The life cycle of a feature: modelling the transitions between feature states
Bibliographic record
Abstract
Purpose Studies have suggested that attributes are dynamic and a life cycle of product and service attributes exists. When an innovative feature is introduced, the feature might attract and delight customers. However, with the passage of time the state of the attractiveness of this feature may change, for better or for worse. The purpose of this paper is to provide a detailed model that shows the factors and related sub-factors that affect the life cycle of a feature and thereby explain the changes that may happen to a feature over time. Design/methodology/approach This model provide detailed explanations of the direct and indirect factors that affect the states of a feature, the ones that affect the rate of adoption, and the ones that trigger the changes between states. The model uses a current-market product’s feature to discuss the effects of these factors on the life cycle of this feature in detail. Findings This paper extends the theory of attractive quality attributes by identified seven states of the feature in its life cycle. These states are as follows: unknown/unimportant state, honey pot state, racing state, required state, standard state, core state, and dead state. This paper also identified eight major factors that affect the transition of the feature from one state to another. These factors include demographic, socioeconomic, behavioural, psychological, geographical, environmental, organisational, and technological factors. Originality/value The findings of this paper provide additional evidence that product and service attributes are dynamic. This paper also increases the validity of the attractive quality attributes theory and the factors that affect the state of the feature in its life cycle. The understanding of the state of the feature in its life cycle, and the factors that influence this change, helps not only in the introduction of completely new features but also in knowing when to remove obsolescent ones.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".