Understanding the intention to use medical big data processing technique from the perspective of medical data analyst

Abstract

Purpose

The purpose of this paper is to apply an extended technology acceptance model to examine the medical data analyst’s intention to use medical big data processing technique.

Design/methodology/approach

Questionnaire survey method was used to collect data from 293 medical data analysts and analyzed with the assistance of structural equation modeling.

Findings

The results indicate that the perceived usefulness, social influence and attitude are important to the intention to use medical big data processing technique, and the direct effect of perceived usefulness on intention to use is greater than social influence and attitude. The perceived usefulness is influenced by perceived ease of use. Attitude is influenced by perceived usefulness, and attitude acts as a mediator between perceived usefulness and usage intention. Unexpectedly, attitude is not influenced by perceived ease of use and social influence.

Originality/value

This research examines the medical data analyst’s intention to use medical big data processing technique and provides several implications for using medical big data processing technique.

Citation

Wang, S., Li, J. and Zhao, D. (2017), "Understanding the intention to use medical big data processing technique from the perspective of medical data analyst", Information Discovery and Delivery, Vol. 45 No. 4, pp. 194-201. https://doi.org/10.1108/IDD-03-2017-0017