Survey on Machine Learning Acceptance in Life Sciences, 213 Professionals from 2022

213 anonymized survey responses from biomedical and life science professionals collected in Q4 2022. The data investigates technology acceptance of machine learning using the Technology Acceptance Model, extended with variables for journal impact factor, hype perception, and prior knowledge. Antonio E. Serrano published this dataset via Harvard Dataverse.

Use Cases

• Modeling technology acceptance factors based on Likert-scale responses for constructs like Perceived Usefulness and Behavioral Intention.
• Analyzing the influence of external variables like journal impact factor and technology hype perception on adoption intent.
• Conducting structural equation modeling or factor analysis based on the described PLS-SEM and PCA methodologies.

Strengths

• Contains 213 survey responses, providing a substantive sample for analysis.
• Data is anonymized, with no personally identifiable information included.
• Survey design is structured around a validated theoretical framework (TAM) with specific external variables.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic bias inherent to the LinkedIn recruitment from EMEA, LATAM, and North America.

Provenance

Source

Harvard Dataverse

Collection Method

Survey responses collected via LinkedIn recruitment.

Time Range

Q4 2022

Freshness

Last updated 2026-04-27 15:55:38; freshness should be verified.

Geography

EMEA, LATAM, and North America