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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

Mem. ASME
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
Professor
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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References

Cross, N. , 2008, Engineering Design Methods: Strategies for Product Design, Wiley, Chichester, UK.
Ulrich, K. , and Eppinger, S. , 2011, Product Design and Development, 5th ed., McGraw-Hill Education, New York.
Otto, K. , and Wood, K. , 2000, Product Design: Techniques in Reverse Engineering and New Product Development, Pearson, Upper Saddle River, NJ.
Hauser, J. R. , and Clausing, D. , 1988, “ The House of Quality,” Harv. Bus. Rev., May, pp. 63–73. https://hbr.org/1988/05/the-house-of-quality
Akao, Y. , 2004, QFD: Quality Function Deployment—Integrating Customer Requirements Into Product Design, Productivity Press, Cambridge, MA.
Chan, L. K. , and Wu, M. L. , 2002, “ Quality Function Deployment: A Literature Review,” Eur. J. Oper. Res., 143(3), pp. 463–497. [CrossRef]
Louviere, J. J. , Hensher, D. , Swait, J. , and Adamowicz, W. , 2000, Stated Choice Methods: Analysis and Application, Cambridge University Press, Cambridge, UK.
Klayman, J. , and Ha, Y. , 1987, “ Confirmation, Disconfirmation, and Information in Hypothesis Testing,” Psychol. Rev., 94(2), pp. 211–228. [CrossRef]
Nest Labs, 2015, “ Meet the Nest Thermostat,” Nest, Palo Alto, CA, accessed Nov. 14, 2015, https://nest.com/thermostat/meet-nest-thermostat/
Burns, A. , Barrett, R. , Evans, S. , and Johansson, C. , 1999, “ Delighting Customers Through Empathic Design,” Sixth International Product Development Management Conference, Churchill College, Cambridge, UK, July 5–6. http://www.gweep.net/~tpollard/IDPA/IDPA_2K7/IDPA_Feb_07/scholar.htm
Burns, A. , and Evans, S. , 2001, “ Empathic Design: A New Approach for Understanding and Delighting Customers,” Int. J. New Prod. Dev. Innovation Manage., 3(4), p. 313.
Lin, J. , and Seepersad, C. C. , 2007, “ Empathic Lead Users: The Effects of Extraordinary User Experiences on Customer Needs Analysis and Product Redesign,” ASME Paper No. DETC2007-35302.
Chaudha, A. , Jain, R. , Singh, A. R. , and Mishra, P. K. , 2010, “ Integration of Kano's Model Into Quality Function Deployment (QFD),” Int. J. Adv. Manuf. Technol., 53(5–8), pp. 689–698.
Olewnik, A. T. , and Lewis, K. , 2005, “ On Validating Engineering Design Decision Support Tools,” Concurrent Eng., 13(2), pp. 111–122. [CrossRef]
Hazelrigg, G. A. , 1998, “ A Framework for Decision-Based Engineering Design,” ASME J. Mech. Des., 120(4), pp. 653–658. [CrossRef]
Jin, Y. , Kim, D. , and Danesh, M. R. , 2006, “ Value Based Design: An Objective Structuring Approach to Design Concept Generation,” ASME Paper No. DETC2006-99497.
Gonzalez-Zugasti, J. P. , Otto, K. N. , and Baker, J. D. , 2001, “ Assessing Value in Platformed Product Family Design,” Res. Eng. Des., 13(1), pp. 30–41. [CrossRef]
Keeney, R. L. , and Raiffa, H. , 1993, Decisions With Multiple Objectives: Preferences and Value Tradeoffs, Cambridge University Press, Cambridge, UK.
Li, H. , and Azarm, S. , 1999, “ Product Design Selection Under Uncertainty and With Competitive Advantage,” ASME J. Mech. Des., 122(4), pp. 411–418. [CrossRef]
Wassenaar, H. J. , and Chen, W. , 2003, “ An Approach to Decision-Based Design With Discrete Choice Analysis for Demand Modeling,” ASME J. Mech. Des., 125(3), pp. 490–497. [CrossRef]
Shiau, C. S. N. , and Michalek, J. J. , 2009, “ Optimal Product Design Under Price Competition,” ASME J. Mech. Des., 131(7), p. 071003. [CrossRef]
Hausman, J. , and McFadden, D. , 1984, “ Specification Tests for the Multinomial Logit Model,” Econometrica, 52(5), pp. 1219–1240. [CrossRef]
Koppelman, F. S. , and Vaneet, S. , 2000, “ Closed Form Discrete Choice Models,” Handbook of Transport Modelling, Pergamon, New York, pp. 211–222.
Train, K. E. , 2009, Discrete Choice Methods With Simulation, Cambridge University Press, Cambridge, UK.
Michalek, J. J. , 2005, “ Preference Coordination in Engineering Design Decision-Making,” Ph.D. dissertation, University of Michigan, Ann Arbor, MI. https://deepblue.lib.umich.edu/handle/2027.42/124888
Sullivan, E. , Ferguson, S. , and Donndelinger, J. , 2011, “ Exploring Differences in Preference Heterogeneity Representation and Their Influence in Product Family Design,” ASME Paper No. DETC2011-48596.
Hoyle, C. , Chen, W. , Wang, N. , and Gomez-Levi, G. , 2011, “ Understanding and Modelling Heterogeneity of Human Preferences for Engineering Design,” J. Eng. Des., 22(8), pp. 583–601. [CrossRef]
Kumar, D. , Hoyle, C. , Chen, W. , Wang, N. , Levi, G. G. , and Koppelman, F. S. , 2009, “ A Hierarchical Choice Modelling Approach for Incorporating Customer Preferences in Vehicle Package Design,” Int. J. Prod. Dev., 8(3), p. 228. [CrossRef]
Olewnik, A. , and Hariharan, V. G. , 2010, “ Conjoint-HOQ: Evolving a Methodology to Map Market Needs to Product Profiles,” Int. J. Prod. Dev., 10(4), pp. 338–368. [CrossRef]
Michalek, J. J. , Feinberg, F. M. , Papalambros, P. Y. , and Wedel, M. , 2005, “ Linking Marketing and Engineering Product Design Decisions Via Analytical Target Cascading,” J. Prod. Innovation Manage., 22(1), pp. 42–62. [CrossRef]
Resende, C. B. , Grace Heckmann, C. , and Michalek, J. J. , 2012, “ Robust Design for Profit Maximization With Aversion to Downside Risk From Parametric Uncertainty in Consumer Choice Models,” ASME J. Mech. Des., 134(10), p. 100901. [CrossRef]
Bradlow, E. T. , 2005, “ Current Issues and a ‘Wish List’ for Conjoint Analysis,” Appl. Stochastic Models Bus. Ind., 21(4–5), pp. 319–323. [CrossRef]
MacDonald, E. F. , Gonzalez, R. , and Papalambros, P. Y. , 2009, “ Preference Inconsistency in Multidisciplinary Design Decision Making,” ASME J. Mech. Des., 131(3), p. 031009. [CrossRef]
Preiser, W. , and Smith, K. H. , 2010, Universal Design Handbook, 2nd ed., McGraw-Hill Education, New York.
Gibson, J., 1977, “The Theory of Affordances,” Perceiving, Acting, and Knowing: Toward an Ecological Psychology, R. Shaw and J. Bransford, eds., Erlbaum Associates, Hillsdale, NJ, pp. 67–82.
Maier, J. R. A. , and Fadel, G. M. , 2008, “ Affordance Based Design: A Relational Theory for Design,” Res. Eng. Des., 20(1), pp. 13–27. [CrossRef]
Maier, J. R. A. , and Fadel, G. M. , 2009, “ Affordance-Based Design Methods for Innovative Design, Redesign and Reverse Engineering,” Res. Eng. Des., 20(4), pp. 225–239. [CrossRef]
Cormier, P. , Olewnik, A. , and Lewis, K. , 2014, “ Towards a Formalization of Affordance Modeling in the Early Stages of Design,” Res. Eng. Des., 25(3), pp. 259–277. [CrossRef]
Lim, S. , and Tucker, C. S. , 2016, “ A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data,” ASME J. Mech. Des., 138(6), p. 061403. [CrossRef]
Kang, S. W. , and Tucker, C. , 2016, “ An Automated Approach to Quantifying Functional Interactions by Mining Large-Scale Product Specification Data,” J. Eng. Des., 27(1–3), pp. 1–24. [CrossRef]
Tuarob, S. , and Tucker, C. S. , 2015, “ Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks,” ASME J. Mech. Des., 137(7), p. 071402. [CrossRef]
Singh, A. , and Tucker, C. S. , 2015, “ Investigating the Heterogeneity of Product Feature Preferences Mined Using Online Product Data Streams,” ASME Paper No. DETC2015-47439.
Kang, S. W. , and Tucker, C. S. , 2016, “ Automated Mapping of Product Features Mined From Online Customer Reviews to Engineering Product Characteristics,” ASME Paper No. DETC2016-59772.
Burnap, A. , Pan, Y. , Liu, Y. , Ren, Y. , Lee, H. , Gonzalez, R. , and Papalambros, P. Y. , 2016, “ Improving Design Preference Prediction Accuracy Using Feature Learning,” ASME J. Mech. Des., 138(7), p. 071404. [CrossRef]
Wang, M. , Sha, Z. , Huang, Y. , Contractor, N. , Fu, Y. , and Chen, W. , 2016, “ Forecasting Technological Impacts on Customers' Co-Consideration Behaviors: A Data-Driven Network Analysis Approach,” ASME Paper No. DETC2016-60015.
Wang, M. , Huang, Y. , Contractor, N. , Fu, Y. , and Chen, W. , 2016, “ A Network Approach for Understanding and Analyzing Product Co-Consideration Relations in Engineering Design,” International Design Conference, Dubrovnik, Croatia, May 16–19, pp. 1965–1976. https://www.designsociety.org/publication/39006/a_network_approach_for_understanding_and_analyzing_product_co-consideration_relations_in_engineering_design
Wang, M. , and Chen, W. , 2015, “ A Data-Driven Network Analysis Approach to Predicting Customer Choice Sets for Choice Modeling in Engineering Design,” ASME J. Mech. Des., 137(7), p. 071410. [CrossRef]
Wang, M. , Chen, W. , Fu, Y. , and Yang, Y. , 2016, “ Analyzing and Predicting Heterogeneous Customer Preferences in China's Auto Market Using Choice Modeling and Network Analysis,” SAE Int. J. Mater. Manf., 8(3), pp. 668–677.
Porter, M. E. , and Heppelmann, J. E. , 2015, “ How Smart, Connected Products are Transforming Companies,” Harv. Bus. Rev., 92(11), pp. 64–88. https://hbr.org/2015/10/how-smart-connected-products-are-transforming-companies
Greenough, J. , 2015, “ The ‘Internet of Things' Will Be the World's Most Massive Device Market and Save Companies Billions of Dollars,” Business Insider, New York, accessed June 2, 2017, http://www.businessinsider.com/the-internet-of-things-market-growth-and-trends-2015-2
van der Vegte, W. F. , 2016, “ Taking Advantage of Data Generated by Products: Trends, Opportunities and Challenges,” ASME Paper No. DETC2016-59177.
Lee, J. , 1995, “ Machine Performance Monitoring and Proactive Maintenance in Computer-Integrated Manufacturing: Review and Perspective,” Int. J. Comput. Integr. Manuf., 8(5), pp. 370–380. [CrossRef]
Johanson, M. , Dahle, P. , and Soderberg, A. , 2011, “ Remote Vehicle Diagnostics Over the Internet Using the DoIP Protocol,” Sixth International Conference on Systems and Networks Communications (ICSNC), Barcelona, Spain, Oct. 23–28, pp. 226–231. https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwj5tsia8p7UAhVn7YMKHfG4BMQQFggtMAA&url=https%3A%2F%2Fwww.thinkmind.org%2Fdownload.php%3Farticleid%3Dicsnc_2011_10_10_20096&usg=AFQjCNEskXbP8UnwC2cL5If64AxWpLR14A
Tahat, A. , Said, A. , Jaouni, F. , and Qadamani, W. , 2012, “ Android-Based Universal Vehicle Diagnostic and Tracking System,” IEEE 16th International Symposium on Consumer Electronics (ISCE), Harrisburg, PA, June 4–6, pp. 137–143.
Redding, L. , 2015, “ Through-Life Engineering Services: Definition and Scope: A Perspective From the Literature,” Through-Life Engineering Services, Springer, Cham, Switzerland, pp. 13–28.
Van Horn, D. , and Lewis, K. , 2015, “ The Use of Analytics in the Design of Sociotechnical Products,” Artif. Intell. Eng. Des. Anal. Manuf., 29(1), pp. 65–81. [CrossRef]
Anderson, J. C. , and Gerbing, D. W. , 1988, “ Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,” Psychol. Bull., 103(3), pp. 411–423. [CrossRef]
Mackenzie, S. B. , 2001, “ Opportunities for Improving Consumer Research Through Latent Variable Structural Equation Modeling,” J. Consum. Res., 28(1), pp. 159–166. [CrossRef]
Niles, H. E. , 1922, “ Correlation, Causation and Wright's Theory of ‘Path Coefficients’,” Genetics, 7(3), pp. 258–273. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1200533/pdf/258.pdf [PubMed]
Tenenhaus, M. , Vinzi, V. E. , Chatelin, Y. M. , and Lauro, C. , 2005, “ PLS Path Modeling,” Comput. Stat. Data Anal., 48(1), pp. 159–205. [CrossRef]
Chin, W. W. , 1998, “ Commentary: Issues and Opinion on Structural Equation Modeling,” MIS Q., 22(1), pp. 7–16. http://www.jstor.org/stable/249674
Alba, J. W. , and Hutchinson, J. W. , 1987, “ Dimensions of Consumer Expertise,” J. Consum. Res., 13(4), pp. 411–454. [CrossRef]
Thompson, D. V. , Hamilton, R. W. , and Rust, R. T. , 2005, “ Feature Fatigue: When Product Capabilities Become Too Much of a Good Thing,” J. Mark. Res., 42(4), pp. 431–442. [CrossRef]
Venkatesh, V. , Thong, J. Y. L. , and Xu, X. , 2012, “ Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology,” MIS Q., 36(1), pp. 157–178. http://misq.org/consumer-acceptance-and-use-of-information-technology-extending-the-unified-theory-of-acceptance-and-use-of-technology.html
Davis, F. D. , 1989, “ Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Q., 13(3), pp. 319–340. [CrossRef]
Ajzen, I. , 1991, “ Theories of Cognitive Self-Regulation: The Theory of Planned Behavior,” Organ. Behav. Hum. Decis. Processes, 50(2), pp. 179–211. [CrossRef]
Ghosh, D. , Kim, J. , Olewnik, A. , Lakshmanan, A. , and Lewis, K. , 2016, “ Cyber-Empathic Design: A Data Driven Framework for Product Design,” ASME Paper No. DETC2016-59642.
Helander, M. G. , 2003, “ Forget About Ergonomics in Chair Design? Focus on Aesthetics and Comfort,” Ergonomics, 46(13–14), pp. 1306–1319. [CrossRef] [PubMed]
Luo, L. , Kannan, P. , and Ratchford, B. T. , 2008, “ Incorporating Subjective Characteristics in Product Design and Evaluations,” J. Mark. Res., 45(2), pp. 182–194. [CrossRef]
Lee, H. , Grosse, R. , Ranganath, R. , and Ng, A. Y. , 2009, “ Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations,” 26th International Conference on Machine Learning, Montreal, QC, Canada, June 14–18, pp. 609–616. http://web.eecs.umich.edu/~honglak/icml09-ConvolutionalDeepBeliefNetworks.pdf
Lee, H. , Largman, Y. , Pham, P. , and Ng, A. Y. , 2009, “ Unsupervised Feature Learning for Audio Classification Using Convolutional Deep Belief Networks,” International Conference on Neural Information Processing Systems (NIPS), Vancouver, BC, Canada, Dec. 7–10, pp. 1096–1104. http://dl.acm.org/citation.cfm?id=2984217
Sakia, R. , 1992, “ The Box-Cox Transformation Technique: A Review,” Statistician, 41(2), pp. 169–178. [CrossRef]
Hair, J. F., Jr. , Black, W. C. , Babin, B. , and Anderson, R. , 2009, Multivariate Data Analysis, 7th ed., Prentice Hall, Upper Saddle River, NJ.
Shevlin, M. , and Miles, J. N. , 1998, “ Effects of Sample Size, Model Specification and Factor Loadings on the GFI in Confirmatory Factor Analysis,” Pers. Individ. Differ., 25(1), pp. 85–90. [CrossRef]
Sharma, S. , Mukherjee, S. , Kumar, A. , and Dillon, W. R. , 2005, “ A Simulation Study to Investigate the Use of Cutoff Values for Assessing Model Fit in Covariance Structure Models,” J. Bus. Res., 58(7), pp. 935–943. [CrossRef]
Browne, M. W. , and Cudeck, R. , 1989, “ Single Sample Cross-Validation Indices for Covariance Structures,” Multivar. Behav. Res., 24(4), pp. 445–455. [CrossRef]
Ghosh, D. , Olewnik, A. , and Lewis, K. , 2016, “ Product ‘In-Use’ Context Identification Using Feature Learning Methods,” ASME Paper No. DETC2016-59645.

Figures

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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. 6

CB-SEM analysis (survey-based model)

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Fig. 7

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

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Fig. 8

CB-SEM analysis (cyber-empathic 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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