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A Fuzzy Logic Perspective on Global Market Entry: An Application to Country Risk Assessment Joshua B. Levy Eunsang Yoon Richard E. Plank University ofMassachusetts Lowell ISBM REPORT 1-1993
A Fuzzy Logic Perspective on Global Market Entry: An Application to Country Risk Assessment
Joshua B. Levy Eunsang Yoon Richard E. Plank University of Massachusetts Lowell Lowell, MA 01854
November, 1992
Key Words and Phrases: country risk assessment, fuzzy logic, global market entry
Correspondence to Joshua B. Levy, College of Management, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854, (508) 9342754. The authors acknowledge the support of the Institute for the Study of Business Markets (ISBM) Affiliated Research Center (ARC) at the University of Massachusetts Lowell. -
ABSTRACT Modeling for business decision making frequently requires oversimplifying basic assumptions, incorporating imprecise knowledge, and treating inputs as highly accurate. The analysis and inference mechanisms often lack the capability to deal with linguistic inexactness and, as a result, generate conclusions that are usually either right or wrong or involve “in-control” or “out-of-control” performance states, with no accommodation for transitional outcomes that normally reflect more realistic decision alternatives. An alternative paradigm to classical models of various business problems is fuzzy logic. It is a suitable means for representing linguistic features of the human reasoning process. Primarily, it provides mathematical tools to handle recursive problematical issues such as inaccurate data, ambiguous facts, and imperfect decision rules. The current research presents a fuzzy logic application to a global market entry problem. Recognizing the elemental vagueness and ambiguity inherent in the problem, we develop specifically. a Fuzzy Evaluation Method to perform general requisite analyses for global market entry decision and, particularly, to assess the level of country risk. A case situation is described to illustrate how the fuzzy logic method analyzes and integrates political, social, and economic risk.
1.
Introduction Researchers and practitioners of global market entry confront
diverse operational issues such as the complexity of interactive influences, inaccuracy of measures, uncertainty of environmental forces, and subjectivity of the decision-making process. Acquiring the information necessary to assess global market opportunities and risks is elaborate, to say the least, and once obtained is liable to be ambiguous, inconsistent, incomplete, or deficient in quality. In addition, decision makers must often apply rules of thumb as well as incorporate their personal intuition and judgment, and derive performance measures based on indefinite linguistic concepts such as “high,” “low,” ‘‘strong,~~ “average,” “weak,” “stable,” and “deteriorating.” For example, if sales potential is “high” and profit potential is “high,” then market opportunity is evaluated as “high.” Similarly, “low” growth of the global market, “strong” competitive entry, and “uncertain” corporate commitment may imply “low” entry pressure. Such imprecise terminology is a natural phenomenon arising from imperfectly defined problem attributes. A distinctive methodology appropriate for investigating various problems characterized by ambiguous language, unreliable data, and unclear decision rules is fuzzy logic. It is founded upon fuzzy sets (Zadeh 1965) and approximate reasoning e.g. Zadeh (1975), Prade (1985) and incorporates a heuristic approach to problem solving. Fuzzy logic exploits the concept that in certain environments sets have inexact boundaries which do not separate them crisply from each other, and carries out deductive inference typically from uncertain premises underlying these sets.
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Institute, Institutional Investor Magazine, and Business International Corporation. The risk assessment method most widely employed by these institutions is the scoring model, which analyzes country risk using a step-by-step approach, e.g. (a) select appropriate risk attributes as evaluation criteria, (b) develop the relative importance of the attributes, (c) evaluate target countries across each attribute, (d) compute the overall risk level by weighting the evaluation with the relative importance of each attribute, and (e) estimate the relative country risk measure among target countries. See Backhaus and Meyer (1986) for details of country risk indices and risk assessment methods. The intent of this research is to develop a fuzzy logic framework for global market entry decision making and, particularly, to provide a fuzzy-based application for country risk assessment. Our work offers a complementary perspective without making a competitive comparison to the scoring model or other techniques used in this problem domain. (Benchmarking our results against non-fuzzy approaches is the subject of future research: see Section 7.) In Section 2 we explore the basic premises of the fuzzy modeling approach and briefly review its applications to business decision problems. We then introduce in Section 3 a decision framework for a firm considering global market entry. We motivate how fuzzy logic can be applied within this paradigm in Section 4. The mathematical basis of the model is the Fuzzy Evaluation Method, which is presented in Section 5. Section 6 depicts how the method works through a numerical illustration. Summary remarks, including a discussion of implementation issues and future research, are provided in Section 7.
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cise measurement. In practice, fuzzy logic promotes different solution alternatives, each of which need not be right or wrong but instead be possibly true to a certain degree. It also permits multiple decision making and fosters interaction among solutions. Fuzzy modeling techniques have been applied to a variety of business decisions and analyses during the past two decades. Table 1 summarizes selected research reports that demonstrate successful linguistic representation of various “fuzzy” environments. In particular, fuzzy logic has been shown to be useful for modeling production and engineering process control (Tong 1977, Bradshaw 1983, Tompkins and White 1984), inventory planning (Negoita 1981, Kacprzyk and Staniewski 1982), Park 1987); production capacity determination (Sommer 1981), and manufacturing-mix selection (Narasimhan 1980). In human resource management, e.g. when estimating the employee’s job performance, it can accommodate mental as well as physical components of human behavior (Velthoven 1977, Ollero and Freire 1981, Saunders 1982, Mital and Karwowski 1984). The use of fuzzy set theory has encouraged more realistic models of cost/benefit estimation (Ragade 1975, Neitzel and Hoffman 1980, Zebda 1984, Hartwig, Olinsky, and Li 1986), security selection (Hammerbacher and Yager 1981), credit estimation (Zimmermann 1983), judgment aggregation (Cooley and Hicks 1983), interest rate determination (Seo and Sakawa 1985), and finan-cial ratio computation (Gutierrez and Carmona 1988). Computerbased models to support dialogue in a natural language were developed for marketing decisions characterized by insufficient information in an uncertain environment. These include new product introduction (Nojiri 1982), consumer brand choice (Yager 1981), and collective preference determination (Buckley 1987).
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determined by the perceived pressure to enter the global market as well as the level of resource availability. For example, “high” entry pressure and “high” resource availability lead to “high” strategic intention. In turn, entry pressure can be inferred from the growth of the global market, the industry’s global competition, and the firm’s longterm global commitment, e.g. “fast” global market growth, “high” international competition, and “probable” long-term corporate commitment may result in a short (i.e. one-year) time-frame for market entry, a determination that signifies “high” entry pressure. Resource availability depends upon the firm’s financial prowess and level of capacity utilization. If for example both parameters are measured as “high,” then resource availability is “high.” Market opportunity is the timely combination of circumstances that motivate the firm’s entry into the preselected target market. Its evaluation requires the estimation of the expected sales and profit potentials. Expected sales potential may be measured from the target country’s GDP (gross domestic product) level, GDP growth rate, and competition, while expected profit potential may be measured from the firm’s competitive cost advantage in production and marketing. In particular, “low” GDP level, “slow” GDP growth and “weak” competition conceivably yield “average” expected sales potential. Similarly, low values for the firm’s production and marketing cost advantage result in “low” expected profit potential. As a consequence, market opportunity may be evaluated as “not above average. Payback risk summarizes the contingency on the return of profits secured from the firm’s investment into the target country. It is determined from both economic and noneconomic risk. For example, if economic risk is “high” and noneconomic risk is “average,” then payback risk is “greater than average.” Economic risk can be measured by the 7
HP
=
{highly positive trade balances}.
(The subscript 162 indicates that trade balance is the second element of the sixth set at the first level, i.e. rightmost column in Figure 1). Then the disjoint intervals, HN HP
=
=
(-1,-.1), SN
=
[-.1,0), SP
=
[0,.1), and
[.1,1], can identify the trade balance level as a percentage of the
GNP, e.g. a country with a trade balance of -$5 billion and a GNP of $200 billion has a trade deficit of 2.5%, which is highly negative according to the scale. In practice, the actual intervals would depend upon the global market expert’s or decision maker’s perception of “highly negative,” “slightly negative,” “slightly positive,” and “highly positive.” 4.
Fuzzy Modeling for Global Market Entry Some variables for global entry decision have an objective scale
of values, e.g. capacity utilization, GOP growth rate, foreign exchange rate, and expected sales potential, while many other variables are scaled subjectively, e.g. political risk, manufacturing synergy, strategic intention, and market opportunity. There are also no universally clear-cut procedures for determining when competitive global entry is “average,” trade balance is “slightly positive,” logistics synergy is “weak,” expected profit potential is “strong,” etc. To complicate matters, coordinating these variables and their descriptors into rules differs among global market decision makers. To show that fuzzy logic can be used for modeling the language of the global market entry problem in general and country risk assessment in particular, and for processing the rules of the decision framework and aggregating their diverse conclusions, we now discuss briefly fuzzy sets, linguistic variables, and production rules.
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gD(u) where a
v b
=
gA(u)
v ~LB(u),
(3)
means max {a,b}. (Other nonstandard fuzzy operations may
be used in place of (1)-(3). See, e.g. Klir and Folger 1988, Chapter 2.) Fuzzy set membership has become a separate topic of study in its own right, spawning a large literature of papers. (See, e.g. Giles 1988 and Hisdal 1988 and the accompanying bibliographies). Determining membership within a fuzzy set is usually highly subjective, since it is often derived from evaluations based on interviews. In practice, fuzzy set developers use qualitative human judgment studies (e.g. Hersh, Caramazza, and Brownell 1979, Freksa 1982, Kempton 1984) as well as statistical techniques (e.g. Bharathi Devi and Sarma 1985, Hall, Szabo, and Kandel 1986) to construct the shapes of these functions. These functions are usually continuous curves, but can be approximated mathematically by simpler functions. To incorporate the subjectivity of fuzziness and ease the calculation of inferential logic, here we use trapezoids for representing the relevant fuzzy sets of country risk evaluation. For real numbers
a
-co
