Published at : 31 Oct 2023
Volume : IJtech
Vol 14, No 6 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i6.6651
Jamaludin Jupir | Faculty of Management, Multimedia University, Cyberjaya Campus, Cyberjaya 63100, Malaysia |
Kamarulzaman Ab. Aziz | Faculty of Business, Multimedia University, Melaka Campus, Melaka 75450, Malaysia |
Hasliza Hassan | Faculty of Management, Multimedia University, Cyberjaya Campus, Cyberjaya 63100, Malaysia |
Project management (PM) has garnered significant
attention from both academia and business stakeholders to understand the
factors that contribute to the success of project delivery. Despite years of
accumulated experience and a significant increase in the number of certified
project managers, the success rate of construction projects remains relatively
low. This study aims to explore possible determinants for collaborative project
management success and validate the framework in the context of the Malaysian
construction industry. Specifically, the study tested project management
maturity, decision-making management, knowledge management, organizational
culture, coordination, and PM certification as antecedents for collaborative
project management success. The study adopted a quantitative research approach
and used a close-ended questionnaire as the data collection instrument. The
questionnaires were distributed to local project practitioners, and the 232
responses were analyzed using Partial Least Squares of the Structural Equation
Modeling (PLS-SEM). The empirical evidence indicates significant relationships
between the identified determinants and project management success. The study's
findings can be applied to manage construction projects or any other collaborative
project and also contribute to the project management body of knowledge and
elaboration of Coordination Theory application in home construction projects.
However, emerging challenges to PM in the post-COVID era suggest that further
studies on PM success factors are necessary.
Business decision making; Collaborative project management; Coordination; Knowledge management; Project management maturity
Project
Management (PM) is vital for success and competitive advantage (PMI, 2020), with its historical roots lie in
efficient resource management (Meirelles et al.,
2019). PM has evolved significantly in the last decade (Söderlund, 2004), becoming a critical field with
practical and theoretical research (Vergopia, 2008).
Despite this, construction project success rates remain low globally and in
Malaysia (Ahmad, Mohd, and Ab, 2021). PM involves the practical
application of knowledge, tools, and techniques to deliver projects (PMI, 2017), distinct from managing operations (PMI, 2019). Success assessment often focuses on
cost, time, and quality, neglecting overall objectives (Carvalho
and Junior, 2015; Davis, 2017;
Vergopia, 2008). Emerging trends include agile, sustainability, and collaborative
management (PMI, 2020a). Collaboration with
stakeholders is crucial for effective PM (Miller and Oliver, 2015). Collaboration thrives when
beneficial outcomes are anticipated (Blomquist et
al., 2010). Collaborative projects involve distinct team members
forming virtual teams (Silva, 2011),
constituting collaborative PM. Jabar et al.
(2019) highlighted the need to understand key competencies for PM
success.
Jupir et al.
(2022) suggest that PM maturity, knowledge management, and
decision-making have an impact on PM success, and these factors are moderated
by culture and certification, with coordination acting as a mediator. PM
maturity refers to an organization's effectiveness in project management (Sopko, 2015), which, in turn, influence
collaborative PM outcomes (Ibbs et al.,
2007). However, evidence linking PM maturity to project value is limited
(Thomas and Mullaly, 2007). Knowledge management (KM)
facilitates shared context (Clarke and Cooper, 2000) and enhances coordination for
project success. Decision-making is vital in complex projects (Goff, 2012), but construction sites present
unique challenges. Examining how coordinating parties facilitate
decision-making in construction projects is essential. PM skills are critical (Henkel et al., 2019), while the impact of
project manager certification is unclear (Natchayangkun,
2014). Organizational culture influences project planning and
coordination (Suda, 2007). Investigating how
cultural components affect decision-making and coordination provides insights.
Lastly, coordination among project team members and tasks, considering
complexity, uncertainty, and organizational structure, impacts project success (Mannan, Haleem,
and Jameel, 2013). Examining coordination's role,
particularly in the construction industry, is valuable (Berawi,
2021).
Thus,
this paper presents the validation of the proposed framework for the successful
management of collaborative projects within the context of the Malaysian
construction industry. To achieve this objective, the study addresses the
primary research question: What are the determinants for successful
collaborative project management (CPM).
Consistent
with previous studies, this study used quantitative methods for data collection
and analysis (Jamieson, Govaart, and Pownall, 2023; Ponto, 2015). Data was collected using self-administered
questionnaires in printed and Google online form to cover a larger target
population (Check and Schutt, 2012).
Invitation to complete the form was distributed to project practitioners in
Malaysia. Out of 350 invitations sent, 232 completed forms were received. The
response rate (66.2%) is considered acceptable to provide fair, confident
estimates (Fosnacht et al., 2017; Chong and
Zin, 2010). Cronbach's alpha scores for internal consistency are above
0.8 for all variables; hence, this research is considered to have an adequate
sampling to test the hypothesis. Data were analyzed using SmartPLS 3.0
software. PLS-SEM is considered an appropriate method to assess the results
since the algorithm permits the unrestricted computation of cause-effect
relationship models for the reflective measurement models employed in this
study (Diamantopoulos and Siguaw, 2006). In
the process of examining the relationships between variables, the study
assessed the measurement model for reliability and construct validity, and also
evaluated the structural model for hypothesis relationships, following the
recommendations of Hair et al. (2018).
2.1. Research Framework
The research framework (see Figure 1), first proposed
in the first phase of this study (Jupir et al.,
2022), consisted of three independent variables, namely Project
Management Maturity (PMM), Decision Making System (DMS), and Knowledge
Management (KMM); one dependent variable namely CPM Success (SCS); two
moderating factors namely Culture (CUL) and Certification (CER); and
Coordination (CDN) as the mediating factor. The following are the finalized
hypotheses tested in this study;
Figure 1 Research
Framework (Jupir et al., 2022)
3.1 Measurement Model
The
proposed model for the framework was tested for internal consistency,
convergent validity, and discriminant validity (Hair
et al.,
2018). Listed in Table 1,
average factor loadings for all items are above 0.7, indicating the model’s constructs
explained more than fifty percent of the indicator's variance; hence, the
factor loading satisfied the cut-off values, and the items in the
questionnaires are reliable for the measurement model assessment. The
constructs' internal consistencies were estimated using Cronbach's Alpha, Rho
Alpha, and Composite Reliability (Samani, 2016; Hair,
Ringle, and Sarstedt, 2012). Table 1 displays
Cronbach's Alpha values for all the constructs, ranging from 0.786 to 0.935.
These values indicate high internal consistency reliability for the constructs'
scale and suggest that the content of the items is not heterogeneous (Taber, 2018; Kline, 2011). Composite Reliability results between 0.885 and 0.953; hence,
the internal consistency of this research is statistically confirmed. Rho Alpha
minimum value of 0.876 is between Cronbach's Alpha (0.786) and CR (0.885); the
maximum value of Rho Alpha of 0.939 is between Cronbach's Alpha of 0.935 and CR
of 0.953. The cut-off value of the coefficient of Rho Alpha must be greater
than 0.7 (Dijkstra and Henseler, 2015).
Convergent
validity measures the relationships between constructs using Average Variance
Extracted (AVE) and Composite Reliability (CR). The AVE for the constructs
falls within the range of 0.518 to 0.854, which exceeds the minimum recommended
value of 0.50, according to Hair et al. (2018). The results showed values of factor
loadings, CR, and AVE are greater than the recommended cut-off values; hence,
the measurement model is confirmed for having a convergent validity. All
constructs have achieved the minimum estimation required, which are 0.70 for
Cronbach Alpha, 0.60 for CR, and 0.50 for AVE. The results indicated DMS, CER,
CUL, CDN, KMM, PMM, and SCS are valid measurements of their respective
constructs. Discriminant validity provided evidence that those constructs that
theoretically should not be related to each other are not found to be related
to each other (Henseler et al., 2015). In Table 2, all constructs in the path
model are not considered distinct from each other; each of the constructs is
trying to validate project success from a different perspective. All constructs
in this research have an HTMT value below 0.90; hence, acceptable discriminant
validity for the model of the constructs (constructs is trying to validate
project success from a different perspective (Henseler
et al.,
2015).
Table 1 Reflective Measurement Model
Item |
Loadings |
Cronbach's Alpha |
rhoA |
Composite
Reliability |
Average Variance Extracted (AVE) | |
DMS |
1 |
0.766 |
0.826 |
0.828 |
0.885 |
0.658 |
2 |
0.812 | |||||
3 |
0.845 | |||||
4 |
0.819 | |||||
CER |
1 |
0.884 |
0.786 |
0.789 |
0.876 |
0.703 |
2 |
0.855 | |||||
3 |
0.772 | |||||
CUL |
1 |
0.824 |
0.864 |
0.868 |
0.908 |
0.710 |
2 |
0.846 | |||||
3 |
0.834 | |||||
4 |
0.867 | |||||
CDN |
1 |
0.609 |
0.883 |
0.895 |
0.906 |
0.518 |
2 |
0.668 | |||||
3 |
0.769 | |||||
4 |
0.798 | |||||
5 |
0.792 | |||||
6 |
0.781 | |||||
7 |
0.699 | |||||
8 |
0.718 | |||||
9 |
0.615 | |||||
KMM |
1 |
0.927 |
0.829 |
0.830 |
0.921 |
0.854 |
2 |
0.921 | |||||
PMM |
1 |
0.818 |
0.837 |
0.838 |
0.902 |
0.755 |
2 |
0.895 | |||||
3 |
0.892 | |||||
SCS |
1 |
0.929 |
0.935 |
0.939 |
0.953 |
0.837 |
2 |
0.932 | |||||
3 |
0.891 | |||||
4 |
0.906 |
3.2. Structural
Model
This study used the
coefficient of determination (R2), estimation of path coefficient (), effect
size (f2), and prediction relevance measure (Q2) for the assessment of the
structural model of CPM as per suggestion by Hair et
al. (2017), and Henseler et al.
(2009). Results indicate that CDN and SCS have moderate fit with R2 of
0.683 and 0.585, respectively, plus Adjusted R2 of 0.677 for CDN and 05.78 for
SCS (Hair et al., 2017; Chin, 1998).
An R2 of 0.683 indicated over 68 percent of the variation in the outcome has
been explained just by predicting the outcome using the CDN variables of the
model. The estimation of the path coefficient tests the significance of the
hypotheses. Table 2 shows the direct effect of the independent variables on the
SCS in the path model. The findings in Table 2 confirmed that the PMM-related
factor significantly influenced project quality (= 0.486, T = 7.506, p =
0.000). Hence, H1 was considered supported. Observing the positive influence of
the DMS-related factors on CDN (H2), the findings endorsed that the DMS factor
positively influenced CDN ( = 0.254, T = 3.903, p = 0.000), and confirmed H2.
The influence of the KMM-related factor on CDN was positive and significant ( = 0.236, T = 3.363, p = 0.000), showing that H3 was supported. The effect of
the CUL-related factors on CDN was significant ( = 0.427, T = 6.586, p =
0.000), therefore supporting H4. Similarly, the findings provided practical
support for H6, where the influence of the CDN-related factors on SCS positively
affected SCS with of 0.206, T at 2.919, and p-value slightly over zero at
0.002 only.
In Table 2, each hypothesis in the model
had a value of over 0.02, with PMM having a moderate value of 0.300 on the
scale. Following Cohen (1988) guideline for
the interpretation of the effect size, the results of f2 suggested the effect
sizes of PMM, CER, and CDN as exogenous latent variables have medium effects on
endogenous variable SCS. The moderate impact of the effect size of the
population suggested that another independent variable(s) would have no
apparent effect on the dependent variable's shared variance. Q² proposed a
model must be able to provide a prediction of the dependent variable's
measuring items for the model to be valid (Hair et
al., 2017). The results (CDN: Q2 = 0.318; SCS: Q2 = 0.451) showed
that the cross-validation redundancy measure Q² is above zero, suggesting the
model has a good predictive relevancy (Henseler et
al., 2009; Fornell and Cha, 1994). Standardized Root Means
Square Residual (SRMR) measured the estimated model fit by transforming the
sample covariance matrix and the predicted covariance matrix into correlation
matrices to avoid model misspecification (Henseler et
al., 2015). The model yielded an SRMR of 0.071, where a value of
0.08 or lower suggests a good model fit (Hussain et
al., 2018).
3.3. Structural Model
Table 2 shows the moderating impact of CER on the relationship between PMM and SCS; the relationship between PMM and SCS is significantly positive (= -0.128, T = 2.394, p = 0.0080); hence, H5a was supported. The direct path standardized beta for the relationship between PMM and SCS is 0.486, which changed to -0.128 after introducing CER as the moderator. The decrease in the relationship between PMM and SCS accounted for by the moderator was 0.614, representing over 127% of the direct effect. The influence of CUL on the relationship between the DMS function factor and CDN factor is positively significant (= 0.149, T = 5.260, p = 0.000), showing that H4a is supported. The direct path standardized beta for the relationship between DMS factors and CDN is 0.254 and changed to 0.149 after the introduction of CUL as the moderator. The decrease in the relationship between the DMS factor and CDN accounted for by the moderator is 0.105, or 41.33% of the direct effect.
3.4. Mediating Effects
The influence of CDN
on the relationship between DMS and a Successful Project is positively
significant ( = 0.122, T = 3.615, p = 0.000), showing that H6a is supported
(Table 8). Looking at the next hypothesis, the influence of the CDN on the
relationship between KMM-related factors and Successful Projects was found to
be positive and significant ( = 0.102, T = 3.054, p = 0.002), showing that H6b
is supported. As the p-value is above 0.050 (p = 0.098), the model shows a
mediation correlation by the mediating variables; hence, DMS and KMM exert
their influence through CDN in ensuring PM success. This study aims to
investigate the structural relationship between collaborative PM components and
project success by examining the hypothesized model, with all ten hypotheses
being supported.
Table 2 Hypotheses Testing
|
MODEL |
Beta |
Lower boundary 5% |
Upper boundary 95% |
Standard error |
T value |
P Value |
f2 |
Decision |
H1 |
PMM -> SCS |
0.486 |
0.377 |
0.587 |
0.065 |
7.506 |
0.000 |
0.300 |
Supported |
H2 |
DMS -> CDN |
0.254 |
0.144 |
0.355 |
0.065 |
3.903 |
0.000 |
0.077 |
Supported |
H3 |
KMM -> CDN |
0.236 |
0.124 |
0.351 |
0.070 |
3.363 |
0.000 |
0.061 |
Supported |
H4 |
CUL -> CDN |
0.427 |
0.320 |
0.533 |
0.065 |
6.586 |
0.000 |
0.203 |
Supported |
H5 |
CER -> SCS |
0.163 |
0.055 |
0.266 |
0.065 |
2.493 |
0.006 |
0.029 |
Supported |
H6 |
CDN -> SCS |
0.206 |
0.098 |
0.330 |
0.071 |
2.919 |
0.002 |
0.040 |
Supported |
Moderation | |||||||||
H4a |
CUL X DMS ->
CDN |
0.149 |
0.103 |
0.195 |
0.028 |
5.260 |
0.000 |
0.104 |
Supported |
H5a |
CER X PMM ->
SCS |
-0.128 |
-0.216 |
-0.041 |
0.054 |
2.394 |
0.008 |
0.044 |
Supported |
Table 3 Mediation Effects of
Coordination (CDN)
|
Indirect Beta |
Lower boundary 5% |
Upper boundary 95% |
Standard error |
T value |
P Value |
Decision | |
H6a |
DMS -> CDN -> SCS |
0.122 |
0.063 |
0.195 |
0.034 |
3.615 |
0.000 |
Supported |
H6b |
KMM -> CDN -> SCS |
0.102 |
0.048 |
0.177 |
0.033 |
3.054 |
0.002 |
Supported |
3.5. Discussion
Organizations with
higher PMM levels tend to deliver more successful projects (Mittermaier and Steyn, 2012; Cooke-Davies and
Arzymanow, 2003), supported by previous research (Gorecki,
2014; Mir and Pinnington, 2014; Prado et al., 2014; Ofori, 2013). Having the right
tools, best practices, and competent personnel enhances project management (Jaleel and Khan, 2013). This study also found a
direct relationship between PM success and project manager certification, with
certification amplifying PMM's impact. Project managers' competencies influence
organizational PMM (Ngonda and
Jowah, 2020),
with a growing trend of organizations considering certification for PM skills
development (PMI, 2020). Project complexity,
uncertainty, and structure determine team coordination levels (Strode, 2012). Construction industry
collaboration leads to task interdependencies (Blomquist
et al., 2010), emphasizing the importance of communication for
decision-making and information-sharing (Cohen et
al., 2007). In collaborative projects, success is a cohesive result,
with common goals and effective communication reducing work delays. A Community
of Practices and specialized social groups highlight collaboration.
Organizational culture and shared context guide employee behavior (Clarke and Cooper, 2000). Effective project teams
rely on trust, cooperation, and continuous teamwork, contrasting with traditional
construction projects marked by competitive relationships and ineffective
communication. In the construction industry, knowledge management systems are
crucial due to staff turnover, external expertise reliance, and inexperienced
project managers. Improving PM capabilities should go hand in hand with
enhancing knowledge management processes, including adopting digital solutions (Tereshko and Rudskaya, 2021). Capturing personal
knowledge for corporate use is essential. Social media platforms like YouTube and
Instagram are emerging alternatives for tacit knowledge sharing, warranting
further study. Coordination affects information-sharing willingness, yet
understanding cognitive structures in practical knowledge-sharing among team
members from different companies requires more research.
Project
Management (PM) is vital for enhancing project delivery and organizational
competitiveness. In construction, where collaboration among diverse parties is
crucial, this research explored factors contributing to project success. It
integrates Coordination Theory, Social Learning Theory, and Social Cognitive
Theory to explain PM success in collaborative construction projects,
emphasizing coordination's role in decision-making and knowledge management.
The study's primary contribution lies in its practical implications. Project
practitioners can apply its principles for effective project planning,
execution, and monitoring. By focusing on the six factors in the collaborative
PM model, project managers can enhance project success. Additionally, the study
underscores the significance of PM certifications in selecting project
managers. Data from Malaysian construction companies informed this study, but
broader industry diversity would enhance understanding and statistical power.
Furthermore, the study lacks specific project details like type, cost,
timeline, and resources, which could offer further insights into PM success
determinants. Including such project details may allow for additional analysis
and a better appreciation of how such aspects influence PM success. Generally,
as CPM become increasingly common, more studies are needed to better understand
it for better project outcomes.
Appreciation
is extended to the respondents, academics, and project management offices that
assisted in the data collection process, as well as to the reviewers for their
constructive feedback. Our gratitude also to the editors.
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