American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
American Journal of Multidisciplinary Research & Review (AJMRR)
Volume-01, Issue-05, pp-29-37
www.ajmrr.com
Research Paper Open Access
Innovative Project Management Concepts and Project
Performance: Construction Project Management in Sri Lanka
Himali Ekanayake 1, Raduwan Bin Idar2
1
(Faculty of Engineering, Open University of Sri Lanka)
2
(Twintech University College for Technology, Malaysia)
ABSTRACT :The research in construction industry suggests that practicing better project management
methods are essential while trying to solve the issue with other process or technology improvement methods.
For establishing better concepts, assessing current practice of innovative concepts and their contribution to the
performance of projects is vital. Currently practiced Innovative concepts based on lean and agile concepts are
identified and their relationship toward the performance of projects was analyzed. Innovation is considered as
an influential factor and the relationship of innovation towards relationship of innovative concepts and project
performance also assessed in this research. A questionnaire survey was conducted among 218 professionals
currently working in construction projects in Sri Lanka. Quantitative analysis by SPSS confirmed the higher
significance level between the relationship of innovative concepts currently practicing and performance.
Further it endorses that innovation act as a mediator and has a significant level of influence to change the
severity of the relationship between the innovative practices and project performance.The results of this study
was used in substantiating the validation of the hybrid project management framework developed in the main
research, in the context of Sri Lanka Construction industry
Keywords: Lean, Agile, construction project management, innovation
I. INTRODUCTION
Several researchers who have done the research in the context of Sri Lanka has suggested factors that
can contribute to solve the problems in construction project performance. (Kesavan, Gobidan, & Dissanayake,
2015; Wijekoon, 2015). Main factors are about management improvement for increase efficiency and
productivity of the processes. Project management approaches has evolved gradually in the construction
industry when compared to the other industries but the complexity has increased rapidly (Dubois &Gadde, 2002
;Nemathullah& Naik, 2016). Among these evolving project management approaches the concept of Lean and
Agile were selected by many researchers to study as these can be used to overcome the deficiencies of managing
projects by traditional Project Management. (Spundak, 2014; DeCarlo, 2004). Presently in Sri Lanka, projects
are managed adopting their own project management methodologies harmonized with Traditional Project
Management (TPM). (Rameezdeen, Construction sector in Sri Lanka, 2006). However, acoording to experts it is
inappropriate categorizing the current practicing project management approach as Traditional , Agile, Lean etc.
They explain that when practicing in the industry they merely follow the principles and may not identify the
academic theory. However, for this study, presently practicing innovative concepts are identified and the project
managers’ perception regarding the outcome of these applications are analysed..
II. LITERAURE REVIEW
2.1 Agile and Lean concepts in Construction Project Management
Agile management or Agile Project Management (APM) is an iterative and incremental method of
managing the design and build activities for engineering, information technology, and new product or service
development projects in a highly flexible and interactive manner (Spundak, 2014). Agile project management is
rapidly evolving and significantly changing the project management profession. (Nerur, Mahapatra, &
Mangalara, 2005) . Owen, Koskela , Henrich and Codinhoto (2006). discussed on the applicability of APM in
construction industry. APM is suitable to manage the project in a regular manner to ensure the value is
generated continuously throughout the project according to them. (Robert Owen, Koskela, Henrich, &
Codinhoto, 2006).
AJMRR Journal P a g e | 29
American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
Rico’s (2010) study defines Agile methods are consisting of four major factors: (1) customer
collaboration, (2) iterative development, (3) self-organizing teams, and (4) adaptability to change. Agile project
management encourages the project manager and other team members to interact more closely with the sponsor
and other business stakeholders. Most important Agile principles identified referring to the literature are in
figure 1 . (Demir, 2013 ; Owen &Koskela, 2006)..
Fig 1- Agile Principles
The general approach of the Lean Project Management (LPM) philosophy is to eliminate waste. “Lean
is doing more with less. Use the least amount of effort, energy, equipment, time, facility space, materials, and
capital – while giving customers exactly what they want.” (Womack, 2008) (Eriksson, Improving construction
supply chain collaboration and performance; A Lean construction pilot project, 2010). Bottirov(2011), it must
be approached as a whole system of thinking and behaviour that is shared throughout the value stream
Lean Concepts that are most significant in this research of construction project management can be derived from
the literature and summarized in figure (Demir.T, Bryde, Fearon, & Ochieng, 2012 ; Demir, 2013 ; Ohno, 1988 ;
Womack, 2008 ; Diekmann, Balonick, Krewedl, &Troendle, 2003).
Fig 2- Most significant Lean principles
2.2 Performance measuring index
Project performance measurement is mainly based on the iron triangle; time, cost and quality (Wi &
Jung, 2010). However, many researchers extended the measuring further with quality, client satisfaction, client
changes, business performance, health and safety. (Shahid, Ahmad, Ahmad, Shafique, & Amjad, 2015).Table 01
Performance indices quantified and normalised by Nassar (2009) is adopted in this research for
assessing the project performance. The eight indices are;
i. Cost performance - The Cost Performance Index (CPI) = BCWP/ACWP ; Where,
BCWP = Budgeted Cost of Work Performed. It is the budgeted amount of cost for work-completed to-
date or the cost allowed (based on budget) to be spent for the actual work done.
ACWP = Actual Cost of Work Performed. It is the cost incurred to complete the accomplished work
to-date.
ii. Schedule performance - The Schedule Performance Index (SPI) = BCWP/BCWS ; Where,
BCWP = Budgeted Cost of Work Performed (budgeted amount of cost for work completed to date) .BCWS =
Budgeted Cost of Work Scheduled (budgeted amount of cost for work scheduled to date.
iii. Safety Performance Index (SFI) = LTI * C /M ; Where,
LTI = Number of Lost Time Incidents to date
M = Total man-hours expended to date; and
C = is a constant (200,000) which represents 100 employees working for a full year (100 x 2,000).
iv. Quality Performance Index (QPI) is best measured by the Construction Field Rework Index
(CFRI)
CFRI = Total direct and indirect rework performed in the field/ Total field construction phase cost.
v. Project Team Satisfaction Index (TSI) is a measure of how satisfied the project team is. It is
evaluated considering twelve factors such as client involvement in the project, client and project manager
AJMRR Journal P a g e | 30
American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
response and individual related areas such as financial benefits, training received, interest in nature of work etc.
hence in this research it is expected to extract project related areas to evaluate the project team satisfaction.
vi. Client Satisfaction Index (CSI) The CSI is determined by satisfaction rating for area of
concern based factors such as fulfilment of client needs and fulfilment of project needs.
Table 1- Project performance measurement in Construction Projects (Silva, Ezcurdia, Gimena, & Guerra, 2013)
Category Author
Cost, Time, Quality, Safety, Health, Cheung & Cheung,
environment, Customer satisfaction 2004
and Communication
Cost, Time, Quality, Owner Ling, 2004, Chan et al
2002
Cost, Schedule Cho & Hyun, 2009
Working team, Continuous Toor & Ogunlana, 2010
improvement, Time, Budget,
Specifications,
Resources/Efficiency, Effectiveness,
Safety,
Defects, Stakeholders, Conflicts
Schedule, Cost, Quality, Arguments Jha & Iyer, 2007
Stakeholders, Time, Cost, Bernroider & Ivanov,
Monitoring of standards, 2011
Implementation, Training
Cost, Time, Customer satisfaction, Luu, Kim, & Huynh,
SGC implementation, Project Team, 2008
Change management, Materials
management, Safety management
2.3 Innovation and project performance
The notion of sustainable competitive advantage is increasingly interwoven with innovation (Barret &
Sexton, 2006). Tomas (2006), have noted that there is a relationship between innovation and project
performance. Innovation is listed as a success criterion for handle with more prominence. (Eriksson &
Westerberg, Effects of procurement in construction project management, 2009).
III. METHODOLOGY
Quantitative method is used as it focuses on gathering numerical data and generalizing it across groups
of people to explain a particular phenomenon and researcher can look for the precision (Babbie,
2010),(Raduwan, 2011). The population considered for this study is comprised with the senior professionals in
the industry. Project managers, engineers, quantity surveyors and project coordinators/administrators who are
employed in ongoing construction projects considered as the population and stratified random sampling method
is used. Kothari (2004), states that various strata may be formed in such a way as to ensure elements being most
homogeneous within each stratum and most heterogeneous between the different strata. Thus, strata were
purposively formed and were based on past experience and personal judgement of the researcher.
Personally administered questionnaire was the selected method of data collection considering the high
response rate. Questionnaire is designed with referring and adopting from the work done by Demir (2013),
Adjei &Rwakatiwana (2009), Bygballe&Sward(2014), HEERY(2015) ,Basebeth&Primiana (2016) and
Mandleyetl. (2009).
Likert scale is the type of interval scale adopted in this research. Initially, factor analysis was
conducted by using a Varimax rotation to minimize the complexity of the factors by increasing the variance
loading of each factor. The size of loading and Cronbach alpha statistic was used to assess the appropriateness
of the scale. Variables having eigenvalue of greater than one considered as significant and will be used in the
analysis. The value of Cronbach alpha is expected to be greater than 0.7.
Pearson’s correlations (r) were performed to determine the strength and direction of the relationships
between the dependent variable and each independent variable. The result of regression is an equation that
represents the best prediction of dependent variables from several independent variables. (Raduwan, 2011).
Therefore, in this research, the relationship among variables areanalysed using multiple regression. In addition
to that, to investigate the relationship of innovation against the relationship between innovative project
AJMRR Journal P a g e | 31
American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
management approaches and performance, three step process for establishing mediation is applied. (Baron, &
Kenny, 1986) cited by Raduwan(2011). SPSS -20 software facilitated all of these statistical analysis functions..
IV. DATA ANALYSIS
4.1 Data Preparation
In the process of data preparation first the data cleaning was done. Then the data was further explored
to find out the readiness to desired correlational analysis using SPSS. This contains checking distribution of
received data in terms of the normality, possible outliers, reliability of the instruments, and validity
i. Data Cleaning.
Data cleaning is simply making sure the correct values are entered in the data sheet. All data were
checked conducting the frequency test.
ii. Assessing Normality
Normality is graphically explored in this study for all the dependent and independent variables using
histogram, normal probability plot and detrended normal plot as suggested by Coakes, Steed and Dzidic(2006).
Simultaneously the statistical test that is available for testing the normality of data was conducted and presented
for each variable..
Table 2 Skewness and the Kurtosis
Statistic Std
Error
Mean 3.4042 .03930
95% Confidence Lower Bound 3.3268
Interval for Mean Upper Bound 3.4817
5% Trimmed Mean 3.4100
Median 3.3750
Variance .337
Perfor
Std. Deviation .58020
mance
Minimum 2.00
Maximum 4.75
Range 2.75
Interquartile Range .88
Skewness -.136 .165
Kurtosis -.502 .328
The descriptive results for the dependent variable shows a mean of 3.404 and a standard deviation of
0.58 according to the table. The negative skewness value as shown in the table for the variable performance
shows a clustering of scores at the right hand side. The negative kurtosis value as explained by Pallant (2007),
indicates that the distribution is rather flatter around the mean. Normality of performance is further backed by
the normal probability plot labels as the Normal Q-Q plot in the given figure 3 and the Detrended normal Q-Q
plot in figure 4.
Fig 3 Normal Q-Q plot for the dependent Fig 4 Detrended Normal Q-Q plot for the
variable dependent variable
Table 3 Test of normality for all the three variables
AJMRR Journal P a g e | 32
American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
Kolmogorov- Shapiro-Wilk
Smirnova
Statistic df Sig. Statistic df Sig.
Performance .056 218 .089 .989 218 .097
Innovation .056 218 .090 .988 218 .060
.200
Innovative PM .050 218 * .989 218 .111
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
Significance level is 0.089, 0.90 and 0.20 respectively for Performance, Innovation and Innovative Project
Management variables and it is greater than 0.05 . Hence the normality of the data is assumed for all the
variables
Outliers also checked inspecting the Mahalanobis distance as instructed by Pallant (2007) and no outliers were
found.
iii. Reliability test
Reliability indicates the degree to which the measurement is consistent over time and whether it accurately
represent the total population under study (Coakes, Steed, &Dzidic, 2006) (Hair, Anderson, Tatham, & Black,
2005) (Sekaran, 2003). Cronbah alpha value of above 0.7 is considered as the acceptable level in this research
Kumar(2011), Kotahari(2004),Sekaran (2003).
Table 4 Reliability of measures
Scale variable Cronbach's Alpha Number of Items
Performance 0.803 8
Innovative PM 0.936 30
Innovation 0.715 10
iv. Correlation of variables
Table 5 Correlations matrix
Performance Innovative PM Innovation
Performance
Pearson 1 0.678** 0.355**
Innovative PM Pearson 0.678** 1 0.388**
Innovation Pearson 0.355** 0.388** 1
**. Correlation is significant at the 0.01 level (2-tailed).
The results indicate that the Performance and InnovativePM (r=0.678), Performance and Innovation (r=0.355),
Innovative PM and Innovation (r = .388), are significantly positively related.
v. Multicollinearity
As explained in Table 06, tolerance value of 0.849 ; 0.1>, .09 < a safely higher. The value for VIF<10. In this
results it is 1.177 and well below the margin. Hence can conclude as no multicollinearity in these two
independent variable (Pallant, 2007).
vi. Factor analysis
AJMRR Journal P a g e | 33
American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
Factor analysis of this study is done using Principle Axis factoring (PAF). (Coakes, Steed, &Dzidic, 2006).The
KMO Kaiser-Meyer-Olkin Measure of Sampling Adequacy. (KMO) value 0.617 for the set and exceed the
minimum of 0.6 and, the significance value indicates that the Bartlett's Test of Sphericity check is also satisfied,
Table 07. Based on the guidelines set by Tabachnick&Fidell (2007) as cited by Raduwan (2012), the decision
rule of 0.30 is considered as the factor loading point at which any factor loading greater than or equal to 0.30
was included in the analysis.
Table 6 Factor matrix for dependent and independent variable
Factor
1
Performance .791
InnovativePM .857
Innovation .451
Extraction Method: Principal Axis Factoring.
a. 1 factors extracted. 17 iterations required.
Table 7- Multicollinearity measure
Model Standardizd Coefficients t Sig. Collinearity Statistics
Beta Tolerance VIF
InnovativePM .635 11.784 .000 .849 1.177
Innovation .109 2.017 .045 .849 1.177
Factor loading revealed that no items were needed to be deleted. It is further explained in the scree plot
indicating that all items belong to one factor.
Fig 5- Scree plot for dependent variable Performance and independent variables Innovative PM & Innovation
4.2 Relationship of currently practiced innovative concepts and performance
The results of regression of Innovative PM and project performance is shown in the following table 8
Table 8- Model summary – Innovative PM
Model R R Adjusted Std. Error Change Statistics
Square R Square of the R Square F df df2 Sig. F
Estimate Change Change 1 Change
1 .678a .459 .457 .42769 .459 183.345 1 216 .000*
a. Predictors: (Constant), Innovative PM concepts
b. Dependent Variable: Performance
*significant at p < .05
Table 9- Coefficients - Innovative PM concepts
Model Unstandardized Standardized t Sig.
Coefficients Coefficients
B Std. Error Beta
Innovative PM concepts .710 .052 .678 13.540 .000*
AJMRR Journal P a g e | 34
American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
a. Dependent Variable: Performance
*significant at p < .05
Table 8 and Table 9 gives adjusted values of r2 = 0.459, significance .000 h < .05, and the beta value of 0.678
indicating that 45.9 % contribution for variance of performance so that supporting the statement that the
“Innovative PM concept has a significant positive relationship towards the construction project performance”
4.3 Relationship of innovation and project performance
These two tables 10, 11 give values of r2 = 0.126, significance .000 hence < .05, and the beta value of 0.355
indicating that 12 % is contributed for the performance variance, so that supporting the statement that the
“Innovation has a significant positive relationship towards the construction project performance”
Table 10- Model summary - Innovation
Model R R Square Adjusted R Change Statistics
Square R Square F Change df1 df2 Sig. F
Change Change
1 .355a .126 .122 .126 31.227 1 216 .000*
a. Predictors: (Constant), Innovation
Table 11- Coefficients- Innovation
Model Unstandardized Coefficients Standardized t Sig.
Coefficients
B Std. Error Beta
Innovation .472 .084 .355 5.588 .000*
4.4 Mediating effect of innovation
Table 12- Model summary- Mediator Analysis
Model R R Adjuste Std. Error of Change Statistics
Square d R the Estimate R Square F Change df1 df2 Sig. F Change
Square Change
1 .678a .459 .457 .42769 .459 183.345 1 216 .000*
2 .685b .469 .464 .42469 .010 4.068 1 215 .045*
a. Predictors: (Constant), Innovative PM concepts
a. Predictors: (Constant), Innovative PM concepts, Innovation
*significant at p < .05
As explained in the table 12, R2 values for two models shows a higher contribution of 45.9% with the presence of
Innovative PM concepts only and when Innovation presence and 46.9 % as the model as whole. It show that r2 change
value is only 1%
Table 13- Coefficient - Mediating variable analysis
Model Unstandardized Standardized t Sig. Correlations
Coefficients Coefficients
B Std. Error Beta Zero-order Partial Part
Innovative PM
.710 .052 .678 13.540 .000* .678 .678 .678
concepts
Innovative PM
2 .666 .057 .735 11.784 .000* .678 .626 .586
concepts
Innovation .144 .072 .109 2.017 .045* .355 .136 .100
AJMRR Journal P a g e | 35
American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
a. Dependent Variable: Performance
*significant at p < .05
According to the table 13, explaining the coefficient table for the analysis of mediating effect of the variable Innovation
towards the Innovative PM concepts and Project Performance, it can be seen that the standardized coefficient of beta
value has increased to .735 in model 2. Which shows a significant change in the contribution of Innovative PM concepts
towards Project performance due to the mediation of Innovation.
These values indicate that Innovation mediate the results of the relationship of Innovative PM concepts and the
performance.
V. FINDINGS AND DISCUSSION
1. The relationship of innovative project management concepts currently practiced and the performance
of the project.
Innovative project management concepts show a significant positive relationship toward the
performance of the project. The results indicated that contribution of the factor towards the performance
variation is 45.9% which is considered as a higher performance rate according to Frost (2013). The result
supported the significance at the p< 0.05[r2 = 0.459, beta 0.678, p= .000]. Therefore it indicate that the
Innovative PM is a predictor of the project performance.
2. Relationship of innovation and performance of projects.
It is a well-established judgement in any business that innovation is comprising of positive drives that
make the improvements in performance. Table 10 and 11 in chapter 4 summarized the results of regression and
hence, it showed r2 value of 0.126, significance level .000, and beta value 0.355, significance .000 level. Results
justified, that innovation has a positive significant relationship toward the performance of projects.
3. Mediating effect of innovation towards the relationship between innovative project management
concepts and performance of projects.
Innovation is a mediator which facilitate the management practice. The hierarchical multiple regression
analysis delivered results of table 12,13. When the model one is considered as the hybrid approach only and the
model two is when the innovation included in the hierarchical analysis, it gave a p<.05( r2 change of 0.01. and
significant value .000) . Further to that the beta value changed from 0.678 to 0.735 making the unique
contribution changes significantly. Hence this shows that innovation is significantly mediate the relationship
between innovative project management concepts and the performance of the project.
VI. CONCLUSION
With results of this research it is attested that innovative concepts, innovation has a positive
relationship for the project performance. Currently practicing innovative concepts and performance appeals that,
the project management approach with lean techniques stringed to agile management concepts may also have
significant influence towards the improvement of project performance. For this the innovation is acting as a
mediator and the improvement of innovation may improve and ease the application of the hybrid project
management approach as well as improve the project performance.
The past literature has also proven on supporting these findings. Thus, the present findings commend
and elaborate the concepts such as lean and agile construction by Iqbal (2015), Agilean by Demir (2013),
hybrid of agile and traditional by of past researchers regarding the new trends in project management. The
extraction and facilitation of lean and agile project management also has discussed by Chen, Riechard and
Beliveau (2007 ) with an introduction of an interface management methodology.
According to (Dess, 2005) concept of innovations and pro- activeness characterized by quick respond
create a competitive advantage. It is applicable in construction industry too. The results show that innovation
significantly moderate the effect of project management approach towards the project performance.
As a conclusion the results of this study directs that the application of lean and agile innovative project
management concepts with innovation may have a positive influence in project performance of construction
industry of Sri Lanka..
REFERENCES
[1]. Chen, Q., Reichard, G., &Beliveau.Y. (2007). Interface management- A facilitator of lean construction
and agile project management. Proceedings IGLC-15. Michigan, USA: 57-66.
[2]. Demir, S. (2013). Agilean project management- A unifying strategic framework to manage
construction projects. UK: Liverpool John Moores University.
[3]. Demir.T, Bryde, D. J., Fearon, D. J., & Ochieng, E. G. (2012). Re-conceptualizing lean in construction
environments –. 48th ASC Annual International Conference Proceedings. Associated Shools of
Construction.
AJMRR Journal P a g e | 36
American Journal Of Multidisciplinary Research &Review (AJMRR) 2022
[4]. Dess, G. G., Lumpkin, G., &Marlyn, L. (2005). Strategic management (2 ed.). New York: Mcgraw
Hill, Irwin
[5]. Dolage, D., &Pathmarajah, T. (2015). Mitigation of delays attributable to the contractors in the
construction industry of Sri Lanka. Engineer. Retrieved 08 04, 2016, from http://www.iesl.lk
[6]. Hass, K. (2007). The blending of traditional and agile project management. Retrieved 07 15, 2016,
from https://www.projectsmart.co.uk: https://www.projectsmart.co.uk/the-blending-of-traditional-and-
agile-project-management.php
[7]. Iqbal, S. (2016, 07 01). http://www.projectmanagement.com/. Retrieved from Leading Construction
Industry to Lean-Agile (LeAgile) Project Management: http://www.projectmanagement.com
[8]. Jayalath, D. (2010). The most significant causes of delay in construction industry of Sri Lanka.
Moratuwa, Sri Lanka: Deapartment of Building Economics , University of Moratuwa.
[9]. Owen, R., Koskela, L., Henrich, G., &Codinhoto, R. (2006). Is Agile Project Management Applicable
to Construction? Proceedings IGLC-14, July 2006, , (pp. 51-66). Santiago, Chile.
[10]. Silva, N., Rajakaruna, R. C., & Bandara, K. (2006). Challenges faced by the construction industry in
Sri Lanka: perspective of clients and contractors. University of Moratuwa, Sri Lanka.
[11]. Spundak, M. (2014). Mixed agile/traditional project management methodology – reality or illusion?
Procedia- Social and Behavioral Sciences, 939-948.
AJMRR Journal P a g e | 37
