Towards a Better Understanding of Consumer Behavior: Marginal Utility as a Parameter in Neuromarketing Research
Bibliographic record
Abstract
Understanding consumers’ decision-making process is one of the most important goal in Marketing. However, the traditional tools (e,g, surveys, personal interviews and observations) used in Marketing research are often inadequate to analyse and study consumer behaviour. Since people’s decisions are influenced by several unconscious mental processes, the consumers very often do not want to, or do not know how to, explain their choices. For this reason, Neuromarketing research has grown in popularity. Neuromarketing uses both psychological and Neuroscience techniques in order to analyse the neurological and psychological mechanisms that underlying human decisions and behaviours. Hence, studying these mechanisms is useful to explain consumers’ responses to marketing stimuli.This paper (1) provides an overview of the current and previous research in Neuromarketing; (2) analyzes the use of Marginal Utility theory in Neuromarketing. In fact, there is remarkably little direct empirical evidence of the use of Marginal Utility in Neuromarketing studies. Marginal Utility is an essential economic parameter affecting satisfaction and one of the most important elements of the consumers’ decision-making process. Through the use of Marginal Utility theory, economists can measure satisfaction, which affects largely the consumer’s decision-making process. The research gap between Neuromarketing and use of Marginal Utility theory is discussed in this paper. We describe why Neuromarketing studies should take into account this parameter. We conclude with our vision of the potential research at the interaction of Marginal Utility and Neuromarketing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".