Cited by 92Innovative Human-Technology InteractionPsychology of Social InfluenceDigital Media and Philosophy

The landscape of persuasive technologies

Open access
King, Phillip, Tester, Jason·1999·Communications of the ACM

Abstract

Aimed largely at children today, persuasion is already part of many technologies, but many more are on the way for everyone else.

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Integrity signals

Registry

Crossref · 10.1145/301353.301398

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4 of 4 references verified0 citation errors · 0 not checkable

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Cites

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Research works printed but not linked: 1

  1. Malone, T., and Lepper, M. Making learning fun: A taxonomy of intrin- sic motivations for learning. In Aptitude, Learning, and Instruction: Cog- nitative and Affective Process Analyses, R. Snow and M. Farr, Eds. Erlbaum, Hillsdale, N.J., 1987, pp. 223-253.

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Authors
King, P., & Tester, J.
Affiliations
  • NetSchools, Mountain View, CA
  • Stanford University (US)
Venue
Communications of the ACM
Published
1999-05
Volume
42
Issue
5
Pages
31-38
ISSN
  • 0001-0782 (ISSN-L)
  • 1557-7317
OpenAlex
W1976618862
Cited by
92
References
4
Citations by year
2025: 1 · 2024: 2 · 2022: 1 · 2021: 6 · 2020: 3 · 2019: 2 · 2018: 2 · 2017: 3 · 2016: 5 · 2015: 5 · 2014: 5 · 2013: 5 · 2012: 1
FWCI
4.93
Type
journal article
Language
en
Open access
Locations
  • Communications of the ACM (publishedVersion)
  • UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Social Sciences › Sociology and Political Science › Psychology of Social Influence
  • Physical Sciences › Computer Science › Computer Science Applications › Digital Media and Philosophy
Keywords
persuasive technology, persuasion, children, technology adoption, user behavior change

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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