Sustainable knowledge-based decision support systems (DSS): perspectives, new challenges and recent advance
| Date | 14 August 2017 |
| Pages | 1318-1322 |
| Published date | 14 August 2017 |
| DOI | https://doi.org/10.1108/IMDS-04-2017-0137 |
| Author | Shaofeng Liu,Boris Delibasic,Lynne Butel,Xue Han |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Editorial
1. Sustainable knowledge-based decision support systems (DSS): perspectives,
new challenges and recent advance
1.1 Evolution of decision making and DSS
Evidence has clearly shown that excellent business performance can only be achieved based
on the right decisions (Liu et al., 2013). The ability to make good decisions is the mark of
successful and promotable business leaders and managers (Martinsons and Davison, 2007).
Research on decision making can be traced back to the preceding work in two main research
streams: theoretical study of organisational decision making undertaken by Simon et al. at
the Carnegie Institute of Technology during late 1950s and early 1960s (Simon, 1960), and
technical work on interactive computer systems carried out by Gerrity et al. at the MIT in
1960s (Gerrity, 1971). Simon’s three-stage human decision-making process (i.e. intelligence,
design and choice) is still one of the most widely cited decision models. However, Gerrity’s
work has identified a key issue with human decision making, that is, the fact that there are
many constraints on effective decision making, for example, with limited information and
limited decision analysis ability. Along with the fast IT advancement over 1970s and 1980s,
it has been widely recognised that computers could be used to overcome many human
limitations. Subsequently, decision support systems (DSS), defined as an interactive
computer-based system to support solving decision problems, have been developed and
widely applied in real-world decisions (Shim et al., 2002). DSS was considered to be one of the
most popular research areas in information systems during 1980s. Most notably, DSS have
evolved from supporting individual decisions to supporting groups and then to supporting
organisation-wide decisions (Liu et al., 2009). In addition, the types of decisions that DSS are
able to support extend from operational to strategic decision making (Martinsons and
Davison, 2007). A number of review papers can be found which have embraced the DSS
success over the time (Keen, 1987; Eom, 1999; Carlsson and Turban, 2002; Shim et al., 2002;
Liu et al., 2010).
However, DSS have entered into a relatively difficult time in 1990s, because DSS users
were no longer satisfied by merely searching for information and obtaining analysis
results from running a decision model (Liu et al., 2010). Based on experience in practice,
decision makers gradually realised, apart from information and decision analysis, the
human element was found to be lacking sufficient knowledge and expertise in order to
make faster and more consistent decisions (Bolloju et al., 2002). In response to the new
issue identified within human decision-making process, many scholars and researchers
invested a significant amount of effort to search for solutions, which resulted in the
emergence of knowledge-based systems (KBS), which is also termed as expert systems in
America (Dhar and Stein, 1997). Since 1990s, KBS and expert systems have been playing
an important role in the new generation of DSS, which led to the development of
knowledge-based decision support systems (KB-DSS) (Courtney, 2001). KB-DSS have been
generally accepted as decision systems that contain a knowledge base and have a function
of inference or reasoning on top of a classical DSS. A comprehensive review of KB-DSS is
available from the guest editors’recent publication (Zarate and Liu, 2016). A special issue
(SI) focussed on the technology perspective of KB-DSS is published earlier in the Journal of
Decision Systems (Liu et al., 2014). However, the current issues of global sustainability
and business performance improvement have presented brand new challenges to decision
making and to KB-DSS.
Industrial Management & Data
Systems
Vol. 117 No. 7, 2017
pp. 1318-1322
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-04-2017-0137
1318
IMDS
117,7
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