Research Papers: Design Theory and Methodology

Design for Sustainable Use of Appliances: A Framework Based on User Behavior Observations

[+] Author and Article Information
Chathura Withanage

SUTD-MIT International Design Centre,
Singapore University of Technology
and Design (SUTD),
Singapore 487372, Singapore
e-mail: chathoo@gmail.com

Katja Hölttä-Otto

Associate Professor
Department of Mechanical Engineering,
Aalto University,
Espoo 02150, Finland
e-mail: katja.holtta-otto@aalto.fi

Kevin Otto

Adjunct Professor
Department of Mechanical Engineering,
Aalto University,
Espoo 02150, Finland
e-mail: kevin.otto@aalto.fi

Kristin Wood

Professor and Head of the Pillar
Engineering Product Development (EPD) Pillar,
Singapore University of Technology
and Design (SUTD),
Singapore 487372, Singapore
e-mail: kristinwood@sutd.edu.sg

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received October 15, 2015; final manuscript received June 22, 2016; published online August 30, 2016. Assoc. Editor: Mathew I. Campbell.

J. Mech. Des 138(10), 101102 (Aug 30, 2016) (12 pages) Paper No: MD-15-1706; doi: 10.1115/1.4034084 History: Received October 15, 2015; Revised June 22, 2016

User behavior can determine over one third of the energy consumed in the residential energy market. Thus, user behavior has become a primary focus in sustainable mechanical device, appliance, and smart-energy systems design. Wasteful user behaviors, termed energy overuse failure modes (EOFMs), offer an opportunity for design engineers to direct users toward more sustainable behavior through design strategies. There are fundamentally two intervention strategies: (1) product or systems solution led or (2) behavioral led. Both are used to achieve increased sustainable user behavior. To ensure expected intervention outcomes, it is equally important to both identify the EOFMs as well as their underlying causes. However, the prevailing sustainable design approaches, such as design for sustainable behavior (DfSB) and ecodesign, depend on stated responses to elicit underlying causes of behavior. Consequently, the outcomes of these approaches are susceptible to response biases. In this paper, a new revealed behavior based framework is introduced to elicit underlying causes of EOFMs and to propose potential intervention strategies to address them. We focus on uncovering two underlying causes that correspond to the intervention strategies: (1) high energy consuming habits and (2) lack of energy awareness. In the proposed framework, user behavior categorization matrices are formulated using a two-phase user study approach with a request to lower the energy use in-between the phases. Based on the observed behavior, each EOFM is matrix categorized on two axes of change and correctness. With this data, the matrices thereby indicate the dominant underlying causes of EOFMs. The EOFMs and proposed interventions can then be prioritized based on the likelihood of occurrence, severity, magnitude or a combinatorial strategy to suit the sustainability objectives. A case study is presented with seven EOFMs that are found in typical day-to-day household electromechanical appliance use including inefficient appliance setup, inefficient selection, inefficient operation, standby energy consumption, and inefficient settings of conditions. Lack of user awareness of energy and power interactions among appliances and household settings is identified as the key underlying cause of considered EOFMs. Potential design solution strategies are also considered to overcome the EOFMs based on likelihoods, severities, and magnitudes, respectively. Each solution strategy carries a varying level of knowledgeable decision-making required of the user, compared with alternatively designing into the product or systems restrictions on use.

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Grahic Jump Location
Fig. 2

User behavior categorization matrix

Grahic Jump Location
Fig. 1

A revealed behavior-based framework for identification and categorization of design opportunities

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Fig. 8

Behavior categorization matrices, severities, likelihoods, and magnitudes

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Fig. 3

(a) Future living lab setup, (b) a participant making cereal, and (c) a participant watching the news

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Fig. 6

Activity flow diagram with energy/power measurements of EOFMs

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Fig. 4

The experimental procedure

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Fig. 5

Tasks to be completed as shown in the cards

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Fig. 7

Occurrence of energy overconsumption failure modes in regular use (first phase) and when trying to reduce consumption (second phase)



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