Mini Review - (2021) Volume 0, Issue 0
Received: 26-Mar-2021
Published:
16-Apr-2021
, DOI: 10.37421/2151-6219.2021.s2.005
Citation: Mitchell ,V incent . “The 4 C’s of Academic Contribution: An Illustration from Marketing” Bus Econ J 12 (2021)S2:005.
Copyright: © 2021 Mitchell V. This is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author
and source are credited.
When thinking about research projects and how to make a contribution, academics can often wonder which element to focus on. In a recently published paper we report on empirical findings which support several hypotheses relating to retailer brand collaborations. However, our article provides a good illustration of a general model for how academics interesting in business and economics can make a contribution to the literature using the 4 C’s of context, concepts, components and computations. In this review, we highlight those four elements using our article as an illustration as well as reiterate the main general takeaways for business researchers. More specifically, our context was the burgeoning but relatively under researched area of retailer collaborations to which we brought the concepts of brand inheritance and using multiple theories like, Construal Theory, Congruity Theory, Categorization Theory, and the Selective Activation, Reconstruction and Anchoring Model to explain our phenomenon. The most revealing component of our conceptual model was the striking difference between symbolic vs. functional brands and our new computational approach to analysing brand image via Double entry intra-class correlation (ICCDE) was not only perfectly suited to our data, but also represented a new approach within the field. The 4C’s model is a useful guide for business and economics researchers to consider for their work.
Retailer • Brand • Stakeholder • Business
When thinking about research projects and how to make a contribution, academics can often wonder which element to focus on. A recently published paper provides a good illustration of a general model for how academics interesting in business and economics can make a contribution to the literature using the 4 C’s of context, concepts, components and computations [1]. In this brief review, we highlight those four elements using our article as an illustration as well as reiterate the main general takeaways for business researchers (Figure 1).
Figure 1: A 4C model of academic contribution.
The context of the research itself can be a contribution if it is novel, underexplored or extremely important it her economically or socially to some significant stakeholder group. In this case, retailer collaborations are an underexploited way for retailers to expand product lines and target new segments relatively quickly and cheaply. Many types of brand collaborations can be found (e.g. Target and UNICEF) and as part of these cobranded products exist (e.g. Apple and Nike) as well as cobranded services (e.g. Kmart and Capital One Bank). The economic significant of such activity is clear when one considers the example of the financially successful Tacobell Doritos collaboration which sold over 1 billion tacos in its first year alone. Such brand collaborations like cobranding are being used because they allow for speedy image change purposes and cheaper new product development as they combine the reputation of two brands to form a new and “unique set of attributes that the parent brands alone cannot offer”. This is especially helpful when brand and consumer personalities are drivers of purchase or when consumers are using heuristic shortcuts to process products and don’t focus on the details. Partners can not only leverage their brand equity to positively affect the image of the parent brands, but also attract new customers and improve firm profits. Jointly developing new products allows retailers to enter a market that may otherwise have been difficult to penetrate particularly international markets. But how retailers should choose a partner to maximize image inheritance or to achieve specific image effects in not an easy thing to do and little work has explored the area and the influence of important factors in the image inheritance process. This explanation justifies why this is an important context for research [2-8].
Highlighting the importance of an ignored or under-valued component of a conceptual model or challenging existing models or measurements is a second way to make a contribution to the literature. For Mitchell and Balabanis our focus was on the component of functional vs. symbolic nature of brands. Functional brands, like Walmart are brands which focus on satisfying immediate consumption and practical needs. Symbolic brands, like Tiffany, focus on more psychological needs such as self-expression and prestige. In cobranding this is a major issue because we often see highly symbolic brands like Tiffany combining with a functional brand like Walmart. To support the importance of this component, recent work has suggested that functional fit and image fit represent independent dimensions, which are not necessarily correlated. Further support comes from findings that functional fit has no effect on expressive (symbolic) brand alliances, but does impacts the evaluation of functional. Since general cobranding largely ignores the effects of symbolic vs. functional brands, we have a knowledge gap in the literature. We provided evidence for the value and important of this component both from a practical viewpoint and from the literature, before showing that most other work has ignored this component [9-15].
Whilst often articles focus on one theory, this may be a restrictive account of what is happening within their context. Conceptual confusion should be avoided, but this does not mean we cannot expand our conceptual understanding my using multiple theories that are relevance to explaining our phenomenon. Mitchell and Balabanis show this by adding further theoretical richness to their context by introducing Construal Theory, Congruity Theory, Categorization Theory, and the Selective Activation, Reconstruction and Anchoring Model as plausible explanations for the observed effects. This also adds to the useful theories previously used in this area of research. To be more specific, construal theory works well for levels of abstraction, which is apparent in symbolic vs. functional images, but not for fit between images. Categorisation and congruity theories work well for fit issues, they work less well for brand strength where anchoring bias theory is useful. This use of multiple theories is also consistent within this area of work and one recent review identifying 9 different theories being used. Following this tradition, we utilised a range of theories which appeared both relevant to the specific context and helped us understand and explain our results [1,12].
In using these multiple theories they linked them to their main model component of symbolic vs. functional brands. Utilitarian/functional values or symbolic values can be related to specific associations. Symbolic brands characteristics tend to be more abstract, e.g., sophistication, and have more variation in associated meaning. In contrast, functional brands are more tangible and rational. These associations are more closely linked with what a product does, e.g., speed, which results in a narrower interpretation of meaning. This invokes the notions inherent within Construal Level Theory which is used to explain the how people think about stimuli in an abstract or concrete way. We proposed that consumers’ perception of functional brands will be low construal and more concrete because of the utilitarian nature of the attributes. In contrast, perceptions of symbolic brands will be higher construal, more abstract, due to the symbolic more abstract nature of the image attributes [16-19].
Next, in cases of uneven brand strength, the Selective Activation, Reconstruction and Anchoring Model suggests an anchoring bias may occur. This is because for strong brands with greater brand awareness consumers have a firmer and more detailed brand knowledge which act as an anchor and make them are more easily recalled than weaker brands. Linking this to functional and symbolic brands, since symbolic brand images are more easily inherited, brand strength will be less relevant in driving the inheritance process. However, for functional brand images which are less easily inherited, brand strength will be more relevant in the image inheritance process [20-22].
Finally, for the fit between the cobrand product-category and that of the parent brand product category, we use Categorization Theory which suggests consumers categorize objects (i.e., brands) into different categories based on their similarities. As it is easier to inherit associations linked with a new retailer cobrand if the object can be classified as member of that category, this is likely to be truer for concrete or functional brand associations where associations are more linked to the nature of the product category, than for symbolic brands, where brand associations are less category specific or representative of that category. So here we explore multiple concepts to make a richer contribution [23-25].
Improving methods, including data collection methods, as well as applying a new method which is either more suitable for the data type or delivers different more insightful results is a final useful way to make a contribution. For Mitchell and Balabanis this meant using a computational technique which was unlike most of the previous research in the area which has simply compared means of brand attitudes, or of brand personality scores or high/low images Instead we used similarity measures of brand personalities which are more sophisticated and consider similarities in the shape, elevation and scatter of an image profile. Our double entry intra-class correlation (ICCDE) method has been shown to be better than other profile similarity coefficients, because they are sensitive to the shape and elevation of image profiles. [26-30].
As it is recommended to remove the normative pattern common to the whole sample known as stereotypic accuracy, they computed ICCDE after standardization of the scores of each personality trait across the entire sample. Measures of brand-image profile similarities were then computed by correlating the scores of the parent brands and cobrand personality traits across all personality-scale items. This methodological advancement enabled us to compute more precisely the extent to which the cobrand was similar in its personality from either of the parent brands. Computationally then, we deployed a new, more holistic way of measuring and comparing brand image which has been shown to be better than other profile similarity coefficients and this may be useful to other researchers in this area [31-34].
Looking at new underexplored contexts, using richer array of concepts, showing the power of important components of a model or adopting new computational methods of analysis is a generalizable four factor model to make contributions to the field of business and economics. Whilst each on their own can be the foundation of a contribution, our paper presents a nice example of how these can also be used together. The other takeaways for researchers are to consider; retail collaborations as an interesting context for your work, the use of multiple explanatory theories in your papers, and consider the use of double entry intra-class correlation (ICCDE) for image and other similar types of data.
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