A Situational Approach to Brand Loyalty

Bernard Dubois, Groupe HEC
Gilles Laurent, Groupe HEC
ABSTRACT - Brand loyalty, although an old idea central to marketing practice, remains a poorly understood and measured construct. Given the renewed interest in brand and branding issues, pleas for fresh perspectives that could rejuvenate brand loyalty research have proliferated. The objective of this paper is to advocate and illustrate the adoption of a situational approach in the analysis of brand loyalty patterns. A situational scale is developed in the context of fast moving consumer goods; its scalability is established, its convergent validity is assessed and its diagnostic power is illustrated.
[ to cite ]:
Bernard Dubois and Gilles Laurent (1999) ,"A Situational Approach to Brand Loyalty", in NA - Advances in Consumer Research Volume 26, eds. Eric J. Arnould and Linda M. Scott, Provo, UT : Association for Consumer Research, Pages: 657-663.

Advances in Consumer Research Volume 26, 1999      Pages 657-663


Bernard Dubois, Groupe HEC

Gilles Laurent, Groupe HEC


Brand loyalty, although an old idea central to marketing practice, remains a poorly understood and measured construct. Given the renewed interest in brand and branding issues, pleas for fresh perspectives that could rejuvenate brand loyalty research have proliferated. The objective of this paper is to advocate and illustrate the adoption of a situational approach in the analysis of brand loyalty patterns. A situational scale is developed in the context of fast moving consumer goods; its scalability is established, its convergent validity is assessed and its diagnostic power is illustrated.


Brands and branding issues have generated considerable interest in recent years both in academic (see for example the special issues of the Journal of Marketing Research in 1994 and of the International Journal of Research in Marketing in 1997) and managerial circles (see Aaker, 1996, and Morris, 1996). To a large extent, this substantial level of attention has been stimulated by the growing awareness, among many companies, that thei brands could represent one of their major assets, an idea encapsulated in the concept of brand equity (Leuthesser, 1988 ; Farquhar, 1990; Barwise, 1990; Kapferer, 1995) . For the most part, the value of a strong brand lies, for a company, in its ability in a competitive environment, to attract and keep satisfied customers over time. As a result, many theoretical and methodological efforts aimed at understanding brand equity have incorporated in one way or another an indicator of brand loyalty (Keller, 1993, Aaker, 1991).

Brand loyalty is a time-honored and rather complex construct which has led to numerous definitions and operationalizations (Copeland, 1923; Brown, 1952; Jacoby and Chestnut, 1978). Today, it would seem that two major approaches predominate. One is behavioral in orientation and typically infers the loyalty status of a given consumer from an observation of his or her purchase record, as obtained for example from purchase diaries or scanner data (Guest, 1944; 1955; 1964; Kahn, Kalwani, and Morrison, 1986; Ehrenberg, 1988). The other is more attitudinal in nature. Brand loyalty is then understood as a systematically favorable expression of preference for the brand, usually measured through self-reported assessments (Dick and Basu, 1994). Both approaches have their merits but also their limitations (Uncles and Laurent, 1997). Behavioral indicators of brand loyalty present the advantage of being based on fairly detailed and reliable data but, unless supplemented by many other indicators, offer limited diagnostic power. Being restricted to a record of what consumers have done, one can only speculate about the extent to which some "psychological commitment" to the brand, consistent with the way the word "loyalty" is normally used in the English language, was really involved in the observed purchase regularity patterns (Jacoby and Chestnut, 1978). Indifference, consumer inertia, a low price, a perceived high switching cost or simply the non-availability of alternatives can all provide plausible rival hypotheses. On the other hand, attitudinal measures, such as brand purchase intentions, better account for the evaluative and affective components of brand loyalty but often suffer from low predictive power (Wind and Lerner, 1979; Dubois, Laurent, and Quaghebeur, 1998). Loyalty is then determined on the basis of what people think and say but with a perhaps distant relation to what they do.

In this paper, we suggest and examine a third conceptual and methodological route to brand loyalty, based on a situational orientation. Situational factors have long been recognized as key explanatory elements to understand consumer behavior (Dickson, 1982). Usually defined as all factors (physical and social environment, time, role context, etc.) "which do not follow from a knowledge of personal and stimulus attributes but still have a demonstrable and systematic effect on current behaviour", situational factors have been found to play a major role in the purchase and consumption of many products and brands (Belk, 1974, 1975). An exploration of situation-driven brand use patterns seems particularly relevant at a time when the marketplace is often described as increasingly unpredictable due to waning brand loyalties (Shocker, Srivastava, and Ruekert, 1994; Schlossberg, 1994), and when cries for perspectives that could bring new life into brand loyalty research have been raised (Fournier and Yao, 1997; Lehmann and Russo, 1996; Lutz, 1987).

Adopting a situational perspective on brand loyalty logically leads to define it as a "propensity to choose a given brand in a variety of situations". In other words, consumers would be defined as more or less loyal to a given brand depending upon the number and nature of situations in which they choose that brand. For example, we will declare that, compared with other people a consumer is more loyal to, let us say, Tropicana fruit juices if (s)he decides not only to purchase that brand when preparing a reception with family or friends (situation n¦1) but also to go to another store to get it if the Tropicana brand is out on stock on the day (s)he goes shopping (situationn¦2). Espoused loyalty in the face of a reason to change (e.g. a different purpose) would appear to be more informative about brand loyalty than is loyalty in the face of no particular reason. Furthermore, if the selected set of situations can be ordered in a sequence according to which fewer consumers stick to their brand as one moves from one situation to the next, a "situational" scale, i.e. a scale made of situations, can be developed. Developing a situational scale for measuring brand loyalty is of both academic and managerial interest. From an academic standpoint, such situational scales would allow interested researchers to better understand the circumstances under which consumers continue or cease to be loyal to their brands and hypotheses or even theories about such behavior could be tested with appropriate instruments. Developing for a given brand a "situational loyalty profile" can also be of managerial interest since this approach has direct implications for market segmentation and targeting. Instead of identifying loyal consumers in terms of socio-demographic or psycho-graphic profiles and positioning brands exclusively in terms of product attributes, the situational approach suggests that efforts to position brands could be developed with particular purchase or usage contexts in mind, beyond permanent people or product characteristics. The objective of this research is to develop and illustrate a procedure for measuring the propensity to stay loyal to one’s brand through an analysis of purchase and consumption situations organized as a scale. In the following sections, we describe the specific context in which this research was developed, the instruments used, the results obtained, and their implications.


The key steps in developing and assessing situational scales for the purpose of measuring brand loyalty involved the selection of: 1) specific brands and therefore product categories; 2) specific purchase and consumption situations and 3) consumers.

Although almost any product category containing a range of brand alternatives could have served our purpose, we preferred to select frequently bought items such as prepackaged food products so as to provide a rich context for analyzing brand loyalty, i.e. a context in which many repeated opportunities would exist over a given period of time (let us say two years) for consumers to exhibit their brand loyalty (or disloyalty). In the end, and after a thorough review of the scanner-based "Marketing Book" compiled by a leading consumer panel company (Secodip), we decided to explore the following six product categories: coffee, pasta, ice cream, fruit juices, biscuits and cheese Although not representative of the full spectrum of food items, the preceding list contains, we believe, enough variety in terms of unit price, nature, and frequency of consumption to allow our conclusions not to be limited to a too narrow product domain. In order to explore the robustness of the phenomenon under investigation, we looked for maximal differences across product categories. Obviously, the selection of products has some influence on the nature of situations to be investigated.

The most critical step in developing a situational scale consists in identifying the nature, number and variety of situations to be considered for inclusion in the measurement instrument. This is no easy task. Ideally, one would like to find situations which are: 1) significantly contrasted from each other so that each additional situation brings a new dimension into the analysis, and 2) collectively representative of the typical purchase and consumption contexts, so that no essential situation is forgotten. Yet, as expressed by Frederiksen, "no prescription can be given to the would-be developer of a taxonomy of attributes of situations with regard to how to proceed (Frederiksen, 1972)". After some pre-testing and considering that the selected situations should appear natural for all six product categories, wedecided to explore three types of situations, two referring to a usage environment and one involving a purchase context. The first one was defined in terms of the social environment, making explicit reference to the preparation of a reception with family or friends. What would consumers do when faced with such a situation? Would they pick their most frequently purchased brand or make another choice? The second included a time perspective. What do consumers do when they realize in their kitchen that they have just run out of a food item (i.e. any of the six products listed above) they urgently need? Do they rush and buy the first brand they find or do they look for their most frequently purchased brand? Finally, the last situation referred to the physical environment of the store where consumers usually shop. What do they decide when they discover that their most frequently bought brand is out of stock ? Do they buy another one instead or refuse to do so ? These three situations illustrate the first three dimensions suggested by Belk. Obviously, for each dimension, a number of other exemplars could have been used (see Belk, 1975, for a description of the sub-components of each dimension). Even though it was difficult to anticipate a priori which situations among the three would affect most brand loyalty, we tried to choose situations that would offer variety in this respect. One may argue that in a reception context for example, consumers would make effort to buy their preferred brand but one may equally hypothesize that consumers in such a situation would decide to buy something exceptional.

Given that our objective was not only to develop a measurement instrument but also obviously to establish its scalability and convergent validity, the latter in relation to the more conventional behavioral and attitudinal measures of brand loyalty, it was essential to have access to a sample of consumers for whom all actual purchases in each of the six product categories could be recorded over time (over two years). The only practical way to do this was to use the members of an already established consumer panel maintained in a "closed zone", i.e. a city where, through prior arrangements with all the retailers active in the zone, all purchases made by all panelists in all stores are electronically recorded as panelists show their identification card when cashing their purchases in the store. We actually used the Scannel 2,500 member consumer panel maintained by Secodip (a major consumer panel operator in France) in the region of ChGteau-Thierry, a geographical area selected by Secodip for its national representativeness, and, as such, regularly used for pre-launch test markets.


Ideally, one would have liked to be in a position to observe what every single member of the consumer panel would have done in each of the three situations identified above, for each of the selected product categories. Given the impossibility of such a task, we decided to send a questionnaire to all panel members asking them to describe what would have been their behavior in each of the three situational scenarios. In doing so, we of course fully recognize that answers given to questions related to hypothetical scenarios may differ from what people would have actually done. It is also obvious that by using a one-shot instrument, we do not explicitly incorporate the time component inherent in the consideration of multiple situations. However, it should be remembered that our main objective is this research is to develop a paper-an-pencil instrument which could be easily used in a variety of contexts. Besides, such an approach is in line with common practice in past situational studies (see for example Miller and Ginter, 1979, Scammon, 1981). It is also more in line with the "situation as subjectively determined" orientation championed by Lutz and Kakkar (1975) than with the "objectively determied" approach supported by Belk (1975).

Practically speaking, a questionnaire was mailed to all panel members who were invited to fill it out and return it to the consumer panel organization. Given that we had available actual purchase data, we developed a series of questionnaire items related to loyalty in order to assess the convergent validity of our approach. The questionnaire itself, identical for each product category, was structured in three parts. In the first part, panel members were asked to determine whether or not, for each product category, they were "almost always buying the same brand" . In case of a positive answer, they were asked to provide the name of this brand. The purpose of the first question was to establish whether respondents perceived themselves as loyal (in a behavioral sense) and the second was to allow us to cross-validate their statements. Of course, several other approaches would have been possible for measuring "perceived loyalty". Perhaps the most obvious one would have been to directly ask consumers whether they were or not "loyal". Given the ambiguity surrounding the word itself, we preferred however to infer loyalty from their perceived purchase behavior. Because we thought that few consumers would say that they buy always the same brand, we used "almost always" as a qualifier (and subsequently checked, on the basis of scanner data, that the exclusive buyers were indeed a rather small group). In the second part, respondents were asked to express their level of agreement (on a dichotomous format) with the following item: "For(product category name), selecting a brand rather than another one is not very important". The purpose of this question was to measure for each product category individual brand sensitivity, a concept often considered as a covariate of brand loyalty (Kapferer and Laurent, 1992). Our situational measure of brand loyalty finally came in the last part. Panel members had to indicate whether when faced with each of the three situations delineated above (reception with friends or family, kitchen run out, in-store out-of-stock position) , they would or not purchase their most frequently purchased brand. In order to limit the data collection burden for respondents (which is a very important consideration when respondents are members of an on-going consumer panel), the 2,500 member panel was randomly split into two parts and in each sub-sample, each respondent only had to answer questions regarding three product categories (fruit juices, cheese and biscuits in one case, coffee, ice cream, and pasta, in the other). A total of 1762 questionnaires ( 858 from the first sample and 904 from the second) were returned and the corresponding response rates (69% and 72% respectively) can be considered as very satisfactory.




As indicated above, each respondent provided answers to the questionnaire in relation to his or her "most frequently purchased brand" (provided of course he or she had mentioned one). Results were then aggregated over respondents. Respondents for whom no purchase act had been observed over the two years under investigation were excluded from the analysis. This explains why the net sample size is not the same for the various product categories. (The average number of purchase occasions was 24 for coffee, 30 for cheese, 14 for ice cream, 32 for fruit juices, 35 for pasta and 56 for biscuits). Table 1 presents, for each product category, the results obtained for each situational question.

Two conclusions emerge from this table. First, the three selected situations, in the order of the table, obviously correspond to increasingly compelling conditions for staying loyal to one’s most frequently purchased brand. This result holds across the six product categories since significant loyalty decreases are always observed for each product and each lvel. To that extent, one could conclude that the three situational scenarios do not appear product-specific but rather generic and therefore potentially applicable to a wider variety of markets. At the same time, strong differences emerge between the six products, a desirable property for scaling purposes. While, in the case of ice cream and cheese, less than one consumer out of three would still continue to purchase "his" or "her" brand in the most stringent situation, more than one out of two would do so for pasta or coffee. To that extent, one may conclude that consumers are more loyal to their brands in the second case than in the first one. But finer phenomena can be detected, illustrating the diagnostic power of our situational instrument. Consider again coffee and pasta. While the percentages of consumers who would stay loyal in the first situation are virtually identical, such is not the case for the second situation, which creates more "damage" for pasta than coffee. In an emergency situation, less than two thirds would continue to buy their most frequently purchased pasta brands, while almost three of four would do so for coffee. The managerial implications are straightforward: Pasta brand managers should encourage their loyal consumers to buy more on each purchase occasion so as to avoid a dangerous "empty pantry" position in the kitchen. This being said, it is interesting to note that, relatively speaking, pasta brands tend to suffer less from store out-of-stock positions, compared with coffee. Inversely, biscuits and cheese exhibit the same final (situation n¦3) attrition rate and yet come from very different starting positions. While, in the first situation, only two consumers out three stay loyal to their brands of biscuits, almost three out four do so for cheese. In the second situation, cheese brands maintain the same advantage over their biscuit counterparts. However, their lead completely vanishes in the third situation. The managerial implications now differ since cheese brands have more to loose than biscuit brands in the store, perhaps because of their shorter life.

In order to establish the scalability of our instrument, aggregate data are obviously inadequate since averages may hide opposite individual moves. In a second step, we therefore decided to analyze our data at the level of each individual respondent. A variety of methods can be used to assess scalability, from the time-honored scalogram approach developed by Guttman more than fifty years ago (Guttman, 1944) to more recent data reduction techniques such as correspondence analysis (BenzTcri et al. 1973, Lebart et al. 1984). Given the exploratory nature of this research, we opted for the conceptually simplest and most appealing Guttman indicator: The percentage of error-free patterns. In calculating these percentages, we closely follow the classical procedure for Guttman scaling, the purpose of which is to obtain numerical descriptors on the basis of nominal data (Torgerson, 1958, pp.307-331). The logic is as follows: If our situational instrument were a perfect scale, no consumers would decide to stay loyal to their brand in the most compelling situation among the three investigated here (i.e. the store out-of-stock position) if they have not done so already in the two previous (and less stringent) ones (i.e. the reception and the kitchen run-out, in that order). Inversely, consumers who decide not to buy their most frequently purchased brand in the least restrictive scenario (here the reception) should not change their mind in the other two more binding cases (binding in the sense that a decreasing number of people decide to stay loyal in such situations). Keeping in mind that, following Guttman, each situational scenario was presented as a dichotomous question, eight possible patterns could theoretically be observed. However, only four of them would be acceptable if our scale were perfect while the other four would be discounted as "errors". Table 2 shows for each product category the percentage of error-free patterns. Such percentages have to be compared with those which would have been obtained by chance, i.e. under conditions of statistical independenc between the situational scenarios and which can be calculated by simply multiplying the frequencies (now dealt with as probabilities) displayed in Table 1.





Results from Table 2 are unambiguous. In all cases, the percentage of correct patterns is extremely high and significantly higher than chance levels. Given such levels, it is clear that more sophisticated indicators such as correspondence analysis scores could only confirm the excellent scalability of our instrument, thus demonstrating that, considered jointly and ordered appropriately, situations can be used to measure brand loyalty. In past research, brand loyalty has often been conceived as "black and white", consumers being either classified as loyal or disloyal depending upon some arbitrary cut-off in purchase patterns. As noted by Fournier and Yao (1997), "such a tendency toward dichotomy not only precludes attention to loyalty levels and types but also blinds the researcher to the value that may exist in patterns classified as disloyal". Our situational scale clearly shows that brand loyalty is more a matter of degree, purchase and consumption situations serving here both as levels and markers.

Following Guttman, one could now create a "brand loyalty score" that would simply correspond to the number of situations in which a given consumer has decided to stay loyal to his or her most frequently purchased brand. In our case, the scale would be a 4-point scale (from 0 to 3) with the breakdown percentages shown in Table 3 (obtained after eliminating all error patterns).

Such results are more interesting in their relative than absolute meaning. They clearly establish that the six product categories do not enjoy the same level of loyalty. While coffee and pasta brands emerge as clear winners, with roughly half of their consumers still loyal after the third "test", ice cream, cheese and biscuits brands have lost almost three quarters of their users, fruit juices occupying an intermediary position. Taking into account the intermediate levels is also important since it allows to build, for each product category and for each brand, a "situational loyalty profile’ which can be established, as in Table 3, at a global level or for any particular consumer segment or product subgroup. For example, Table 4 illustrates, in the case of coffee, different situational profiles for various consumer segments (similar results have been generated at the level of specific brands but confidentiality reasons prevent us from displaying them)

Results from Table 4, which have many managerial implications especially in the area of targeting, reveal that certain consumer indicators have stronger links than others with situational brand loyalty patterns. For example, age seems to be a rather strong discriminator since senior consumers appear much more brand loyal than younger ones. While only 7% of the 65 years and over fail to score a single point, more than two thirds would buy their most frequently purchased brand in all three situations. Income on the other hand seems to have little impact since the profiles of upper and lower income groups are very close and similar to the global average. Household size is even more discriminant than age, to which it is obviously related. While less than half of the large families (5 people and over) would stay loyal to their brand in all situations, almost three out of four single-person households would do so. Finally usage status occupies an intermediate position. Heavy users, defined here as those for whom more than 30 purchase acts were recorded over two years are only marginally more loyal than low users (less than ten purchase records), even though they are about the same number.

The fact that we have presented a number of results obtained from our specific scale should not be interpreted as meaning that we would recommend to use this scale for any fast moving consumer good or even pre-packaged food item. Our purpose here is primarily illustrative. We just want to show that, when appropriately selected, purchase and consumption situations offer a freh and operational approach to brand loyalty measurement.

Having established the reliability or rather "scalability" of our situational instrument along the lines suggested by Guttman, we could proceed to explore its convergent validity. Since, as discussed before, brand loyalty had traditionally been assessed on the basis of either attitudinal or behavioral indicators (sometimes referred to as the "soft" and the "hard" side of brand loyalty, see Fournier and Yao, 1997), we decided to explore both dimensions.

To assess the convergent validity of our scale with the "soft" side, we relied upon two indicators. First, since, in the first part of the questionnaire, each respondent was invited to indicate whether in each product category, there was one brand that he or she "almost always buys", a simple measure of "perceived brand loyalty" could be based upon the percentage of positive answers to this question. This measure could in turn be compared with "situational brand loyalty", as inferred from our 4-point scale. A second measure could be provided by the answer given to the item designed to measure brand sensitivity. Presumably, brand loyal consumers should be expected to display a higher level of brand sensitivity, compared with non loyals. Finally, a behavioral index of brand loyalty was obtained by calculating, for each respondent, the ratio of the number of purchase acts he or she made on the brand most frequently bought over the total number of purchases recorded over two years (Share of Category Requirements). Table 5 reveals the results obtained on the three indicators for each level of situational brand loyalty.





Results reveal strong links between our situational scale and the three brand loyalty indicators. Regular patterns are observed (with only two exceptions out of 4x6x3 or 72 cases) and all relationships are statistically significant (at p<.05 level). One can therefore conclude to a good level of convergent validity. It should be simply observed that the links are much stronger for attitudinal measures than for behavioral ones. This is often the case when comparing attitudinal data to behavioral data, specially when attitudinal variables are measured within the same questionnaire while behavioral data come from a disjoint source.


In this research, we have advocated the adoption of a situation-based approach in the analysis of brand loyalty. To illustrate how such an approach could be implemented in practice, we have developed and tested a simple but operational research instrument based on three purchase and consumption situations in the context of prepackaged food items. The scalability properties of this instrument have been established over six product categories and two separate samples of about 700 respondents each. The relationships between our scale and more conventional attitudinal and behavioral indicators of brand loyalty have been systematically explored and have resulted in a high degree of convergent validity. Although empirically consistent with previous brand loyalty measures, we however believe that a situational perspective offers more diagnostic power than either the attitudinal or behavioral approaches used in the past. In particular, we have shown repeatedly how brand loyalty situational profiles could shed new light on various levels and types of loyalty as well as lead to significant managerial implications.

As already mentioned, the specific instrument developed in this research primarily served an illustrative purpose. In no way do we suggest that this 3-situation scale should be used without adaptation for other product categories and research contexts. But we do believe that the general approach is transferable to many other environments. The procedure we recommen is in three steps. One first has to establish an appropriate list of situations. This can be done in a variety of ways and should benefit from inputs from past experience and intuition but also qualitative research, whether based on in-depth interviews, usage diaries, focus groups or more anthropological or phenomenological investigations (Sherry, 1987, McCracken, 1993, Olsen, 1995). Depending upon the objective of the investigation, one could decide to limit oneself to usage situations or, on the contrary, include both usage and purchase contexts. Similarly, situations may be elaborated from the consumer or the researcher perspective. Furthermore, when hypotheses are available about the more or less compelling nature of the selected situations (corresponding for example to different reasons for buying the product or the brand), explicit hypotheses regarding the rank-order of the situations could be incorporated in the scale. Finally, one may also decide to explore variability within rather than across situations.

The second step consists in checking the reliability and scalability of the set of selected situations through pre-testing and psychometric analysis. When an adequate inventory of situations has been obtained, we would recommend to use multiple methods for assessing scalability (from Guttman scaling to Correspondence Analysis). Finally, convergent validity should be checked with both attitudinal and behavioral indicators before the newly created scale can be used routinely.

Of course, the present study is not without limitations. First, the nature of the specific situations, product categories, and consumer samples may limit the external validity of our findings. Next, the use of hypothetical situational scenarios may preclude researchers from determining whether individuals would make exactly the same decisions in real situations. Even though it would be quite cumbersome to implement in practice, an alternative approach would consist in querying informants about actual purchases whenever they occur. Finally, we have limited ourselves to a rather narrow set of convergent validity checks.

There are many ways in which the present research could be extended. First and still considering the same product categories, one may wish to enrich the repertoire of situations under investigation. In this research, we have limited ourselves to the first three of the five dimensions suggested by Belk (1975): The physical context, the social environment, and the time perspective. While each of these could certainly lead to many alternative formulations, the other two (i.e. the role context and the antecedent psychological states) are also worth exploring. For example, one could investigate the impact of gift-giving versus self-purchase contexts upon brand loyalty patterns. Similarly, the potential influence of moods on brand loyalty would be a legitimate territory to charter. Whether the situations selected for the analysis should be product-specific or on the contrary rather generic should ultimately depend upon the primarily objective of the researcher. While the former may be more adequate in a managerial context, for example when conducting a systematic brand audit, the latter may be more appropriate when one is more interested in comparative research. Beyond situations, another obvious extension of the present study would involve the investigation of other product categories, within or outside the context of fast moving consumer goods. Finally, our study being limited to one consumer panel maintained in one particular country, a cross-cultural validation of the approach developed here should be established. We hope that our first exploratory efforts will stimulate other researchers to contribute their skills to a better understanding of brand loyalty, an old concept on which much remains to be done.


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