Understanding the intention to use medical big data processing technique from the perspective of medical data analyst
| Published date | 20 November 2017 |
| DOI | https://doi.org/10.1108/IDD-03-2017-0017 |
| Pages | 194-201 |
| Date | 20 November 2017 |
| Author | Shanyong Wang,Jun Li,Dingtao Zhao |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
Understanding the intention to use medical
big data processing technique from the
perspective of medical data analyst
Shanyong Wang, Jun Li and Dingtao Zhao
School of Management, University of Science and Technology of China, Hefei, China
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.
Keywords Technology acceptance model, Social influence, Structural equation modelling, Usage intention,
Medical big data processing technique, Medical data analyst
Paper type Research paper
1. Introduction
Large amounts of medical data are generated from hospital
due to the development of hospital information system and the
widely usage of cloud computing (Azar and Hassanien, 2015).
Recently, there is a specific term to define these data, namely,
big data. According to Pawar (2016), big data refers to the
structured data, semi-structured data and unstructured data
which accumulated from various data sources. There are three
basic characteristics of big data: volume, velocity and variety.
Volume means the data keep growing rapidly and the size of
data could reach exabytes, zettabytes and more; velocity
means the data generate very fast; variety means the type of
data and the data source are diverse.
These medical big data is beneficial to patient care and
treatment, health-care services, hospital management and
scientific research. However, considering the nature of these
medical data (e.g. complex, distributed and highly
interdisciplinary), traditional medical data analysis techniques
are unable to help medical data analyst to access, storage,
process, analyze, distribute and share these data (Yao et al.,
2015). Recently, several new medical data-processing
techniques such as Hadoop, MapReduce and RapidMiner
have been developed to help medical data analyst to process
vast amounts of data (Choi et al., 2015). These new medical
data-processing techniques can help medical data analyst to
analyze medical big data intelligently, mine several features of
patients and redesign hospital information system to be much
easier and intelligent. Given these advantages, it is reasonable
to use medical big data processing technique. However, many
medical data analysts are still struggling with the usage of
medical big data-processing technique. Thus, research on the
antecedents of medical big data processing technique usage
from the perspective of medical data analyst is of great
significance and interest.
In this research, technology acceptance model (TAM) was
selected as the basic theoretical framework. TAM proposes a
link between the acceptance of technology and utilization
behavior, and it is a widely accepted model to predict the
determinants of technology adoption and usage in various
settings (Nikkheslat et al., 2012). However, little research has
been conducted within the medical big data context. Thus, in
this research, TAM was selected as the basic theoretical
framework to examine the factors that influence medical data
analyst’s intention to use medical big data-processing
technique. In addition, despite the usefulness of the TAM, it
has received criticism (Akman and Mishra, 2015;Wu and
Chen, 2017). Critics have charged that TAM only concerns
the short-term beliefs and assumes the decisions are only
based on rational self-interested motivation but neglects the
effect of other factors, such as social influence (Toft et al.,
2014). To address these issues, this research attempts to add
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/2398-6247.htm
Information Discovery and Delivery
45/4 (2017) 194–201
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-03-2017-0017]
Received 9 March 2017
Revised 31 May 2017
Accepted 5 June 2017
194
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