An Exploratory Study of Brand Success: Evidence From the Food Industry

Davcik, Nebojsa S.; Rundquist, Jonas

doi:10.1080/08974438.2012.645747

Cited by 19 publications

(23 citation statements)

References 53 publications

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“…There is a general agreement in marketing literature that brand knowledge represents a set of associations in consumers' minds regarding the brand's features, benefits, uses, and overall attitude (e.g. Aaker 1991;Keller 1993;Broniarczyk and Gershoff 2003;Hoeffler & Keller 2003;Davcik & Rundquist 2012), and that knowledge about the brand arises as "a result of prior brand marketing activities and investments in advertising and promotion" (Broniarczyk and Gershoff 2003, p. 163).…”

Section: Brand Equity As a Measure Of Brand Valuementioning

confidence: 99%

“…Due to the fact that it is not possible to directly observe and measure the consumers' assessments of perceived value, the applied innovation in the production process is used as a proxy for the perceived quality (Sriram et al 2007; cf. Kamakura & Russell 1993;Aaker 1991;Yoo et al 2000;Bogue & Sorenson 2009;Davcik & Rundquist 2012;cf. Sorenson & Bogue 2007).…”

Section: Drivers Of Brand Equitymentioning

confidence: 99%

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An empirical investigation of brand equity: drivers and their consequences

Davcik

2013

British Food Journal

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Purpose -The author aims to present a model of the brand value drivers, measured by brand equity. The goal of this research is to identify the drivers, and determine how they influence brand equity performance in the researched industry, in order to develop a more effective brand strategy. Design/methodology/approach -The author studied an aggregate dataset for 739 food brands. Six predictors were controlled for (i.e. marketing investments, price, revenue, perceived quality [organic and functional] and brand ownership), while the impact of the brand equity drivers on brand value was estimated. The model was formulated and estimated using a robust OLS procedure. Several data sources have were in this study, such as market-based data from ACNielsen, as well as information and variable constructs using data from the Bureau Van Dijk Electronic Publishing AIDA financial statements database. Findings -Results suggest that marketing investment, price, revenue, brand ownership and perceived quality are highly associated with brand equity, and consequently with a higher brand value in the food industry. Research limitations/implications -This study has only studied one industry (food), one industry segment (enriched-food) and one country (Italy). Originality/value -The majority of marketing studies apply a single research approach and measures. This is the first study of brand equity that combines consumer, financial and marketing approaches. The model contributes to theory and practice in terms of suggesting which business drivers create brand value and what type of brand strategy a firm can apply in order to create brand value.

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Section: Brand Equity As a Measure Of Brand Valuementioning

confidence: 99%

Section: Drivers Of Brand Equitymentioning

confidence: 99%

An empirical investigation of brand equity: drivers and their consequences

Davcik

2013

British Food Journal

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“…Stability of estimates is assessed only by the bootstrapping, but the authors failed to report the jack-knifing assessment of the model. The study of Davcik & Rundquist (2012) is a good example for the VBSEM approach. They justified theoretical approach due to the exploratory nature of study, data distribution assumptions and less stringent sample requirements in comparison to the CBSEM approach.…”

Section: Research Illustrationmentioning

confidence: 99%

“…We present papers on brand loyalty / success published in the world-known peer reviewed journals such as Management Decision, Journal of the Academy of Marketing Science, Journal of Brand Management, etc. Labrecque et al (2011) and Mazodier & Marunka (2011) applied the CBSEM approach, and Hur et al (2011) and Davcik & Rundquist (2012) applied the VBSEM approach; presented in Table 5.…”

Section: Research Illustrationmentioning

confidence: 99%

The use and misuse of structural equation modeling in management research

Davcik

2014

Journal of Advances in Management Research

134

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Purpose – The research practice in management research is dominantly based on structural equation modeling (SEM), but almost exclusively, and often misguidedly, on covariance-based SEM. The purpose of this paper is to question the current research myopia in management research, because the paper adumbrates theoretical foundations and guidance for the two SEM streams: covariance-based and variance-based SEM; and improves the conceptual knowledge by comparing the most important procedures and elements in the SEM study, using different theoretical criteria. Design/methodology/approach – The study thoroughly analyzes, reviews and presents two streams using common methodological background. The conceptual framework discusses the two streams by analysis of theory, measurement model specification, sample and goodness-of-fit. Findings – The paper identifies and discusses the use and misuse of covariance-based and variance-based SEM utilizing common topics such as: first, theory (theory background, relation to theory and research orientation); second, measurement model specification (type of latent construct, type of study, reliability measures, etc.); third, sample (sample size and data distribution assumption); and fourth, goodness-of-fit (measurement of the model fit and residual co/variance). Originality/value – The paper questions the usefulness of Cronbach's α research paradigm and discusses alternatives that are well established in social science, but not well known in the management research community. The author presents short research illustration in which analyzes the four recently published papers using common methodological background. The paper concludes with discussion of some open questions in management research practice that remain under-investigated and unutilized.

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“…In the agribusiness sector, such preferences refer to specific characteristics that consumers have learned they prefer, such as taste or tenderness. Considering that Davčik and Rundquist (2012) show that consumer perceptions of brand quality can drive firms' success in the market, we posit that Lancaster's theory might be congruent with consumer behaviour in the food industry. Bell et al (2005) explain that the vast range offered in product categories consists of thousands of SKUs, which poses a major challenge to researchers and store managers, who must decide what to offer within each category.…”

Section: Technological Complexitymentioning

confidence: 99%

Technological Complexity: A Tool for Understanding the Behaviour of Consumers of High Value-Added Foodstuffs

Cotes-Torres

Gallego

Torres

2016

Journal of Business Economics and Management

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Abstract. This article proposes and develops the concept of technological complexity (TC) as a useful and simple tool for grouping key attributes that give added value to a product. In addition, it reports an empirical application of this concept to two different food products (cured ham and cured sausage). The authors used a mixed-effects multinomial logistic regression model and show that in the cured pork product agribusiness, a low frequency of consumption favours the acceptance of high TC products. The results also confirm that marketing high TC products in stores with a large assortment decreases the chances of success for agribusiness companies that produce cured pork food products. These finding can be used by the managers for designing complementary attributes that improve their product portfolio. Besides, advertising expenditures associated with introducing new products could be reduced if companies strengthened their presence in specialty stores.

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An Exploratory Study of Brand Success: Evidence From the Food Industry

Cited by 19 publications

References 53 publications

An empirical investigation of brand equity: drivers and their consequences

An empirical investigation of brand equity: drivers and their consequences

The use and misuse of structural equation modeling in management research

Technological Complexity: A Tool for Understanding the Behaviour of Consumers of High Value-Added Foodstuffs

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