skip to main content
10.1145/3313831.3376502acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

A Literature Review of Quantitative Persona Creation

Authors Info & Claims
Published:23 April 2020Publication History

ABSTRACT

Quantitative persona creation (QPC) has tremendous potential, as HCI researchers and practitioners can leverage user data from online analytics and digital media platforms to better understand their users and customers. However, there is a lack of a systematic overview of the QPC methods and progress made, with no standard methodology or known best practices. To address this gap, we review 49 QPC research articles from 2005 to 2019. Results indicate three stages of QPC research: Emergence, Diversification, and Sophistication. Sharing resources, such as datasets, code, and algorithms, is crucial to achieving the next stage (Maturity). For practitioners, we provide guiding questions for assessing QPC readiness in organizations.

Skip Supplemental Material Section

Supplemental Material

References

  1. An, J. et al. 2018. Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data. Social Network Analysis and Mining. 8, 1 (2018). DOI: https://doi.org/10.1007/s13278-018-0531-0.Google ScholarGoogle ScholarCross RefCross Ref
  2. An, J. et al. 2018. Imaginary People Representing Real Numbers: Generating Personas from Online Social Media Data. ACM Transactions on the Web (TWEB). 12, 4 (2018), Article No. 27. DOI: https://doi.org/10.1145/3265986.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Aoyama, M. 2005. Persona-and-scenario based requirements engineering for software embedded in digital consumer products. Proceedings of the 13th IEEE International Conference on Requirements Engineering (RE'05) (Washington, DC, USA, Aug. 2005), 85--94.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Aoyama, M. 2007. Persona-Scenario-Goal Methodology for User-Centered Requirements Engineering. Proceedings of the 15th IEEE International Requirements Engineering Conference (RE 2007) (Delhi, India, Oct. 2007), 185--194.Google ScholarGoogle ScholarCross RefCross Ref
  5. Bamman, D. et al. 2013. Learning Latent Personas of Film Characters. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Sofia, Bulgaria, 2013), 10.Google ScholarGoogle Scholar
  6. Blanco, E. et al. 2014. Role of personas and scenarios in creating shared understanding of functional requirements: an empirical study. Design Computing and Cognition'12. Springer. 61--78.Google ScholarGoogle Scholar
  7. Brickey, J. et al. 2010. A Comparative Analysis of Persona Clustering Methods. AMCIS 2010 Proceedings (2010).Google ScholarGoogle Scholar
  8. Brickey, J. et al. 2012. Comparing Semi-Automated Clustering Methods for Persona Development. IEEE Transactions on Software Engineering. 38, 3 (May 2012), 537--546. DOI: https://doi.org/10.1109/TSE.2011.60.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Chapman, C. et al. 2015. Profile CBC: Using Conjoint Analysis for Consumer Profiles. Sawtooth Software Conference Proceedings (2015).Google ScholarGoogle Scholar
  10. Chapman, C.N. and Milham, R.P. 2006. The Personas' New Clothes: Methodological and Practical Arguments against a Popular Method. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Oct. 2006), 634--636.Google ScholarGoogle Scholar
  11. Chu, E. et al. 2018. Learning Personas from Dialogue with Attentive Memory Networks. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Brussels, Belgium, Oct. 2018), 2638--2646.Google ScholarGoogle ScholarCross RefCross Ref
  12. Cooper, A. 1999. The Inmates Are Running the Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity. Sams - Pearson Education.Google ScholarGoogle Scholar
  13. Dang-Pham, D. et al. 2015. Demystifying online personas of Vietnamese young adults on Facebook: A Q-methodology approach. Australasian Journal of Information Systems. 19, 0 (Nov. 2015). DOI: https://doi.org/10.3127/ajis.v19i0.1204.Google ScholarGoogle ScholarCross RefCross Ref
  14. De Souza, C.R. et al. 2004. How a good software practice thwarts collaboration: the multiple roles of APIs in software development. ACM SIGSOFT Software Engineering Notes. 29, 6 (2004), 221--230.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Del Vecchio, P. et al. 2017. Creating value from Social Big Data: Implications for Smart Tourism Destinations. Information Processing & Management. (2017).Google ScholarGoogle Scholar
  16. Del Vicario, M. et al. 2017. News consumption during the Italian referendum: A cross-platform analysis on facebook and twitter. 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (2017), 648--657.Google ScholarGoogle ScholarCross RefCross Ref
  17. Dhakad, L. et al. 2017. SOPER: Discovering the Influence of Fashion and the Many Faces of User from Session Logs using Stick Breaking Process. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17 (Singapore, Singapore, 2017), 1609--1618.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Dillahunt, T.R. et al. 2017. The sharing economy in computing: A systematic literature review. Proceedings of the ACM on Human-Computer Interaction. 1, CSCW (2017), 38.Google ScholarGoogle Scholar
  19. Dupree, J.L. et al. 2016. Privacy Personas: Clustering Users via Attitudes and Behaviors Toward Security Practices. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2016), 5228--5239.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Epasto, A. et al. 2017. Ego-Splitting Framework: From Non-Overlapping to Overlapping Clusters. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2017), 145--154.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Fiesler, C. and Proferes, N. 2018. ?Participant" Perceptions of Twitter Research Ethics. Social Media+ Society. 4, 1 (2018), 2056305118763366.Google ScholarGoogle Scholar
  22. Fisher, R.J. 1993. Social Desirability Bias and the Validity of Indirect Questioning. Journal of Consumer Research. 20, 2 (1993), 303--315.Google ScholarGoogle ScholarCross RefCross Ref
  23. Forrester Research 2010. The ROI Of Personas.Google ScholarGoogle Scholar
  24. Friess, E. 2012. Personas and Decision Making in the Design Process: An Ethnographic Case Study. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2012), 1209--1218.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Gaiser, B. et al. 2006. Community Design-The Personas Approach. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (2006), 520--525.Google ScholarGoogle Scholar
  26. Goodman-Deane, J. et al. 2018. Evaluating Inclusivity using Quantitative Personas. (Jun. 2018).Google ScholarGoogle Scholar
  27. Guo, A. and Ma, J. 2018. Archetype-Based Modeling of Persona for Comprehensive Personality Computing from Personal Big Data. Sensors. 18, 3 (Mar. 2018), 684. DOI: https://doi.org/10.3390/s18030684.Google ScholarGoogle ScholarCross RefCross Ref
  28. Hajian, S. et al. 2016. Algorithmic bias: From discrimination discovery to fairness-aware data mining. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (2016), 2125--2126.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Hill, C.G. et al. 2017. Gender-Inclusiveness Personas vs. Stereotyping: Can We Have it Both Ways? Proceedings of the 2017 CHI Conference (Denver, Colorado, USA, 2017), 6658--6671.Google ScholarGoogle Scholar
  30. Hirskyj-Douglas, I. et al. 2017. Animal Personas: Representing Dog Stakeholders in Interaction Design. Proceedings of the 31st British Computer Society Human Computer Interaction Conference (Swindon, UK, 2017), 37:1--37:13.Google ScholarGoogle Scholar
  31. Hoffmann, A.L. and Jonas, A. 2016. Recasting justice for Internet and online industry research ethics. Internet Research Ethics for the Social Age: New Cases and Challenges. M. Zimmer and K. Kinder-Kuranda (Eds.), np Bern, Switzerland: Peter Lang, Forthcoming. (2016).Google ScholarGoogle Scholar
  32. Holden, R.J. et al. 2017. Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure. International Journal of Medical Informatics. 108, (Dec. 2017), 158--167. DOI: https://doi.org/10.1016/j.ijmedinf.2017.10.006.Google ScholarGoogle ScholarCross RefCross Ref
  33. Holmgard, C. et al. 2014. Evolving personas for player decision modeling. Computational Intelligence and Games (CIG), 2014 IEEE Conference on (2014), 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  34. Huh, J. et al. 2016. Personas in online health communities. Journal of Biomedical Informatics. 63, (Oct. 2016), 212--225. DOI: https://doi.org/10.1016/j.jbi.2016.08.019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Ishii, R. et al. 2018. Monte-Carlo Tree Search Implementation of Fighting Game AIs Having Personas. 2018 IEEE Conference on Computational Intelligence and Games (CIG) (Maastricht, Aug. 2018), 1--8.Google ScholarGoogle Scholar
  36. Jansen, A. et al. 2017. Personas and Behavioral Theories: A Case Study Using Self-Determination Theory to Construct Overweight Personas. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA, 2017), 2127--2136.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Jansen, B.J. et al. 2000. Real life, real users, and real needs: a study and analysis of user queries on the web. Information processing & management. 36, 2 (2000), 207--227.Google ScholarGoogle Scholar
  38. Jenkinson, A. 1994. Beyond segmentation. Journal of targeting, measurement and analysis for marketing. 3, 1 (1994), 60--72.Google ScholarGoogle Scholar
  39. Jung, S. et al. 2019. Personas Changing Over Time: Analyzing Variations of Data-Driven Personas During a Two-Year Period. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, UK, 2019), LBW2714:1--LBW2714:6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Kanno, T. et al. 2011. Integrating Human Modeling and Simulation with the Persona Method. Universal Access in Human-Computer Interaction. Users Diversity (2011), 51--60.Google ScholarGoogle Scholar
  41. Kim, H.M. and Wiggins, J. 2016. A Factor Analysis Approach to Persona Development using Survey Data. Proceedings of the 2016 Library Assessment Conference (2016), 11.Google ScholarGoogle Scholar
  42. Kwak, H. et al. 2018. What We Read, What We Search: Media Attention and Public Attention Among 193 Countries. Proceedings of the Web Conference (Lyon, France, 2018).Google ScholarGoogle Scholar
  43. Laporte, L. et al. 2012. Using Correspondence Analysis to Monitor the Persona Segmentation Process. Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design (New York, NY, USA, 2012), 265--274.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Leong, L.-Y. et al. 2017. Understanding impulse purchase in Facebook commerce: does Big Five matter? Internet Research. 27, 4 (2017), 786--818.Google ScholarGoogle ScholarCross RefCross Ref
  45. Li, J. et al. 2016. A Persona-Based Neural Conversation Model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Berlin, Germany, Aug. 2016), 994--1003.Google ScholarGoogle ScholarCross RefCross Ref
  46. Long, F. 2009. Real or imaginary: The effectiveness of using personas in product design. Proceedings of the Irish Ergonomics Society Annual Conference (2009).Google ScholarGoogle Scholar
  47. Luo, Y. et al. 2019. Co-Designing Food Trackers with Dietitians: Identifying Design Opportunities for Food Tracker Customization. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19 (Glasgow, Scotland Uk, 2019), 1--13.Google ScholarGoogle Scholar
  48. Mari, M. and Poggesi, S. 2013. Servicescape cues and customer behavior: a systematic literature review and research agenda. The Service Industries Journal. 33, 2 (2013), 171--199.Google ScholarGoogle ScholarCross RefCross Ref
  49. Marsden, N. and Haag, M. 2016. Stereotypes and Politics: Reflections on Personas. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, USA, 2016), 4017--4031.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Masiero, A.A. et al. 2011. Multidirectional Knowledge Extraction Process for Creating Behavioral Personas. Proceedings of the 10th Brazilian Symposium on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction (Porto Alegre, Brazil, Brazil, 2011), 91--99.Google ScholarGoogle Scholar
  51. Matthews, T. et al. 2012. How Do Designers and User Experience Professionals Actually Perceive and Use Personas? Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Austin, Texas, USA, 2012), 1219--1228.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. McGinn, J.J. and Kotamraju, N. 2008. Data-driven persona development. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Florence, Italy, 2008), 1521--1524.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Mesgari, M. et al. 2015. Affordance-based User Personas?: A mixed-method Approach to Persona Development. AMCIS 2015 Proceedings (Jun. 2015).Google ScholarGoogle Scholar
  54. Miaskiewicz, T. et al. 2008. A latent semantic analysis methodology for the identification and creation of personas. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2008), 1501--1510.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Miaskiewicz, T. and Kozar, K.A. 2011. Personas and user-centered design: How can personas benefit product design processes? Design Studies. 32, 5 (2011), 417--430.Google ScholarGoogle ScholarCross RefCross Ref
  56. Miaskiewicz, T. and Luxmoore, C. 2017. The Use of Data-Driven Personas to Facilitate Organizational Adoption--A Case Study. The Design Journal. 20, 3 (2017), 357--374.Google ScholarGoogle ScholarCross RefCross Ref
  57. Mijac, T. et al. 2018. The potential and issues in data-driven development of web personas. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (May 2018), 1237--1242.Google ScholarGoogle ScholarCross RefCross Ref
  58. Minichiello, A. et al. 2018. Bringing User Experience Design to Bear on STEM Education: A Narrative Literature Review. Journal for STEM Education Research. 1, 1--2 (2018), 7--33.Google ScholarGoogle ScholarCross RefCross Ref
  59. Minichiello, A. et al. 2017. Work In Progress: Methodological Considerations for Constructing Nontraditional Student Personas with Scenarios from Online Forum Usage Data in Calculus. Technical Report #Paper ID #17980. American Society for Engineering Education.Google ScholarGoogle Scholar
  60. Mulder, S. and Yaar, Z. 2006. The User is Always Right: A Practical Guide to Creating and Using Personas for the Web. New Riders.Google ScholarGoogle Scholar
  61. Nielsen, L. 2019. Personas - User Focused Design. Springer.Google ScholarGoogle Scholar
  62. Pruitt, J. and Grudin, J. 2003. Personas: Practice and Theory. Proceedings of the 2003 Conference on Designing for User Experiences (San Francisco, California, USA, 2003), 1--15.Google ScholarGoogle Scholar
  63. Radjenovic, D. et al. 2013. Software fault prediction metrics: A systematic literature review. Information and software technology. 55, 8 (2013), 1397--1418.Google ScholarGoogle Scholar
  64. Rahimi, M. and Cleland-Huang, J. 2014. Personas in the Middle: Automated Support for Creating Personas As Focal Points in Feature Gathering Forums. Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering (New York, NY, USA, 2014), 479--484.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Salminen, J. et al. 2018. Are personas done? Evaluating their usefulness in the age of digital analytics. Persona Studies. 4, 2 (Nov. 2018), 47--65. DOI: https://doi.org/10.21153/psj2018vol4no2art737.Google ScholarGoogle ScholarCross RefCross Ref
  66. Salminen, J. et al. 2019. Automatic Persona Generation for Online Content Creators: Conceptual Rationale and a Research Agenda. Personas - User Focused Design. L. Nielsen, ed. Springer London. 135--160.Google ScholarGoogle Scholar
  67. Salminen, J. et al. 2019. Detecting Demographic Bias in Automatically Generated Personas. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2019), LBW0122:1--LBW0122:6.Google ScholarGoogle Scholar
  68. Salminen, J. et al. 2018. From 2,772 segments to five personas: Summarizing a diverse online audience by generating culturally adapted personas. First Monday. 23, 6 (Jun. 2018). DOI: https://doi.org/10.5210/fm.v23i6.8415.Google ScholarGoogle ScholarCross RefCross Ref
  69. Salminen, J. et al. 2018. Persona Perception Scale: Developing and Validating an Instrument for Human-Like Representations of Data. CHI'18 Extended Abstracts: CHI Conference on Human Factors in Computing Systems Extended Abstracts Proceedings (Montréal, Canada, 2018).Google ScholarGoogle Scholar
  70. Salminen, J. et al. 2019. Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas. International Journal of Human--Computer Interaction. 0, 0 (Nov. 2019), 1--13. DOI: https://doi.org/10.1080/10447318.2019.1688946.Google ScholarGoogle ScholarCross RefCross Ref
  71. Salminen, J. et al. 2019. The future of data-driven personas: A marriage of online analytics numbers and human attributes. ICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems (Heraklion, Greece, Jan. 2019), 596--603.Google ScholarGoogle Scholar
  72. dos Santos, T.F. et al. 2014. Behavioral persona for human-robot interaction: a study based on pet robot. International Conference on Human-Computer Interaction (2014), 687--696.Google ScholarGoogle Scholar
  73. Siegel, D.A. 2010. The Mystique of Numbers: Belief in Quantitative Approaches to Segmentation and Persona Development. CHI '10 Extended Abstracts on Human Factors in Computing Systems (New York, NY, USA, 2010), 4721--4732.Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Stevenson, P.D. and Mattson, C.A. 2019. The Personification of Big Data. Proceedings of the Design Society: International Conference on Engineering Design. 1, 1 (Jul. 2019), 4019--4028. DOI: https://doi.org/10.1017/dsi.2019.409.Google ScholarGoogle ScholarCross RefCross Ref
  75. Tanenbaum, M.L. et al. 2018. From Wary Wearers to d-Embracers: Personas of Readiness to Use Diabetes Devices. Journal of Diabetes Science and Technology. 12, 6 (Nov. 2018), 1101--1107. DOI: https://doi.org/10.1177/1932296818793756.Google ScholarGoogle ScholarCross RefCross Ref
  76. Tempelman-Kluit, N. and Pearce, A. 2014. Invoking the User from Data to Design. College & Research Libraries. 75, 5 (Sep. 2014), 616--640. DOI: https://doi.org/10.5860/crl.75.5.616.Google ScholarGoogle ScholarCross RefCross Ref
  77. Thoma, V. and Williams, B. 2009. Developing and Validating Personas in e-Commerce: A Heuristic Approach. Human-Computer Interaction -- INTERACT 2009 (2009), 524--527.Google ScholarGoogle Scholar
  78. Torgerson, C. 2003. Systematic Reviews. A&C Black.Google ScholarGoogle Scholar
  79. Tu, N. et al. 2010. Using cluster analysis in Persona development. 2010 8th International Conference on Supply Chain Management and Information (Oct. 2010), 1--5.Google ScholarGoogle Scholar
  80. Turner, P. and Turner, S. 2011. Is stereotyping inevitable when designing with personas? Design studies. 32, 1 (2011), 30--44.Google ScholarGoogle Scholar
  81. Tychsen, A. and Canossa, A. 2008. Defining Personas in Games Using Metrics. Proceedings of the 2008 Conference on Future Play: Research, Play, Share (Toronto, Ontario, Canada, 2008), 73--80.Google ScholarGoogle ScholarDigital LibraryDigital Library
  82. Van Laar, E. et al. 2017. The relation between 21st-century skills and digital skills: A systematic literature review. Computers in human behavior. 72, (2017), 577--588.Google ScholarGoogle Scholar
  83. Vosbergen, S. et al. 2015. Using personas to tailor educational messages to the preferences of coronary heart disease patients. Journal of Biomedical Informatics. 53, (Feb. 2015), 100--112. DOI: https://doi.org/10.1016/j.jbi.2014.09.004.Google ScholarGoogle ScholarDigital LibraryDigital Library
  84. Wang, L. et al. 2018. Analysis of Regional Group Health Persona Based on Image Recognition. 2018 Sixth International Conference on Enterprise Systems (ES) (Oct. 2018), 166--171.Google ScholarGoogle Scholar
  85. Watanabe, Y. et al. 2017. ID3P: Iterative Data-driven Development of Persona Based on Quantitative Evaluation and Revision. Proceedings of the 10th International Workshop on Cooperative and Human Aspects of Software Engineering (Piscataway, NJ, USA, 2017), 49--55.Google ScholarGoogle Scholar
  86. Williams, K.L. 2006. Personas in the design process: a tool for understanding others. Georgia Institute of Technology.Google ScholarGoogle Scholar
  87. Wöckl, B. et al. 2012. Basic Senior Personas: A Representative Design Tool Covering the Spectrum of European Older Adults. Proceedings of the 14th International ACM SIGACCESS Conference on Computers and Accessibility (New York, NY, USA, 2012), 25--32.Google ScholarGoogle ScholarDigital LibraryDigital Library
  88. Zaugg, H. and Ziegenfuss, D.H. 2018. Comparison of personas between two academic libraries. Performance Measurement and Metrics. 19, 3 (Aug. 2018), 142--152. DOI: https://doi.org/10.1108/PMM-04--2018-0013.Google ScholarGoogle ScholarCross RefCross Ref
  89. Zhang, X. et al. 2016. Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA, 2016), 5350--5359.Google ScholarGoogle Scholar
  90. Zhu, H. et al. 2019. Creating Persona Skeletons from Imbalanced Datasets - A Case Study using U.S. Older Adults' Health Data. Proceedings of the 2019 on Designing Interactive Systems Conference - DIS '19 (San Diego, CA, USA, 2019), 61--70.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A Literature Review of Quantitative Persona Creation

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
      April 2020
      10688 pages
      ISBN:9781450367080
      DOI:10.1145/3313831

      Copyright © 2020 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].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 23 April 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate6,199of26,314submissions,24%

      Upcoming Conference

      CHI '24
      CHI Conference on Human Factors in Computing Systems
      May 11 - 16, 2024
      Honolulu , HI , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format