Towards a Design Theory for Hedonic Systems: Delivering Superior User
Experience in the Digital Home Entertainment Context
YoungKi Park and Omar A. El Sawy
Marshall School of Business, University of Southern California
[email protected], [email protected]
Abstract
Since the advent of information system design theory (ISDT) by Walls et al. (1992) in the
mainstream IS literature, most of the design science research has focused on utilitarian systems
in work environments. Therefore, we have less knowledge about the design of information
systems that are used by consumers in their everyday lives, and especially those that focus on
entertainment rather than productivity. This paper tackles the design issues of delivering superior
user experience in the digital home entertainment (DHE) context, in which hedonic technologies
rather than utilitarian technologies play a key role. We first articulate the differences between
hedonic and utilitarian technologies and then use March’s theory of exploration and exploitation
as a kernel theory to conceptualize mutual learning between service/content providers and
consumers. We argue that the effective mutual learning is a key driver for superior user
experience. The paper then builds the core elements of a design theory for hedonic systems and
ends by exploring alternative empirical avenues for further developing and field testing the
theory.
Key words: hedonic systems, user experience, information systems design theory, digital
home entertainment, exploitation & exploration, mutual learning.
In Proceedings of the Third International Conference on Design Science
Research in Information Systems and Technology. (V. Vaishnavi & R.
Baskerville, Eds). May 7- 9, 2008, Atlanta, Georgia: Georgia State University.
© The Authors 2008. Permission to make digital or hard copies of all or part
of this work for personal or classroom use is granted without fee.
Many information technologies have both hedonic and utilitarian characteristics. The goal of
utilitarian technologies is to achieve high performance, while that of hedonic technologies is to
provide self-fulfilling value such as enjoyment and fun through prolonged use (Sun and Zhang
2006; Van der Heijden 2004; Voss et al. 2003)1. The following table summarizes comparisons
between hedonic and utilitarian technologies upon multiple dimensions.
Purpose
Place
Design Objective
Motivation (Brief
& Aldag 1977)
Scale (Voss et al.
2003)
Use-related
Elements
Exemplar Tactics
for Development
Hedonic Technology
Utilitarian Technology
Pleasure, Fun, Enjoyment
Home, Theme or Amusement Park,
Leisure Activities
Encouraging Prolonged Use
Intrinsic
Productivity, Performance
Business Workplace, Home Office
Fun, Exciting, Delightful, Thrilling,
Enjoyable
Effective, Helpful, Functional,
Necessary, Practical
Multi-sensory, Imagery, Emotive,
Fantasy
Attributes and Functions
Include hedonic content, animated
images, and a focus on colors,
sounds and esthetically appealing
visual layouts.
Align functionalities with task
requirements and provide as little
distraction as possible to help users
perform tasks (task technology fit).
Productive Use
Extrinsic
Considering the differences presented in Table 1, theories of use developed for utilitarian
systems may not hold for hedonic systems. For example, Van der Heijden (2004) showed that
the technology acceptance model (TAM) for utilitarian systems is not directly applicable for
hedonic systems. Although design science research has developed many relevant theories for
utilitarian systems, we have few design theories for hedonic systems that are used by consumers
in their everyday lives. This paper proposes a design theory for hedonic systems delivering
superior user experience in the DHE context. Here, we define the term “hedonic user experience”
as the “personal, emotional, and subjective perception of a user to any direct or indirect contacts
with hedonic systems over time” (Meyer and Schwager 2007; Pine and Gilmore 1998). The
existing literature shows the great impact of user experience on high customer loyalty,
repurchases or revisits, credibility, promotion through word of mouth (Pullman and Gross 2004;
Pine and Gilmore 1998), and user satisfaction (Meyer and Schwager 2007). However, the
literature has not identified the relevant drivers for delivering superior user experience, and
content/service providers in practice never seem to understand how to deliver superior user
experience. For example, Bain & Company’s recent survey of user experience finds that only 8%
of the customers of 362 companies reported their experience as superior, while 80% of the
companies believe that they are providing superior experience (Meyer and Schwager 2007).
A Design Theory for Hedonic Systems in the DHE Context
Following Walls et al.’s ISDT framework and its modified versions, our design theory consists
of meta-requirements, design principles, and testable hypotheses supported by kernel theories.
1
For convenience, by hedonic (utilitarian) technology we mean technology used for hedonic (utilitarian) purposes.
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1) Meta-Requirements for Hedonic Systems
A hedonic system is composed of several hedonic technologies which are connected with each
other for DHE. These include set-top boxes, TVs, audio systems, gaming consoles, and other
devices as well as associated software for interface, gaming, privacy control, etc. People interact
with such technologies and content, which provokes affective involvement and results in hedonic
user experience (Hirshman and Holbrook 1982). Thus, technologies must be configured in a way
to deliver better fun and enjoyment, yielding the following meta-requirement.
 Meta-Requirement: Hedonic systems for DHE should be able to deliver superior
hedonic user experience.
2) Design Principles and Corresponding Testable Hypotheses
Provider-side
Consumer-side
The above figure depicts how to design superior hedonic user experience using the concepts of
learning and personalization. We assume that effective learning helps providers to better
personalize content and service, which in turn helps consumers to learn and use hedonic
technologies effectively and easily. Personalized use of hedonic systems enables consumers to
enjoy DHE effectively, thus creating superior hedonic user experience. Continuous personalized
learning helps consumers to sustain superior hedonic user experience by enhancing their mastery
of hedonic technologies. This theoretical logic is based on our kernel theory, the theory of
exploitation and exploration (March 1991).
Exploitation is a process of enhancing existing alternatives and competencies, while exploration
is a search for new alternatives and innovations. When available knowledge is insufficient for
exploitation, individuals or organizations learn to acquire new knowledge which can complement
existing knowledge. They also learn through exploration for new alternatives reflecting
environmental changes (McGrath 2001). Thus, exploitation-initiated learning enhances reliability
and short-run performance while exploration-initiated learning enhances variety and long-run
survival. Levinthal and March (1993) argued that a proper balance between exploitation and
exploration should be achieved in order to overcome both the problem of obsolescence by
exploitation and the uncertainty of performance by exploration. In the DHE context, people
exploit their knowledge for current enjoyment and explore new technologies, content, and
services for enhancing their future enjoyment. To design every touch point between consumers
and their products in a way that superior user experience can be delivered, providers should
continuously learn how consumers interact with their products and exploit intelligence to provide
personalized services. In sum, both providers and users should balance between exploitative and
explorative learning.
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Mutual learning between users and providers is an effective way to achieve these balances. If the
learners find inconsistencies among their assumptions or between their assumptions and those of
others, they learn by exploring these inconsistencies (Majchrzak et al. 2005). Since these
inconsistencies are more likely found by others, providers and users can have effective learning
chances by communicating with each other. In addition to these externally initiated mutual
learning chances, learners have internally initiated learning chances through their-own routines.
For the user-side, “provider-directed instructions (e.g., expert instruction at customer support
centers)” are externally initiated learning chances, while “user-directed self-discoveries (e.g.,
fiddle and try new features)” are internally initiated learning chances. For the provider-side,
“structured feedbacks (e.g., customer technical support records)” are externally initiated learning
chances, while “open-ended information gatherings (e.g., chance conversations with vendors,
friends, or colleagues)” are internally initiated learning chances. In sum, mutual learning enables
the balance between exploitative and explorative learning mode. This balance is further
supported by balancing external and internal learning chances. Based on this rationale, we
propose the following two design principles.
Design Principle 1: Hedonic systems should provide communication channels through which
providers and users can exchange information, resulting in continuous mutual learning.
Design Principle 2: Hedonic systems should support the balance between internally initiated
and externally initiated learning.
To verify the validity of these principles, we suggest the following hypotheses.
Hypothesis 1: The balanced mix of two learning modes between structured feedbacks and
open-ended information gatherings enhances the quality of provider intelligence.
Hypothesis 2: The balanced mix of two learning modes between user-directed self-discoveries
and provider-directed instructions enhances the quality of users’ mastery of services and
technologies in DHE.
Pine and Gilmore (1998) argued that “no two people can have the same experience because each
experience derives from the interaction between the staged event and the individual’s state of
mind.” Also by its definition, user experience is personal and subjective perception. Therefore,
personalization should be a key factor in designing superior user experience. Users are more
interested in personalized content and services, perceive them to be more useful, and tend to
adopt and interact with them more than non-personalized content and services (Tam and Ho
2006; Komiak and Benbasat 2006). Users can recall personalized content faster and more
accurately and spend less time and effort to process personalized information (Tam and Ho
2006). Therefore, personalization supports the delivery of superior user experience indirectly by
helping users to learn and use hedonic systems easily and effectively.
Design Principle 3: Hedonic systems should support personalized learning and use through
the exploitation of providers’ intelligence.
To verify the validity of this principle, we suggest the following two hypotheses.
Hypothesis 3: Personalization helps each user to learn hedonic technologies easily.
Hypothesis 4: Personalization helps each user to use content and services easily.
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Thompson et al. (2005) revealed that customers choose a product with more features even at the
expense of usability, but once they have used a product, their preference changes, that is, ease of
use matters much more than utility. “Functionality is not always more important than hedonics,
rather once a cut-off level of functionality is met, hedonics assume greater weight in consumers’
minds (Chitturi et al. 2007).” Because ease of use is the measure of easiness of the interaction
and hedonic systems focus more on the interaction than on the outcome, ease of use is more
related to hedonic behavior than to utilitarian behavior. Van der Heijden (2004) proved that ease
of use take dominance over usefulness in hedonic systems. We now have the last design
principle.
Design Principle 4: Hedonic systems for DHE should have more focus on usability than
functionality.
We suggest the following hypothesis to verify the validity of this principle.
Hypothesis 5: Usability of technologies has more positive impact on delivering superior user
experience than functionality.
Testing the Theory
Since hedonic systems for DHE are emerging and little user data is currently available, experts’
in-use knowledge can be one of the best resources to test our hypotheses (Gregor and Jones
2007). We collected data from the survey for 222 industry experts. Each respondent was given
one of five highly possible evolving scenarios which project near future: home theatre, gaming
arcade, mobile family, Internet PC TV, and Internet DVR scenarios. Data analysis is underway
to evaluate the proposed hypotheses.
Conclusion
Design science research should suggest either innovative ways to solve important problems or
more effective or efficient ways to enhance existing solutions (Gregor and Jones 2007; Hevner et
al. 2004). This study addresses a new design problem of delivering superior hedonic user
experience in the emerging DHE context and then suggests a design theory for hedonic systems
as a new solution. We did not include learner types into our design theory. The enjoyment
behaviors can be different depending on the learner’s types such as learners enjoying exploratory
learning, learners only interested in specific areas, or resistant learners. We did not consider
cultural and demographic factors or system features such as interoperability and connectivity
between hedonic technologies. Taking these factors into account should be an interesting
research topic for enhancing our understanding of the emerging hedonic systems and context.
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