Unraveling corporate brand equity: a measurement model based on consumer perception of corporate brands

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-01-08 DOI:10.1108/jm2-04-2023-0067
Rafael Barreiros Porto, Gordon Robert Foxall, Ricardo Limongi, Débora Luiza Barbosa
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Abstract

Purpose

Consumer perception of corporate brand equity has primarily focused on product brand dimensions, neglecting considerations at the firm analysis level. Assessing corporate brands requires different criteria relevant to the competitiveness of companies, such as their prominence, management and meeting society’s demands. In this sense, this study aims to develop and validate a scale of corporate brand equity founded on consumer perceptions, transcending industry boundaries and comparing its relationship with companies' market share.

Design/methodology/approach

The authors used an integrative approach to clarify the construct’s domain, building on previous measures. They took several steps to select appropriate items, refine the measure, validate it through reliability tests and convergent and discriminant analyses, test the validity of the second-order formative structure of corporate brand equity and assess associations between first-order factors, the second-order factor and market share.

Findings

The model identifies three first-order dimensions of corporate brands (presence, outstanding management and responsible) that shape the second-order factor (corporate brand equity). They are directly related, but not proportionally, to market share, contributing to the general and joint assessment of the company’s competitive performance considering the consumer.

Originality/value

To the best of the authors’ knowledge, this study is the first attempt to develop a comprehensive measurement model of corporate brand equity that considers the firm level of analysis, combines metrics from previous research on corporate brand evaluation criteria and includes consumer perceptions of the company’s competitiveness, unifying branding theory with the theory of the marketing firm.

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解读企业品牌资产:基于消费者对企业品牌认知的测量模型
目的消费者对企业品牌资产的认知主要集中在产品品牌层面,忽视了企业分析层面的考虑。评估企业品牌需要与企业竞争力相关的不同标准,如企业的知名度、管理和满足社会需求等。从这个意义上讲,本研究旨在开发和验证一个基于消费者感知的企业品牌资产量表,超越行业界限,并比较其与企业市场份额的关系。研究结果该模型确定了企业品牌的三个一阶维度(存在感、杰出管理和责任感),这些维度塑造了二阶因子(企业品牌资产)。它们与市场份额直接相关,但不成正比,有助于消费者对公司竞争表现的总体联合评估。原创性/价值 据作者所知,本研究首次尝试建立企业品牌资产的综合测量模型,该模型考虑了企业层面的分析,结合了以往企业品牌评价标准研究的指标,并包含了消费者对公司竞争力的看法,将品牌理论与营销企业理论统一起来。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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