Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-25T16:25:52.308Z Has data issue: false hasContentIssue false

A FRAMEWORK FOR PREDICTING POTENTIAL PRODUCT IMPACT DURING PRODUCT DESIGN

Published online by Cambridge University Press:  27 July 2021

Christopher S. Mabey*
Affiliation:
Brigham Young University
Andrew G. Armstrong
Affiliation:
Brigham Young University
Christopher A. Mattson
Affiliation:
Brigham Young University
John L. Salmon
Affiliation:
Brigham Young University
Nile W. Hatch
Affiliation:
Brigham Young University
*
Mabey, Christopher S., Brigham Young University, Mechanical Engineering, United States of America, [email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The impact of products is becoming a topic of concern in society. Product impact may fall under the categories of economic, environmental, or social impact and is defined by the effect of a product on day-to-day life. Design teams lack sufficient tools to predict the impact of products they are designing. In this paper we present a framework for the prediction of product impact during product design. This framework integrates models of the product, scenario, society, and impact into an agent-based model to predict product impact. Although this paper focuses on social impact, the framework can also be applied to economic or environmental impacts. An illustration of using the framework is also presented. Agent-based modeling has been used previously for adoption models, but it has not been extended to predict product impact. Having tools for impact prediction allows for optimizing the product design parameters to increase potential positive impact and reduce potential negative impact.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Ajzen, I. (1991), ‘The theory of planned behavior’, Organizational Behavior and Human Decision Processes 50(2), 179211. URL: https://doi.org/10.1016/0749-5978(91)90020-TCrossRefGoogle Scholar
Axelrod, R. (1997), ‘Advancing the art of simulation in the social sciences’, Complexity 3(2), 1622.3.0.CO;2-K>CrossRefGoogle Scholar
Benoît, C., Norris, G. A., Valdivia, S., Ciroth, A., Moberg, A., Bos, U., Prakash, S., Ugaya, C. and Beck, T. (2010), ‘The guidelines for social life cycle assessment of products: just in time!’, The International Journal of Life Cycle Assessment 15(2), 156163. URL: https://doi.org/10.1007/s11367-009-0147-8CrossRefGoogle Scholar
Bonabeau, E. (2002), ‘Agent-based modeling: Methods and techniques for simulating human systems’, Proceedings of the National Academy of Sciences of the United States of America 99(SUPPL. 3), 72807287. URL: https://doi.org/10.1073/pnas.082080899CrossRefGoogle ScholarPubMed
Brock, W. A. and Durlauf, S. N. (2001), ‘Discrete choice with social interactions’, Review of Economic Studies. URL: https://doi.org/10.1111/1467-937X.00168CrossRefGoogle Scholar
Burdge, R. J. (2015), A community guide to social impact assessment, fourth edn, Social Ecology Press, Huntsville.Google Scholar
Chen, X. and Kelly, T. F. (2015), ‘B-Corps—A Growing Form of Social Enterprise: Tracing Their Progress and Assessing Their Performance’, Journal of Leadership & Organizational Studies 22(1), 102114. URL: https://doi.org/10.1177/1548051814532529CrossRefGoogle Scholar
Esteves, A. M., Franks, D. and Vanclay, F. (2012), ‘Social impact assessment: the state of the art’, Impact Assessment and Project Appraisal 30(1), 3442. URL: https://doi.org/10.1080/14615517.2012.660356CrossRefGoogle Scholar
Fischer, E. P., Fischer, M. C., Grass, D., Henrion, I., Warren, W. S. and Westman, E. (2020), ‘Low-cost measurement of face mask efficacy for filtering expelled droplets during speech’, Science Advances 6(36). URL: https://doi.org/10.1126/sciadv.abd3083CrossRefGoogle ScholarPubMed
Fontes, J., Gaasbeek, A., Goedkoop, M., Contreras, S. and Evitts, S. (2016), ‘Handbook for Product Social Impact Assessment 3.0’. URL: https://doi.org/10.13140/RG.2.2.23821.74720CrossRefGoogle Scholar
Fontes, J., Tarne, P., Traverso, M. and Bernstein, P. (2018), ‘Product social impact assessment’, The International Journal of Life Cycle Assessment 23(3), 547555. URL: https://doi.org/10.1007/s11367-016-1125-6CrossRefGoogle Scholar
Gargiulo, F., Ternes, S., Huet, S. and Deffuant, G. (2010), ‘An iterative approach for generating statistically realistic populations of households’, PloS one 5(1), e8828. URL: https://doi.org/10.1371/journal.pone.0008828CrossRefGoogle Scholar
Gelles, D. and Yaffe-Bellany, D. (2019), ‘Shareholder Value Is No Longer Everything, Top C.E.O.s Say’. URL: https://www.nytimes.com/2019/08/19/business/business-roundtable-ceoscorporations.htmlGoogle Scholar
He, L., Wang, M., Chen, W. and Conzelmann, G. (2014), ‘Incorporating social impact on new product adoption in choice modeling: A case study in green vehicles’, Transportation Research Part D: Transport and Environment 32, 421434. URL: http://doi.org/10.1016/j.trd.2014.08.007CrossRefGoogle Scholar
Howard, M. C. (2020), ‘Understanding face mask use to prevent coronavirus and other illnesses: Development of a multidimensional face mask perceptions scale’, British Journal of Health Psychology 25(4), 912924. URL: https://doi.org/10.1111/bjhp.12453CrossRefGoogle ScholarPubMed
Igielnik, R. (2020), Most Americans say they regularly wore a mask in stores in the past month; fewer see others doing it, Technical report, Pew Research Center. URL: https://www.pewresearch.org/fact-tank/2020/06/23/most-americans-say-they-regularly-wore-amask-in-stores-in-the-past-month-fewer-see-others-doing-it/Google Scholar
Latora, V. and Marchiori, M. (2001), ‘Efficient Behavior of Small-World Networks’, Phys. Rev. Lett. 87(19), 198701. URL: https://doi.org/10.1103/PhysRevLett.87.198701CrossRefGoogle ScholarPubMed
Li, H. and Azarm, S. (2000), ‘Product design selection under uncertainty and with competitive advantage’, Journal of Mechanical Design, Transactions of the ASME. URL: https://doi.org/10.1115/1.1311788CrossRefGoogle Scholar
Macal, C. M. and North, M. J. (2005), ‘Tutorial on agent-based modeling and simulation’, Proceedings - Winter Simulation Conference 2005, 215.Google Scholar
Mattson, C. A. and Winter, A. G. (2016), ‘Why the Developing World Needs Mechanical Design’, Journal of Mechanical Design 138(7). URL: https://doi.org/10.1115/1.4033549CrossRefGoogle Scholar
Müller, K. and Axhausen, K. W. (2010), ‘Population synthesis for microsimulation: State of the art’, Arbeitsberichte Verkehrs-und Raumplanung 638.Google Scholar
Norman, W. and MacDonald, C. (2004), ‘Getting to the Bottom of “Triple Bottom Line”’, Business Ethics Quarterly. URL: https://doi.org/10.5840/beq200414211CrossRefGoogle Scholar
Pack, A. T., Rose Phipps, E., Mattson, C. A. and Dahlin, E. C. (2019), ‘Social Impact in Product Design, An Exploration of Current Industry Practices’, Journal of Mechanical Design 142(7). URL: https://doi.org/10.1115/1.4045448Google Scholar
Pakravan, M. and MacCarty, N. (2020), ‘an Agent-Based Model for Adoption of Clean Technology Using the Theory of Planned Behavior’, Journal of Mechanical Design pp. 119. URL: https://doi.org/10.1115/1.4047901Google Scholar
Pavlou, P. A. and Fygenson, M. (2006), ‘Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior’, MIS Quarterly 30(1), 115143. URL: https://doi.org/10.2307/25148720CrossRefGoogle Scholar
Rai, V. and Robinson, S. A. (2015), ‘Agent-based modeling of energy technology adoption: Empirical integration of social, behavioral, economic, and environmental factors’, Environmental Modelling & Software 70, 163177. URL: https://doi.org/10.1016/j.envsoft.2015.04.014CrossRefGoogle Scholar
Rainock, M., Everett, D., Pack, A., Dahlin, E. C. and Mattson, C. A. (2018), ‘The social impacts of products: a review’, Impact Assessment and Project Appraisal. URL: https://doi.org/10.1080/14615517.2018.1445176CrossRefGoogle Scholar
Rogers, E. M. (1995), Diffusion of Innovations, Fourth Edition, fourth edn, Free Press, New York.Google Scholar
Schelling, T. C. (1971), ‘Dynamic models of segregation’, The Journal of Mathematical Sociology 1(2), 143186. URL: https://10.0.4.56/0022250X.1971.9989794CrossRefGoogle Scholar
Schwarz, N. and Ernst, A. (2012), ‘Agent-based modeling of the diffusion of environmental innovations — An empirical approach’, International Journal of Agent Technologies and Systems 4, 497511. URL: https://doi.org/10.1016/j.techfore.2008.03.024Google Scholar
Smaldino, P. E., Pickett, C. L., Sherman, J. and Schank, J. (2012), ‘An Agent-Based Model of Social Identity Dynamics’, Journal of Artificial Societies and Social Simulation 15(4), 117. URL: https://doi.org/10.18564/jasss.2030CrossRefGoogle Scholar
Squazzoni, F. (2010), ‘The Impact of Agent-Based Models in the Social Sciences After 15 Years of Incursions’. URL: https://doi.org/10.2307/23723517CrossRefGoogle Scholar
Stevenson, P. D., Mattson, C. A. and Dahlin, E. C. (2020), ‘A Method for Creating Product Social Impact Models of Engineered Products’, Journal of Mechanical Design, Transactions of the ASME. URL: https://doi.org/10.1115/1.4044161CrossRefGoogle Scholar
United States Bureau of Labor Statistics (2020), American Time Use Survey (ATUS): Arts Activities, [United States], 2003–2018.Google Scholar
Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003), ‘User acceptance of information technology: Toward a unified view’, MIS Quarterly: Management Information Systems. URL: https://doi.org/10.2307/30036540CrossRefGoogle Scholar
Vicsek, T. (2002), ‘Complexity: The bigger picture’, Nature 418(6894), 131. URL: https://doi.org/10.1038/418131aCrossRefGoogle Scholar
Wassenaar, H. J. and Chen, W. (2003), ‘An Approach to Decision-Based Design With Discrete Choice Analysis for Demand Modeling ’, Journal of Mechanical Design 125(3), 490497. URL: https://doi.org/10.1115/1.1587156CrossRefGoogle Scholar
Watts, D. J. and Strogatz, S. H. (1998), ‘Collective dynamics of ‘small-world’ networks’, Nature 393(6684), 440442. URL: https://doi.org/10.1038/30918CrossRefGoogle ScholarPubMed
Wilensky, U. (1999), ‘NetLogo. http://ccl.northwestern.edu/netlogo/.’, Center for Connected Learning and ComputerBased Modeling Northwestern University Evanston IL.Google Scholar
Wilensky, U. and Rand, W. (2015), Verification, Validation, and Replication, in ‘An Introduction to Agent-based Modeling’, The MIT Press, Cambridge, pp. 325336.Google Scholar
Xu, Z., Glass, K., Lau, C. L., Geard, N., Graves, P. and Clements, A. (2017), ‘A synthetic population for modelling the dynamics of infectious disease transmission in American Samoa’, Scientific Reports 7(1), 19. URL: https://doi.org/10.1038/s41598-017-17093-8CrossRefGoogle ScholarPubMed