The application of multi-attribute utility theory for a market share-based design evaluation
Bibliographic record
Abstract
Purpose The purpose of this paper is to use the concepts of the multi-attribute utility theory to develop a model to evaluate the design of low-density residential units to increase the profit of the company from a certain design, by assessing the changes in the market shares as a result of the built unit’s attributes. Design/methodology/approach The proposed platform consists of two stages: Stage I or relational model development and Stage II or design evaluation. Stage I is concerned with developing a mathematical model that links design variables (e.g. the R-Value of the building envelope and construction material) with the assessment attributes (e.g. price and carbon emissions). Stage II ensures the fulfillment of the corporation’s goals in maximizing profit and market shares using multi-attribute utility theory. Findings The application of the proposed model on a case study – a single-family house – shows that reducing the selling price of the unit is not always the best marketing strategy builders should pursue to increase their sales and accordingly their profit, as accounting for other attributes (e.g. performance, operational cost and environmental impact) leads to larger changes in the market shares and accordingly in profit. Research limitations/implications The limitations of this research are manifested in the following points: it does not account for the impact of the marketing campaigns on the market shares; it considers the profit as a percentage of the construction cost; and it has not been validated on high-density residential buildings. Practical implications This research provides speculative builders with a platform that allows the objective evaluation of houses’ designs prior to introducing them to the market so builders can increase their market shares and consequently their profit. The proposed platform also contributes to increasing the sustainable performance of the housing industry, as it allows for the assessment of the design against economic, environmental and social attributes concurrently, which ensures a balanced consideration of the built houses on sustainability pillars. Social implications The proposed platform for design evaluation extends the assessment attributes beyond the traditionally considered economic and environmental attributes. By doing so, it assists decision-makers in evaluating the potential social influence of the proposed design and, as a result, reduces the unwanted impact. Originality/value This research combines the concepts of multi-attribute utility with market studies to develop an objective decision support tool for evaluating the design of speculative houses to increase the sustainable performance of the builders without compromising on their profit.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".