Cyber-Empathic Design: A data-driven framework for product design

• Main Authors: GHOSH, Dipanjan, OLEWNIK, Andrew, LEWIS, Kemper, KIM, Junghan, LAKSHAMAN, Arun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: cyber-empathic design; data-driven design; design methodology; product design; product development; Marketing; Technology and Innovation
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/5316; https://ink.library.smu.edu.sg/context/lkcsb_research/article/6315/viewcontent/md_139_09_091401_pv.pdf
• Institution: Singapore Management University

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.