Evaluation of country attractiveness for foreign direct investment in the e-retail industry

Evaluation of country attractiveness for foreign direct investment in the e-retail industry

Zhao, Hongxin

A model for ranking countries is developed that will enhance foreign direct investment (FDI) decision-making in the e-retail industry. Existing country attractiveness rankings offer valuable information for FDI decision-making but they are too aggregated to be useful at the industry level. Different industry characteristics necessitate different assessment for specific industries. A list of criteria relevant to FDI decision-making in e-retail was compiled for this model. Information regarding the relative weight of the criteria as they apply to e-retail was compiled based on a mail survey. This model provides a more refined and pertinent ranking of countries for FDI decision-making in the e-retail industry than is found in the published general country rankings. A paired-sample test indicates significant differences between the country ranking for FDI in the e-retail industry obtained by our model and the general published country ranking.

INTRODUCTION

In today’s global marketplace, firms find it strategically necessary to engage in foreign direct investment (FDI) in multiple foreign countries. FDI involves the acquisition and management of both tangible and intangible assets in foreign markets. The tangible assets of FDI are in contrast to the financial assets like stocks and bonds that are part of indirect foreign investment. Because FDI assets are owned, they are distinguished from such entry strategies as trade and licensing agreements. Like domestic investment, FDI involves the control and management of assets and profit generation. Essentially, FDI is direct investment outside the boundary of the investors’ home country and is often the natural extension of direct exports.

As the Internet has grown rapidly in popularity, it has become the preferred platform for an increasing number of business transactions. It is used in business to business transactions as well as business to consumer transactions. The e-retail industry was one of the first e-commerce industries that evolved with the proliferation of the Internet. As several of the established e-retail companies expanded their market penetration to foreign countries, they found themselves engaged in FDI.

One notable phenomenon, along with the explosive growth of electronic commerce, is the rapid international presence by e-commerce companies (Zhao and Du, 2000). However, as their global businesses grow, FDI has become more preferred choices since FDI allows businesses to exploit location specific endowments and ownership advantages (Dunning and Rugman, 1985; Dunning, 1988). Though, theoretically, Internet technology permits e-retail firms to reach a larger number of geographical markets at fast speed, various political systems, cultural differences, and unbalanced infrastructure call for careful assessments of country attractiveness, if they opt for FDI as the desirable strategy to enter and serve the foreign markets.

The objective of this paper is to provide a model for ranking countries that will enhance FDI decision-making in the e-retail industry. Our main argument is that although existing country attractiveness rankings offer valuable information, they may be too aggregated to be useful at industry level as different industry characteristics require different assessment. In this paper, we compile a list of criteria relevant to FDI decision-making in the e-retail industry. Based on a mail survey, we also compile information regarding the relative weight of the criteria as they apply to e-retail. We integrate this information with the ranking of the individual criteria obtained in the above mentioned publications. The end result of the the e-retail industry than is found now in the published general country rankings.

COUNTRY ENVIRONMENT AND FDI

Firms undertaking international business typically follow an incremental process from low-cost, low-risk entry strategies like exporting to higher-cost, higher-risk strategies, such as wholly-owned subsidiaries (Buckley and Casson, 1976, 1985). Apart from firm-level risks related to opportunism and asset specificity in the internationalization process (Williamson, 1985), volatile and dynamic country environments pose both opportunities and threats. The political, legal, and economic factors of national environments tend to exert significant impact on firms’ entry strategies and operations but are often beyond the control of multinational corporations. Hence, national environments must be understood and assessed, as they are crucial to FDI decisions. Critical factors need to be identified and incorporated into decision making.

Firms are attracted to foreign direct investment because it may offer them: a competitive advantage over local firms, a lower cost for labor and/or physical resources, secure access to physical resources, proximity to major markets and increased market share. Foreign countries are identified as candidates for foreign direct investment if they can satisfy one or more of these strategic reasons for investing abroad. Once a foreign country is identified as a potential candidate for FDI, the investing firm’s management must select the best entry mode from among those, which the host country offers. A host government may restrict entry modes like wholly owned subsidiaries, for example. Host governments have the option, however, of offering: wholly owned subsidiaries, majority owned joint ventures, minority owned joint ventures and no entry.

When executives of e-retail firms consider market expansion to foreign countries, they must evaluate and select countries with favorable and attractive business environments for their business. This issue faces all executives considering FDI regardless of the industry. Publications like The Global Competitiveness Report published by the World Economic Forum (WEF) rank 59 countries on the basis of a large number of criteria such as Internet usage and tax incentives. While the general rankings are very valuable to executives considering FDI regardless of the industry, they are insufficient for making specific FDI decisions. First, each industry is likely to have at least some unique criteria that are not considered in the general rankings. Furthermore, the general country ranking includes criteria that may not be relevant to a specific industry and their inclusion in the ranking may bias the ranking as applied to a specific industry. Additionally, none of the publications weight the importance of one criterion relative to another. This would be difficult to do for a general ranking as weighting would of necessity vary by industry. Industry executives making informed decisions about FDI, however, need access to such information.

A MULTIPLE CRITERIA MODEL FOR

FDI IN THE E-RETAIL INDUSTRY

To develop an industry-based multiple criteria ranking model for e-retail, we take a two-step approach. The first step is identifying the criteria that affect FDI decisions in this industry. This can be done by analyzing the factors that affect the industry and by obtaining input from executives familiar with both FDI and the e– retail industry. The following criteria were identified based on the literature of country environment studies. Criteria 1, 2, and 3 are related to country infrastructure. Criteria 4, 6, and 7 are related to country market conditions. Criteria 5, 8, 9, 10 and 11 are related to country regulatory system.

The second step in the model development is determining the relative weight of the identified criteria. The approach which this study uses for determining the relative weight is accomplished by compiling the input from executives who are familiar with FDI, information technology, e– commerce in general and e-retail in particular. A survey distributed to this type of executive can be an appropriate instrument for determining the relative weight of the criteria. The executives surveyed will be asked to rank the importance of each criterion in relation to FDI decisions in the e-retail sector on a scale of 1 to 5 with 5 being very important and 1 being very unimportant. By analyzing the results of such surveys, one can determine the weight of the criteria.

Once the criteria and their relative weights are determined, the ranking of country j (R^sub j^) is computed as the weighted sum of the values of the criteria for country j:

DETERMINING THE RELATIVE

WEIGHTS OF THE CRITERIA

To determine the relative weights of the criteria listed above, a questionnaire was developed and sent to two groups of managers. Group I included current students and alumni of the Executive Master in International Business program offered by St. Louis University. All those surveyed currently hold a leadership managerial position. A total of 202 questionnaires were sent and 12 were returned due to an undeliverable mailing address. That resulted in a survey sample size of 190. There were 60 surveys mailed back. Of the 60 returned 9 recipients declined to participate in the survey. That yielded a useable return rate of 27% (51/90). A summary of the survey results, labeled Group I, is given in Table 1. To overcome the possible bias in sampling, we also surveyed 47 e-commerce companies located in the Saint Louis area. This was the second group surveyed. The 47 e-commerce companies were identified from the Fast Forward, published by St. Louis County Economic Council. Questionnaires, similar to those sent to Group I, were sent to senior executives of the identified companies. Of the 47 questionnaires, 16 were returned. That gave a useable return rate of 34%. A summary of the survey results, labeled Group II, is given in Figure 1.

Since two groups of executives were surveyed, a paired-sample test was performed to examine whether there were significant differences between the two groups. The t-test result showed no significant differences between the two groups (t = 1.007; p> 0.10). This indicates that, in reference to the e-retail, the two groups did not perceive the importance of the criteria differently.

The survey results from Group I indicate that the growth rate of Internet users is the most important criteria when the input from all the replies was considered (average weight of 4.54). The effectiveness of the local infrastructure for package delivery criterion and the government willingness to allow local profits to be converted into hard currency and transferred abroad criterion were also considered as very important The average weight for these criteria were 4.34 and 4.26, respectively. The least important criterion was the degree of bureaucracy having an average weight of 3.26. The degree of incentives for foreign direct investment criterion, the currency convertibility criterion and the growth rate of the gross national product criterion also obtained a relatively lower ranking, ranging from 3.42 to3.47. The two groups were broken down into two subgroups: respondents having at least two years of international experience and respondents having both e– commerce and at least two years of international experience. The subgroup of Group I composed of those having at least two years of international experience weighted the consumer purchasing power criterion substantially higher (4.86) than Group I as a whole (4.06). The other criteria that were weighted higher by this Group I subgroup included: government restrictions on ownership of foreign operations, degree of incentives for foreign direct investment, currency convertibility and availability of local personnel trained in computer and information technology.

The subgroup of Group I having e-commerce experience along with at least two years of international experience weighted the effectiveness of the local infrastructure for package delivery criterion higher than Group I as a whole or the aforementioned subset. Other criteria that he subgroup of Group I having both e-commerce experience and at least two years of international experience weighted higher includes: availability of local personnel trained in computer and information technology and degree of bureaucracy. The criteria which this subgroup weighted lower than either the entire Group I or the foreign experience only subgroup includes: degree of incentives for foreign direct investment, growth rate of the gross national product, and political stability.

The survey results from Group II (e– commerce executives) indicate that the growth rate of Internet users is the most important criterion when the input from all the replies was considered (average weight of 4.50). The availability of local personnel trained in computer and information technology criterion was also considered very important, having an average weight of 4.31. The least important criterion was the currency convertibility having an average weight of 3.06. The effectiveness of, the local infrastructure for package delivery and the consumer purchasing power also obtained relatively lower rankings, 3.25 and 3.38 respectively.

The subgroup of Group II composed of those having at least two years of international experience weighted the availability of local personnel trained in computer and information technology as the most important criterion, having an average weight of 4.43. This weight is higher than that obtained by the entire Group II (4.31). The growth rate of Internet users criterion was the second most important criterion for the aforementioned subset of Group II (4.14). However, the weight of this criterion by the Group II subgroup was lower than that obtained from the entire Group II (i.e., 4.50). The growth rate of the gross national product criterion obtained the lowest weight by this Group II subset (2.86). This was substantially lower than the weight obtained by Group II as a whole (3.50). The other criteria that were weighted lower by this Group II subset when compared to Group II as a whole included: government willingness to allow local profits to be converted into hard currency and transferred abroad, degree of bureaucracy, and degree of incentives for foreign direct investment. The criteria that were weighted higher by this Group II subset when compared to Group II as a whole (in addition to the availability of local personnel trained in computer and information technology criterion) included: effectiveness of the local infrastructure for package delivery, currency convertibility, consumer purchasing power, political stability and government restriction on ownership of foreign operation.

By analyzing the survey results from both groups and their subgroups, it becomes evident that the growth rate of Internet users and availability of local personnel trained in computer and information technology were the most important criteria for FDI decision-making in the e-retail industry.

A paired-sample test was performed to examine whether there were significant differences between the average weights of the criteria obtained by all the replies to the survey (the combined Group I and II) and: 1) the average weights obtained by those who have at least two years of international experience, 2) the average weights obtained by those having both e-commerce experience and at least two years of international experience. The t-test result showed significant difference in the first case and no significant difference in the second case (t = -2.473, p=0.033 for the first comparison and t = -0.337, p=0.743 for the second comparison).

DETERMINING COUNTRY RANKING

BASED ON MULTIPLE CRITERIA

MAODEL

Data regarding country ranking based on individual criteria were obtained from The Global Competitiveness Report (1999) published by the WEF and from Business Risk Index (1999). Both publications have been used in the literature as sources of country attractiveness information. The World Economic Forum measures and rankings compare how a country’s environment influences domestic and global competitiveness for the companies operating within its borders. The final rankings are derived from two sources of data: hard data and a survey. Hard data contain 139 criteria (www.imd.ch/wcy/criteria.cfn). Data are also collected from an annual survey of 3,500 executives worldwide based on 110 specific questions.

The ten criteria from The Global Competitiveness Report that were identical or similar to the ten criteria identified earlier in this paper as being relevant to the e-retail industry were used to provide the data for country ranking in Figure 2. The country ranking based on the infrastructure criterion provided in Figure 2 is the mean ranking of four criteria published by the WEF report: roads, railroads, air transport and ports. When applying infrastructure criterion to FDI decision making in the e-retail sector, all four modes of transportation must be considered. This is why the data was averaged. These ten criteria were defined by the WEF as follows:

Internet Use: ranking of countries based on the extent of Internet usage

Training: ranking of average years of schooling of the labor force

Infrastructure: the average of four individual infrastructure rankings (road, railroad, air transportation and port facilities)

Currency convertibility: ranking of countries according to the availability of foreign exchange for importing purposes

Exchange control: ranking of the degree of capital control

Purchasing power: ranking of GDP adjusted purchasing power

Growth rate: ranking based on real GDP growth rates

Bureaucracy: ranking of countries based on the time spent by company executives dealing with governments

Tax incentives: ranking of countries based on the extent that the tax system in a country promotes competitiveness

Government influences: ranking of the degree of country government influence on businesses

Data regarding country rankings based on the criterion of political risk were obtained from the Business Risk Index. These data are given in Figure 2 as well.

To obtain country ranking for the e-retail sector (R^sub j^) the multiple criteria model given in Section 2 was implemented. In this model, criteria weights (W^sub i^), reported in Figure 1 were applied. The values of criterion i for country j (F^sub ij^) are the different country rankings, each based on a single criterion. These country rankings are given in Figure 2. The country ranking for the e-retail industry based on the multiple criteria model is given in Figure 3. The results in this table were rounded to the second digit after the decimal point. Since no information regarding the political risk was provided by the Business Risk Index for Hong Kong, Israel and New Zealand, these countries were excluded from the overall ranking provided by the model.

The multiple criteria model ranked countries using three sets of criteria weight obtained from survey data for Groups I and II. These sets were: average weight of all replies; average weight of replies having at least two years of international experience and average weight of replies having e-commerce experience and at least two years of international experience. The resulting country ranking is provided in Figure 3. Figure 3 also provides the country ranking obtained by the World Economic Forum (1999).

The resulting country ranking indicates that there is very little change in the country ranking obtained by the three different sets of criteria weights. However, comparing the country ranking obtained from the multiple criteria model for the e-retail industry with the general country ranking obtained by the WEF shows a significant difference. The results of a paired-sample test between the World Economic Forum overall country ranking and the country ranking for FDI in the e-retail industry obtained by our model showed significant difference among the two country rankings (t = 1.971, p = 0.056). This result should be expected since a country ranking compiled for a specific sector (i.e., the e-retail industry) and using data relevant only for that industry represents more accurately the country ranking for that industry in comparison to the country ranking obtained by the WEF based on the general characteristics of the countries. For instance, according to our industry- adjusted country ranking, the Netherlands and Switzerland appeared to be more competitive than the United States and Singapore, the two countries that were ranked most competitive by WEF.

SUMMARY AND CONCLUSIONS

Country environment consists of various factors critical to the success of FDI. The risks and uncertainties existing in the foreign markets can be major hurdles to e-commerce transactions, particularly when such transactions take place across national borders. While the current national rankings may provide useful information for country assessments by firms in their FDI decision-making process, these rankings at the high level of aggregation need to be adjusted for industry specifics. Drawing on the current ranking criteria, we developed in this study an industry-specific ranking model with e– retail as a focus. First, criteria that affect FDI decisions in the e-retail industry were identified. Then, the relative weights of the identified criteria were determined based on the analysis of the replies to a questionnaire. The ranking of countries for enhancing FDI decision-making in the e-retail industry was determined using a normalized weighted aggregation of the different country rankings, each based on a single criterion. The data for country ranking based on a single criterion was obtained from WEF and the Business Risk Index. We chose data from these publications that were relevant to FDI decision-making in the e-retail industry (i.e. criteria that were identical or similar to the identified criteria).

The results show that the ranking based on e– retail adjusted weights are significantly different than the overall ranking provided by the World Economic Forum. This result suggests that future rankings of countries may need to be finetuned to take into account differences of specific industries in order to be more relevant and useful for firms. One managerial implication is that firms are able to optimize their choice of foreign markets by subsuming the country environment factors within the specific industrial characteristics. When firms search foreign markets for their FDI, overall national rankings of country attractiveness should be utilized with industry-adjusted rankings, since the degree of firms’ tolerance and acceptance of foreign country risks and uncertainties may vary due to the different characteristics of different industries.

REFERENCE

Business Environment Risk Index, S.A. 1999. Business Risk Index, London, United Kingdom.

Buckley, Peter J. and Mark Casson, (1976). The Future of Multinational Enterprise. Holmes & Meier Publishers, Inc.: New York.

Buckley, Peter J. and Mark Carson. 1985. “Foreign investment success for smaller firms”, Multinational Business, 3(1): 12– 19.

Dunning, John H. and Alan Rugman. 1985. “The influence of Hymer’s dissertation on theories of foreign direct investment”. American Economic Review, 75(2): 228– 32.

Dunning, John H. 1988. “The eclectic paradigm of international production: A restatement and some possible extension”. Journal of International Business Studies, 19(1): 1– 31.

Fast Forward. 2000. St. Louis County Economic Council.

Williamson, O. E. 1985. Economic Institutions of Capitalism. New York, NY: Free Press.

World Economic Forum (WEF), 1999. The Global Competitiveness Report 1999, Oxford University Press, Oxford, UK.

Zhao, Hongxin and Jianjun Du. 2000. “E– commerce and international business: A new test of internationalization theory”. A competitive paper presented at the special conference on E-commerce and Global Business, organized by University of California-Los Angels, University of Washington and Anderson Consulting. Santa Cruz, California

Hongxin Zhao (email: zhaoxk@slu.edu)

Reuven R. Levary (email: levarypr@slu.edu)

Saint Louis University

Copyright College of Business Administration. University of Detroit Mercy Spring 2002

Provided by ProQuest Information and Learning Company. All rights Reserved