Methods of Country Risk
Assessment for International
Market-Entry Decision
Joshua B. Levy
Eunsang Yoon
University of Massachusetts Lowell
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Methods. of Country Risk Assessment for International Market-Entry Decision
Joshua B. Levy and Eunsang Yoon*
University of Massachusetts Lowell
February 1995
Revised, April 1996
*Correspondence to Eunsang Yoon, Department of Management, College of Management,
University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854, Tel.: (508)
934-28 14, Fax: (508) 934-3035
Methods of Country Risk Assessment for International Market-Entry Decision
ABSTRACT
Researchers and practitioners of international market entry typically have a difficult task
obtaining and processing requisite information to evaluate potential opportunities and risks. Essential
analysis is often confounded by inappropriate measures of input requirements, inadequately defined
information categories, and the overall complex nature of the decision process. In partial response to
these issues, this research introduces a three-stage guiding framework for market-entry decision and
presents alternative methodologies for country risk assessment, a principal component in the final
stage. A variety of discrete methods are included such as subjective interaction by deliberating experts,
scoring models, the analytic hierarchy process, simulation, and statistical designs using regression or
factor analysis. New, formal analytic rule-based non-discrete techniques utilizing fuzzy logic are also
introduced. Fuzzy logic simulates natural discourse and analogical reasoning through inference about
nebulous facts and inexact concepts, using rules that do not require a perfect match between input data
and their antecedental values in order to fire. It provides formal mathematical structure for
representing, evaluating, and interpreting linguistic context. It is especially useful for handling
problematical issues such as imprecise data, ambiguous information, vague meanings of terms, and
inconsistent analyses that characterize the general market-entry problem and risk assessment in
particular. Numerical examples demonstrate how discrete and fuzzy methods work to integrate
political, social, and financial risks.
1. Introduction
A fm’s initial business for international market entry is to screen countries for segmenting and
selecting appropriate targets and perform in-depth evaluation for discerning which specific markets
to enter. To provide support, research in global marketing must resolve several problematical issues
including the complexity of interactive influences, questionable or missing source data, inaccuracy of
measures, unclear meaning of terms, inability to classify occurring events accurately, uncertainty of
environmental forces, and subjectivity of the decision-making process (Mascarenhas 1982, Milliken
1987, Ring, Lenway, and Govekar 1990). Practitioners also face a field-research problem of
obtaining and processing the requisite information to evaluate opportunities and risks (March and
Shapira 1987, Erramilli 1991, Miller 1992). Securing the requisite knowledge to assess country risk,
a critical component of market entry analysis, is particularly elaborate and, once established, is
potentially incomplete, incongruent, vague, or quality-deficient (BUM and Mustafaoglu 1978, Dunn
1983, Karakaya and Stahl 1991, Gannon 1993).
The literature has reported a variety of methodologies for country risk assessment. The most
common are nonobjective techniques that rely on experts’ perceptions and evaluations (Backhaus and
Meyer 1984, Miller 1992), scoring models that aggregate index data on various risk variables (Hake
1982, Miiller-Berghoff 1984, Dahringer and Miihlbacher 1991), the analytic hierarchy process which
estimates relative importance of relevant variables from judgmental data (Saaty 1972, 1980, Sauber,
Sanchez, and Tummala 1991), simulation surveys which develop scenario-based risk-perception data
(Karakaya and Stahl 1991,
Punnett 1994), and other statistical methods, such as regression and factor
_
analyses (Backhaus and Meyer 1984, 1986, Erramilh 1991 j.
Recently, an alternative technique based on fuzzy logic has been suggested to deal with the
vagueness and ambiguity of knowledge conveyed through the everyday language of researchers and
practitioners who assess country risk (Levy and Yoon 1995). Fuzzy logic mirrors the way humans
think by replicating analogy and similarity in the reasoning process and performing inference from
uncertain premises about imprecise concepts (Prade 1985, Dubois, Lang, and Prade 1991, Lano
1992). These concepts are represented by fuzzy sets, whose elements belong to them only parriafly,
and which overlap each other with unsharp boundaries. Each element of a fuzzy set exhibits a degree
(or grade) of belonging to the set between O.and 1. To illustrate, suppose political conditions - one of
the input measures for country risk - can be represented numerically as a (political) risk index, a
number between 0 and 1. Then the set of “Zow political risk” can be considered fuzzy since an index
of, e.g. .35 may belong to it with a certain degree, e.g. .25 or 25%, but an index of, e.g. .22 clearly
belongs more to it with degree, e.g. 90%. (These degrees are deterministic functional values: see
Section 6.1.) Different degrees of membership of two distinct elements in a fuzzy set exhibit the
relative belonging of both to the set. Also, the fuzzy set of low political risk overlaps with the fuzzy
set of “average political risk,” since conceivably a country with an index of .35 can exhibit average
political risk with some degree, e.g. 67%. Fuzzy set membership contrasts with the usual notion of
crisp set membership of either belonging to or not belonging to, e.g. if the set of low political risk
were defined to be those indices not exceeding .20, then both a country with index .19 and another
with index .10 belong equally to the set regardless of relative lowness of political risk. Conversely,
neither a country with index .22 nor one with index .35 would belong to this traditional set, with no
distinction made between their relative degrees of not belonging. Thefuzzy set of “not low political
risk,” however, would in fact contain both indices .22 and .35 with respective degrees 10% and 75%.
(Formal definitions and concepts are presented in Section 6.1.)
Since L. Zadeh’s seminal paper on fuzzy sets appeared nearly thirty years ago (Zadeh 1965),
fuzzy logic has been applied to a wide range of problems in engineering, industrial control, medicine,
economics, computer science, physics, pattern classification, and human resource organization,
among other fields (e.g. Maiers and Sherif 1985, Kandel 1986, Klir and Folger 1988, Dubois, Prade,
and Sessa 1994). Some -business applications of fuzzy logic have been advanced in production,
human resources, finance, accounting, and marketing planning. (See Levy and Yoon 1995 for a
review.) A fuzzy approach has also been applied to risk analysis with applications for management
(Neitzel and Hoffman 1980, Kangari and Boyer 1981, Bruce and Kandel 1983, Schmucker 1983,
Kangari and Riggs 1989, Gleason 1991) and to international marketing with primary focus on new
product planning (Sanatani 198 1, Nojiri 1982, Siskos 1982, Wedel and Steer&amp 1991).
This research examines various methodologies used for country risk assessment, an integral
.
component of market entry decision, and presents a unique approach based on fuzzy logic.
2
Specifically, Section 2 develops an international market-entry framework that integrates major
categories of information requirements and analyses. Section 3 discusses basic issues in defining and
measuring parameters of country risk and develops risk indices derived from case-study data.
Different methods for evaluation of country risk are described in Section 4. A typical discrete
scoring model and numerical example derived from the indices are then presented in Section 5. In
comparison, Section 6 presents two fuzzy-logic models which are also applied using the same data.
Section 7 summarizes this work while Section 8 concludes with directions for continuing research.
2. A decision framework for international market entry
Extensive research has been published about information categories required for international
market-entry decision, including the two initial analytical stages of country screening and market
estimation (e.g. Cavusgil 1985, Root 1987, Papandoupolis and Denis 1988, Higgins, McIntyre, and
Raine 1991, Malhotra 1991). Screening includes segmentation and selection of target markets on the
basis of country-specific data such as geographical area, political climate, sociocultural
characteristics, and economic development (e.g. Sethi 1971, Goodnow and Hansz 1972, Ayal and Zif
1978, Russow 1989, Koh 1991, Wind and Thomas 1994). Subjective inputs may also be combined
for strategic reasons (Hisey and Caves 1985, Day, Fox, and Huszagh 1988, Cavusgil and Zou 1994).
Statistical techniques such as regression analysis and ANOVA as well as clustering analysis have
been utilized for segmenting potential target countries (Tse, Lee, Vertinslcy, and Wehrung 1988,
Clark 1990). Diffusion models have been applied to obtain comparative insight into patterns of target
markets (Gatignon, Eliashberg, and Robertson 1989, Takada and Jain 1991).
For a specific country, market opportunities can be estimated through various methods, e.g.
statistical analysis of the country’s market potential (Moyer 1968, Samli 1977, Douglas, Lemaire,
and Wind 1982), econometric analysis of demand models (Armstrong 1970, Lindberg 1982), and
computation of shift-share impact (Green and Allaway 1985). A portfolio approach has been
proposed to integrate country screening and market estimation into a dynamic process of a firm’s
market segmentation and resource allocation (Wind 19’78, Leontides 1988, Schuster and Plank 1989,
Wind and Thomas 1994). Despite a vigorous body of work on country screening and analysis for
3
market entry, relatively little progress has beenmade in defining data requirements and examining
how data can be converted to useful intelligence for the decision maker (Ghosal 1987, Dahringer and
Miihlbacher 199 1). Here, we introduce an information-processing framework that integrates
important measures for market analysis.
A firm’s international market-entry decision caxi be illustrated by a conceptual framework in
Figure 1. It consists of three stages linking four levels of components, each level consisting of
different sets with certain factors comprising a given set. Information and decision making is
propagated forward, sequentially in stages from right to left across Figure 1. Initially, stage 1
analyzes 19 components in level one, organized into eight sets of two or three factors per set, to make
inferences about eight level-two components organized into four sets each with two factors. Stage 2
then accumulates knowledge from these results to derive conclusions about the major components at
level three which comprise a single set of four factors: the company’s strategic intention, market
opportunity, synergistic effects, and payback risk. The final stage 3 then analyzes these to infer Go
vs. No Go at level four: whether or not to enter a target market. This configuration, which contains
principal categories for analysis deemed essential by both practitioners and researchers in global
business (Root 1987, Datta 1988, Jain 1990, Jeannet and Hennessey 1992), is flexible and expandable
in handling other evaluation criteria, information requirements, and data sources different users may
require, as well as methodologies for collection, analysis, and systematic decision making. For its
strategic use, ours is similar to the decision framework of market segmentation that Wind and
Thomas (1994) demonstrate in their application to industrial market analysis. In their segmentation
framewdrk a target market or country is a descriptor variable, but it is a segmentation basis for our
international market-entry analysis.
_____-______________-~~~~~~~~~
Insert Figure 1 about here.
__________________-_~~~~~~~~~~~
Figure 1 indicates that strategic intention embodies the firm’s long-term perception of its plans
and performance in the target country. It is determined by the manifest pressure to expand global
business and the degree of resources available to the fm. Entry pressure is inferred from growth of
the global market, the industry’s global business competition, and the firm’s long-term commitment,
while resource availability depends upon the fii’s fiiancial prowess and measure of capacity
utilization. Market opportunity is the expedient conjunction of circumstances and events that
motivates the firm’s entry decision. Its evaluation requires estimation of expected sales and profit
potentials. Sales potential can be measured from the target country’s gross domestic product (GDP)
.
and global/local competition, while profit potential is determined from the fnm’s competitive cost
advantage in production and marketing. Synergy is the effectual combination of all concordant
actions the firm undertakes. Product synergy is created for research and development in engineering,
manufacturing, and logistics, while management and marketing experience generate the fii’s global
business synergy. Payback risk, the final category for a Go/No Go decision, summarizes the
contingency on the return of profits secured from the firm’s investment into the target country. It is
based on economic and noneconomic risk (Baird and Thomas 1985, Sharma and Johanson 1987).
3. Measures of country risk
Country risk, which represents uncertainty of payback from international business, is perceived
and measured linguistically as well as numerically (Johnson 198 1, Terpstra and Yu 1988, Miller and
Bromiley 1990). Based on Figure 1, country risk is assessed in two stages. At stage 2, the level-two
components noneconomic risk and economic risk are processed. Noneconomic risk can be
interpreted as the probability of the firm losing its investment, which can be measured by two factors
at level one, political conditions and social conditions (Robock 1971, Dunn 1983, Simon 1984,
Weiner 1992). Combining these two factors is at stage 1; their interaction is expressible, for
example, as “stable” political conditions and “stable” social conditions lead to a “smail” probability
of investment loss, a situation management may interpret as ‘Vow” noneconomic risk. Economic risk
can be measured as a probability that the payback can be completed by the end of the firm’s planning
horizon, e.g. 5 years (Lessard 1989, Ahmed and Summers 1992). It depends upon the level-one
factors, foreign exchange and trade balance. The foreign exchange rate is often estimated via a
country’s inflation (Jacque 198 1, Jacque and Lorange 1984). Trade balance is computed as a
percentage of the gross national product (GNP). Integrating these factors also occurs at stage 1, e.g.
5
large values for both the foreign exchange and trade deficit (negative trade balance) imply that
economic risk is “high.”
We demonstrate measures of country risk for market-entry decision from a case study for a
multinational firm that plans to enter Spain and/or Norway in the near future to expand its European
business in waste management. (See Levy and Yoon 1995 for background material.) To exhibit
different methods of risk analysis, we develop numerical examples requiring source data for political
conditions, social conditions, foreign exchange, and trade balance. To initiate the examples, factor
indices will now be derived from the source data. In general, let the notation Uijk denote the index
for the k’th component factor of the j’th set at the i’th level if k is at least two but, if k is 1, then the
notation is uij for simplicity. From Figure 1, then the indices for p~litkal conditions and social
conditions, the seventh set at level one, are labeled 11171 and ~172, and for foreign exchange and trade
balance, the eighth set at level one, they are labeled 11181 and u182. Theoretically, each index can be
any real number. Consultation with an expert on international marketing (see Section 6. l), however,
has suggested that ~171, ~172, and ~1181 be resticted to the interval [O,l] while 11182 lie in C-1,1].
An index for political conditions is obtained from the five-year forecast for 1990-94 of
“political turmoil” by Coplin and O’Leary (1989, Introduction, p. 7). Turmoil considers large-scale
protests, strikes, demonstrations, riots, terrorism, guenilla warfare, civil or international war, and the
impact of a government’s reaction to unrest. These are measured on a nine-point scale from 0 to 8:
“very low” = 0, “low” = 1, “slightly low” = 2, “slightly-less-than-moderate” = 3, “moderate” = 4,
“slightly-more-than-moderate” = 5, “slightly high” = 6, “high” = 7, and “very high” = 8. We next
compute scalar input foi-political conditions using the data shown in Table 1. The first row indicates
the estimated probability that a specific government will be in power by 1994. The second row
shows the “base” (or current) turmoil and the “projected” turmoil dependent upon the ruling
government-to-be. The number in parentheses is the score associated with the category, based on our
nine-point scale. For example, Spain’s base turmoil is moderate (4), but the projected turmoil if the
Socialists are in power in 1994 is slightly less than moderate (3). By (i) computing the expected
value of projected turmoil across the three possible regimes and then (ii) taking the simple. weighted
average of this expected value and the base turmoil, a scalar in the interval [0,8] is obtained which
6
.
(iii) is modified via the normalizing linear transformation u + u/8 to a number in [O,l]. AS a result,
an index of political conditions is u171= SO6 for Spain and u171= .125 for Norway.
___________-_______~~-~~~~~~~
Insert Table 1 about here.
____________________---------From Daume (1991), five factors that comprise social conditions were obtained: ethnic makeup,
religious makeup, literacy rate, language dispersion, and income distribution. Using the simple
scoring assignment of “least risky” = 1, “risky” = 2, and “most risky” = 3 for each factor, we obtain a
cumulative value for all factors lying in the range [S,lS]. Table 2 displays the authors’ categories and
evaluations based on data from Daume (1991). For example, since the dominant ethnic group in
Spain comprises 60-95% of the population, 2 is assigned. Religious makeup, literacy rate, and
language dispersion are scored in the same way. Income distribution was figured by taking the
difference between the proportion of total income earned by the top 10% of the population and the
proportion earned by the bottom 20%. In Spain the top 10% earned 25.4% of the total income while
the bottom 20% earned only 4.0%, a difference of 21.4%. The respective numbers for Norway are
24.5% and 6.9%, a difference of 17.6%. In both cases, income distribution is assigned 2.
Consequently, the five factors for Spain and Norway sum to a total of 8 and 6. The linear
transformation u + (u - S)/lO rescales [5,15] to [O,l]. Thus, an index of social conditions is ~172 =
.300 for Spain and ~172 = . 100 for Norway, respectively.
________~___________~~~~~~~~~~~
Insert Table 2 about here.
____________________~~~~~~~~~~~
An index for foreign exchange is estimated by the three-year average from 1987 to 1990 of a
country’s annual proportional increase in consumer prices. Based on the source data, taken from the
OECD Main Economic Indicators (June 1991, pp. 148, 152), the average (index) lies in [0, l]: it is
u181 = 6.115% (.06115) for Spain and u181= 5.145% (.05145) for Norway. (See Table 3.)
____________________~~~~~~~~~~~
Insert Table 3 about here.
__________________-_-~~~~~~~~~
Scalar input for the final component, trade balance, is measured by the three-year average from
1988 to 1990 of the ratio of a country’s annual trade balance to its GNP. The data, which is from
7
OECD Mai! Economic Indicators (June 1991, pp. 25,172), leads to the average or index ~182 =
-.01088 for Spain and ~1182 = .00410 for Norway. (See Table 4.)
________-___________-~____
Insert Table 4 about here.
_______-_-_________________
For ease of reference, the indices are summarized in Table 5.
__________________~_~~~~~~~~~~
Insert Table 5 about here.
_______U____________~~~~~~~~
4. Methodologies
Approaches for country risk assessment vary from subjective and interactive deliberation by a
group of experts, through priority ranking and weight estimation of information components as well
as statistical designs using regression or factor analysis, to formative rule-based methods for
evaluating risk variables from a linguistic rather than numerical perspective. Collaboration by
experts assists unstructured decision making through its intrinsic process of fostering a combination
of different solutions from decision makers, while the other methods support semistructured decision
making through integration of routine, repetitive structured decisions with unique, nonrecurrent
unstructured decisions. All approaches can be incorporated as useful model management techniques
and linked to a database management system with appropriate support tools (e.g. user interfaces,
graphical analysis, on-line help, and means for error correction and control) for design of a formal
decision support system (DSS) with specific application to country risk analysis. Table 6 compares
major risk assessment techniques with respect to measures.of input and output and summarizes their
main advantages (benefits) and disadvantages (limitations).
_~__~_______________~~~~~~~~~~~
Insert Table 6 about here.
____~_______________~~~~~~~~~~~
Country risk assessment can be conducted by a panel of experts striving for concurrence. This
amenable approach to group decision making is experiential, judgmental, and intuitive, typically
using a nominal scale of raw risk measures for input. It combines a wide variety of knowledge and
practice elicited from experts into a common understanding to assess cumulative entry risk for a
8
-
target market. “Experts” are usually considered by whether they can enhance the performance of
global-market entry decision and include experienced managers, field practitioners, industry
protagonists, and professional consultants. Panel consensus, however, is often criticized on several
points, including a comparatively long process to reach acceptable conclusions, a general lack of
formal extrinsic analyses, and the tendency for bias shared among experts, besides the persistent
difficulty of identifying qualified “experts” (Backhaus and Meyer 1984, Miller 1992).
A more common approach is the (discrete) scoring model that averages indices from different
risk categories (Blank, La Palombara, and Sacks 1982, Hake 1982, MCiller-Berghoff 1984). Typical
steps include: (1) select appropriate risk attributes as evaluation criteria, (2) develop the relative
importance of the attributes, (3) evaluate target countries across attributes, and (4) estimate the
overall risk level for each country by weighting the evaluation with the relative importance of every
attribute. Backhaus and Meyer (1986) compare, for example, 23 risk indices developed from scoring
models reported in the business literature. Factor analysis can be employed to identify underlying
dimensions of various risk attributes and develop an appropriate weighting scheme for scoring
models (Backhaus, Meyer, and Weiber 1985). It analyzes a set of interval-scale indices for various
risk variables, computes covariance matrices, and determines an interval average of factor loadings
which serve as weights to assess overall country risk. The scoring model will be most useful for
processing numerical infoxmation when a framework for analysis, e.g. Figure 1, has already been
determined by the decision maker. It is best applied for evaluating quantitative data such as market
potential or economic risk because of its simplicity and relative ease of use for comprehension,
computation, and interpretation. However, its principal limitation is that it often requires arbitrary
data manipulation when processing qualitative information, particularly to estimate attribute weights.
The analytic hierarchy process (AHP) is a technique that has been successfully applied for
identifying an appropriate structure (typically a hierarchical tree) of various information components
in a group decision model and estimating their relative importance to a decision (Saaty 1972, 1980,
Jensen 1986, Sauber, Sanchez, and Tummala 1991). The process of integration using the framework
of Figure 1 (or a variation of it designed for a specific market-entry case) requires the weighted
contributions of these components. AHP estimates the weights so that the analysts’ evaluation of
9
.
relevant information best fit their practical or hypothetical Go/No Go decision. AHP is most useful
for coordinating actual data and the results of other quantitative models with subjective information
obtained from a group of experts’ general knowledge, experience, and intuition, particularly amidst
personal conflict, e.g. strategic intention which depends on the positional policies of the decision
participants as well as their own personal goals and career plans. Disadvantages include potential
bias or inconsistency in the experts’ derivation of different information categories. For an application
of AHP to international market attractiveness see, e.g. Liang and Sheng (1990) and Saaty and Vargas
(1994, ch. 11).
A simulation survey utilizes a qualitative scenario to create risk evaluation and entry decision
for different combinations of market barriers and entry conditions (Karakaya and Stahl 1991, Punnett
1994). Suppose, for example, the barriers for a specific country are simultaneously characterized in
terms of “low” cultural differences, “low” product adaptability, “high” channel accessibility, “stable”
currency exchange rate, and “favorable” foreign government policies. Then a decision maker may
conclude there is, e.g. a 70% chance of an early market-entry opportunity but only, e.g. a 40% chance
of a late market-entry opportunity. If, instead, channel accessibility were deemed only “adequate”
and foreign government policies “indifferent,” then the opportunity might shift to, e.g. 60% for early
entry and 45% for late entry. This method is flexible, since a country’s risk can be evaluated for
various taxonomic combinatWs of risk factors. Additionally, the data created by the simulation
survey can be analyzed through statistical regression or discriminant analysis to estimate the
association between the scenario components of market barriers (explanatory variables) and the
probabilistic assessments about early/late entry (dependent variable). The resulting model is also a
useful guideline to assess entry risk for other target markets. The method’s main limitations are the
relatively long process and high cost of scenario and questionnaire design, survey and data collection,
and analysis and evaluation.
Finally, we introduce two fuzzy-logic techniques that are based on fuzzy sets and production
rules to describe the fundamental relationships between the framework’s variables. These are thefull
fuzzy scoring model and the reducedfuzzy scoring model, which categorically analyze each
. risk
attribute to develop a composite linguistic representation of country risk through two successive
10
stages of production rules. This process enables problem solving by deriving new (fuzzy) facts about
country risk from previously known (fuzzy) facts. A generic production rule has the form “If X is A,
then Y is B ,” where the “if’part is the premise or antecedent and the “then” part is the conclusion or
consequent of the rule, X and Y are Linguistic instead of numerical variables, and A and B are terms
instead of real values designated by fuzzy sets. In a fuzzy rule-based system, at each stage of analysis
fuzzy input A’ is matched against rule antecedent A to reach a conclusion B’ that only approximates
the intended conclusion B, since A’ is not exactly A. This is an example offuzzy modus ponens, an
inference mechanism often used as a systematic approach for accommodating uncertainty based on
discourse and imprecise reasoning. It contrasts with classical modus ponens, in which terms are crisp
and, for a rule to fire, input must match precisely the antecedent to infer the given consequent. All
outputs B’ are summarily combined into a representative fuzzy set, an envelope, which is the ultimate
linguistic evaluation, or score, for that stage.
The two models differ in the type of information exchanged between stages. The full fuzzy
scoring model (or fuzzy evaluation method: see Levy and Yoon 1995) transmits the entire envelope
as input, maintaining complete information from one stage to the next, encouraging multiply
descriptive interpretations consistent with the decision maker’s innate feeling for different but
conformable solutions. Of course, potentially wide variation among decision makers interpreting
output is a disaiantage. The reduced fuzzy scoring model converts the envelope to a single scalar
input for the next stage, thereby conceding information entirety for facility of use but possibly
restricting interpretation (Levy and Yoon 1993). A typical user at first may find the reduced model
easier and more comforttible to work with than pe full model. Moreover, while the accompanying
envelope is available for analysis, interpreting scalar output is intuitively more appealing. (See
Section 6.) Both approaches provide a formal structure for integrating categorical input data,
linguistic variables, and production rules to systematically generate and aggregate knowledge, as well
as embody the flexibility of output interpretation desired by users and often obtained through “What
if?” analysis from applying the previous non-fuzzy procedures.
These methods enable and enhance support for country-risk assessment or any other. decision
category for market-entry analysis including the final Go/No Go evaluation. Any of the previous
11
,
approaches can be incorporated into a model management base for building a group decision support
system (GDSS), in which conciliation and cooperation among all participants with a wide variety of
styles and thought processes are ultimately required in practice for successful decision making (e.g.
Mallach 1994). For example, Dyer and Forman (1992) show why AHP is well suited for group
decision making and offer a GDSS approach in different contexts. (See also Section 8.)
For application to international market-entry analysis, we generally assume that level-one
components require raw information which has already been developed through, e.g. judgments and
the scoring model partially using hard facts (see Tables l-4), while the relative importance of leveltwo or -three components can be determined by, e.g. interviews with industry experts, scenario
survey, or AHP. In the sequel, however, we demonstate specifically how country risk can be
assessed by using the indices for Spain and Norway from Table 5 as initial input for stage 1, and then
applying the discrete and fuzzy scoring models for purpose of comparison at stage 2.
5. A discrete scoring model
We apply a discrete (i.e. crisp or non-fuzzy) scoring model that initially averages equally
weighted indices for the four level-one components of payback risk to obtain scalar values for
noneconomic and economic risk. These are then averaged (equally) together to obtain a value for
payback risk. Specifically, at the beginning of stage 1, the pair of indices (u 171 ,u172) for political
conditions and social conditions is averaged to obtain a value ~241 for noneconomic risk, the first
component in the fourth set at level two. Similarly, the pair (u 18 1 ,Ul& representing foreign
exchange and trade balance is averaged, getting a value ~242 for economic risk, the second
component in the fourth set at level two. This completes the analysis of stage 1. The pair
(~241,~242)
is then input for stage 2. Their average in turn is a value ~134 for payback risk, the fourth
component at level three. Obtaining ~34, an index invariably in [O,l], concludes the evaluation of
payback risk at stage 2. The higher ~34, then the greater the risk. For purpose of decision support,
partitioning [O,l] into smaller disjoint intervals categorizes the degree of risk, e.g. [O,.lO] represents
“very low” payback risk, (. 10,.20] is “low,” (.20,.35] is “below average,” (.35,.65] is “average,”
(.65,.80] is “above average,” (.80,.90] is “high,” and (.90, l] is “very high.” Additionally, combining
12
94 with similar scores from the other level-three components, input is obtained for the final stage 3
analysis of Go/No Go.
Applying the data in Table 5, the model yields for Spain first the scores 11241= .403 and ~242 =
.02X4, so u314 = .214. Similarly, for Norway, ~241= .113, ~242 = .02778, whereby ~314 = .070
(Table 7). Based on the preceding categories, one can conclude payback risk is below average for
Spain and very low for Norway, both reassuring conclusions for venturesome firms, although Spain
poses, by comparison, conceivably three times the risk than Norway. By contrast, in the next section
fuzzy logic will capture the linguistic structure of the output and evaluate the entire structire,
offering an alternative to averaging point estimates for purpose of analysis.
--_--____--__________________
Insert Table 7 about here.
-____-______________~~~~~~~~~~
6. New fuzzy-based techniques
6.1. Fuzzy sets, linguistic variables , and production rules
Our approach utilizes fuzzy logic to handle subjectively defined parameters for market-entry
analysis in general and, particularly, for country risk assessment. It is based on fuzzy sets, linguistic
variables, and production rules which we will briefly review within the current framework. A fuzzy
set is a mathematical formalism for representing linguistic imprecision within the context of human
discourse. Unlike classical sets that have sharp boundaries to distinguish membership from nonmembership, a fuzzy set. contains universal elements which have only partial membership within it.
Formally, a fuzzy set F in a universe of discourse U is a collection of ordered pairs, ((u, pF(u))} ,
where u is an element in U and PF: U + [O,l] is the membership function of F that describes the
degree of belonging or compatibility pF(u) of’u to F. cLF(u) is also called the membership grade of u
in F. In general, 0 < pF(u) c 1, i.e. u has partial membership in F, while the extreme cases pF(u) = 1
and /+(u) = 0 signify, respectively, full or no membership of u in F. A fuzzy set measures vagueness
in context by partial membership of its elements. An element can also belong to more than one fuzzy
set with a different membership grade in each one. Partial membership in various fuzzy ‘sets is a
measure of contextual ambiguify.
13
Standard operations among fuzzy sets are extensions of complementation, intersection, and
union for classical sets, but they manipulate membership functions. If A s: U and B c U are fuzzy
sets in a universe U, then basic fuzzy set operations am defined as follows. The complement x of A
is the fuzzy set with membership function
PAW = 1 - 1.LA(u)=
(1)
The intersection of A and B is the fuzzy set C = A n B with membership function
PC(U) = &&d A b&(u),
(2)
where a A b means min (a,b 1. The union of A and B is the fuzzy set D = A u B where
&$u) = &4(u) ” &J(u),
(3)
is its membership function and a v b means max { a,b).
Arguably, a variety of techniques can be used to develop membership functions, from
subjective interviews for obtaining consensus, e.g. Hersh, Caramazza, and Brownell 1979, Freksa
1982, Kempton 1984 to more definitive approaches including statistical analysis of empirical data
using human concepts, payoff functions, conditional probabilities, parameter identification, and
possibility distributions, e.g. Mabuchi 1992. For our work, informal inquiries with an academic expert
with extensive practical experience in international market entry, who has especial knowledge of the
prescreened targets Spain and Norway, have been carried out based on the framework of Figure 1 to
obtain meaningful membership functions for each of the 32 components. Firstly, the expert
methodically evaluated the context of every component and assigned labels based on this evaluation.
Secondly, he developed a suitable scale of values signifying a designated universe. Thirdly, he
interpreted the appropriateness of each label to its context by sketching a figure of height between 0
and 1 over the scale. The shape of this figure expresses the expert’s feelings on the degree of
compatibility between any scale value u and the corresponding label he assigned. In this way, a label
is associated with a fuzzy set and the corresponding figure is its membership function. T&e expert on
his own selected trapezoidal membership functions to elucidate his labels for ease of use and
14
flexibility. For real numbers a I b I c I d in U, a trapezoid T with amplitude one is the real-valued
function defined by
i
I
T(u) =
u-a
b-a
,
a<ulb
I
9
be&c
d-.u,
d-c
cculd
0
d < u.
9
Otherwise, if a = b, then T(a) = 1 or if c = d, then T(d) = 1. Formally we write T = (a,b,c,d).
Consider foreign exchange, a component of country risk (Figure 1). It is computed as a
percentage of the rate of inflation which can be any real number, but the consulting expert resealed
the universe to be the interval U = [O,l] from his knowledge about Spain and Norway. Low
(respectively, high) percentages in [O,l] indicate stable (respectively, unstable) rates of exchange.
The labels “unstable,” “deteriorating,” and “stable” were introduced to describe the basic linguistic
nature of foreign exchange from the expert’s perspective. “Deteriorating” means declining from
“stable” to “unstable.” The expert then sketched separate trapezoids to convey his own interpretation
of the innate context expressed by the labels. In this manner, for these two target countries, the fuzzy
sets U, D, and S were derived as representing unstable, deteriorating, and stable foreign exchange
with membership functions FLU = (.15,.2,1,1), &) = (.05,.13,1,1), and ps = (O,O,.OS,.l) (Figure 2).
Thus, from (4), a foreign exchange reflecting 8% of the annual inflation rate is “stable” with degree
.40, but also “deteriorating” with degree .375 and “unstable” with degree 0.
______________~_____~~~~~~~~~~~~
Insert Figure 2 about here.
~_____~_____________~~~~~~~~~~~~
Another component is trade balance, which is measured as a percent of GNP. The expert
devised the scale U = [-l,l] with the labels “highly negative” (I-IN), “slightly negative” (SN),
“slightly positive” (SP), and “highly positive” (HP). By configuring trapezoidal membership
functions, he then created the fuzzy sets HN, SN, SP, and HP for which &JN = (-l,-l,-.l,O), ~SN =
I
(-.l,-.075,-.025,0), J.LSP = (0,.025,.075,1), and pm = (O,.l,l,l) (Figure 3). If, e.g. ~country’s trade
balance as a percent of GNP is -.015, then its trade balance is highly negative with degree .15 and
slightly negative with degree .60, while it is slightly positive or highly positive with degree zero.
__-______I____-_-_________
Insert Figure 3 about here.
I____________~_-_--_-~~~~~~~~~~
An opportune means of delineating a portion of the expert’s knowledge is a linguistic variable.
It takes on values - his own labels - with appropriate fuzzy sets and membership functions. Among
the 32 potential linguistic variables in Figure 1, those for risk assessment are Political Conditions
(POLC), Social Conditions (SOLC), Foreign Exchange (FREX) (Figure 2), Trade Balance (TRBL)
(Figure 3), Noneconomic Risk (NONER), Economi;c Risk (ECONR), and Payback Risk (RPAYBK).
The expert assigned U = [O,l] as the scale for each variable except TRBL. The basic terms “low,”
“average,” and “high” he created for FREX and TRBL are also the values of POLC, SOLC, NONER,
and ECONR, but are interpreted differently with unequal membership functions per variable. “Low”
is described for POLC by PL = (0,0,.2,.4), but by PL = (0,0,.2,.35), PL = (0,0,.25,.35), and PL =
(0,0,.25,.4) for SOLC, NONER, and ECONR, respectively. Expressing “average” and “high” are PA
= (.25,.4,.6,.75) and j.~f~ = (.7,.85,1,1) for POLC, PA = (.3,.45,.65,.8) and pi = (.75,.85,1,1) for
SOLC, PA = (.3,.4,.6,.7) and PH = (.6,.8,1,1) for NONER, and PA = (.35,.45,.53,.67) and &L =
(.65,.85,1.1) for ECONR. RPAYBK has five basic terms: “low,” having PL = (0,0,.25,.33); “below
average,” /.LBA = (0,0,.35,.5); “average,” PA = (.3,.45,.55,.7); “above average,” ~AA = (.5,.85,1,1);
and “high,” &-I = (.67,.9,1,1).
The expert introduced additional terms for values of .linguistic variables to accommodate more
complete knowledge of the problem domain. Some of these terms are constructed from modifiers,
adverbs, e.g. “very,” “more than,” “less than,‘: “not as much,” etc. that adjoin basic labels. POLC and
SOLC for example take on the modified values “very high” and “less than average” (but with
different membership functions). Supplemental terms also arise from simple manipulation of the
basic operations (l)-(3). For instance, TRBL takes on additional linguistic values “not highly
negative,” HN, “slightly negative or slightly positive,” SN u SP, and “slightly positive or highly
positive,” SP u HP, having respective membership functions
16
.
Pm
by (1) and,
=1-/&N=
1 - (-l,-l,-.l,O) = (-.l,O,l,l)
from (3,
@N usp = (-.l,-.075,-.025,O).v (0,.025,.075,.1),
CLSP UHP = (0,.025,.075,.1) v (O,.l,l,l) = (0,.025,.075,.1) = psp.
Production rules associate linguistic variables to provide structure for representing the
knowledge and inference of market entry and specifically analyzing country risk. Typically, the rules
have multiple antecedent terms but a single consequent term: “IfXl is Al, X2 is Ap..., and Xn is An,
then Y is B,” where X1, X2 ,..., Xn , and Y are linguistic variables and Al, A2,*.*, A,, and B are values
induced by fuzzy sets. Each Ai belongs to a universe of discourse Ui corresponding to Xi and B
belongs to a universe V corresponding to Y. More than one rule usually leads to the same conclusion
“Y is B” since at least one Xi can take on multiple values Ai in the antecedent. If this were a
traditional rule base in which the values of the variables were crisp instead of fuzzy sets, then the
number of possible rules would explode geometrically, since ostensibly each antecedent variable
would assume only one value at a time (e.g. if each Xi could take on ni discrete values Ai, then the
number of antecedent concurrences is the product lir,ni). On the contrary, the fuzziness of the
=
values, expressed by the underlying membership functions having continuous scale, induces a
gradation of rule combinations of reasonably more manageable order. In this way the rules
instantiate the inferential process of human reasoning.
The same expert devised three stages of production rules linking four levels of linguistic
variables for market-entry decision, from the lowest level on the far right of Figure 1 to the highest
level at the far left. A rule set is defined to be, a collection of rules having the same consequent
variable Y. There are eight rule sets at stage 1, four sets at stage 2, and one set at stage 3. Country
risk assessment is part of stages 1 and 2, its first stage having two distinct rule sets and its second
having one (Figure 4).
_~__________________~~~~~~~~~~~
Insert Figure 4 about here.
________________________I______
17
A typical rule in the first set of stage 1 for country risk assessment infers Noneconomic Risk:
(POLC is Low Or Average)
AND (SOLC is Not High)
THEN
(NONER is Average).
IF
-
In the second rule set at stage 1 which has Economic Risk in the consequent, an example rule is
IF
(FREX is Stable)
AND (TRBL is Slightly Negative Or Slightly Positive)
THEN
(ECONR is Low).
At stage 2, two rules for risk assessment that generate the same conclusion are, e.g.
(NONER is Low)
AND (ECONR is Not High)
THEN
(RPAYBK is Below Average).
IF
(NONER is Average)
AND (ECONR is Low)
THEN
(RPAYBK is Below Average).
IF
See Levy and Yoon (1995) for comprehensive rule sets about country risk assessment.
6.2. The fuzzy techniques
Fuzzy logic systematizes approximate relationships among linguistic variables and implements
deductive reasoning using production rules. The full and reduced fuzzy scoring models are designed
with inference schemes based on fuzzy modus ponens. While other fuzzy approaches can be used
(Levy and Yoon 1993), during
a typical analysis these two specifically examine all production rules
_
at both stages of country risk assessment and
ali three stage3 of market-entry decision. Initial input
data to stage ‘1 triggers the analysis through a comparison tith appropriate rule antecedents to
generate approximate output of the intended conclusion of each rule. These results are then
integrated across all rules and passed forward as input to stage 2 - differently, depending on the
methodology - where the analysis is again performed. This produces summary output for payback
risk, which concludes the assessment. Together with other outputs similarly generated by stage 2, it
initializes the third and final stage of rule evaluations for ultimately making the Go/No Go decision.
18
6.2.1. The fullfuzzy scoring model. This consists of three components: matching, resolution,
and aggregation. At each stage, for every rule in any rule set, matching fast compares fuzzy (or nonfuzzy) input data, i.e. a vector A’= (A’l,... ,A’,), with the corresponding antecedent terms, i.e. the
vector A = (Al, . . . . A,), where each data component A’i and term Ai belong to universe Ui. Since the
data is rarely a subset of or coincident with the rule premises, this results in a partial match, namely a
vector A’n A = (A’1 n A1 , . . . , A’, n An) in the product universe U = FlUi that is not fully
=
compatible with A.
Resolution then implements fuzzy modus ponens among all rules in a set with the same
consequent “Y is B,T’ generating output B’ that approximates B as closely as possible, but is unequal
to it since A’is different from A. It does this in three successive steps.
(i) It determines the degree of compatibility between A’and A as follows. Firstly, the partial
match A’n A is unified into a cumulative fuzzy set iA1(A1i n Ai). The amplitude hof the associated
=
membership function which, by (2) equals iil[JLAfi(Ui) Aph(Ui)], is then found. It is the largest
=
value (denoted by sup, for supremum) of pAti Apb(Ui) taken over all possible vectors u =
(Ul ,...,u,) in U:
h = sup {i’lMA’i(ui) AcLA;(ui)]}.
u =
(5)
h is taken to be the joint compatibility factor between A’and A. It also measures how simultaneously
true are. the statements “Xl is Al, . . ., X, is An” for this input. Although x 5 I from (1) and (5),
usually h < 1, i.e. A’and A are not fully compatible.
In practice, the input A’i is often a scalar ui, which can be considered the singleton fuzzy set
{ (ui,l)) . SO , matching A’i
with Ai yields another singleton fuzzy set ((Ui,
pAi(U
For a vector of
scalars u, then in (5) the joint compatibility factor reduces to the smallest membership grade
h =
iAl= pAi(
(6)
19
.
(ii) Next, the largest scalar 3L is selected among joint compatibility factors computed for all
rules in a rule set that have the same consequent value B in universe V. Denoted by h*, it signifies
the highest conformance among A’ and A for that particular B. It satisfies h* < 1 when h c 1.
(iii) The third step of resolution completes fuzzy modus ponens by generating an approximate
value B’ for B. Specifically, B’ is the intersection of h* with B, i.e.
&+‘) = h* A pB(v)
is its membership function, for v in V. B’is afuzzr subenvelope. Since PB is a trapezoid, e.g.
(a,b,c,d) and h* is a scalar, then PB’ is also a trapezoid, (a’,b’,c’,d’), defined fkom (7) by
(a’,b’,c’,d’) = h” A(a,b,c,d) = h*(a, (l- h*)a + h”b, h’c + (l- h*)d, d).
(8)
Thus, h* is the amplitude of (a’,b’,c’,d’) which, by (8), dampens the ideal conclusion “Y is B”. h* can
also be regarded as the possibility or degree of truth to the inferred output “Y is B’.”
Summarizing (i)-(iii), resolution produces a statement “Y is B’ (A*)” that is the “best”
approximation to “Y is B.” In fact, if Y can assume different consequent values, e.g. B 1, B2,..., B&j,
then resolution formulates M fuzzy subenvelopes with accompanying possibilities hl*, x2*,..., &* in
a collective output statement “Y is B’l (xl*), B’2 (x2*),..., B’M (XM*).”
The final component is aggregation. It accumulates all “best” outcomes or subenvelopes B’
generated by resolution from each rule set into afuzzy envelope B”. From the preceding notation, the
M
fuzzy envelope would be defined by B” = mV1 B’, where, by (3),
=
_ .
B” is the ultimate outcome of executing fuzzy modus ponens for a rule set enacted by input A’. The
resolution statements nlso
demonstrate the weighted contribution &* to B” from each subenvelope
0
B’,, m = l,...,M, e.g. those subenvelopes with relatively small possibilities will contribute marginally,
if at all, to the envelope. This completes a single stage of analysis by the model.
20
There exist other fuzzy logic techniques that can be applied to match input with rule
antecedents (e.g. Cho, Ersoy, and Lehto 1992), as well as determine the joint compatibility factor in
(5) and the fuzzy subenvelope in (7) (e.g. Zimmermann 1987) and aggregate different outputs (9)
(e.g. Dubois and Prade 1985, Dubois and Koning 1991).
The fuzzy envelope advantageously conveys full information about the output variable. The
envelopes B” from each analyzed rule set become the fuzzy input that fires the next stage of rules, for
which matching, resolution, and aggregation are then repeated. For example, from stage 2 at the
conclusion of risk analysis, Payback Risk can ideally be either Low, Below Average, Average, Not
Low And Not High, Above Average, or High. Thus, B” for Payback Risk is a six-fold union of
subenvelopes and is a fuzzy subset of V = [O,l]. Accompanying this are also the resolution
statements, “RPAYBK is Low (xl*), Below Average @2*), Average (X3*), Not Low And Not High
(x4*), Above Average (X5*), and High (he*),” for computed 0 I Xrn* 5 1, m = 1,...,6. The shape of
the envelope conveys an overview of risk assessment and the resolutions provide more detail for
decision support. Results can be interpreted differently according to the decision maker’s attitude
towards risk. Depending on the particular contribution to the envelope, some users may give more
consideration to the subenvelopes “above average” or “high,” while others will pay more attention to
“below average” or “low.” Still another group may focus on the “average” or “not low and not high”
subenevelopes. Evidently, the user has a degree of flexibility absent from the discrete scoring model
which, by contrast, relies only on a one-dimensional scale for crisply interpreting the output.
Subsequently, the full fuzzy scoring model integrates the envelope for Payback Risk with other
envelopes generated for Strategic Intention (STRINT), Market Opportunity (MKTOPT), and Synergy
Effects (SYNEFT). This produces a vector of fuzzy inputs propagated forward to initiate the last
stage 3 of market entry analysis. The method then
examines all rules which involve the single level,
four variable Go/No Go (GO). For example, one of these rules is
(STRINT is High)
AND (MKTOPT is Not Very Weak)
AND (RPAYBK is Above Average)
AND (SYNEFT is Very Strong)
THEN
(GO is Strong).
IF
21
-
At the end of this last stage, an envelope for Go/No Go is displayed with the resolutions “GO is Weak
(al), ” “GO is Average (az),” and “GO is Strong (a$’where the q’s are possibilities. These can be
used to assist decision makers to select specific target markets for entry.
To Wstrate country risk assessment for Spain and Norway, we use the data from Table 5 to
trigger the first stage of analysis. Since it is scalar input, matching and resolution are simplified by
(6). Instead of showing specific numerical results for each of the method’s components we summarize
the fuzzy output from aggregation in stages 1 and 2 for both countries. (See Levy and Yoon 1995 for
complete details.) At the end of stage 1, the resulting envelope from the first rule set about
Noneconomic Risk (NONER) is PB” = (.3,.4,.6,.7) for Spain and PB” = (0,0,.25,.35) v (.3,.4,.6,.7) for
Norway (Figure 5). For the second rule set about Economic Risk (ECONR), Spain’s derived envelope
is &y’ = .435(0,0,.335,.4) v .777(.35,.428,.561,.67) v .109(.65,.672,1,1) while Norway’s is PB” =
.164(0,0,.375,.4) v ,971(.35,.447,.534,.67) (Figure 6).
_____~______________~~~~~~-~~~~~~~~~~~~
Insert Figures 5 and 6 about here.
~___________________~~~~~~~~~~~~~~~~~~~~
The envelopes for NONER and ECONR are inputs for stage 2, where the method is again
enacted. This time, matching and resolution (5) are implemented forfizzy sets. Aggregation produces
the enVdOpe
pfj" = .400(0,0,.808,.9) v
.777(.3,.417,.583,.7) v .206(.5,.572,1,1) for Spain and PB” =
.200(0,0,.854,.9) v .971(.3,.446,.554,.7) v .206(.5,.572,1,1) for Norway (Figure 7). It can also be
concluded that “RPAYBK is Low (.250), Below Average (.400), Average (.777), Not Low And Not
High (.400), Above Average (.206), and High (. log),” for Spain. For Norway, the summary is
“RPAYBK is Low (.1&i), Below Average (.2OO), Average (.972), Not Low And Not High (.200),
Above Average (.206), and High (.059).”
____________________~~~~~~~~~~~
Insert Figure 7 about here.
____________~_______~~~~~~~~~~~
Since, by design, the fuzzy envelope is linguistic rather than numerical, its interpretation can
vary from user to user. Examining Figure 7, many decision makers may conclude that payback risk
is best described by “average” (the highest possibility by far) for both countries, although less so for
Spain (.777 vs. .971). However, some of these in turn may view Spain less risky because of its larger
22
possibility for (the impartial) “not high and not low” (A00 vs. .200). The flatter envelope and larger
“low” (.2SO vs. .164) and even much higher “below average” possibilities (.4OO vs. 200) also suggest
much less risk for Spain. The larger “high” possibility (.109 vs. .059) of risk may be too small versus
these other comparisons to discourage this assessment. Conversely, other users may interpret
Norway less risky, based on its smaller “high” and larger “average” possibilities. These simple but
tractable interpretations contrast noticeably from those of the discrete scoring model that generates
only the scalar output, ~34 = .214 (Spain) and u34= .070 (Norway) (Table 7), whereby all users
would merely infer Spain is significantly more risky than Norway. Such variability interpreting the
fuzzy envelope can be considered a disadvantage by users desiring simplified discrete outcome for
purpose of quick decision aid, especially in a short timeframe.
6.2.2. A reducedfizzy scoring model. This technique simplifies the output from the full fuzzy
scoring model by converting the envelope into a representative scalar to be used for input at the next
stage of analysis. Thus, (6) always determines the joint compatibility factor. The conversion is
called defuzziflcation, since it collapses a fuzzy set into a point of the underlying universe. It is an
“averaging” process that can be carried out in several ways. Here, we defuzzify the envelope B” into
its center of gravity, which is defined (e.g. see Mizumoto 1988, p. 137) by
where necessarily v* is an element of V. Two other defuzzification techniques are (i) the mean of all
points in V that have the maximum membership grade sup ~B”(v) and (ii) the point in V that divides
V
the area of PB” in half. (See also Yager and Filev 1993 for more procedures.) In any case, the
reduced fuzzy scoringQmodel produces different resolutions and envelopes in stages 2 and 3 than the
full fuzzy scoring model.
To evaluate risk assessment by the reduced fuzzy scoring model, starting with the data
from
.
Table 5, then the results of matching, resolution, and aggregation at the end of stage 1 are the same
23
envelopes for NONER and ECONR obtained in Figures 5 and 6. Defbzifjhg them into their
centers of gravity via (lo), we get v* = .5 (Spain) and v* = .3258 (Norway) for NONER and v* =
.3988 (Spain) and v* = .4282 (Norway) for ECONR. These scalars are now the inputs for stage 2
instead of the envelopes. From the analysis at this stage, resolution then generates “RPAYBK is Low
(0), Below Average (.008), Average (1). Not Low And Not High (JO@, Above Average (0), and
High (0)” for Spain and “RPAYBK is Low (0), Below Average (.242), Avkrage (1), Not Low And
Not High (.242), Above Average (0), and High (0)” for Norway. Aggregation now produces a new
envelope, FB” = .008(0,0,.898,.9) v (.3,.45,.55,.7) for Spain and mtf = .242(0,0,.844,.9) v
(.3,.45,.55,.7) for Norway with v* = .4986 and v* = .4640, respectively (Figure 8).
__-___________________________
Insert Figure 8 about here.
-___-________________________
Although it sacrifices complete information, this method is preferable to the full fuzzy scoring
model for those decision makers who feel identifying and interpreting scalar output is easier than a
potentially complex envelope. Especially at stage 3, one may opt in conclusion for the numerical
simplicity of v* as opposed to B”. Since, in this example, v* is close to .5 (the middle of the scale)
for either country, he may decide risk is “average,” but Spain is slightly less risky since its v* is
marginally closer to S. B” is still available, however, for decision support, e.g. the shape of either
envelope in Figure 8 may induce this one or another decision maker who feels comfortable using it to
infer, also, “average” risk with possibility 1 for either country since v* lies squarely underneath the
“average” subenvelope. In fact, one may boost the decision that Spain is less risky (without the
modifying “slightly”) &cause, unlike Norway, nearly all of Spain’s envelope evidently consists of the
“average”,subenvelope (and a center of gravity closer to SO). One who is impartial, however, may
conclude, e.g. neither is more or less risky than the other, encouraged by both countries’ zero
possibility of “high” and “above average” risk. Still another user may deem Norway less risky since
it has a greater “not low and not high” possibility (.008 vs. .242), as well as exhibiting larger “below
average” risk (also, .OOS vs. .242).
24
.
7. Summary Discussion
This research has proposed a framework for global market-entry analysis which applies
alternative methodologies for analysis of information components across three stages. These include
consensus by experts, the discrete scoring model, analytic hierarchy process, simulation survey, and
two fuzzy scoring techniques. Table 6 summarizes the advantages and disadvantages of the methods.
Specific applications for Spain and Norway of the discrete and two fuzzy scoring models have been
made to evaluate in two stages country risk, one of four categories for final analysis at stage 3 of the
Go/No Go decision. (These countries are of special interest to a company examined in an earlier case
study by Levy and Yoon 1995). By summary evaluation, the discrete scoring model suggests risk is
much higher for Spain than Norway. In comparison, depending on the risk perspective of the
decision maker, different inferences can be drawn from. the results of the applied fuzzy models.
Using the full fuzzy scoring model, evaluating the fuzzy envelope for payback risk some users, e.g.
those with high risk avoidance, would see Norway as less risky but others, e.g. less risk-averse users,
would perceive Spain less risky. Applying the reduced fuzzy scoring model, Spain would be
regarded slightly less risky by some users because its fuzzy envelope has a center-of-gravity a little
bit closer to S. However, if also analyzing the envelope, others would be impartial or, depending on
their perspective, feel one is less risky than the other.
The fuzzy methods can be used to bolster support for country-risk assessment or any other
decision category including the final Go/No Go evaluation, through manipulation of input data or
information, linguistic variables,
and production ruies. However, the full fuzzy scoring model is
.
theoretically more desirable since it preserves the entire operational output from the preceding stage
of analysis by forwarding the fuzzy envelope as input to the next stage: nothing is lost in this
transmission. Integrity and quality of information are maintained from stage to stage. By contrast,
the reduced fuzzy scoring model is an averaging process that modifies the envelope to a single scalar
input for the next stage: it forfeits complete information in favor of easier use and understanding. A
manager or decision maker may find the reduced model faster and more palatable to apply, especially
if choosing to interpret the scalar output by itself without benefit of the fuzzy envelope. Thus, the
full fuzzy approach offers greater flexibility to derive and coordinate output. Both methods forge the
extensibility of output interpretation consistent with the decision maker’s proclivity towards payback
risk, which can often be achieved by the preceding discrete methods through ‘What if?” analysis.
We have not tried to show here how or why fuzzy logic can be better than the discrete scoring
model or other non-fuzzy methods in providing managerial decision support for country risk
assessment. Model or methodology selection normally depends on the nature of the problem domain,
flexibility, system requirements, and relative ease of impiementation (Urban 1974, Schultz and Henry
198 1, Lilien, Kotler, and Moorthy 1992). (See Table 6). As a technical support tool, however, fuzzy
logic has an intrinsic advantage over the discrete methods since the principal relationships between
the framework’s components can be best characterized by fuzzy sets rather than numerical values.
Fuzzy sets express these variables’ interdependencies through the natural language of practitioners
and researchers of international market-entry analysis. Production rules handle effectively the
complexity and subjectivity of phraseology and embedded analogical reasoning. By allowing
multiple representation of categorical variables through membership grades, performing calculations
based on these grades for rule evaluations, and aggregating consequential output, the fuzzy methods
offer promise for an effective, practical, and systematic approach to country risk assessment.
8. Conchsion
Our framework offers a perception for international market-entry analysis rather than a rigorous
formulation of how to approach this problem or derive universal solutions. It is a guideline for
design and operational use to facilitate decision support. It is not intended as a unique configuration
for meeting each decision maker’s requirements, but is suitably adaptable for incorporating different
data sources, evaluation measures, categorical information components, and methodologies.
Several principal issues have been identified for future research. Firstly, the current fuzzy
methods although computerized are not complete decision support systems. We expect that
modification of database design and enhancement of graphic interface capabilities combined with the
fuzzy models will develop a formal on-line DSS. Secondly, the inherent complexities evaluating
country risk or any framework component encourage design of a group decision support system that
actually incorporates consensus by experts, AHP, or any other of these methods into a management
.
26
.
model base. Thirdly, once the (G)DSS has been established the fuzzy approach must be validated
done through implementation. A classical interactive field experiment (e.g. a salesperson planning
model, CALLPLAN, Fudge and Lodish, 1977) or a rule-based consistency test (e.g. a medical
diagnosis model, MYCIN, Buchanan and Shoxtiiffe, 1984) is a potential basis for validation. The
following test questions could be examined: How wduld a highly functional fuzzy system for risk
assessment be designed for a multinational ~~IRI? Would the firm require continual updating of the
system? What specific roles would country managers or local staff play in the system? How
important is the expert, how many experts are necessary or better for both design and implementation
of the fuzzy approach, and how would they be chosen at manageable cost to enact country risk
assessment? Our firsthand research specifically used an available expert who has provided fuzzy
labels, trapezoidal membership functions, linguistic variables, and production rules. By answering
these and other questions through iterative development and validation, the fuzzy methods can evolve
a marked degree of functionality, user-interactiveness, and reliability for analyzing country risk or
any other phase of international market entry analysis.
Fourthly, the fuzzy methods must be validated relative to the other techniques. Conditions
under which the fuzzy models would either outperform the others - or be outperformed by - should be
established. Some of these are amenability to group decision making (e.g. if in fact groups are used
fo implement the fuzzy approach, then how does their performance compare to that of AHP?), the
number of simultaneous countries for market-entry consideration (e.g. are the fuzzy methods better
for a large number?), handling of incomplete or inaccurate data on measures of country risk, and
timeliness (e.g. are the discrete methods quicker and more effective under a short decision
timeframe?). Another condition is stagewise suitability in the decision framework, e.g. are the fuzz!
methods more valid for analysis in stage 3 since, hypothetically, they are better able to process
intactiy a larger volume of compiled linguistic information, in particular, leading to the crucial No/Go
No decision. Conversely, are the discrete methods more expeditious for analysis in stage 1 or 2?
Greater comprehensive empirical study is needed to endorse their performances, expected benefits.
and implementation costs in comparison to, e.g. the consensus of experts, discrete scoring. model,
AHP, and simulation survey.
27
Finally, once the fuzzy methods have been upgraded and presented as formal decision support
technologies, the next stage is designing a knowledge-based System to perform risk analysis. While
there have been operational expert systems in marketing (see, e.g. LiIien et. al. 1992, ch. 12), no
fuzzy-based DSS or expert system for risk assessment in particular or market-entry analysis in
general has been reported to our knowledge. Establishing appropriate, justifiable, and complete rules
for a prototype expert system of country risk assessment within an international market-entry
framework is a natural design strategy. It involves a process that is evolutionary and incrementally
progressive (e.g. Holsapple and Whinston 1987, ch. 8; Rangaswamy 1993). Ongoing rule elicitation,
examination, and refinement are required (Eliashberg and Lilien 1993), i.e. an assessment of
knowledge acquisition methodology, performance, and system utility (e.g. a new product planning
model, NNOVATOR, Ram and Ram, 1996). In this way, for purpose of practical implementation, a
sustainable knowledge-based approach to country risk assessment or other categories within an
international market-entry framework can be developed and then validated with respect to existing
methodologies.
Acknowledgment: The authors would like to thank an anonymous referee for helpful comments and
criticisms that improve the content and readability of this paper.
28
Growth of global market
t
Competitors ’ global business
Entry
pressure
Strategic
intention
Long-term corporate
Financial resources
i Resource
availability
Capacity utilization
r
Expected
sales
potential
Market
opportunity
Expected
profit
potential
Production cost advantage
1
r
L
Payback
risk
L
Figure 1.
R&D/engineering synergy
bai
business
experience
Glo
Noneconomic
r i s k -
_ .
GDP growth rate
1 Marketing cost advantage
Decision
Synergistic
effects
GDP Ievel
Global/local competition
’ _
Go/No Go
Economic
risk
Management experience
Marketing experience
Political conditions
c
Social conditions
--c
Foreign exchange
Trade balance
An International Market-Entry Decision Framework
29
commitment
.
.os
0
.l .15 2
1.0
pu = (.15,.2,1,1), ID = (.05,.‘13,1,1), & = (0,0,.05,.1)
0
Figure 2.
Basic Fuzzy Sets for Foreign Exchange
30
t
Membershir,
grade A
I
,SP
SN
-. 1
-1.0
0
.l
1.0
Percent of GNP
PHN = (-I,-1,-.l,O), PSN = (-.l,-.075,-.02&O), &p = (0,.025,.075,.1), FLH~ = (0,.1,1,1)
Figure 3.
Basic Fuzzy Sets for Trade Balance
31
Noneconomic
Risk (NONER)
Payback
Risk
(RPAYBK)
Economic Risk
(ECONR)
Figure 4.
Political Conditions (POLC)
Social Condi tons (S OLC)
Foreign Exchange (FREX)
Trade Balance (TRBL)
A Decision Framework for Country Risk Assessment
32
II
i%
2.
..
c
33
Spain:
pBtt = .435(0,0,.335;.4) v .777(.35,.428,.561,.67)
v .109(.65,.672,1,1)
Norway: j&” = .164(0,0,.375,.4)~.971(.35,.447,.534,.67)
1.0
.777
w
-P
.435
.200 1
.I09
,058
___-.-___
,_e_
me.
_ mm_
0
I
I
-,---a __
J._
__
.;7
_ _ _ _
1
662
I
1.0
4
.I64
0
Figure 6. Fuzzy Envelope for ECONR
I
8
I
I
I
-- __
r-_____
I
I
1
I
I
I
.
I
-ewe
L_____
I
I
I
I
I
I
I
1
;
I
I
,,
I
I
i
35
TABLE 1
Determination of Index (q 71) for Political Conditions
-11--111--“11-11--1----~-~~------------------~---------------~-~~~--------~-~---~--~~-----------------------------Spain
---1-11--1--1--1~~~----~--~-~---------------------------------------------~-~----.-~~~----------------------------Socialist Coalition 40% Center Right 15%
Regimes & Probabilities:1 Socialists 45%
Turmoil2
Base =
Moderate (4) Slightly Less (3)
Slightly More (5)
Slightly More (5)
~171 = ((.5)4 + (.5)[(.45)(3) + (.40)(5) + (.15)(5)])/8 = .506
-1-1------1111-1--11~--~~--~-~~----------------.------------------------------~--~-------------------------------~~
-1~~1”
1-11111~~--111~~~~~~~~-~-------~----------------------------------------------------------------------------
Norway
1--111111-111-~---~-~~~-~~~~~~---------------------------------------------------~~~-----------------------------Middle Parties 15%
Regimes & Probabilities? Labor 45% Conservatives 40%
Turmoil4
Base =
Low (1)
Same (1)
Same (1)
~171 =
192Coplin and O’Leary, 1989, p. 266.
3y41bid., p. 210.
37
l/8 = .125
Same (1)
TABLE 2
Determination of Index (~172) for Social Conditions
-1-----~-~111~1~~~-~~~~~-~~~~~~~~~~~~~~~~~~--------------~-~-~---.~~~~~~~~~~~~~-~---------------------------.---
Factor
Spain
Norway
Score
Category
--1--111--1111~1~-~-~-~-~~~~~-~~~~~~~~~~~~-~--~-----------------~--~~~~-~-~~~~~~~-~-------------------------------Ethnic
X
1
Dominant group is over 95%
1
makeup
of population.
(p-674)
Religious
makeup
Literacy
rate
Language
dispersion
Income
distribution
Dominant group is 60-95%.
2
2
(p.702).
X
Dominant group is under 60%.
3
X
X
Dominant group is over 85%
of population.
1
1
(P.763)
1
(p-763 >
Dominant group is 60-85%.
X
X
Dominant group is under 60%.
X
X
1
(P-702)
(~674)
60-95% is literate.
X
X
Under 60% is literate.
X
X
Over 95% speak the same
language.
X
1
(P-760)
Over 95% of population is
literate.
1
60-95% speak the same
language.
2
2
(P-761)
X
Under 60% speak the same
language.
3
X
X
Percentage difference between
high and low is less than 15%.
1
X
X
Percentage difference between
high and low is 15-35%. ’ -
2
2
(P-852)
2
(p.852)
Percentage difference between
high and low is more than 35%.
3
X
X
----1-----1111-.~~11~~~~~~~~~~~~~~~~~~~~~~~~~~.~~--------------.----------------------------------------------~-~~
Total,
U172 = (U -
u
5)/10
8
.300
6
.lOO
Source: Daume (1991). Note: This is the only source containing all of the above data. Additional
references were used to corroborate the data and are available from the authors.
38
’
TABLE 3
Determination of Index (~181) for Foreign Exchange
__-----1---1o11--------------------------------------------------------------------------Year
Consumer Prices (1985 = 100)
Annual Increase (%)
Spain1
Norway2
Spain
Norway
__1_1-__---------------------------------------------------------------------------------116.5
114.5
X
X
124.3
120.0
1988
6.695
4.803
130.0
198’9
128.2
6.833
4.586
1990
136.8
135.4
6.708
4.154
1~-1~-11~-1----1----------------------------------------------------------------~---~--~~~
Three-year average of yearly increase, ~18 1
6.115
5.145
1-1--11-----111-.------------------------------------------------------------------------1987
lOECD Main Economic Zndicators,~June 1991, p. 152.
21bid., p. 148.
39
TABLE 4
Determination of Index (u&for Trade Balance
1-11------1-----1-------------------------------~~~~~~~~~~~~-~--------------------~-~
Spain
.--11---1-1-------11--------------------------------~---~---~-------“
----------------~-~
Trade Balance
(E-I)/(GDP+E)
Year
Imports1
(I)
Exports2
(E)
GDP3
1988
5.036B
3.353B
190.43B
-.00869
1989
5.914
3.617
199.70
-.01130
-.01265
206.7
4.607
7.28 1
1990
-11---1--1-------11--.-----------------------------------~----~---~------~~~-~~~-~~~~
Three-year average of trade balance, ~182
-.01088
Norway
-~1---11-------------------------.-.-----------------------~-----------~.---~-~~----~~
4
Imports
Exports5
GDP6
Trade Balance
(E-I)/(GDP+E)
(I)
(E)
1-~-.-11-1-------1-------.--------------------------------------------------~-~~---~~~
Year
1988
1.922B
1.870B
61.89B
-.00082
1989
1.969
2.262
62.64
.0045 1
2.229
2.823
.00862
66.10
1990
-----111-1111-1------------------.---.-.-------------------.-----.-----------------------Three-year average of trade balance, 11182
.00410
1~2~4~50ECD Main Economic Indicators, June 199 1, p. 25.
3hbid., p. 172.
40
TABLE 5
Summary Scalar Indices for Risk Analysis
----1---1-1-----1-------------------------~~~_-~-~_~________~_~_-~_-~-~-~-~_~_
Component
Index
Spain
Norway
_~11_-1----1-~---~--~-----~~-----~~~~~~~~-~______________.____________________
Political Conditions
.506
Y71
.125
Social Conditions
.300
U172
.I00
Foreign Exchange
.06115
Wl
.0X45
Trade Balance
-.01088
u182
.00410
-------.-----.-1---------------------------_-_.“______________________________
41
TABLE 6
Methods for Country Risk Assessment
*
_______________~~~~~~~~~~~~~~~~~--~----~----------------~-~~~~~~---~--~--~-~~~~~-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.~~~~~~~~~~~~~~~~~~~~~~~~.
Risk assessment
method
Jnput
measures ,
Ourpu 1
measures
Advantages
Disadvantages
Selected
references
Panel of experts
Perception of
country risk
Consensus
risk index
Combines experts’
knowledge and practice;
amenable to groupdecision process
Time-consuming;
nonobjective; experts’
Backhaus and Meyer (1984)
Miller (1992)
bias; difficulty identifying
qualifkl experts
Discrete scoring
model
Interval index
for each risk
attribute
Average risk
index or
average
factor-score
risk
Easy application of
quantitative techniques;
ease of comprehension,
computation, and
interpretation
Arbitrariness in estimating
weights of attributes for
qualitative information
Blank et al. (1982)
Hake (1982)
Miiller-Berghoff (1984)
Backhaus et al. (1985)
Backhaus and Meyer (1986)
Analytic
hierarchy process
Judgmental
assessmcnl
for each risk
attribute
Relative
weights of risk
attrtibutes
Combines management
judgment and intuition;
amenable to groupdecision process
Possible inconsistency
or bias in determining
information categories
.
Jensen (1986)
Saaty and Vargas (1994)
Simulation
survey
Intention of
early/late entry
for different
risk scenarios
Probability
estimates for
entry decision
Flexible for scenario
design; combines
regression or
discriminant analysis
Time-consuming and costly
for survey design, data
collection, and analysis
and evaluation
Karakaya and Stahl (1991)
Punnett (1994)
Full fuzzy
scoring model
Categorical
assessment of
each risk
variable
Fuzzy envelope
for country risk
Performs linguistic
analysis; propagates
complete information
from stage to stage
User subjectively interprets
fuzzy envelope; interprclalion
may vary among users
Levy and Yoon (1995)
. Reduced fuzzy
scoring model
Categorical
assessment of
each risk
variable
Point estimate
of fuzzy
envelope for
country risk
Performs linguistic
analysis; propagates
easy-lo-interpret scalar
from stage lo stage
Loss of full information;
potentially restrictive
single-category summary;
subjective interpretation of
fuzzy envelope that may
vary among users
Levy ar,d Yoon (1993)
TABLE 7
Applying a Discrete Scoring Model to Payback Risk
-111-----11---------------------------------------.----------------------------------------------------------Spain
-------1----1---111--------------”-------------------------------------------------.---------------------.---Input for
u171 = S O 6 11172 = .300
U181 = .06115 ~182 = -.01088
stage 1
Stage 1 analysis;
input for stage 2
u241 = .5(u171 + u172) = 403
1.1242 = .5(ulgl + ~182) = .02514
Stage 2 analysis;
~314 = S(u241 + 1.1242) = .214
input for stage 3
---------------------------------~-------------------------------.----------------~----~~~-----.-~------~~~~
11---1----111111---------------.----------~------------------------~----------------~--~-~---~---.---~--~-~~~~.
Norway
---1---1-----1--------------.-------------------------------------------------------~--~-~~~~~-~-~-~~~~.~~..~~.
Input for
u171 = .125 ~172 = .lOO
UK31 = .05145 u182 = .00410
stage 1
Stage 1 analysis; U241 = .5(u171 fq72) = .113
input for stage 2
Stage 2 analysis;
input for stage 3
u242 = .5(ulSl + u182) = .02778
U314 = .5(U241 + U242) = .070
43
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