Consumer choice modeling: comparing and contrasting the MAAM, AHP, TOPSIS and AHP-TOPSIS methodologies
Bibliographic record
Abstract
While making decisions, consumers are often confronted with choosing between multiple product and brand alternatives that may be viewed as specific bundles of attributes/criteria. Researchers, attempting to understand this decision-making process, employ multi-criteria decision making (MCDM) models in numerous ways for predicting ultimate brand choice. This thesis compares and contrasts four types of MCDM models within a laptop brand choice context—specifically, the Multi Attribute Attitude Model (MAAM; Fishbein 1967), Analytical Hierarchy Process (AHP; Saaty, 1980), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS; Hwang & Yoon, 1981), and a mixed AHP-TOPSIS model (Ghosh, 2011; Bhutia & Phipon,2012). While Fishbein’s MAAM model evaluates brand choice by multiplying attribute belief ratings with their importance weights, the AHP does a pair-wise comparison to elicit relative weights of brand attributes and alternatives. The TOPSIS method, on the other hand, proposes that consumers choose brands that are nearest to (i.e., the shortest distance from) their ideal brand solution as well as the farthest from (i.e., the greatest distance from) their worst solution. Advantages and disadvantages of each of these methods are reviewed, and a mixed AHP-TOPSIS method that addresses some of the drawbacks is proposed here. The results attained via TOPSIS and AHP-TOPSIS are the same. However, it is coincidental in the chosen laptop choice example. By applying the two models within an alternative hotel choice scenario, the rankings obtained are demonstrated as being different. Sensitivity analyses conducted also demonstrate these differences across models. This thesis has both theoretical and practical implications. From a theoretical perspective, it brings the knowledge of decision making methodologies from the supply chain management field to further the understanding of marketing related issues. Furthermore, this research is the first to apply a mixed AHP-TOPSIS model that demonstrates greater accuracy in predicting consumer brand choice. In terms of practical significance, it allows companies to improve the impression that customers hold about its performance on specific attribute types.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".