Brand Halo: Understanding its Implications ... - ANZMAC

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The phenomenon has further implications as brands are compared to one another. In Figure 1, the arrow labelled C represents the process that occurs as a new brand enters a consideration set. At this stage, in a pursuit to maintain coherence in judgement, consumers who favour Qantas, or simply hold it as a status quo in their decision-making, will modify their perceptions of attributes to fit their evaluation against a competing company. In the airflight example, a consumer might shift importance to Qantas’ Flight Frequency, which had not been prioritized in the decision task until now, and/or construct negative inferences regarding JetStar’s Flight Frequency, one of the D arrows in Figure 1. Erdem, Swait and Louviere (2002) show that consumers have different price sensitivities for products with distinct brands despite their equivalent configurations, which could be a function of different consumer brand valuation itself, as well as a result of different expected utilities from specific aspects of products from different brands. This same process would be complemented by the opposite effects, with JetStar as the focal brand of analysis. Hence, beliefs regarding attributes and attribute levels of the product category would mould the attitude towards the brand JetStar. This brand attitude would feedback into the evaluation process both in regards to its own attribute beliefs, but also to the beliefs of additional brands in the consideration set, in this example Qantas. Measurement of Brand Halo The extension of halo effects to brands is a not new concept, with initial steps in the literature circa 1970s (Bass & Talarzyk, 1972; Bass & Wilkie, 1973; Beckwith & Lehmann, 1975, 1976; Huber & James, 1978; Johansson et al., 1976; Wilkie, McCann, & Reibstein, 1974). Albeit these studies stressed the value in comprehending this phenomenon with multiattribute attitudinal models, the operationalization of these proved a more complicated endeavour, especially in regards to the delineation of this brand halo in a choice context. Since its identification, halo effects have been verified by observance of inter-attribute correlations of rating data, where factor analysis is used to identify highly related attributes, characterized by having 0.6-0.7 as a rule of thumb for defining high level of bias in judgement (Leuthesser et al., 1995; Thorndike, 1920). Wu and Petroshius (1987) mention that the effectiveness of this alternative rating procedures as a halo reduction tool is elusive, which receives support from Bagozzi (1996), who distinguishes affective-ignited brand halo from simple inter-attribute correlations because the latter are an empirical observation that cannot be objectively be ascertained as a result of the former. A more refined approach was presented by Russo, Meloy and Medvec (1998), with experiments designed to investigate the distortion of information in consumer judgment. In this study, one group received a favourable irrelevant additional piece of information, prompting positive attitude towards a specific fictitious brand, whereas for another condition different brands were presented at each choice set, eliminating the possibility of formation of brand-specific affective response (Russo et al., 1998). Russo, Meloy and Medvec (1998) stress that understanding distortion of information is particularly important for studies that rely on conjoint measurement techniques: the increased use of discrete choice experiments and econometric modelling techniques in the marketing discipline begs for a refinement on how this phenomenon can be addressed for a utility-model implementation.
Research Propositions Identifying sources of halo in choice may have implications in creating more reliable attitudinal models via the manipulation of distortions. This section highlights variables that can alleviate the introduction of brand halo in judgment. Firstly, Nisbett and Wilson’s (1977) studies report that assessors claimed not to have been influenced by halo effects, despite their results indicating high correlational levels. If interattribute correlations and real halo have some common ground, this implies that halo distortion is involuntary. Additionally, Bagozzi (1996) reports that both positive and negative beliefs could be influenced by emotional arousal in the context of blood donation. Despite contextual limitations, Bagozzi’s (1996) results seem to indicate that prompting consumers’ awareness of halo effects could incline them to make more cognitive-driven assessments. That is, if consumers were conscious of the potential halo effect-type bias and its consequences, it is likely that decision makers will attempt to minimise its practice. This leads us to our first research proposition. P1: Prompting raters’ awareness of own halo effects will reduce brand halo in choice. Providing accurate information to respondents regarding diagnosticity of attributes assessed should reduce halo effects (Nisbett & Wilson, 1977; Van Osselaer & Alba, 2003). Russo et al. (1998) selected attributes purposefully with low diagnosticity to prompt reliance on brand in the choice task. Moreover, Leuthesser et al. (1995) and Russo et al. (1998) highlight the importance of attribute specification for understanding halo effects, although manipulation regarding diagnosticity of information showed that halo effects were still present in spite of highly diagnostic information. Leuthesser et al. (1995) further points out that halo may be induced in a task if attributes are few or lack concreteness. This motivates the proposition of the following hypothesis. P2: Experts will be less prone to brand halo in choice than novices. Consumers seem to modify their judgments in line with the confidence in a choice (Russo et al., 1998). This finding supports brand signalling theory such that familiarity with a choice context motivates consumers to rely on the signals emitted by credible brands (Erdem & Swait, 1998; Swait & Erdem, 2007). If this is the case, more brand halo is expected when consumers have familiarity with brands presented on the basis of preestablished decisionsetting preferences. This leads to the third proposition. P3: Confidence in decision setting increases brand halo. Final considerations The extension of the halo effects literature seems to complement some of the findings in the consumer behaviour literature. It provides support to the idea that consumers have different price sensitivities to products described by the equivalent aspects (Swait, Erdem, Louviere, & Dubelaar, 1993), in a manner consistent with compensatory inferences (Chernev, 2007; Chernev & Carpenter, 2001; Chernev & Hamilton, 2009), a strategy employed by consumers to justify the price/ benefit relationship expected from different products. This paper consolidates the literature on brand halo through a literature review in economics, marketing and psychology, laying foundations to the conceptual development of the phenomenon. Current measurement techniques and their shortcomings were also debated. Finally, research
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