Research Papers: Design Automation

Cyber-Empathic Design: A Data-Driven Framework for Product Design

[+] Author and Article Information
Dipanjan Ghosh

Department of Mechanical and
Aerospace Engineering,
University at Buffalo—SUNY,
805 Furnas Hall,
Buffalo, NY 14260
e-mail: dipanjan@buffalo.edu

Andrew Olewnik

Department of Mechanical and
Aerospace Engineering,
University at Buffalo—SUNY,
412 Bonner Hall,
Buffalo, NY 14260
e-mail: olewnik@buffalo.edu

Kemper Lewis

Fellow ASME
Department of Mechanical and
Aerospace Engineering,
University at Buffalo—SUNY,
208 Bonner Hall,
Buffalo, NY 14260
e-mail: kelewis@buffalo.edu

Junghan Kim

Marketing Department,
School of Management,
University at Buffalo—SUNY,
234B Jacobs Management Center,
Buffalo, NY 14260
e-mail: junghank@buffalo.edu

Arun Lakshmanan

Marketing Department,
School of Management,
University at Buffalo—SUNY,
215A Jacobs Management Center,
Buffalo, NY 14260
e-mail: alakshma@buffalo.edu

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 20, 2016; final manuscript received April 22, 2017; published online July 12, 2017. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 139(9), 091401 (Jul 12, 2017) (12 pages) Paper No: MD-16-1658; doi: 10.1115/1.4036780 History: Received September 20, 2016; Revised April 22, 2017

A critical task in product design is mapping information from consumer to design space. Currently, this process largely depends on designers identifying and mapping psychological and consumer level factors to engineered attributes. In this way, current methodologies lack provision to test a designer's cognitive reasoning and could introduce bias when mapping from consumer to design space. In addition, current dominant frameworks do not include user–product interaction data in design decision making, nor do they assist designers in understanding why a consumer has a particular perception about a product. This paper proposes a framework—cyber-empathic (CE) design—where user–product interaction data are acquired using embedded sensors. To gain insight into consumer perceptions relative to product features, a network of psychological constructs is utilized. Structural equation modeling (SEM) is used as the parameter estimation and hypothesis testing technique, making the framework falsifiable in nature. To demonstrate effectiveness of the framework, a case study of sensor-integrated shoes is presented, where two models are compared—one survey-only and one using the cyber-empathic framework model. Covariance-based SEM (CB-SEM) is used to estimate the parameters and the fit indices. It is shown that the cyber-empathic framework results in improved fit over a survey-only SEM. This work demonstrates how low-level user–product interaction data can be used to understand and model user perceptions in a way that can support falsifiable design inference.

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Fig. 1

Analytical model of cyber-empathic design framework

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Fig. 2

Representative cyber-empathic design causal model derived from technology adoption model

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Fig. 3

Foot areas and sensor integrated shoe insert: (a)1 foot areas for sensor placement and (b) sensor integrated shoe insert

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Fig. 4

Cyber-empathic hypothesis for case study

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Fig. 5

Case study analysis procedure

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Fig. 8

CB-SEM analysis (cyber-empathic model)

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Fig. 7

Re-evaluated CB-SEM analysis (survey-based model)

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Fig. 6

CB-SEM analysis (survey-based model)

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Fig. 9

Re-evaluated CB-SEM analysis (cyber-empathic model)

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Fig. 10

Sensor groups from factor analysis2




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