An evaluation of the fuzzy attractiveness of market entry (FAME) model for market selection decisions and the related modelling process E-Book

Marvin Mertens

• Herausgeber: GRIN Verlag
• Sprache: Englisch

Veröffentlichungsjahr: 2015

Beschreibung

Scientific Essay from the year 2015 in the subject Business economics - Operations Research, grade: 74.00/100.00, University of Strathclyde (Strathclyde Business School), course: Foundations of Operational Research and Business Analysis, language: English, abstract: This essay is concerned with a Fuzzy Attractiveness of Market Entry (FAME) model, devel-oped for the Bulgarian winery Vinprom Svishtov (VS). VS had to decide whether to expand two of its wines, a cabernet sauvignon and a chardonnay, into either a regional or a national market (Shipley et al., 2013). The model’s purpose was to assist VS’s management in deciding whether the firm should expand two wines into a regional or a national market. The success of the modelling exercise discussed in this essay can be assessed from two per-spectives. The first perspective relates to the question whether the model generated satisfactory results given the problem VS faced. The second perspective focuses on potential learning process on the part of VS’s management, stimulated through the modelling exercise.

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Table of Contents

1. Introduction

2. Characteristics of a good model and a good modelling process

3. Evaluation of the FAME model and the modelling process

4. Evaluation of the success of the modelling exercise

References

1. Introduction

This essay is concerned with a Fuzzy Attractiveness of Market Entry (FAME) model, developed for the Bulgarian winery Vinprom Svishtov (VS). VS had to decide whether to expand two of its wines, a cabernet sauvignon and a chardonnay, into either a regional or a national market (Shipley et al., 2013).

The model’s purpose was to assist VS’s management in deciding whether the firm should expand two wines into a regional or a national market. One market had to be chosen for both wines. Because VS had never before operated in either of the markets in question, there was no historical data available. Accounting for this lack of data availability, the model builders used expert judgment to feed the model. Experts included vineyard owners, academicians, experienced wine producers, and managers from VS (Shipley et al., 2013). The key formula used in the model is stated below:

 denotes the current attractiveness of a particular market for a wine, e.g. the attractiveness of the national market for the chardonnay.  represents the current “Best Market Fit for the Firm’s Marketing Mix” (Shipley et al., 2013). This variable indicates how similar VS’s marketing mix (price, product, advertising, distribution) for the wine is to that of the currently dominant competitor in the market in terms of its fit with customer preferences. A value close to one indicates that VS’s marketing mix is approximately as suitable to serve customer preferences as the marketing mix of the currently dominant winery. Since  increases in line with , the underlying assumption is that the marketing mix of the currently dominant winery is particularly suitable to serve customer preferences. The remaining variables represent the current market environment (), i.e. economic and social conditions, political climate and infrastructure, and the perceived future strategic importance of the market (). The latter incorporates the expectable profit margin and sales growth resulting from a market entry (Shipley et al., 2013). The values of all independent variables were based on expert judgment generated through questionnaires. Once  had been computed for each wine/market combination, VS’s management could compare the attractiveness scores across markets.

Since the relationships between the variables are expressed using a mathematical formula, the FAME model can be classified as being mathematical (Hull, Mapes and Wheeler, 1976). More precisely, it is linear because it only contains exponents equal to one and multivariate since  is sensitive to changes in more than one independent variable (McWilliams, 1987). The model generates several decision alternatives, i.e. different wine/market combinations, which can be rank-ordered according to their attractiveness scores. Hence,  measures the effectiveness associated with each combination, thereby allowing VS’s management to choose the most attractive market. Therefore, the model is normative as it provides a decision aid for VS’s management (Krajewski and Thompson, 1981). With , the model contains an independent variable, which is affected by time because it measures the perceived future strategic importance of a market. Hence, the model is dynamic (Krajewski and Thompson, 1981).

While the above classifications were largely unambiguous, the classification of the model as either probabilistic or deterministic is less clear. According to Hull, Mapes and Wheeler (1976), “a probabilistic model (…) recognises that the values of some variables are uncertain and deals with this, using concepts from probability theory”. Krajewski and Thompson (1981) define a probabilistic model as a model “in which at least one parameter or exogenous variable is assumed to be a random variable”. The FAME model is purely based on human judgment. Therefore, assuming that human beings are incapable of predicting the future with absolute certainty, the model contains a substantial amount of uncertainty. The model builders addressed this issue by applying elements of fuzzy set theory, a concept from probability theory (Shipley et al., 2013). Because different experts were questioned, the model also features a certain level of randomness, as the value of each independent variable may vary across experts. However, some of its randomness is taken from the model because individual expert judgments are averaged in order to obtain an aggregate value for each independent variable. This makes the model slightly more deterministic (Hull, Mapes and Wheeler, 1976; Krajewski and Thompson, 1981). With respect to the further discussion, it is sufficient to conclude that the model contains probabilistic elements due to the inclusion of human judgments.

The model “revealed” that the national market was more attractive than the regional market for both wines. In accordance with this result, VS expanded both wines into the national market. However, the relative attractiveness of each market was not the only result the modelling process generated. Because each element of VS’s marketing mix was assessed separately, VS’s management was able to detect potential weaknesses in the company’s marketing mix that could be an obstacle to a successful market entry (Shipley et al., 2013).

2. Characteristics of a good model and a good modelling process

First of all, a good model addresses the problem it is supposed to address. As Hull, Mapes and Wheeler (1976) point out: “The construction of a realistic model (…) is of no practical use to the decision maker unless the model can be used to solve the original problem.” While it appears to be obvious that a model should tackle the problem it has been designed for, a mismatch between model and problem can indeed be a major reason for perceived failure in management science interventions (Tilanus, 1985).

A good model also features appropriate levels of detail and complexity. On the one hand, a model cannot include every single characteristic of the system to be represented (Urry, 1991). Indeed, it would even be counterproductive to develop a model that represents reality in every detail. Such a model would likely be over-complicated and probably not easier to analyse than the original system, which a model should actually simplify (Salt, 2008). On the other hand, a model must not be over-simplistic in that it excludes variables and/or aspects that are essential to understand the system to be modelled (Williams, 2008). In accordance with Hull, Mapes and Wheeler (1976), a good model is therefore not over-complicated, but includes all aspects that are necessary to represent the system under consideration appropriately.

Another characteristic of a good model is flexibility. Flexibility, as defined in this essay, means that the model can be easily altered if necessary. This enables the model builder to make changes to the model should the model in its current form turn out to be inadequate given the problem at hand. Flexibility is likely to be high in comparatively simple models because, for instance, the removal of a variable is less likely to have a great impact on the overall model because of complex interrelations with other variables (Ward, 1989). The possibility to alter the model easily may also increase the client’s acceptance of the model because he theoretically has a greater chance of influencing its design.        

Furthermore, a good model is easy to understand. That is to say, the variables used in the model should be defined unambiguously (Krajewski and Thompson, 1981). The possibility of the model being misinterpreted is, ceteris paribus, likely to be low if the meaning of the variables is clear.

Lastly, a good model is valid. A model is usually validated by comparing outputs generated by the model with data from the real situation (Krajewski and Thompson, 1981). If model outputs and real data are similar, the model can be considered to be a good representation of reality (Williams, 2008). However, there are situations in which no past data are available, e.g. when a model addresses a problem never before faced by the decision maker. In these situations, the model has to be examined carefully in order to detect logical inconsistencies, and the results of the model have to be checked for abnormities (Krajewski and Thompson, 1981).

In a good modelling process, the model builder gains an understanding of the system to be modelled prior to building the model (Hull, Mapes and Wheeler, 1976). Particularly because a model should address the problem for which it has been developed, it is important that the model builder understands the characteristics of the underlying system, so that he can identify those factors that should be incorporated in the model. In order to gain an understanding of the system to be modelled, the model builder can for example talk to people involved in the system (Hull, Mapes and Wheeler, 1976). However, the model builder may face a situation in which there is neither experience within the client organisation available nor any data to collect, e.g. when the model is to be used in a completely new decision situation. In this case, the model builder should seek guidance from models that were developed for similar problems, e.g. by reviewing relevant literature (Krajewski and Thompson, 1981).

Furthermore, in a good modelling process the client is involved in the design of the model. First, by involving the client the model builder can make use of the client’s knowledge of the system to be modelled. Therefore, he may be able to develop a more adequate model than he would be without involving the client. Second, client involvement is likely to increase the client’s understanding of the model. The importance of the client understanding the model cannot be overemphasised. After all, it is the client who is supposed to make use of the model and the information it generates. By involving the client in the development of the model, the model builder can make sure that the client is able to interpret the information he derives from the model and to evaluate the plausibility of the model output (Ackoff, 1968). Third, involving the client is likely to increase not only the client’s understanding, but also his acceptance of the model. If the client is involved in the setup of the model, he is more likely to accept the model because he has the opportunity to influence it to a certain extent according to his own ideas and requirements. Krajewski and Thompson (1981) summarise the importance of model acceptance vividly by saying: “A model builder may arrive at a model that can be shown to save thousands of dollars per year, yet it is worth nothing if the person who is to use it does not accept it.”

3. Evaluation of the FAME model and the modelling process

First, the fit between the model and the problem at hand needs to be evaluated. The problem at hand is that of deciding whether the regional or the national market is “better” for two of VS’s wines. The model evaluates the attractiveness of both markets for each of the two wines, taking multiple aspects, including economic conditions and potential sales growth, into account. All these factors can be said to have an impact on a market’s “goodness”. For instance, it seems plausible that, ceteris paribus, a market that promises high sales growth is “better” than a market that promises low sales growth. In conclusion, it can be said that the model addresses the problem at hand.

It is worth noting that the model builders limited the model’s complexity deliberately in order to ensure its ease of use (Shipley et al., 2013). Nevertheless, the model’s level of complexity still seems to be appropriate given the problem at hand. The decision to expand into a new market clearly is of strategic nature, as it is complex and can have substantial implications for a firm (BBC, 2014). Moreover, a market entry decision is marked by limited and possibly ambiguous information as well as considerable uncertainty (Shipley et al., 2013). It is therefore unlikely that such a decision is based on the output of a single model. Hence, models aiming to address strategic problems should provide a general, uncomplicated decision aid that assists decision makers in reflecting upon a problem, rather than a concrete and detailed “action plan”, which is more associated with tactical and operational decisions (Ward, 1989). The FAME model is linear and multiplicative, which makes it constructively uncomplicated. Simultaneously, it incorporates multiple variables and yields attractiveness scores for both markets under consideration, thereby allowing VS’s management to reflect upon the key drivers of each market’s attractiveness as well as the attractiveness of each market compared to the other candidate.

Concerning the model’s level of detail, it can certainly be argued that the model does not incorporate all factors that should influence a market entry decision. For instance, VS’s financial situation is not represented in the model, although financial considerations play an important role in market entry decisions (Koch, 2001). However, as indicated above, the output of the FAME model is likely to be combined with other data, such as financial figures, before any final decision is made. It therefore appears to be unreasonable to criticise the model simply for not including enough variables, especially since a higher number of variables may increase the model’s complexity.

However, the model’s level of detail can be said to be insufficient regarding one factor, which is particularly important in strategic decisions: competition (Rosenzweig, 2013). First, the model represents competition through the variable , which evaluates VS’s marketing mix in terms of its similarity to that of the currently dominant competitor in the market. The model thereby assumes implicitly that this competitor will still be dominant at the time VS enters the market. This does not necessarily have to be true. Indeed, it is even possible that the competitor in question is already losing market share because his marketing mix does no longer fit customer preferences. The model might therefore give VS a wrong impression concerning the suitability of its marketing mix by comparing it with that of the currently dominant competitor. Besides, the model does not explicitly include potential reactions to VS’s market entry. The firm’s market entry is likely to provoke reactions from competitors who are unwilling to lose market share to VS without a fight. For instance, they could decrease prices in order to restrain customers from switching over to VS’s products (Porter, 1980). One could argue that the FAME model reflects this competitive dynamic to some extent through the experts’ evaluation of the expectable profit margin and sales growth since these two measures are likely to be affected by competitive actions. However, the model does not encapsulate this dynamic in a stand-alone variable, which makes it difficult for VS’s management to assess the extent of competitive retaliation they would have to expect when entering the market.

The model’s flexibility can be considered as high. Because of the linear-multiplicative relationship between the variables, alterations to the model can be made without major implications. Furthermore, the model is highly flexible with regard to its input, because the questions posed to the experts can theoretically be adjusted to the wishes of the model builders and those of VS’s management.

The majority of variables in the model are defined unambiguously, which makes it comparatively easy to understand. Nevertheless, it should be noted that the notations of the variables  and  may be prone to misinterpretation.  is supposed to represent the perceived future strategic importance of a particular market for VS. However, because of the index , the variable appears to refer to the next time period. The variable  may be difficult to interpret because it integrates two different concepts: the similarity of VS’s marketing mix to that of the currently dominant competitor and the ability of the competitor’s marketing mix to meet customer preferences.

Given the nature of the problem at hand, validating the model by comparing its output with actual data was impossible. VS had never before operated in either of the markets under consideration, and entering each market only in order to compare the market’s actual attractiveness with the attractiveness as predicted by the model would have been prohibitively costly. At the same time, the article does not contain any information on whether the model was validated by screening it for logical mistakes. However, the fact that the FAME model was influenced by other models, which had been successfully used in similar contexts (Shipley et al., 2013) speaks in favour of the model’s validity.

As to the modelling process, the article remains rather vague. It can certainly be said that the model builders had attempted to gain an understanding of the problem situation before they started to build the model by looking for similar models in the academic literature. In fact, all independent variables in the model are based on factors, which have been incorporated in similar models. For instance, the variable  is based on research of Callaghan and Morley (2002) who found out that both profitability and sales volume are important measures for managers in the context of market entry decisions (Shipley et al., 2013). At the same time, the article does not provide any explicit information on whether the model builders consulted VS’s management or other experts in order to gain a more thorough understanding of the problem situation. However, it seems plausible to suppose that the model builders talked to experts prior to the model building because they identified several experts whose knowledge was to be used to feed the model.

With respect to client involvement, members of the client body certainly participated in the process of generating expert judgments because they were among the people who were questioned. However, at least based on the information given in the article, VS’s management was apparently not heavily involved in the concrete design of the model since most of the model’s variables are originated in the academic literature on market entry decisions. At the same time, it cannot be said with certainty that VS’s management was not involved in the development of the model at all. After all, the model builders’ declared aim was to design a model that could be implemented in a spreadsheet (Shipley et al., 2013). It appears to be unrealistic that such a high degree of ease of use can be achieved without involving the client at all.  

4. Evaluation of the success of the modelling exercise

The success of the modelling exercise discussed in this essay can be assessed from two perspectives. The first perspective relates to the question whether the model generated satisfactory results given the problem VS faced. The second perspective focuses on potential learning process on the part of VS’s management, stimulated through the modelling exercise.

With respect to the former, the modelling exercise can be considered as relatively successful. The outcome of the model was that the national market would be more attractive for both the cabernet sauvignon and the chardonnay than the regional market. VS acted accordingly and expanded both wines into the national market.[1] According to the model builders, VS’s actual performance in the national market reflected the model’s assessment of the market’s attractiveness. In particular, the model apparently turned out to be highly accurate in terms of its evaluation of the economic, social, and political conditions in the national market as well as the market’s infrastructure (Shipley et al., 2013).

In spite of this seemingly clear proof of the FAME model’s predictive power, one must be careful not to overinterpret the results of the modelling exercise. After all, it cannot be said with certainty that the national market truly was the best choice for VS. The winery never expanded neither its cabernet sauvignon nor its chardonnay into the regional market, so it is practically impossible to compare VS’s actual performance in both markets. Furthermore, it should be noted that the model builders did not comment on the model’s accuracy regarding the other factors it incorporates, such as the expectable profit margin and sales growth. However, to argue that this indicates a lack of accuracy with regard to these factors would be idle speculation.

With respect to the potential learning processes on the part of VS’s management, the modelling exercise can be evaluated as highly successful. First, because the model incorporated uncertainty in the form of human judgment, VS’s managers were once again reminded of the uncertainty inherent in the market entry decision they faced and the necessity to analyse the situation carefully before making a final decision. Besides, they were now able to identify some of those factors tainted with a particularly high level of uncertainty, and reflect upon them. Second, because ventures into new markets are rather knowledge-based than data-driven (Shipley et al., 2013), the very fact that the model was based on expert judgment was beneficial for VS’s managers. The incorporation of the opinions of experts with extensive experience in the wine industry enabled VS’s management to question their own perceived attractiveness of the two markets and therefore arrive at a more thought-out solution. At this point, it should also be noted that the integration of expert judgment probably increased the acceptance of the model, as it reflected, among others, the business knowledge of people from VS’s direct business environment, such as vineyard owners and grape suppliers, rather than only the perhaps superficial business knowledge of the model builders (Churchman, 1964). Third, because each element of VS’s marketing mix was evaluated separately, the model assisted VS’s decision makers not only on a strategic, but also on a tactical level. For instance, the model “revealed” the need to improve both distribution and promotion should VS decide to enter the national market (Shipley et al., 2013). This can also be considered as beneficial because decision makers tend to be more confident in strategic, highly uncertain decision situations when they are able to link them to concrete, tactical decisions, which increases their perceived level of control over the situation (Eisenhardt, 1990).

Despite this positive evaluation of the learning process, the learning outcomes should be treated with due caution. Based on the information the article provides, the expert judgments may not have been completely unbiased. Some of the experts who were questioned, such as grape suppliers, might have had particular (business) interests in the outcome of VS’s market entry decision, which might have influenced their judgments. Moreover, this author shares the view that decisions in an organisation, especially strategic ones, are always to some extent influenced by politics and power relationships within the organisation (Eden, 1990). Therefore, the judgments of people from within VS might have also to some extent reflected their political (perhaps even personal) interests instead of their pure, unbiased evaluation of the attractiveness of a particular market.

References

Ackoff RL (1968). Management misinformation systems. Management Decision2(1): 4-8

BBC (2014). Types of decisions. URL: http://www.bbc.co.uk/bitesize/higher/business_ enterprise/decision_making_business/revision/1/

Callaghan B and Morley C (2002). The hierarchy of target market selection criteria. In: ANZMAC (2002) Conference Proceedings. Deakin University: Geelong, Australia: 761-767

Churchman CW (1964). Managerial Acceptance of Scientific Recommendations. California Management Review7(1): 31-38

Eden C (1989). Using cognitive mapping for strategic options development and analysis (SODA). In: J. Rosenhead (1989) (ed.). Rational Analysis for a Problematic World: Problem Structuring Methods for Complexity, Uncertainty and Conflict. Wiley & Sons: Chichester, West Sussex: 21-42

Eisenhardt KM (1990). Speed and Strategic Choice: How Managers Accelerate Decision Making. California Management Review32(3)

Hull J, Mapes J and Wheeler B (1976). Model building techniques for management. SAXON HOUSE: Farnborough, Hampshire