Background: A major and continuing problem in the information technology (IT) profession is the high rate of failure of new information systems (IS) or upgraded versions of them.
Aim: This paper suggests a new normative model that attempts
to analyze why the improvement of versions of existing decision support systems
does not necessarily increase the effectiveness of the productivity of
decision-making processes. Moreover, this research deals with various
principles that were examined and identified during Decision-Making processes
and integrates them into a comprehensive methodology for building an analytical
model which instructs how to design a DSS properly.
Methodology: The paper suggests some constructive ideas,
formulated through a mathematical analytic model, on how to select a strategy
for the design and switch to a new version of a decision support system,
without having to immediately run through a mega conversion and training
process while temporarily losing productivity. The analysis employs the
information structure model prevailing in Information Economics. The study
analytically defines and examines a systematic informativeness ratio between
two information structures. The analysis leads to a better understanding of the
performances of decision-support information systems during their life cycle.
Findings and Conclusions: The suggested approach explains
normatively the phenomenon of “leaks of productivity”, namely, the decrease in
productivity of information systems, after they have been upgraded or replaced
with new ones. Such an explanation may partially illuminate findings regarding
the phenomenon known as the Productivity Paradox. It can be assumed that the
usage of the methodology that is presented in this paper to improve or replace
information structure with systematically more informative versions of information
structures over time may facilitate the achievement of the following major
targets: Increase the expected payoffs over time, reduce the risk of failure of
new versions of information systems, and reduce the need to cope with
complicated and expensive training processes. The theoretical approach
presented here may also apply to the introduction of new AI systems that
support decision-making.
Limitations: The implementation of a normative approach
includes basic assumptions that do not necessarily exactly reflect daily
phenomena. However, that kind of approach facilitates illustrating various
aspects of the "real world".
Author (s) Details
Niv Ahituv
Faculty of Management, Tel Aviv University, Israel.
Gil Greenstein
Faculty of Industrial Engineering and Technology Management, Holon
Institute of Technology, Holon, Israel.
Please see the book here:- https://doi.org/10.9734/bpi/nabme/v2/3972
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