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Christoph Schneider ( email@example.com)
is an assistant professor in the Department of Information
Systems at City University of Hong Kong, Kowloon, Hong
Markus Weinmann ( firstname.lastname@example.org) is an
assistant professor in the Department of Information
Systems at the University of Liechtenstein, Vaduz,
Jan vom Brocke ( email@example.com) is a professor of
information systems, the Hilti Chair of Business Process
Management, Director of the Institute of Information
Systems, and Vice President for Research and Innovation
at the University of Liechtenstein, Vaduz, Liechtenstein.
Copyright held by the authors.
design cycle we have described here
to deliberately develop such choice
One final note of caution is that the
design of nudges should not follow a
“one-size-fits-all” approach, as their
effectiveness often depends on a decision maker’s personal characteristics. 16 In digital environments, characteristics of users and their environment
can be inferred from a large amount of
data, allowing nudges to be tailored.
System designers might design the
choice environment to be adaptive on
the basis of, say, users’ past decisions
or demographic characteristics. Likewise, big-data analytics can be used to
analyze behavioral patterns observed
in real time to infer users’ personalities, cognitive styles, or even emotional
states. 12 For example, Bayesian updating can be used to infer cognitive styles
from readily available clickstream data
and automatically match customers’
cognitive styles to the characteristics
of the website (such as through “
morphing” 11). Designers of digital choice
environments can attempt to “morph”
digital nudges on the basis of not only
the organizational goals but also users’
Any designer of a digital choice
environment must be aware of its
effects on users’ choices. In particular, when developing a choice environment, designers should carefully
define the goals, understand the
users, design the nudges, and test
those nudges. Following the digital-nudging design cycle we have laid
out here can help choice architects
achieve their organizational goals
by understanding both the users
and the potential nudging effects so
intended effects can be maximized
and/or unintended effects minimized.
This work was partially supported
by research grants from the University of Liechtenstein (Project No. wi-
2-14), City University of Hong Kong
(Project No. 7004563), and City University of Hong Kong’s Digital Innovation Laboratory in the Department
of Information Systems. We wish to
thank Joseph S. Valacich for valuable
comments on earlier versions, as well
as the anonymous reviewers for their
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