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Using Social Comparisons to Facilitate Healthier Choices

This exploratory research examines how we might nudge consumers towards making healthier food choices in online grocery shopping or other digitally mediated food consumption contexts. Our pilot study investigated how different forms of social comparisons could be used to encourage consumers to reduce the number of calories contained in their online grocery basket. Our findings show that participants who were less interested in trying new diets were more willing to reduce calories when presented with a comparison to people unlike them, an out-group member comparison, while those who were interested in trying new diets were more willing to reduce calories regardless of social comparison type. These findings imply that one size does not fit all when nudging. More research is needed to see how social comparisons influence the effectiveness of digital health behavior projects.

DiCosola, Blake & Gina Neff. 2020. “Using Social Comparisons to Facilitate Healthier Choices in Online Grocery Shopping Contexts.” CHI 2020, April 25–30, 2020, Honolulu, HI, USA. https://doi.org/10.1145/3334480.3382877

Developing a Research Agenda for Human-Centered Data Science

The study and analysis of large and complex data sets offer a wealth of insights in a variety of applications. Computational approaches provide researchers access to broad assemblages of data, but the insights extracted may lack the rich detail that qualitative approaches have brought to the understanding of sociotechnical phenomena. How do we preserve the richness associated with traditional qualitative methods while utilizing the power of large data sets? How do we uncover social nuances or consider ethics and values in data use?

 

Cecilia Aragon, Clayton Hutto, Andy Echenique, Brittany Fiore-Gartland, Yun Huang, Jinyoung Kim, Gina Neff, Wanli Xing, Joseph Bayer. Developing a Research Agenda for Human-Centered Data Science. Proceeding CSCW ’16 Companion Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion Pages 529-535. ISBN: 978-1-4503-3950-6 doi>10.1145/2818052.2855518

Technical Boundary Spanners and Translation: A Study of Energy Modeling for High Performance Hospitals

High performance buildings—buildings with the aim of reduced energy and resource use— require that engineering analysis be at the center of an iterative and complex design process that assesses trade-offs, goals, and priorities across engineering and other fields of expertise. It has been observed that teams rarely get this right. Historical, cultural, and technical issues all get in the way of open communication and the integration of technical analysis. In this research, we ask what organizational and communication practices are needed for engineering to translate and design teams to synthesize complex energy modeling into the design of hospital buildings? In this paper we introduce a detailed ethnography of energy modeling during the conceptual phase of a new hospital design where energy modeling falls short of its potential. With cross case comparison, we found that a technically-knowledgeable boundary spanner in the owner organization enriches collaboration between the design team and the owner organization for more accurate and impactful energy modeling and improved translation of the model between team and owner. The energy modeling process became almost more important than the results of the energy model wherein the owner and design team had design-critical conversations about the model inputs and clear knowledge about the owner’s goals for the data. We propose that it is in this socially constructed knowledge where real high performance design can occur.

 

Dossick, Carrie Sturts, Gina Neff, Laura Osburn, Chris Monson, and Heather Burpee. “Technical Boundary Spanners and Translation: A Study of Energy Modeling for High Performance Hospitals.” Cle Elum, Washington: Engineering Project Organization Conference, 2016. http://www.epossociety.org/EPOC2016/papers/Dossick%20et%20al%20_EPOC_2016.pdf.

 

 

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