Philosophy of science perspective


Marketing Research

There have come to be two kinds of commercial marketing research. One is commonly called qualitative, the other quantitative. For most marketers, qualitative research is defined by the nonexistence of numerical measurement and statistical analysis. Qualitative research provides an in-depth, if necessarily subjective, understanding of the consumer. In practice, qualitative research has become almost synonymous with the focus group interview. This technique involves convening a cluster of respondents, usually eight to 10, for a more or less open-ended discussion about a product. The discussion "moderator" makes certain that topics of marketing significance are brought up. The research report summarizes what was said, and perhaps draws inferences from what was said and left unsaid, in the discussion. One can identify in several quarters conflicting feelings about focus groups. The results do seem useful to management. But there is concern about the subjectivity of the technique, and a feeling that any given result might have been different with different respondents, a different moderator, or even a different setting. Most commercial reports hold a cryptic statement acknowledging this conflict. The statement cautions that focus group research should be regarded as preliminary. Results should not be generalized without additional quantitative research. Most users probably have a vague sense of uneasiness with the technique. As aptly put by [Wells, W. D. 1974, p. 2-145], "How can anything so bad be good?" In addition to the general uneasiness, plentiful procedural questions surround 0 the uses of focus groups. The following are typical questions. Should focus group research ideally be generalized through additional quantitative research? When should focus group research be used? How many focus groups constitute a project? What is the role of interface among the group members? Should focus groups be composed of homogeneous or heterogeneous people? What expertise and credentials should a moderator need? How important is the moderator's interviewing technique? Should management observe focus group sessions? What should a focus group report look like? These questions currently are debated by marketing researchers on the basis of their professional experiences. Neither the conflict between the apparent effectiveness of focus groups and the reservations expressed about them, nor the typical procedural questions have been the subject of systematic argument. The marketing literature has been of little aid to qualitative marketing researchers. There have been occasional descriptions of applications [e.g., Cox, K. K., J. B. Higginbotham, and J. Burton, 1976] and expositions of techniques [e.g., Wells, W. D: 1974, Goldman, A. E: 1962, Wagner, H. R. Alfred Schutz: 1970], but this work has not established a common framework for thinking about focus group research. Qualitative marketing research is considered first from a philosophy of science outlook. This perspective is not used simply to hold up the focus group technique to a list of idyllic criteria for scientific methods. The author fully realizes that many practitioners are not interested in being "scientists." They are, however, interested in developing understanding from research. The philosophy of science provides a valuable perspective on knowledge-not just scientific knowledge, but the entire realm of knowledge. The point of the philosophy of science perspective developed here is to examine the type of knowledge sought by qualitative research, be it scientific knowledge or otherwise, to determine what this implies about the use of the focus group technique. The implications of seeking either nonscientific or scientific knowledge through focus group research are not well understood. Though many practitioners might shun the "scientist" label, the distinction is not as simple as it may seem. There are actually three different approaches to focus group research in existing practice. Drawing upon the philosophy of science perspective developed, this article shows that each of these approaches reflects a different kind of knowledge being sought. Though none of the three approaches seeks scientific knowledge in its strictest form, two are meant to yield knowledge which is in some sense scientific.


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What comes to mind when most people think of research is the image of "scientific" research. This image is somewhat fluffy, and it is not easy to articulate. Thus it may help to begin with a consensus view of what science is. Science is a particular way of trying to understand the real world. For social scientists the real world is the full physical complexity of substance and behaviors. But the real world is much too complex to be understood in and of it. At the heart of science is the process of conceptualization, which seeks to represent the real world in a simple enough way to permit understanding. Scientific constructs are abstracted forms and represent only limited aspects of real-world objects and behaviors. If scientific constructs mirrored the full complexity of the real world, one could no more understand science than one can directly understand the real world. Constructs are simplifications and idealizations of reality. They are, in short, abstractions of the real world. Some may seem more "real" than others-say, "taste buds" as opposed to "attitudes"-but they are all abstractions; they "exist" only within the realm of scientific discourse. Scientific theory consists of constructs and the inter-relationships among them [Bunge, M, 1967]. The value of this theory depends on the detail that abstract conceptualization is not a one-way process. As depicted in Figure 1, scientific conceptualization must work in reverse, too. One must be able to use constructs to interpret the real world, to determine whether real objects and behaviors possess the properties and relationships embodied in scientific theory [Zaltman, G., C. R. Pinson, and R. Angelma, 1973]. This is the business of theory testing. It is the most visible part of science, for it entails all of the methods and procedures associated with "being scientific." Basically, these methods are merely systematic procedures for determining whether a theory is consistent with the workings of the real world. If consistency is detected, the theory is retained, though it is not considered proved; otherwise the theory is modified. The uniqueness of science is in the logical rigor and documentation employed in testing scientific constructs and relationships against the real world. Let us return to the nature of scientific constructs. An important question is, how do we develop scientific constructs? Where do they come from? In all of science, the derivation of constructs is somewhat problematic [Kaplan, 1964]. Part of the answer seems to be that good theory spawns its own constructs (the best example being particle physics). There is also the process of modifying constructs on the basis of empirical confirmation. Still, there must be an external derivation at some point in theory development, and this origin is the world of everyday thought and experience. As shown in Figure 1, the world of everyday thought is separate from scientific discourse. It is composed of the requisites and ordinary language that people use to give meaning to the world in their everyday lives. As such, its function is analogous to that of science. It allows one to interpret the actual world by use of simplified ideas. The only difference is that scientific be subject to more rigorous and critical verification than are everyday ideas. Although everyday thought may initially supply ideas for scientific constructs, are supposed to be more powerful and to be subject to more rigorous and critical verification than are everyday ideas.

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Although everyday thought may initially supply ideas for scientific constructs, knowledge is subject to its own rules of proof. But this independence is not absolute. Modern philosophers of science agree that all knowledge is highly presumptive [Feyerabend, P. K, 1970: Lakatos, I, 1970: Toulmin, S. E. Human, 1972]. No single hypothesis can be examined without at the same time assuming the truth of the mass of all other knowledge, both scientific and everyday. Neither scientific explanations of consumer behavior nor explanations based on everyday knowledge can be proved. All knowledge reduces to the choice between alternative explanations. It is thus entirely reasonable to contrast scientific and everyday explanations. The truly scientific explanation may be expected to have advantages, but it is not automatically superior. In the case of social science, these advantages are seen by many as more assumed than real. Such considerations have led [Campbell, D, and 1976.] to argue for the cross-validation of social science by qualitative common-sense explanation. This step rarely is taken, and is probably generally considered to be "unscientific." Nonetheless, some form of contrast between scientific and everyday explanation should be part of a sophisticated view of science, and this relationship accordingly appears in Figure 1.

Quantitative research commonly is associated, at least implicitly, with the realm of science. This connotation is not always correct, however. Actually, there are two approaches to quantitative research. What can be referred to as the descriptive approach supplies numerical information relevant to everyday, first-degree constructs? Demographic analyses, such as breakdowns of consumption figures by age, are a prime example. This research, in itself, bears more upon everyday than scientific explanation. Age, used purely descriptively, is not a scientific construct. Quantitative research which does seek scientific clarification can be referred to simply as the scientific approach. Here, quantitative means much more than merely working with numerical amounts or rating scales. It implies the use of second-degree constructs and causal hypotheses which are subjected to scientific methods. The methods in common use are the experiment, some types of cross-sectional and panel surveys, and time series analysis. Scientific quantitative marketing research, in sum, aspires to the scientific familiarity depicted in the philosophy of science outlook. Qualitative marketing research similarly cannot be restricted to a literal definition of "doing research without numbers." Unlike the case of quantitative research, the relationship of qualitative research to the scientific and everyday knowledge dichotomy is very indistinct.


Qualitative marketing research regularly is under-taken with the belief that it is provisional in nature. Focus groups frequently are conducted before the fielding of a large sample survey. This exploratory approach can take one of two somewhat different forms. Researchers may be interested in simply "pilot testing" certain operational aspects of anticipated quantitative research [Bobby .J. Calder, 1977]. Their objective might be to check the wording of questions or the instructions accompany product placements. Alternatively, researchers may have the much more ambitious goal of using qualitative research to create or select theoretical ideas and hypotheses which they plan to verify with future quantitative research. For this purpose, focus groups are usually less structured; respondents are allowed to talk more freely with each other. When focus groups are conducted in anticipation of scientific quantitative research, their principle is really to stimulate the thinking of the researchers. They represent an explicit attempt to use everyday thought to generate or operationalize second-degree constructs and scientific hypotheses (cf. Fig. 1). Though the subject of exploratory qualitative research is everyday knowledge, the information desired is best described as pre-scientific. The basis of exploratory focus groups is that considering a problem in terms of everyday explanation will somehow facilitate a subsequent scientific approach. Focus groups are a way of accomplishing the construct-generation process shown in Figure 1. As was noted, however, the process of generating second-degree constructs from first-degree ones, of moving from the everyday to the scientific, is very poorly understood. The philosophy of science supplies no precise guidelines. Nor has any thought been given to this process in the marketing research literature. This is not to say that the exploratory approach is not valuable, only that it is being attempted without benefit of any well-developed ideas of how to do it [Bobby .J. Calder, 1977]. The most relevant sources to which qualitative marketing researchers might turn are sociologists concerned with the notion of "grounded theory." This term refers to theory analytically generated from qualitative as well as quantitative research as opposed to theory generated by its own inside logic. The idea is that "grounded theory is a way of arriving at theory suited to its supposed uses" [Glaser, B. G. and A. L. Strauss, 1967 p. 3]. In other words, such theory is developed within the context of its application. The aim of the exploratory approach might well be described as grounded theory. Much qualitative research follows the exploratory approach even though it never leads, to quantitative research. The presumed second-degree constructs and hypotheses developed from focus groups frequently are not subjected later to scientific methods. Most often this omission is due to the high costs of a second quantitative level. In such cases, concern commonly is expressed about the risk of generalizing from the small samples of qualitative research. But there is much more at risk than sample generalizability. What happens with this abridged exploratory approach is that what is still essentially everyday knowledge (that of the researchers and focus group participants) is cast in ostensibly scientific terms and treated as if it were a scientific finding, instead of being at best a pre-scientific starting point. The problem is that this knowledge has not been subjected to scientific methods for any sample; to assume that it is scientific is risky indeed. Exploratory qualitative research which is not followed by a quantitative stage is not necessarily ineffective. Taken as everyday knowledge, it may well be very useful. The mistake is to represent pre-scientific every-day explanation as fully scientific but merely lacking sample generalizability. One final spot with regard to the exploratory approach is almost never recognized in marketing research practice. The approach concentrates solely on the construct-generation relationship from the everyday to the scientific (cf. Fig. 1). Of equal importance in terms of the philosophy of science is the comparison relationship from the scientific to the everyday. It is useful to think of this relationship as cross-validating scientific explanations against everyday ones. If the two explanations are not reliable, a choice must be made. Given the current expansion of social science, this choice sometimes will favor the everyday explanation. That is, consumers' explanations will sometimes be favored over theoretical hypotheses. Thus, it is potentially misleading to assume that qualitative research must always be impermanent. It is also desirable to conduct independent exploratory qualitative research. In this way, scientific explanations can be compared with everyday ones. Contrary to current practice, it is just as appropriate to conduct focus groups after a quantitative project as before it. Scientific explanations should be treated as provisional also. The exploratory approach to qualitative research seeks pre-scientific knowledge. This knowledge is not meant to have scientific standing. It is meant to be a precursor to scientific knowledge. Its status is ultimately rooted in the creativity of the individual. The exploratory approach could be adopted to compare scientific with everyday explanations. In this case, the objective would be not pre-scientific, but everyday knowledge.

Distinct of Market Research

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Market research information may have at least two different contributions to marketing knowledge and practice. First, insight is obtained about aspects of a market exchange process involving a product (re-search results), a producer group (researchers), and a consumer group (managers) of sole interest to the marketing profession. Second, studying elements of the profession's knowledge system may provide insights which could lead to improvements in that system. We offer just a few reasons why more attention should be devoted to knowledge system issues such as factors affecting the use of market research information. Each year substantial resources are expended in the conduct of market research. The top 10 U.S. private market research agencies alone had transactions of more than 700 million dollars in 1980 (Honomichl 1981). These monies are spent on formal, problem-oriented re-search to help determine day-after recall for an advertisement, the best location for a new retail outlet, what product line modifications are desirable, and so on. Formal research is undertaken because managers expect the resulting information to reduce uncertainty when they are making important decisions. The market research industry, in fact, exists largely because of this anticipation among managers. Thus, understanding what factors affect the use of research by managers is of major outcome to both the market research industry and its customers. Do managers think about research results while making product or service decisions? What factors influence and improve the consideration of research results? Additionally, if we give credibility to the frequent observation that much problem-oriented research in marketing is not used or not used for its intended purpose (Adler and Mayer 1977; Dyer and Shimp 1977; Ernst 1976; Kover 1976; Kunstler 1975), the study of these factors becomes even more imperative. The general issue of market research use has been cited as an extremely important one in need of official investigation. A special joint commission of the AMA and the Marketing Science Institute surveyed the contributions of more than 25 years of marketing's "R & D." They were "struck by the discrepancies between the volume of the new knowledge generated over [the 25 surveyed years] and a comparatively low rate of adoption at the line manager level" (Myers, Greyser, and Massy 1979, p. 25). The commission's major recommendations were to develop improved ways "to bridge the gaps between knowledge-generation and knowledge-utilization" (Myers, Greyser, and Massy 1979, p. 27). These sentiments have been echoed in a study of European managers by Permut (1977).

Marketing Research Strategies Actual and Recommended

Until recently, there has been a strong fondness in social science research in the direction of preserving data integrity through the use of quantitative/ deductive research methods whenever possible (e.g., Mitroff 1974). This preference also is evident in marketing. A random sample of 10 issues of the Journal of Marketing Research for the years 1977-1982, for example, shows marketing's research methods to be characterized by (1) substantial methodological attention and self-study, ordinarily advocating quantitative or "objective" methodological innovations,( 2) no qualitative studies of any sort, and (3) considerable use of indirect measures of behavior( e.g., verbal reports) rather than direct assessments of the phenomena (e.g., purchases) under consideration. In other disciplines, growing dissatisfaction with the use of quantitative research methods and strategies has emerged, particularly as they are applied to phenomena not easily operationalized or easily visible outside the natural settings in which they occur (for examples, see the special issue of the Administrative Science Quarterly 1979, or the Sage Series in Qualitative Research, e.g., van Maanen, Dabbs, and Faulkner 1982c ). Van Maanen (1982a) gives some reasons for this re emergence of qualitative research in the disciplines of sociology and psychology: "The sources of disenchantment [with quantitative/deductive tools] are many, but deserving of passing note are: the relatively trivial amount of explained variance, the abstract and remote nature of key variables, the lack of comparability across studies, the failure to achieve much analytical validity . . . and the causal complexity of multivariate analysis, which, even when understood, makes change-oriented actions difficult to contemplate" (p. 13). A rising number of researchers in economics (e.g., Piore 1979), medicine (e.g., Feinstein 1977), organizational behavior (e.g., Fombrun 1982; van Maanen 1979a), sociology (McGrath, Martin, and Kulka 1982; Mitroff 1974), and psychiatry have advocated and helped foster rebirth of qualitative research in the social sciences. Some of these researchers have gone so far as to say that, given the small level of theoretical knowledge about phenomena in which social science is interested, coupled with the known complexities and context-sensitivities of these same phenomena, qualitative research is the major or even the only valid knowledge-accrual device open to scientists whose interests are focused on human behavior. Though we do not go so far, it may be noted that many important marketing phenomena meet the dual conditions of little theoretical knowledge and high complexity. Such phenomena should be suited to the application of qualitative research methods. However, little trend toward qualitative research has yet been observed in marketing Because of marketing's quantitative/deductive research roots, many marketing subject areas not amenable to study by the methods oriented toward the top-left apex of Figure 1 have received little research notice of any sort. For instance, though much is written about normative pricing strategy formation, almost zero is known descriptively about how (or whether!) managers engage these strategies under real-world pressures. Indeed, little is known about what constitutes effective marketing management in practice (or whether practice is consistent with what little is known from theory, survey verbal reports, or student simulations). What is known about such questions often evolves from practical experience, undocumented analogies with other disciplines, and common-sense reasoning. The apparent researches bias toward types of investigation that protect data integrity at the expense of currency results in a methodological one-sidedness that may impair the development and testing of sound theories. In sum, there is a role and a need for a much broader set of knowledge-accrual mechanisms than those conventionally employed in marketing research In particular, methods toward the lower-right apex of Figure 1 seem especially well-suited to aspects of marketing where there is a relatively thin theoretical base or complex observational task. One such method found promising by many researchers (e.g., Duncan 1979; McClintock, Barnard, and Maynard-Moody 1979) is case research.


Case studies are most familiar to marketers as a pedagogical device, or as a way of generating exploratory insights prior to more "rigorous" investigations. Here, neither of these uses of cases is viewed as case research; rather, the use of cases as research tackle is our focus. Though examples of case research qua research can be found (c.f., Bonoma, in press; Corey 1978; Corey and Star 1971), little guidance about how to conduct marketing case research is available, except in literatures not often examined by marketing researchers (e.g., Geertz 1973; van Maanen 1982a). In this section, therefore, we discuss the nature of a case, then differentiate the use of cases for teaching, prescientific, and research purposes, and set the stage for discussion of a four-stage qualitative research process intended to guide qualitative and case-based research endeavors

Case Studies

Defined most generally, a case study is a description of a management state of affairs. As such, it is the marketing analogue of the physician's clinical examination (e.g., MacLeod 1979), and relies on a alike appeal to multiple data sources for reliable diagnosis (cf. Leenders and Erskine 1978). Though case studies familiar from class-room use usually spotlight on some problem of high currency to firm management and have broad pedagogical appeal, cases without any problem focus can be constructed to learn about the operation of a healthy management or marketing organization. Thus, though management "disease" often is the stimulus for case construction, a problem focus is not required. Second, case construction implicates multiple data sources. Like other qualitative methods, cases frequently rely heavily on verbal reports (personal interviews) and unobtrusive observation as primary data sources. However, case method is distinguished from other qualitative methods in that it involves numerous other data sources, some of which are quantitative. These other data sources serve as a means of "perceptual triangulation"4 and pro-vide a full picture of the business unit under study. Prime among these sources are financial data (e.g., budgets, operating statements), market performance data (e.g., share, sales by territory), and market and competitive information (e.g., product replacement rates, competitive spending levels). Additional data sources consulted include written archives (e.g., memoranda), business plans, and direct observations of management interactions. Third, cases should mirror and be sensitive to the context within which management's acts occur and to the temporal dimension through which events unfold. They go beyond providing a static snapshot of events, and cut across the temporal and contextual gestalt of situations. Finally, cases require direct observation of management behavior by a trained observer who applies his/her own construal of the ongoing events, while also trying to understand the construal's of the actors. Case method, in short, requires skilled clinical judgments about what to look at and what it means. Thus, like other qualitative methods, case method is concerned basically with the researcher's interpretation of management's signification of events, information, and reality-that is, it depends on the researcher's perceptions about management's meanings, not on some "objective reality."Unlike some other qualitative methods, case methodology draws on numerous other data sources to triangulate these perceptions and significations within a broader context.

Organizational Context of Market Research Use

Largely as a function of developments in its environment, marketing is asking introspective questions about its own competence. At the beginning of the 1980s we have seen the quick growth of the marketing function over the past two decades slowed under the impacts of inflation, raw material shortages, unemployment and recession. These economic changes necessitate a reconsideration of strategies that had earlier proved successful. The drive now is to become leaner, more well-organized in the use of available resources and more oriented toward the future (Wind 1980).

If we are to believe that the U.S. and other post-industrial economies are moving from an "Age of Product Technology" to a "Knowledge-based Society" (Bell 1976), we should be increasingly concerned with our ability to deal with our corporate knowledge systems. The growth and even survival of today's business entities will depend on their strategies for handling and processing information. The more present this information, the greater the ability of managers to make policy decisions based upon it. In turn, the effectiveness of those decisions will be measured in terms of market information. The marketing purpose is somewhat unique in that the information gathering and analysis processes in firms have been institutionalized as marketing research departments or divisions. Although these specialized information processing units have existed for some time, very little examination has been given to the effectiveness of research in providing information at the right place for the right decision. Additionally, it is only very recently that any attention has been paid to the factors that have an effect on the usefulness of marketing research. The issue of examining marketing's R&D has not gone ignored. The critical costs of inadequate utilization of marketing tools and techniques have been mentioned lately by a special AMA/Marketing Science Institute joint commission (Myers, Massy and Greyser 1980). The commission's members were surprised at the relatively low rate of acceptance at the line manager level of new marketing knowledge generated over a period encompassing the past 25 years. Their major recommendation was to develop better ways "to bridge the gaps between knowledge-generation and knowledge-utilization"(Myers, Greyser and Massy 1979, p. 27). Both marketing practitioners and academics support these observations and agree that much problem oriented research is not used (Dyer and Shimp 1977, Ernst 1976, Kover 1976, Kunstler 1975). However, little formal research has been conducted in this area (Greenberg, Goldstucker and Bellenger 1977; Krum 1978; Luck and Krum 1981). Most observations about the factors affecting use of marketing research have been limited to introspective, albeit careful, analyses of personal experiences (Hardin 1973, Kunstler 1975, Newman 1962). The issue of inadequate utilization of available research information is not unique to marketing. Under use occurs in all areas of applied research activity. Most recently it has received much empirical attention in the policy sciences and has led to the creation of the area of inquiry called Knowledge deployment (Caplan, Morrison and Stambaugh 1975; Rich 1975; Weiss 1977; Weiss and Bucuvalas 1980). Developments in this area indicate that an understanding of the research use phenomenon lies in examining the organizational contexts in which policy decisions are made. The design of the decision making structures of organizations sometimes provides clues as to why some of them are more well-organized at using research than others. As Day and Wind (1980) have commented, senior management has come to believe that focusing only on a customer-oriented search for competitive advantage may be shortsighted. There is a need to widen the scope of empirical attention in marketing by looking at relationships beyond those of the company and its customers. One set of these relationships deals with managers within an organization. Unless the arrangement of work relationships in a firm has been de-signed to optimize managerial effectiveness, the company customer dealings will suffer and, in turn, negatively impact on the firm's long-term success. Yet the influence of organizational structure on the marketing function has hardly ever been studied systematically (Bonoma, Zaltman and Johnston 1977; Silk and Kalwani 1980; Spekman and Stern 1979). This issue is particularly important in the knowledge utilization area since parallel findings in the policy sciences, as mentioned earlier; indicate the importance of organizational design in influencing research use. In the pursuit of marketing effectiveness it may be useful to examine what forms of marketing organization appear best suited to manage the marketing research process efficiently (Wind 1980).

Market Characteristics

Competitive Pressures in Environment

The timing of the special subject on competition in marketing is particularly appropriate because of the growing significance of competition in marketing activities. With a slowdown in world economic growth, firms must take business away from competitors if they are to sustain their own growth rate. Deregulation, globalization of markets, flexible manufacturing, and rapidly changing technology are producing new sources of com-petition and altering the nature of competition in markets. The articles in the special issue respond to the needs of marketers to develop a better understanding of the impact of competition on marketing decisions. Competition and marketing research competition is the process by which independent sellers vie with each other for customers in a market. Be-cause substitutes exist for most products and services, firms typically meet competitors when marketing their offerings. Consequently, the effectiveness of marketing programs typically depends on the reaction of both customers and competitors. However, marketing theories and research have emphasized issues related to customer response and have directed less attention to competitive response. This lack of attention to competitive effects is surprising because it is hard to imagine a marketing decision that is not affected by competitive activity. The marketing concept, a keystone of marketing thought, stresses the importance of satisfying customer needs and considering customer responses in the development of marketing programs. Recently, marketers have called for a expansion of the marketing concept to ad-dress explicitly the role of competitive considerations in marketing decision making (Day and Wensley 1983; Oxenfeldt and Moore 1978). These scholars suggest that customers be viewed as a "prize" gained by satisfying customer needs better than competitor firms. The entire range of research in this area cannot be addressed in this short note. The introduction is organized around the following five questions.

1. Who are the firm/brand's competitors?

2. How intense is the competition in a market?

3. How does competition affect market evolution and structure?

4. How do competitive actions affect the firm's marketing decisions?

5. How do firms achieve and maintain a competitive advantage?


A market is defined as "a group of potential customers with similar needs and sellers offering goods and services to satisfy those needs" (McCarthy and Perreault 1984). The identification of market limits and the competing firms within those boundaries pervades all levels of marketing decisions. Market definition is crucial for assessing strategic opportunities, identifying competitive threats, developing marketing programs, and assessing market share to assess performance.

What Are the Boundaries of a Market?

The identification of market boundaries and competing firms is subjective. Competition among firms and brands is a matter of degree. At one extreme, all firms and products compete indirectly against each other for the restricted resources of customers. At the other extreme, Coke and Pepsi compete against each other using similar production and marketing strategy to satisfy almost identical customer needs. Thus, the degree of similarity in needs satisfied and methods used to satisfy those needs deter-mines the degree to which firms and brands compete against each other. The different market definitions are determined by discontinuities in supply and demand characteristics. Economists highlight supply considerations when they define an industry as a set of rival firms using similar technologies and/or manufacturing processes. Marketers have focused on demand considerations when they define markets in terms of common needs such as transportation (Levitt 1965). The nature of the marketing decision determines the appropriate boundary for defining the competitive set. The development of functional marketing mix decisions typically involves a contracted definition of the competitive set focusing on directly competing brands in a market segment. In contrast, long-term strategic marketing decisions require a broader definition of competitors and customers-product markets (Day 1981a) or industry segments (Porter 1985)-so that un-served potential needs and competitive threats are identified. In general, marketing research on market boundaries has resolute on consumer needs related to functional decisions involving brands. The rich tradition of segmentation research in marketing (Wind 1978) centers primarily on the structure of buyers in the market, ignoring the sellers participating in the market. Research on product positioning considers both customer needs and customer perceptions of market offerings. A assortment of analytical approaches are available for identifying the structure of competing products from assessments of the degree to which customer-based in formation indicates the substitutability of products( Day, Shocker, and Srivastava 1979).


The attractiveness of an industry, product market, or market section as a strategic investment opportunity is related to the profit potential of the market and the firm's ability to exploit that potential. Porter (1980) indicates that the evaluation of competitive intensity is a crucial input for evaluating the profit potential of a market. Much of the research in industrial organization (IO) economics addresses issues related to assessing competitive intensity. The dominant IO prototype, structure-conduct-performance, argues t hat industry structure determines the conduct within an industry. Thus, the conduct of firms, the nature of the competitive activity within an industry, determines industry performance (profit-ability, innovativeness, cost efficiency). Most IO research ignores conduct, focusing simply on the relation-ship between structure and performance (Porter1 981). Within this tradition, Porter (1980) suggests a check list of structural variables that can be used to determine the level of competitive strength within an industry Marketers are more concerned with the performance of products and firms than with the performance of entire industries. Because of this orientation, marketers have concentrated on directly assessing the conduct o r behavior of competing firms rather than the structural properties that presumably affect conduct's or example, Gatignon (1984) developed a measure of competitive intensity in a market based on competitive reactions to marketing activities rather than the structural properties of the competitive such as the her find ahl index. Research related to the assessment and implications o f competitive intensity is not represented in the special issue. However, the area is a promising one for prospect research. Research is needed to test the extent to which the structural properties postulated by Porter (1980) are related to actual conduct, competitive reaction, and performance. Is the strength of competitive reactions in a market related to the number and size distribution of competitors in the market? How does the level of fixed costs, market growth rate, product differentiation, and exit and entry barriers influence the intensity of competitive reactions? Is the level of competitive intensity in a market related to the performance of the industry and specific firms in an industry? In addition, we need to explore how the level of competitive intensity in a market influences the effectiveness o f marketing activities. For example, Gatignon (1984) found that the intensity of competition moderates the effect of advertising on consumer price compassion. In markets with high competitive intensity, advertising increases price sensitivity, whereas in markets with low competitive intensity t he effects of advertising on price sensitivity are weaker.

Firm Resources

The industrial organization and marketing strategy literature place considerable emphasis on the size of a firm, especially because of the resource advantages that it possesses and can use to compete. This factor can strongly affect a new product's performance (Day 1984; Narver and Slater 1990). The better the resources of a firm, the more market power, which is a competitive advantage that translates into better performance of the new product. These advantages can be due, in part, to the capability to invest greater resources into the design of superior innovations (Capon et al. 1992), which might be more radical, have a greater relative advantage, and cost less. These effects need to be included in a model of the impact of strategic orientations

Product Characteristics

Product quality is speedily becoming an im­portant competitive issue. The superior reli­ability of many Japanese products has sparked considerable soul-searching among American managers. [W. J. Abernathy, K. B. Clark, and A. M. Kantrow, 1983] In addition, a number of surveys have voiced consumers' dissatisfac­tion with the existing levels of quality and service of the products they buy [Barksdale et al., 1982]. In a recent study of the business units of major North American companies, managers ranked "producing to high quality standards" as their chief current concern [G. Miller, 1983].

Despite the attention of managers, the aca­demic literature on quality has not been re­viewed extensively; the problem is one of coverage: scholars in four disciplines — phi­losophy, economics, marketing, and opera­tions management — have considered the subject, but each group has viewed it from a dissimilar vantage point. Philosophy has fo­cused on definitional issues; economics, on profit maximization and market balance; marketing, on the determinants of buying behavior and customer satisfaction; and op­erations management, on engineering prac­tices and manufacturing control. The result has been a host of competing perspectives, each based on a different analytical frame­work and each employing its own terminol­ogy.

At the same time, a number of ordinary themes are apparent. All of them have im­portant management implications. On the conceptual front, each discipline has wres­tled with the following questions: Is quality objective or subjective? Is it timeless or so­cially determined? Empirically, interest has focused on the correlates of quality. What, for example, is the connection between qual­ity and price? Between quality and advertis­ing? Between quality and cost? Between quality and market share? More generally, do quality improvements lead to higher or lower profits?

Five Approaches to Defining Quality

Five major approaches to the definition of quality can be identified: (1) the transcen­dent approach of philosophy; (2) the prod­uct-based approach of economics; (3) the user-based approach of economics, market­ing, and operations management; and (4) the manufacturing-based and (5) value-based approaches of operations management [Garvin, D. A. 1984].

Dimensions of Quality:

Dimensions can be identified as a framework for thinking about the basic ele­ments of product quality:

1. Performance,

2. Features,

3. Reliability,

4. Conformance,

5. Durability,

Each is self-contained and dissimilar, for a product can be ranked high on one dimen­sion while being low on another.


Garvin, D. A. (1984) first on the list is performance, which refers to the main operating characteristics of a product. For an automobile, these would be characteristics like acceleration, handling, cruising speed, and ease; for a television set, they would include sound and picture clarity, color, and ability to receive distant stations. This dimension of quality combines ele­ments of both the product and user-based approaches. Measurable product attributes are involved, and brands can usually be ranked objectively on at least one dimension of performance. The connection between performance and quality, however, is more ambiguous. Whether performance differ­ences are perceived as superiority differences normally depends on individual prefer­ences. Users typically have a wide range of interests and needs; each is likely to equate quality with high performance in his or her area of immediate interest. The connection between performance and quality is also af­fected by semantics; among the words that describe product performance are terms that are frequently associated with quality as well as terms that fail to carry the association. For example, a 100-watt light bulb provides superior candlepower (performance) than a 60-watt bulb, yet few consumers would re­gard this difference as a measure of quality. The products simply belong to different per­formance classes. The smoothness and quietness of an automobile's ride, however, is typically viewed as a direct reflection of its quality. Quietness is therefore a performance dimension that readily translates into quality, while candlepower is not. These differ­ences appear to reflect the conventions of the English language as much as they do per­sonal preferences.

There is a clear analogy here to Lancaster's theory of consumer demand. [K. Lancaster, 1966] the theory is based on two propositions: [Lancaster, 1971] All goods hold objective characteristics rele­vant to the choices which people make among different collections of goods. The relationship between ... a good . . . and the traits which it possesses is essentially a technical rela­tionship, depending on the objective characteris­tics of the good. . . .

Individuals fluctuate in their reaction to different characteristics, rather than in their assessments of the characteristics.... It is these characteristics in which consumers are interested . . . the various characteristics can be viewed ... as each helping to satisfy some kind of "want."

In these terms, the performance of a product would match to its objective charac­teristics, while the relationship between per­formance and quality would reflect individ­ual reactions.


The same approach can be applied to prod­uct features, a second dimension of quality. Features are the "bells and whistles" of products, those secondary characteristics that complement the product's basic func­tioning. Examples include free drinks on a plane flight, permanent press as well as cot­ton cycles on a washing machine, and auto­matic tuners on a color television set. In many cases, the line separating primary product characteristics (performance) from secondary characteristics (features) is difficult to draw. Features, like product per­formance, involve objective and measurable attributes; their conversion into quality dif­ferences is equally affected by individual preferences. The distinction between the two is primarily one of centrality or degree of importance to the user.


Reliability is a third dimension of quality. It reflects the chance of a product's failing within a specified period of time. Among the most common measures of reliability are the mean time to first failure (MTFF), the mean time between failures (MTBF), and the fail­ure rate per unit time. (Juran, 1974)Because these mea­sures require a product to be in use for some period, they are more relevant to durable goods than they are to products and services that are consumed instantly. Japanese man­ufacturers typically pay great notice to this dimension of quality, and have used it to achieve a competitive edge in the automotive, consumer electronics, semiconductor, and copying machine industries.


A related dimension of quality is confor­mance, or the degree to which a product's design and operating characteristics match pre-established standards. Both internal and external elements are involved. Within the factory, conformance is usually measured by the incidence of defects: the proportion of all units that fail to meet specifications, and so require rework or repair. In the field, data on conformance are often difficult to gain, and proxies are frequently used. Two com­mon measures are the incidence of service calls for a product and the frequency of re­pairs under warranty. These measures, while suggestive, disregard other deviations from standard, such as misspelled labels or shoddy construction, which do not lead to service or repair. More comprehensive mea­sures of conformance are required if these items are to be counted [Garvin, D. A. 1984].

Both reliability and conformance are closely joined to the manufacturing-based ap­proach to quality. Improvements in both measures are normally viewed as translating directly into quality gains because defects and field failures are regarded as undesirable by virtually all consumers. They are, there­fore, relatively objective measures of quality, and are less likely to reflect individual pref­erences than are rankings based on perfor­mance or features.


Durability, a gauge of product life, has both economic and technical dimensions. Technically, durability can be denned as the amount of use one gets from a product before it physically deteriorates. A light bulb pro­vides the perfect example: after so many hours of use, the filament burns up and the bulb must be replaced. Repair is impossible. Economists call such products "one-hoss shays," and have used them widely in modeling the production and consumption of capital goods. [C. J. Bliss, 1975]

Garvin, D. A. (1984) Durability becomes more difficult to inter­pret when repair is possible. Then the con­cept takes on an added dimension, for prod­uct life will vary with changing economic conditions. Durability becomes the amount of use one gets from a product before it breaks down and replacement is regarded as preferable to continued repair. Consumers are faced with a series of choices: each time a product fails; they must weight the expected cost, in both dollars and personal inconve­nience, of future repairs against the invest­ment and operating expenses of a newer, more reliable model. In these circumstances, a product's life is determined by repair costs, personal valuations of time and inconve­nience, losses due to downtime, relative prices, and other economic variables, as much as it is by the quality of components or materials, this approach to durability has two im­portant implications. First, it suggests that durability and reliability are closely associated. A product that fails frequently is likely to be scrapped earlier than one that is more reli­able; repair costs will be correspondingly higher, and the purchase of a new model will look that much more desirable. Second, this approach suggests that durability figures should be interpreted with care. An increase in product life may not be due to technical improvements or to the use of longer-lived materials; the underlying economic envi­ronment may simply have changed, For ex­ample, the expected life of an automobile has risen steadily over the last decade, and now averages fourteen years.[ Retiring Autos at 14, 1983] Older automobiles are held for longer periods and have become a greater percentage of all cars in use.[ S. W. Burch, 1983] Among the factors thought to be responsible for these changes are rising gasoline prices and a weak economy, which have reduced the average number of miles driven per year, and federal regulations governing gas mileage, which have resulted in a reduction in the size of new models and an increase in the attractiveness to many consumers of re­taining older cars. In this case, environmen­tal changes have been responsible for much of the reported increase in durability.

Product as Symbol

Products have a significance that goes beyond their functional usefulness. This significance stems from the ability of products to communicate meaning (Hirschman, 1981; McCracken, 1986). Products are symbols by which people convey something about themselves to themselves and to others (Holman, 1981; Solomon, 1983). This symbolic meaning is known to influence consumer preference.

All commercial objects have a symbolic character, and making a purchase involves an assessment - implicit or explicit - of this symbolism ... (Levy, 1959, p. 119).

The symbolic meaning of products has become increasingly significant. Nowadays, differentiating products based on their technical functions or quality is difficult (Dumaine, 1991; Veryzer, 1995). Since the wave of the quality controls in the 1980s, products can be expected to fulfill their functions reasonably well. Symbolic meaning provides another way to differentiate products. Due to representative meaning otherwise indistinguishable products become differentiated in the eyes of the consumer. Similarly Salzer-Mo¨rling and Strannega˚rd (2004) recently stated:

With the abundance of products in the western world, the managerial challenge, it seems, has become that of differentiating similar products (p. 224).

The relationship between physiological product char­acteristics and consumer quality perception is at the heart of market-oriented product development: In order to design products which will be accepted by consumers, it is necessary to convert consumer demands into pro­duct specifications that are actionable from the produ­cer's point of view. With regard to food, this relationship is particularly complicated because the way consumers perceive expected quality before a purchase is often dif­ferent from the way quality is perceived after consump­tion, and may be related to various physiological product characteristics.

While this has been acknowledged repeatedly in the literature (e.g. Grunert et al., 1996; Poulsen et al., 1996; Steenkamp and van Trijp, 1996), despite the apparent practical consequences of better knowledge on how phy­siological product characteristics and quality perception before purchase and after consumption interact, research shedding light on this issue has been very sparse.

The study by Steenkamp and van Trijp (1996) combined physiological product characteristics, quality cues and quality criteria. It was done with blade steak as product category. Six physiological characteristics were calculated, some of them by several indicators: color, fatness, pH value, water-binding capacity, and shear force and sarcomere length. Eight quality cue measures were combined into three latent constructs: freshness, visible fat and appear­ance, which together determined quality expectations. Likewise, seven quality criteria measures were com­bined into three latent constructs: tenderness, non-meat components and flavor, which together determined quality experience. The main results were as follows:

* color has a significant impact on quality expecta­tions only

* fatness has a negative impact on quality expecta­tions and a positive impact on quality experience

* water-binding capacity, sarcomere length and pH value have an effect on both quality expectations and quality experience

* shear force affects quality experience only

* There is no significant relationship between quality expectation and quality performance.

To have a clear understanding of the issues surrounding the impact of product characteristics, a debate on product classifications is warranted. When looking at product classifications, marketers divide products and services based on the types of consumers that use them - consumer products and business to business products. This discussion will be limited to consumer products. Consumer products are those which are purchased by the final consumer for his/her consumption. These products are further classified into convenience, shopping, specialty and unsought products. Convenience goods are those that are purchased often with little planning or shopping effort. They are usually at stumpy prices and widely available. Shopping goods are those which are purchased less frequently, such as furniture and major appliances, and which are compared on the basis of suitability, quality, price and style. Specialty goods enjoy strong brand preference and loyalty. Consumers of these goods are willing to make a special purchase effort, do little brand comparisons and have low price sensitivity. Both producers and sellers of these products use carefully targeted promotion. Unsought products are consumer goods that the consumer either does not know about or knows about but does not normally think of buying; for example, Red Cross blood donations (Kotler et al, 1998).

Peterson et al., (1997), suggest another classification system which they argue is more relevant. In this system the products and services are categorized along three dimensions: cost and frequency of buy, value proposition and degree of differentiation. Goods in the first dimension range from low cost, frequently purchased goods to high cost infrequently purchased goods. The usefulness of this dimension lies in that it highlights the differences in operation and distribution costs depending on whether and how the Internet is used.

The value proposition dimension classifies merchandise according to their tangibility. Products are classified as tangible and physical or intangible and service related. Internet commerce is especially well-suited for goods consisting of digital assets - which are intangible - (Rayport and Sviokla, 1995), such as computer software, music and reports. The third dimension, differentiation, deals with how well the seller has been able to create a sustainable competitive advantage through differentiation.

Information about the product attributes plays a vital role in consumers' product evaluation process. For most product evaluations, only incomplete information is available, thus consumers often form evaluations for various products on the basis of the available information and form attribute covariance inferences about the missing information (Pechmann and Ratneshwar, 1992; Ross and Creyer, 1992).