Brand image and brand loyalty: do they show the same deviations from a common underlying pattern?

Transcription

1 Brand image and brand loyalty: do they show the same deviations from a common underlying pattern? This is the peer reviewed author accepted manuscript (post print) version of a published work that appeared in final form in: Stocchi, Lara, Driesener, Carl & Nenycz-Thiel, Magda 2015 'Brand image and brand loyalty: do they show the same deviations from a common underlying pattern?' Journal of Consumer Behaviour, vol. 14, no. 5, pp This un-copyedited output may not exactly replicate the final published authoritative version for which the publisher owns copyright. It is not the copy of record. This output may be used for noncommercial purposes. The final definitive published version (version of record) is available at: Persistent link to the Research Outputs Repository record: General Rights: Copyright and moral rights for the publications made accessible in the Research Outputs Repository are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights. Users may download and print one copy for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the persistent link identifying the publication in the Research Outputs Repository If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

2 The Library Educating Professionals, Creating and Applying Knowledge, Engaging our Communities This is the peer reviewed version of the following article: Stocchi, Lara, Driesener, Carl and Nenycz Thiel, Magda (2015) Brand image and brand loyalty: do they show the same deviations from acommon underlying pattern?, Journal of Consumer Behaviour, Article in Press which has been published in final form at This article may be used for non commercial purposes in accordance with Wiley Terms and Conditions for self archiving.

3 Brand image and brand loyalty: do they show the same deviations from a common underlying pattern? Abstract The aim of the paper is to investigate whether deviations from the Double Jeopardy pattern observed in brand buying (panel) data (Ehrenberg, 1972), such as niche brands (brands with few users, but high levels of brand loyalty) and change-of-pace brands (brands with many users, but low levels of brand loyalty), correspond with deviations from the same Double Jeopardy pattern in brand image data (i.e. brand image associations gathered through consumer surveys). The analysis of a unique data set in three categories, including brand purchases and brand image associations from the same consumers and for the same brands, shows that such deviations in brand buying data and in brand image data seldom correspond. Moreover, deviations from the Double Jeopardy pattern in brand image data are rare and imply the existence of an underlying impact of a brand s associative rate (i.e. the average number of associations a brand obtains in a brand image consumer survey) on brand loyalty (measured as the frequency of brand buying). These findings provide contribution to buyer behaviour theory and have some practical implications for branding strategies. For instance, to obtain any impact on brand loyalty marketers may need to develop branding strategies aimed at increasing a brand s associative rate, whilst maintaining or enlarging its level of market penetration. Key words: Brand Image Associations, Brand Usage, Double Jeopardy, and Purchase Frequency. 1

4 Brand image and brand loyalty: do they show the same deviations from a common underlying pattern? In branding research, the existence of a positive relationship between brand image (defined by Keller in 1993 as the lasting perceptions of brands held in memory by consumers) and brand usage is one of the earliest empirical patterns documented (see the work of Bird and Ehrenberg; and Bird, Channon and Ehrenberg, 1970). Importantly, this relationship (commonly referred to as brand image and usage relationship) has been used to justify a positive impact of brand image on brand loyalty (expressed as frequency of brand buying), with numerous implications for branding strategies (Low and Lamb, 2000; Dillon, Maddern, Kirmani & Mukherjee, 2001; Myers, 2003; Romaniuk & Sharp, 2004). Additional empirical evidence in support to the importance of the brand image and brand usage relationship can be found in later studies, such as Barwise and Ehrenberg (1985), Castleberry and Ehrenberg (1990), Barnard and Ehrenberg (1990), and Dall Olmo-Riley et al. (1997). In particular, these later studies analysed the effects on the brand image and brand usage relationship in the context of consumer surveys measuring brand image for brands differing in their level of market share. It was found that brands with a greater market share obtained far greater levels of brand image associations than brands with a small market share. This mechanism, commonly referred to as the Double Jeopardy pattern (McPhee, 1963), was documented in the analysis of brand buying behaviour through consumer panel data, whereby brands with low market share (relatively to competitors) typically have fewer users and slightly lower levels of brand loyalty (e.g. lower levels of brand purchase frequency) (Ehrenberg, 1972). Importantly, in analyses of brand buying, the Double Jeopardy pattern has been used as a benchmark in understanding unexpected patterns in buying behaviour. For instance, it has been used to detect niche brands (brands with few users and high levels of brand loyalty) and change-of-pace brands (brands with many users and low levels of brand 2

5 loyalty) as exceptions (deviations) to the Double Jeopardy that are imputable to brand characteristics, such as advantage or disadvantage in physical (Fader and Schmittlein, 1993) and mental availability (Bhattacharya, 1997). The viewpoint offered by the present research is that the existence of a common underlying pattern (Double Jeopardy) offers the tantalising possibility of a benchmark against which to assess the consistency between brand image and brand buying behaviour. More specifically, we propose to draw on the Double Jeopardy pattern to determine whether brand image consumer survey data and brand buying (panel) data display matching deviations from the Double Jeopardy pattern. For example, we propose to use this pattern to determine whether a brand that classifies as being niche in brand buying data (a brand with few users and high levels of loyalty) will also result in high levels of brand image (e.g. a high rate of brand image associations in consumer survey data). The opportunity for this analysis is offered by the availability of a unique data set comprising brand image associations and brand purchases from the same consumers, gathered over the same time period. Therefore, we analyse both brand image associations and brand purchases in an identical manner in order to detect and crosscheck deviations from the Double Jeopardy patterns. As such, this study: (i) pioneers the explicit examination of the deviations from the Double Jeopardy pattern in the context of brand image associations at the individual brand level (an aspect that was not examined in existing research); and (ii) contributes to examining the link between brand image associations and brand loyalty, which, to date, has generally provided inconclusive results (Christodoulides and de Chernatony, 2010). It also provides practical insights, given that marketers often assume that branding and positioning strategies, leveraging brand image, are likely to affect market share by influencing brand loyalty. This research, however, shows that brand managers are likely to over-estimate their ability to affect brand loyalty through brand image related activities as we show that unusual 3

6 brand loyalty (i.e. obtaining unexpectedly high or low values of brand loyalty given the brand s market share) are only seldom linked with matching deviations in brand image. Consequently, it is likely that the return on marketing investments with such aims may be lower than, for instance, the returns provided by activities aimed at increasing brand loyalty through enlarging the customer base (Sharp et al. 2012). The remainder of the paper proceeds as follows. First, we discuss in greater detail the conceptual and empirical basis of this work. Second, we outline our proposed methodology for examining the links between brand image and brand loyalty. Lastly, we outline and discuss the outcome of the empirical analysis, and clarify the implications of our findings. Background Brand image Brand image has been defined by Keller (1993) as the set of information consumers associate with a brand in their memory, commonly referred to as brand image associations. Importantly, this definition of brand image builds on the Associative Network Theory (ANT) of human memory by Anderson and Bower (1973). According to this theory, memory is conceptualised as a network of interlinked concepts (e.g. the brand itself and all attributes consumers might associate with it), whereby information processing (i.e. thinking of a specific concept) typically occurs as a result of network activation by a relevant cue such as a physiological need that requires fulfilment. For example, an individual may be needing energy which brings to mind a range of options to satisfy that need that are linked to food items that can provide an energy boost such as a Snickers. In the light of Keller s (1993) widely used conceptualisation of brand image, it is assumed that the processing of brand information in memory and, more importantly, the chance that a brand will be thought of by the consumer, is a function of the number of brand 4

7 image associations activated in memory (i.e. the overall level of activation within the network) (Collins and Loftus, 1975, Reder, 1988, Anderson et al., 1996). More specifically, it is assumed that the size of the network (how many brand image associations are held in memory by consumers) and the frequency of past activation or strength of the network (the number of times the brand image associations held in memory by consumers have been previously activated) determine a brand s retrieval propensity, i.e. its chances to be thought of in buying situations (Romaniuk and Sharp, 2004). For instance we could easily imagine that Snickers, from our earlier example, is well supported with marketing activities, has a large market share and has many strong brand image associations for many consumers. In comparison, we could easily imagine that Bounty (a much smaller brand of chocolate) is not so well supported with marketing activities, has a much smaller market share and has fewer, weaker associations in the minds of many consumers. Consequently, Snickers would have a greater chance of being thought of in a buying situation than Bounty, especially if the Snickers network of associations had recently been activated. In the light of the characteristics of brand image discussed so far, previous research has identified brand image as a lasting and an important determinant of crucial aspects of brand buying behaviour, such as brand loyalty (e.g. the frequency of purchase) (Aaker, 1991; Keller, 1993; Low and Lamb, 2000). Nonetheless to date, research examining the link between brand image and brand buying behaviour has produced inconclusive results (Christodoulides and de Chernatony, 2010). Arguably, this may be caused by the scarcity of studies that explicitly link the analysis of brand image to the analysis of brand buying behaviour, drawing on the existence of underlying similarities, such as common patterns. One such instance of commonalities between brand image and brand buying is represented by the Double Jeopardy pattern, which is documented to characterise brand image associations 5

8 gathered through consumer surveys, as well as brand purchases gathered through consumer panel data. A common underlying pattern: Double Jeopardy Early research on brand image data (Bird and Ehrenberg, 1970, Bird et al., 1970) demonstrated the existence of a simple and systematic pattern binding brand image and brand usage. More specifically, this early research highlighted a positive relationship between brand image and brand usage (known as the brand image and usage effect). Later, research by Barwise and Ehrenberg (1985) and Dall Olmo Riley et al. (1997) added to this empirical evidence by showing that smaller brands (i.e. brand with low market share and fewer users) usually have fewer brand image associations per user than larger brands (i.e. brands with high market share and more users), thus confirming the existence of the Double Jeopardy effect (McPhee, 1963) in brand image data. The Double Jeopardy pattern was, in origin, typical of brand buying behaviour, whereby it is expected that brands with lower market share have fewer users with lower loyalty (i.e. they repeat purchase less frequently) than larger brands that have more users who exhibit higher loyalty, (i.e. they are purchased more frequency) (Ehrenberg, 1972). In the analysis of brand buying, the Double Jeopardy pattern has been used to assess the effectiveness of niche positioning or price premium strategies (Kahn et al., 1988, Fader and Schmittlein, 1993, Bhattacharya, 1997). In particular, this pattern has been utilised to detect exceptions (deviations) from the Double Jeopardy pattern in brand buying, in the attempt to draw a link between brand buying behaviour and branding and positioning strategies. Importantly, existing research pertaining to the analysis of the Double Jeopardy pattern in brand buying (panel) data, has led to the identification of four typical deviations from this pattern, which are: (i) niche brands, i.e. brands that have a small base of highly loyal 6

9 customers, showing an unusually high frequency of purchase or loyalty compared to penetration (Kahn et al., 1988); (ii) change-of-pace brands, i.e. brands that are purchased by many consumers infrequently, displaying lower levels of brand loyalty (Kahn et al., 1988); (iii) private label brands, i.e. brands whose limited distribution can lead to unusually high loyalty patterns (Scriven and Bound, 2004); and lastly (iv) market leading brands, i.e. brands that show excess behavioural loyalty (Ehrenberg, Goodhart and Barwise 1990; Fader and Schmittlein, 1993). To the best of the authors knowledge, the existence of these deviations from the Double Jeopardy pattern in brand image data has never been examined. In brand image research pertaining to the analysis of brand image associations gathered through consumer surveys, the Double Jeopardy pattern has been used primarily as a benchmark to distinguish between different types of brand attributes. For example, Hoek et al. (2000) used the Double Jeopardy pattern to identify brand image attributes that deviate from this pattern (to detect evaluative or descriptive brand image attributes). No existing study has undertaken the logical step of drawing on the existence of the Double Jeopardy pattern in brand image data to examine the link between brand buying behaviour (specifically brand loyalty or the frequency of brand purchasing) and brand image. This absence of studies is somewhat surprising considering the link between brand image and brand loyalty theorised by brand equity researchers (Aaker, 1991; Keller, 1993; Low and Lamb, 2000). The viewpoint offered by the present research is that the existence of a common underlying pattern between brand image and brand buying behaviour provides scope to examine the extent to which deviations from the Double Jeopardy pattern in purchases correspond with deviations in brand image. This insight can then be used to draw a series of conclusions in relation to the link between brand image and brand loyalty, e.g. to determine the extent to which niche positioning strategies can effectively correspond to a niche brands. 7

10 The availability of three sets of data that include brand image associations and brand purchases collected from the same consumers (for the same brands), gathered over the same time period provides a unique opportunity to explore the aspects illustrated above. Thus, the key aim of the present research is to address the following key research questions: RQ1: Are there brands that deviate from the Double Jeopardy pattern in brand image consumer survey data? And if so: RQ2: Do deviations from the Double Jeopardy pattern in brand image data match deviations from the same pattern in brand buying data? We now explain the analytical approach used to pursue these objectives. Methods Data description The data used in this research are panel data for three packaged goods categories in the UK including the count of brand purchases and brand image associations for the same brands, by the same consumers over a 12-month period. The product categories analysed are all hot beverages frequently consumed by the UK population and include national and private label brands. The purchase data are scanner data from a chain s loyalty card panel. All panellists are category users that had opted in to participate in online surveys. The brand image associations used are typically used in market research in the relevant categories and were collected via a consumer online survey at the end of the 12-month period. Therefore, the data set effectively offered directly comparable purchases and brand image associations. Furthermore, although not specifically captured at the exact same point in time, in line with Keller s (1993) definition of brand image associations, it is plausible to assume that the 8

11 associations gathered were: (i) a proxy for the lasting brand perceptions held by the sample; and thus (ii) likely to be drawn upon in the context of the observed brand purchases. The brand image associations comprised in the data were collected using the freechoice pick-any approach, whereby respondents are prompted with a list of brands and a range of brand attributes and asked to determine whether they associated any of the brands with those attributes (Driesener and Romaniuk, 2006). Sample sizes for the three categories ranged from n = 6,532 to n= 8,219. All data were analysed at the individual brand level and at the aggregate (category) market level to generate the following metrics: From the brand buying data: market share, purchase penetration and purchase frequency (our measure of brand loyalty), which we calculated for each individual brand; and purchase penetration and purchase frequency for the whole product category (as we explain later on, category level metrics are required in order to detect deviations from the Double Jeopardy pattern). From brand image data: mental market share, associative penetration and associative rate, which similarly to the above metrics we calculated for each individual brand; and associative penetration and associative frequency for the whole product category (again, as we explain shortly, category level metrics are needed to detect deviations from the Double Jeopardy pattern). The next sections provide a detailed description of how each metric was calculated and, importantly, how we examined these metrics to detect deviations from the Double Jeopardy pattern. 9

12 Detecting deviations from the Double Jeopardy pattern in brand buying data For each product category considered, we analysed the count of brand and overall category purchases made by each individual consumer observable in the panel data and calculated the relevant metrics listed above and as follows (Ehrenberg, 1972): Market share was calculated as the proportion of a brand s purchases, out of the total purchases made in the category by the sample. Purchase penetration was calculated as the proportion of the sample making at least one brand purchase in the time period considered (i.e. the proportion of brand buyers). This metric was calculated for both the whole product category, as well as each brand within the category (i.e. the proportion of category buyers). Purchase frequency was calculated as the average number of purchases made by brand buyers and treated as an indicator of the level of loyalty of each brand. The same metric was also calculated for the whole product category. These metrics were then further examined as inputs to a mathematical model, the Dirichlet (Goodhardt et al., 1984). More specifically, we used the Dirichlet software developed by Kearns (2010) to generate equivalent theoretical values of these metrics. We then compared the observed and theoretical values of all metrics as a way to identify deviations from the Double Jeopardy pattern. That is, in line with previous research, this comparison was carried out by looking at the Absolute Percentage Error (APE) (Wright et al., 2002) for each brand s purchase frequency, which we calculated as follows: # Obs Th& APE = Abs% ( $ Obs ' Previous literature (Wright et al., 2002) indicates that a value of APE equal to or greater than 15% is a suitable indicator of a deviation from the Double Jeopardy pattern, i.e. an indicator 10

13 of brands showing more or less than expected levels of brand loyalty, given the observed level of brand buying metrics for the brand itself and for the whole category. Detecting deviations from the Double Jeopardy pattern in brand image data Whilst all purchases were analysed to calculate the metrics based on purchases, it is important to clarify that not all brand image associations available in the data were analysed. More specifically, only a sub-selection of brand image associations was selected for the analysis, following Romaniuk s (2013) guidelines. Importantly, the selection of attributes was carried out independently from the process of calculating the relevant brand image metrics. The final list of brand image attributes used in our analysis reflects attributed commonly used in market research for the categories examined and is provided in the Appendix. The metrics calculated following Romaniuk s (2013) guidelines and are defined as follows: Mental market share is the ratio between the total number of a brand s associations and the total number of brand image associations provided by the sample for all brands in the category. Associative penetration is the proportion of respondents providing at least one association with the brand, out of the total sample size. Similarly to purchase penetration, this metric is calculated for both the category and all brands. Associative rate is the average number of brand image associations provided by those who gave at least one association with the brand. Again, similarly to purchase frequency, this metric is calculated for both the category and all brands. Importantly these metrics mirror the behavioural metrics derived from purchases described earlier (market share, penetration and purchase frequency) in how they are calculated (Romaniuk, 2013). As with the behavioural metrics, these brand image metrics are the inputs of the Dirichlet model, and thus can be utilised to generate equivalent theoretical values 11

14 (Romaniuk 2013) that can be used to benchmark actual values against expected values. We use this approach to identify deviations from the Double Jeopardy pattern in brand image data by comparing observed and expected values of these metrics similar to the analysis of brand buying illustrated earlier, i.e. by looking at APEs in the brand s associative rate being greater than 15%. We used this approach to address the key research aims introduced before. In particular, mimicking the typical deviations from the Double Jeopardy pattern that characterise brand buying data, we anticipate that there could be two possible types of potential deviations from the Double Jeopardy pattern in brand image data: (i) brands showing a higher than expected associative rate, given their level of associative penetration (equivalent of a niche brand in brand buying data); and (ii) brands showing a lower than expected associative rate, given their level of associative penetration (equivalent of a changeof-pace brand in brand buying data). We would then define a match between deviations from the Double Jeopardy pattern in brand image data and deviations from the Double Jeopardy pattern in behavioural data as brands appearing as niche in both sets of data and brand appearing as change-of-pace in both sets of data. Finally, to gather additional insights on the link between brand image and brand loyalty, we also conducted a multiple regression analysis to assess the relative impact of mental market share, associative penetration and associative rate (independent variables) on purchase frequency (dependent variable). Results Due to the requirements of the data provider, product categories cannot be specifically identified, nor the brands named. Consequently the three categories of hot beverages have been labelled Category 1, 2 and 3 respectively, and the brands in each category are identified 12

15 as Brand A, B, C etc. (note that brand A has higher mental market share, than Brand B and so on). With regards to the identification of deviations in brand image associations, the analysis shows that there are only a few instances of brands showing large differences between the observed and theoretical associative rates. Table 1 (Category 1), 2 (Category 2) and 3 (Category 3) show for each brand the values of the associative penetration and the associative rate. *** Insert Table 1 about here. *** Insert Table 2 about here. *** Insert Table 3 about here. When comparing the observed and estimated levels of brand image associations with the Dirichlet model estimates, the identified deviations were: In Category 1 (see Table 1): Brand I, with a lower than expected associative rate. This is a brand that many consumers could provide at least one association for, but that obtained, on average, many fewer associations than expected (APE of 24%). This brand displays a change-of-pace deviation in its brand image. In Category 2 (see Table 2): there are several instances of deviations. Brands A, B and D are all showing a lower than expected associative rate, (APE of 31%, 16% and 31% respectively); again these brands display a change-of-pace deviation in their brand image. Brands E and H are both showing a higher than expected associative rate, (APE of 15% and 33% respectively), indicating they are brands not many consumers could provide an association for, yet those that could provided a relatively high number of associations. These two brands display a niche deviation in brand image. 13

16 It is worth noting that instances of deviation in brand image were relatively uncommon (six instances out of 26 brands analysed across the three categories), and in one category (Category 3) not even present. The multiple regression analysis performed on all three categories shed further light on the conditions that are likely to impact such deviations. For Category 1 (with only one deviation from the Double Jeopardy pattern) the model only approached significance (F 3, 5 = 5.243, p =.053, adjusted R 2 =.614) and there was: (i) a non-significant impact of mental market share (p >.05); (ii) a significant (p <.05) but negative impact of associative penetration (b-coefficient = ); and (iii) a significant (p <.05) and positive impact of the association rate (b-coefficient = 2.133) on purchase frequency. For Category 2 (with five deviations) the model was significant (F 3, 4 = 8.274, p <.05, adjusted R 2 =.757) and it highlighted: (i) a significant (p <.05) but negative impact of mental share (b-coefficient = ); (ii) a non-significant (p >.05) impact of the associative penetration; and (iii) a significant (p <.05) but negative impact of the associative rate (b-coefficient = -.786) on purchase frequency. For Category 3, where there were no deviations from the underlying pattern, the model was not significant. These results would suggest that only in product categories where there is an underlying impact of brand image associations on purchase frequency is it possible to detect instances of deviations from the Double Jeopardy pattern in brand image data, and that the associative rate has a greater impact on brand loyalty than the brand s associative penetration. When checking whether the deviations identified in brand image were matched by deviations in brand buying, this mechanism appears even more obvious. A close examination of Categories 1 and 2 (categories with a quasi-significant and significant interplay between brand image associations and brand loyalty) suggests that the instances of deviations in brand image data being consistent with deviations in purchase data are rather limited, with only two 14

17 consistent brand image and behavioural deviations, out of the six brand image deviations identified (see Tables 4 and 5). *** Insert Table 4 about here. *** Insert Table 5 about here. In Category 1, Brand I had a lower than expected associative rate, matched with lower than expected brand loyalty (the brand had an average purchase frequency of 2.1 cf an expected 3.3; APE of 62%). In Category 2, Brand A had a lower than expected associative rate which was matched with a deficit in loyalty (average purchase frequency of 1.5 cf an expected 3.3; APE of 55%). The remaining instances of deviations in brand image data either did not show a sufficiently large discrepancy in brand loyalty (see Brand H), or highlighted a deviation in the opposite direction. Brand D which showed a deficit in associative rate, had excess brand loyalty, while Brand E showed a higher than expected associative rate and a deficit in brand loyalty. Discussion The results of our analysis revealed that instances of brands deviating from the Double Jeopardy pattern in brand image associations are rare and rarely match deviations from the same pattern in brand loyalty (measured as the brand s purchase frequency). Moreover, deviations from the Double Jeopardy pattern in brand image data were detected only in categories where the associative rate (i.e. the average number of associations a brand obtains in a brand image survey) had a significant underlying impact on purchase frequency. We therefore show that deviations in brand image associations and purchase frequency (or brand loyalty) generally do not align. When we identified a deviation in the associative rate for a brand, it was generally not matched with a deviation in the loyalty to that 15

18 brand. This indicates that while there may be links between brand image and brand loyalty as postulated in brand equity research (Aaker, 1991; Keller, 1993; Low and Lamb, 2000), this relationship is mediated by other factors not accounted for by brand image associations. Therefore, it may not be possible to directly influence brand loyalty through the management of brand image associations by brand managers. This is of course obvious in hindsight when we consider that mental availability is only one part of the buying process, and many other factors like distribution, competitor activities and mother-in-law visits play a role. The analytical framework we utilised in this research did not explicitly account for the effect of such factors, however, the Dirichlet model implicitly accounts for their impact (Goodhardt et al., 1984; Sharp et al., 2012). Arguably, the limited matching between the metrics representative of brand image and brand purchasing may also have other explanations. For example, although the brand image associations provided in the survey are likely to be the result of past branding activities (see Keller, 1993), the metrics derived from those associations are a snapshot of the brand image associations at the moment of the survey, and thus may be subject to all sorts of biases (including memory biases, such as forgetting, interference, or lack of attention). Nonetheless, our findings have some practical implications. Our modelling showed that there was not always a link between brand loyalty and brand image. Where there was a link it was mostly driven by the associative rate, however, such links were unusual. For example we found that niche brands (those with lower penetration and high brand loyalty) do not display a corresponding pattern in the brand image associations of consumers; that is the brand did not have a higher rate of association than expected. This suggests that trying to create a situation where fewer consumers have a higher association rate (i.e. a unique position relative to the market) may (i) not be possible (or at the very least is difficult), and more importantly (ii) is not required in order to create a niche brand. What seems crucial is 16

19 ensuring that branding strategies are aimed at increasing the strength of a brand s image associations (its associative rate), whilst maintaining the current level of associative penetration in the population. These findings also stimulate some interesting conceptual considerations for brand equity theories. In particular, the traditional view of a straightforward relationship between brand image and brand loyalty, as theorised by brand equity models such as Keller s (1993) framework, seems to neglect the fact that the depth of the brand image associations (particularly the strength of the associations) may alter this relationship. Moreover, the outcome of this research suggests that the link between brand image and loyalty may be far more complex than originally postulated in the literature; yet, it can be parsimoniously examined by cross-checking exceptions from a benchmark such as the Double Jeopardy pattern in both brand image and brand purchase data. Conclusions and future research directions The present research has provided insights into the potential for brand image manipulation to affect brand loyalty, or at least brand image as is typically measured. The main finding of the empirical analysis suggests that deviations in brand image associations from the Double Jeopardy pattern are rather rare and have a limited impact on brand loyalty. We analysed three large sets of data that allowed an explicit comparison of brand image associations and brand purchases for the same consumers and the same brands over a 12-month time frame. Whilst data of this nature is rare, it is important to extend the same analysis to different markets and to multiple subsequent time periods in order to generalise our results. Replication would provide additional evidence in support of the effects identified in this research, especially in relation to differences across product categories and the persistence of deviations over multiple time periods, but also in relation to the impact of other 17

20 properties of brand image associations on brand loyalty such as isolating brand image associations for users and non-users. Finally, a key limitation to consider in future research is the relative time frames of the data. Due to the nature of the data considered (purchases and brand image associations by the same consumers), we were effectively merging two different time frames. The purchasing data is collected over 12 months and reflects the totality of all forces acting on a consumer that effect each and every recorded purchase in that time. In comparison, the brand image data is collected at the end of that period in a single survey. This is a complex issue that may not be possible to resolve in the absence of longitudinal brand image data. 18

21 Tables Table 1 Brand image associations for Category 1 Associative Pen % Associative Rate Obs Th Obs Th APEs Brand A Brand B Brand C Brand D Brand E Brand F Brand G Brand H Brand I Table 2 Brand image associations for Category 2 Associative Pen % Associative Rate Obs Th Obs Th APEs Brand A Brand B Brand C Brand D Brand E Brand F Brand G Brand H Table 3 Brand image associations for Category 3 Associative Pen % Associative Rate Obs Th Obs Th APEs Brand A Brand B Brand C Brand D Brand E Brand F Brand G Brand H Brand I

22 Table 4 Category 1 comparison between brand image associations and purchases Associative Rate Purchase frequency (Brand Loyalty) Obs Th APE Obs Th APE Matching deviations Brand A Brand B Brand C Brand D Brand E Brand F Brand G Brand H Brand I YES Table 5 Category 2 comparison between brand image associations and purchases Associative Rate Purchase frequency (Brand Loyalty) Obs Th APE Obs Th APE Matching deviations Brand A YES Brand B NO Brand C Brand D NO Brand E NO Brand F Brand G Brand H NO 20

23 Appendix List of brand attributes used in the analysis following application of Romaniuk s (2013) guidelines. Category 1 Category 2 Category 3 Would warm me up inside Calm me down After dinner When I want to indulge myself Good first thing in the morning Would taste good Natural Great aroma Helps me to wake up When I want to indulge myself When I want to relax Would taste good Natural Smooth Down to earth Wholesome Helps me to wake up To have with friends When I want to indulge myself When I want to relax Good value for money Would taste good High quality ingredients Full-bodied 21

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