نقشه های مفهومی پیشرفته نام تجاری (برند) : رویکرد جدید برای ارزیابی موافقت شبکه های تشکل نام تجاری
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|1985||2012||10 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Research in Marketing, Volume 29, Issue 3, September 2012, Pages 265–274
John, Loken, Kim, and Monga (2006) have introduced brand concept maps (BCM) as a powerful approach to measuring brand image according to the structure of the underlying brand association networks and reveal the strength and uniqueness of brand associations. Interestingly, BCM, as well as other consumer mapping techniques, do not incorporate explicit measures for the favorability of brand associations. This study extends the original BCM approach with explicit information on the favorability of single brand associations and, further, develops a new metric, brand association network value (BANV), which quantifies overall network favorability. Our advanced BCM approach and the new BANV metric are managerially relevant in that they allow for comparison of the favorability of networks at both individual brand association and aggregate network levels. We illustrate the relevance of our BANV metric within an empirical application and demonstrate its validity.
Brand image constitutes an important element of customer-based brand equity (Keller, 1993). Understanding brand image demands the identification of a network of strong, unique, and favorable brand associations because consumers store brand information in the form of associative networks (Anderson, 1983, John et al., 2006 and Keller, 1993). Brand association networks identify, for instance, which associations are directly or indirectly linked to the brand and how these brand associations are connected to one another. Association networks also indicate the brand's value to consumers and suggest ways to leverage its equity in the marketplace (Aaker, 1996 and John et al., 2006). Two categories of techniques are specifically designed to measure brand association networks: consumer mapping techniques and analytical techniques (John et al., 2006). The former, including brand concept maps (BCM) and Zaltman's metaphor elicitation technique (ZMET), elicit individual brand association networks directly from consumers ( John et al., 2006 and Zaltman and Coulter, 1995). That is, respondents reveal how their brand associations relate to the brand and others by constructing their own network of associations. With these individual maps, researchers can aggregate the information to produce a consensus brand association network. Analytical techniques instead uncover brand associations through consumer surveys (e.g., repertory grids) and employ analytical methods to reveal the underlying consensus brand association network (Henderson, Iacobucci, & Calder, 1998). Among consumer mapping approaches, the BCM method is particularly promising (John et al., 2006). Unlike analytical techniques, such as network analysis (Joiner, 1998 and Lynch and Srull, 1982), the BCM technique allows for the analysis of brand association networks at both individual and aggregate levels because brand maps emerge for each respondent. In contrast to other mapping techniques, such as ZMET, BCM also gathers consumer perceptions using structured association elicitation, mapping, and aggregation procedures, which result in an easy-to-administer, less costly, and less subjective approach (John et al., 2006). John et al. (2006) also offer empirical evidence of the high reliability and validity of the BCM approach. Because brand image is defined by the strength, uniqueness, and favorability of brand associations, organized in a network (Keller, 1993), it is surprising that BCM and other techniques do not incorporate explicit measures of the favorability of brand associations. That is, the BCM technique identifies relevant brand associations, groups them in a network, and offers information on the uniqueness of the associations (e.g., in terms of the multitude of brand-specific associations in a brand map thereby assuming that additional associations in the brand map increase the probability that associations are unique in comparison with competitors) and their strength (e.g., in terms of the degree to which associations are directly linked to the brand node or the strength of the associations´ linkage to the brand node (e.g., weak, moderate, or strong)) but does not provide explicit favorability information. Prior research reveals that the favorability of brand associations, as evaluated by consumers, varies substantially, particularly with regard to (1) individual evaluative judgments (i.e., how favorable each association is pronounced to be for the specific brand) and (2) their importance to the overall purchase decision (Fishbein and Ajzen, 1975, Keller, 1993 and Wilkie and Pessemier, 1973). Therefore, we extend the original BCM approach by integrating explicit information on the favorability of brand associations within brand association networks (i.e., advanced BCM). Specifically, we include the original information about uniqueness and strength but also integrate explicit favorability information regarding (1) evaluative judgments of each brand association (Keller, 1993), as well as (2) the individual importance of each brand association to a consumer's purchase situation. This paper demonstrates that the added information has valuable management implications in that it makes the resulting networks more meaningful. Because the original BCM technique and other mapping and analytical techniques do not account for explicit measures for the favorability of brand associations, they do not provide information regarding the favorability of the underlying association network.3 Therefore, we introduce a new metric, the brand association network value (BANV) metric, which, for the first time, quantifies the overall favorability of brand association networks by combining network structure (i.e., the uniqueness and strength of brand associations) and the favorability of single brand associations (i.e., the evaluative judgment and the importance to the specific purchase decision) into a single measure. The new metric enhances the usefulness of the BCM methodology for comparisons of the network favorability at individual and aggregate network levels. In the next section, we briefly outline the original BCM approach and our extended advanced BCM approach and derive our new BANV metric. We then describe the research design for our empirical application and demonstrate the validity of our new metric. In the final section, we discuss how the advanced BCM technique and the BANV approach contribute to brand image measurement and derive further research applications and limitations.
نتیجه گیری انگلیسی
Existing methodologies measure brand image using brand association networks and have thus uncovered the structure of these networks and provided information about the strength and uniqueness of brand associations. However, existing methodologies, such as the BCM technique, do not reveal explicit measures regarding the favorability of brand associations. This study extends the original BCM approach with explicit information about favorability, that is, an association's evaluative judgment and individual importance within a purchase situation. We provide initial evidence that the added information (i.e., advanced BCM) can offer valuable management implications in that it renders the resulting networks more meaningful. We further propose a new metric, brand association network value (BANV), which quantifies the overall favorability of brand association networks using information from the original BCM approach and includes the additional information on the favorability of brand associations, which is collected with Likert scales. Our metric builds on a widely accepted multi-attribute attitude model (Wilkie & Pessemier, 1973) and well-established concepts and theories. With our empirical application, we demonstrate the managerial relevance of BANV and its plausibility in terms of face, nomological, and predictive validity. Overall, our new metric satisfactorily quantifies the overall favorability of consumers’ brand association networks. Unlike the original BCM approach, the BANV metric can differentiate “good” brand association networks from “bad” ones because it offers a standardized approach for quantifying a network's overall favorability. The metric also substantially enhances the applicability of the original BCM approach because it can quantitatively compare networks for (groups of) subjects or over time. For example, with the BANV metric, managers can analyze whether specific marketing activities (e.g., brand enrichment, marketing communication, celebrity endorsements, and sponsorships; Keller, 2008) enhance consumers' brand association networks. Such implications are not available with the original BCM approach, which provides information on the effects on the network structure without combining the information with additional favorability information regarding single brand associations into an overall evaluative judgment of the favorability of the corresponding marketing activity. Furthermore, BANV not only quantifies the differences in the overall favorability of brand association networks (as simple favorability ratings of the overall strategies might do) but also provides detailed explanations for the differences between the two brand association networks by quantifying the favorability of underlying single brand associations. This step provides valuable implications for managers regarding how to influence consumers’ brand perceptions in terms of specific associations. For example, suppose that a longitudinal study reveals an unfavorable alteration in the overall BANV of brand X, although the overall brand association structure (e.g., first-order associations, core associations, and brand association links) remains unchanged. Information about evaluations of brand associations (i.e., Eaj) can enable managers to identify which associations deteriorated and caused the unfavorable alteration of the BANV over time. In turn, managers can address those associations promptly with marketing communication activities. Furthermore, information about the importance of brand associations for the overall product evaluation (i.e., Iaj) reveals which association managers should address through marketing communication if many associations have worsened. The important brand associations can then be addressed primarily through an appropriate allocation of marketing communication resources and ensure a greater impact on consumers’ product evaluations, especially in purchase situations. Moreover, our new metric is easy to implement and requires limited additional information (i.e., for Eaj and Iaj) in comparison with the original BCM approach. Therefore, the additional time effort demanded of consumers is minimal, and the required number of respondents remains unchanged. Although our empirical application provides initial evidence that BANV effectively quantifies the favorability of consumers' brand association networks, several limitations remain that should be addressed by future research. First, we focused our empirical study on merely two brands. Second, the predetermined association set, as well as the associations' importance information within our advanced BCM procedure, is product category-specific. Thus, the new BANV-metric is primarily applicable to quantitative comparisons of networks regarding one specific brand (e.g., networks of different consumer segments of Volkswagen) and brands of one specific product category (e.g., networks of different car manufacturers). Furthermore, importance weights for single subjects might be situationally dependent (e.g., single consumers might deem an association to be important for the short-term, whereas they generally would not deem the association to be relevant at all). Therefore, comparisons of BANV values across subjects might suffer from situational dependence bias. One possible way for overcoming this bias is to use an appropriate method of selecting respondents. For instance, if Volkswagen decision makers wonder whether they should target the consumer segment with low involvement or that with high involvement with prospective advertising campaigns (see Section 4.2), the sample should only contain, for instance, consumers from both segments with a substantial interest in purchasing a new car. Furthermore, researchers might test for response styles across subjects, which might be a further source of situational dependence bias (Adler, 1983). Moreover, if brand managers, for instance, are interested in comparing BANV values for two brands, they might use a within-subject design (i.e., each respondent evaluates both brands) to assure that importance weights for both brands are evaluated for the same situation. Third, we assume that all types of brand associations, including brand-specific attributes and product category associations, are equally weighted. However, product category associations might be shared by the brand's competitors, whereas brand-specific attributes explicitly differentiate the brand from its competitors in an attempt to generate a sustainable competitive advantage (Fitzsimons et al., 2008, Keller, 1993 and Ries and Trout, 1979). Therefore, further research should consider different (i.e., higher) weights for brand-specific attributes than for product category associations to take these issues into account. Finally, we based our new metric on the BCM approach because it offers the capability of analyzing brand association networks at an individual disaggregated level. However, because the BANV metric has the aim of quantifying the overall favorability of single brand association networks, it is not limited to the BCM approach and might be extended to other network approaches, such as analytical mapping techniques (e.g., consensus networks within the network analysis). Such analyses provide structural network indices, such as network density, and support comparisons of different networks with respect to consumers’ brand associative structures (e.g., number of existing association linkages), but they do not reveal information about the overall favorability of the underlying association networks (Henderson et al., 1998 and Henderson et al., 2002). Additional research should determine if the BANV metric might offer a meaningful extension to these approaches as well.