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Modeling foreign equity control in Sino-foreign joint ventures with neural networks
European Journal of Operational Research 159 (2004) 729–740 www.elsevier.com/locate/dsw
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Modeling foreign equity control in Sino-foreign joint ventures with neural networks Michael Y. Hu b
a,*
, G. Peter Zhang b, Haiyang Chen
c
a Department of Marketing, Graduate School of Management, Kent State University, Kent, OH 44242-0001, USA Department of Management, J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30303, USA c E-Trade Financial Learning Center, Christos M. Cotsakos College of Business, William Paterson University, 1600 Valley Road, Wayne, NJ 07474, USA
Received 20 January 2003; accepted 25 June 2003 Available online 5 December 2003
Abstract Equity control is one of the key areas of research in international business. This study employs artificial neural networks (ANNs) to model foreign equity control. Comparisons are made with traditional statistical modeling approaches. It was found that ANNs produce a more parsimonious set of independent variables that yield higher classification rates than logistic regression. Thus, it can be concluded that ANNs, with their complex, nonlinear structure, are able to model the relationship between transaction cost factors and majority/minority ownership; and percent equity ownership more accurately than the linear statistical approaches. Ó 2003 Elsevier B.V. All rights reserved. Keywords: Neural networks; Joint ventures; Foreign equity control; Variable selection; Parsimony
1. Introduction Foreign direct investment is one of the focal issues in international business. Within this broad area of foreign direct investment, equity control of foreign operations emerges as a key area of interest for academic researchers and international business managers. Control has been identified to be a major factor in explaining the performance of multinational joint ventures (Geringer and Hebert,
*
Corresponding author. Tel.: +1-330-672-1261; fax: +1-330672-5006. E-mail address: [email protected] (M.Y. Hu).
1989). Equity control is basically a form of control the foreign parent can exercise over its overseas operations. Internalization and eclectic theories have been used to explain the broader issues of why multinationals (MNCs) would own and control their operations overseas. The question of why MNCs would use a higher or lower level of equity ownership is the focus of several studies (Hennart, 1988; Gatignon and Anderson, 1988). The transaction cost argument presumes that the choice between complete or partial ownership depends on the benefits and costs of shared ownership. The transaction cost theory originally proposed by Buckley and Casson (1976) and later extended
0377-2217/$ - see front matter Ó 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2003.06.002
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by Teece (1983) has been used to explain the dichotomy between wholly owned subsidiaries and joint ventures. Empirical evidence of its effectiveness can be found in Gatignon and Anderson (1988) and Gomes-Casseres (1989). Hu and Chen (1993) extended this framework to embody the full range of percent equity ownership, and empirically tested the usefulness of the framework in Sinoforeign joint ventures. The difficulty in modeling such equity ownership behavioral pattern arises from sources of error in measurement and model specification. First, data obtained from presumably the most reliable published sources in China are not precise and often contain systematic biases. Every year the Chinese government publishes every year information related to a small subset of Sino-foreign joint ventures. The sample selection criterion is never clearly spelled out. Second, the transaction cost argument does not preclude interaction among the economic factors used in the model. Model specification as a result of the complexity of the relationship and the interplay among the independent variables is a nontrivial task. Thirdly, the relationships between the dependent variable, percent ownership and transaction cost factors need not be linear. Thus, linear statistical models traditionally used for such modeling efforts often yield sub-par results. Lastly, from the perspective of modeling, parsimony is to be desired. One would like to identify a small subset of relevant independent factors while maintaining a high level of predictability. In this study we employ a versatile nonlinear modeling tool-artificial neural networks (ANNs)–– to model the relationship between transaction cost factors and equity control in Sino-foreign joint ventures. Our choice of neural network models was motivated by several factors. First, as a relatively new methodology, ANNs have drawn a lot of attention in various areas of quantitative modeling, and a large number of successful applications have been reported in the literature. Second, ANNs have many desirable features for practical applications, as they are nonparametric and nonlinear. Thus they are able to capture more complex patterns involving interactive effects without having to make unrealistic assumptions about the
underlying relationships. Finally, although ANNs have effectively been applied in several crosscultural studies (for example, Veiga et al., 2000), the use of ANNs in international business applications is still meager. This study aims to provide empirical evidence on the usefulness of this modeling technique in modeling foreign joint venture equity control. Our aim is to show that with ANNs some of the modeling difficulties as previously mentioned can be overcome while attaining parsimony in the process. The ANN model will be compared to the traditional statistical modeling approaches such as multiple regression and logistic regression. The transaction cost framework originates from economics, and embodies a wide spectrum of applications. It is, therefore, not the intent of this paper to delve deeply into this framework and justify why it is applicable for describing equity control of Sino-foreign joint ventures; rather the thrust of the paper is on using ANNs to model equity control. Thus, a lengthy review of transaction cost is not warranted. The following section will provide a brief justification and operationalization of transaction cost factors in equity control. Section 3 shows the data used in this study. Section 4 lays out the ANN model and the procedure used for variable selection. Section 5 contains the results for ANN and comparisons made with logistic regression. Section 6 concludes.
2. Transaction cost factors in equity control Williamson (1985) employed the transaction cost framework to theoretically explain the conditions under which a firm may exercise more or less control over its investments. Anderson and Gatignon (1986) operationalized this framework and identified testable propositions for the case of foreign market entry decisions. Four categories of propositions are identified. They are proprietary nature of assets, host countryÕs environment, cultural difference between host and home countries, and commitment. Due to data limitations, this study will employ some of these propositions, and empirically show how neural networks can be used to model these relationships.
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2.1. Proprietary assets The transaction cost theory stresses the importance of proprietary assets possessed by firms (Williamson, 1975, 1985; Buckley and Casson, 1976, 1988, 1996; Hennart, 1988). These assets include a firmÕs proprietary knowledge in product development, brand name, and marketing skills. Anderson and Gatignon (1986) propose that high control entry modes are more efficient for products that are highly proprietary. We argue that this line of logic is particularly true for multinational corporationsÕ (MNCs) entry mode decisions in China. Hu and Chen (1993) provided some empirical evidence of the effects of proprietary assets on levels of control in entry mode decisions in China. In this study, proprietary assets will be operationalized using measures of high/low tech, and advertising intensity. 2.2. Host country’s potentials Transaction benefits increase with the size of the host countryÕs market potential. These benefits can take the form of revenue or internalization. Thus, high control modes are necessary to protect these high market potentials. Agarwal (1994) found that high control mode was more likely when the host country market increased in size. The argument being put forth is generally applied to country markets. China is a large market comprised of distinctly different regional markets (Gao, 1996). Regional segmentation and protectionism persist in China. MNCs vying for the China market have to enter each regional market one at a time, as in the case of Coca Cola and Pepsi. It is expected that high entry modes are more likely in geographic regions or industrial sectors with high potentials. 2.3. Cultural distance Culture is a set of values and beliefs. Cultural distance is the difference in values and beliefs between the host and foreign countries. It is argued that large cultural distance has consequences for high transaction costs. Padmanabhan and Cho (1996) find Japanese investors utilize high control modes when cultural distance is large. On the
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other hand, unfamiliarity with a foreign country will require the MNCs to be more dependent on local partners for their position and knowledge in the local network. This position is supported by Gatignon and Anderson (1988), Kogut and Singh (1988a,b) and Erramilli and Rao (1993). 2.4. Commitment Time and equity commitments are two other ways an MNC can exercise control over risks. Foreign investors must specify a planned duration, defined as the number of years a joint venture is to exist when registering in China. At the end of the specified period, the foreign investment enterprise must cease its operation unless both parties decide to reapply and extend the life span of the joint venture. Longer duration means less uncertainty in re-negotiation, and the partners are more committed to the success of the project. Thus, it is expected that longer duration and larger total capital investment be positively related to the level of equity control.
3. Artificial neural networks The idea of neural computing grows out of the pattern recognition capabilities of a biological brain. McCulloch and Pitts (1943) developed the first neural model, which instantly became the basis for ANNs, where nodes are likened to neurons and arcs to dendrites. Neural networks have been applied in numerous business settings such as bank failures (Tam and Kiang, 1992), corporate bond ratings (Surkan and Singleton, 1990), and more recently international business (Hu et al., 1996, 1999; Veiga et al., 2000). For a comprehensive review of neural network applications in classification and time-series problems, readers are referred to Zhang (2000) and Zhang et al. (1998). Several distinguishing features of ANNs make it an attractive modeling tool. First, ANNs are known to be an effective tool for pattern recognition particularly when the data contain a lot of noise (Patuwo et al., 1993). Second, ANNs are data-driven and do not need pre-established model form. This is in contrast with traditional statistical
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modeling approaches, where specific model forms have to be pre-specified and normality of the error terms is assumed. Thus, ANNs are well suited for problem situations where the solutions require knowledge that is difficult to specify ahead of time. Third, ANNs are universal functional approximators. It has been shown that ANNs can approximate any continuous function to any desired level of accuracy (Cybenko, 1989; Funahashi, 1998; Hornik, 1991). Complexity can be a result of interaction or measurement errors associated with the variables. Traditional statistical methods have limitations in estimating this underlying function in face of the complexity of a problem. Fourth, ANNs are nonlinear. Although many linear statistical models are simple and easy to interpret, they are simply inappropriate for nonlinear relationships that persist in most real world business problems. Finally, ANNs as a modeling tool often lead to more parsimonious results than linear statistical approaches, because they can fit any arbitrary function to a data set, thus requiring a smaller set of dimensions. A three layer feedforward network is the most popular network paradigm. Fig. 1 shows a network with one input layer, one hidden layer, and one output layer. Input nodes correspond to the independent variables used in the study. There is
only one output node for the dependent variable. The dependent variable in our study can be either categorical (majority foreign equity ownership or minority foreign ownership) or continuous (percent foreign equity ownership). Hidden nodes play an important role for ANNs to capture nonlinear patterns in the data. As the number of hidden nodes increases, more complex functions can be created to map the inputs to the output. However, as in the case of statistical models, more hidden nodes means less number of degrees of freedom, raising the possibility of overfitting. In most empirical applications, the researcher would experiment on the number of hidden nodes to arrive at the best neural architecture. At each processing node of hidden and output layer, there is an activation function that transforms information received from the previous layer. The activation function used at the hidden 1 layer is the logistic function: f ðxÞ ¼ 1þexpðxÞ . In addition, bias terms are used for both hidden and output layer. For the output node, we use the logistic function when the task is to classify an object (i.e. for binary dependent variable) and the linear function for a continuous output variable. There are linking arcs (connections) from the input nodes to hidden, and then to output nodes. The arcs connecting the input to the hidden nodes allow one
Y
Dependent variables
Output node
1
Hidden nodes
..... 1
..... X1
X2
Input nodes
Xp
Independent variables
Fig. 1. A three-layer feedforward neural network.
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to model data transformation as well as interactions among the independent variables. To use neural networks, they have to be ÔtrainedÕ first. Neural network training is the process of estimating the connecting weights (the model parameters) by minimizing some overall error measures such as the sum of squared errors (SSE). The trained network can apply its knowledge to unseen observations and the power of the trained network can be tested using a separate test sample. According to the theory of least squares, the network outputs are the unbiased estimates of the posterior probabilities in the cases of classification problems (Richard and Lippmann, 1991), which is an important advantage of ANNs used for classification (Zhang, 2000). In training an ANN, a key element is the nonlinear optimization procedure. Numerous nonlinear methods have been proposed, and in this study, the networks are trained with the GRG2based system of Subramanian and Hung (1993). GRG2 is a widely distributed nonlinear optimization algorithm (Lasdon and Waren, 1986). Subramanian and Hung (1993) showed that the GRG2 algorithm is much more efficient than other nonlinear optimizers in training neural networks.
4. Data The data for this study are obtained from the Statement of Sino-Foreign Joint Ventures, 1979– 1990 in the Almanac of ChinaÕs Foreign Economic Relations and Trade (Ministry of Foreign Economic Relation and Trade, 1980–1991). The Statement contains a description of 3071 Sinoforeign joint ventures. It provides information on the joint ventures/their parent companies, including geographic location, number of years the venture is to exist, amount of total investment, primary scope of the business, and percent of foreign equity investment in the venture. Of the 3071 joint ventures, 2416 fall into the manufacturing category. After editing for missing observations, a total of 908 observations are retained for this study. Of the 908 observations, 339 (37.33%) have foreign equity ownership less than 50%, and the rest 569 (62.67%) greater than or equal to 50%.
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In this study we use two related dependent variables for measuring foreign ownership––the percent of foreign equity ownership (PERCENT) and a dichotomous variable FCONTROL. FCONTROL is coded as Ô1Õ if PERCENT is greater than or equal to 50% and coded as Ô0Õ otherwise. That is, we aim to model foreign ownership via two separate analyses. The first is to model the exact percentage of foreign ownership in a joint venture (a function mapping problem), and the second is to model majority/minority control (a classification problem). From the managerial decision-making perspective, majority ownership often implies complete control. The classification problem is usually of greater interest to MNCs. On the other hand, the actual percentage of foreign ownership can provide more detailed information and help to validate the accuracy of the classification model. Using the transaction cost theory, we utilize four sets of predictor variables in this study: (1) proprietary assets related factors––technology and advertising intensity of the project; (2) host market potential by industrial sector, and by geographic region; (3) cultural distances between foreign investorsÕ home countries and China; and (4) commitment in capital and time to an investment projects, and planned duration of the projects. We first classify the line of business of the foreign investment enterprises into two-digit SICs. Then we group them into high (coded as Ô1Õ) or low (coded as Ô)1Õ) technology industries (TECH). Low technology industries include food and kindred products (SIC 20), tobacco (SIC 21), textile mill (SIC 22), apparel (SIC 23), lumber and wood (SIC 24), furniture (SIC 25), paper (SIC 26), printing (SIC 27), rubber and plastics (SIC 30), leather (SIC 31), stone, clay and concrete (SIC 32), primary metal (SIC 33), fabricated metal (SIC 34), and miscellaneous manufacturing (SIC 39). High tech industries include chemicals and allied products (SIC 28), petroleum and coal (SIC 29), industrial machinery (SIC 35), electrical and electronic products (SIC 36), transportation equipment (SIC 37), and measuring equipment (SIC 38). Advertising intensity (ADV) is measured as the ratio of advertising expenditure to sales. This ratio
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by SIC is provided by the Annual Line of Business Report (Federal Trade Commission, 1975–1977). This ratio captures the level of marketing skills and the relative position of a product in the marketplace (Gatignon and Anderson, 1988; Kogut and Singh, 1988b). Host market potential is measured by the growth rate by industrial sectors and geographic regions in China. Growth rates of industrial output (IGROWTH) by 2-digit SICs in China from 1980 to 1985 were obtained from the National Industrial Survey Group (1986). Growth rates by provinces (RGROWTH) from 1978 to 1984 were gathered from the Statistics of ChinaÕs Industrial Economy. Culture as defined by Hofstede (1980) has four key dimensions: power distance, uncertainty avoidance, individuality/collectivity, and masculinity/femininity. Cultural distance corresponds to differences in culture between the host and foreign countries. This study employs the single, summated index (CDIST) developed by Kogut and Singh (1988b) and used by other researchers in international business (Erramilli and Rao, 1993; Pan, 1996). As China is the only host country examined in this study, cultural distances refer to the differences between China and the foreign countries. The amount of commitment all parties bring to a project is measured in terms of capital investment and time. The sample data show that the distribution of total amount of investment in a joint venture projects some very large observations. We applied the natural logarithmic transformation to this variable (LINVEST) as in Hu and Chen (1993) and also Gatignon and Anderson (1988). Level of time commitment in a project is reflected in the proposed duration of the project (LDURATION). It is expected that the longer the duration, the greater the time commitment to the project. Foreign investors would prefer a larger equity stake in such projects.
5. Model building The primary objective of this study is to use ANNs to model equity control in Sino-foreign joint ventures. As in all modeling exercises, parsi-
mony is a key consideration. One would like to achieve the highest degree of explanatory power with the fewest number of explanatory variables. For this objective, two related questions have to be answered: 1. Which combination of explanatory variables allows for highest degree of parsimony? 2. What is the appropriate neural network model for this data set? In neural networks for classification purposes, this question corresponds to the number of hidden nodes. The second question was addressed with the cross-validation approach that splits the training sample data into an estimation sample and a validation sample. Then the neural network with different hidden nodes is trained with the estimation sample, and its performance is monitored in the validation sample. The best architecture in terms of the lowest SSE in the validation sample is the appropriate model, and will be used for all the subsequent analyses. In this work, we experiment with 10 different levels of hidden nodes, ranging from 1 to 10, and the network with seven hidden nodes was found the best. The first question is more difficult to answer with ANNs. Unlike in statistics where variable selection approaches are based on formal statistical theory and assumed probability distribution, distributional forms are difficult or impossible to specify for neural network models. In this study, a backward elimination method based on a heuristic technique of ANNs is employed for determining the ideal combination of a subset of explanatory variables. Starting with the full set of explanatory variables, variables are eliminated one at a time until some stopping criterion is met. This procedure is similar to that used in linear regression models, whereby the F -statistic is sequentially updated and revised, and compared to a predetermined cutoff value from the F distribution with a desirable significant level. Here in our study, we start with a neural network containing all seven explanatory variables, referred to as the full model, and each of the seven sub-models with six variables is then constructed. A sub-model is being referred to as the reduced
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model. A reduced model is constructed by removing an explanatory variable in the full model and all of its associated nodes and arcs. We can define: SSEF ¼ SSE of the full model; SSER ¼ SSE of the reduced model: It can be expected that the SSE for the reduced model should be larger than that of the full model. The F -ratio thus can be defined as ðSSER SSEF Þ=ðdf R df F Þ F ¼ ; SSEF =df F where df R , df F are the degrees of freedom for the reduced and full models, respectively. In linear statistical models, the F -statistic defined above follows an exact F distribution, which is derived from the assumption of normal distribution for error terms. In the case of neural networks, the theoretical distributional form cannot be derived. Thus, the F -ratio is not used for statistical testing. Nevertheless, it serves as a useful heuristic variable to measure the relative contribution of a particular variable to the modeling ability. In this study, we set the cutoff value for the F ratio to 9 as the stopping criterion for the variable selection procedure. This heuristic value was also used in a previous study by Hu et al. (1996). It is important to note that the exact value of the cutoff point is not possible to determine by theoretical statistical distribution. However, setting higher cutoff values is equivalent to specifying a lower significant level in linear statistical models.
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At each stage of the backward elimination procedure, an F -ratio is calculated for each of the sub-models. The reduced model with the smallest F -ratio is chosen, and becomes the full model in the next iteration. This process continues until finally F -ratios for all remaining variables are all larger than a cutoff F -value, suggesting no candidate can be removed.
6. Results Of the 908 joint ventures in the data set, approximately 70% (636 observations) were randomly selected for the training sample. The remainder was the test sample (272 observations). Of the 636 ventures in the training sample, 399 (62.74%) have majority foreign ownership. Roughly the same percentage (62.5%) in the test sample are of this type. Table 1 provides summary statistics for all the variables used in this study. It appears that the training and test samples shared of much the same characteristics. First, we present the results from using neural networks and logistic regression in modeling majority/minority foreign equity control. Results of logistic regression via SAS with all seven independent variables are given in Table 2. Table 2 shows that overall the independent variables are quite correlated with the dependent variable (2Log L ¼ 85:963; p-value ¼ 0.0001). Two variables, LDURATION and LINVEST are significantly related to majority ownership. Their coefficients take on a value of 1.936 and )0.250.
Table 1 Summary statistics Variables
TECH LDURATION LINVEST ADV CDIST IGROWTH RGROWTH PERCENT
Training sample
Test sample
Mean
Standard deviation
Minimum
Maximum
Mean
Standard deviation
Minimum
Maximum
)0.428 2.867 8.723 1.365 1.295 0.912 0.750 0.426
0.036 0.018 0.041 0.035 0.032 0.018 0.013 0.007
)1.000 2.303 6.551 0.250 0.870 0.510 0.370 0.030
1.000 4.595 13.581 6.100 3.180 2.210 1.630 0.990
)0.294 2.845 8.708 1.416 1.314 0.880 0.729 0.422
0.058 0.028 0.062 0.053 0.048 0.028 0.019 0.010
)1.000 2.303 6.551 0.250 0.870 0.510 0.290 0.200
1.000 4.595 12.986 2.940 3.180 2.210 1.630 0.920
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Table 2 Logistic regression result Variable
Coefficients
Wald chi-square
p-value
Odds ratio
Intercept TECH LDURATION LINVEST ADV CDIST IGROWTH RGROWTH
)4.105 0.051 1.936 )0.250 0.151 )0.041 0.168 )0.143
19.633 0.194 63.494 6.270 2.002 0.135 0.609 0.236
0.0001 0.6595 0.0001 0.0123 0.1571 0.7131 0.4353 0.6269
– 1.05 6.93 0.78 1.16 0.96 1.18 0.87
2Log L ¼ 85:963; p-value ¼ 0.0001.
The odds ratios of these two variables are 6.93 and 0.78, respectively. Table 3(panel A) shows the classification results using the neural network model with all seven explanatory variables. Of the 636 observations in the training sample, 454 (71.38%) are correctly classified. Among them, 88.72% (354/399) are correctly classified into group 1 and 42.19% (100/ 237) into group 2. In the test sample, the classification rate for group 1 actually improved to 90.59% (154/170), while for group 2 classification rate declined to 41.18% (42/102). Overall, the correct classification rate in the test sample is stable at 72.06%. Table 3(panel B) gives the classification results with logistic regression. We find that the overall correct classification rates for both training and test samples are lower than those reported for neural networks. These numbers are 69.81% in the training sample and 69.86% in the test sample. The logistic regression model per-
forms about the same as in group1 in both the training set (89.22% vs. 88.72%) and the test set (91.18% vs. 90.59%). However, its performance is inferior to neural networks in classifying observations into group 2. Note that roughly 63% of the observations in both samples are in group 1, thus classification into the smaller group 2 tends to be a more difficult task. Statistical approaches such as logistic regression used for classification are likely to be impacted by the unequal sample size in each group. In the case when the imbalance is severe, statistical procedures tend to ignore the information contained in the data and classify all the observations into the larger sample. Therefore, results from Table 3 suggest that ANNs may have the advantage over logistic regression when groups are quite unbalanced. Next, we subject the training sample to the backward elimination procedure in neural networks previously described. We employ a similar
Table 3 Neural network (panel A) and logistic regression (panel B) classification results with all predictor variables Actual/predict Panel A Group 1 Group 2 Total
Training
Test
Group 1
Group 2
Total
Group 1
Group 2
Total
354 (55.66%) 137 (21.54%) 491 (77.20%)
45 (7.07%) 100 (15.72%) 145 (22.80%)
399 (62.74%) 237 (37.26%) 636 (100.00%)
154 (56.62%) 60 (22.06%) 214 (78.68%)
16 (5.88%) 42 (15.44%) 58 (21.32%)
170 (62.50%) 102 (37.50%) 272 (100.00%)
Overall classification rate ¼ 71.38% Panel B Group 1 356 (55.97%) Group 2 149 (23.43%) Total 505 (79.40%) Overall classification rate ¼ 69.81%
Overall classification rate ¼ 72.06% 43 (6.76%) 88 (13.84%) 131 (20.60%)
399 (62.74%) 237 (37.26%) 636 (100.00%)
155 (56.99%) 67 (24.63%) 222 (81.62%)
15 (5.51%) 35 (12.87%) 50 (18.38%)
Overall classification rate ¼ 69.86%
170 (62.50%) 102 (37.50%) 272 (100.00%)
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backward elimination procedure in SAS for logistic regression. Results are shown in Table 4. Logistic regression selects LDURATION, LINVEST and ADV as the final reduced set of variables, whereas the neural network model keeps only two variables, LDURATION and RGROWTH. Thus, a more parsimonious model is selected by the neural network. Note that both ANNs and logistic regression select the variable LDURATION, suggesting the importance of time commitment in explaining equity control for the joint venture business. Table 5 contains the classification results for the two procedures using the reduced set of variables, three for logistic regression, and two for neural networks. It is important to note that when using the reduced set of explanatory variables to classify observations in the training and test samples neural networks still provide higher overall classification rates in the test sample compared to the logistic model. Overall classification rates in the training sample are about the same for both techniques: 70.13% for logistic regression and 69.97% for neural networks. Furthermore, for smaller group 2, neural networks again outperform logistic regression with a 38.24% classification rate for neural networks and a 34.31% classification rate for logistic regression. To model the percentage of foreign ownership, we use transformed logit variable as the desired output or dependent variable. As previously no-
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ted, neural networks used in this context are equivalent to a nonlinear regression problem that performs function mapping from the input variable space to the output space. Multiple regression is first used to serve as the baseline model and examine the possible linear relationship. Results are presented in Table 6. The model is highly significant with two variables LDURATION and LINVEST again significantly related to percent foreign ownership. The regression coefficient for LDURATION is 0.707 and for LINVEST is )0.128. Thus, results for percent foreign ownership are basically similar to those for majority ownership reported in Table 2. Accuracy of multiple regression and neural networks for the continuous dependent variable is evaluated with four metrics: the mean error (ME),
Table 6 Multiple regression on training set with lnðy=1 yÞ as dependent variable Variable
Coefficients
t-statistic
p-value
Intercept TECH LDURATION LINVEST ADV CDIST IGROWTH RGROWTH
)1.295 )0.020 0.707 )0.128 0.042 )0.046 0.103 )0.022
)4.030 )0.489 9.315 )3.720 1.109 )1.157 1.347 )0.215
0.0001 0.6249 0.0000 0.0002 0.2677 0.2478 0.1785 0.8302
F -statistic ¼ 13.372; p-value ¼ 0.000.
Table 4 Variable selection result Method
Full variables
Reduced variables
Logistic regression
TECH, LDURATION, LINVEST, ADV, CDIST, IGROWTH, RGROWTH TECH, LDURATION, LINVEST, ADV, CDIST, IGROWTH, RGROWTH
LDURATION, LINVEST, ADV
Neural networks
LDURATION, RGROWTH
Table 5 Comparison of classification results with reduced models Method Logistic regression Neural network
Training
Test
Group 1
Group 2
Overall
Group 1
Group 2
Overall
359 (89.97%) 361 (90.48%)
87 (36.71%) 84 (35.44%)
446 (70.13%) 445 (69.97%)
154 (90.59%) 157 (92.35%)
35 (34.31%) 39 (38.24%)
189 (69.49%) 196 (72.06%)
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Table 7 Full model comparison: ANNs versus linear regression (panel A) and reduced model comparison (panel B) Error measure
Training ANN
Test Regression
ANN
Regression
Panel A ME RMSE MAE MAPE
0.0002 0.1597 0.1272 0.3458
0.0000 0.1616 0.1287 0.3482
0.0016 0.1582 0.1249 0.3230
0.0002 0.1593 0.1257 0.3257
Panel B ME RMSE MAE MAPE
)0.0002 0.1597 0.1269 0.3493
0.0000 0.1618 0.1293 0.3513
)0.0018 0.1533 0.1226 0.3238
)0.0011 0.1581 0.1265 0.3296
the root mean squared error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). Results for the full model and the reduce model are given in Table 7, panels A and B, respectively. Several observations are made from the tables. First, both the full model and the reduced model with both neural network and multiple regression models give very similar overall error measures, indicating that the reduced model is capable of explaining and predicting the percentage of foreign ownership. Second, for both regression and neural networks, results in the test sample do not differ much from those in the training sample, suggesting the robustness of the models. Finally, except for ME, compared with neural networks, results from regression models in both training and test samples are consistently worse, though the differences are not substantial.
7. Conclusions Foreign ownership is an important research area in international business. This study demonstrates the usefulness of neural networks in modeling foreign ownership of Sino-foreign joint ventures that result from several transaction cost factors. As in most other areas in international business, the complexity of the problem does not allow a solid grounding in the development of a theoretical framework. The advantages of using
ANNs are that they are largely data-driven and are nonlinear. They can approximate many complex patterns, and result in more parsimonious models. In addition, a neural network model allows interactions to be modeled explicitly. In addition to the above-mentioned advantages of neural network, another benefit of using neural networks over traditional models is their use for unbalanced classification problems. As mentioned earlier, statistical models are more sensitive to the imbalance of sample size in group compositions. They have a tendency to classify all available observations into the larger sample and ignore the information contained in the smaller sample. Neural networks, on the other hand, are better positioned to handle this problem. Our neural network analysis yields a more parsimonious model than the logistic regression model, a statistical counterpart of neural networks (Zhang, 2000), while at the same time achieving better performance in out-of-sample prediction. This can have important practical advantages, because a parsimonious model is less computationally intensive and less prone to the overfitting problem often encountered in neural network modeling. Moreover, with a small number of independent variables, MNCs are able to focus their efforts on those variables that have critical influence on joint venture equity control. Our neural network analysis reveals that both the time commitment in term of the duration of the joint venture (LDURATION), and the regional growth rate (RGROWTH) could be the most significant factors for equity control. It has been argued that the longer duration of the joint venture may create more economic uncertainty or risk, and therefore, is to be compensated with greater equity control or ownership. Because of the extremely uneven growth rates among ChinaÕs many provinces, it may not be surprising that the regional growth rate can be significantly impacting the foreign ownership decision of Sino-foreign joint venture. Since theories in entry mode decisions are not well developed, it would be difficult for one to conclude whether the variables selected by ANNs is more or less consistent with the theories as compared to those selected by logistic regression.
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This study further demonstrated the usefulness of the heuristic variable selection approach based on the F -like statistic originated from linear statistical models. This approach overcomes the problem of assigning the correct statistical distribution for the neural network results. Though no formal statistical inference can be readily made by using this F -ratio, the procedure does have good intuitive appeal for practical uses. References Agarwal, S., 1994. Socio-cultural distance and the choice of joint ventures: A contingency perspective. Journal of International Marketing 2 (2), 63–80. Anderson, E., Gatignon, H., 1986. Modes of foreign entry: A transaction cost analysis and propositions. Journal of International Business Studies 17 (3), 1–26. Buckley, P.J., Casson, M., 1976. The Future of Multinational Enterprises. Macmillan, London. Buckley, P.J., Casson, M., 1988. A theory of co-operation in international business. Management International Review, 19–37, special issue. Buckley, P.J., Casson, M., 1996. An economic model of international joint venture strategy. Journal of International Business Studies, 19–37, special issue. Cybenko, G., 1989. Approximation by superpositionÕs of a sigmoidal function. Mathematical Control Signals Systems 2, 303–414. Erramilli, M.K., Rao, C.P., 1993. Service firmsÕ international entry-mode choice: A modified transaction-cost approach. Journal of Marketing 57 (July), 19–38. Federal Trade Commission, 1975–1977. Annual Line of Business Report: Staff Report to the Federal Trade Commission, Washington, DC. Funahashi, K., 1998. Multi-layer neural networks and Bayes decision theory. Neural Networks 11, 209–213. Gao, S., 1996. ChinaÕs Economic Reform. St. MartinÕs Press, New York. Gatignon, H., Anderson, E., 1988. The multinational corporationÕs degree of control over foreign subsidiaries: An empirical test of a transaction cost explanation. Journal of Law, Economics and Organization 4 (2), 305–336. Geringer, M., Hebert, L., 1989. Control and performance of international joint ventures. Journal of International Business Studies 20 (summer), 235–254. Gomes-Casseres, B., 1989. Ownership structures of foreign subsidiaries: Theory and evidence. Journal of Economic Behavior and Organization 11 (1), 1–25. Hennart, J.-F., 1988. The transaction costs theory of joint ventures. Strategic Management Journal 9 (4), 361–374. Hofstede, G., 1980. CultureÕs Consequences: International Differences in Work-related Values. Sage Publications, Beverly Hills, CA.
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Hornik, K., 1991. Approximation capabilities of multiayer feed-forward networks. Neural Networks 4, 251– 257. Hu, M.Y., Chen, H., 1993. Foreign ownership in Chinese joint ventures: A transaction cost analysis. Journal of Business Research 26 (2), 149–160. Hu, M.Y., Hung, M.S., Shanker, M., Chen, H., 1996. Using neural networks to predict performance of Sino-foreign joint ventures. International Journal of Computational Intelligence and Organizations 1 (3), 134–143. Hu, M.Y., Hung, M.S., Patuwo, B.E., Shanker, M., 1999. Estimating the performance of Sino–Hong Kong joint ventures using neural network ensembles. Annals of Operations Research 87, 213–232. Kogut, B., Singh, H., 1988a. Entering the United States by joint venture: Competitive rivalry and industry structure. In: Contractor, F.J., Lorange, P. (Eds.), Cooperative Strategies in International Business. Lexington Books, Lexington, MA. Kogut, B., Singh, H., 1988b. The effect of national culture on the choice of entry mode. Journal of International Business Studies 19, 411–432. Lasdon, L.S., Waren, A.D., 1986. GRG2 UserÕs Guide. School of Business Administration, University of Texas at Austin, Texas. McCulloch, W.S., Pitts, W.A., 1943. A logical of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133. Ministry of Foreign Economic Relation and Trade, 1980–1991. Various editions, Almanac of ChinaÕs Foreign Economic Relations and Trade, Beijing, China. Padmanabhan, P., Cho, K.R., 1996. Ownership strategy for a foreign affiliate: An empirical investigation of Japanese firms. Management International Review 36 (1), 45– 65. Pan, Y., 1996. Influences of foreign equity ownership level in joint ventures in China. Journal of International Business Studies 28 (1), 1–26. Patuwo, E.B., Hu, M.Y., Hung, M.S., 1993. Two-group classification using neural networks. Decision Science 24 (4), 825–845. Richard, M.D., Lippmann, R., 1991. Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation 3, 461–483. Subramanian, V., Hung, M.S., 1993. A GRG2 based system for training neural networks: Design and computational experience. ORSA Journal on Computing 5 (4), 386– 394. Surkan, A.J., Singleton, J.C., 1990. Neural networks for bond rating improved by multiple hidden layers. Proceedings of the International Joint Conference on Neural Networks 2, 151–162. Tam, K.Y., Kiang, M.Y., 1992. Managerial applications of neural networks: The case of bank failure predictions. Management Science 38 (7), 926–947. Teece, D.J., 1983. Technological and organizational factors in the theory of the multinational enterprises. In: Casson, M.
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M.Y. Hu et al. / European Journal of Operational Research 159 (2004) 729–740
(Ed.), The Growth of International Business. George Allen and Irwin, New York, pp. 51–62. Veiga, J.F., Lubatkin, M., Calori, R., Very, P., Tung, Y.A., 2000. Using neural network analysis to uncover the trace effects of national culture. Journal of International Business Studies 31 (2), 223–238. Williamson, O.E., 1975. Markets and Hierarchies: Analysis and Antitrust Implications. Free Press, New York.
Williamson, O.E., 1985. The Economic Institutions of Capitalism. Free Press, New York. Zhang, G.P., 2000. Neural networks for classification: A survey. IEEE Transactions on Systems, Man, and Cybernetics 30 (4), 451–462. Zhang, G., Patuwo, B.E., Hu, M.Y., 1998. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14 (1), 35–62.
Interfaces with Other Disciplines
Modeling foreign equity control in Sino-foreign joint ventures with neural networks Michael Y. Hu b
a,*
, G. Peter Zhang b, Haiyang Chen
c
a Department of Marketing, Graduate School of Management, Kent State University, Kent, OH 44242-0001, USA Department of Management, J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30303, USA c E-Trade Financial Learning Center, Christos M. Cotsakos College of Business, William Paterson University, 1600 Valley Road, Wayne, NJ 07474, USA
Received 20 January 2003; accepted 25 June 2003 Available online 5 December 2003
Abstract Equity control is one of the key areas of research in international business. This study employs artificial neural networks (ANNs) to model foreign equity control. Comparisons are made with traditional statistical modeling approaches. It was found that ANNs produce a more parsimonious set of independent variables that yield higher classification rates than logistic regression. Thus, it can be concluded that ANNs, with their complex, nonlinear structure, are able to model the relationship between transaction cost factors and majority/minority ownership; and percent equity ownership more accurately than the linear statistical approaches. Ó 2003 Elsevier B.V. All rights reserved. Keywords: Neural networks; Joint ventures; Foreign equity control; Variable selection; Parsimony
1. Introduction Foreign direct investment is one of the focal issues in international business. Within this broad area of foreign direct investment, equity control of foreign operations emerges as a key area of interest for academic researchers and international business managers. Control has been identified to be a major factor in explaining the performance of multinational joint ventures (Geringer and Hebert,
*
Corresponding author. Tel.: +1-330-672-1261; fax: +1-330672-5006. E-mail address: [email protected] (M.Y. Hu).
1989). Equity control is basically a form of control the foreign parent can exercise over its overseas operations. Internalization and eclectic theories have been used to explain the broader issues of why multinationals (MNCs) would own and control their operations overseas. The question of why MNCs would use a higher or lower level of equity ownership is the focus of several studies (Hennart, 1988; Gatignon and Anderson, 1988). The transaction cost argument presumes that the choice between complete or partial ownership depends on the benefits and costs of shared ownership. The transaction cost theory originally proposed by Buckley and Casson (1976) and later extended
0377-2217/$ - see front matter Ó 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2003.06.002
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by Teece (1983) has been used to explain the dichotomy between wholly owned subsidiaries and joint ventures. Empirical evidence of its effectiveness can be found in Gatignon and Anderson (1988) and Gomes-Casseres (1989). Hu and Chen (1993) extended this framework to embody the full range of percent equity ownership, and empirically tested the usefulness of the framework in Sinoforeign joint ventures. The difficulty in modeling such equity ownership behavioral pattern arises from sources of error in measurement and model specification. First, data obtained from presumably the most reliable published sources in China are not precise and often contain systematic biases. Every year the Chinese government publishes every year information related to a small subset of Sino-foreign joint ventures. The sample selection criterion is never clearly spelled out. Second, the transaction cost argument does not preclude interaction among the economic factors used in the model. Model specification as a result of the complexity of the relationship and the interplay among the independent variables is a nontrivial task. Thirdly, the relationships between the dependent variable, percent ownership and transaction cost factors need not be linear. Thus, linear statistical models traditionally used for such modeling efforts often yield sub-par results. Lastly, from the perspective of modeling, parsimony is to be desired. One would like to identify a small subset of relevant independent factors while maintaining a high level of predictability. In this study we employ a versatile nonlinear modeling tool-artificial neural networks (ANNs)–– to model the relationship between transaction cost factors and equity control in Sino-foreign joint ventures. Our choice of neural network models was motivated by several factors. First, as a relatively new methodology, ANNs have drawn a lot of attention in various areas of quantitative modeling, and a large number of successful applications have been reported in the literature. Second, ANNs have many desirable features for practical applications, as they are nonparametric and nonlinear. Thus they are able to capture more complex patterns involving interactive effects without having to make unrealistic assumptions about the
underlying relationships. Finally, although ANNs have effectively been applied in several crosscultural studies (for example, Veiga et al., 2000), the use of ANNs in international business applications is still meager. This study aims to provide empirical evidence on the usefulness of this modeling technique in modeling foreign joint venture equity control. Our aim is to show that with ANNs some of the modeling difficulties as previously mentioned can be overcome while attaining parsimony in the process. The ANN model will be compared to the traditional statistical modeling approaches such as multiple regression and logistic regression. The transaction cost framework originates from economics, and embodies a wide spectrum of applications. It is, therefore, not the intent of this paper to delve deeply into this framework and justify why it is applicable for describing equity control of Sino-foreign joint ventures; rather the thrust of the paper is on using ANNs to model equity control. Thus, a lengthy review of transaction cost is not warranted. The following section will provide a brief justification and operationalization of transaction cost factors in equity control. Section 3 shows the data used in this study. Section 4 lays out the ANN model and the procedure used for variable selection. Section 5 contains the results for ANN and comparisons made with logistic regression. Section 6 concludes.
2. Transaction cost factors in equity control Williamson (1985) employed the transaction cost framework to theoretically explain the conditions under which a firm may exercise more or less control over its investments. Anderson and Gatignon (1986) operationalized this framework and identified testable propositions for the case of foreign market entry decisions. Four categories of propositions are identified. They are proprietary nature of assets, host countryÕs environment, cultural difference between host and home countries, and commitment. Due to data limitations, this study will employ some of these propositions, and empirically show how neural networks can be used to model these relationships.
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2.1. Proprietary assets The transaction cost theory stresses the importance of proprietary assets possessed by firms (Williamson, 1975, 1985; Buckley and Casson, 1976, 1988, 1996; Hennart, 1988). These assets include a firmÕs proprietary knowledge in product development, brand name, and marketing skills. Anderson and Gatignon (1986) propose that high control entry modes are more efficient for products that are highly proprietary. We argue that this line of logic is particularly true for multinational corporationsÕ (MNCs) entry mode decisions in China. Hu and Chen (1993) provided some empirical evidence of the effects of proprietary assets on levels of control in entry mode decisions in China. In this study, proprietary assets will be operationalized using measures of high/low tech, and advertising intensity. 2.2. Host country’s potentials Transaction benefits increase with the size of the host countryÕs market potential. These benefits can take the form of revenue or internalization. Thus, high control modes are necessary to protect these high market potentials. Agarwal (1994) found that high control mode was more likely when the host country market increased in size. The argument being put forth is generally applied to country markets. China is a large market comprised of distinctly different regional markets (Gao, 1996). Regional segmentation and protectionism persist in China. MNCs vying for the China market have to enter each regional market one at a time, as in the case of Coca Cola and Pepsi. It is expected that high entry modes are more likely in geographic regions or industrial sectors with high potentials. 2.3. Cultural distance Culture is a set of values and beliefs. Cultural distance is the difference in values and beliefs between the host and foreign countries. It is argued that large cultural distance has consequences for high transaction costs. Padmanabhan and Cho (1996) find Japanese investors utilize high control modes when cultural distance is large. On the
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other hand, unfamiliarity with a foreign country will require the MNCs to be more dependent on local partners for their position and knowledge in the local network. This position is supported by Gatignon and Anderson (1988), Kogut and Singh (1988a,b) and Erramilli and Rao (1993). 2.4. Commitment Time and equity commitments are two other ways an MNC can exercise control over risks. Foreign investors must specify a planned duration, defined as the number of years a joint venture is to exist when registering in China. At the end of the specified period, the foreign investment enterprise must cease its operation unless both parties decide to reapply and extend the life span of the joint venture. Longer duration means less uncertainty in re-negotiation, and the partners are more committed to the success of the project. Thus, it is expected that longer duration and larger total capital investment be positively related to the level of equity control.
3. Artificial neural networks The idea of neural computing grows out of the pattern recognition capabilities of a biological brain. McCulloch and Pitts (1943) developed the first neural model, which instantly became the basis for ANNs, where nodes are likened to neurons and arcs to dendrites. Neural networks have been applied in numerous business settings such as bank failures (Tam and Kiang, 1992), corporate bond ratings (Surkan and Singleton, 1990), and more recently international business (Hu et al., 1996, 1999; Veiga et al., 2000). For a comprehensive review of neural network applications in classification and time-series problems, readers are referred to Zhang (2000) and Zhang et al. (1998). Several distinguishing features of ANNs make it an attractive modeling tool. First, ANNs are known to be an effective tool for pattern recognition particularly when the data contain a lot of noise (Patuwo et al., 1993). Second, ANNs are data-driven and do not need pre-established model form. This is in contrast with traditional statistical
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modeling approaches, where specific model forms have to be pre-specified and normality of the error terms is assumed. Thus, ANNs are well suited for problem situations where the solutions require knowledge that is difficult to specify ahead of time. Third, ANNs are universal functional approximators. It has been shown that ANNs can approximate any continuous function to any desired level of accuracy (Cybenko, 1989; Funahashi, 1998; Hornik, 1991). Complexity can be a result of interaction or measurement errors associated with the variables. Traditional statistical methods have limitations in estimating this underlying function in face of the complexity of a problem. Fourth, ANNs are nonlinear. Although many linear statistical models are simple and easy to interpret, they are simply inappropriate for nonlinear relationships that persist in most real world business problems. Finally, ANNs as a modeling tool often lead to more parsimonious results than linear statistical approaches, because they can fit any arbitrary function to a data set, thus requiring a smaller set of dimensions. A three layer feedforward network is the most popular network paradigm. Fig. 1 shows a network with one input layer, one hidden layer, and one output layer. Input nodes correspond to the independent variables used in the study. There is
only one output node for the dependent variable. The dependent variable in our study can be either categorical (majority foreign equity ownership or minority foreign ownership) or continuous (percent foreign equity ownership). Hidden nodes play an important role for ANNs to capture nonlinear patterns in the data. As the number of hidden nodes increases, more complex functions can be created to map the inputs to the output. However, as in the case of statistical models, more hidden nodes means less number of degrees of freedom, raising the possibility of overfitting. In most empirical applications, the researcher would experiment on the number of hidden nodes to arrive at the best neural architecture. At each processing node of hidden and output layer, there is an activation function that transforms information received from the previous layer. The activation function used at the hidden 1 layer is the logistic function: f ðxÞ ¼ 1þexpðxÞ . In addition, bias terms are used for both hidden and output layer. For the output node, we use the logistic function when the task is to classify an object (i.e. for binary dependent variable) and the linear function for a continuous output variable. There are linking arcs (connections) from the input nodes to hidden, and then to output nodes. The arcs connecting the input to the hidden nodes allow one
Y
Dependent variables
Output node
1
Hidden nodes
..... 1
..... X1
X2
Input nodes
Xp
Independent variables
Fig. 1. A three-layer feedforward neural network.
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to model data transformation as well as interactions among the independent variables. To use neural networks, they have to be ÔtrainedÕ first. Neural network training is the process of estimating the connecting weights (the model parameters) by minimizing some overall error measures such as the sum of squared errors (SSE). The trained network can apply its knowledge to unseen observations and the power of the trained network can be tested using a separate test sample. According to the theory of least squares, the network outputs are the unbiased estimates of the posterior probabilities in the cases of classification problems (Richard and Lippmann, 1991), which is an important advantage of ANNs used for classification (Zhang, 2000). In training an ANN, a key element is the nonlinear optimization procedure. Numerous nonlinear methods have been proposed, and in this study, the networks are trained with the GRG2based system of Subramanian and Hung (1993). GRG2 is a widely distributed nonlinear optimization algorithm (Lasdon and Waren, 1986). Subramanian and Hung (1993) showed that the GRG2 algorithm is much more efficient than other nonlinear optimizers in training neural networks.
4. Data The data for this study are obtained from the Statement of Sino-Foreign Joint Ventures, 1979– 1990 in the Almanac of ChinaÕs Foreign Economic Relations and Trade (Ministry of Foreign Economic Relation and Trade, 1980–1991). The Statement contains a description of 3071 Sinoforeign joint ventures. It provides information on the joint ventures/their parent companies, including geographic location, number of years the venture is to exist, amount of total investment, primary scope of the business, and percent of foreign equity investment in the venture. Of the 3071 joint ventures, 2416 fall into the manufacturing category. After editing for missing observations, a total of 908 observations are retained for this study. Of the 908 observations, 339 (37.33%) have foreign equity ownership less than 50%, and the rest 569 (62.67%) greater than or equal to 50%.
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In this study we use two related dependent variables for measuring foreign ownership––the percent of foreign equity ownership (PERCENT) and a dichotomous variable FCONTROL. FCONTROL is coded as Ô1Õ if PERCENT is greater than or equal to 50% and coded as Ô0Õ otherwise. That is, we aim to model foreign ownership via two separate analyses. The first is to model the exact percentage of foreign ownership in a joint venture (a function mapping problem), and the second is to model majority/minority control (a classification problem). From the managerial decision-making perspective, majority ownership often implies complete control. The classification problem is usually of greater interest to MNCs. On the other hand, the actual percentage of foreign ownership can provide more detailed information and help to validate the accuracy of the classification model. Using the transaction cost theory, we utilize four sets of predictor variables in this study: (1) proprietary assets related factors––technology and advertising intensity of the project; (2) host market potential by industrial sector, and by geographic region; (3) cultural distances between foreign investorsÕ home countries and China; and (4) commitment in capital and time to an investment projects, and planned duration of the projects. We first classify the line of business of the foreign investment enterprises into two-digit SICs. Then we group them into high (coded as Ô1Õ) or low (coded as Ô)1Õ) technology industries (TECH). Low technology industries include food and kindred products (SIC 20), tobacco (SIC 21), textile mill (SIC 22), apparel (SIC 23), lumber and wood (SIC 24), furniture (SIC 25), paper (SIC 26), printing (SIC 27), rubber and plastics (SIC 30), leather (SIC 31), stone, clay and concrete (SIC 32), primary metal (SIC 33), fabricated metal (SIC 34), and miscellaneous manufacturing (SIC 39). High tech industries include chemicals and allied products (SIC 28), petroleum and coal (SIC 29), industrial machinery (SIC 35), electrical and electronic products (SIC 36), transportation equipment (SIC 37), and measuring equipment (SIC 38). Advertising intensity (ADV) is measured as the ratio of advertising expenditure to sales. This ratio
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by SIC is provided by the Annual Line of Business Report (Federal Trade Commission, 1975–1977). This ratio captures the level of marketing skills and the relative position of a product in the marketplace (Gatignon and Anderson, 1988; Kogut and Singh, 1988b). Host market potential is measured by the growth rate by industrial sectors and geographic regions in China. Growth rates of industrial output (IGROWTH) by 2-digit SICs in China from 1980 to 1985 were obtained from the National Industrial Survey Group (1986). Growth rates by provinces (RGROWTH) from 1978 to 1984 were gathered from the Statistics of ChinaÕs Industrial Economy. Culture as defined by Hofstede (1980) has four key dimensions: power distance, uncertainty avoidance, individuality/collectivity, and masculinity/femininity. Cultural distance corresponds to differences in culture between the host and foreign countries. This study employs the single, summated index (CDIST) developed by Kogut and Singh (1988b) and used by other researchers in international business (Erramilli and Rao, 1993; Pan, 1996). As China is the only host country examined in this study, cultural distances refer to the differences between China and the foreign countries. The amount of commitment all parties bring to a project is measured in terms of capital investment and time. The sample data show that the distribution of total amount of investment in a joint venture projects some very large observations. We applied the natural logarithmic transformation to this variable (LINVEST) as in Hu and Chen (1993) and also Gatignon and Anderson (1988). Level of time commitment in a project is reflected in the proposed duration of the project (LDURATION). It is expected that the longer the duration, the greater the time commitment to the project. Foreign investors would prefer a larger equity stake in such projects.
5. Model building The primary objective of this study is to use ANNs to model equity control in Sino-foreign joint ventures. As in all modeling exercises, parsi-
mony is a key consideration. One would like to achieve the highest degree of explanatory power with the fewest number of explanatory variables. For this objective, two related questions have to be answered: 1. Which combination of explanatory variables allows for highest degree of parsimony? 2. What is the appropriate neural network model for this data set? In neural networks for classification purposes, this question corresponds to the number of hidden nodes. The second question was addressed with the cross-validation approach that splits the training sample data into an estimation sample and a validation sample. Then the neural network with different hidden nodes is trained with the estimation sample, and its performance is monitored in the validation sample. The best architecture in terms of the lowest SSE in the validation sample is the appropriate model, and will be used for all the subsequent analyses. In this work, we experiment with 10 different levels of hidden nodes, ranging from 1 to 10, and the network with seven hidden nodes was found the best. The first question is more difficult to answer with ANNs. Unlike in statistics where variable selection approaches are based on formal statistical theory and assumed probability distribution, distributional forms are difficult or impossible to specify for neural network models. In this study, a backward elimination method based on a heuristic technique of ANNs is employed for determining the ideal combination of a subset of explanatory variables. Starting with the full set of explanatory variables, variables are eliminated one at a time until some stopping criterion is met. This procedure is similar to that used in linear regression models, whereby the F -statistic is sequentially updated and revised, and compared to a predetermined cutoff value from the F distribution with a desirable significant level. Here in our study, we start with a neural network containing all seven explanatory variables, referred to as the full model, and each of the seven sub-models with six variables is then constructed. A sub-model is being referred to as the reduced
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model. A reduced model is constructed by removing an explanatory variable in the full model and all of its associated nodes and arcs. We can define: SSEF ¼ SSE of the full model; SSER ¼ SSE of the reduced model: It can be expected that the SSE for the reduced model should be larger than that of the full model. The F -ratio thus can be defined as ðSSER SSEF Þ=ðdf R df F Þ F ¼ ; SSEF =df F where df R , df F are the degrees of freedom for the reduced and full models, respectively. In linear statistical models, the F -statistic defined above follows an exact F distribution, which is derived from the assumption of normal distribution for error terms. In the case of neural networks, the theoretical distributional form cannot be derived. Thus, the F -ratio is not used for statistical testing. Nevertheless, it serves as a useful heuristic variable to measure the relative contribution of a particular variable to the modeling ability. In this study, we set the cutoff value for the F ratio to 9 as the stopping criterion for the variable selection procedure. This heuristic value was also used in a previous study by Hu et al. (1996). It is important to note that the exact value of the cutoff point is not possible to determine by theoretical statistical distribution. However, setting higher cutoff values is equivalent to specifying a lower significant level in linear statistical models.
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At each stage of the backward elimination procedure, an F -ratio is calculated for each of the sub-models. The reduced model with the smallest F -ratio is chosen, and becomes the full model in the next iteration. This process continues until finally F -ratios for all remaining variables are all larger than a cutoff F -value, suggesting no candidate can be removed.
6. Results Of the 908 joint ventures in the data set, approximately 70% (636 observations) were randomly selected for the training sample. The remainder was the test sample (272 observations). Of the 636 ventures in the training sample, 399 (62.74%) have majority foreign ownership. Roughly the same percentage (62.5%) in the test sample are of this type. Table 1 provides summary statistics for all the variables used in this study. It appears that the training and test samples shared of much the same characteristics. First, we present the results from using neural networks and logistic regression in modeling majority/minority foreign equity control. Results of logistic regression via SAS with all seven independent variables are given in Table 2. Table 2 shows that overall the independent variables are quite correlated with the dependent variable (2Log L ¼ 85:963; p-value ¼ 0.0001). Two variables, LDURATION and LINVEST are significantly related to majority ownership. Their coefficients take on a value of 1.936 and )0.250.
Table 1 Summary statistics Variables
TECH LDURATION LINVEST ADV CDIST IGROWTH RGROWTH PERCENT
Training sample
Test sample
Mean
Standard deviation
Minimum
Maximum
Mean
Standard deviation
Minimum
Maximum
)0.428 2.867 8.723 1.365 1.295 0.912 0.750 0.426
0.036 0.018 0.041 0.035 0.032 0.018 0.013 0.007
)1.000 2.303 6.551 0.250 0.870 0.510 0.370 0.030
1.000 4.595 13.581 6.100 3.180 2.210 1.630 0.990
)0.294 2.845 8.708 1.416 1.314 0.880 0.729 0.422
0.058 0.028 0.062 0.053 0.048 0.028 0.019 0.010
)1.000 2.303 6.551 0.250 0.870 0.510 0.290 0.200
1.000 4.595 12.986 2.940 3.180 2.210 1.630 0.920
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Table 2 Logistic regression result Variable
Coefficients
Wald chi-square
p-value
Odds ratio
Intercept TECH LDURATION LINVEST ADV CDIST IGROWTH RGROWTH
)4.105 0.051 1.936 )0.250 0.151 )0.041 0.168 )0.143
19.633 0.194 63.494 6.270 2.002 0.135 0.609 0.236
0.0001 0.6595 0.0001 0.0123 0.1571 0.7131 0.4353 0.6269
– 1.05 6.93 0.78 1.16 0.96 1.18 0.87
2Log L ¼ 85:963; p-value ¼ 0.0001.
The odds ratios of these two variables are 6.93 and 0.78, respectively. Table 3(panel A) shows the classification results using the neural network model with all seven explanatory variables. Of the 636 observations in the training sample, 454 (71.38%) are correctly classified. Among them, 88.72% (354/399) are correctly classified into group 1 and 42.19% (100/ 237) into group 2. In the test sample, the classification rate for group 1 actually improved to 90.59% (154/170), while for group 2 classification rate declined to 41.18% (42/102). Overall, the correct classification rate in the test sample is stable at 72.06%. Table 3(panel B) gives the classification results with logistic regression. We find that the overall correct classification rates for both training and test samples are lower than those reported for neural networks. These numbers are 69.81% in the training sample and 69.86% in the test sample. The logistic regression model per-
forms about the same as in group1 in both the training set (89.22% vs. 88.72%) and the test set (91.18% vs. 90.59%). However, its performance is inferior to neural networks in classifying observations into group 2. Note that roughly 63% of the observations in both samples are in group 1, thus classification into the smaller group 2 tends to be a more difficult task. Statistical approaches such as logistic regression used for classification are likely to be impacted by the unequal sample size in each group. In the case when the imbalance is severe, statistical procedures tend to ignore the information contained in the data and classify all the observations into the larger sample. Therefore, results from Table 3 suggest that ANNs may have the advantage over logistic regression when groups are quite unbalanced. Next, we subject the training sample to the backward elimination procedure in neural networks previously described. We employ a similar
Table 3 Neural network (panel A) and logistic regression (panel B) classification results with all predictor variables Actual/predict Panel A Group 1 Group 2 Total
Training
Test
Group 1
Group 2
Total
Group 1
Group 2
Total
354 (55.66%) 137 (21.54%) 491 (77.20%)
45 (7.07%) 100 (15.72%) 145 (22.80%)
399 (62.74%) 237 (37.26%) 636 (100.00%)
154 (56.62%) 60 (22.06%) 214 (78.68%)
16 (5.88%) 42 (15.44%) 58 (21.32%)
170 (62.50%) 102 (37.50%) 272 (100.00%)
Overall classification rate ¼ 71.38% Panel B Group 1 356 (55.97%) Group 2 149 (23.43%) Total 505 (79.40%) Overall classification rate ¼ 69.81%
Overall classification rate ¼ 72.06% 43 (6.76%) 88 (13.84%) 131 (20.60%)
399 (62.74%) 237 (37.26%) 636 (100.00%)
155 (56.99%) 67 (24.63%) 222 (81.62%)
15 (5.51%) 35 (12.87%) 50 (18.38%)
Overall classification rate ¼ 69.86%
170 (62.50%) 102 (37.50%) 272 (100.00%)
M.Y. Hu et al. / European Journal of Operational Research 159 (2004) 729–740
backward elimination procedure in SAS for logistic regression. Results are shown in Table 4. Logistic regression selects LDURATION, LINVEST and ADV as the final reduced set of variables, whereas the neural network model keeps only two variables, LDURATION and RGROWTH. Thus, a more parsimonious model is selected by the neural network. Note that both ANNs and logistic regression select the variable LDURATION, suggesting the importance of time commitment in explaining equity control for the joint venture business. Table 5 contains the classification results for the two procedures using the reduced set of variables, three for logistic regression, and two for neural networks. It is important to note that when using the reduced set of explanatory variables to classify observations in the training and test samples neural networks still provide higher overall classification rates in the test sample compared to the logistic model. Overall classification rates in the training sample are about the same for both techniques: 70.13% for logistic regression and 69.97% for neural networks. Furthermore, for smaller group 2, neural networks again outperform logistic regression with a 38.24% classification rate for neural networks and a 34.31% classification rate for logistic regression. To model the percentage of foreign ownership, we use transformed logit variable as the desired output or dependent variable. As previously no-
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ted, neural networks used in this context are equivalent to a nonlinear regression problem that performs function mapping from the input variable space to the output space. Multiple regression is first used to serve as the baseline model and examine the possible linear relationship. Results are presented in Table 6. The model is highly significant with two variables LDURATION and LINVEST again significantly related to percent foreign ownership. The regression coefficient for LDURATION is 0.707 and for LINVEST is )0.128. Thus, results for percent foreign ownership are basically similar to those for majority ownership reported in Table 2. Accuracy of multiple regression and neural networks for the continuous dependent variable is evaluated with four metrics: the mean error (ME),
Table 6 Multiple regression on training set with lnðy=1 yÞ as dependent variable Variable
Coefficients
t-statistic
p-value
Intercept TECH LDURATION LINVEST ADV CDIST IGROWTH RGROWTH
)1.295 )0.020 0.707 )0.128 0.042 )0.046 0.103 )0.022
)4.030 )0.489 9.315 )3.720 1.109 )1.157 1.347 )0.215
0.0001 0.6249 0.0000 0.0002 0.2677 0.2478 0.1785 0.8302
F -statistic ¼ 13.372; p-value ¼ 0.000.
Table 4 Variable selection result Method
Full variables
Reduced variables
Logistic regression
TECH, LDURATION, LINVEST, ADV, CDIST, IGROWTH, RGROWTH TECH, LDURATION, LINVEST, ADV, CDIST, IGROWTH, RGROWTH
LDURATION, LINVEST, ADV
Neural networks
LDURATION, RGROWTH
Table 5 Comparison of classification results with reduced models Method Logistic regression Neural network
Training
Test
Group 1
Group 2
Overall
Group 1
Group 2
Overall
359 (89.97%) 361 (90.48%)
87 (36.71%) 84 (35.44%)
446 (70.13%) 445 (69.97%)
154 (90.59%) 157 (92.35%)
35 (34.31%) 39 (38.24%)
189 (69.49%) 196 (72.06%)
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Table 7 Full model comparison: ANNs versus linear regression (panel A) and reduced model comparison (panel B) Error measure
Training ANN
Test Regression
ANN
Regression
Panel A ME RMSE MAE MAPE
0.0002 0.1597 0.1272 0.3458
0.0000 0.1616 0.1287 0.3482
0.0016 0.1582 0.1249 0.3230
0.0002 0.1593 0.1257 0.3257
Panel B ME RMSE MAE MAPE
)0.0002 0.1597 0.1269 0.3493
0.0000 0.1618 0.1293 0.3513
)0.0018 0.1533 0.1226 0.3238
)0.0011 0.1581 0.1265 0.3296
the root mean squared error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). Results for the full model and the reduce model are given in Table 7, panels A and B, respectively. Several observations are made from the tables. First, both the full model and the reduced model with both neural network and multiple regression models give very similar overall error measures, indicating that the reduced model is capable of explaining and predicting the percentage of foreign ownership. Second, for both regression and neural networks, results in the test sample do not differ much from those in the training sample, suggesting the robustness of the models. Finally, except for ME, compared with neural networks, results from regression models in both training and test samples are consistently worse, though the differences are not substantial.
7. Conclusions Foreign ownership is an important research area in international business. This study demonstrates the usefulness of neural networks in modeling foreign ownership of Sino-foreign joint ventures that result from several transaction cost factors. As in most other areas in international business, the complexity of the problem does not allow a solid grounding in the development of a theoretical framework. The advantages of using
ANNs are that they are largely data-driven and are nonlinear. They can approximate many complex patterns, and result in more parsimonious models. In addition, a neural network model allows interactions to be modeled explicitly. In addition to the above-mentioned advantages of neural network, another benefit of using neural networks over traditional models is their use for unbalanced classification problems. As mentioned earlier, statistical models are more sensitive to the imbalance of sample size in group compositions. They have a tendency to classify all available observations into the larger sample and ignore the information contained in the smaller sample. Neural networks, on the other hand, are better positioned to handle this problem. Our neural network analysis yields a more parsimonious model than the logistic regression model, a statistical counterpart of neural networks (Zhang, 2000), while at the same time achieving better performance in out-of-sample prediction. This can have important practical advantages, because a parsimonious model is less computationally intensive and less prone to the overfitting problem often encountered in neural network modeling. Moreover, with a small number of independent variables, MNCs are able to focus their efforts on those variables that have critical influence on joint venture equity control. Our neural network analysis reveals that both the time commitment in term of the duration of the joint venture (LDURATION), and the regional growth rate (RGROWTH) could be the most significant factors for equity control. It has been argued that the longer duration of the joint venture may create more economic uncertainty or risk, and therefore, is to be compensated with greater equity control or ownership. Because of the extremely uneven growth rates among ChinaÕs many provinces, it may not be surprising that the regional growth rate can be significantly impacting the foreign ownership decision of Sino-foreign joint venture. Since theories in entry mode decisions are not well developed, it would be difficult for one to conclude whether the variables selected by ANNs is more or less consistent with the theories as compared to those selected by logistic regression.
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This study further demonstrated the usefulness of the heuristic variable selection approach based on the F -like statistic originated from linear statistical models. This approach overcomes the problem of assigning the correct statistical distribution for the neural network results. Though no formal statistical inference can be readily made by using this F -ratio, the procedure does have good intuitive appeal for practical uses. References Agarwal, S., 1994. Socio-cultural distance and the choice of joint ventures: A contingency perspective. Journal of International Marketing 2 (2), 63–80. Anderson, E., Gatignon, H., 1986. Modes of foreign entry: A transaction cost analysis and propositions. Journal of International Business Studies 17 (3), 1–26. Buckley, P.J., Casson, M., 1976. The Future of Multinational Enterprises. Macmillan, London. Buckley, P.J., Casson, M., 1988. A theory of co-operation in international business. Management International Review, 19–37, special issue. Buckley, P.J., Casson, M., 1996. An economic model of international joint venture strategy. Journal of International Business Studies, 19–37, special issue. Cybenko, G., 1989. Approximation by superpositionÕs of a sigmoidal function. Mathematical Control Signals Systems 2, 303–414. Erramilli, M.K., Rao, C.P., 1993. Service firmsÕ international entry-mode choice: A modified transaction-cost approach. Journal of Marketing 57 (July), 19–38. Federal Trade Commission, 1975–1977. Annual Line of Business Report: Staff Report to the Federal Trade Commission, Washington, DC. Funahashi, K., 1998. Multi-layer neural networks and Bayes decision theory. Neural Networks 11, 209–213. Gao, S., 1996. ChinaÕs Economic Reform. St. MartinÕs Press, New York. Gatignon, H., Anderson, E., 1988. The multinational corporationÕs degree of control over foreign subsidiaries: An empirical test of a transaction cost explanation. Journal of Law, Economics and Organization 4 (2), 305–336. Geringer, M., Hebert, L., 1989. Control and performance of international joint ventures. Journal of International Business Studies 20 (summer), 235–254. Gomes-Casseres, B., 1989. Ownership structures of foreign subsidiaries: Theory and evidence. Journal of Economic Behavior and Organization 11 (1), 1–25. Hennart, J.-F., 1988. The transaction costs theory of joint ventures. Strategic Management Journal 9 (4), 361–374. Hofstede, G., 1980. CultureÕs Consequences: International Differences in Work-related Values. Sage Publications, Beverly Hills, CA.
739
Hornik, K., 1991. Approximation capabilities of multiayer feed-forward networks. Neural Networks 4, 251– 257. Hu, M.Y., Chen, H., 1993. Foreign ownership in Chinese joint ventures: A transaction cost analysis. Journal of Business Research 26 (2), 149–160. Hu, M.Y., Hung, M.S., Shanker, M., Chen, H., 1996. Using neural networks to predict performance of Sino-foreign joint ventures. International Journal of Computational Intelligence and Organizations 1 (3), 134–143. Hu, M.Y., Hung, M.S., Patuwo, B.E., Shanker, M., 1999. Estimating the performance of Sino–Hong Kong joint ventures using neural network ensembles. Annals of Operations Research 87, 213–232. Kogut, B., Singh, H., 1988a. Entering the United States by joint venture: Competitive rivalry and industry structure. In: Contractor, F.J., Lorange, P. (Eds.), Cooperative Strategies in International Business. Lexington Books, Lexington, MA. Kogut, B., Singh, H., 1988b. The effect of national culture on the choice of entry mode. Journal of International Business Studies 19, 411–432. Lasdon, L.S., Waren, A.D., 1986. GRG2 UserÕs Guide. School of Business Administration, University of Texas at Austin, Texas. McCulloch, W.S., Pitts, W.A., 1943. A logical of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133. Ministry of Foreign Economic Relation and Trade, 1980–1991. Various editions, Almanac of ChinaÕs Foreign Economic Relations and Trade, Beijing, China. Padmanabhan, P., Cho, K.R., 1996. Ownership strategy for a foreign affiliate: An empirical investigation of Japanese firms. Management International Review 36 (1), 45– 65. Pan, Y., 1996. Influences of foreign equity ownership level in joint ventures in China. Journal of International Business Studies 28 (1), 1–26. Patuwo, E.B., Hu, M.Y., Hung, M.S., 1993. Two-group classification using neural networks. Decision Science 24 (4), 825–845. Richard, M.D., Lippmann, R., 1991. Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation 3, 461–483. Subramanian, V., Hung, M.S., 1993. A GRG2 based system for training neural networks: Design and computational experience. ORSA Journal on Computing 5 (4), 386– 394. Surkan, A.J., Singleton, J.C., 1990. Neural networks for bond rating improved by multiple hidden layers. Proceedings of the International Joint Conference on Neural Networks 2, 151–162. Tam, K.Y., Kiang, M.Y., 1992. Managerial applications of neural networks: The case of bank failure predictions. Management Science 38 (7), 926–947. Teece, D.J., 1983. Technological and organizational factors in the theory of the multinational enterprises. In: Casson, M.
740
M.Y. Hu et al. / European Journal of Operational Research 159 (2004) 729–740
(Ed.), The Growth of International Business. George Allen and Irwin, New York, pp. 51–62. Veiga, J.F., Lubatkin, M., Calori, R., Very, P., Tung, Y.A., 2000. Using neural network analysis to uncover the trace effects of national culture. Journal of International Business Studies 31 (2), 223–238. Williamson, O.E., 1975. Markets and Hierarchies: Analysis and Antitrust Implications. Free Press, New York.
Williamson, O.E., 1985. The Economic Institutions of Capitalism. Free Press, New York. Zhang, G.P., 2000. Neural networks for classification: A survey. IEEE Transactions on Systems, Man, and Cybernetics 30 (4), 451–462. Zhang, G., Patuwo, B.E., Hu, M.Y., 1998. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14 (1), 35–62.