An Analysis of the Requirements for Smart Guiding Services in Museums Using the Kano-AHP Method
Bibliographic record
Abstract
This study aims to enhance the visiting experience in museums by investigating visitor demands for a smart guiding service system. Through the utilization of semi-structured interviews and field observations, the research explores visitor needs comprehensively. The Kano model is employed to categorize the various requirements, and the hierarchical analysis approach (AHP) is used to determine attribute weights, ensuring a thorough evaluation. By creating a hierarchical structure model for the smart guiding service system in museums and obtaining a comprehensive weight ranking for each indicator, the study provides a solid foundation for the development of effective solutions. Based on the ranking, several proposals are generated, presenting actionable insights for the implementation of a smart guiding service system. The findings emphasize the value of the Kano-AHP model in analyzing visitor demands, offering valuable guidance for museums aiming to establish an efficient and user-centric smart guiding service system.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".