Industrial sector, perceived environmental uncertainty and entry mode strategy

Journal of Business Research 55 (2002) 495 – 507

Industrial sector, perceived environmental uncertainty and entry mode strategy Keith D. Brouthersa,*, Lance Eliot Brouthersb,1, Steve Wernerc,2 a

b

University of East London, Duncan House, High Street, London E15 2JB, UK Division of Management and Marketing, University of Texas at San Antonio, San Antonio, TX, USA c Department of Management, University of Houston, Houston, TX 77204-6283, USA Accepted 1 April 2000

Abstract Utilizing a multi-dimensional measure of perceived environmental uncertainty (PEU), we investigate both similarities and differences between services and manufacturing with respect to the impact of PEU (risk) on their international entry mode strategies. We hypothesize that due to unique product characteristics and modes of delivery, (1) for certain types of PEU (government/political, product market), services and manufacturing will respond similarly, while (2) services will respond quite differently from manufacturing to other particular types of perceived risk. The hypotheses were generally supported, showing that the use of a multi-dimensional measure of PEU is important in gaining a better understanding of strategic behavior in different industries. D 2002 Elsevier Science Inc. All rights reserved. Keywords: Entry mode; Risk; Empirical

Studies by Das and Teng (1996) and Miller (1998) suggest that global corporations may benefit from choosing strategies that take international risk factors into consideration in the strategy formulation process. Gerloff et al. (1991) summarize the risk/uncertainty literature into two categories: contingency views and perceptual views. The more traditional work on uncertainty (Duncan, 1972; Downey et al., 1975; Milliken, 1987) tends to fall into the perceptual views category. ‘‘The perceptual views of environmental uncertainty are process oriented in that they seek to describe stages involved in noticing, interpreting, or learning the environment’’ (Gerloff et al., 1991, p. 750). Conversely, the contingency view attempts to find relationships between ‘‘observed variable levels of environmental uncertainty’’ and the organization’s ‘‘need for ifferentiation and integration’’ (Gerloff et al., 1991, p. 749). Here, we take the contingency view and examine perceived environmental uncertainty’s (PEU’s) impact on mode choice.

* Corresponding author. Fax: +44-208-2233395. E-mail address: [email protected] (K.D. Brouthers). 1 Fax: +1-210-458-5833. 2 Fax: +1-713-743-4652.

As suggested by Sitkin and Pablo (1992), we predict that higher levels of uncertainty will be related to higher levels of risk. We build on the work of Das and Teng (1996), Miller (1992, 1993, 1998), Brouthers (1995) and Vermon (1985). All have suggested that: (1) international risk plays a crucial role in entry strategy formation, and (2) a multidimensional perspective of risk yields a more complete understanding of risk’s impact on entry strategy decisions. More specifically, we focus on how differences between service and manufacturing firms influence risk’s impact on entry mode strategy. Scholars have yet to address this issue. While Miller (1993) found that uncertainty/risk perceptions were consistent across industry sectors, he did not examine how these similar risk perceptions influenced strategic decisions. Brouthers (1995) examined the strategic impact of risk perceptions, but only in one service industry, and did not attempt to identify differences between service and manufacturing firms. Services differ from tangible products, both in physical characteristics and in methods of production and delivery (Li, 1994; Edgett and Parkinson, 1993; Nicoulaud, 1989). For these reasons, service and manufacturing firms may face unique challenges in their foreign market entry and expansion processes (Li, 1994; Erramilli and Rao, 1993; Nicou-

0148-2963/02/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 0 1 4 8 - 2 9 6 3 ( 0 0 ) 0 0 1 5 4 - 5

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laud, 1989; Vandermerwe and Chadwick, 1989; Boddewyn et al., 1986 and may therefore respond quite differently to comparable risk levels. Several scholars have suggested that service providers tend to prefer higher control and lower resource commitment entry modes than do manufacturing firms (Erramilli and Rao, 1993; Sarathy, 1994). Service firms will therefore tend to use more integrated (wholly owned) modes of entry than manufacturing firms, given similar risk conditions (Campbell and Verbeke, 1994; Thomas, 1978; Carman and Langeard, 1980; Erramilli and Rao, 1993). However, this proposition has never been empirically tested. The purpose of this paper is to use a multi-dimensional measure of PEU to predict service providers and manufacturing firms’ international entry mode strategies. In this paper, we hypothesize that given similar risk, services prefer more integrated modes. We further hypothesize that for certain types of risk (government/political, product market) services and manufacturing respond similarly (what we call main effects). We suggest that services respond differently from manufacturing to other particular types of risk (what we call interactive effects). Finally, previous entry mode studies have assumed that firms are risk neutral (e.g. Agarwal and Ramaswami, 1992; Erramilli and Rao, 1993). In this study, we maintain this traditional risk neutrality assumption. To explore the potential differences between manufacturing and service firms, we use the multi-dimensional measure of risk first developed by Miller (1992). This measure, developed from the literature, has been tested for consistency by Miller (1993) and reliability by Werner et al. (1996). The refined measure (dubbed PEU2 by the authors) includes five risk factors: (1) political/governmental policies; (2) product market; (3) materials; (4) macroeconomic; and (5) competitive. Using Werner et al.’s (1996) PEU2 measure, we develop and test six hypotheses, which predict similarities and differences between services and manufacturing firms in their responses to international risk perceptions in choosing their international entry mode strategy. We offer three reasons why the approach presented here adds a relevant set of elements to the emerging international riskstrategy paradigm. First, as Murray and Kotabe (1999) state, only a few studies have empirically examined services (Murray and Kotabe, 1999; Erramilli, 1992, 1996; Erramilli and Rao, 1993; Agarwal and Ramaswami, 1992). These studies tend to concentrate on a variety of factors that may affect mode choice. None of these studies attempts to empirically compare the influence of international risk on entry mode selection for service providers and manufacturing firms in a single study (Kogut and Singh, 1988). In this study, we predict and empirically compare service and manufacturing firms’ responses to PEU. Second, past mode choice studies tend to use a single measure of international risk. For instance, Murray and

Kotabe (1999) use a perceived level of demand uncertainty in their study of US Fortune 500 service firms. Agarwal and Ramaswami (1992) use a single composite index in their analysis of a single US service industry. Erramilli and Rao (1993) use a dummy variable to measure ‘‘country risk’’ when examining US service firm mode choices. Single aggregate measures of risk may mask contingent relationships within specific types of risk (Miller, 1998; Werner et al., 1996). For instance, services providers and manufacturers may respond quite differently to distinctive types of risk. More specifically, we suggest that manufacturing will be more sensitive to materials risk than services because physical products tend to rely more on material inputs than service products (Campbell and Verbeke, 1994). Thus, it is important to incorporate a number of PEU variables into investigations of international risk. In this paper, using the five PEU2 measures developed and tested by Werner et al. (1996), we predict a variety of relationships between different types of PEU and international entry mode strategies for services and manufacturing. Third, what little empirical research has been done on international risk and entry strategy tends to suffer from a US bias (Murray and Kotabe, 1999; Brouthers, 1995; Erramilli and Rao, 1993; Agarwal and Ramaswami, 1992). This study attempts to extend the generalizability of previous research by examining European firms.

1. Theory and hypotheses We hypothesize that due to unique product characteristics and modes of delivery, (1) for certain types of PEU, services and manufacturing will respond similarly (main effects) while (2) services will respond quite differently from manufacturing to other particular types of perceived risk (interactive effects). An explanation of our three main effects and three interactive effects hypotheses is offered below. 1.1. Main effect of sector Given similar risk perceptions, service firms are more likely to prefer integrated (wholly owned) entry modes than manufacturing firms (Carman and Langeard, 1980) for a variety of reasons. First, the level of resource commitment is commonly different between service and manufacturing firms. Manufacturing frequently requires large commitments for plant and equipment. Services typically require fewer resources, especially in the business services sector (consulting, software services, advertising). Because of lower resource commitments, service firms tend to have less exposure to international risks such as nationalization (Erramilli and Rao, 1993; Boddewyn et al., 1986). Thus, even in areas of high international risks, there is a tendency

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for service firms to select more integrated (wholly owned) entry modes because ‘‘. . . control can be acquired at comparatively low expense by many service firms’’ (Erramilli and Rao, 1993, p. 22). Second, because services are typically more people intensive than manufacturing firms, switching costs tend to be much lower (Erramilli and Rao, 1993). Lower switching costs allow service providers to more easily relocate operations if risk becomes too great. Third, Erramilli and Rao (1993, p. 23) suggest that ‘‘the benefits of high integration are so immense for high idiosyncratic firms that they shun shared control (joint venture and independent) modes, regardless of resource commitment.’’ Service providers, due to their more specialized or idiosyncratic nature (professional skills, knowledge, customization) will typically select more integrated entry modes (Erramilli and Rao, 1993). In contrast, in high-risk markets, manufacturing firms will be less willing to use more integrated entry modes in order to minimize resource commitments (Gatignon and Anderson, 1988). Finally, there is a greater need for multi-site locations (Nicoulaud, 1989), speedy entry (Nicoulaud, 1989) and local adaptation (Erramilli and Rao, 1993; Li and Guisinger, 1994) for service providers than for manufacturing firms because typically location is more important for services than for manufacturing (Rushton and Carson, 1989; Boddewyn et al., 1986). Each of these objectives can be achieved more easily through more integrated (wholly owned) entry modes. Service firms do not pursue integrated entry modes exclusively (Erramilli and Rao, 1993). What the literature suggests is that, given similar risk perceptions, service providers tend to prefer higher control, lower resource commitment entry modes than do manufacturing firms (Erramilli and Rao, 1993; Sarathy, 1994). Thus, under risk conditions where manufacturing firms tend to select cooperative modes, service firms are more likely to select integrated (wholly owned) entry modes. Under risk conditions where manufacturing firms tend to select more independent (licensing) entry modes, service providers may prefer more cooperative and/or integrated modes. This leads to the following general hypothesis: Hypothesis 1: In general, given similar aggregate levels of risk perception, service firms will tend to utilize more integrated entry modes than manufacturing firms.

1.2. Main effect of government and political risk Government and political risk refers to instability in government policies that affect the business community, such as laws or regulations affecting international business and/or tariffs on imported goods. The literature suggests that manufacturing firms may choose more integrated entry modes as a result of trade barriers in the host country (Dunning, 1993). Calvet explains the circumstances that

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commonly motivate foreign MNEs to chose more integrated (wholly owned, foreign direct investment) entry modes: Tariffs, other trade barriers (for example, quotas), and, it might be added, non-tariff barriers (for example, regulations for imported goods) are often regarded as a major cause of direct investment. Other things being equal, an increase in trade barriers (or the expectation of their rise) may be the necessary incentive for firms to establish a subsidiary inside the protected market, rather than export to it. (Calvet, 1981, p. 46)

Similarly, Erramilli and Rao (1993) suggest that when government and political risk is high, service firms may choose more integrated entry modes in order to gain access to markets that would otherwise be unavailable to the firm, due to host-country protectionistic policies. Thus, in high political risk markets, both manufacturing (Dunning, 1993; Calvet, 1981) and service firms (Erramilli and Rao, 1993) will favor more integrated entry modes. Hypothesis 2: Greater PEU of governmental/political policies will lead to greater use of integrated entry modes for both manufacturing and services.

1.3. Main effect of product-market risk Product-market uncertainty refers to the unexpected changes in product demand, client’s preferences, and/or changes in the availability of substitute and/or complementary products. When product markets or product quality changes, the intangibility of services creates difficulties for service firms because potential consumers have trouble identifying differences in service offerings (Campbell and Verbeke, 1994; Mitchell and Greatorex, 1993). Intangibility leads to increased risk perception for buyers (Campbell and Verbeke, 1994; Mitchell and Greatorex, 1993; Rushton and Carson, 1989), which creates increased transaction costs for service firms (Boddewyn et al., 1986). Under such conditions, services prefer to internalize processes that minimize transaction costs. Service firms therefore tend to select more integrated entry modes when product-market risk is high (Boddewyn et al., 1986). Similarly, rapidly changing product-markets can also have a major impact on manufacturing firms (Kogut and Kulatilaka, 1994). Under such conditions, manufacturing firms not only frequently need to adapt their products, but also change the way in which they communicate these product changes to the consumer. For these reasons, both manufacturing and service firms will desire high levels of local market knowledge, which are more readily obtained through more integrated entry modes. Thus, in markets with high product-market uncertainty, both manufacturers and service providers will tend to prefer more integrated entry modes (Anderson and Gatignon, 1986). Hypothesis 3: Greater PEU of product-markets will lead to greater use of more integrated modes for both manufacturing and service firms.

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1.4. Interactive effect of competitive risk and sector

1.5. Interactive effect of materials risk and sector

Competitive risk refers to instability in competitor’s prices, strategies, the markets they choose, as well as threats of new entrants both foreign and domestic. Competitive uncertainties are such that even with size, it is difficult to restrict entry into a service business (Trondsen and Edfelt, 1987; Carman and Langeard, 1980; Thomas, 1978). Service industries are usually easy to enter because of the relatively low initial start-up costs (Erramilli and Rao, 1993; Boddewyn et al., 1986). In addition, services are for the most part not patentable and therefore easily duplicated by competitors (Nicoulaud, 1989). Thus, the threat of new entrants is typically greater for service firms than for manufacturing firms (Nicoulaud, 1989; Trondsen and Edfelt, 1987; Thomas, 1978). However, many service firms gain competitive advantage through their ‘‘idiosyncratic nature,’’ which requires specialized skills and knowledge (Bharadwaj et al., 1993; Erramilli and Rao, 1993). In such cases, these ‘‘idiosyncratic assets’’ become the firm’s basis of sustainable competitive advantage. Transaction cost theory suggests that for idiosyncratic assets, full-control modes of entry will be preferred, especially in markets with high risk (Murray and Kotabe, 1999; Erramilli and Rao, 1993). According to Ekeledo and Sivakumar (1998), the best way for services to safeguard the idiosyncratic nature of their processes is through high control modes of entry. Consequently, Boddewyn et al. (1986) and Ekeledo and Sivakumar (1998) suggest that service firms may prefer high control modes of entry in highly competitive markets. Service firms react in this manner for two reasons. First, because of inseparability, many services must be present in the market and face the customer, although initial start-up costs tend to be low (Erramilli and Rao, 1993; Boddewyn et al., 1986). Second, because service firm competitive advantage is normally derived from idiosyncratic resources, safeguarding this advantage from potential competitors may require high control modes of entry (Ekeledo and Sivakumar, 1998). Contrary to this, Kim and Hwang (1992) and Kwon and Konopa (1993) suggest manufacturing firms will be less willing to make resource commitments in markets characterized by high competitive risks. Because of the large number of competitors, entry by integrated mode may be less profitable (Kim and Hwang, 1992; Anderson and Gatignon, 1986). Therefore, in highly competitive markets manufacturing firms tend to prefer more independent entry modes (Kim and Hwang, 1992; Harrigan, 1985). Hence, while manufacturing firms tend to rely on independent modes and export into highly competitive markets, service firms often lack this option and thus have to utilize high control modes of entry.

The uncertain quality of inputs causes different problems for service firms than for manufacturing companies (Trondsen and Edfelt, 1987). Services are commonly people intensive rather than materials intensive (Campbell and Verbeke, 1994). Thus, service firms are less likely to be affected by materials uncertainty than manufacturing firms. Conversely, manufacturing firms are much more threatened by materials risk due, in large measure, to their reliance on physical assets as opposed to personnel assets. Manufacturing firms rely on the physical product as a method of communicating quality, meeting deadlines, and providing customer service (Parasuraman et al., 1985). The tangible nature of manufactured products makes them susceptible to variations in quality, quantity, and timing (Richardson, 1993). For these reasons, where materials risks are high, Hennart (1991), as well as Anderson and Gatignon (1986), suggests that manufacturers prefer more integrated entry modes, rather than relying on local sources of raw materials and/or developing networks of outside suppliers. In markets where materials risks are low, manufacturers are more willing to rely upon local firms (through licensing, franchising, and/or joint ventures), which already possess reliable sources of materials (Hennart, 1991; Anderson and Gatignon, 1986). Thus, the PEU of materials inputs does not seem to affect service firms, but is related to the entry mode choices of manufacturing firms. For this reason, we posit that service firms’ mode choices are relatively unaffected by materials uncertainty. We also hypothesize that manufacturing firms prefer more independent modes of entry in low-risk markets but tend to prefer more integrated modes in high-risk markets.

Hypothesis 4: Greater PEU of competition will lead to greater use of integrated modes for service firms, and greater use of independent modes for manufacturing firms.

Hypothesis 5: Lower PEU of materials inputs will lead to greater use of independent entry modes for manufacturing firms but not for service providers.

1.6. Interactive effects of macroeconomic risk and sector Macroeconomic uncertainty deals with fluctuations in the level of economic activity, inflation, exchange rates, and interest rates that affect services and manufacturing differently. Changes in macroeconomic variables commonly result in price and/or production level changes. For example, increases in inflation may lead to decreased demand for both products and services. This may result in lower production levels or products/services being put into inventory for later sale. Further, inflation may lead to increases in prices because of increases in labor and other costs. Services are often locally produced because of their characteristic of inseparability. For this reason, services are typically more exposed to macroeconomic uncertainty than manufacturing firms (Sarathy, 1994; Erramilli, 1992; Nicoulaud, 1989). For instance, human resource costs represent a

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higher percent of total expenditures for service firms (Campbell and Verbeke, 1994). Changes in macroeconomic activity tend to increase these costs, which may lead to price increases. In addition, because of intangibility, many services cannot be inventoried and sold at a later time. Thus, changes in macroeconomic activity that affect demand may also have a significant impact on service firm costs. To gain better control over these potential fluctuation-in demand/costs and cost/price changes, service firms tend to prefer local control. Local control allows service providers to respond in a timely fashion to macroeconomic fluctuations, e.g. price and production changes can be implemented more quickly. Hence, service providers in markets with high macroeconomic uncertainty tend to prefer more integrated entry modes in order to provide greater control over pricing and production policies (Carman and Langeard, 1980). Conversely, manufacturing firms can gain control of pricing and production policies through a variety of techniques. For instance, in times of high macroeconomic uncertainty, final products can be imported. Second, manufacturing firms can import components from existing outof-country suppliers. Third, manufacturers are able to inventory their products and sell them later (Edgett and Parkinson, 1993). This ability to respond to fluctuations in macroeconomic risk provides a safety net to manufacturing firms that service firms typically lack. For these reasons, we hypothesize that service firms tend to be more sensitive to macroeconomic changes than manufacturing firms. Hypothesis 6: Higher PEU of macroeconomics will lead to greater use of integrated entry modes for service providers but not for manufacturing firms.

2. Method We tested our hypotheses on an original sample of European firms using one-way analysis of variance and discriminant analysis.

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2.1. Sample We tested our hypotheses on the EU500 (see International Management, 1993). The EU500 are the 500 largest firms based in European Union nations. The EU500 includes service organizations like: retailing (Tengelmann, REWE, Carrefour), construction (LdE-Dumez, Bouygues, Poliet), business services (Havas, BET), airlines (British Air, Alitalia), and telecommunications (British Telcom, Stat); as well as manufacturing firms: chemicals (Hoechst, BASF, Bayer), automotive (Daimler-Benz, Volkswagen, Fiat), electronics (Siemens, Philips), and food/consumer products (Unilever, Grand Met, BSN). European companies native to non-member countries, foreign-owned European companies, and subsidiaries of EU-based companies were excluded. Questionnaires were sent to 436 EU500 firms. We were unable to obtain the mailing address for 23 firms on the listing. As in previous US studies, firms that had international sales of less than 10% were excluded from this study (e.g. Habib and Victor, 1991). Of the 436 questionnaires sent to firm headquarters, 64 were returned indicating that the firms elected not to participate, and 79 were returned substantially complete (44 manufacturing firms and 35 from service firms). Thus, our overall response rate was 33%, and our overall survey response rate was 18%. To assess whether the respondents of the completed surveys were representative of the total population, we compared sectors, sales, and number of employees of the two groups. A test of proportions determined that the proportion of respondents who were in the manufacturing sector did not significantly differ ( P < .05) from the proportion of the population in the manufacturing sector (Z = 1.51). The average sales (in millions of ecu) of the total population is 4821 (standard deviation = 7565), while the average sales (in millions of ecu) of the completed respondents is 7335 (standard deviation = 15,247; range 894 – 105,504). A Z test analyzing the mean differences

Table 1 Correlation matrix for variables used in the analyses Mean Standard deviation (1) (2) (3) (4) (5) (6) (7) (8) (9)

PEU2 — competition PEU2 — macroeconomics PEU2 — government policy PEU2 — materials PEU2 — product Entry mode — wholly owned Entry mode — joint venture Entry mode — independent Manufacturing N = 116. * P < .05. ** P < .01.

1 3.13 0.66 1.00 0.30** 0.33** 0.08 0.14 0.11 0.04 0.10 0.09

2 3.09 0.91 1.00 0.63** 0.37** 0.04 0.01 0.07 0.10 0.14

3 3.28 0.71

1.00 0.73** 0.30** 0.12 0.02 0.20 * 0.08

4 3.49 0.91

1.00 0.41** 0.14 0.06 0.12 0.00

5 3.34 0.59

1.00 0.26** 0.15 0.16 0.00

6 0.48 0.50

1.00 0.76** 0.39** 0.36**

7 0.38 0.49

8 0.14 0.35

9 0.61 0.49

1.00 0.31** 0.29**

1.00 0.11

1.00

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Table 2 Analysis of variance of entry mode by sector and perceived environmental uncertainty Duncan procedure Analysis of variance (1) Sector Means Wholly owned: 0.43 Joint venture: 0.80 Independent: 0.75 (2) PEU2 — government policies Means Wholly owned: 3.37 Joint venture: 3.30 Independent: 2.93 (3) PEU2 — product Means Wholly owned: 3.50 Joint venture: 3.23 Independent: 3.11 (4) PEU2 — competition Means Wholly owned: 3.20 Joint venture: 3.09 Independent: 2.97 (5) PEU2 — materials Means Wholly owned: 3.63 Joint venture: 3.43 Independent: 3.22 (6) PEU2 — macroeconomics Means Wholly owned: 3.10 Joint venture: 3.17 Independent: 2.86 (7) PEU2 — competition: service firms Means Wholly owned: 3.14 Joint venture: 2.91 Independent: 2.79 (8) PEU2 — competition: manufacturing firms Means Wholly owned: 3.29 Joint venture: 3.14 Independent: 3.03 (9) PEU2 — materials: manufacturing firms Means Wholly owned: 3.78 Joint venture: 3.41 Independent: 3.15 (10) PEU2 — materials: service firms Means Wholly owned: 3.51 Joint venture: 3.47 Independent: 3.44 (11) PEU2 — macroeconomics: service firms Means Wholly owned: 3.13

Wholly owned F = 8.69 **

* * F = 2.40 *

Table 2

(continued ) Duncan procedure

Analysis of variance Joint venture

Joint venture: 2.75 Independent: 1.88 (12) PEU2 — macroeconomics: manufacturing firms Means Wholly owned: 3.06 Joint venture: 3.28 Independent: 3.19

Wholly owned

Joint venture

* F = 0.49

* P < .05. ** P < .01. * F = 4.37 *

* * F = 0.89

was found to be insignificant at the 0.05 level (Z = 1.44). The average number of employees of the total population is 37,771 (standard deviation = 53,612), while the average number of employees of the completed respondents is 48,653 (standard deviation = 67,283; range 5165 – 298,000). A Z test analyzing the mean differences was found to be insignificant at the 0.05 level (Z = 1.37). Thus, we found no significant differences between the population and the sample means.

F = 1.46

2.2. Measures 2.2.1. Perceived environmental uncertainty PEU was assessed using the PEU2 instrument developed and tested by Werner et al. (1996), based on the work of Miller (1993). PEU2 consists of 28 items aggregated to measure the predictability of a country’s government policies, macroeconomics, materials, product market, and competition (see Appendix A for details). For each question, we asked the raters to evaluate the predictability of the country their firm most recently entered. The reliability (as measured with Cronbach’s alpha) of the government policy scale was 0.86, of the macroeconomic scale was 0.88, of the materials scale was 0.87, of the product-market scale was 0.70, and of the competition scale was 0.81.

F = 0.69

F = 0.81

F = 0.70

2.2.2. Sector Firms were categorized as manufacturing or service based on their listing in the EU500 (International Management, 1993).

F = 3.18 *

* F = 0.01

F = 3.15 *

2.2.3. Entry mode The entry mode decision was the unit of analysis, hence, we asked which entry mode firms used for manufacturing (or service production), marketing activities, and research and development activities. The options were (1) wholly owned, (2) joint venture/strategic alliance, (3) independent (licensing, agents, distributors, franchise, etc.), or (4) not applicable. Because most firms responded to only one entry mode area, our total sample was 116 entry decisions.

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market and demand were more likely to use wholly owned entry modes.

2.3. Analysis The analysis consisted of two steps. The first step was to use one-way ANOVA to assess the statistical significance between entry mode decision and sector and each PEU dimension. The second step was to look at the relationship between entry mode, sector, PEU, and the sector – PEU interactions with a multivariate analysis. This was done using discriminant analysis.

3. Results Our first step in the analysis was to calculate a correlation matrix to examine the multicollinearity of the independent and dependent variables (Hair et al., 1995). Table 1 shows the correlation matrix, means, and standard deviations for all variables used in the analyses. The correlation matrix shows some significant bivariate relationships between entry mode and sector, and entry mode and PEU. In general, our correlations show that (1) manufacturing firms were more likely to use joint ventures; (2) service firms were more likely to use wholly owned entry modes; (3) firms that perceived high uncertainty in government policy were less likely to use independent modes; and (4) firms that perceived high uncertainty in product-

3.1. One-way ANOVA To test if there was a direct relationship between sector and entry mode, or PEU and entry mode we used one-way analysis of variance following the methodology of Brouthers et al. (1996). Table 2 reports our results. To test Hypothesis 1, we looked at the main effects of sector on entry mode. Panel 1 shows that the sector composition of firms who chose wholly owned was significantly different from the sector composition of those who chose joint venture and independent modes. That is, of the firms that chose wholly owned, 43% were manufacturing firms, while of the firms that chose joint ventures and independent, 80% and 75% were manufacturing, respectively. Table 2 shows that this is a statistically significant difference. Thus, it appears that manufacturing firms were more likely to choose joint ventures and independent modes than service firms, while service firms were more likely to choose integrative (wholly owned) modes. The statistical significance in the differences was determined using Duncan’s multiple range test because it has been shown to be sensitive in detecting true differences between means while not excessively downplaying

Table 3 Discriminant analysis of entry mode by sector and unidimensional perceived environmental uncertainty and their interaction (1) Structure matrix (A) Pooled within-groups correlations between discriminating variable and canonical discriminant functions (variables ordered by size of correlation within functions) Variable Sector Sector and PEU Interaction PEU — all

Function 1

Function 2

0.83 0.72 0.40

0.54 0.69 0.86

(B) Canonical discriminant functions evaluated at group means (group centroids) Group Function 1 Function 2 Wholly owned Joint venture Independent

0.47 0.39 0.56

0.02 0.13 0.30

(2) Classification results Predicted group membership Actual group Wholly owned Percentage hit (miss) rate Joint venture Percentage hit (miss) rate Independent Percentage hit (miss) rate Total decisions in analysis Percentage of cases correctly classified Percentage of improvement over chance

No. of cases 56 44 16

116 50.00% 16.67%

1 31 55.4% 9 (20.5%) 3 (18.8%)

2

3

15 (26.8%) 19 43.2% 5 (31.3%)

10 (17.9%) 16 (36.4%) 8 50.0%

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protection against type I errors (Carmer and Swanson, 1973). The Duncan procedure controls for family-wise error rates (alpha = 0.05) while comparing group mean differences simultaneously. To test Hypotheses 2 and 3, we looked at the main effects of PEU on entry mode. Panels 2– 6 in Table 2 report each of the five PEU areas and whether different levels of PEU were related to different entry modes. We found significant differences in PEU by entry mode for government policy PEU and product-market PEU, supporting Hypotheses 2 and 3. No significant differences in PEU by entry mode were found in the areas of competition, materials, or macroeconomics, perhaps because of interactions. Although interactions cannot be detected directly using one-way ANOVA, we indirectly tested for them by comparing the relationship between PEU and entry mode separately for manufacturing and service firms. This analysis was performed on the PEU of competition, materials, and macroeconomics, the three areas for which we hypothesized interactions. Panels 7 – 12 of Table 2 report the results. We found no difference in PEU competition by entry mode for either manufacturing or service firms (panels 7 and 8). Thus, Hypothesis 4 was not supported. We found that for

manufacturing firms, those who chose the wholly owned mode had significantly higher PEU of materials than those that chose independent modes (panel 9). Unlike the case in manufacturing, no significant differences were found for service firms (panel 10), providing support for the interactive effects suggested by Hypothesis 5. For macroeconomic risk, service firms who chose the wholly owned mode had significantly higher PEU than those that chose independent modes (panel 11). Unlike the case in service firms, no significant differences were found for manufacturing firms (panel 12), providing support for the interactive effects suggested by Hypothesis 6. 3.2. Discriminant analysis We used discriminant analysis for two reasons. First, because the previous methods of analysis could not detect interactions directly, and were univariate in nature, we used discriminant analysis to further test Hypotheses 4– 6 and to show that sector and PEU of materials, macroeconomics and competition interact to affect entry mode. Second, discriminant analysis was used to show the increased predictive power of a multi-dimensional measure of PEU over a unidimensional measure.

Table 4 Discriminant analysis of entry mode by sector and multi-dimensional perceived environmental uncertainty main effects (1) Structure matrix (A) Pooled within-groups correlations between discriminating variable and canonical discriminant functions (variables ordered by size of correlation within functions) Variable Function 1 Function 2 Sector PEU2 — PEU2 — PEU2 — PEU2 — PEU2 —

product government policy macroeconomics materials competition

0.74 0.53 0.26 0.03 0.29 0.22

(B) Canonical discriminant functions evaluated at group means (group centroids) Group Function 1 Wholly owned Joint venture Independent

0.37 0.22 0.86 0.60 0.33 0.28

Function 2

0.53 0.45 0.61

0.02 0.17 0.40

(2) Classification results Predicted group membership Actual group Wholly owned Percentage hit (miss) rate Joint venture Percentage hit (miss) rate Independent Percentage hit (miss) rate Total decisions in analysis Percentage of cases correctly classified Percentage of improvement over chance

No. of cases 56 44 16

116 49.14% 15.81%

1

2

3

33 58.9% 7 (15.9%) 4 (25.0%)

11 (19.6%) 18 40.9% 6 (37.5%)

12 (21.4%) 19 (43.2%) 6 37.5%

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Discriminant analysis is a multivariate technique that examines the relationship among variables and is used for the analysis of groups for the following purposes: (1) to determine the separation between the number of hypothesized groups; (2) to discriminate with respect to dimensions and variates; (3) to examine the relationship between the total variables and group membership variables; and (4) to classify cases to specific groups (Hair et al., 1995; Bray and Maxwell, 1985). The resulting canonical variate correlations are similar to factor loadings and depict the level of variance a given variable shares with the underlying composite (Hair et al., 1995; Bray and Maxwell, 1985). The canonical discriminant function coefficients served as the basis for assigning the firm decisions to the three entry mode types. Based on the coefficients, a discriminant score was the basis for a rule classifying the entry mode decisions into one of the three entry modes. Using the Bayes’ rule, which is the probability that a firm decision with a particular discriminant score belongs in a group, each firm decision was assigned to the group predicted by the quantitative variables (Hand, 1981). For our sample, the actual decision was known and was therefore compared to the predicted decision. Classifica-

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tion results (comparison of ‘‘hits’’ and ‘‘misses’’) were compared to determine the percent of cases classified correctly, which was an index of the effectiveness of the discriminant function and the efficiency of underlying quantitative variables. We first used discriminant analysis on a model using a univariate measure of PEU (in our case, the five dimensions averaged). We included sector and the interaction between sector and PEU in the model. The standardized function coefficients, the pooled within-groups correlations of the two discriminant functions used to group the 116 decisions, and the classification results using only main effects are reported in Table 3. The discriminant function using a univariate measure of PEU, sector, and their interaction correctly classified 50.00% of the decisions, a 16.67% improvement over chance. Secondly, we used discriminant analysis on a model using the multi-dimensional measure of PEU and sector. However, the interactions were not included. Thus, the model had six independent variables (sector and the five PEU dimensions), which examined only the main effects. The standardized function coefficients, the pooled withingroups correlations of the two discriminant functions used

Table 5 Discriminant analysis of entry mode by sector, multi-dimensional perceived environmental uncertainty and the hypothesized interactions (1) Structure matrix (A) Pooled within-groups correlations between discriminating variable and canonical discriminant functions (variables ordered by size of correlation within functions) Variable Function 1 Function 2 Macro and sector interaction Sector PEU2 — product PEU2 — government policy Materials and sector interaction PEU2 — macroeconomics Competition and sector interaction PEU2 — materials PEU2 — competition

0.59 0.58 0.43 0.22 0.42 0.04 0.49 0.24 0.18

(B) Canonical discriminant functions evaluated at group means (group centroids) Group Function 1 Wholly owned Joint venture Independent

0.57 0.50 0.09 0.72 0.58 0.54 0.52 0.24 0.21

Function 2

0.66 0.52 0.88

0.04 0.20 0.42

(2) Classification results Predicted group membership Actual group Wholly owned Percentage hit (miss) rate Joint venture Percentage hit (miss) rate Independent Percentage hit (miss) rate Total decisions in analysis Percentage of cases correctly classified Percentage of improvement over chance

No. of cases 56 44 16

116 64.66% 31.33%

1

2

3

42 75.0% 8 (18.2%) 1 (6.3%)

4 (7.1%) 22 50.0% 4 (25.0%)

10 (17.9%) 14 (31.8%) 11 68.8%

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to group the 116 decisions, and the classification results using only main effects are reported in Table 4. The discriminant function using only main effects correctly classified 49.14% of the decisions, a 15.81% improvement over chance. Table 5 reports the standardized function coefficients, the pooled within-groups correlations of the two discriminant functions used to group the 116 decisions, and the classification results using main effects and the three hypothesized interactions between PEU and sector. This discriminant function correctly classified 64.66% of the decisions, a 31.66% improvement over chance. Thus, the model using the interactions in addition to the main effects almost doubled the predictive power over chance. These results show that (1) including the interactions substantially increases the predictive power of the model, and (2) the multi-dimensional measure of PEU and its interactions with sector has substantially greater predictive power than the unidimensional measure. Because the one-way ANOVAs were not consistent with Hypothesis 4, we removed the competition PEU and sector interaction from the discriminant analysis to further test the viability of Hypothesis 4. We found that the removal of the competition PEU and sector interaction from the discriminant analysis slightly increased the functions’ correct classifications to 66.38%. This provides further evidence that the PEU of materials and macroeconomics each interact with sector as hypothesized to influence entry mode decisions, but that PEU of competition does not. Thus, one-way analysis of variance, and discriminant analysis support all hypotheses except for Hypothesis 4.

entry than manufacturing firms given similar international risk conditions. Further, we hypothesized that due to unique product characteristics and modes of delivery, services and manufacturing firms respond very differently to specific types of PEU. Three distinctive contingent relationships were specified: (1) service firms tend to be more sensitive to macro-economic risks than manufacturing firms; (2) manufacturing firms tend to be more sensitive to materials risk perception than service providers; and (3) greater PEU of competition will lead to greater use of integrated modes for service firms, and greater use of independent modes for manufacturing firms. We found support for five of our six hypotheses. We found strong support for all three main effects hypotheses: (1) service firms tended to utilize more integrated (wholly owned) modes of entry than manufacturing firms; (2) greater governmental/political risks lead to increased use of integrated modes for both service and manufacturing firms; and (3) greater product-market risks resulted in more integrated entry strategies for both industry sectors. In addition, we found support for two of the interactive hypotheses: (5) materials risk had an impact on manufacturing firm strategy but not service firm strategy; and (6) macroeconomic risk had an impact on service firm strategy but not manufacturing firm strategy. Our findings are consistent with past research in that (1) service firms tended to use integrative entry mode strategies more than manufacturing firms, and (2) PEU affected service more than manufacturing firms. We also found significant differences in how manufacturing and service firms related to different dimensions of PEU, which has important implications for future research and managerial activities.

4. Discussion and conclusions 4.1. Limitations of the study While scholars agree that international risks have an impact on the strategic processes of firms, little research exists to explain the extent and nature of these influences. Several researchers offer theoretical relationships between risk perceptions and strategic behavior, yet, these theories have not been empirically tested (Das and Teng, 1996; Miller, 1992; Vermon, 1985). What little empirical research does exist shows that: (1) manufacturing and service firms tend to perceive risks similarly (Miller, 1993), and (2) at least for the US computer software industry, risk does appear to influence entry mode strategy (Brouthers, 1995). In this study, we attempted to enhance what is known about risk – strategy relationships. We employed an empirically reliable multi-dimensional measure of risk (Werner et al., 1996) to predict similarities and differences in service and manufacturing firms’ international entry strategies. We also attempted to extend the generalizability of previous risk research by empirically examining the activities of a sample of European firms. We hypothesized that (as suggested by previous scholarship) service firms typically prefer more integrated modes of

There are empirical limitations to this study. First, because of the size of our sample, it was difficult to fully explore the interactions between specific types of risk and entry mode strategy. Future research could use larger samples to boost the statistical power of their studies. Second, because our study was cross-sectional, the longitudinal effects of international risk on entry strategy remains unexplored. It may turn out that these relationships change over time. Future research could do much to answer this question. Last, because we explored only one type of firm strategy — entry mode selection — our findings may be limited. Future efforts focusing on other strategies may help determine how generalizable our findings are. 4.2. Additional suggestions for future research While the findings tended to support our hypotheses, future research efforts may wish to focus on a number of unexamined issues. How reasonable is it to treat all

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manufacturing firms/service providers as having the same response to risk factors? Additional variables, such as the specific type of service/manufacturing industry, could be incorporated into the model in an effort to more completely understand the relationship between international risk and strategic choice. Are firms more interested in some risk factors than others, and what variables moderate this interest? Are some factors generally more important to firms than others? Are they strategy and/or industry dependant? For example, some macroeconomic factors such as tariffs may be less important to service firms than manufacturing firms. Similarly, firm strategies based upon specific industry characteristics and/or governmental policies are likely to be significant determinants of which risk factors a firm deems important. While found to be statistically reliable in their present state (Werner et al., 1996), it is possible that different contexts and/or theories may alter some of the five PEU2 factors. For this reason, future studies may also wish to examine the components and boundaries of the PEU2 multi-dimensional measure. In addition, examining the relationship between risk and strategy for other home/ host-country firms/locations may extend the generalizability of our findings. We performed a post hoc analysis exploring other possible interactions. We ran separate ANOVAs for government/ political risk and product-market risk by sector. For manufacturing firms, we found that, for both types of risk, wholly owned entry modes perceived different levels of risk than independent modes. However, for service firms, no such risk perception differences were noted. This suggests that interactions may indeed also exist between sector and these two risk types. Future research may find it useful to consider and explore this possibility. Furthermore, previous scholarship from other areas has shown that firms may not always be risk neutral; they may differ in terms of risk propensity (Sitkin and Pablo, 1992). In this study, we maintained the traditional risk neutrality assumption in order to provide consistency and comparability with past entry mode studies. However, future efforts may wish to examine how the risk propensity of decision makers affects risk based decisions, such as mode of entry. In addition to industry differences (which were examined in this paper), future research efforts may wish to theorize and examine how firms establishing different types of foreign operations (production operations vs. marketing/ sales operations, for example) respond similarly/differently to uncertainty in their mode choice decisions. Although consistent with past entry mode research, we compressed entry mode types into three categories (Brouthers, 1995; Erramilli and Rao, 1993). Future studies may wish to examine differences in mode choice within each of these categories. For example, do franchising agreements provide benefits for risk management different from licensing agreements? Do wholly owned acquisitions

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have different risk management implications than greenfield ventures? Our study explored how manufacturing and service firms may react to each of the five PEU2 risk factors. We did not examine the interactions between and among the five factors. Future research may want to explore how manufacturing and service firms will react to the combined affects of the five PEU2 factors, given industry differences. Finally, the next step in examining international risk and entry mode strategy may be to link them to firm performance. Miller (1998) and Kwon and Konopa (1993) have suggested that firms choosing a strategy that incorporates international risk perform better than firms that do not take risk into consideration. In addition, as Woodcock et al. (1994) have pointed out, for entry mode strategy research to be of use to firm managers the relationship between entry mode selected and performance needs to be explicated. In order to do this, future research efforts may wish to clarify: (1) the relationship between international risk perception, entry mode strategy and firm performance, and (2) whether findings related to manufacturing can be extended to services.

Acknowledgments The authors would like to thank Kent D. Miller, Purdue University for his helpful comments on an earlier draft of this paper. Authors are listed alphabetically.

Appendix Summary of PEU2 items from questionnaire

(1) Government/political policies (Cronbach alpha = 0.86) (a) Ability of the party in power to maintain control of the government (b) Threat of armed conflict (c) Tax policies (d) Monetary policy (e) Prices controlled by the government (f) National laws affecting international business (g) Legal regulations affecting the business sector (h) Tariffs on imported goods (i) Enforcement of existing laws (j) Public service provision (2) Macroeconomic factors (Cronbach alpha = 0.88) (a) Inflation rate (b) Exchange rate with your domestic currency (c) Interest rate (d) Results of economic restructuring.

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(3) Materials/infrastructure (Cronbach alpha = 0.87) (a) Quality of inputs, raw materials and components (b) Availability of inputs, raw materials, and components (c) Transportation system within the country (d) Transportation system to foreign countries (4) Product, market, and demand (Cronbach alpha = 0.70) (a) Customer preferences (b) Product demand (c) Availability of substitute products (d) Availability of complementary products (5) Competition (Cronbach alpha = 0.81) (a) Changes in competitors’ prices (b) Changes in the markets served by competitors (c) Changes in competitors’ strategies (d) Entry of new firms into the market (e) Domestic competitors (f) Foreign competitors All items were evaluated on a scale of 1 (very unpredictable) to 5 (very predictable).

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