ACM Conference on Human Factors in Computing Systems (also known simply as ‘CHI’) is the top conference in human-computer interaction (HCI). Personas, as a design technique, strongly related to the domain of HCI, which is why our team’s research efforts are centered around CHI as well as other HCI conferences and journals.
Anyhow, this year, we were fortunate enough to get 2 full papers and 5 late-breaking work (LBR) papers accepted to CHI’20. Both of the full papers are about personas, and three of the LBR papers are about personas.
While the full PDF versions will be made publicly available in CHI Proceedings, we are sharing below the abstracts, summarizing the main findings of each paper. If you are curious to learn more or collaborate with us around persona research, please reach out (jsalminen@hbku.edu.qa).
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Personas and Analytics: A Comparative User Study of Efficiency and Effectiveness for a User Identification Task
Personas are a well-known technique in human computer interaction. However, there is a lack of rigorous empirical research evaluating personas relative to other methods. In this 34 participant experiment, we compare a persona and an analytics system, both using identical user data, for efficiency and effectiveness for a user identification task. The results show that personas afforded faster task completion than the analytics system, as well as outperforming analytics with significantly higher user identification accuracy. Qualitative analysis of think-aloud transcripts shows that personas have other benefits regarding learnability and consistency. However, the analytics system affords some insights and capabilities that personas cannot due to their inherent design limitations. Findings support the use of personas to learn about users, empirically confirming some of the stated benefits in the literature, while also highlight the limitations of personas that may necessitate the use of accompanying methods.
A Literature Review of Quantitative Persona Creation
Quantitative persona creation (QPC) has an unquestionable potential for HCI research and practice, particularly in utilizing data from online analytics and digital media platforms to better understand users/customers. However, there is a lack of a systematic overview of the methods applied and progress made, with no standard methodology for researchers and practitioners to choose from. To address this need, we reviewed 49 quantitative persona articles published between 2005 and 2019. Results indicate three stages in the evolution of QPC research: Emergence, Diversification, and Expansion and present steps for achieving the next stage (Maturity). We provide useful takeaways for researchers and practitioners, including strategies for coping with QPC and checklists for assessing if QPC is suitable for a given use case. The development of shared resources, such as guidelines, annotated datasets, and algorithms, is crucial to advancing the field.
The Ethics of Data-Driven Personas
Quantitative methods are becoming more common for persona creation, but it is not clear to which extent online data and opaque machine learning algorithms introduce bias at various steps of data-driven persona creation (DDPC) and if these methods violate user rights. In this conceptual analysis, we use Gillespie’s framework of algorithmic ethics to analyze DDPC for ethical considerations. We propose five design questions for evaluating the ethics of DDPC. DDPC should demonstrate the diversity of the user base but represent the actual data, be accompanied by explanations of their creation, and mitigate the possibility of unfair decisions.
The Effect of Experience on Persona Perceptions
User perceptions of personas affect the adoption of personas for decision-making in real organizations. To investigate how experience affects the way an individual perceives a persona, we conduct an experimental study with individuals less and more experienced with personas. Quantitative results show that previous experience increases several important perceptions, including willingness to use, empathy, likability, and completeness. Results suggest that methods that increase experience (e.g., training, workshops, scenarios) should be applied alongside persona deployment, as desirable persona perceptions increase with individuals’ experience.
Designing Prototype Player Personas from a Game Preference Survey
The competitiveness of the video game market has increased the need for understanding players. We generate player personas from survey data of 15,402 players’ 195,158 stated game preferences from 130,495 game titles using the methodology of automatic persona generation. Our purpose is to demonstrate the potential of data-driven personas for segmenting players by their game preferences. The resulting prototype personas provide potential value for game marketing purposes, e.g., targeting gamers with social media advertising, although they can also be used for understanding demographic variation among various game preference patterns.
Salminen, J., Jung, S.G., Chowdhury. S., and Jansen, B. J. (2020) Analyzing Demographic Bias in Artificially Generated Facial Pictures. ACM CHI Conference on Human Factors in Computing Systems (CHI’20) (Extended Abstract), Honolulu, HI, USA. 25–30 April, 1-8.
Salminen, J., Froneman, W., Jung, S.G., Chowdhury. S., andJansen, B. J. (2020) The Ethics of Data-Driven Personas. ACM CHI Conference on Human Factors in Computing Systems (CHI’20) (Extended Abstract), Honolulu, HI, USA. 25–30 April, 1-9.
Salminen, J., Jung, S.G., Santos, J., Chowdhury. S. G, and Jansen, B. J. (2020) The Effect of Experience on Persona Perceptions. ACM CHI Conference on Human Factors in Computing Systems (CHI’20) (Extended Abstract), Honolulu, HI, USA. 25–30 April. 1-9.
Salminen, J., Vahlo, J., Jung, S.G., Chowdhury. S., and Jansen, B. J. (2020) Designing Prototype Player Personas from Game Preference Surveys. ACM CHI Conference on Human Factors in Computing Systems (CHI’20) (Extended Abstract), Honolulu, HI, USA. 25–30 April, 1-8.
Salminen, J., Jung, S.G., Chowdhury. S., Ramirez-Robillos, D., and Jansen, B. J. (2020) Things Change: Comparing Results Using Historical Data and User Testing for Evaluating a Recommendation Task. ACM CHI Conference on Human Factors in Computing Systems (CHI’20) (Extended Abstract), Honolulu, HI, USA. 25–30 April, 1-7.
Salminen, J., Jung, S.G., Chowdhury, S. Şengün, S., and Jansen, B. J. (2020) Personas and Analytics: A Comparative User Study of Efficiency and Effectiveness for a User Identification Task. ACM CHI Conference on Human Factors in Computing Systems (CHI’20), Honolulu, HI, USA. 25–30 April, 1-13.
Salminen, J., Guan, K., Jung, S.G., Chowdhury, S., and Jansen, B. J. (2020) A Literature Review of Quantitative Persona Creation. ACM CHI Conference on Human Factors in Computing Systems (CHI’20), Honolulu, HI, USA. 25–30 April. 1-14.