Cyber-Empathic Design: A Data-Driven Framework for Product Design
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...
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| Autor*in: |
Ghosh, Dipanjan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of mechanical design - New York, NY : ASME, 1978, 139(2017), 9 |
|---|---|
| Übergeordnetes Werk: |
volume:139 ; year:2017 ; number:9 |
| Links: |
|---|
| DOI / URN: |
10.1115/1.4036780 |
|---|
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OLC1997809842 |
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| 520 | |a 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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10.1115/1.4036780 doi PQ20171228 (DE-627)OLC1997809842 (DE-599)GBVOLC1997809842 (PRQ)a912-21bcf93cac3321a482da7664f1a24e49ab052442cbaefad65de31eb7acb5e9d60 (KEY)0128972120170000139000900000cyberempathicdesignadatadrivenframeworkforproductd DE-627 ger DE-627 rakwb eng 620 DNB Ghosh, Dipanjan verfasserin aut Cyber-Empathic Design: A Data-Driven Framework for Product Design 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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. Olewnik, Andrew oth Lewis, Kemper oth Kim, Junghan oth Lakshmanan, Arun oth Enthalten in Journal of mechanical design New York, NY : ASME, 1978 139(2017), 9 (DE-627)129615293 (DE-600)243787-9 (DE-576)01511290X 1050-0472 nnns volume:139 year:2017 number:9 http://dx.doi.org/10.1115/1.4036780 Volltext http://dx.doi.org/10.1115/1.4036780 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 AR 139 2017 9 |
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10.1115/1.4036780 doi PQ20171228 (DE-627)OLC1997809842 (DE-599)GBVOLC1997809842 (PRQ)a912-21bcf93cac3321a482da7664f1a24e49ab052442cbaefad65de31eb7acb5e9d60 (KEY)0128972120170000139000900000cyberempathicdesignadatadrivenframeworkforproductd DE-627 ger DE-627 rakwb eng 620 DNB Ghosh, Dipanjan verfasserin aut Cyber-Empathic Design: A Data-Driven Framework for Product Design 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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. Olewnik, Andrew oth Lewis, Kemper oth Kim, Junghan oth Lakshmanan, Arun oth Enthalten in Journal of mechanical design New York, NY : ASME, 1978 139(2017), 9 (DE-627)129615293 (DE-600)243787-9 (DE-576)01511290X 1050-0472 nnns volume:139 year:2017 number:9 http://dx.doi.org/10.1115/1.4036780 Volltext http://dx.doi.org/10.1115/1.4036780 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 AR 139 2017 9 |
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Cyber-Empathic Design: A Data-Driven Framework for Product Design |
| abstract |
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. |
| abstractGer |
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. |
| abstract_unstemmed |
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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| container_issue |
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| title_short |
Cyber-Empathic Design: A Data-Driven Framework for Product Design |
| url |
http://dx.doi.org/10.1115/1.4036780 |
| remote_bool |
false |
| author2 |
Olewnik, Andrew Lewis, Kemper Kim, Junghan Lakshmanan, Arun |
| author2Str |
Olewnik, Andrew Lewis, Kemper Kim, Junghan Lakshmanan, Arun |
| ppnlink |
129615293 |
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| doi_str |
10.1115/1.4036780 |
| up_date |
2024-07-04T03:44:43.570Z |
| _version_ |
1803618545647484928 |
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7.400137 |