Combining Direct and Indirect User Data for Calculating Social Impact Indicators of Products in Developing Countries

Bryan J. Stringham · Daniel O. Smith · Christopher A. Mattson · Eric C. Dahlin

7 citations0/0 files storedJournal of Mechanical Design

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Innovation and Socioeconomic Development Innovative Human-Technology Interaction ICT in Developing Communities

Abstract

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.

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References

2018PRODUCT DESIGN AND DEVELOPMENT 2015What is big data? A consensual definition and a review of key research topics 2015Design for the Developing World: Common Pitfalls and How to Avoid Them 2009The Future of Design for Development: Three Questions 2018Toward a Universal Social Impact Metric for Engineered Products That Alleviate Poverty 2016Why the Developing World Needs Mechanical Design 2017Towards a Universal Social Impact Metric for Engineered Products That Alleviate Poverty 2019Analysis of Perceived Social Impacts of Existing Products Designed for the Developing World, With Implications for New Product Development

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2022Systematic Review and Classification of the Engineering for Global Development Literature Based on Design Tools and Methods for Social Impact Consideration 2021Design of remote data collection devices for social impact indicators of products in developing countries

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