How consumers’ use of brand vs attribute information evolves over time
Bibliographic record
Abstract
Purpose – Prior research has identified that brands have a differential impact on consumer evaluations across various brand benefits. This paper investigates whether these effects are stable over time, or evolve in a consistent way. Design/methodology/approach – Consumer evaluations of brand benefits into overall brand and detailed attribute-specific sources through a standard confirmatory factor analysis approach have been decomposed. Two unique datasets have been analyzed; the first contains cross-sectional data from Kodak across four different consumer goods categories, and the other is a longitudinal dataset from the USA and Canada in the surface-cleaning category, covering seven brands over five years (2007-2011). Findings – A systematic evolution in brand effects has been demonstrated: a general trend is that over time and with experience, consumers rely more heavily on overall brand information to develop their evaluations. However, early in a brand’s life, or later when circumstances compel consumers to actively consider the attributes, ingredients or features of a brand, consumers may rely more heavily on, detailed attribute-specific information to evaluate brand benefits. Research limitations/implications – The systematic evolution in consumers’ use of information from attribute to brand is hypothesized in this paper and is found to occur contrary to the speculation of Dillon et al. (2001) regarding the direction of such an evolution. Further, our results indicate the sensitivity of our approach to detect changes in consumers’ use of the two sources that should be expected, given the various exogenous factors. Practical implications – Brand managers can use the results from our procedure to alter their messages to more strongly emphasize either overall brand information or detailed attribute-specific information, depending on the consumer segment or key benefit in question. The research offers insights for the kind of information managers should communicate for brands trying to extend into new categories. The research also raises interesting questions regarding the extent to which brands can own a strong position on a particular benefit over time. Originality/value – No prior work has evaluated brand effects (i.e. the relative use of brand vs attribute sources) to evaluate brand benefits over time. Our results demonstrate the value of the decompositional procedure we recommend and the importance of knowing which source is relied upon more heavily as consumers evaluate brands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".