وفاداری رفتاری به برند و ارتباطات برند مصرف کننده
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2031||2013||6 صفحه PDF||سفارش دهید||5220 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Business Research, Volume 66, Issue 1, January 2013, Pages 67–72
Brand associations are a core part of Consumer Based Brand Equity (CBBE), and behavioral brand loyalty is a desirable outcome of CBBE. In this research, data from purchase panel and consumer surveys merge to reveal the relationship between a consumer's past behavioral loyalty and their current propensity to give brand associations. The results show a positive relationship, where those with a higher buying frequency and a higher share of category requirements are more likely to give brand associations. The findings also show that share of category requirements is a greater driver of brand association responses than buying frequency. This finding suggests that the use of competitors has a greater dampening effect on brand associations than the reinforcement effect of repeated brand buying. These results have important implications for modeling brand associations, particularly using cross-sectional data.
Keller, 1993 and Keller, 2003 conceptualizes Consumer Based Brand Equity (CBBE) as the aspects of customers' brand knowledge that create a differential effect in behavior towards the brand. One of the key objectives of marketing research is to determine how CBBE influences customers' future brand buying behavior. To this end, there has been considerable effort to conceptualize and measure the different facets of CBBE across a wide range of contexts (e.g., Hsieh, 2004, Keller, 1993 and Leone et al., 2006). However, very limited evidence concerning how CBBE relates to changes in customer buying behavior exists. A potential factor holding back discoveries in this area is that CBBE models rarely include past buying behavior. The focus of this paper is on behavioral brand loyalty, which is the relative weight or frequency of customer purchases (Ehrenberg, 2000). Behavioral brand loyalty combines with penetration, which is how many people buy the brand within a timeframe to make up market share. Penetration is a potential antecedent of CBBE (Keller, 2003). Penetration is a binary variable, representing the instance of brand purchasing in the timeframe or not. Therefore, penetration does not vary amongst a brand's customers. All customers, as defined by penetration, have bought the brand at least once. However, consumers display considerable heterogeneity in their behavioral loyalty, with different customers having different weights of purchase of the brand over a particular timeframe. The widespread fit of the NBD-Dirichlet Model (Goodhardt, Ehrenberg, & Chatfield, 1984) shows that this heterogeneity in brand loyalty across consumers is a normal characteristic of customer bases of brands in packaged goods markets. One can find an appropriate analogy in horseracing. Horses in the same race have been racing for differing periods, with varying levels of success, so they do not all start with the same potential to win. Thus betters look to the horse's prior form, in an attempt to improve the accuracy of their wagers for the next race. Consumers of a brand have similarly heterogeneous past experiences with the brand and its competitors (Rust, Lemon, & Zeithaml, 2004). This heterogeneity implies that when assessing brand equity, a variety of customers, each with potentially different brand equity baselines, are likely to exist. Understanding the nature and drivers of the variation in baselines will improve accuracy in measuring any change in CBBE over time. Such knowledge also provides insight into customers' potential for change, through highlighting segments with more/less room to move in CBBE. This information can be valuable when targeting marketing activities to build brand equity. Therefore, like the aforementioned horses, at any single point in time, not all customers in a brand's customer base start with the same potential, which, in turn, leads to the question about how the differential loyalty levels may affect current CBBE. CBBE is multifaceted, including dimensions such as brand awareness and brand image (Keller, 2003). CBBE can also encompass attitudes toward a brand, brand personality traits, and perceived quality ratings (Aaker, 1996, Aaker, 1997 and Buil et al., 2008). However, the key component of CBBE is the associations that customers hold about the brand in memory. These associations are the concepts that have links to the brand name in consumer memory (Keller & Lehmann, 2006). Examples of associations include representations of purchase and consumption situations, functional qualities or provided benefits (Holden & Lutz, 1992). Stronger behavioral loyalty is a desirable differential effect of CBBE. The basic premise is that if CBBE shifts, then so should loyalty (Kaynak et al., 2007 and Leone et al., 2006). However, much of the past research takes a cross-sectional approach, where the researchers use claimed behavioral loyalty and measure the construct at the same time as the CBBE (e.g. Brakus, Schmitt, & Zarantonello, 2009). This approach raises serious issues about the direction of causality, particularly if a relationship between past behavioral loyalty and current brand associations exists. Broyles, Schumann, and Leingpibul (2009) model brand loyalty as an antecedent of CBBE and find a weak/insignificant relationship between stated behavioral loyalty and imagery. However, the research has two limitations. The first limitation is that the study collects both measures at the same time. The second limitation lies in the antecedent brand loyalty measure, which takes form of verbalized past behavior in comparison to other brands. This study overcomes the limitations of this research. The aim of this paper is to understand the degree to which a customer's past behavioral brand loyalty is an antecedent of their current brand associations. The paper takes a unique approach by merging two data sources from the same consumers: scanner buying data collected over one year, and survey data collected at the end of the year. This approach provides a clear direction of behavior-to-brand associations, and reduces the inaccuracy of using claimed behavior to assess brand loyalty and the common method bias inherent in cross-sectional studies (Podsakoff, MacKenzie, Jeong-Yeon, & Podsakoff, 2003).
نتیجه گیری انگلیسی
This paper examines whether two behavioral brand loyalty metrics, buying frequency and SCR, are antecedents to the current brand associations held by customers. A major strength of this research is that while the data is from the same people, the sources vary. Buying behavior collected from scanner data fuses with brand associations collected via an online survey of the same individuals. The findings suggest a positive relationship between past behavioral loyalty and the current propensity to give brand associations. This relationship is evident for the two behavioral loyalty metrics of buying frequency and SCR. For example, customers who had previously bought the brand five or more times have a higher propensity to give brand associations than customers who had only bought the brand once. This finding is in line with the theory of the strong memory reinforcement effects of using a brand (e.g., Kempf and Smith, 1998 and Singh et al., 2000). Furthermore, those customers who bought the brand for 51–99% of their SCR have a higher propensity to give brand associations than did those customers with an SCR of 1–20%. This finding supports the theory of the interference of brand associations when more competitor links are present (Meyers-Levy, 1989). The exception to this is the 100% loyals, where evidence shows a levelling off of brand associations. Results of this research offer a number of theoretical and managerial implications. First, a substantive difference exists in the response level of brand associations of those with low behavioral loyalty compared to those with high behavioral loyalty. This difference creates a problem for researchers in trying to determine cause and effect when modeling CBBE in cross-sectional data. Those with high behavioral loyalty will already have a higher propensity to give brand associations than other segments. Therefore, they are expected to have higher response levels to brand associations. To overcome this problem, marketing researchers need to include the influence of past behavior, and particularly SCR, in the modeling, and draw upon a dependent variable from a different data source or collect data at a later point in time to capture future buying behavior. Importantly, the results also shed some light on the potential of different segments to increase in brand associations. More room to move exists for those who just buy the brand once, and have weaker links in memory, than for those who buy the brand five times and have stronger links in memory. This result suggests that marketing activities such as advertising, which is an indirect influence on consumer memory, might achieve greater ROI in terms of shifts in brand associations if targeted at light and non-users than at heavy users. This result might also suggest that the expected effects of marketing activities could vary across segments, as a reflection of this differing initial propensity. Light buyers may react more to advertising exposure than heavy buyers because of lower initial starting points. Since recent research shows that light buyers tend to be light television viewers (Taylor, 2010) and light viewers are more responsive to advertising, accounting for share of voice (Roberts, 1999), the assumption about light buyers being more receptive to advertising is plausible. A final insight from this paper is that of the two loyalty behaviors tested, past SCR and buying frequency, past SCR has the stronger relationship with current brand associations. This finding is important, as it suggests that models incorporating brand associations need to include the strength of competitors in memory as well as the strength of the brand itself. This finding highlights the importance of understanding market structures, and identifying key competitors. The most obvious future research generated from this study would be to model the effects of CBBE taking into account past behavioral loyalty, in particular SCR. Controlling for SCR may facilitate detecting the relationship between the dimensions of CBBE and future buying behavior. While obtaining two sources of data is obviously more difficult and costly, such practice seems to be a better investment for future research to measure the predictive power of CBBE. A limitation of this research is that the study only examines one aspect of CBBE. Extensions into whether past behavioral loyalty influences other dimensions such as awareness, salience and attitude should follow. Additionally, other types of brand associations exist where replication would be beneficial. An example is brand associations that link to the company (for example see Brown & Dacin, 1997). These associations are less rooted in past brand usage, and as such, may be less subject to loyalty influences. Future research could also investigate the relationship between behavioral loyalty and negative brand equity, which would need to include former brand usage as well (Winchester and Romaniuk, 2003 and Winchester and Romaniuk, 2008). Extensions into other packaged goods categories would also be useful, as well as other types of markets. An interesting question is whether behavioral loyalty has the same impact in service and durable markets, where different purchase patterns exist for brands and for competitors.