Predicting the Diversity of Foreign Entry Modes

ReseaRch aRticle

Abstract:0 this paper expands the entry mode literature by referring to multiple modes exerted simul-

taneously 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—in-ternalization 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.

0 analyzing a unique dataset we show that high technological knowledge intensity is strongly associated with high 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.

Keywords: entry mode diversity · Value chain activities · technological knowledge intensity

Manag int Rev (2010) 50:659–681DOi 10.1007/s11575-010-0059-7

Technological Knowledge Intensity and Entry Mode Diversity

Niron Hashai · Christian G. Asmussen · Gabriel R. G. Benito · Bent Petersen

Received: 08.07.2008 / Revised: 09.02.2009 / Accepted: 31.08.2009 / Published online: 17.11.2010© Gabler-Verlag 2010

Dr. N. hashai ()Jerusalem school of Business administration, the hebrew University, Mount scopus, Jerusalem, israele-mail: [email protected]. Prof. c. G. asmussen · Prof. B. Petersencenter for strategic Management and Globalization, copenhagen Business school, Frederiksberg, Denmark

Prof. G. R. G. BenitoDepartment of strategy and logistics, Bi Norwegian school of Management, Oslo, Norway

660 N. hashai et al.

Introduction

Firms’ foreign market entry mode1 choice is one of the most researched topics in interna-tional business (e.g. anand and Delios 1997; Benito et al. 2005; Brouthers and hennart 2007; chen and hennart 2004; Datta et al. 2002; Delios and henisz 2003; erramilli et al. 1997; hennart 1991; Kim and hwang 1992; Kogut and Zander 1993; Madhok 1997; Malhotra et al. 2003; Martin and salomon 2003; tse et al. 1997; Yiu and 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 subsidi-aries, 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 and 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 et al. 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 et al. 2009; Buck-ley and hashai 2004, 2005; hill et al. 1990; Petersen et al. 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 ana-lysis of multiple entry modes of a firm across its value chain and across foreign markets. this approach implies that entry mode 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, inter-nalization theory (Buckley and casson 1976, 1998; Rugman 1981) as well as knowl-edge transfer efficiency considerations (Kogut and Zander 1993; Martin and 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 and 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.

661technological Knowledge intensity and entry Mode Diversity

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 concep-tual 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.

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 level2, a few studies have indicated the importance of referring to a firm’s variety of entry modes (Asmussen et al. 2009; Ben-ito et al. 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 and casson 1998; Buckley and hashai 2004, 2005; casson 2000). Other studies have empirically shown that firms often do not stick to one particu-lar entry mode, but instead simultaneously employ a variety of entry modes at the value chain activity level (Benito and Welch 1994; Fina and Rugman 1996; Petersen and 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 acti-vity is performed in the given host market. the general form of the entry mode matrix is presented in Fig. 1.

the matrix depicts three levels of aggregation in which entry mode diversity can be discussed3:0 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, mi2, . . . , miJ ) of activity-level decisions. the larger the variation in entry modes within this vector, the higher the area-level diversity.

0 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 ≡

�m1j , m2j , . . . , mIj

.

0 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 and Delios 1997; Delios and henisz 2003; erramilli et al. 1997; Gatig-non and anderson 1988; Padmanabhan and cho 1999; tan et al. 2001; tse et al. 1997; Yiu and Makino 2002). technological knowledge intensity represents the level of tech-nological knowledge contained in each unit of output that the firm produces (Almor et al. 2006; hashai and 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 and casson 1976; Rugman 1981; teece 1986a). The theory highlights firms’ technological 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 firm-specific know-how (most often referring to techno-logical 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 techno-logical 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 presum-

Fig. 1: entry mode diversity matrix. (source: adapted from Petersen et al. (2008))


ably 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 and McFetridge 1984) between the focal firm and potential collaborators cou-pled with the high uncertainty of managing and coordinating multiple entry modes for highly technology-intensive firms (Contractor 1990; Kim and hwang 1992; Osborn and 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 and almor 2008; Kogut and Zander 1992, 1993; Martin and 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 and salomon 2003).

Greater technological knowledge intensity often implies greater complexity of cod-ing and decoding the transferred knowledge (Kogut and Zander 1992, 1993; Martin and 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 coordination of knowledge transfer between multiple parties engag-ing 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 arrange-ments while avoiding the appropriation of its knowledge by other firms.

Overall, the above views imply that higher 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 et al. 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 et al. 2000; Milliken and Martins 1996). this is so since knowledge diversity stimulates problem-solving and enhances innovation by pro-viding multiple viewpoints (Page 2007). Highly technology-intensive 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 absorptive capacity is contingent on the availability of external learning opportunities (cohen and levinthal 1989).

In the case of internationalizing firms which operate across national boundaries, expo-sure to diverse technological knowledge is particularly pronounced (Ghoshal 1987; Zahra et al. 2000). Firms which already possess strong technological capabilities are motivated to seek out technological knowledge abroad, in order to enhance their knowledge diver-sity (cantwell and Janne 1999; chung and 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 com-petitive advantage (Vermeulen and 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 interna-tionalizing 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 spe-cific to their markets (Almor et al. 2006; hirsch 1989; Zahra et al. 2000). Porter (1998) suggested that technological innovation is propelled by having a “window on the mar-ket”, 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 and White iii 2008; Petersen and 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.

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 a means of fostering competitive advantage (Dyer and Singh 1998; Kale et al. 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 et al. 1997; Reuer and tong 2005). in particular, technological knowledge which is teachable but not codifiable (Kogut and 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 technol-ogy, are revealed through trial and error (arora and 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 knowl-edge is likely to be the most powerful way for a firm to enhance its technological competi-tive 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 simultaneously conducting R&D, production, distribution, and servicing activities under different modes in a given host country. This is so since a firm’s tech-nological 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 (Kumar 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 to entry mode diversity.

The original list included Israel’s one hundred and fifty largest industrial firms. Com-bined exports of these 150 firms represented about 80% 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, con-glomerates and firms which were not publicly traded we were left with a sample of 101


firms. To obtain a balanced panel we 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 data4. comparisons between the 67 participating firms and the 34 non-participating firms did not show evi-dence 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 elabo-rated for four different 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 com-mon in extant literature (e.g. almor et al. 2006; Kim et al. 1993; Rugman and Verbeke 2004; Yeung et al. 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 per-forming 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)

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 com-puting 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

4i=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 area-level 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 diver-sity. entropy is commonly used to measure diversity (e.g. hitt et al. 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 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. Fig. 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 govern-ance 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 technologi-cal knowledge embodied in the firm’s output. Following earlier studies (e.g. Almor et al. 2006; cohen and levinthal 1990; hashai and 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 and 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 performed logarithmic transforma-tions 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 “liabil-ities” 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 internation-alized and would need to overcome their liability of foreignness (contractor et al. 2003; coviello and Munro 1997; lu and 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 diversity5.

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 trans-formed 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 diver-sity of the firm’s foreign operations, operationalized with an entropy measure based on its sales distribution across the different foreign regions, i.e. as

6i=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 re- gulation, 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 & 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 & clothing, electron-ics & 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 Us 128.0 mio. $ (92.3 mio in 1995 and 163.6 mio in 1999), and R&D expenditures consti-tuted 13% of revenue (12% in 1995 and 14% 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 activ-ity-level entry mode diversity than of area-level entry mode diversity; note however that 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.


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 mod-els 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 con-cluded 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 (indus-try and firm year of establishment) since these are perfectly collinear with the firm dum-mies. 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-invari-ant 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 effec-tively decomposes the firm-specific fixed effect into two orthogonal components: One which is explained by the time-invariant variables—in our case, an industry-specific and age-related component—and a residual component of firm effects not explained by these

Table 1: Descriptive statistics and correlationsVariable Mean Median sD 1 2 3 4 5 6 71. area-level entry mode diversity

0.30 0.23 0.34 1

2. activity-level entry mode diversity

0.35 0.32 0.34 0.78** 1

3. corporate entry mode diversity

0.49 0.59 0.42 0.87** 0.87** 1

4. technological knowledge intensity

0.13 0.07 0.20 0.36** 0.34** 0.34** 1

5. Firm size (sales in millions of UsD)

128.0 54.9 200.3 0.05 −0.11 0.03 −0.41** 1

6. international diversity

1.03 1.09 0.35 0.18* 0.19* 0.20* −0.03 0.21* 1

7. Value chain scope

1.92 2.00 0.74 0.34** 0.10 0.21* 0.34** 0.15 −0.03 1

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


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 time-invariant” 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 and troeger 2004).

Results

the results of our panel data models regressions are presented in tables 2, 3 and 4. For each model we present the regression results with and without the control variables. the reported coefficients are standardized betas, which make 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,

Table 2: Panel data regression analysis (dependent variable = Area-level entry mode diversity, standardized coefficients)Dependent variable: area-level entry mode diversity

Pooled Ols Fixed effects (Ols)

Random effects (Gls)

ViF

Model 1 2 3 4 5 6 4R&D intensity 0.35** 0.30** 0.20 0.21** 0.30** 0.25* 2.12Firm size 0.24* 0.20** 0.20* 1.72establishment year 0.19* 0.16** 0.18 1.41international diversity 0.21* −0.02 0.09 1.27Value chain scope 0.24** 0.58** 0.60** 1.60Metal 0.11 0.15** 0.12 1.30Rubber/plastic −0.03 0.03 −0.01 1.31textiles 0.13* 0.14** 0.13 1.44electronics 0.13 0.18** 0.18 1.28telecom 0.23* 0.14** 0.13 1.25

R2 0.13 0.31 0.86 0.91 0.08 0.31F 19.1** 5.5** 5.3** 76.5**hausman m 0.98 –Breusch-Pagan LM 30.7** 31.8**N 67 67 67 67 67 67Intercept, firm dummies (model 3), and residual firm-specific effects (model 4) suppressedF-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 dummiesTime-invariant control variables explain 11% 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


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).

For all dependent variables, adding the fixed firm-specific effects to the pooled OLS regression increases the variance explained from about 30% to about 90%. The F-test con-firms that these group effects are significant, which implies that the pooled OLS regres-sion without group effects may be biased. the pooled Ols regression is also rejected by the significance of the LM statistics, which in all cases favors the random effects model (Breusch and 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 coef-ficients confirms 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 cap-tured by the firm dummies, which reflects the general advantage of using a random effects

Table 3: Panel data regression analysis (dependent variable = Activity-level entry mode diversity, standardized coefficients)Dependent variable: activity-level entry mode diversity



ViF

Model 1 2 3 4 5 6 4R&D intensity 0.34** 0.20 0.04 0.16* 0.24** 0.17 2.13Firm size 0.12 0.25** 0.16 1.72establishment year 0.21* 0.28** 0.24* 1.41international diversity

0.21* −0.01 0.13 1.29

Value chain scope −0.01 −0.20** −0.04 1.53Metal 0.04 −0.04 0.01 1.29Rubber/plastic −0.06 −0.13* −0.08 1.31textiles 0.02 −0.08 −0.02 1.44electronics 0.22* 0.16** 0.20 1.28telecom 0.22* 0.25** 0.23* 1.25



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 et al. 1985).

Overall, the results of all the models presented in tables 2, 3 and 4 indicate that R&D intensity is positively correlated with 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 activity-level 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 techno-logical learning considerations have a greater impact on these types of entry mode diver-sity than internalization and knowledge transfer efficiency considerations. Hypothesis 2

Table 4: Panel data regression analysis (dependent variable = Corporate entry mode diversity, standardized coefficients)Dependent variable: corporate entry mode diversity



ViF

Model 1 2 3 4 5 6 4R&D intensity 0.34** 0.29* 0.12 0.19** 0.26** 0.23* 2.13Firm size 0.26* 0.23** 0.26** 1.72establishment year 0.20* 0.19** 0.20 1.41international diversity

0.29* −0.07 0.07* 1.29

Value chain scope 0.07 0.35** 0.18 1.53Metal 0.08 0.09 0.07 1.29Rubber/plastic −0.08 −0.04 −0.08 1.31textiles 0.09 0.07 0.07 1.44electronics 0.12 0.09* 0.11 1.28telecom 0.19* 0.24** 0.22 1.25



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 meas-ures. Firm age is negatively correlated with 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 corre-lated with area-level (with the largest impact in term of its coefficient) and corporate entry mode diversity, but negatively correlated with activity-level entry mode diversity.

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 between our dependent variables indi-cate that the three entry mode diversity types are strongly interrelated6. 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 analy-sis. This could indicate that entry mode diversity is indeed a firm-level construct.

We were generally able to support the strong positive relationship between technologi-cal knowledge intensity and entry mode diversity. The finding that firms with high tech-nological 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 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 self-developed technological knowl-edge on the expense of others knowledge (Katz and 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 trans-fer complexity effects detailed above, see Williamson 1985; Kogut and Zander 1993) our results suggest that the ability of highly technology-intensive firms to build on their high absorptive capacity (cohen and levinthal 1990) and garner multiple technologi-cal 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 et al. 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 techno-logical learning in affecting entry mode diversity, other types of learning and in particular learning about specific foreign market traits in order to overcome the liability of foreign-ness 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 internationali-zation (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 con-sistent with extant literature on the regional spread of multinational firms (Rugman and 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 con-sider 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’ 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 tradition-ally 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 and almor 2004). the latter observation is especially true for technological knowledge-intensive firms that need to exploit and explore techno-logical 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 internali-zation (Buckley and 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 technolo-gies. 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 and Welch 2002) is a potentially important line of research, as under-standing how and why firms change their entry 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 comple-mentary 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 fac-


tors 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 asso-ciated 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 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 transna-tional, see Bartlett and 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 inten-sity 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 techno-logical 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 firm-specific 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 vari-ous 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 owner-ship 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 diver-sity of entry modes are potentially important factors as they enhance our conceptualiza-tion of foreign market entry modes from an ordinal, 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 diver-sity. Unravelling the relationship between entry mode ownership and diversity and per-formance has several empirical complexities (shaver 1998), but is of utmost importance for better understanding the normative implications of the foreign entry mode choice.


Acknowledgements: We wish to thank Ulf andersson, Ram Mudambi and the anonymous re-viewers 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 Entrepreneur-ship at the hebrew University.

Endnotes

1 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 mar-ket, 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.

2 Often used in extant research under the assumption that it represents the aggregation of trans-actions that a given firm faces in a given host market.

3 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.

4 Data was obtained from the financial statements of the firms and through structured interviews with ceO and VP level executives.

5 We are in debt to an anonymous reviewer for highlighting this issue.6 it is noteworthy that corporate diversity is composed of area and business activity diversities.

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Predicting the Diversity of Foreign Entry Modes

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