Tag: decision-making

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Connecting sustainable and well-being-enhancing behaviors: Reflections through daily practices of young adults

Publication

  • Kowalski, M. & Yoon, J. (2024). Connecting sustainable and well-being-enhancing behaviors: Reflections through daily practices of young adults. DRS: Design Research Society, Boston, MA. Download
  • Kowalski, M. & Yoon, J. (2024). Connecting Sustainable Behavior and Subjective Well-being: An Experiential Model for Design, Journal of Design Research (Under review)

An experience sampling study was conducted to further understand daily activities of young adults with implications for Environmentally Sustainable Behavior (SB) and Subjective Well-being (SWB) simultaneously. Studies on SB and SWB are pre- sent in established bodies of design research, though connection across these strands appears limited. Analysis of 209 survey responses from 27 participants showed that while many activities were reported with mutually positive outcomes for SB and SWB, when there was conflict, individuals were more likely to prioritize their own subjective well-being over environmental sustainability. Activities that included designed prod- ucts and environments that more readily supported SB and SWB without imposing an external conflict, and those that included social bonding and sharing of resources led to more mutually positive outcomes. The findings present avenues for design research- ers and practitioners in developing designs that can address individuals’ well-being and environmentally sustainable behavior in a more positive and complimentary manner.

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Decision-making experiences of maximizers and satisficers in human-design interactions

Publication

  • Shin, Y., Ranjan, K., Kowalski, M., and Yoon, J. (2023). Investigating the Decision-making Experiences of Maximizers and Satisficers: The Case of Interactions with Conversational Music Recommender Systems. Human Factors in Computing Systems (CHI 2023). ACM. (Under review)

This project investigates a human-centered perspective for creating a recommender system by focusing on users’ different decision-making styles and information processing modes. According to emerging research in behavioral science, people’s decision-making tendencies can be broadly classified into two styles: (1) maximizers, who strive to maximize the expected utility and, (2) satisficers, who want to reach their own sufficiency. When users exert a high level of cognitive elaboration in decision-making, they engage in analytical processing, while imagery processing takes place when cognitive elaboration is low. Utilizing these theoretical distinctions, we developed Arlo Assistant, a conversational user interface that differentiates ways of recommending music. Through an in-lab experiment, we tested the effects of using Arlo Assistant on both maximizers’ and satisficers’ decision-making experiences. The study provides initial evidence that a personalized recommender system tailored to users’ different decision-making styles can facilitate more positive experiences and discusses implications for developing recommender systems.