Development and determinants of end-user intention: usage of expert systems
| Date | 13 May 2019 |
| DOI | https://doi.org/10.1108/JSIT-08-2018-0108 |
| Published date | 13 May 2019 |
| Pages | 166-185 |
| Author | Khaled A. Alshare,Mohammad Kamel Alomari,Peggy L. Lane,Ronald D. Freeze |
| Subject Matter | Information & knowledge management |
Development and determinants of
end-user intention: usage of
expert systems
Khaled A. Alshare and Mohammad Kamel Alomari
Qatar University, Doha, Qatar
Peggy L. Lane
University of Louisiana Monroe, Monroe, Louisiana, USA, and
Ronald D. Freeze
Sam M. Walton College of Business, University of Arkansas, Fayetteville,
Arkansas, USA
Abstract
Purpose –Expert systems (ES) design emulates expertise with the intentionthat the ES be used by non-
experts. This study aims to predict end-user intention and use of ES by proposing a research model that
extends the basic components of the unified theory of acceptance and use of technology (UTAUT) by
including additional relevant factors to ES, including the expert domain, perceived relevance, reliability,
quality of ES and management support, which directlyand indirectly influence the end-user intention to use
an expert system.
Design/methodology/approach –A structural equation model(SEM), using LISREL, was used to test
the measurement andstructural models using a sample of 205 end-users of expert systems in the USA. These
users of expert systems come from a variety of domains. The factors include both internal and external
factors forthe individual level of analysis design of this study.
Findings –The results showed behavioral intention had the strongest effect on usage, followed by
perceived relevance. With respectto the factors that impact intention, perceived relevance had the strongest
total effect, followed by attitude. For attitude, effort expectancy had the strongest total effect, followed by
managementsupport and perceived relevance.
Research limitations/implications –The results of this study should assist decision-makers in
planning training and communications about the use of expert systems so that the expert systems will be
used as intended.
Originality/value –The originality of this work residesin the addition of external factors to the UTAUT
model that helps provide advice to practitioners in the support needed to insure expert system
implementationsuccess.
Keywords UTAUT, Behavioral intention, End-user, Expert systems use
Paper type Research paper
1. Introduction
Expert systems (ES) are computer programs that are capable of performing specialized
tasks based on an understanding of how human experts perform the same tasks (Ye and
Johnson, 1995). An ES includes a knowledge base, an inference engine, an explanation
module and a user interface. These components are used to mimic expert decision-making
(Yoon, Guimaraes, and O’Neal, 1995). Expert systems are unique in that they are often
developed to help in making unstructured decisions. These real-world decisions have
JSIT
21,2
166
Received22 August 2018
Revised9 November 2018
12April 2019
Accepted6 May 2019
Journalof Systems and
InformationTechnology
Vol.21 No. 2, 2019
pp. 166-185
© Emerald Publishing Limited
1328-7265
DOI 10.1108/JSIT-08-2018-0108
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1328-7265.htm
financial, legal, political and social implications (Ye and Johnson, 1995). Service industries
such as accounting, banking and finance were among the first industries to use expert
systems (Wagner, 2017).
Over the years, ESs have successfully been implemented to assist the decision-making
activity of end-users in business over a vast number of areas and situations ranging from
medical diagnosis to buying a PC. As businesses invest larger amounts of money on
technology to increase the efficiency and effectiveness of decision-making, the need for
businesses to evaluate the payback also increases. With the focus on analyzing big data to
make better business decisions, the demand for expert systems, especially, those related to
cognitive computing to derive useful insights, has grown in importance. It has been
estimated that by 2020, the global text analytics market could reach $6.5bn with North
America expected to be the largest regional market (EDISON, 2018). Because companies
continue to invest in ES technology, it has become more important for information
technology personnel to gain an understanding of factors that influence the acceptanceand
intent to use ES technology (Yoon, Guimaraes, and O’Neal, 1995). The understanding of
these factors speed implementationand use of an ES system whose results are summarized
by the Founder of the World Economic Forum, Klaus Schwab, “It used to be that the big
used to eat the small, now it is the fast eat the slowand I’m seeing it every day.”(Choudhury,
2017).
Businesses invest significant amounts of money to design and build ES with a
moderate-sized system consisting of about 300 decision rules, generally costing between
$250,000 and $500,000 (Reference for Business, 2017). Despite the size of this investment,
there is no guarantee that a system will be accepted and used by the end-user. To insure a
return on an investment in an ES, the approach followed for investigating ES acceptance
and use would be different from other information systems as ES methodologies “are
tending to develop towards expertise orientation and ES applications development is a
problem-oriented domain”(Liao, 2004, p.99). The current research paper contributes to
the business domain by selecting factors, such as top management support, that are
thought to be essential for business-oriented ESs vs other information systems because of
the impact of the ES on end-users’jobs and the need for management support to increase
ES usage (Yoon and Guimaraes, 1995;Yoon et al., 1998). On the other hand, Liao (2004)
recommended, “different social science methodologies, such as psychology, cognitive
science, and human behavior could implement ES as another kind of methodology”
(p. 99). Therefore, the current study considers the nature of ES by using a theoretical
framework that includes contributing factors related to end user characteristics such as
end user attitudes and behavioral intention. End user characteristics, including attitude,
are considered to be one of the critical factors related to ES usage success (Guimaraes
et al., 1996).
This research paper categorizesthe factors of acceptance and use of ES into internal and
external groups. Prior studies that have recognized internal vs external factor differences
include a knowledge sourcingstrategy where the origin is internally and externally oriented
(Choi and Lee, 2012), the five trigger model that focused on internalvs external constraints
on team leaders (Thomas and Bostrom, 2010) and internal/external facilitators on project
success (Tsai et al.,2011). Internal factors include end-user’sattitude toward expert systems,
end-user’s perception of system’s ease of use, system’s usefulness, the quality of the
system’s outputs, and the reliability of the system. External to end-user control are
organizational factors that include the role that senior level management plays in
supporting the ES, the perceived relevance of the ES and the ES domain. These external
factors may play a more significant role for a business oriented ES than other systems. An
Usage of
expert systems
167
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