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research-article

CYBER- EMPATHIC DESIGN - A DATA DRIVEN FRAMEWORK FOR PRODUCT DESIGN

[+] Author and Article Information
Dipanjan Ghosh

Department of Mechanical and Aerospace Engineering 805 Furnas Hall University at Buffalo - SUNY Buffalo, New York - 14260
dipanjan@buffalo.edu

Andrew Olewnik

Department of Mechanical and Aerospace Engineering 412 Bonner Hall University at Buffalo - SUNY Buffalo, New York - 14260
olewnik@buffalo.edu

Kemper Lewis

Department of Mechanical and Aerospace Engineering 208 Bonner Hall University at Buffalo - SUNY Buffalo, New York - 14260
kelewis@buffalo.edu

Junghan Kim

Marketing Department School of Management 234B Jacobs Management Center University at Buffalo - SUNY Buffalo, New York - 14260
junghank@buffalo.edu

Arun Lakshmanan

Marketing Department School of Management 215A Jacobs Management Center University at Buffalo - SUNY Buffalo, New York 14260
alakshma@buffalo.edu

1Corresponding author.

ASME doi:10.1115/1.4036780 History: Received September 20, 2016; Revised April 22, 2017

Abstract

Background: 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. Method of approach: This paper proposes a new framework - Cyber-Empathic Design - where user-product interaction data is acquired via embedded sensors. To understand the motivations behind consumer perceptions, a network of latent constructs forms a causal model framework. Structural Equation Modeling (SEM) is used as the parameter estimation and hypothesis testing technique, making the framework falsifiable in nature. Results: To demonstrate the framework, a case study of sensor-integrated shoes is presented, where two models are compared - one Survey-based and one using the Cyber-Empathic framework model. Two methods are used to estimate the parameters and the fit indices - Covariance based SEM and Partial Least Square SEM. It is shown that the Cyber-Empathic framework results in improved fit using both estimation techniques over survey-only SEM. Conclusion: 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.

Copyright (c) 2017 by ASME
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