Technology Acceptance Survey for Raman Spectroscopy & AI in Biomedical Diagnostics (2026)
Description
A conjoint analysis / discrete choice experiment surveying 1,890 respondents on their acceptance of Raman spectroscopy combined with AI for biomedical diagnostics. Includes 14 choice tasks, demographic profiles, and attitudinal scales measuring technology awareness, AI trust, and adoption willingness. Data collected via LinkedIn, email, Facebook, postal mail, and Zalo across Vietnam.
Acquisition parameters
Acquisition conditions are recorded in full so you can judge whether the dataset fits your purpose. An empty field means the information has not been recorded, not that it does not apply.
| Parameter | Value |
|---|---|
| Instrument | Online survey (conjoint analysis / DCE design, 20 blocks) |
| Laser wavelength (nm) | — |
| Power at sample (mW) | — |
| Wavenumber range (cm⁻¹) | — |
| Spectral resolution (cm⁻¹) | — |
| Objective / numerical aperture | — |
| Sample preparation | — |
| Wavenumber calibration standard | — |
| Most recent calibration date | — |
| Number of samples | — |
If you need a parameter that is not listed here, contact us — we keep the raw record of every measurement.
How to cite
Please cite this dataset exactly as shown below in any publication that uses it. Full citation lets the community trace data provenance and is a condition of the licence.
Plain-text citation
BIRALAB (2026). Technology Acceptance Survey for Raman Spectroscopy & AI in Biomedical Diagnostics. Vietnam National University, Hanoi.
BibTeX
@misc{biralab2026surveytechacceptance2026,
title = {Technology Acceptance Survey for Raman Spectroscopy \& AI in Biomedical Diagnostics (2026)},
author = {{BIRALAB}},
year = {2026},
howpublished = {BIRALAB Data Hub},
version = {1.0},
url = {https://biralab.org/en/data-hub/datasets/survey-tech-acceptance-2026},
note = {dataset}
}DOI
No DOI yet
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By downloading you agree to comply with the dataset licence and to cite it as described above.
Data dictionary
The meaning of every field in the data files, with its type and unit.
| Field name | Type | Unit | Description |
|---|---|---|---|
kenh | — | — | Recruitment channel: linkedin, email, fb, thu (postal), zalo |
block | — | — | Survey version/block (1-20, ~95 per block) |
AI1-AI4 | — | — | AI attitude Likert scales (1-5) |
DN1-DN8 | — | — | Demographics: age, gender, region, city size, education, field, profession, income |
NT1-NT5 | — | — | Awareness/perception Likert scales (1-5) |
ms_1-14 | — | — | Response time in ms per choice task |
resp_id | — | — | Unique respondent identifier (UUID) |
flag_bot | — | — | Detected as bot |
nhan_luc | — | — | Response submission timestamp |
flag_fail | — | — | Failed comprehension check |
choice_1-14 | — | — | Conjoint choice per task: alt1, alt2, or none |
flag_speeder | — | — | Completed too fast |
total_seconds | — | — | Total completion time in seconds |
flag_attention | — | — | Failed attention check |
flag_duplicate | — | — | Duplicate fingerprint |
fingerprint_hash | — | — | Browser fingerprint hash |
flag_straight_likert | — | — | Straight-lining on Likert scales |
DST1-4, SST1-4, BAHN1-4, SS1-4, TS1-3 | — | — | Conditional follow-up questions (mixed ordinal + Yes/No) |
Preprocessing applied
This dataset was processed with the BIRALAB preprocessing standard: cosmic ray removal, wavenumber-axis calibration against a reference material, baseline correction, Savitzky–Golay smoothing, intensity normalisation and resampling onto a common wavenumber grid. The exact parameters of each step are recorded in the metadata shipped with the download.
Publications using this dataset
BIRALAB publications that reference this dataset or its DOI.
No publication in our records references this dataset yet. If you have used it in your own research, send us the reference and we will add it to this list.