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Abstract Engineered products often have more social impacts than are realized. A product review was conducted to bring this to light. In this paper, we show the extent to which different social impacts in 11 impact categories are co-present in 150 products and how this can help engineers and others during the product development process. Specifically, we show how social impact categories not previously considered can be identified. The product review resulted in 13,200 data points that were divided into two data sets, one with 8800 data points from which a social impact probability table was created. The remaining data points were then used to validate the table. All data points were then combined to create a final social impact probability table. This table provides insight for how various social impact categories correlate and can assist engineers in expanding their views to include additional social impact objectives and thus achieve a design with broader social impact or a design with minimized unwanted negative social impact. A simple method for predicting social impact is also created in order to assist engineers when developing products with social impacts in mind.
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A Method for Creating Product Social Impact Models of Engineered Products
Abstract All products impact the lives of their users, this is called social impact. Some social impacts are commonly recognized by the engineering community, such as impacts to a user’s health and safety, while other social impacts can be more difficult to recognize, such as impacts on families and gender roles. When engineers make design decisions, without considering social impacts, they can unknowingly cause negative social impacts. Even harming the user and/or society. Despite its challenges, measuring a program’s or policy’s social impact is a common practice in the field of social sciences. These measurements are made using social impact indicators, which are simply the things observed to verify that true progress is being made. While there are clear benefits to predicting the social impact of an engineered product, it is unclear how engineers should select indicators and build predictive social impact models that are functions of engineering parameters and decisions. This paper introduces a method for selecting social impact indicators and creating predictive social impact models that can help engineers predict and improve the social impact of their products. As a first step in the method, an engineer identifies the product’s users, objectives, and requirements. Then, the social impact categories that are related to the product are determined. From each of these categories, the engineer selects several social impact indicators. Finally, models are created for each indicator to predict how a product’s parameters will change these indicators. The impact categories and indicators can be translated into product requirements and performance measures that can be used in product development processes. This method is used to predict the social impact of the proposed, expanded U.S. Mexico border wall.
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- Award ID(s):
- 1761505
- PAR ID:
- 10192001
- Date Published:
- Journal Name:
- Journal of Mechanical Design
- Volume:
- 142
- Issue:
- 4
- ISSN:
- 1050-0472
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Though academic research for identifying and considering the social impact of products is emerging, additional insights can be gained from engineers who design products every day. This paper explores current practices in industries used by design engineers to consider the social impact of products. Forty-six individuals from 34 different companies were interviewed to discover what disconnects exist between academia and industry when considering a product’s social impact. These interviews were also used to discover how social impact might be considered in a design setting moving forward. This is not a study to find “the state of the art,” but considers the average engineering professional’s work to design products in various industries. Social impact assessments (SIA) and social life cycle assessments (SLCA) are two of the most common processes discussed in the literature to evaluate social impact, both generally and in products. Interestingly, these processes did not arise in any discussion in interviews, despite respondents affirming that they do consider social impact in the product design. Processes used to predict social impact, rather than simply evaluate, were discussed by the respondents. These tended to be developed within the company and often related to industry imposed government regulations. To build on this study, the findings herein should be further validated for executives, managers, and engineers. A study specific to these roles should be designed to understand the disconnect better. Additionally, processes should be developed to assist engineers in considering the social impact of their products. Work should also be done to help educate engineers and their leaders on the value of considering the social impact in product design.more » « less
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Abstract Evaluating the social impacts of engineered products is critical to ensuring that products are having their intended positive impacts and learning how to improve product designs for a more positive social impact. Quantitative evaluation of product social impacts is made possible through the use of social impact indicators, which combine the user data in a meaningful way to give insight into the current social condition of an individual or population. Most existing methods for collecting these user data for social impact indicators require direct human interaction with users of a product (e.g., interviews, surveys, and observational studies). These interactions produce high-fidelity data that help indicate the product impact but only at a single snapshot in time and are typically infrequently collected due to the large human resources and cost associated with obtaining them. In this article, a framework is proposed that outlines how low-fidelity data often obtainable using remote sensors, satellites, or digital technology can be collected and correlated with high-fidelity, infrequently collected data to enable continuous, remote monitoring of engineered products via the user data. These user data are critical to determining current social impact indicators that can be used in a posteriori social impact evaluation. We illustrate an application of this framework by demonstrating how it can be used to collect data for calculating several social impact indicators related to water hand pumps in Uganda. Key to this example is the use of a deep learning model to correlate user type (man, woman, or child statured) with the raw hand pump data obtained via an integrated motion unit sensor for 1200 hand pump users.more » « less
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Abstract The impact of engineered products is a topic of concern in society. Product impact may fall under the categories of economic, environmental or social impact, with the last category defined as the effect of a product on the day-to-day life of people. Design teams lack sufficient tools to estimate the social impact of products, and the combined impacts of economic, environmental and social impacts for the products they are designing. This paper aims to provide a framework for the estimation of product impact during product design. To estimate product impact, models of both the product and society are required. This framework integrates models of the product, scenario, society and impact into an agent-based model to estimate product impact. Although this paper demonstrates the framework using only social impact, the framework can also be applied to economic or environmental impacts individually or all three concurrently. Agent-based modelling has been used previously for product adoption models, but it has not been extended to estimate product impact. Having tools for impact estimation allows for optimising the product design parameters to increase the potential positive impact and reduce potential negative impact.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4044161
