research-article

Personalizing Time Loss Aversion to Reduce Social Media Use

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Ziwei GaoAuthors Info & Claims

UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

Pages 78 - 84

https://doi.org/10.1145/3565472.3592951

Published: 19 June 2023 Publication History

Abstract

This study examines the effectiveness of a novel personalization approach for persuasive and behavior change systems: time loss aversion. Focusing on time instead of money, it influences behavior. Two interventions were developed and tested to reduce daily social media use and boost non-digital activities. Participants received information about their average daily social media use in terms of a week, month, and year, with one intervention offering substitute activities. Among 231 participants, both interventions successfully reduced intentions for future social media use, revealing generational effects, with Gen Zs using social media more than Millennials. These results have practical implications for personalizing interventions that reduce excessive digital engagement.

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Cited By

Echterhoff JMelkote AKancherla SMcAuley J(2024)Avoiding Decision Fatigue with AI-Assisted Decision-MakingProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659569(1-11)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659569
Gao Z(2023)Time Loss AversionHCI International 2023 Posters10.1007/978-3-031-35989-7_72(571-577)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35989-7_72

Index Terms

Personalizing Time Loss Aversion to Reduce Social Media Use
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
  2. Interaction design
    1. Empirical studies in interaction design

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cover image ACM Conferences

UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

June 2023

333 pages

ISBN:9781450399326

DOI:10.1145/3565472

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 19 June 2023

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Cited By

Echterhoff JMelkote AKancherla SMcAuley J(2024)Avoiding Decision Fatigue with AI-Assisted Decision-MakingProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659569(1-11)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659569
Gao Z(2023)Time Loss AversionHCI International 2023 Posters10.1007/978-3-031-35989-7_72(571-577)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35989-7_72

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