Niron Hashai/ Christian G. Asmussen/ Gabriel R.G. Benito/ Bent Petersen
Technological Knowledge Intensity and Entry Mode
Diversity
Abstract

This paper expands entry mode literature by referring to multiple modes exerted
simultaneously in different value chain activities within and across host markets, rather
than to a single entry mode at the host market level. We apply competing theoretical
perspectives – internalization theory and knowledge transfer efficiency considerations on
the one hand, and organizational learning theory on the other – to argue that firms'
technological knowledge intensity affect their entry mode diversity across value chain
activities, across host markets, and at the overall corporate level.
Key Results

Analyzing a unique dataset we show that greater technological knowledge intensity is
strongly associated with greater entry mode diversity across value chain activities and at
the corporate level, but only weakly associated with greater entry mode diversity across
geographic host markets.
Authors
Niron Hashai, Jerusalem School of Business Administration, The Hebrew University, Israel.
Christian G. Asmussen, Center for Strategic Management and Globalization, Copenhagen
Business School, Denmark
Gabriel R.G. Benito, Department of Strategy and Logistics, BI Norwegian School of
Management, Oslo, Norway
Bent Petersen, Center for Strategic Management and Globalization, Copenhagen Business
School, Denmark
Key words: entry mode diversity, value chain activities, technological knowledge
intensity.
Abbreviated heading: Entry Mode Diversity
We wish to thank Ulf Andersson, Ram Mudambi and the anonymous reviews for their useful comments,
and Tamar Almor and Seev Hirsch for allowing us to use their dataset. Niron Hashai acknowledges the
financial support of the Asper Center for Entrepreneurship at the Hebrew University.
Introduction
Firms’ foreign market entry modei choice is one of the most researched topics in international
business (e.g. Anand/Delios, 1997; Benito/Pedersen/Petersen, 2005; Brouthers/Hennart, 2007;
Chen/Hennart,
2004;
Datta/Herrmann/Rasheed,
2002;
Delios/Henisz,
2003;
Erramilli/Agarwal/Kim, 1997; Hennart, 1991; Kim/Hwang, 1992; Kogut/Zander, 1993; Madhok,
1997; Malhotra/Agarwal/Ulgado, 2003; Martin/Salomon, 2003; Tse/Pan/Au, 1997; Yiu/Makino,
2002) . Yet, despite the considerable attention devoted to this topic, most studies still refer to a
specific mode exerted by a firm in a given foreign market – be it joint ventures, wholly owned
greenfield subsidiaries, or acquisitions – and often with reference to a specific value chain
activity, such as when the choice is between exports, manufacturing subsidiaries, and licensed
production. This simplified view of entry modes, while convenient and useful for theory building
and empirical investigation, stands in contrast to the variety of combined entry modes that can be
observed in real-world firms.
The general approach in extant literature has been to view each geographic area-value chain
activity combination independently, thereby disregarding additional areas and activities.
However, managerial decisions on such entry modes are not independent but are rather
interdependent. For example, transaction cost concerns (Buckley/Casson, 1976) may motivate the
firm to standardize its use of entry modes across geographies and activities, while the firm’s
search for diverse knowledge (Zahra/Ireland/Hitt, 2000) on the other hand may motivate it to
diversify its entry modes. Thus, the usefulness of analyzing a specific entry mode at the activity
and country level, without regarding the overall set of entry modes a given firm may have, might
be
quite
limited
(Asmussen/Benito/Petersen,
2009;
Buckley/Hashai,
2004,
2005;
Hill/Hwang/Kim, 1990; Petersen/Benito/Welch/Asmussen, 2008).
The aim of this paper is to expand extant foreign market entry mode research by switching
the unit of analysis from activity- and location-specific entry mode to the analysis of multiple
2
entry modes of a firm across its value chain and across foreign markets. This approach implies
that entry modes decisions are likely to be interdependent across host markets and value chain
activities, and are not taken independently of each other as implicitly assumed by extant
literature.
More specifically, we aim to investigate how firms' level of technological knowledge
intensity affects their foreign entry mode diversity, defined as their propensity to vary entry
modes across locations and activities. The direction of such an effect is not clear as different
theoretical perspectives predict contradictory effects. On the one hand, internalization theory
(Buckley/Casson, 1976, 1998; Rugman, 1981) as well as knowledge transfer efficiency
considerations (Kogut/Zander, 1993; Martin/Salomon, 2003) essentially imply that greater
technological knowledge intensity limits entry mode diversity. On the other hand, greater
technological knowledge intensity is also associated with a capacity for learning due to greater
absorptive capacity (Cohen/Levinthal, 1990). The exposure to different types of technological
learning through multiple types of entry modes is likely to leverage diverse technological
knowledge and skills in foreign markets, thus leading firms with greater technological knowledge
intensity to engage in more diverse entry modes. By empirically comparing these two
contradictory theoretical predictions we provide a partial answer to the question why some firms
use diverse entry modes while others apply only few modes.
The structure of the paper is as follows. In the next section we conceptualize entry mode
diversity according to different levels of analysis (area level, activity level, and corporate level)
and derive hypotheses as to how technological knowledge intensity may affect entry mode
diversity at these three levels. The hypotheses derived from our conceptual framework are tested
on unique data of entry modes used by a sample of Israeli-based firms. This is followed by an
analysis and a discussion of the results. Finally we suggest further research avenues and conclude.
3
Conceptualizing Entry Mode Diversity
While extant research on foreign market entry mode mostly refers to a firm's entry mode decision
as a general decision at the location-activity levelii, a few studies have indicated the importance of
referring to a firm's variety of entry modes (Asmussen et al., 2009; Benito/Petersen/Welch, 2009;
Petersen et al., 2008). Some of these studies have theoretically advanced the conceptualization of
internationalizing firms as a locus of value chain activities to which firms simultaneously
determine the location and entry mode in order to minimize their overall costs (Buckley/Casson,
1998; Buckley/Hashai, 2004, 2005; Casson, 2000). Other studies have empirically shown that
firms often do not stick to one particular entry mode, but instead simultaneously employ a variety
of entry modes at the value chain activity level (Benito/Welch, 1994; Fina/Rugman, 1996;
Petersen/Welch, 2002). Taken together, it is therefore implied that firms may often
simultaneously use multiple entry modes in different locations and value chain activities.
Petersen et al. (2008) used an entry mode matrix to illustrate this point. Assuming that an
international firm operates in I host markets and has J identifiable activities in its value chain, its
entry mode matrix at a given point in time can be denoted M=(mij), where i=1...I indexes host
markets and j=1...J indexes value chain activities. Each cell in the matrix (mij) may then contain
one or multiple entry modes under which the given activity is performed in the given host market.
The general form of the entry mode matrix is presented in Figure 1.
[Insert Figure 1 about here]
The matrix depicts three levels of aggregation in which entry mode diversity can be
discussediii:

Area-level diversity refers to different entry modes exerted by a firm within a given
foreign area – country or region – and can therefore be evaluated by looking at a row
vector mi  mi1 , mi 2 ,, miJ  of activity-level decisions. The larger the variation in
entry modes within this vector, the higher the area-level diversity.
4

Activity-level diversity is about how a specific value chain activity is performed in
different geographical areas (countries or regions), as measured by each column in the
matrix. Activity-level diversity for a given activity is therefore described by a column


vector of the form: m j  m1 j , m2 j , , m Ij .

Corporate diversity represents the variety of entry modes in the entire matrix M, as
represented by all the combinations of area-level and activity-level entry mode decisions.
Petersen et al. (2008) discuss what may potentially affect the diversity within the entry
mode matrix. However, to our knowledge no study has attempted to develop and test hypotheses
about the predictors of entry mode diversity. One such hypothesis may pertain to the firm’s
technological knowledge intensity, which has been emphasized in foreign market entry mode
research as a distinctive variable affecting foreign market entry mode choice (e.g. Anand/Delios,
1997; Delios/Henisz, 2003; Erramilli et al., 1997; Gatignon/Anderson, 1988; Padmanabhan/Cho,
1999; Tan et al., 2001; Tse et al., 1997; Yiu/Makino, 2002). Technological knowledge intensity
represents the level of technological knowledge contained in each unit of output that the firm
produces (Almor/Hashai/Hirsch, 2006; Hashai/Almor, 2008; Jones, 1999). Since technological
knowledge intensity, often measured as the ratio of research and development (R&D)
expenditures to sales, has been shown to affect the entry mode decision of firms, it is quite likely
that the diversity of firms' entry mode portfolio across countries and value activities is affected by
this variable as well. Hence, we aim to investigate what is the likely impact of this variable on
foreign market entry mode diversity. As mentioned above, internalization theory and
organizational learning theory constitute two perspectives that may inform us about this
relationship.
Internalization theory and entry mode diversity
Internalization theory explains the existence and growth of multinational enterprises
(Buckley/Casson, 1976; Rugman, 1981, Teece, 1986a). The theory highlights firms' technological
5
knowledge intensity as a dominant determinant of internalization and externalization decisions.
This stream of literature is primarily focusing on the impact of failures in the market for firmspecific know-how (most often referring to technological know-how) on firms' choice between
licensing and wholly owned subsidiaries. In essence, the major prediction of this school of
thought is that higher levels of technological knowledge imply a higher risk of market failure and
hence lead an internalized mode of operation in foreign markets, i.e. wholly owned entry modes.
Consequently, if a knowledge-intensive firm were to engage in diverse entry modes, it would
presumably face higher transaction costs. This is the result of information asymmetry (difficulty
of evaluating and transferring high levels of technological knowledge, see Arrow 1982;
Davidson/McFetridge, 1984) between the focal firm and potential collaborators coupled with the
high uncertainty of managing and coordinating multiple entry modes for highly technologyintensive firms (Contractor, 1990; Kim/Hwang, 1992; Osborn/Baughn, 1990; Williamson, 1975).
It therefore follows that higher levels of technological knowledge intensity are likely to be
associated with lesser entry mode diversity.
A complementary view refers to the relationship between the relative efficiency of
technological knowledge transfer for internationalizing firms using different types of entry
modes. The technological knowledge developed by highly technology-intensive firms is often
complex, hard to codify and to teach, and, hence, is relatively difficult to transfer (Hashai/Almor,
2008; Kogut/Zander, 1992, 1993; Martin/Salomon, 2003; Teece, 1977). Externalization of such
knowledge is likely to result in knowledge dissipation costs associated with the misappropriation
of transferred knowledge, and with higher control and monitoring costs to protect technological
knowledge, as well as higher negotiation and litigation costs (Martin/Salomon, 2003).
Greater technological knowledge intensity often implies greater complexity of coding and
decoding the transferred knowledge (Kogut/Zander, 1992, 1993; Martin/Salomon, 2003). Higher
entry mode diversity is therefore likely to result in greater costs of transferring complex
knowledge for highly technological knowledge intensive firms, since it requires tight
6
coordination of knowledge transfer between multiple parties engaging in different contractual
arrangements. For example, if a technology-intensive firm were to use a mix of sales agents,
licensing agreements, joint ventures, and wholly-owned subsidiaries in a given foreign market, it
would presumably have to incur large costs and efforts in order to manage, organize, and transfer
knowledge across these diverse arrangements while avoiding the appropriation of its knowledge
by other firms.
Overall, the above views imply that greater levels of technological knowledge intensity are
expected to be associated with lower entry mode diversity. We therefore hypothesize that:
Hypothesis 1: Technological knowledge intensity is negatively associated with entry mode
diversity.
Organizational learning theory and entry mode diversity
While the above hypothesis mainly draws on internalization theory, there have been recent calls
to incorporate a resource-based view into entry mode research (Madhok, 1997; Zhao/Luo/Zhu,
2004) and thus complement the (transaction) cost minimization concern of internalization theory
with a value generation perspective. Indeed, highly technology-intensive firms are arguably
dependent on having diverse technological knowledge in order to create and sustain their
competitive advantage. Strategic management research has shown that a firm’s ability to draw on
diverse knowledge is an important source of competitive advantage (Kilduff/Angelmar/Mehra,
2000; Milliken/Martins, 1996). This is so since knowledge diversity stimulates problem-solving
and enhances innovation by providing multiple viewpoints (Page, 2007). Highly technologyintensive firms are likely to obtain the capability to observe and mobilize new types of knowledge
due to their high absorptive capacity because — as noted by Cohen and Levinthal (1990), Autio,
Sapienza and Almeida (2000) and others — greater levels of technological knowledge intensity
are associated with a greater capacity for learning. Yet, a firm’s ability to benefit from this
7
absorptive capacity is contingent on the availability of external learning opportunities
(Cohen/Levinthal, 1989).
In the case of internationalizing firms which operate across national boundaries, exposure
to
diverse
technological
knowledge
is
particularly
pronounced
(Ghoshal,
1987;
Zahra/Ireland/Hitt, 2000). Firms which already possess strong technological capabilities are
motivated to seek out technological knowledge abroad, in order to enhance their knowledge
diversity (Cantwell/Janne, 1999; Chung/Alcácer, 2002). This may in turn affect a given firm’s
choice of entry mode portfolio, since its entry modes constitute its organizational interface with
different host country environments. All else being equal, a more diverse set of entry modes
allows sourcing from a more diverse pool of technological knowledge. Interaction with different
types of partner firms through multiple types of organizational arrangements is likely to leverage
diverse technological knowledge and skills in foreign markets through which firms can source
knowledge to facilitate and strengthen their competitive advantage (Vermeulen/Barkema, 2002).
In fact, a review of the entry mode literature suggests that different types of entry modes – e.g.
market-based, contractual, jointly owned, and wholly owned – convey different learning
experiences for internationalizing firms.
Market-based entry modes such as arms length relationships with sales agents and
distributors enable firms to learn from these local agents about technologies that are specific to
their markets (Almor et al., 2006; Hirsch, 1989; Zahra/Ireland/Hitt, 2000). Porter (1998)
suggested that technological innovation is propelled by having a “window on the market”, by
benchmarking against technologically advanced competitors and by targeting the preferences of
sophisticated customers in knowledge-intensive locations. A cost-effective way for foreign firms
to acquire these benefits may be to interact with local agents, who have extensive experience with
the market and broad knowledge of local technological developments (Canabal/White III, 2008;
Petersen/Pedersen, 2002). Agents may also act as “filters” through which the R&D-intensive
entrant firm can evaluate the local applicability and relevance of its own proprietary technologies.
8
Contractual entry modes (strategic alliances, OEM agreements, etc.), on the other hand,
enable firms to gain deeper technological understanding from their partners and acquire
complementary competencies directly from them (Hamel, 1991; Teece, 1986b). Indeed, recent
observations indicate that firms operating in high tech industries are those which are most likely
to engage in multiple contractual agreements through which they combine their technological
capabilities with complementary technological capabilities of partner firms as means of fostering
their competitive advantage (Dyer/Singh, 1998; Kale/Dyer/Singh, 2002; Lavie, 2006).
Partly owned entry modes (joint ventures) enable internationalizing firms to learn from
their partners and acquire the type of operational and tacit technological knowledge that can only
be transferred by close collaboration and supervision (Barkema/Shenkar/Vermeulen/Bell, 1997;
Reuer/Tong, 2005). In particular, technological knowledge which is teachable but not codifiable
(Kogut/Zander, 1993) could be effectively appropriated through a jointly owned arrangement,
since this provides the opportunity to work alongside a local firm’s employees in a common
organizational framework.
Finally, wholly owned entry modes facilitate “learning by doing” where specific knowledge
about host country technologies, their operational competency requirements, and their
complementarity and compatibility with the entrant firm’s proprietary technology, are revealed
through trial and error (Arora/Fosfuri, 2000). Where market based entry modes enable relatively
broader technological learning, wholly owned entry modes enable a much deeper learning as a
result of doing business in a particular foreign setting (Almor et al., 2006; Hirsch, 1989).
Nevertheless, acquiring both broad and deep knowledge is likely to be the most powerful way for
a firm to enhance its technological competitive advantage.
At the value chain activity level, increased entry mode diversity should thus enable highly
technology-intensive firms to learn from multiple foreign partners with whom they interact in
different contractual ways. At the host market level, increased entry mode diversity of
technology-intensive firms may be motivated by the learning opportunities arising from
9
simultaneously conducting R&D, production, distribution, and servicing activities under different
modes in a given host country. This is so since a firm's technological knowledge is likely to have
an effect not only on the R&D function but on all value chain activities. Firms with greater
absorptive capacity are likely to have a greater capacity to learn from such diverse entry modes.
Since greater technological knowledge intensity is associated with greater absorptive capacity it
therefore follows that highly technology-intensive firms are likely to engage in more diverse entry
modes which will serve as a vehicle for obtaining more diverse technological knowledge through
the use of market based-, contractual, partly owned, and wholly owned entry modes. We therefore
hypothesize that:
Hypothesis 2: Technological knowledge intensity is positively associated with entry mode
diversity.
Data and methods
Our hypotheses were tested on data obtained through a survey of Israel’s leading publicly traded
industrial firms. The data was collected for the years 1995 and 1999. A time span of four years
was considered long enough so that changes in, and additions to, the firms’ entry modes could be
observed, while not long enough as to introduce a large amount of entries and exits (ShyamKumar, 2009). The dataset is quite unique as it portrays different entry modes at both the activity
and area levels. This refined level of aggregation on entry modes data does not exist, to the best
of our knowledge, in publicly available secondary datasets and is essential for testing hypotheses
relating entry mode diversity.
The original list included Israel’s one hundred and fifty largest industrial firms. Combined
exports by these 150 firms represented about 80 percent of Israel’s industrial exports in 1999. The
list was based on data received from Israel’s Ministry of Industry and Trade and data provided by
Dun & Bradstreet (2000). After eliminating foreign affiliates, conglomerates and firms which
were not publicly traded we were left with a sample of 101 firms. To obtain a balanced panel we
10
further eliminated all firms with missing data for any variable for either of the years of 1995 and
1999. Hence, the final sample consisted of 67 firms that provided useable information, including
questionnaire dataiv. Comparisons between the 67 participating firms and the 34 non-participating
firms did not show evidence of any response bias in terms of firm sales, number of employees,
age, industrial classification and percentage of foreign sales.
As noted above, the chosen dataset is unique compared to traditional datasets as it includes
data on the specific entry modes of firms in specific host markets, and is elaborated for four value
chain activities (R&D, production, distribution and customer support) and six major regions
(United States, Rest of America, European Union, Rest of Europe, South East Asia, and Rest of
the World). Since entry mode data collection on per country and per value chain activity level is
extremely complex we decided to focus on region-specific entry modes at the value chain activity
level. This approach is quite common in extant literature (e.g. Almor et al., 2006;
Kim/Hwang/Burgers, 1993; Rugman/Verbeke, 2004; Yeung/Poon/Perry, 2001) and reflects the
tendency of firms to configure their operations at a regional, rather than at a country level. Such
an approach is especially feasible for small and medium-sized firms which are resource
constrained. As shown later, this firm size characteristic fits our sample well.
Dependent variables
A firm is defined as having a foreign entry if it performs, or have other organizations performing
on its behalf, value chain activities in a certain foreign location. Hence, for each value chain
activity and each region it was assessed whether one or more of the following categories of entry
modes could be assigned:
1. Market based (e.g. arms length transactions with an agent/distributor who performs
distribution activities for the firm)
2. Contractual (e.g. formalized strategic alliance or original equipment manufacturer (OEM)
relationship with local firm)
11
3. Partly Owned (e.g. joint venture with local firm)
4. Wholly Owned (e.g. wholly owned greenfield or acquired subsidiaries conducting R&D,
production, sales, or customer support).
Overall, there were 204 market based entry modes in our sample in 1995 (and 251 in 1999),
38 (102) contractual entry modes, 5 (32) partly owned entry modes, and 297 (427) wholly owned
entry modes. This entry mode classification served as the basis for computing the three measures
of entry mode diversity, following the aggregation levels of diversity suggested in the conceptual
framework.
Area-level diversity describes the variation in entry modes across value chain activities
within a given location. For each area (region) in which the firm had at least one foreign entry, we
calculated an entropy measure of its entry modes, defined as

4
i 1
mi ln( 1 / mi ) , where mi is the
share of the firm’s entry modes in that area that fall into category i as defined above. These arealevel entropy values were then averaged over the number of regions in which the firm had activity
to arrive at its overall area-level entry mode diversity. Entropy is commonly used to measure
diversity (e.g. Hitt/Hoskisson/Kim, 1997). In the context of this study it has the advantage that it
does not only take into account the number of different entry modes used by the firm but also the
distribution of entries across these entry modes. Nevertheless, we also tried a simple count
measure and got very similar results from our model (not reported here). This is not surprising as
the two measures were highly correlated.
Activity-level diversity describes a firm’s tendency to vary its entry modes of a specific
value chain activity across locations (regions). For each value chain activity, we therefore
measured the diversity of entry modes worldwide using an entropy measure similar to the one
defined above, and these activity-level entropy measures were then averaged over the number of
12
activities in which the firm had foreign entries to arrive at the firm’s overall activity-level entry
mode diversity.
These two variables – area- and activity-level diversity – capture variations along the two
dimensions of the entry mode diversity matrix (cf. Figure 1). For example, a firm which always
uses joint ventures for production and always wholly-owned subsidiaries for R&D would have a
higher degree of area-level diversity than of activity-level diversity as it does not standardize its
governance form within the individual locations. Conversely, a firm using wholly-owned
subsidiaries for all activities in Europe and joint ventures for all activities in Asia would have a
higher degree of activity-level diversity than of area-level diversity as it does not distinguish
between different value chain activities in its governance forms.
Finally, corporate entry mode diversity uses the entry modes found in the entire entry mode
diversity matrix of the firm as an input to calculate an entropy measure of diversity. This captures
variations along both the area and business activity dimensions.
Independent Variable
Technological knowledge intensity, as defined earlier, represents the level of technological
knowledge embodied in the firm’s output. Following earlier studies (e.g. Almor et al., 2006;
Cohen/Levinthal, 1990; Hashai/Almor, 2008; Jones, 1999), we measured this variable as the ratio
of R&D expenditures to sales. This ratio reflects the investment share directed towards the
creation and absorption of technological knowledge and hence is a major source of firms’
technological knowledge (Hashai/Almor, 2008). Naturally, not all R&D investments are likely to
result in increased technological knowledge. However, on average, higher outlays (as a
proportion of total sales) on the creation of technological knowledge are expected to result in
higher levels of such knowledge. R&D expenditures were used by Cohen and Levinthal (1989,
1990) as an indication of firms’ absorptive capacity - a concept which our second hypothesis
builds upon. The R&D per sales ratio in our sample was heavily skewed to the left, so we
13
performed logarithmic transformations on it in order to bring skewness values down from above 3
to below 0.5.
Control Variables
We also used several control variables to ensure that our results really captured the effect of
technological knowledge intensity on entry mode diversity and not any spurious relation caused
by, for example, differential learning needs caused by technology-intensive firms being smaller or
larger, younger or older, more or less internationalized, or performing more or less value chain
activities than other firms. One may argue that a number of “liabilities” unrelated to technological
intensity would lead firms to rely more heavily on learning from their agents and partners, and
thereby influence their foreign entry mode diversity. This argument is relevant for relatively small
firms (facing a liability of smallness), for young firms (a liability of newness), as well as for firms
that are relatively less internationalized and would need to overcome their liability of foreignness
(Contractor/Kundu/Hsu, 2003; Coviello/Munro, 1997; Lu/Beamish, 2004). Such learning needs
often do not relate to technological aspects but rather to local market information and knowledge
in foreign countries, yet they are likely to result in greater engagement in multiple collaborations
of different types and hence in greater entry mode diversity. We therefore need to control for the
possible effects of firm size, age, and level of internationalization when analyzing the relationship
between technological knowledge intensity and entry mode diversityv.
We controlled for firm size, measured as total revenues (in USD) in a given year. As was
the case for R&D intensity, firm size was heavily skewed to the left and therefore transformed
with logarithms. The year of establishment of the firm—effectively, the inverse of firm age—was
used to control for the impact of accumulated managerial experience on entry mode diversity.
Internationalization level was measured by the international diversity of the firm’s foreign
operations, operationalized with an entropy measure based on its sales distribution across the
14
different foreign regions, i.e. as

6
i 1
pi ln( 1 / pi ) where pi is the share of the firm’s international
sales generated in region i.
We also controlled for the firm’s foreign value chain scope, based on an expectation that
firms performing a larger variety of value chain activities in foreign countries also have an
opportunity to use a greater variety of entry modes. We therefore counted the number of activities
with entry modes in each region where the firm operates (ranging from 1-4), and averaged this
count over the number of regions in which the firm operates.
Dummy variables were used to control for industry effects (such as: per industry regulation,
industry-specific transaction costs, and industrial organization) on entry mode choice and hence
on entry mode diversity. Our sample did not include conglomerates (all firms operated in a single
industry), so we could classify the firms in our sample into the following industries: (1)
chemicals; (2) food & beverage; (3) metal; (4) rubber, plastic, wood & paper; (5) textile &
clothing; (6) electronics and computer hardware; (7) software; (8) telecommunication; (9)
pharmaceuticals and (10) other. After controlling for other effects five of these industries were
identified as having relatively more diversified entry modes than other industries: Rubber, plastic,
wood & paper, textile and clothing, electronics and computer hardware, telecommunication and
metal. Industry dummies for these five industries were therefore used as control variables.
Table 1 depicts the descriptive statistics and correlations of our sample. The mean
establishment year of the firms in the sample was 1975. The average sales revenue was USD
128.0 million (92.3 million in 1995 and 163.6 million in 1999), and R&D expenditures
constituted 13 percent of revenue (12 percent in 1995 and 14 percent in 1999). This implies that
the firms in our sample are typically small to medium sized, but with high growth rates, and that
many of them can be considered R&D-intensive. These firms have a slightly higher level of
activity-level entry mode diversity than of area-level entry mode diversity; note however that
15
there are high correlations between the three measures of diversity. Overall entry mode diversity
(corporate level) increased from 0.42 to 0.56 between 1995 and 1999.
[Insert Table 1 about here]
We used panel data models to analyze our sample. Panel data models allow estimation of
cross-sectional (firm) effects, time effects, or both. Initially we estimated all three types of models
to evaluate the importance of each of these two dimensions. The two-way models with both time
and firm effects were almost identical to the one-way cross-sectional models, and the time effect
was insignificant for all dependent variables except corporate entry mode diversity where it was
only significant at the p=0.05 level. Therefore, we concluded that incorporating time-varying
intercepts or errors would not justify the resulting decline in parsimony and degrees of freedom,
and we proceeded to estimate a series of one-way models with only firm-specific effects.
For each of the three dependent variables we developed three models: a pooled OLS
regression, a fixed effects model allowing for firm-specific intercepts, and a random effects
model treating the error term as firm-specific. Each of these models is reported both with and
without the control variables, i.e. firm size, international diversity, value chain scope of foreign
operations, industry, and firm year of establishment. Note that the traditional fixed effects
estimator does not allow time-invariant control variables (industry and firm year of
establishment) since these are perfectly collinear with the firm dummies. Hence, to include these
variables in the fixed effects model we used the unit effect vector decomposition technique
developed by Plumper and Troeger (2004).
In this approach the estimated firm-specific intercepts are regressed on the time-invariant
variables and the residual from this regression is used as a predictor in a pooled OLS regression
along with the time-varying and time-invariant variables. This effectively decomposes the firmspecific fixed effect into two orthogonal components: one which is explained by the timeinvariant variables – in our case, an industry-specific and age-related component – and a residual
16
component of firm effects not explained by these variables (and hence caused by other,
unobserved variables). While it produces the same R-square, the technique is more efficient than
the fixed effects model, especially if the time-varying independent variables are “almost timeinvariant” and if the sample is small (as in our case). It has also been shown in Monte Carlo
simulations to outperform the pooled OLS, random effects, and Hausman-Taylor instrumental
variables approaches in terms of consistency and unbiasedness (Plumper/Troeger, 2004).
Results
The results of our panel data models regressions are presented in Tables 2 - 4. For each model we
present the regression results with and without the control variables. The reported coefficients are
standardized betas, which makes us able to compare the impact of different variables. The
interpretation is such that one standard deviation change in an independent variable leads to β
standard deviations change in the dependent variable, where β is the coefficient reported in the
table. Overall we use 134 observations (one observation per year (1995, 1999) for each of the 67
firms).
[Insert Tables 2-4 about here]
For all dependent variables, adding the fixed firm-specific effects to the pooled OLS
regression increases the variance explained from about 30 percent to about 90 percent. The F-test
confirms that these group effects are significant, which implies that the pooled OLS regression
without group effects may be biased. The pooled OLS regression is also rejected by the
significance of the LM statistic, which in all cases favors the random effects model
(Breusch/Pagan, 1979).
For all three diversity measures, the Hausman m-value is insignificant, implying that the
estimates produced by the fixed and random effects models are similar and that the random
effects model is not biased (Hausman, 1978). A casual comparison of the coefficients confirms
17
this. The somewhat lower significance for the fixed effects coefficients can be attributed to the
lower efficiency of this model and the large share of variance captured by the firm dummies,
which reflects the general advantage of using a random effects specification in small samples.
Alternatively, the vector decomposition model (model 4 in all three tables) is similar to the fixed
effects model but more efficient. The results of all the entry mode diversity models are generally
robust to different model specifications. Variance inflation factors are reported for model 4, and
as they are all quite low (much lower than the recommended threshold of 10), multicollinearity
can be assumed not to significantly bias the results (Neter/Wasserman/Kutner, 1990).
Overall, the results of all the models presented in Tables 2 - 4 indicate that R&D intensity is
positively correlated to the three measures of entry mode diversity, although this correlation
seems to be more significant for area-level and corporate entry mode diversity than for activitylevel entry mode diversity (model 4 in Table 3 is significant, but models 2 and 6 are not). Hence,
hypothesis 2 is strongly supported while hypothesis 1 is rejected for area-level and corporate level
entry mode diversity, indicating that technological learning considerations have a greater impact
on these types of entry mode diversity than internalization and knowledge transfer efficiency
considerations. Hypothesis 2 is weakly supported for activity-level entry mode diversity,
implying that technological learning has less pronounced impact on the benefit of differentiating
entry modes across geographic regions than within geographic regions.
As for the control variables, firm size is positively correlated to all entry mode measures.
Firm age is negatively correlated to all entry mode diversity measures. The impact of these two
control variables in terms of the standardized coefficients is quite similar in magnitude to that of
R&D intensity. The results for international diversity are inconsistent across the three entry mode
diversity measures and value chain scope is positively correlated to area-level (with the largest
impact in term of its coefficient) and corporate entry mode diversity, but negatively correlated to
activity-level entry mode diversity.
18
Finally, industry effects indicate that relative to other industries area-level entry mode
diversity is higher in the metal, textiles & clothing, electronics & computer hardware, and
telecom industries, whereas activity-level entry mode diversity is relatively higher in the
electronics & computer hardware and telecom industries but lower in the rubber, plastic, wood &
paper industry. Corporate entry mode diversity is relatively higher in the electronics and telecom
industries. These industry effects imply that industries in which the value chain relatively easy
can be split into distinct value chain activities tend to have higher area-level diversity (e.g. the
textiles industry which is characterized by multiple stages of R&D, production and marketing)
whereas more technology oriented industries (e.g. telecom) are characterized by multiple entry
modes per value chain activity. Since telecom is traditionally considered to be R&D intensive, the
latter finding further supports our arguments and findings regarding the positive association
between R&D intensity and entry mode diversity.
Discussion
Our analysis of entry mode diversity at the area, activity and corporate levels reveals several
interesting findings. The high correlations among our dependent variables indicate that the three
entry mode diversity types are strongly interrelatedvi. Furthermore, the empirical analysis shows
that the entry mode diversity types are more or less determined by the same organizational
characteristics. As a scale, the three items have Cronbach’s alpha of 0.94 and they all load on the
same factor in a post-hoc confirmatory factor analysis. This could indicate that entry mode
diversity is indeed a firm-level construct.
We were generally able to support the strong positive relationship between technological
knowledge intensity and entry mode diversity. The finding that firms with high technological
knowledge intensity pursue more diverse entry modes is by no means a trivial one in a theoretical
perspective since entry mode diversity is predicted to provide both increased costs and increased
benefits to technology-intensive firms. Our results indicate that the learning opportunities that
19
might be derived from entry mode diversity for highly technology-intensive firms overrule
transaction cost and knowledge efficiency transfer effects. Moreover, these learning opportunities
seem to overrule the impact of the ”not invented here” syndrome which leads firms to prefer selfdeveloped technological knowledge on the expense of others knowledge (Katz/Allen, 1982). One
explanation for this might be the fact that most of the entry modes in our sample are either market
based or wholly owned, hence implying a relatively lesser extent of technological learning from
partner firms through alliances and joint ventures.
While greater levels of R&D intensity is often associated with an increased propensity for
internalization (due to the information asymmetry, uncertainty and knowledge transfer
complexity effects detailed above, see Williamson, 1985; Kogut/Zander, 1993) our results
suggest that the ability of highly technology-intensive firms to build on their high absorptive
capacity (Cohen/Levinthal, 1990) and garner multiple technological learning opportunities drives
them to diversify their foreign market entry modes. Of course, these predictions need not be
mutually exclusive, as R&D-intensity may well lead to entry modes that are both more diversified
and—on average—more internalized. Nevertheless, our conclusion presents a challenge to the
existing foreign market entry mode literature, which traditionally has seemed more preoccupied
with the question which particular entry mode gives the "best" learning opportunity for firms.
Such an approach bears the implicit assumption that learning is an outcome of an either-or choice
of
a
specific
entry mode
which
may lead to greater
or
lesser learning (e.g.
Barkema/Bell/Pennings, 1996). In contrast, this study proposes that different types of
complementary learning can be combined by having a diverse foreign entry mode portfolio, thus
leading firms with the capacity to conduct such learning (for instance, technology-intensive firms)
to pursue greater entry mode diversity.
In this respect it is noteworthy that while this study has focused on the role of technological
learning in affecting entry mode diversity, other types of learning and in particular learning about
20
specific foreign market traits in order to overcome the liability of foreignness may also have an
impact. Our results reveal that firm age is negatively related to entry mode diversity, reflecting
the role of the "liability of newness" in generating a need for learning from agents and partners
through multiple and differing entry modes. On the other hand, contrary to our expectations we
found that size is positively correlated with entry mode diversity. This may merely reflect that
large firms with more diverse operations can have higher diversity between those operations than
can firms with only a limited scope of activity. We did not find any clear impact of firms' level of
internationalization (proxied by their international diversity) and their entry mode diversity.
While our conclusions are general to the entry mode diversity construct, our results also
reveal interesting differences between the different types of diversity, where greater technological
knowledge intensity has a weaker association with activity-level diversity across geographical
areas. Our interpretation of this result is that cross-national/regional differences (culture,
language, laws and regulations, etc.) have a considerable impact on the learning opportunities
faced by technology-intensive firms. Perhaps intra-regional learning is relatively easier than
inter-regional learning - an assumption which is consistent with extant literature on the regional
spread of multinational firms (Rugman/Verbeke, 2004). Alternatively, it may be interpreted to
mean that firms can learn just by performing a certain activity in multiple locations, without
necessarily diversifying its entry modes across these locations, and that other factors than
technological knowledge intensity thus drive activity-level entry mode diversity. Taken together,
this implies that future research on entry mode diversity should aim to explicitly incorporate
factors of cultural distance and host market institutional differences as explanatory variables.
Overall, the findings suggest that managers of technology-intensive firms should consider
their entry mode decisions by taking an overall view of their specific value chain activities and
their worldwide dispersion rather than taking such decisions in isolation for each entry mode.
Such a change in the unit of analysis is likely to have considerable implications on managers’
21
choice of foreign market entry modes and in particular on the implications of engaging in
multiple entry modes as means of fostering organizational learning.
A key contribution, but also a potential limitation, of this study is that it analyses an
understudied population of firms originating in Israel. Our sample differs from traditionally
analyzed samples of firms from the United States or Europe, since the small domestic market of
Israeli firms may lead them to larger foreign markets comparatively early and rapidly in their life
span (Hashai/Almor, 2004). The latter observation is especially true for technological knowledgeintensive firms that need to exploit and explore technological advantages in a world where
product life cycles are getting shorter. The fact that young, inexperienced, knowledge-intensive
firms need to rapidly expand their foreign market presence could lead them to seek more diverse
modes of operations in comparison to US or European knowledge-intensive firms that usually
exhibit high levels of internalization (Buckley/Casson, 1976, 1998). Such diverse entry modes
enable this type of firms to share costs (mainly marketing related costs), build on indigenous
foreign markets familiarity of their partners as well as to learn about foreign complementary
technologies. Thus, our results may be at least partially driven by our sample characteristics, and
additional studies with larger samples of firms from multiple countries are required in order to
enhance the external validity of our results. Future research on this subject may therefore analyze
the entry mode diversity of firms originating in different countries and learn about their
association with technological knowledge intensity.
There are several other avenues for future research on entry mode diversity. First of all,
more research is required in order to analyze the impact of additional factors on entry mode
diversity. Also, while our suggested conceptual framework is not expected to be time-specific, it
may help to analyze entry mode diversity for more recent time periods and over larger spans of
time. Exploring the dynamics in entry mode diversity (Benito et al., 2009; Petersen/Welch, 2002)
is a potentially important line of research, as understanding how and why firms change their entry
22
mode diversity should garner further insights on the process of entry mode selection. From a
dynamic perspective, the finding that age is negatively correlated with entry mode diversity is
particularly interesting. This may imply that younger firms are in greater need for learning
through their agents and partners through multiple entry modes (as discussed above). It could also
be interpreted to mean that in their early years in a certain foreign market, firms experiment with
different types of entry modes, but that after a period of trial and error a relatively narrow set of
the most efficient entry modes is chosen within that specific location.
Furthermore, while we have attempted to control for a large number of variables that may
influence entry mode diversity, we realize that we cannot completely rule out the existence of
alternative explanations of our results. We see our framework as a complementary perspective
that does not invalidate well-known determinants of the entry mode choice, such as
considerations of market failure, risk sharing, and managerial or financial resource constraints. By
leading firms to favor certain entry modes over others, these factors would also have an indirect
impact on entry mode diversity - an effect that we are not able to tease out with our current data
set.
Another possible limitation of the current study is the focus on regions rather than on
specific host countries; hence data collection on entry modes at the host country level may further
garner insights as to entry mode diversity. Future research may also incorporate other important
variables which may potentially affect entry mode diversity but which were not included in our
study. For instance, our finding that firm size is positively associated with entry mode diversity
implies that there are economies of scale in entry mode diversity. Here also the significance of
this result is lower for activity-level diversity, thus indicating that larger firms benefit more from
having diverse entry modes across value chain activities (within a specific region) than from
having diverse entry modes across host markets (for the same value chain activity). Evidently,
firms are able to derive greater economies of scale from entry mode diversity when they are
23
vertically integrated than when they pursue a division of labor for specific value chain activities.
This might imply that the international strategy of firms (international, multidomestic, global and
transnational, see Bartlett/Ghoshal, 1989) is likely to be associated with different levels of entry
mode diversity as a function of value chain disaggregation across host markets and hence is
another avenue for future research.
While technological knowledge intensity was found to be significant in explaining entry
mode diversity, it might still be that, beyond a certain threshold of R&D intensity, transaction
costs reduce the learning benefits of increased diversity. In fact, since we use a logarithmic
transformation of R&D intensity, the functional form of our model contains exactly such an
effect: at higher levels, a larger (untransformed) increase in R&D intensity is needed to induce a
given increase in entry mode diversity. Still, similar studies relating to larger firms and to firms
with a more diverse range of R&D intensity may help us to strengthen the external validity and
functional form of the linkage between technological knowledge intensity and entry mode
diversity. Furthermore, since our findings are not fully consistent for area-level diversity and
activity-level diversity there seems to be much room for studies analyzing the impact of firmspecific characteristics versus region-specific institutional and cultural characteristics on entry
mode diversity.
Finally, a plausible avenue for future research will be to explore the relationship between
entry mode ownership level and entry mode diversity. When looking at the various entry modes
chosen for different value chain activities in different host countries we may not only calculate
various diversity measures but also refer to an “average” degree of ownership. Entry mode
ownership level can be thought of as the “mean” degree of ownership or internalization across a
given firm’s value chain, where entry mode diversity can be thought of as the “variance” of such
ownership degrees. Both the ownership and diversity of entry modes are potentially important
factors as they enhance our conceptualization of foreign market entry modes from an ordinal,
24
categorical variable to a continuous variable which may be characterized by its mean and
variance. This should also pave the way for investigating the performance implications of entry
mode ownership and diversity. Unravelling the relationship between entry mode ownership and
diversity and performance has several empirical complexities (Shaver, 1998), but is of utmost
importance for better understanding the normative implications of the foreign entry mode choice.
25
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35
Figure 1. Entry mode diversity matrix.
Host Market
Manufacturing
Distribution
Service
.
.
.
.
.
.
.
1
.
.
.
.
.
2
.
(mij)
.
.
.
.
3
.
.
.
.
.
.
4
.
.
.
.
.
.
5
.
.
.
.
.
.
6
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
M=
J
R&D
I
Source: Adapted from Petersen et al. (2008)
36
Table 1. Descriptive Statistics and Correlations.
Variable
Mean
Median St. Dev. 1
1. Area-Level Entry Mode Diversity
0.30
0.23
0.34
-
2. Activity-Level Entry Mode Diversity
0.35
0.32
0.34
0.78**
-
3. Corporate Entry Mode Diversity
0.49
0.59
0.42
0.87**
0.87**
-
4. Technological Knowledge Intensity
0.13
0.07
0.20
0.36**
0.34**
0.34**
-
5. Firm Size (Sales in millions of USD)
128.0
54.9
200.3
0.05
-0.11
0.03
-0.41**
-
6. International Diversity
1.03
1.09
0.35
0.18*
0.19*
0.20*
-0.03
0.21*
-
7. Value Chain Scope
1.92
2.00
0.74
0.34**
0.10
0.21*
0.34**
0.15
-0.03
-
8. Establishment Year
1975
1983
17.6
0.27**
0.34**
0.27**
0.40
-0.42
-0.02
0.13
* Significant at p=0.05
** Significant at p=0.01
2
3
4
5
6
7
Table 2. Panel data regression analysis (dependent variable = Area-level entry mode
diversity, standardized coefficients).
Pooled OLS
Fixed Effects (OLS)
Random Effects (GLS)
VIF
Model
1
2
3
4
5
6
4
R&D intensity
0.35**
0.30**
0.20
0.21**
0.30**
0.25*
2.12
Dependent
variable:
Area-Level
Entry
Mode Diversity
Firm Size
0.24*
0.20**
0.20*
1.72
Establishment Year
0.19*
0.16**
0.18
1.41
International Diversity
0.21*
-0.02
0.09
1.27
Value Chain Scope
0.24**
0.58**
0.60**
1.60
Metal
0.11
0.15**
0.12
1.30
Rubber/Plastic
-0.03
0.03
-0.01
1.31
Textiles
0.13*
0.14**
0.13
1.44
Electronics
0.13
0.18**
0.18
1.28
Telecom
0.23*
0.14**
0.13
1.25
R2
0.13
0.31
0.86
0.91
F
19.1**
5.5**
5.3**
76.5**
0.08
0.31
Hausman m
0.98
-
Breusch-Pagan LM
30.7**
31.8**
67
67
N
67
67
67
67
Intercept, firm dummies (model 3), and residual firm-specific effects (model 4) suppressed.
F-test reported for model 3 is a test of fixed effects, i.e. joint significance of the firm dummies. F-test for
model 4 is a test of the entire model including independent variables and firm dummies.
Time-invariant control variables explain 11 per cent of the firm-specific effect (model 4).
Model 4 t-values deflated by 66 degrees of freedom to compensate for the three-stage approach.
* Significant at p=0.05
** Significant at p=0.01
Table 3. Panel data regression analysis (dependent variable = Activity-level entry mode
diversity, standardized coefficients).
Dependent
Pooled OLS
variable:
Activity-Level
Fixed Effects (OLS)
Random Effects (GLS)
VIF
Entry
Mode Diversity
Model
1
2
3
4
5
6
4
R&D intensity
0.34**
0.20
0.04
0.16*
0.24**
0.17
2.13
Firm Size
0.12
0.25**
0.16
1.72
Establishment Year
0.21*
0.28**
0.24*
1.41
International Diversity
0.21*
-0.01
0.13
1.29
Value Chain Scope
-0.01
-0.20**
-0.04
1.53
Metal
0.04
-0.04
0.01
1.29
Rubber/Plastic
-0.06
-0.13*
-0.08
1.31
Textiles
0.02
-0.08
-0.02
1.44
Electronics
0.22*
0.16**
0.20
1.28
Telecom
0.22*
0.25**
0.23*
1.25
R2
0.12
0.29
0.86
0.87
F
17.3**
4.9**
5.4**
49.6**
0.05
0.19
Hausman m
3.7
-
Breusch-Pagan LM
29.6**
25.1**
67
67
N
67
67
67
67
Intercept, firm dummies (model 3), and residual firm-specific effects (model 4) suppressed.
F-test reported for model 3 is a test of fixed effects, i.e. joint significance of the firm dummies. F-test for
model 4 is a test of the entire model including independent variables and firm dummies.
Time-invariant control variables explain 29 per cent of the firm-specific effect (model 4).
Model 4 t-values deflated by 66 degrees of freedom to compensate for the three-stage approach.
* Significant at p=0.05
** Significant at p=0.01
39
Table 4. Panel data regression analysis (dependent variable = Corporate entry mode
diversity, standardized coefficients).
Dependent
variable:
Pooled OLS
Fixed Effects (OLS)
Random Effects (GLS)
VIF
Model
1
2
3
4
5
6
4
R&D intensity
0.34**
0.29*
0.12
0.19**
0.26**
0.23*
2.13
Corporate Entry Mode
Diversity
Firm Size
0.26*
0.23**
0.26**
1.72
Establishment Year
0.20*
0.19**
0.20
1.41
International Diversity
0.29*
-0.07
0.07*
1.29
Value Chain Scope
0.07
0.35**
0.18
1.53
Metal
0.08
0.09
0.07
1.29
Rubber/Plastic
-0.08
-0.04
-0.08
1.31
Textiles
0.09
0.07
0.07
1.44
Electronics
0.12
0.09*
0.11
1.28
Telecom
0.19*
0.24**
0.22
1.25
R2
0.11
0.27
0.86
0.89
F
16.8**
4.62**
5.5**
88.7**
0.06
0.22
Hausman m
1.8
-
Breusch-Pagan LM
31.2**
29.2**
67
67
N
67
67
67
67
Intercept, firm dummies (model 3), and residual firm-specific effects (model 4) suppressed.
F-test reported for model 3 is a test of fixed effects, i.e. joint significance of the firm dummies. F-test for
model 4 is a test of the entire model including independent variables and firm dummies.
Time-invariant control variables explain 15 per cent of the firm-specific effect (model 4).
Model 4 t-values deflated by 66 degrees of freedom to compensate for the three-stage approach.
* Significant at p=0.05
** Significant at p=0.01
40
Endnotes
i
While the term "entry mode" seems to refer to the starting of operations in a foreign market, traditionally it
is also used when describing the long term operations of a firm in a foreign market, regardless of its timing.
In what follows we therefore adopt this notion and use the term "entry mode" to portray the variety of long
term foreign operation modes.
ii
Often used in extant research under the assumption that it represents the aggregation of transactions that a
given firm faces in a given host market.
iii
It is noteworthy that more than one entry mode may apply to a specific value chain activity in a host
market, hence duplicable value chain activities can be included as the matrix columns.
iv
Data was obtained from the financial statements of the firms and through structured interviews with CEO
and VP level executives.
v
We are in debt to an anonymous reviewer for highlighting this issue.
vi
It is noteworthy that corporate diversity is composed of area and business activity diversities.
41