of Fit (GoF)
Another useful approach to measuring structural model is Goodness of fit (GoF). GoF is defined as a geometric mean of the average commonality and the R2 whose purpose is to measure the performance of the PLS model both at the measurement model assessment stage and at the structural model assessment stage [15].
GoF is calculated using the below equation.
GoF = √ AVE * R2 (1) The cut off values are between 0 – 1, where 0.10, 0.25, and 0.36 are deemed small, medium, and large respectively. In this model, the GoF value is 0.663 which demonstrated the model has better predictive power.
Figure 1. PLS Algorithm Results on The Path Model
5. CONCLUSION AND SUGGESTION
Besides high rating of growth, it has been understood generally housing projects in Indonesia have been built by the small to medium qualification of housing contractors who carry out low-quality management system on overall project activities, thus the possibility of quality failure due to improper working process happened repeatedly.To improve more on labor productivity, customer satisfaction as well as reducing rework process drastically, housing contractors need to pay more attention on the importance of managing good quality by establishing effective quality program, procedures, working instructions, and provide clear quality plans since in the beginning of the project. This finding provides wider insight for construction practitioners especially those who involved in
managing housing project how quality can bring positive implication on overall project performance.
Based on data analysis on the previous section, it clearly indicates that quality has a strong relationship towards labor productivity, rework, and customer satisfaction. Thus, all hypotheses on the final model are accepted and scientifically proven.
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