Measuring CBBE across brand portfolios: Generalizability theory perspective

Wang, Luming; Finn, Adam

doi:10.1057/jt.2012.9

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…While common in psychology, it is not always appropriate in marketing especially for CBBE measurement. Wang and Finn 6 introduced MGT and developed a brand level scale to fi ll this gap. It is designed to maximally differentiate brands from each other and reliably capture their differences.…”

Section: Discussion and Implicationmentioning

confidence: 99%

“…The fully crossed (brands by respondents) data set used by Wang and Finn 6 is examined here to demonstrate the complementary roles played by MFIRT to MGT analysis. It was collected in 2008 from 254 North American undergraduate students, who received course credit for their participation.…”

Section: Empirical Applicationmentioning

confidence: 99%

“…Wang and Finn 6 used Multivariate Generalizability Theory (MGT) to develop the fi rst brand level CBBE scale. It was designed to maximally differentiate brands from each other and reliably capture their differences in a brand portfolio context.…”

Section: Introductionmentioning

confidence: 99%

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Measuring consumer-based brand equity across brand portfolios: Many-facet Item Response Theory perspective

Wang

Finn²

2012

J Target Meas Anal Mark

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Luming Wangcompleted her PhD at the University of Alberta (Canada) in 2010. Her research interests include branding, brand equity and brand management, research methodology, Bayesian modeling, consumer choice modeling, and international marketing. Adam Finncompleted his PhD at the University of Illinois (Urbana, USA) in 1983. His research interests include marketing measurement, retailing and service assessment, new product management, retailing and service management, application of choice modeling to policy issues in marketing and the cultural industries.ABSTRACT Wang and Finn (in press) used Multivariate Generalizability Theory (MGT) to develop the fi rst brand level consumer-based brand equity (CBBE) scale. It is an important addition to CBBE measurement literature. However, in general, the goal of MGT is to examine measurement scale in terms of reliability and to deal with the precision of observed raw data. It has little interest in correcting the observed measures (that is, brand ratings) for differences among the various elements encountered by each brand during a data collection design. The elements such as individual differences, dimensions and items are all confounded with CBBE measures. In order to address this issue, we introduce Manyfacet Item Response Theory to complement the information provided by MGT analysis. These two measurement approaches, used together, allow for balanced and thorough interpretations of brand performance assessment data and offer better ways of improving performance measurement.

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Section: Discussion and Implicationmentioning

confidence: 99%

Section: Empirical Applicationmentioning

confidence: 99%

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Measuring consumer-based brand equity across brand portfolios: Many-facet Item Response Theory perspective

Wang

Finn²

2012

J Target Meas Anal Mark

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“…We feel that, if the one term can mean three very different things, then we should separate them into three different terms. A notable exception is the recent work of Wang and Finn (2012a, 2012b, 2014, which uses an item-response perspective to operationalise CBBE as the probability of brand selection from an array of competitors as a function of awareness and perceived quality.…”

Section: Construct and Measurement Issuesmentioning

confidence: 99%

Brand competitiveness

Winzar

Baumann

Chu

2018

IJCHM

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Purpose This paper aims to introduce the concept of consumer-based brand value (CBBV), a change in the perspective of brand-equity and brand value from one where the brand is considered separately from other brands. The purpose of this paper is threefold: highlighting the theoretical difference between brand equity and brand value (Raggio and Leone, 2007); conceptually linking brand equity with brand value and then with brand competitiveness; and demonstrating a straightforward method for scholars and practitioners to measure brand value and then to forecast and simulate brand competitiveness. Design/methodology/approach On a sample of 190 postgraduate students, the authors apply the conjoint analysis and best-worst scaling to illustrate our CBBV model. Following Keller (1993), the authors operationalise brand equity as the outcome of consumers’ internal mental processes, in the form of brand preference, and show how preferences data can be transformed into estimates of brand value in the form of price premium. Finally, the model allows market share simulation based on manipulation of branding and brand features. Findings The paper is more a conceptual piece, highlighting the distinction between brand equity and brand value. It also demonstrates a relatively new measurement technique for transforming measures of brand equity, in the form of brand preferences, into estimates of brand value. The paper used airlines as a service-provider example, but the technique can be applied to many hospitality and service environments. Research limitations/implications The study demonstrates how brand equity drives brand value via consumer utility, and proposes a CBBV–competitiveness chain. The authors convert individual consumer preference data to brand value with subsequent preference-based market segmentation, and estimate competitiveness in two ways: market share ceteris paribus and price premium for brand indifference. They also demonstrate how market simulation can be performed so that it allows forecasting of competitiveness (market share) based on product attributes that affect brand value. Practical implications The CBBV–competitiveness chain constitutes a (new) mindset in the marketing of hospitality, tourism and other services. The study provides a method to measure and test the components of this model and determine brand competitiveness. It used airlines in the example, but the method can be applied to many different settings such as, for example, hotel management group (Starwood, Accor, Intercontinental, Hilton). The authors show how the method allows for benefit-based market segmentation, market-share forecasting and estimation of price premium. Originality value The CBBV chain provides a conceptual link between brand equity, brand value and brand competitiveness. When equity and related constructs are often conceptualised as relative to competition, they are rarely actually measured in that manner. The study shows how brand equity measured as preferences can be transformed into brand value and competitiveness relative to the competition. The combination of conjoint analysis and best–worst scaling is relatively new to the hospitality and services industries. The authors show that these tools can be applied to these industries without depending on costly software or high-priced consultants.

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“…Applications of IRT are now starting to show up in the marketing literature (de Jong et al, 2007, 2008, 2009; Salzberger, 2009; Salzberger and Koller, in press). But they are traditional IRT applications in terms of the number of facet with two exceptions as Wang and Finn (2012a, 2012b). We introduce many facet IRT (MFIRT), developed by Linacre (1989) in educational psychology to extend IRT from single facet (i.e., consumers) to model one additional facet (e.g., brands).…”

Section: Literature Reviewmentioning

confidence: 99%

Dual-Faceted Multidimensional IRT Models with Hierarchical Structure

Wang

Finn

2013

Australasian Marketing Journal

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In marketing, there are many important multidimensional constructs, such as service quality, market orientation, and consumer-based brand equity (CBBE). Survey methods and multidimensional scales are used to measure these constructs. But the survey responses collected for these constructs are also dual-faceted. Except for consumers, the other facet (such as services, firms or brands) is involved too. Assessment of multidimensional marketing scales (such as SERVQUAL and MARKOR) has relied on confirmatory factor analysis (CFA) and structural equation modeling (SEM), which may not give accurate and unbiased evidence about the estimates and dimensionality across both facets. The authors introduce a hierarchical extension of many facet item response theory (MFIRT) to empirically investigate the causal relationships between a construct and its dimensions for scales that generate dual-faceted data. CBBE data for soft drink brands are used in an empirical study, which demonstrates the method and finds CBBE best modeled as formative from its dimensions along the consumer facet.

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Measuring CBBE across brand portfolios: Generalizability theory perspective

Cited by 7 publications

References 22 publications

Measuring consumer-based brand equity across brand portfolios: Many-facet Item Response Theory perspective

Measuring consumer-based brand equity across brand portfolios: Many-facet Item Response Theory perspective

Brand competitiveness

Dual-Faceted Multidimensional IRT Models with Hierarchical Structure

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