CLASSIFYING THE UNIQUE FEATURES OF MASS HOUSING
PROJECTS
E. Adinyira1
1
D. Ahadzie2
and
T. E. Kwofie1
Department of Building Technology, KNUST-Kumasi, Ghana
2
Centre for Settlement Studies, KNUST-Kumasi, Ghana
Corresponding Author: [email protected],
[email protected]
ABSTRACT
Projects are unique and share distinguishing characteristics from
one project to the other. Mass Housing projects (MHPs) are said to
differ significantly from 'one-off' traditional projects and thus
require unique managerial skills and efforts to deliver them
successfully. The unique nature of MHPs accounts for inefficient
communication among project teams resulting in considerable
amount of unproductive time, managerial ineffectiveness and
failures in its delivery especially in developing countries. Clearly
known features of MHPs will be a significant step towards evolving
frameworks, developing managerial competencies, concentration of
effort etc. to enhance its delivery. Through a questionnaire survey
and performance of means scores and Kruskall- Wallis analysis on
the survey data, this study revealed 3 unique physical features, 4
unique organisational features and 3 unique operational features of
MHPs. This work is a preliminary stage of an on-going investigation
into the impact of unique features of Mass Housing projects on
communication performance among project team in developing
countries towards managerial effectiveness.
Keywords:
Mass
management.
Housing,
project
features
and
project
INTRODUCTION
Construction projects possess unique physical, organisational and
operational features (PFs) that are critical and characterize their
planning, management and procurement (Manu et al., 2010; Favié
and Maas, 2008). Mass Housing projects (MHPs) share unique
features and particularities that make their management inherently
more difficult in comparison to 'one-off' traditional projects and
thus require distinct, unique management approach and skills in its
implementation (Thorpe et al., 1999; Ahadzie et al., 2007; Turner,
and Müller, 2003). The unique features of MHPs impose enormous
impact on the operational, organisational and managerial functions
during the construction process and thus lead to managerial
inefficiencies (Ahadzie et al., 2007). However, what constitute the
unique features of mass housing projects (MHPs) remains to be
interrogated and its implications for managing MHPs among the
project team is not well researched.
The findings presented represent the unique features of MHPs and
its implication for management.
CLASSIFYING THE UNIQUE FEATURES OF MHPs
Project Features (PF) or characteristics refer to the physical
attributes of the project or the items which define the technical
nature of the work (Cho et al., 2009). The lack of consistency and
agreement in approach in classifying construction projects remains
a critical challenge (Crawford et al 2005). Several authors have
sought to classify project features (PFs) from different perspectives
(Hernández, 2008; Kipp et al., 2008; Thorpe et al., 1999;
Kumaraswamy, 1997). The approach in determining the features
by assessing the related cost, size of project, number of
participants, volume of resources, managerial and construction
challenges have been the dominant criteria used (Kumaraswamy,
1997: Kipp et al., 2008). Though these approach offer useful
lessons for building efficient practices, it is important to stress that
the main study from which this emanates is towards managerial
effectiveness. Against this background, it can be said that in
management practice, operational and organisational actions are
the key components of effective management systems. A study by
Manu et al., (2010) affirmed that all construction projects share
distinguishing ‘physical, operational and organisational’ features
and these component features have implications for its
management and health and safety. It is also opined that the
organisational, physical and operational features have critical
influence on the communication, human resource, procurement
and management planning in the implementation of projects (Favié
and Maas, 2008). Crawford et al, (2005) further contend that
project management must rigorously be pursued to embrace the
unique particularities of projects life cycle models, methods,
planning, execution and organisation so as to increase delivery
success.
Dwelling on this and the study by Manu et al., (2010), their
approach was suitable to meet the objective of this study. Also,
drawing on the theoretical, management and practical context of
the Ghanaian construction industry as well as the situation in other
developing countries, this approach to the classification of the
project features remains very relevant and was thus adopted.
METHOD OF STUDY
The study was approached through extensive review of literature
by comparing ‘one-off’ traditional projects to Mass Housing projects
(MHPs). From this, 14-features perceived to be unique
characteristics of MHPs were identified (see Table 1.0). These 14features were modelled into a questionnaire survey which allowed
respondents with extensive experience in MHPs and project
management practice to determine their extent of agreement to
the features (variables). They were also to classify these features
into physical, operational and organisational features of MHPs.
The respondents were to determine their extent of agreement to
these features (variables) from a 5-point likert scale interpreted as:
1=Strongly Disagree, 2=Disagree, 3=Neutral, 4 Agree and
5=Strongly Agree. For a variable to be accepted as a unique
feature by the respondents, the mean score of the variable must be
greater than 3.0. The results of this are presented in Table 1.0. The
respondents were also to classify the variables by selecting 1.
Physical 2. Organisational and 3. Operational features. This is
presented in Table 3.0.
Table 1.0 Mean Scores of the Unique Features of Mass Housing
Projects (MHPs)
S/No.
Variables (Features)
1
2
3
4
5
MULTIPLE SITE FOR VARIOUS UNITS
MULTIPLE STANDARDIZED DESIGN-UNITS IN SCHEME
MULTIPLE ENVIRONMENTAL IMPACT
MULTIPLE GEOGRAPHICAL LOCATION FOR SCHEMES
MULTIPLE INTERDEPENDENT SUB-CONTRACTING
UNDER SCHEME
MULTIPLE ONE-OFF INFRASTRUCTURE
COMPLEX NETWORK OF PROCUREMENT SYSTEMS
MULTI-COLLINEAR REPEATED PRELIMINARY
ACTIVITIES ON UNITS
REPETITIVE TASKS ON STANDARDIZED UNITS
VIRTUAL TEAM PARTICIPANTS
COMPLEX CONSTRUCTION METHOD
COMPLEX NETWORK OF RISK FROM VARIOUS UNITS
COMPLEX NETWORK OF TEAM RELATIONSHIP
MULTIPLE DURATION FOR UNITS UNDER SCHEMES
6
7
8
9
10
11
12
13
14
Source: Field Data
Mean
Std. Deviation
4.42
4.28
1.83*
4.06
.554
.615
.811
.715
4.22
.637
1.64*
3.78
.961
.681
4.39
.549
4.14
4.17
1.67*
1.72*
4.44
4.03
.639
.655
.894
.815
.558
.654
Remarks
ACCEPTED
ACCEPTED
REJECTED
ACCEPTED
ACCEPTED
REJECTED
ACCEPTED
ACCEPTED
ACCEPTED
ACCEPTED
REJECTED
REJECTED
ACCEPTED
ACCEPTED
The respondents were assessed to have experience and
involvement in MHPs and also have knowledge in project
management practice through Research, Construction, Education
and Policy and/or Management from industry to classify the unique
features as the 1.'physical', 2. 'organisational' and 3. 'operational'
features of MHPs (see Table 2.0). Following the means core
analysis, frequency scores were used to classify the variables into
the various groups. Also, Kruskall- Wallis which is a non-parametric
version of ANOVA was used to assess the level of agreement
between the various groups of respondents on the variables given
the relatively small sample size of the respondents. This was done
to avoid the extreme violations to the assumption of normality and
the assumption of homogeneity of variance required on parametric
tests (e.g. ANOVA) due to the relatively small sample size used for
the study (Coates, 2001).
RESULTS AND DISCUSSIONS
Background of Respondents
In all, a total of 58 questionnaires were distributed to persons with
experience in Mass housing and project management with their
involvement in MHPs as either in Construction, Research, Education
or Policy and Management. In Ghana these domain of respondents
are key stakeholders in Mass Housing planning, development and
policy formulation and thus were useful for this study. Out of these,
36 questionnaires were received giving a response rate of 62% as
represented in Table 2.0.
From Table 2.0, on the experience (years of involvement) of the
respondents, 30 (83.2 %) out of the 36 respondent had experience
above 5years. This suggests that the respondents have
considerable experience in MHPs and Project Management and are
thus more likely to give accurate interpretation of the variables and
are thus suitable to contribute to the study. Also on the main
domain of the respondents: Research (28%), Educational (11%),
Construction (42%) and Policy and Management (19%), the
distribution reflects a fairly balanced source of contribution from
the key stakeholders and participants in housing development.
Table 2.0 Characteristics and Analysis of the
Respondents
VARIABLES
0-5 year
FREQUENCY
PERCENTAGE
6
16.7%
Years of Involvement in MHPs
Nature of Involvement in
MHPs
Total
6-10 years
11-15 years
16 years and above
Construction
Research
Educational
Policy & Management
10
12
8
15
10
4
7
36
27.8%
333.2%
22.2%
41.7%
27.8%
11.1%
19.4%
100%
Source: Field Data
CLASSIFICATION OF THE UNIQUE FEATURES OF MHPS
From Table 1.0, most variables had mean scores above 3.0 except
variables 3, 6, 11, and 12 with means less than 3.0 respectively.
These variables were thus rejected because the results indicate
weak agreement among the respondents as the unique features of
MHPs. This generated 10-unique features of MHPs with mean
scores greater than 3.0 suggesting a higher agreement among the
respondents. Also, drawing on the standard deviations, all variables
registered a standard deviation less than 1. This suggests that
there is high level consistency and low variability in the
interpretation and responses given by the respondents on these
variables and thus make the results credible and reliable (Coates,
2001; Field, 2005).
In classifying the unique features, respondents were asked to
select each of the 10-features as either the unique Physical,
Organisational and Operational features of MHPs. The result of this
is presented in Table 3.0. Through the use of frequency scores, the
variables with the dominant frequency among the three groups
were determined to belong to the group. From Table 3.0, for
example, variable PF1 scored 33 for physical and 3 for
organisational. It was seen that physical feature had the dominant
frequency and as such the variable PF1 was accepted as unique
physical feature of MHPs.
Physical Features
From Table 3.0 above, this group of features, PF1, PF2 and PF3
were classified as unique physical features based on the frequency
of choice by the respondents. Out of the 36 respondents 33
classified PF1, 32 classified PF2 and PF3 were 31 as the unique
physical features of mass housing projects.
Table 3.0 Classification of Unique Features of Mass Housing Project
(MHPs)
S/No.
PF1
PF2
PF3
PF4
PF5
PF6
PF7
PF8
PF9
PF10
VARIABLES
MULTIPLE SITES FOR VARIOUS
UNITS
MULTIPLE STANDARDIZED
DESIGN-UNITS IN SCHEME
MULTIPLE GEOGRAPHICAL
LOCATION FOR SCHEMES
VIRTUAL TEAM PARTICIPANTS
COMPLEX NETWORK OF TEAM
RELATIONSHIP
MULTIPLE INTERDEPENDENT SUBCONTRACTING UNDER SCHEME
COMPLEX NETWORK OF
PROCUREMENT SYSTEMS
MULTIPLE DURATION FOR UNITS
UNDER SCHEMES
MULTI-COLLINEAR REPEATED
PRELIMINARY ACTIVITIES ON
UNITS
REPETITIVE TASKS ON
STANDARDIZED UNITS
PHYSICAL
33
RESPONSES TO VARIABLES
ORGANISATIONAL
OPERATIONAL
3
0
REMARKS
PHYSICAL
32
2
2
PHYSICAL
31
5
0
PHYSICAL
3
31
2
1
30
5
0
30
6
0
29
7
2
10
24
ORGANISATION
AL
ORGANISATION
AL
ORGANISATION
AL
ORGANISATION
AL
OPERATIONAL
3
5
28
OPERATIONAL
0
7
29
OPERATIONAL
Source: Field Data
Organisational Features
Also, drawing from Table 3.0, PF4 (31), PF5 (30), PF6 (30) and PF7
(29) had the dominant frequencies (in brackets) for organisational
group and were thus classified as unique organisational features of
MHPs.
Operational Features
Again, three (3) out of the 10-unique features were classified as
unique operational features of MHPs from the study. These are
'multiple duration for units under schemes' (24), 'multi-collinear
repeated preliminary activities on units' (28) and 'repetitive tasks
on standardized units' (29).This is because the variables had
dominant frequencies (in brackets) for operational feature
classification.
LEVEL OF AGREEMENT ON THE CLASSIFICATION OF THE
UNIQUE FEATURES
It is extremely crucial to further assess the results in the
classification as the frequency score(s) does not seem to be a
rigorous test (Field, 2005). In accepting these classifications from
the results, it is extremely significant to assess the level of
agreement among and/or within the groups on the variables. The
Kruskal-Wallis test is essentially useful to compare means and/or
variance of more than two groups of samples on an independent
variable with relatively small sample size data (Coates, 2001; Field,
2005). The responses on the classification of the variables were
subjected to Kruskal-Wallis analysis. The results of the KruskalWallis are presented in Table 4.0. As indicated earlier, four main
groups of respondents were identified namely persons involved in
mass housing development from 1.Research, 2. Construction,
3.Education and 4. Policy and Management perspectives. The
Kruskal-Wallis test was performed at a 95% confidence level to
determine the extent of agreement on the variables among and
within the various groups.
Hypothesis
For this study, the null hypothesis was that the variation in the
interpretation and responses by all respondents was not significant
at 95% confidence level (H0:U = U0) and the alternative hypothesis
was that the variation in the interpretation and response by all
respondents was significant, i.e (Ha: U ≠ U0), where U0 is the
population mean of the various groups of respondents. In this
regard, the significance (i.e. p-value) of each independent variable
as displayed in Table 4.0 is analyzed.
From Table 4.0, the Sig. values represent the overall values
between the groups for the independent variables. In determining
the variations in the responses between the groups as significant or
otherwise, the Sig.-value (p) is critically examined (Coates, 2001;
Field, 2005). When the 'p'- value (sig-value) is less than 0.05
(p<0.05), it suggests that variations in the means being compared
between the sample groups is significant and thus suggests that
there is inconsistencies in the interpretations given to the variables
between the various groups. It also means there is low level of
agreement among the groups on the variables (Field, 2005).
Table 4.0 Test of Variation in Variables Interpretations among the
Groups of Respondent
Test Statisticsa,b
Variables
ChiSquare
2.758
IN 5.706
MULTIPLE SITE FOR VARIOUS UNITS
MULTIPLE STANDARDIZED DESIGN-UNITS
SCHEME
MULTIPLE
GEOGRAPHICAL
LOCATION
FOR 1.806
SCHEMES
MULTIPLE INTERDEPENDENT SUB-CONTRACTING .650
Df
3
3
Asymp.
Sig.
.431
.127
3
.614
3
.885
UNDER SCHEME1
COMPLEX NETWORK OF PROCUREMENT SYSTEMS
MULTI-COLLINEAR
REPEATED
PRELIMINARY
ACTIVITIES ON UNITS1
REPETITIVE TASKS ON STANDARDIZED UNITS
VIRTUAL TEAM PARTICIPANTS
COMPLEX NETWORK OF TEAM RELATIONSHIP
MULTIPLE DURATION
FOR
UNITS UNDER
SCHEMES
3.759
1.422
3
3
.289
.700
1.542
2.670
.280
3.865
3
3
3
3
.673
.445
.964
.276
a. Kruskal Wallis Test
3b. Grouping Variable: HOW ARE YOU INVOLVED IN HOUSING DEV'T: 1. RESEARCH,
2. CONSTRUCTION, 3. EDUCATIONAL, 4. POLICY AND MANAGEMENT
From Table 4.0 above, it can be deduced that, in comparing the
results between the groups of respondents, the values recorded
were all greater than 0.05 (p>0.05). This indicates that there is no
significant variation in the interpretations between the groups on
the variables. Therefore the variability or diversity between the
groups is not significant at a 95% confidence level and that there is
high level of consistencies, low variability and strong agreement in
the interpretations and responses given between the various
groups on the variables as the classified unique features of MHPs.
CONCLUSIONS
From the results, it can be concluded that generally there is no
significant variation in the responses between the groups and that
they gave consistent interpretation to the variables and thus makes
the classification of the variables (results) consistent and reliable
as presented in Tables 3.0 and 4.0. There is an indication that
generally the various groups agreed to the classification of the
variables resulting in three (3) physical features, four (4)
organisational features and three (3) operational features on MHPs.
The study also lend support to Manu et al., (2010) and Favié and
Maas, (2008) that construction projects share unique physical,
operational and organisational features and thus these features
significantly have implications on its procurement, communication
and resource planning and decisions.
REFLECTIONS ON THE FINDINGS
It was contended that, the unique nature of Mass Housing projects
(MHPs) should require unique skills and managerial approach in its
delivery (Ahadzie et al., 2007). This led to identifying the unique
features of MHPs by considering the critical components of
management practice (e.g. operations and organisation). It is
posited that the critical challenges inherent from the unique
particularities and nature of MHPs is managerial and
communication ineffectiveness among the project team (Enhassi,
1997; Enhassi et al., 2007; Thorpe et al., 1999). Against this
background, in classifying the unique features by this approach will
help to clearly engender the understanding of MHPs developers and
stakeholders to develop the needed unique skills, strategies and
managerial approaches that are crucial to perform the tasks
involved on these features. The findings are also crucial for project
teams to develop the appropriate competency behaviours
necessary for superior outcomes in the delivery.
Drawing on these findings, in an attempt to achieve the needed
managerial effectiveness on MHPs to register the delivery
successes in developing countries, the inherent impact of these
unique features on performance, management practices and
communication performance must rigorously be explored to enable
for a more pragmatic management framework as well as
communication performance models towards efficient delivery of
MHPs in developing countries.
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