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Entry Location and Entry Timing (ELET) Decision Model for International Construction Firms

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This paper proposes a model for entry location (EL) and entry timing (ET) decisions to guide construction firms in accessing targeted international markets. Neglecting to properly choose the right combination of the entry location and entry timing (ELET) decisions can lead to poor performance of the firms' international ventures. The sampling frame was from the Malaysian construction firms that have undertaken and completed projects abroad. Survey questionnaires sent to 115 firms registered with Construction Industry Development Board (CIDB) Malaysia, operating in more than 50 countries, achieved a 39.1 per cent response rate. Based on a comprehensive statistical analysis of survey data it was found that the mutually inclusive significant factors that influenced the firms' ELET decisions were: the firm's ability to assess market signals and opportunities, international experience, financial capacity, competencies and capabilities (project management, specialist expertise and technology), resources (level of knowledge based on research and development), experience in similar works, financial support from the home country banks, technical complexities of projects and availability of funds for projects. Hence, the present research builds on and extends the literature on the ELET decisions in a more integrated way.
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Entry Location and Entry Timing (ELET)
Decision Model for International Construction
Firms
Che Maznah Mat Isa, Hamidah Mohd Saman (Universiti Teknologi MARA,
Malaysia)
Christopher Nigel Preece (Universiti Teknologi Malaysia, Malaysia)
Abstract
This paper proposes a model for entry location (EL) and entry timing (ET) decisions to guide
construction firms in accessing targeted international markets. Neglecting to properly
choose the right combination of the entry location and entry timing (ELET) decisions can
lead to poor performance of the firms’ international ventures. The sampling frame was from
the Malaysian construction firms that have undertaken and completed projects abroad.
Survey questionnaires sent to 115 firms registered with Construction Industry Development
Board (CIDB) Malaysia, operating in more than 50 countries, achieved a 39.1 per cent
response rate. Based on a comprehensive statistical analysis of survey data it was found
that the mutually inclusive significant factors that influenced the firms’ ELET decisions were:
the firm’s ability to assess market signals and opportunities, international experience,
financial capacity, competencies and capabilities (project management, specialist expertise
and technology), resources (level of knowledge based on research and development),
experience in similar works, financial support from the home country banks, technical
complexities of projects and availability of funds for projects. Hence, the present research
builds on and extends the literature on the ELET decisions in a more integrated way.
Keywords: Entry location, entry timing, resource-based view, international markets, Malaysian
construction firms.
Introduction
Market expansion is one of the most critical business strategies made by firms to exploit
opportunities in international markets. These firms have gained access to foreign countries
using combinations of market entry strategies and have been gradually extending their
operations including Malaysian firms. However, the Engineering News Record (2013)
revealed none of Malaysian construction firms was listed in the top 250 international
contractors. Despite government encouragement through various plans such as the 10th
Malaysian Plan (10MP), 3rd Industrial Malaysia Plan (IMP) and Construction Industry
Malaysian Plan (CIMP), only 115 Malaysian construction firms have worked abroad
undertaking various construction projects, ranging from infrastructure, building and other
construction related projects (CIDB Malaysia 2013) since 1986. CIDB Malaysia records
about 35% of these firms have been operating within the ASEAN (Association of Southeast
Asian Nations), while more than 65% of the firms have penetrated the non-ASEAN. The
ASEAN include Malaysia, Singapore, Thailand, Vietnam, Laos, Myanmar, Cambodia,
Indonesia, Philippines and Brunei.
Within the international market domain, some of the main obstacles or barriers to entry
identified by previous researchers on Malaysian construction firms were trade and
investment barriers (Kaur & Sandhu 2014), insufficient information to access markets, lack of
financial, advanced technology and technical resources, lack of economies of scale and
scope, high market structure and competition against other foreign firms (Che Ibrahim et al.
2009). In addition, there were studies on other aspects of international Malaysian
construction firms such as the firms’ business locations (Abdul-Aziz & Sing-sing 2008; Che
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
35
Ibrahim et al. 2009), risks and challenges, competitive assets (Abdul-Aziz & Wong 2010)
and strength, weakness, opportunity and threat attributes (Mat Isa et al. 2012).
Research has shown that adopting suitable market entry strategies is crucial in a firm’s
decision to enter and subsequently to perform in international markets. Hence, many
researchers have proposed different plans for crafting the right combination of international
market entry strategies (Luo 1998; Luo & Peng1998; Yean et al. 2008; Chen & Orr 2009;
Polat & Donmez 2010; Lee et al. 2011). During market expansion, firms addressed three
major entry strategic questions specifically; which location to enter (EL), when to enter (ET)
and how to enter (EM) (Gaba et al. 2002). Similarly, Ekeledo (2007) contended that the
choice of which country to enter, when to enter and how to enter commits a firm to operating
on a given terrain and lays the foundation for its future international expansion. Even though
Huang and Sternquist (2007) have stressed that these interlinking decisions were among the
dilemmas that challenge firms during their international expansion, previous studies have
primarily addressed each entry decision in an isolated way as shown by Dacko (2002) on
ET, Somlev and Hoshino (2005) on EL and Chen and Messner (2009) on EM decisions.
Generally, solving these problems was attempted by the researchers based on the three
streams of research in international business and strategic management related to the EL,
ET and EM decisions respectively. Out of these three entry decision dimensions, most of
the classic and current literature regarding internationalization process has focused mainly
on the EM decision (Agarwal & Ramaswami 1992; Ekeledo & Sivakumar 1998; Tawanda
2006; Chen et al. 2007; Che Ibrahim et al. 2009; Chen & Chang 2011). In comparison, the
EL decision has received less attention from researchers in international business (Koch
2001a; Koch 2001b; Gallego et al. 2009; Gaston-Breton & Martín 2011). Even though the
importance of EL decision that emerged from the relationship between EL and EM decision
(Koch 2001a; Koch 2001b; Boeh & Beamish 2012) and segmentation in the process leading
to the identification of promising European target markets (Gaston-Breton & Martín 2011)
has been somewhat acknowledged, the ET decision dimension has been the most neglected
in international research areas (Luo & Peng 1998; Gao & Pan 2010). It is important to
understand the firms’ ET decision as claimed by Dacko (2002), where firms normally face a
particularly difficult decision when planning the right time to enter a foreign market. Thus,
the ET decision of foreign direct investment (FDI) also plays a critical role in multinational
corporations’ (MNCs) market entry strategy (Luo & Peng 1998). Green, Sedef and Bjorn
(2004) asserted that ET decision may affect the firm’s competitive position, especially on the
ability and competency of a firm to fulfill its objectives in order to attain or even sustain its
competitive edge.
Gallego et al (2009) argued that very few studies have attempted to establish relationships
between these three interlinking decisions in an integrative manner. Hence, the issues were
further addressed by establishing a model that illustrates the influence of ET on EM,
influence of ET on EL and influence of EL on EM into international markets (Gallego et al.
2009). However, this model only focused on the influence of dependent variables on each
other and only considered five dimensions of independent variables, that is; knowledge,
resources, product and process innovation, mimicry and situational uncertainty that have a
bearing on these three entry decisions and has ignored the influence of significant factors on
them. Thus there still remain a number of questions that need to be addressed, especially in
choosing a different combination of market entry decisions such as the EL and ET decisions
and determining the significant influential factors on these entry decisions to enter
international markets. Hence, the research on which this paper is based, aims to empirically
determine the factors influencing EL and ET decisions of construction firms in international
markets.
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
36
Literature Review
Factors Influencing Entry Location (EL) Decisions
Based on the reviews of factors related to EL decision, the following discussions are
grouped into four main factors: country, market/industry, firm and project. It was found that
most of the researchers have considered firm factors in their studies.
Country factors were amongst the important factors considered in the EL decision.
Pioneering research by El-higzi (1999) on Australian construction firms, focused on the risk
factors that influenced the firms’ decision to internationalize their operations, how the
interplay to influence the EL decision of a foreign country is related to country factors such
as the host country’s political stability, foreign exchange control, trade barriers, tax
discrimination, home government policy towards foreign investment, tax incentives with host
country, and home country market demand fluctuation. Further, AbdulAziz and Wong (2010)
evaluated a wider range of country factors influencing the Malaysian contractors in making
the go/no go decision to enter foreign markets. The factors include political stability, taxation
and incentive, law and order, host government delivery system/bureaucratic efficiency, host
government's integrity and transparency, host government's encouragement, language and
culture similarity and visa requirements. More recent literature focuses on more in-depth
research related to factors influencing the EL decision, however still dwelling on the similar
country factors namely; home country government support, well-established host country
institutions (Lu et al. 2014), attitude and intervention of host government, host government
control on licensing, restrictions and other FDI requirements (Mat Isa et al. 2013), travel time
and liability of distance (Boeh & Beamish 2012), host–home country linkages, host–home
country specific advantages (Buckley, Forsans & Munjal 2012), local density of home-
country affiliates (Zhu et al. 2012), and distance factors (Malhotra, Sivakumar & Zhu 2009).
The next important influential factor is related to the market factors, for example the market
size, high economic performance, competition intensity in host country, availability of
finance, intensity of competition in home country, and reliable and timely information (El-higzi
1999); market growth, rapid economic development, market size, business cost, financial
freedom, market openness, exchange rate, foreign competition, connection and degree of
business interaction (AbdulAziz & Wong 2010). Sakarya et al. (2007) claimed that traditional
market EL analysis relies on purely macroeconomic and political factors and fails to account
for the market’s dynamism such as growth and opportunities for future works. The findings
indicated that strong future market potential, manageable level of cultural distance,
supportive and developing local industry and positive customer receptiveness for foreign
products and business were very important in the firms’ market EL.
The firm factors being studied were the profit repatriation, desire to expand strategically,
firm’s strength in know-how (El-higzi 1999), capital requirement, local competition,
technological capability, geographical distance, trained workforce, ease of obtaining financial
funding (AbdulAziz & Wong 2010), enhanced organizational capabilities, accumulation of
experiential knowledge and capabilities based on prior entry experience (Lu et al. 2014),
strong financing capacity, experience in similar works (Mat Isa et al. 2013), prior experience
in the local market (Zhu et al. 2012), resources of firms, firm internationalization market-
seeking and labor-seeking strategies (Jain 2010), financial strength, project management
skill, and international network (Che Ibrahim et al. 2009).
Relative to the resource based view, firm factors such as resources and capabilities are the
main elements being investigated by the majority of the researchers. These firm resource-
based capabilities are the internal factors that shape the firm’s competitive advantages
commonly used during their international operations. It started with the firms’ vision and
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
37
mission with the desire to expand using market and labor seeking strategies. Targeting
profit repatriation and using the firm’s know-how and project management skill, international
network, accumulated experiential knowledge and enhanced organizational capabilities the
firms were encouraged to penetrate the highly risky international markets. However, based
on the project capital requirements, they must have strong financing capacity or be able to
get financial funding in order to compete and sustain their place in the international markets.
In addition, some of the project factors based on previous research were project funding,
project nature and future potential (El-higzi 1999), infrastructure and other related and
supporting industries (Abdul-Aziz and Sing-sing 2008), and the availability of project funds
(Mat Isa et al. 2013a). Hence, to address the gap, the research question in this paper was
whether and how significantly these factors influenced the firms’ EL decision.
Factors Influencing Entry Timing (ET) Decisions
This section further analyzes the influential factors related to ET decisions based on
previous studies. The application of the resource-based view is also incorporated in the firm
factors discussion, since the majority of the researchers have also considered these factors
in their studies.
Previously, a number of researchers have attempted to determine influential factors for ET
decisions for international firms under various industries. Under country factors, in their
study, Lilien and Yoon (1990) have included the competition elements such as the entry
competition and demand potential. The reviews revealed that the researchers also focused
on the market factors such as the demand potential, market evolution and marketing rivalry
(Lilien & Yoon 1990), foreign market stability, and degree of globalization in the industry
(Petersen & Pedersen 1999), environmental conditions, namely market dynamism and
market rivalry (Villaverde & Ortega 2007) and type of industry (Tsou, Yu & Lin 2009). In
addition to the recent literature, Stevens and Dykes (2013) studied the influence of the
country factors namely; home country cultural attributes, host country’s political environment.
Next, under the firm factors Stevens and Dykes (2013) focused on the firms’ high
performance orientation, high power distance and high uncertainty avoidance cultures.
Other firm factors studied by other researchers are international experience, level of
knowledge, research & development, project management, specialist expertise and
technology, financing capacity (Mat Isa et al. 2013b), managerial, marketing, technological
capabilities (Villaverde & Ortega, 2007), firms’ resources and capabilities (Lieberman &
Montgomery 1998), whether the company is producing manufactured goods or services,
foreign market entry motives, company size, similar experience with foreign markets entered
(Petersen & Pedersen 1999), firms’ product technology strategies for example in offering
products based on the technology standard and products incorporating the latest technology,
pre-entry experience (Bayus & Agarwal 2007), firm size, research and development intensity
(Tsou, Yu & Lin 2009; Lilien & Yoon 1990); firm factor (research and development
competition) and also the project factor (product competition).
The earlier study by Lieberman and Montgomery (1998) explained that the firms’ ET
decisions were subject to the firms’ internal factors, whether they are strong and confident
enough to be the early movers or whether they have to wait-and-see to be the late movers.
Hence, larger firms with strong tangible assets, having greater access to financing, were
found to enter the foreign market early (Petersen and Pedersen 1999). In his review, Peng
(2001) claimed that the resource-based view (RBV) of the firm has influenced the theoretical
perspective in international business research. The RBV indicates that firms’ resource
capabilities allow the firms to achieve sustainable competitive advantages and successful
firms usually possess proprietary assets to sustain better performance. Hence, the previous
studies show that firms with strong resource capabilities and competencies have significantly
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
38
influenced that firm’s ET decision into an international market. Thus, these firms are likely to
be among the early movers entering into a particular market, since their competitive
advantages will offset the uncertainty and information disadvantages that are most profound
for international entrants (Delios, Gaur & Makino 2007). In addition, Bayus and Agarwal
(2007) indicated that ET decision plays an important role related to the pre-entry experience
and firm survival, while Tsou, Yu and Lin (2009) later found that in addition to the industry
type and host country environment, the firm size, and research and development intensity
were the factors influencing the ET decision for Taiwanese firms in China. Hence, firms with
competitive advantages entered early and performed better as compared to the late
entrants. However, low growth market was also found to influence the firms to enter late into
foreign markets.
In summary, the internal factors that commonly influence the ET decision are related to the
firm’s resource capabilities in terms of size, financial, physical and intangible assets. These
are the manifestation of the firm’s strengths in terms of skills, competencies and competitive
advantages in order to be sustainable and perform in the international markets. On the other
hand, external factors found to relate to the international market environment were identified
as competition, market growth, uncertainties and information. Hence, to address the gap, the
research question in this paper was whether and how significantly these factors influenced
the firms’ ET decision.
A Proposed Model of Factors influencing ELET Decisions
The evaluation on the literature reviewed has identified forty-four (44) factors influencing the
EL and ET decisions related to both construction and non-construction businesses in
international markets. These are the independent variables representing the following:
country factors such as attitude and intervention of host government, similarity of host
country/market (social/cultural/religious), proximity to host country, anticipated non-economic
risks (political, technology) and economic risks (currency fluctuations, interest rates), other
foreign competitors in the host country, promotion of export efforts of home government,
financial support from home country banks, trade relationship between two countries,
diplomatic relationship between two countries, host government control: licensing,
restrictions and other foreign direct investment requirements, market/industry factors such as
market profit potential/attractiveness, market intensity on competition, product/service
market growth, market entry barriers, availability of innovative and entrepreneurial
opportunities, and construction demands related to finance, labor, material, transport,
utilities, firm factors such as, size, ability to assess market signals and opportunities, level of
international experience, long-term and strong management strategic orientations/objectives,
superior management & organizational dynamic capabilities, financing capacity,
competencies in project management, specialist expertise and technology, resources based
on level of knowledge and research & development, risk management attitude, quality
management of product, service, human resource, profit targets (return on
investment/sales/assets), level of knowledge and international experience, uncertainty
avoidance, international business network for example relationship with foreign partners,
product differentiation with strong brand name, reputation and good track record /competitive
advantage, and project factors such as, project size, project types (building, manufacturing),
technical complexity of projects, type of clients (public, private), availability of funds for
projects, contract types /procurement methods: lump sum, cost-plus, D&B, experience in
similar works, existence of strict time and quality requirements, and availability of
partner/alliance.
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
39
The proposed ELET decision model will be developed by identifying the common shared
factors significantly influencing both EL and ET factors using mutually inclusive principle as
shown in Figure 1.
Figure 1 Conceptual framework for EL and ET decision model (Developed for this study)
The proposed ELET decision model consists of two dependent variables which are the EL
and ET decisions adopted by the firms. These are the variables of primary interest, the
variances in which are attempted to be explained by the forty four factors as independent
variables identified from the literature review.
Research Method
The study adopts an exploratory approach utilizing a quantitative method. This approach is
a common strategy in business and management research and particularly suitable when
the aim is to understand the “what” and “how” factors influencing the firms’ EL and timing
decisions (Saunders, Lewis & Thornhill 2009). The target population was chosen from the
cross-section of Malaysian construction firms that had undertaken and completed projects in
international markets. However, an official registry that contains the total number of
construction firms in international markets is not available. Hence, the sampling frame of the
population was based on CIDB (2103) record with 115 firms registered as international
players operating in more than 49 countries. Their involvement in international projects
includes various sectors such as building, infrastructure, branches of engineering,
mechanical and electrical, power transmission and plant, and oil and gas. The unit of
analysis is an individual construction firm. Thus, the target respondents from these firms
were the general managers, senior managers, project managers, assistant project
managers, project engineer, project planners, contract managers and project coordinators;
those directly involved who have acquired international experience in handling construction
projects in international markets.
This paper is part of on-going research based on a section of the survey questionnaire to
seek the experts’ opinion on their firms’ EL and ET decisions. The purposes of the
questionnaire are: (1) to determine individually the significant factors influencing EL and ET
decisions and, (2) to determine the commonly shared or mutually inclusive significant factors
that the respondents considered in their EL and ET decisions using factor analysis from
each model. Hence, the respondents were asked to select their firms’ (a) preferred ET
(early mover/late mover) and (b) international business locations (a list of countries was
given as recorded by CIDB Malaysia and respondents were encouraged to state other non-
listed locations). The preceding question seeks the experts’ opinions on the significant level
of 44 factors on their EL and ET decisions. The level of significance of influence for each
opinion was measured using a 5-point Likert scale (1: Not critical; 2: A little critical; 3:
Critical; 4: Very critical; and 5: Extremely critical).
ASEAN
EL Decision
Factors
Non-ASEAN
Early Mover
ET Decision Late Mover
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
40
The purpose of each analysis, results and discussion on factors influencing the EL and ET
decisions are outlined in the next section. The binary logistic regression analysis dependent
(EL and ET decisions) was used while factor analysis was carried out to measure the
independent variables (factors influencing the EL and ET decisions). Other statistical
analysis techniques such as descriptive analysis validity test, reliability test, normality test,
correlation were adopted.
Results and Discussions
Based on a total of 115 firms, 45 firms responded giving a 39.1 percent response rate. In
order to increase the rate of response, personal distribution, follow-up letters and phone calls
were carried out. Hence, the response rate for this study is acceptable since most of the
surveys done in Malaysia generated a rate between 10 to 20 per cent for example, 10.8%
from previous studies by Ahmed et al. (2002), 19.8% from Abdul Aziz, Wong and Awil (2008)
and 12.1% by Abdul-Aziz and Awil (2010).
The respondents’ designation indicates a diverse background is required during international
operations. In general, the respondent’s designation varied from being managing director
(2), technical director (2), vice president (2), general manager (1), senior project manager
(3), project manager (5), project/architecture coordinator (2), senior project engineer (1),
project engineer (3), design/civilvengineer (9), contract manager (3), quality/financial/human
resource manager (3), quantity surveyor (2), project planner(1) and other managerial
positions (6). Results demonstrate that 25% of the respondents have more than 10 years of
international experience, 29% having experience between 5 to 10 year and the rest (47%)
have between 1 to 4 years of experience. Hence, the respondents have the required
international related construction background to participate and give reliable opinions.
They were asked to choose their international business locations based on a list of 43
countries adopted from the CIDB record (2013) which are: Algeria, Australia, Bangladesh,
Cambodia, China, Egypt, Hong Kong, India, Indonesia, Iran, Iraq, Ireland, Japan, Kuwait,
Libya, Maldives, Mauritius, Myanmar, Mongolia, Morrocco, Nepal, Nigeria, Oman, Pakistan,
Philiphines, Qatar, Saudi Arabia, Seychelles, Singapore, Spain, South Africa, South Korea,
Sudan, Syria, Sri Lanka, Taiwan, Thailand, Turkmenistan, United Arab Emirates, United
Kingdom, Vietnam and Yemen and were also encouraged to state other non listed locations.
The results indicate about 88% similarity in terms of countries recorded by CIDB and the
countries penetrated by the firms from this study. However, eight (8) additional countries
were found in this study namely; Austria, Botswana, Brunei, France, Germany, Tobago,
United States of America and United Kingdom which were not in the CIDB list.
To ensure consistency and a proper comparison with the CIDB Malaysia (2013) report on
the international business locations of Malaysian construction firms, this study also grouped
the countries under ASEAN and non-ASEAN. This classification alllows the measure of the
EL and ET decisions as binary variables as required by the logistic regression analysis.
Using descriptive analysis, it was found that about 73% of firms (33 firms) have chosen the
non-ASEAN while the other 27% (12 firms) have chosen the ASEAN. The findings in this
study are supported by the CIDB Malaysia (2013) statistics where more than 65% of the
construction firms have penetrated non-ASEAN.
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
41
Measurement of Independent Variables
Validity Tests
In order to determine the suitability of data using factor analysis, two main issues were
validated, accordingly; (a) a sampling adequacy issue and (b) the strength of the correlations
among the independent variables (factors) using Kaiser-Mayer-Olkin (KMO) measure of
sampling adequacy (MSA) and Bartlett's tests of Sphericity (Pallant 2011). William and
Brown (2012) stated that KMO static varies between 0 and 1 and recommend accepting
values greater than 0.5, which indicates that the sample meets the fundamental
requirements for factor analysis. In addition, the Bartlett’s test of Sphericity should be
signicant (p < 0.05) for factor analysis to be considered appropriate. Table 1 shows the
values for KMO MSA and Bartlett’s test of Sphericity for factors influencing the EL and ET
decisions.
Table 1: KMO Measure of Sampling Adequacy and Bartlett’s Test of Sphericity for EL and
ET Decisions
Entry Decisions EL Decision ET Decision
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.727 0.779
Bartlett's Test of Sphericity Approx. Chi-Square 553.194 725.122
Df 136 190
Sig. .000 .000
The results show that the KMO MSA values are 0.727 and 0.779 for EL and ET decisions,
respectively; both are greater than 0.5 (Williams & Brown 2010). Further, the results for
Bartlett’s test of Sphericity have given χ2 (136) = 553.194, p < 0.001, and χ2 (190) = 725.122,
p < 0.001, for EL and ET decisions, respectively which indicate that correlations between
items were sufficiently large and strong for PAF.
Total Variance Explained
In this study, the data reduction process follows three criteria. First, Kaiser’s criteria that only
factors with eigenvalue greater than one are retained. Second, factors with just one item
were excluded from the analysis and thirdly, the cumulative percent of variance extracted
are presented. Table 2 and Table 3 show the results for the EL and ET decisions
respectively, extracted from the PAF analysis.
The results from Table 2 for EL decision reveal the presence of five (5) components with
eigenvalue exceeding 1. These five factor components explain a total of 71.032% of the
variance contributed by component 1 (42.763%), component 2 (9.182%), component 3
(7.865%), component 4 (6.527%) and component 5 (4.695%).
Table 3 also reveals the presence of five components with eigenvalue exceeding 1 for ET
decision. These five components explained a total of 72.186% of the variance contributed
by component 1 (48.247%), component 2 (7.984%), component 3 (6.339%), component 4
(5.212%) and component 5 (4.404%).
Thus, results for the EL and ET decisions demonstrate a good cumulative percentage of
variance of 71.032% and 72.186%, respectively; both are well above the common
percentage of the explained variance for humanities research which is commonly in the
range of 50% up to 60% (Hair et al. 1995).
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
42
Table 2: Total Variance Explained for EL Decision
Facto
r
Initial Eigenvalues Extraction Sums of Squared
Loadings Rotation Sums of
Squared Loadings a
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
Total
1 7.556 44.449 44.449 7.270 42.763 42.763 5.561
2 1.838 10.810 55.260 1.561 9.182 51.945 3.152
3 1.609 9.464 64.724 1.337 7.865 59.810 3.504
4 1.357 7.981 72.705 1.110 6.527 66.337 3.129
5 1.090 6.414 79.119 0.798 4.695 71.032 2.728
6 0.773 4.549 83.668
7 0.566 3.327 86.994
8 0.496 2.916 89.910
9 0.426 2.507 92.417
10 0.318 1.871 94.288
11 0.241 1.418 95.706
12 0.201 1.183 96.889
13 0.168 0.988 97.877
14 0.133 0.785 98.662
15 0.109 0.640 99.302
16 0.070 0.410 99.712
17 0.049 0.288 100.000
Extraction Method: Principal Axis Factoring.
a. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.
Table 3: Total Variance Explained for ET Decision
Facto
r
Initial Eigenvalues Extraction Sums of Squared
Loadings Rotation Sums of
Squared Loadingsa
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative % Total
1 9.920 49.599 49.599 9.649 48.247 48.247 4.744
2 1.842 9.212 58.811 1.597 7.984 56.231 4.506
3 1.523 7.615 66.427 1.268 6.339 62.571 4.830
4 1.327 6.634 73.061 1.042 5.212 67.782 5.733
5 1.166 5.828 78.889 0.881 4.404 72.186 5.883
6 0.738 3.690 82.578
7 0.604 3.019 85.597
8 0.529 2.645 88.243
9 0.415 2.074 90.316
10 0.340 1.699 92.016
11 0.291 1.453 93.469
12 0.268 1.340 94.809
13 0.236 1.178 95.987
14 0.193 0.965 96.952
15 0.170 0.848 97.800
16 0.141 0.707 98.508
17 0.121 0.606 99.113
18 0.088 0.438 99.552
19 0.058 0.292 99.844
20 0.031 0.156 100.000
Extraction Method: Principal Axis Factoring.
a. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.
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43
Factor Analysis: Principal Axis Factoring Analysis (PAF)
Factor analysis involves factor extraction and rotation methods. The extraction methods that
are commonly used in the published literature for both exploratory factor analysis (EFA) and
confirmatory factor analysis (CFA) are the principal component analysis (PCA) and principal
axis factoring (PAF) also known as factor analysis (FA)(Williams & Brown 2010).
Firstly, following the PCA analysis, PAF was conducted for comparison and assessment for
best fit as suggested by William and Brown (2010). According to Williams and Brown
(2010), PAF is more correct theoretically but more complicated than PCA. In other words,
whichever rotated solution produces the best fit and factorial suitability, both intuitively and
conceptually, should be used. The difference between these analyses lies on the
communalities used where in PCA, the communalities are assumed to be 1, assuming that
the total variance of the variables can be accounted for by means of its components and
hence, there is no variance. However, in the PAF method, the initial communalities are not
assumed to be 1, hence the variables do not account for the 100 percent of the variance.
Since, the sample size in this study (45 respondents) is less than 100; communalities above
0.60 were sought.
The second step is factor rotation method, in which the interpretation and naming of factors
or component are carried out by altering the pattern of the factor loadings. Instead of
orthogonal, the oblique rotation with direct-oblimin was used in this study. Oblique rotations
assume that the factors are correlated. Oblique rotation is the best method to use in factor
rotation as it is also looks at the correlations among the factors. The results are presented in
pattern matrix and used later in the interpretation of factors.
In this study, the PAF was empirically found to offer best fit and used in this study to analyze
the responses to the forty four (44) factors used in the questionnaires. Table 4 and Table 5
show the results from the PAF used as the data reduction technique using direct-oblimin with
Kaiser Normalization for EL and ET decisions respectively.
Table 4 reveals a five-factor solution which has resulted in 17 items with factor loadings
above 0.50 with eigenvalues above Kaiser’s criterion of 1, thus in combination explained
71.032% of variance. According to Williams and Brown (2012), for study that has sample
size less than 100 the communalities values must be above 0.6. The results indicate that all
seventeen (17) factors have values above 0.6 hence there is no violation of the assumption
of communalities. The good communalities shown in the last column indicate that the factor
analysis is suitable even though the sample size for this study is only 45 respondents
(Williams & Brown 2010). The five components extracted were grouped as: (1) firm factor;
(2) country factor; (3) market factor; (4) project factor; and (5) management factor.
Table 5 also reveals a five-factor solution which has resulted in 20 items with factor loadings
above 0.50 with eigenvalues above Kaiser’s criterion of 1, thus in combination explained
72.186 percent of variance. The results indicate that all 20 factors have values above 0.6
hence there is no violation of the assumption of communalities. The five components
extracted were grouped as: (1) firm factors; (2) project factors; (3) performance factors; (4)
management; and (5) market factors.
Reliability Test
Once the factors were obtained and interpreted, the tests for consistency and stability took
place by conducting reliability test using Cronbach's alpha. It is a reliability coefficient to
indicate the extent to which all item in a set are positively correlated to one another and is
obtained by computing the average inter-correlation among all items. The closer Cronbach’s
alpha is to 1, the higher the internal consistency reliability. In general, reliability less than
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44
0.60 is considered poor, a result of 0.70 is acceptable and above 0.80 is considered as good
(Sekaran & Bougie 2013).
Results of the reliability test for EL decision in Table 4 demonstrates that the components
extracted from this analysis show high reliability of internal consistency where the value for
each component exceeds 0.70; Firm factors (α= 0.912); country factors (α= 0.792); market
factors (α= 0.812); project factors (α= 0.819) and management factors (α= 0.791). Moreover,
all items when combined indicate a very good overall internal consistency (α= 0.917).
Results for ET decision in Table 5 show that the components extracted from factor analysis
indicate high reliability of internal consistency where the value for each component exceeds
0.70; Firm factors (α= 0.873); Project factors (α= 0.823); Performance (α= 0.905);
Management factors (α= 0.890) and Market factors (α= 0.862). In addition, all items when
combined indicate a very good overall internal consistency (α= 0.945). Hence, the factors for
EL and ET decisions have high reliability of internal consistency.
Table 4: Factor loadings using Principal Axis Factoring (PAF) for EL Decision
Component Factor Loading Commu-
nalities
1234 5
Firm Factors
Strong competencies (project management,
specialist expertise and technology)
0.823 0.812
Strong financing capacity 0.760 0.620
Strong resources : (Level of knowledge and
Research & Development)
0.750 0.712
Experience of firm in similar works 0.667 0.715
Management quality (product, service, human
resource)
0.611 0.621
Availability of funds for projects 0.595 0.777
International business network : Strong
relationship with foreign partners in host
countries
0.580 0.647
Country Factor
Trade relationship between two countries 0.933 0.932
Diplomatic relationship between two countries 0.761 0.699
Financing support of home country banks 0.568 0.671
Market Factor
Product/Service market growth 0.863 0.801
Market entry barriers 0.750 0.685
Project Factor
Firm ability to assess market signals &
opportunities
-0.786 0.687
Firm level of international experience -0.686 0.755
Technical complexity of projects -0.569 0.699
Management Factor
Uncertainty avoidance 0.665 0.686
Long-term and strong management strategic
orientation/objectives
0.620
0.756
Eigenvalues 7.556 1.838 1.609 1.357 1.090
% of Variance 42.763 9.182 7.865 6.527 4.695
Cumulative of Variance% 42.763 51.945 59.810 66.337 71.032
Cronbach’s Alpha (n) 0.912
(7)
0.792
(3)
0.812
(2)
0.819
(3)
0.791
(2)
Overall items Cronbach’s Alpha (n) 0.917 (17)
Extraction Method: Principal Axis Factoring
Rotation Method: Direct-Oblimin with Kaiser Normalization
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45
Table 5 Factor loadings using Principal Axis Factoring (PAF) for ET Decision
Normality Test
A univariate analysis known as Skewness and Kurtosis was used to determine whether the
data is normally distributed or not. The distribution is considered normal if the value lies
between -1 and +1. In addition, the normal quantile-quantile (Q-Q) plot method was also
used. If the majority of values (smaller dots) lie on the straight line in the plot, the data are
approximately normally distributed. In order to meet the assumption of normality, the
Skewness and Kurtosis statistics was performed on the factors for the EL and ET decisions.
For the EL decision factors, the results revealed the followings; Firm factors (Skewness = -
0.809; Kurtosis = 0.298), country factors (Skewness = -0.192; Kurtosis = -0.745), market
Component Factor Loadings Commu-
nalities
1 2 3 4 5
Firm Factors
Firm’s international experience 0.795 0.686
Firm’s resources: Level of knowledge and
Research & Development
0.622 0.768
Firm’s competencies: Project management,
specialist expertise &technology
0.592 0.703
Firm’s financing capacity 0.515 0.749
Project Factors
Performance: Increase level of knowledge and
international experience
0.783 0.775
Availability of funds for projects 0.588 0.731
Technical complexity of projects 0.505 0.742
Performance factors
Project size -0.859 0.855
Good track record and competitive advantage -0.806 0.844
Type of clients: public, private -0.544 0.845
Firm’s reputation -0.505 0.682
Management factors
Financing support of home country banks -0.796 0.704
Experience of firm in similar works -0.746 0.840
Existence of strict time limitations -0.706 0.821
Superior management and organizational
capabilities
-0.548 0.715
Market Factors
Construction demand: Finance, labor, material,
transport and other utilities
0.805 0.748
Availability of partner/alliance 0.625 0.677
Attitude and intervention of host governments 0.598 0.666
Similarity of host country/market: social, cultural,
religions
0.575 0.513
Firm’s ability to assess market signals and
opportunities
0.558 0.624
Eigenvalues 9.920 1.842 1.523 1.327 1.166
% of Variance 48.247 7.984 6.339 5.212 4.404
Cumulative of Variance % 48.247 56.231 62.571 67.782 72.186
Cronbach’s Alpha (n) 0.873
(4)
0.823
(3)
0.905
(4)
0.890
(4)
0.862
(5)
Overall items Cronbach’s Alpha (n) 0.945 (20)
Extraction Method: Principal Axis Factoring
Rotation Method: Direct Oblimin with Kaiser Normalization
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46
factors (Skewness = -0.772; Kurtosis = 0.4770, project factors (Skewness = -0.698; Kurtosis
= 0.030), and for management factors (Skewness = -0.046; Kurtosis = -0.680). While for the
ET decision factors, the results are as follows; the firm factors (Skewness = -0.360; Kurtosis
= -0.461), project factors (Skewness = -0.001; Kurtosis = -1.092), performance factors
(Skewness=-0.524; Kurtosis= -0.659), management factors (Skewness = -0.115; Kurtosis = -
0.431) and market factors (Skewness = -0.365; Kurtosis = -0.223).
The descriptive statistics shows that all variables are normally distributed since the values of
Skewness and Kurtosis coefficients are in the range of ±1.0 with majority of values lying on
the straight line in the Q-Q plots. Hence, all factors influencing the EL and ET decisions are
considered approximately normally distributed.
Omnibus Tests of Model Coefficient
The omnibus test indicates an overall significant of the influential factors on the EL and ET
decisions. For EL decision, the result shows that the Omnibus test of Model Coefficients is
significant; [χ² (5) = 22.207, p < 0.001]. While for the ET decision, the result reveals that the
Omnibus test of Model Coefficients is also significant; [χ² (5) = 21.792, p < 0.05]. Therefore,
the models have a good set of independent variables for both EL and ET decision factors.
Model Summary Using Cox and Snell R 2 and Nagelkerke R2
The Cox and Snell R2 and Nagelkerke R2 are statistics that provide an indication and
quantify the 2R proportion of explained “variation” in the logistic regression model.
Precisely, for the EL decision, the value of Cox and Snell R2 is 0.418 which reveals about
41.8% of the variation in the outcome variable is explained by the model. Likewise, the
Nagelkerke R2 is 0.642 which indicates that about 64.2% of the variation in the outcome
variable is explained by the logistic regression model. Similarly, for the ET decision, the Cox
and Snell R2, and Nagelkerke R2 suggest that between 43.6% and 59.6% of the variability is
explained by the logistic regression model.
Goodness-of-Fit of the Model using Hosmer and Lemeshow Test
Using Hosmer-Lemeshow Goodness of Fit Test, a good fit is indicated by a significant value
more than 0.05. For the EL decision, results indicate that the data supports the model with
χ2 (8) = 5.734 with a significance level of 0.263 (p> 0.05). While for the ET decision, the
results also indicate that the data supports the model with χ2 (7) = 8.857 with a significance
level of 0.263 (p> 0.05). Thus, both results for the EL and ET decisions indicate sufficient
evidence to claim that the model is worthwhile and fits the data adequately.
Assumptions of Logistic Regression Model (LRM)
Prior to performing the logistic regression analysis, a correlation statistics and outlier
diagnosis were prepared to investigate possible signs of multi-collinearity and the presence
of outliers. The following sections discuss on the multi-collinearity and outliers assumptions
that have to be met in order to have a valid model.
Multi-collinearity Diagnosis for Factors influencing EL and ET Decisions
Multi-collinearity problems exist when there are strong relationships among independent
variables. The variance inflation factor (VIF) is calculated for all variables with the aim of
verifying the possible existence of multi-collinearity. This test measures the extent to which
the variances of the coefficients estimated in a regression are inflated when compared to the
cases in which the independent variables are not linearly related. When the VIF values are
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47
greater than 10, the cut-off point can become indicators of the existence of multi-collinearity
(Pallant 2011).
Table 6: Coefficients a and collinearity statistics for EL Decision
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
Step
1
(Constant) 11.643 12.249 .951 .348
Firm .050 3.500 .003 .014 .989 .507 1.971
Country -1.555 2.699 -.103 -.576 .568 .754 1.326
Market -3.798 2.668 -.257 -1.424 .163 .737 1.357
Project 3.946 3.141 .247 1.256 .217 .617 1.620
Management 4.274 3.465 .254 1.233 .226 .565 1.769
a. Dependent Variable: series
Table 6 and Table 7 depict the coefficients and linear statistics which explain whether there
is existence of multi-collinearity for EL and ET decisions respectively. The results show the
highest VIF values of 1.971 and 2.427 respectively, which are well below 10, the cut-off point
recommended by Pallant (2011). Furthermore, all tolerance values for both tables are
greater than 0.1 which rules out the presence of multi-collinearity in the data. Hence, multi-
collinearity problems do not exist for either EL or ET decision factor models.
Table 7: Coefficientsa and collinearity statistics for ET Decision
The Presence of Outliers
Preliminary analyses were performed to ensure no violation on the assumptions of normality
and outlier cases. For the EL decision, the analysis detected 1 outlier which can harm the
logistic regression analysis and correlation analysis results for a small sample size. The
outlier was removed leaving only 44 out of 45 items. For the ET decision, the analysis
detected 7 outliers. Similarly, the outliers were removed leaving only 38 out of 45 items.
Consequently, the results depicted by the Box-plots indicate no outlier for the entire
variables which shows the data set is clean from any outlier cases. The output in the logistic
regression tables also shows that the case wise plot was not produced due to the absence
of outliers. Since these two assumptions were met, both logistic regression models for EL
and ET decisions together with their results are valid. In summary, the findings have proven
that the multi-collinearity problems did not exist in either EL or ET decisions models and are
supported by all VIF values (less than 10.00) with tolerance values, all above 0.10.
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
Step 1 (Constant) 12.019 15.687 .766 0.449
Firm -7.754 3.818 -.428 -2.031 0.051 0.583 1.716
Project 3.811 5.045 .190 .755 0.455 0.412 2.427
Performance 2.536 3.727 .154 .680 0.501 0.508 1.967
Management 7.087 5.465 .319 1.297 0.204 0.428 2.335
Market -2.954 4.517 -.142 -.654 0.518 0.552 1.813
a. Dependent Variable: series
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Measurement of Dependent Variable
The objective of this study is to examine the influence of factors on firms’ EL decisions in
choosing either ASEAN or non-ASEAN countries and on the firms’ ET decisions to be either
an early or late mover. Descriptive analysis shows that more than 70% of the firms have
penetrated the non-ASEAN with more than 50% of the firms choosing to be late movers.
Thus, in order to have a better understanding of the factors influencing these decisions, a
binary logistic regression analysis was carried out. As a limitation to this study, the actual
distance from Malaysia to the foreign locations was not considered. However, the actual
distance from host to home country is proposed as an observable measurement for future
research on EL decision to develop a decision tool for construction firms.
A binary logistic regression is similar to a linear regression except that it is used when the
dependent variable is dichotomous/binary, while multinomial logistic regression is used when
the dependent or outcome variable has more than two categories (Leech, Barrett, & Morgan
2005). It is used as an appropriate multivariate procedure for describing and testing
relationships between a dichotomous/binary (0/1) outcome variable and a number of
categorical and/or continuous variables. Hence, a binary logistic regression model was used
in this study to determine the effect an increment of each independent variable (factors) on
how likely the binary variable (EL) decision is to take value 1 (non-ASEAN countries) as
opposed to value 0 (ASEAN countries). Similarly, a binary logistic regression model was
also used to determine the effect an increment of each independent variable (factors) on
how likely the binary variable (ET) decision is to take value 1 (late movers) as opposed to
value 0 (early mover). An assessment of the goodness-of-fit of the model using Hosmer and
Lemeshow test carried out earlier has determined the appropriateness of the model.
Table 8 and Table 9 known as the classification tables summarize the results with the
predictor variables in the EL and ET decision models respectively. Table 8 shows that the EL
decision model has correctly classified 90.6% of construction firms have chosen the non-
ASEAN countries, while 55.6% have chosen the ASEAN countries. As a result, in the overall
model, 82.9% of the sample population has been correctly classified.
Table 8: Classification Table with predictor variables for EL Decisiona
Observed Predicted
EL Percentage
Non-ASEAN ASEAN Correct
Step 1 EL Non-ASEAN 29 3 90.6
ASEAN 4 5 55.6
Overall Percentage 82.9
a. The cut value is .500
Similarly, as shown in Table 9, 95.8% of the construction firms were correctly classified in
the late mover group, while 78.65% in the early mover group. As a result, in the overall
model, 89.5% of the sample population has been correctly classified. Hence, both
classification tables have indicated that each model has predicted the correct category for
each case well.
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49
Table 9: Classification table with predictor variables for ET Decisiona
Variables in the Equation for EL Decision
The binary logistic regression was also performed to assess the impact of a number of
factors on the likelihood of the firms’ EL decision in international market. Table 10 known as
“variables in the equation” provides the information on the contribution or importance of each
independent variable (factors for EL decision) on the model. The Wald statistics (fourth
column) was also used to identify the independent variables that are good predictors. This
method of assessing the successive accuracy of a model is to evaluate its ability to correctly
predict the category for cases for which the outcome is known.
Table 10 shows a model contains five independent variables named as the firm, country,
market, project and management factors. The factors corresponding to the values under the
sixth column labeled Sig. which are less than 0.05 are the variables that contribute
significantly to the predictive ability of the model. This suggests that the model was able to
distinguish between firms that chose to enter the ASEAN or Non-ASEAN countries.
However, the model shows only three (3) predictors namely; firm, country and market were
statistically significant, as shown earlier, where χ2(5) = 22.207, p < 0.001, with B values of -
3.177, -4.780 and 4.150 respectively. Hence, for the firms to choose either to enter the
ASEAN or non-ASEAN countries depends, among other factors, on the firm, country and
market factors that are needed to be established before stepping into the chosen foreign
market. While, the other two factors namely project and management with Sig. values of
0.073 and 0.224 did not influence the EL decision of the firms in international market
expansion.
Table 10: Variables in the Equation for EL Decision
Factors B S.E. Wald df Sig. Exp (B) 95% C.I.for
EXP(B)
Lower Upper
Step
1a
Firm -3.177 1.554 4.181 1 .041 0.042 .002 .877
Country -4.780 2.136 5.007 1 .025 0.008 .000 .552
Market 4.150 1.920 4.672 1 .031 63.404 1.472 2730.816
Project -2.550 1.422 3.214 1 .073 0.078 .005 1.269
Management 1.725 1.418 1.479 1 .224 5.611 .348 90.368
Constant 14.238 6.981 4.159 1 .041 1525167.612
a. Variable(s) entered on step 1: firm, country, market, project, management.
Observed Predicted
ET Percentage
Correct
Early Mover Late Mover
Step 1 ET Early Mover 11 3 78.6
Late Mover 1 23 95.8
Overall Percentage 89.5
a. The cut value is .500
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50
The other useful information in Table 10 is provided in the Exp (B) (seventh column). The B
values provided in the second column are the values that are used in an equation to
calculate the probability of a case falling into a specific category (independent variables
influencing EL decision). These values are the odds ratios (OR) for each of the independent
variables. The odds ratio represents ‘the change in odds of being in one of the categories of
outcome when the value of a predictor increases by one unit. It is shown as the strongest
predictor. Since each predictor is a continuous variable, “increase” is reported for value more
than 1 (decrease if less than 1) of the odds for each unit increase in the predictor variable.
Hence, based on the overall results, the strongest predictor is the country factor (B = -4.780,
p < 0.05), recording an odds ratio of 0.008. The odds ratio for this variable, however, as
.008, is a value less than 1. This indicates that a firm with less knowledge on the country
factors is 1 times less likely to choose the ASEAN countries as compared to those who have
more knowledge on the country factors, all other factors being equal.
Variables in the Equation for ET Decision
Similarly, Table 11 provides information about the contribution or importance of each of the
independent variables (factors) on the model for the ET decision. The factors corresponding
to the values under column labeled Sig. which are less than 0.05 are the variables that
contribute significantly to the predictive ability of the model.
Table 11: Variables in the Equation for ET Decision
Factors B S.E. Wald df Sig. Exp
(B)
95.0% C.I.for EXP(B)
Lower Upper
Step
1a
Firm -4.660 2.024 5.300 1 0.021 0.009 0.000 0.500
Project 3.501 1.760 3.959 1 0.047 33.155 1.054 1.043E3
Performance 1.669 1.342 1.546 1 0.214 5.304 0.382 73.624
Management -4.035 1.995 4.090 1 0.043 0.018 0.000 0.883
Market 3.786 1.769 4.578 1 0.032 44.064 1.374 1.413E3
Constant 1.014 3.436 0.087 1 0.768 2.756
a. Variable(s) entered on step 1: firm, project, performance, management and market
Choosing either to enter early or late depends, among other factors, on the firms’
background and resources to be established before stepping into the chosen foreign market.
Hence, the logistic regression model fitted well with the predictor variables (ET decision)
under components namely; firm, project, performance, management and market factors. In
this case, the model reveals that four out of five independent variables which are the firm,
project, management and market factors with Sig. values of 0.021, 0.047, 0.043 and 0.032
respectively, have made a unique statistically significant contribution to the model. However,
another factor, performance (Sig. = 0.214) did not influence the ET decision. While the B
value provided in the second column are the values that are used in an equation to calculate
the probability of a case falling into a specic category (an independent variable that
influences the ET decision).
Similarly for the ET decision based on Table 11 (Exp (B) column) the strongest predictor is
the firm factor (B = -4.660, p < 0.05), recording an odds ratio of 0.009. The odds ratio for this
variable, however, as .009, is a value less than 1. This indicates that the more knowledge a
firm has on the firm factors, the less likely the firm chooses to be the late mover. Hence, the
odds of a firm choosing to be a late mover decrease by a factor of 0.009, all other factors
being equal.
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51
Proposed ELET decision model
Common factors influencing both EL and ET decisions are consolidated towards developing
the ELET decision model based on extracted loadings from factor analysis. Due to the
limitation of using logistic regression analysis in the SPSS software, the analysis has to be
carried out individually for both EL and ET decision models. Hence, the ELET decision
model is developed by consolidating the common factors shared by both EL and ET
decisions. The integration of decision using structural equation modelling that can model the
constructs/factors influencing the two dependent variables simultaneously, is in progress.
The logistic regression models have successfully determined the effect of factors on EL and
ET decisions in which 90.6% of the respondents have been correctly classified under non-
ASEAN while 78.5% of the respondents have been correctly classified as late movers.
Factor analysis has resulted in 17 significant factors for the EL decision and 20 significant
factors for the ET decision. Table 12 shows the significant factor loadings for EL and ET
decisions extracted from Tables 4 and 5.
Table 12: Summary of significant factors influencing EL and ET decisions based on factor
loadings
Factors influencing EL and ET decisions Loadings
for EL Loadings
for ET
C1 Host government attitude and intervention 0.595
C2 Similarity with host country/market (social/cultural/religious)
environment
0.575
C8 Financial support from home country banks 0.568 0.796
C9 Trade relationship between two countries 0.933
C10 Diplomatic relationship between two countries 0.761
M3 Product/service market growth 0.863
M4 Market entry barriers 0.750
M6 Construction demand (e.g. finance, labor, material, transport and
other utilities)
0.805
F2 Firm’s ability to assess market signals and opportunities -0.786 0.558
F3 Firm’s level of international experience -0.686 0.795
F4 Firm’s long-term and strong management strategic
orientation/objectives
0.620
F5 Firm’s superior management & organizational dynamic capabilities 0.548
F6 Firm’s financing capacity 0.760 0.515
F7 Firm’s competencies (project management, specialist expertise and
technology)
0.823 0.592
F8 Firm’s resources (level of knowledge and Research & Development) 0.750 0.622
F10 Firm’s management of quality (product, service, human resource) 0.611
F12 Firm’s perfomance in terms of increase level of knowledge and
international experience
0.783
F13 Uncertainty avoidance 0.665
F14 International business network (strong relations with foreign partners) 0.580
F16 Firm’s reputation 0.505
F17 Firm’s good track record /competitive advantage 0.806
P1 Project size 0.859
P3 Technical complexity of projects -0.569 0.505
P4 Type of client (public vs. private) 0.544
P5 Availability of funds for projects 0.595 0.588
P7 Experience of company in similar works 0.667 0.746
P8 Existence of strict time limitations 0.706
P10 Availability of partner/alliance 0.625
Total number of factors 17 20
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
52
Further consolidation has resulted in nine (9) factors that are commonly shared or mutually
inclusive for both EL and ET decisions as shown in Table 13. These factors were found to
significantly influence the majority of firms’ ET decision as late movers by entering the
selected market locations mostly in the non-ASEAN countries far from their home country as
shown in the ELET model in Figure 2.
Table 13: Mutually inclusive significant factors influencing ELET decisions of firms in
international market expansion
NO. Factors influencing ELET decisions
1 Financial support from home country banks
2 Firm’s ability to assess market signals and opportunities
3 Firm’s level of international experience
4 Firm’s financing capacity
5 Firm’s competencies (project management, specialist expertise & technology)
6 Firm’s resources (level of knowledge and Research & Development)
7 Technical complexity of projects
8 Availability of funds for projects
9 Firm’s experience in similar works/projects
Figure 2: ELET decision model for Malaysian construction firms in international market
(Developed from this study)
These nine (9) factors are further grouped into six (6) internal factors related to the firms’
resource capabilities and three (3) external factors which contributed to their entry decisions.
The factors related to the firms’ resource capabilities were the firms’ ability to assess market
signals and opportunities, international experience, financial capacity, competencies and
capabilities in project management, specialist expertise and technology, resources (level of
knowledge & research and development) and experience in similar works, while the external
factors are the financial support from the home country banks, technical complexities of
projects and availability of funds for projects.
Internal factors (firm’s
resource capabilities)
ability to assess market
signals and opportunities
international experience
financial capacity,
competencies and
capabilities in project
management, specialist
expertise and technology
resources (level of
knowledge & research and
development
experience in similar works
External factors
financial support from the
home country banks
technical complexities of
projects
availability of funds for
projects
EL Decision
Non-ASEAN countries:
Algeria, Australia, Austria, Bangladesh,
Botswana, China, Egypt, France, Hong
Kong, India, Indonesia, Iran, Iraq, Ireland,
Germany, Japan, Kuwait, Libya,
Maldives, Mauritius, Myanmar, Mongolia,
Morocco, Nepal, Nigeria, Oman,
Pakistan, Qatar, Saudi Arabia,
Seychelles, Spain, South Africa, South
Korea, Sudan, Syria, Sri Lanka, Taiwan,
Tobago, Turkmenistan, United Arab
Emirates, United States of America,
Late Mover
ET Decision
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
53
The ability of a firm to assess the emerging market growth and sourcing opportunities was
crucial and should not be overlooked, as agreed by Sakarya, Eckman and Hyllegard (2007).
Firms should not rely purely on macroeconomic and political factors. Gaston-Breton and
Martín (2011) have recommended that firms should look for market signals such as the
market size and its potentials in order to select the right location for their businesses. Hence,
the findings have fulfilled the suggestion that the significant firm’s resource based
capabilities which have influenced the firms’ ELET decisions to enter international markets,
include the ability to assess market signal, financial capacity, competencies and capabilities
in project management, specialist expertise and experience in similar works with no
significant factor related to the political factor. According to previous studies, many business
opportunities exist for construction firms to operate in the international market due to its high
volume of construction demand and its growing economy either within the ASEAN or non-
ASEAN regions. Hence, in a firm’s strategic planning it is very important to assess the
market signals and explore opportunities by gathering the required information on ELET
decisions in potential international markets.
As indicated by Gunhan and Arditi (2005), financial strength is one of the essential firm
resources in international construction. Capital requirement during market entry is very high
and firms with large resources are able to cover the capital requirement and enter the foreign
market earlier. However, these findings from this study are in line with Söderblom (2011)
who pointed out that firms with strong financing capability or that have easier access to
financing were able to enter the foreign market early.
International construction projects are known to be more complex with many known and
unknown risk factors as compared to domestic projects. Hence, project management skills
including specialist expertise and technological capabilities are very much needed to handle
the complex nature of international projects (Gunhan & Arditi 2005). Thus, firms with a high
level of competencies were among the early movers entering into a foreign market, since its
competitive advantages will offset uncertainty and information disadvantages which are most
profound for international entrants (Villaverde & Ortega 2007). Firms’ capabilities, measured
by the firm size, project management skills, specialist expertise level, firm reputation,
technology knowledge and firm network, are some of the factors that influence the ET
decision. Hence, when firms have low levels of competencies and capability, the implication
is late entry (Dacko 2002).
The findings show that the firms’ resources related to their level of knowledge based on
Research and Development has significantly influenced their ELET decisions, which may
indicate that low level of knowledge has resulted in lack of power to gain access to suppliers,
markets, customers and other assets as accentuated by Soderblom (2011). However,
Soderblom emphasized that learning creates substantial entry barriers for the late movers as
compared to the early movers especially in unstable situations related to customer needs,
where the early movers grasp opportunities that exist when entering the market that later will
limit market opportunities for late movers. Guler and Guillén (2009) deliberated that the level
of knowledge and technology increases as the firm’s international experience increases.
Hence, firms should plan properly to increase their knowledge and overcome the entry
barriers in order to understand the needs of customer and predict the market trends (Kerin,
Varadarajan & Robert 1992). Hence, late movers must acquire greater knowledge and other
intangible assets to help reduce the risks and competition during the exploitation of
opportunities in international markets. The factors affecting the firm’s resources availability
include satisfying capital requirement, lowering risk, more flexibility in decision making and
increasing market power.
The findings indicate that firms’ international experience significantly influenced the firms’
ELET decisions to enter international markets. This finding is consistently supported by
Australasian Journal of Construction Economics and Building
Mat Isa, C.M., Mohd Saman, H & Preece, C.N. 2014, ‘Entry Location and Entry Timing (ELET) Decision Model for
International Construction Firms’, Australasian Journal of Construction Economics and Building, 14 (3), 34-57.
54
another study where firms with a high level of experience in similar projects have entered
early compared to those having less experience (Schwens & Kabst 2009). As found by Guler
and Guillén (2009), as firms gained more international experience they were more likely to
overcome the entry barriers by entering as an early mover and to prepare for cross-border
by improving their knowledge and experience (Liu, Low & Niu 2011). The findings suggest
that the Malaysian firms’ resource capabilities such as experience in similar projects plays
important roles related to the ELET decisions. Hence, the construction firms contended that
based on their firm’s international experience in similar projects, they have chosen to be late
entrants to the non-ASEAN countries.
Conclusions
In light of the work presented in this paper a number of conclusions can be made. As the
Malaysian construction firms go international, the EL and timing decisions are perceived as
very important strategies to fulfill the firms’ missions and long-term objectives for their global
operations. Before expanding internationally, the firms need to decide on suitable market EL
and ET business’ strategies. Both entry decisions were found to be contingent upon qualities
of the firms’ resource based capabilities. The findings revealed resource-based factors such
as a firm’s financial capacities, human capabilities, competencies and specialist expertise,
knowledge and experience complemented by some external factors such as the financial
supports from banks, are crucial in order to enter international markets.
The decision to enter a foreign market and concurrently choose the right time is a complex
decision making process for construction firms. Malaysian construction firms contended that
they have chosen to be the late entrants to enter the non-ASEAN countries based on the
level of a firm’s resource capabilities. This present study builds on and extends the literature
on EL and ET decisions in a more integrated way and brings forward the significant mutually
inclusive factors influencing both the EL and ET dimensions toward developing an integrated
ELET decision model.
If firms require new resources in order to enter foreign markets, entry modes via acquisition
and joint venture may enable them to be the early movers. Therefore, a further research to
add entry mode (EM) decision in the ELET model would seem to be needed in order to
develop a complete and comprehensive decision model, namely ELETEM model for
international markets entry. It is recommended that a complex analysis such as structural
equation modeling that can model constructs (factors) influencing two or more dependent
variables simultaneously should be adopted to integrate these ELETEM decisions.
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... A detailed analysis of the selected ICJV papers within the studied period primarily covered the following: (1) entry modes, formation decision strategies and operation (e.g., Ling et al. 2005;Chen 2008;Ling et al. 2008;Chen and Messner 2009;Isa et al. 2014); (2) risk assessment and management practices (e.g., Kapila and Hendrickson 2001;Hsueh et al. 2007;Zhang and Zou 2007;Zhao et al. 2013;Al-Sabah et al. 2014;Hwang et al. 2017;Razzaq et al. 2018;Chang et al. 2018); (3) performance evaluation elements (e.g., Mohamed 2003;Pheng et al. 2004;Ozorhon et al. 2007aOzorhon et al. , b, 2010Ozorhon et al. , 2011; (4) dispute resolution mechanisms (e.g., Chan and Suen 2005a;Maemura et al. 2018); (5) management issues in ICJVs (e.g., Luo 2001;Neves and Bugalho 2008;Ho et al. 2009;Girmscheid and Brockmann 2010); (6) influential factors for ICJV practice (e.g., Kreitl et al. 2002;Gale and Luo 2004;Ozorhon et al. 2008b); and (7) technology transfer (e.g., Carrillo 1996; Ganesan and Kelsey 2006;Zhang et al. 2010). A summary of all the seven broad research topics and their subtopics together with the CM journals publishing those articles as well as the percentage of papers falling under each research topic is provided in Table 6. ...
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The adoption of international construction joint ventures (ICJVs), of late, for more complex and large-scale construction projects across the globe, has drawn an increasing broad range of research interest since early 1990s. A number of studies on ICJVs focusing on diverse perspectives have been published by globally renowned construction management (CM) journals over the past two decades. However, a systematic review of research development in this domain is still lacking. Thus, it is critical to conduct a comprehensive review to detect current research priorities and future research directions for development. This study aims to fill this gap through a comprehensive and systematic analysis of selected ICJV research papers published in 17 selected CM journals from 1990 to 2018. Using Scopus search engine and keywords, a systematic desktop search was conducted, followed by the selection of journals and papers. It analyzed the trend of ICJV research in terms of annual publication, countries’ contributions, contributions by institutions and researchers, data collection and analysis methods adopted, and research interests. The results highlighted an increasing attention to ICJV research within the studied period. Also, the results indicated that while the largest contribution to ICJV research has come from developed countries like Singapore, the UK, and the US, developing countries like China and Turkey have also made enormous contribution. Key research topics covered include entry modes, formation decision strategies, and operation; risk assessment and management practices; performance evaluation; dispute resolution mechanisms; management issues in ICJVs; influential factors for ICJV practice; and technology transfer. This study also suggests useful directions for future research. The findings provide in-depth understanding of ICJV research to practitioners and researchers and stimulate future research based on the identified gaps.
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... Research on IJVs has long been of interest to scholars in the last two decades (Tetteh et al., 2019. A few topics have been emphasized: formation decision strategies and operation, entry modes (Isa et al., 2014), management practices and risk assessment (Zhao et al., 2013;Hwang et al., 2017), key performance indicators (Mohamed, 2003), mechanisms of dispute resolution (Chan and Suen, 2005;Maemura et al., 2018), technology transfer (Zhang et al., 2010), influential factors for IJVs practice (Gale and Luo, 2004;Ozorhon et al., 2008), and management issues in IJVs (Luo et al., 2001;Ho et al., 2009). ...
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The mileage of high-speed rail (HSR) is expected to grow in the foreseeable future. Fierce international competition and HSR business uniqueness have facilitated the broad application of international joint ventures (IJVs) globally. However, the interaction between competition and cooperation complicates IJVs, and IJVs partners’ implementation has rarely been informed concerning the fluent operation of IJVs for HSR projects. This study aims to examine the determinants of IJVs coopetition for HSR projects. We identified 35 variables along with their clusters through literature review and pilot study, and collected data from the international HSR industry using a questionnaire survey. Eight clusters verified by the confirmatory factor analysis come from three levels: macro-level, project-level, and firm-level. Based on the partial least squares structural equation modeling (PLS-SEM), a path model for the coopetition relationship has been established, demonstrating that macro-level and project-level determinants affect the coopetition relationship via the firm-level determinants. Furthermore, the path model indicates recommendations corresponding to various situations (isolating situation, partnering situation, contending situation, and adapting situation) for IJVs members to find a way to improve the coopetition relationship. Thus, this study not only contributes to the existing knowledge body on coopetition theory, but also enhances HSR contractors’ comprehension of the coopetition relationship determinants as well as the interactions.
... Most of the forty-four (44) identified factors are drawn largely from the studies conducted at the developed countries with some from developing countries.These factors were then grouped under four themes: country, market, firm and projects factors that were used in the questionnaire survey are shown in Table 1. Attitude and intervention of host governments (Owhoso, Gleason, Mathur, and Malgwi, 2002), similarity to host country/market in terms of social, cultural and religious (Javernick-Will and Scott, 2010), proximity to host country (Ahmad and Kitchen, 2008a), anticipated non-economic risk such as political, technological etc. (He and Wei, 2011), anticipated economic risks such as currency fluctuation, interest rate, etc. (Zaradiah, 2008), other foreign competitors in the host country (Ramayah, Mohamad, Jaafar, Abdul Aziz, and Wong, 2010), promotion of export efforts of home government (Abdul Aziz et al., 2011), financial support from home country banks (Mat Isa, Mohd Saman, and Preece, 2014), trade relationship between two countries (Braunerhjelm, Oxelheim, and Thulin, 2005), diplomatic relationship between two countries (Chen, 2005), host government control on licensing, restrictions and other FDI requirements (Ozorhon et al., 2010). ...
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... Among the respondents, senior managers accounted for 47.4% of the respondents and project level managers accounted for 38.6%. As data acquisition in international projects is somewhat difficult, the literature sample in similar research in the field of international projects is only 45 [56], and regression analysis only requires a minimal sample of 30 [57][58][59]; although the sample is not very large, the number of valid questionnaires has met the requirements of research. ...
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With the active support of the national policy “One Belt and One Road” Initiative, Chinese contractors seized this historic opportunity to accelerate strategic globalization, and they gradually stood out in international construction projects owing to their low-cost advantage. However, despite China having large-scale contractors and wide-range business, compared to developed countries, a considerably large gap still exists. China is confronted with complex and changeable international projects filled with increasing competition. Thus, it is both a focus issue and a major task for Chinese international contractors, and many scholars, to consider how Chinese contractors can obtain and maintain long-term competitive advantages to improve their capabilities in response to dynamic environmental changes. Therefore, the objectives of this study are (1) to study the influence of the dynamic capability of Chinese contractors on competitive advantage in a project and (2) to explore the moderating effect of Chinese guanxi on the dynamic capability and competitive advantage of Chinese international contractors. This study primarily aimed at researching the impact of dynamic capacity of Chinese contractors on competitive advantage and the moderating effects of Chinese guanxi. The findings suggest that the environmental perception capability and the integration and coordination capability of the dynamic capability have a significant positive effect on the project competitive advantage; business guanxi positively moderates the relationship between the environmental perception capability and the competitive advantage. Business guanxi also negatively moderates the relationship between learning ability and competitive advantage, while political guanxi negatively moderates the relationship between the environmental perception capability and competitive advantage. This paper contributes to the construction management literature not only by providing empirical evidence on the dynamic capability and competitive advantage of Chinese contractors but also by expanding guanxi research. The results may also help Chinese contractors by providing strategic reform guidance and sustainable development in international construction projects.
... These prospects are the guarantee that engaging a production system for building materials would be profitable. Entrepreneurial opportunities and construction demands are among the factors identified as entry location and entry timing essential for market expansion in foreign markets such as Nigeria (Abdul-Aziz and Wong, 2010;Mat Isa et al., 2014). The nation as endowed as it is, has its own peculiar challenges. ...
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