It is an empirical study of strategic management practices in the construction industry. It examines the dynamic capabilities paradigm within the context of the Indonesian construction industry. The characteristics of asset-capability combinations were found to be significant determinants of the competitive advantage of the Indonesian construction enterprises, and that such advantage sequentially contributes to organizational performance. In doing so, this study fills an important gap in the empirical literature and reinforces the dynamic capabilities framework’s recognition as a rigorous theory of strategic management. As the dynamic capabilities framework can work in the context of Indonesia, it suggests that the framework has potential applicability in other emerging and developing countries

1. university for the
real world
R
Strategic Management
Practices in Construction
Industry: A Study of
Indonesian Enterprises
FINAL SEMINAR
MUHAMMAD SAPRI PAMULU
BEng (Hons.), MEng (PM)
Presentation Outline
1. Introduction
2. Literature Review
3. Conceptual Model & Hypothesis
4. Research Methodology
5. Analysis and Results
6. Conclusions & Recommendations

2. Introduction - Background
• The construction 8.0%
7.7% 7.8%
industry is one of 7.5%
the key 7.0%
6.6%
contributors to 6.5%
most economies.
6.0%
– The gross domestic 5.5%
5.5%
product (GDP)
5.0%
– Investment
00
01
02
03
04
05
06
07
08
09
10
11
12
– Labour employed 20
20
20
20
20
20
20
20
20
20
20
20
20
Introduction - Background
• The promising
prospects but, many
local construction
firms have poor
performance and
low
competitiveness.

3. Introduction - Background
• Strategic management research related to
the Indonesian construction industry
remain scarce. This has potentially
become one of the factors hampering
efforts to guide Indonesian construction
enterprises.
Research Objectives
• Major aims is to construct a conceptual
model to enable Indonesian construction
enterprises to develop sound long-term
corporate strategy that generates
competitive advantage and superior
performance.

4. Research Objectives
Specific objectives:
• Explore a number of strategic factors and their
characteristics and inter-relationships that may
potentially affect the competitive advantage and
the performance of a firm.
• Construct a conceptual model that captures the
linkages with specific factors, competitive
advantage and performance
• Verify the characteristics and inter-relationships
of the factors and setting within the conceptual
model based on survey feedback.
Research Scope
• Specific focus on exploring the “Dynamic
Capabilities Framework” (Teece et al.
1997; Teece 2007).
• Limited to those Indonesian construction
enterprises belonging to the first-class
qualification (Grade 6-7)

5. Research Significance
• Filling the gap between theoretical
construct and practical evidence of
dynamic capabilities framework within the
construction industrial context
• Introduces the framework for Indonesian
construction firms which has never
adopted previously by others.
Literature Review
Strategy Paradigms
Competitive Strategic Resource- Dynamic
Conflict based Capabilities
Forces
Strategy Paradigms (Teece et al. 1997)
Strategy Paradigms (Teece et al. 1997)

6. Literature Review
Processes
New Paths
and
Positions
Positions Dynamic
(assets) Capabilities
Competitive
Prior Paths
Advantage
Dynamic Capabilities Framework (Teece et al. 1997, Teece 2007)
Dynamic Capabilities Framework (Teece et al. 1997, Teece 2007)
Research Gap
Strategy Research in Construction:
• Static vs. Dynamic Approach
• Single vs. Integrated Approach
• The standard vs. Multi-stage models
• Specific vs. All asset/capabilities
• Competitive advantage = Organisational
Performance
• Construction Industry in Developing
countries

7. Conceptual Model
Combination
+ +
Competitive Performance
of Asset- Advantage
capabilities
Conceptual Model
Conceptual Model
Research Hypothesis
H1
Value of
Asset-
capabilities
combination
H3
Competitive
Advantage Performance
H2
Rareness of
Asset-
capabilities
combination

8. Research Hypothesis
Competitive
A Advantage B
Asset- C’
Capabilities
Combination Performance
/ Dynamic
Capabilities
Research Methodology
Review of the
Provision of theoretical
mainstream
Stage 1 foundation and skeleton of the
Strategic
Literature model
Management
Review Theories
Provision of theoretical
foundation in the context of
Construction Industry
Dynamic Identify Critical
Capabilities variables in the Conceptual
Stage 2
Model Framework model
Development (Assets & Model
Capabilities)
Identify Critical variables and
interrelationships among
variables
Questionnaire
Survey Hypotheses
Stage 3 Test
Model (Sampling, Design,
Verification & Construct)
Conceptual Model Verification

9. Research Methodology
Why Survey?
• The type of research question (Yin 2003).
• 70% of empirical studies on dynamic capabilities
used surveys and case-based data sources
(Arend and Bromiley, 2009)
• Data Access to private firms
• Limited time resources (Cross Section)
Research Methodology
Sample Required
1. More than 84 cases (Kish, 1965)
2. More than 106 cases (Tabachnick and
Fidell, 2007)
Respondents
1. Contractors (AKI, GAPENSI, AKLI)
2. Consulting/Eng. Firms (INKINDO)

10. Research Methodology
Questionnaire Survey Construct *)
Questionnaire Survey Construct *)
Research Construct Scale/Measurement item
Performance 4 items: P1 – P4
Competitive Advantage 3 items: CA1 – CA3
Value of Asset-Capabilities 6 items: V1 – V6
Rarity of Asset-Capabilities 3 items: R1 – R3
Environmental Hostility 3 items: H1 – H3
Micro-foundation of dynamic 12 items: DC1 – DC12
capabilities
*) English, Bahasa & Japanese version
*) English, Bahasa & Japanese version
Response Analysis
• Response Rate
Number of Replies 120
Returned Undelivered 75
Total Number of Forms 503
Sent
Response Rate (%) 28,04 %
(delivered)
23,86 % (of total)

11. Response Analysis
Research Survey Author (year) Response Rate
Strategic management in Chinowsky, P.S., & 26.5% (106/400)
construction Meredith, J.E (2000)
Changing strategic management Price, A.D.F., Ganiev, 22.5% (45/200)
practice within UK construction B.V., & Newson, E.
industry (2003)
Strategic analysis of large local Cheah, C.Y.J, Kang, J. & 28.3% (85/300)
construction firms in China Chew, D.A.S (2007)
Strategic assets driving Wetyavivorn, 25.1% (258/1027)
organizational capabilities of Charoenngam, &
Thai construction firms Teerajetgul, W. (2009)
Strategic management practices in Kazaz, A. & Ulubeyli, S. 37.4% (52/139)
Turkish construction firms (2009)
Response Analysis
• Non-response Bias
Table ANOVA Result: Significant Group Response
Table ANOVA Result: Significant Group Response
Item Group Mean F-statistic
Performance Early 11.66 0.069+
Respondents
Late 11.77
Respondents
Employees Early 3.15 2.861+
Respondents
Late 3.70
Respondents
+ p>0.05
+ p>0.05

12. Construct Analysis
The item scales are suitably reliable and
valid.
• All Alpha coefficients are above the 0.7 threshold
(Nunnaly, 1978).
• All loading coefficients are above the 0.5 cut-off
(Tosi et al. 1973).
Construct Analysis
Table Reliability & Validity Analysis
Table Reliability & Validity Analysis
Construct Item Reliability *) Validity **)
Performance 4 .839 .773
Competitive Advantage 21 .936 .556
Value of Asset-Capabilities 42 .973 .525
Rareness of Asset-Capabilities 21 .955 .540
Environment Hostility 3 .734 .806
Dynamic Capability Processes 12 .872 .616
*) Alpha ;;N=120
*) Alpha N=120
**) Min. Loading ;;N=120
**) Min. Loading N=120

13. Results of Statistical Analysis
Table Regression Results for Hypothesis 1 and 2
Table Regression Results for Hypothesis 1 and 2
Technological Complementar Financial Reputational
Assets and y Assets and Assets and Assets and
Capabilities Capabilities Capabilities Capabilities
(Model 1) (Model 2) (Model 3) (Model 4)
Regression Stage Stage Stage Stage Stage Stage Stage Stage
Model 1 2 1 2 1 2 1 2
Environment (β) -.11 ns -.07 ns -.13 ns -.05 ns .02 ns .09 ns -.19* -.01 ns
Value (β) .55*** .48*** .45*** .36***
Rarity (β) .28* 15+ .30** .39***
ns Not sig., +p<0.1, **p<0.05, **p<0.01, ***p<0.001
ns Not sig., +p<0.1, p<0.05, **p<0.01, ***p<0.001
Results of Statistical Analysis
Table Regression Results for Hypothesis 1 and 2 (Cont.)
Table Regression Results for Hypothesis 1 and 2 (Cont.)
Structural Institutional Market Assets Average Assets
Assets and Assets and and and
Capabilities Capabilities Capabilities Capabilities
(Model 5) (Model 6) (Model 7) (Model 8)
Regression Stage Stage Stage Stage Stage Stage Stage Stage
Model 1 2 1 2 1 2 1 2
Environment (β) -.30*** -.17* -.19* -.05 ns -.21* -.02 ns -.20* -.04 ns
Value (β) .41*** .49*** .47*** .25***
Rarity (β) .19+ .24* .26* .45**
ns Not sig., +p<0.1, **p<0.05, **p<0.01, ***p<0.001
ns Not sig., +p<0.1, p<0.05, **p<0.01, ***p<0.001

14. Results of Statistical Analysis
Table Regression Results for Hypothesis 3
Table Regression Results for Hypothesis 3
Technological Complementar Financial Reputational
Assets and y Assets and Assets and Assets and
Capabilities Capabilities Capabilities Capabilities
(Model 1) (Model 2) (Model 3) (Model 4)
Hierarchical Stage Stage Stage Stage Stage Stage Stage Stage
Reg. Model 1 2 1 2 1 2 1 2
Environment (β) -.30*** -.28** -.26** -.22* -.28** -.30*** -.30*** -.23**
Competitive .19* .28** .32*** .33***
Advantage (β)
ns Not sig., +p<0.1, **p<0.05, **p<0.01, ***p<0.001
ns Not sig., +p<0.1, p<0.05, **p<0.01, ***p<0.001
Results of Statistical Analysis
Table Regression Results for Hypothesis 3 (Cont.)
Table Regression Results for Hypothesis 3 (Cont.)
Structural Institutional Market Assets Average
Assets and Assets and and Assets and
Capabilities Capabilities Capabilities Capabilities
(Model 5) (Model 6) (Model 7) (Model 8)
Hierarchical Reg. Stage Stage Stage Stage Stage Stage Stage Stage
Model 1 2 1 2 1 2 1 2
Environment (β) -.26** -.20* -.19* -.18* -.30*** -.23** -.30*** -.24**
Competitive .27** .27*** .36*** .31***
Advantage (β)
ns Not sig., +p<0.1, **p<0.05, **p<0.01, ***p<0.001
ns Not sig., +p<0.1, p<0.05, **p<0.01, ***p<0.001

15. Results of Statistical Analysis
Table Regression Results for Hypothesis 4
Table Regression Results for Hypothesis 4
Mediated Relationships 1 Sobel Aroian Goodman
Rareness of Reputational Asset-
capability combinations and 1.75 + 1.72 + 1.77 +
Performance
Rareness of Market Asset-
capability combinations and 2.45 * 2.40* 2.50 *
Performance
ns Not sig., +p<0.1, **p<0.05
ns Not sig., +p<0.1, p<0.05
Results of Statistical Analysis
Table Regression Results for Hypothesis 5
Table Regression Results for Hypothesis 5
Mediated Relationships Sobel Aroian Goodman
Transforming capability and
Performance are mediated by 2.66 * 2.62* 2.71 *
reputational competitive advantage
Transforming capability and
Performance are mediated by 1.74 + 1.69+ 1.79 +
institutional competitive advantage
Transforming capability and
Performance are mediated by 2.69 ** 2.65** 2.74**
market competitive advantage
Transforming capability and
Performance are mediated by 2.35 * 2.31 * 2.41 *
average competitive advantage
ns Not sig., +p<0.1, **p<0.05, **p<0.01
ns Not sig., +p<0.1, p<0.05, **p<0.01

16. Summary of Results
Hypotheses Findings
1. The value of asset-capability combinations Supported
that an enterprise exploits will have positive
relations to its competitive advantage
2. The rarity of asset-capability combinations Supported
that an enterprise exploits will have positive
relations to its competitive advantage
3. An enterprise’s competitive advantage will Supported
have a positive correlation to its
performance.
Summary of Results (cont.)
Hypotheses Findings
4. An enterprise’s competitive advantage will Partially
mediate the relationship between the value Supported
and rareness of the dynamic capability
combinations and its performance.
5. An enterprise’s competitive advantage will Partially
mediate the relationship between the Supported
dynamic capability combinations and its
performance

17. Discussion of Results
Model Evaluation – H1/H2
• All regression models fully support hypotheses 1 and 2 :
all asset-capability combinations fully exhibit the
characteristics of value and rarity
• All value variables contribute more to the competitive
advantage than rarity variables (except in the
reputational model)
• Reputational model records the largest contributor of
rarity, the technological model contributes the highest
value
Discussion of Results
Model Evaluation – H3
• Market and reputational asset-capability
combinations are major contributors in
determining the competitive advantage and
performance of Indonesian construction
enterprises
• The technological advantage model is the lowest
contributors to the performance.

18. Discussion of Results
Model Evaluation – H4 / H5
• Competitive advantage fully plays it mediation
role in the relationship between characteristics
of asset-capabilities combination and
performance of the firm.
• The results affirm previous studies that
competitive advantage and performance are two
distinct construct (Tang and Liou’s 2009;
Grahovac & Miller 2009; O’Shannassy 2008;
Newbert 2007).
Conclusions
1. This study provides empirical evidence in
support of the notion that a competitive
advantage via the implementation of
dynamic capability framework is an
important way by the construction
enterprise in improving its organisational
performance.

19. Conclusions
2. The value and rarity characteristics of
asset-capability combinations contribute
to the competitive advantage of the
Indonesian construction enterprises, and
that such an advantage, sequentially
contribute to its organisational
performance (Hypotheses 1,2,3).
Conclusions
3. This study offers practical evidence of
positively direct relationship between
characteristics of the enterprises’ asset-
capability, dynamic capability,
competitive advantage, and its mediating
effect on organisational performance
(Hypothesis 4 & 5).

20. Contributions & Implications
• For academics, this study fills an
important gap in the empirical literature.
• Hence, the present findings reinforce the
dynamic capabilities framework’s
recognition as a rigorous theory of
strategic management.
Contributions & Implications
• For practitioners, this study’s finding that
a competitive advantage stems from the
combination of valuable and rare assets
and capabilities may inform the way in
which managers make decisions to alter
their firms’ asset/capability bases.

21. Contributions & Implications
• This study suggest that importance
knowledge asset as micro-foundation for
dynamic capabilities.
• To sustain competitive advantage, it is
important that managers develop and/or
renewal dynamic capabilities by focusing
on elements of knowledge assets through
learning process.
Limitations & Recommendations
• Cross sectional -> Longitudinal studies
• Single respondents and method ->
Multiple respondents and methods
• Large firms sample and level -> different
company size and level of analysis
• Indonesia focus –> different emerging
countries

22. Publication
• Publication
– Conference Paper (Published)
• Pamulu, M. S, S. Kajewski and M. Betts (2009) Financial
management effectiveness of Indonesia's
construction state-owned enterprises. In: Infrastructure
Research Theme Postgraduate Student Conference 2009, 26
March 2009, Queensland University of Technology, Brisbane.
• Pamulu, Muhammad Sapri and Kajewski, Stephen L. and
Betts, Martin (2008) Financial ratio analysis of
Indonesian construction firms. In: Fourth International
Conference on Global Research in Business & Economics,
December 27-30, 2008, Bangkok, Thailand.
• Pamulu, Muhammad Sapri and Kajewski, Stephen L. and
Betts, Martin (2007) Evaluating financial ratios in
construction industry : a case study of Indonesian
firms. In: 1st International Conference of European Asian
Civil Engineering Forum (EACEF), 26 - 27 September 2007,
Jakarta, Indonesia.
Publication
• Publication
– Book Part (Published)
• Pamulu, M. S, S. Kajewski and M. Betts (2007) Management
of Information Technology. In Indonesian Construction
Firms, in Construction: Industry, Management and
Engineering. Ed. M. Abduh, 73-83. Bandung: ITB Press.
ISBN 979-3507-98-5
– Journal Paper (in progress)
• Pamulu, M. S, S. Kajewski and M. Betts (2010) Dynamic
capabilities framework in construction: A Study of
Indonesian Enterprises.
• Pamulu, M. S, S. Kajewski and M. Betts (2011) Micro-
foundations of dynamic capabilities: A Study of
Indonesian Construction Firms.

23. Acknowledgements
• Supervisory Team
– Professor Stephen Kajewski
– Professor Martin Betts
• Scholarships providers
– QUT
– BEE
Questions?
?