Big Data, Small Personas : How Algorithms Shape the Demographic Representation of Data-Driven User Segments

Derived from the notion of algorithmic bias, it is possible that creating user segments such as personas from data results in over- or under-representing certain segments (FAIRNESS), does not properly represent the diversity of the user populations (DIVERSITY), or produces inconsistent results when hyperparameters are changed (CONSISTENCY). Collecting user data on 363M video views from a global news and media organization, we compare personas created from this data using different algorithms. Results indicate that the algorithms fall into two groups: those that generate personas with low diversity–high fairness and those that generate personas with high diversity–low fairness. The algorithms that rank high on diversity tend to rank low on fairness (Spearman's correlation: −0.83). The algorithm that best balances diversity, fairness, and consistency is Spectral Embedding. The results imply that the choice of algorithm is a crucial step in data-driven user segmentation, because the algorithm fundamentally impacts the demographic attributes of the generated personas and thus influences how decision makers view the user population. The results have implications for algorithmic bias in user segmentation and creating user segments that not only consider commercial segmentation criteria but also consider criteria derived from ethical discussions in the computing community.©2022, Mary Ann Liebert, Inc., publishers.fi=vertaisarvioitu|en=peerReviewed

Strengths and Weaknesses of Persona Creation Methods:Guidelines and Opportunities for Digital Innovations

Persona is a technique for enhancing user understanding and improving the user-centered design of digital products. Persona creation has traditionally been divided into Qualitative, Quantitative, and Mixed Methods approaches. However, no literature systematically contrasts the strengths and weaknesses of these approaches. We review the literature to map the strengths and weaknesses of these approaches and evaluate the potential of personas for the domain of digital innovation. We provide insights for better creation and use of personas by both researchers and practitioners, especially those that are new to personas, deploying personas in a new domain, or familiar with only one of the persona creation approaches

A Survey of 15 Years of Data-Driven Persona Development

Data-driven persona development unifies methodologies for creating robust personas from the behaviors and demographics of user segments. Data-driven personas have gained popularity in human-computer interaction due to digital trends such as personified big data, online analytics, and the evolution of data science algorithms. Even with its increasing popularity, there is a lack of a systematic understanding of the research on the topic. To address this gap, we review 77 data-driven persona research articles from 2005-2020. The results indicate three periods: (1) Quantification (2005-2008), which consists of the first experiments with data-driven methods, (2) Diversification (2009-2014), which involves more pluralistic use of data and algorithms, and (3) Digitalization (2015-present), marked by the abundance of online user data and the rapid development of data science algorithms and software. Despite consistent work on data-driven personas, there remain many research gaps concerning (a) shared resources, (b) evaluation methods, (c) standardization, (d) consideration for inclusivity, and (e) risk of losing in-depth user insights. We encourage organizations to realistically assess their data-driven persona development readiness to gain value from data-driven personas

How to Create Personas : Three Persona Creation Methodologies with Implications for Practical Employment

Background: Personas are a technique for enhanced understanding of users and customers to improve the user-centered design of systems and products. Their creation can be categorized using three persona creation methodologies: Qualitative, Quantitative, and Mixed Methods. Despite the apparent differences in these methodologies, no previous review has systemically compared and contrasted the strengths and weaknesses of each of these methodologies for persona development. Method: This manuscript maps and navigates persona literature to identify the benefits and challenges of these three persona creation methodologies. Furthermore, the strategies and opportunities of the different methodologies are presented. Results: The results summarize the strengths and weaknesses of each of the three principal persona creation methodologies and offer suggestions of the benefits of their employment. Conclusion: In conclusion, we offer insights into the construction and usage of personas for practitioners and researchers, and we propose a framework to determine which persona creation methodology is most suitable for a given context.© 2022 by the Association for Information Systems. 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 full citation on the first page. Copyright for components of this work owned by others than the Association for Information Systems must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior specific permission and/or fee. Request permission to publish from: AIS Administrative Office, P.O. Box 2712 Atlanta, GA, 30301-2712 Attn: Reprints, or via email from [email protected]=vertaisarvioitu|en=peerReviewed

How to Create Personas: Three Persona Creation Methodologies with Implications for Practical Employment

Background: Personas are a technique for enhanced understanding of users and customers to improve the user-centered design of systems and products. Their creation can be categorized using three persona creation methodologies: Qualitative, Quantitative, and Mixed Methods. Despite the apparent differences in these methodologies, no previous review has systemically compared and contrasted the strengths and weaknesses of each of these methodologies for persona development. Method: This manuscript maps and navigates persona literature to identify the benefits and challenges of these three persona creation methodologies. Furthermore, the strategies and opportunities of the different methodologies are presented. Results: The results summarize the strengths and weaknesses of each of the three principal persona creation methodologies and offer suggestions of the benefits of their employment. Conclusion: In conclusion, we offer insights into the construction and usage of personas for practitioners and researchers, and we propose a framework to determine which persona creation methodology is most suitable for a given context. Keywords: Algorithmically-Generated Personas, Persona Analytics, Persona Science

How to Create Personas: Three Persona Creation Methodologies with Implications for Practical Employment

Background: Personas are a technique for enhanced understanding of users and customers to improve the user-centered design of systems and products. Their creation can be categorized using three persona creation methodologies: Qualitative, Quantitative, and Mixed Methods. Despite the apparent differences in these methodologies, no previous review has systemically compared and contrasted the strengths and weaknesses of each of these methodologies for persona development.Method: This manuscript maps and navigates persona literature to identify the benefits and challenges of these three persona creation methodologies. Furthermore, the strategies and opportunities of the different methodologies are presented.Results: The results summarize the strengths and weaknesses of each of the three principal persona creation methodologies and offer suggestions of the benefits of their employment.Conclusion: In conclusion, we offer insights into the construction and usage of personas for practitioners and researchers, and we propose a framework to determine which persona creation methodology is most suitable for a given context.</p

Developing Persona Analytics Towards Persona Science

Much of the reported work on personas suffers from the lack of empirical evidence. To address this issue, we introduce Persona Analytics (PA), a system that tracks how users interact with data-driven personas. PA captures users’ mouse and gaze behavior to measure users’ interaction with algorithmically generated personas and use of system features for an interactive persona system. Measuring these activities grants an understanding of the behaviors of a persona user, required for quantitative measurement of persona use to obtain scientifically valid evidence. Conducting a study with 144 participants, we demonstrate how PA can be deployed for remote user studies during exceptional times when physical user studies are difficult, if not impossible.© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.fi=vertaisarvioitu|en=peerReviewed

How does varying the number of personas affect user perceptions and behavior? Challenging the ‘small personas’ hypothesis!

Studies in human-computer interaction recommend creating fewer than ten personas, based on stakeholders’ limitations to cognitively process and use personas. However, no existing studies offer empirical support for having fewer rather than more personas. Investigating this matter, thirty-seven participants interacted with five and fifteen personas using an interactive persona system, choosing one persona to design for. Our study results from eye-tracking and survey data suggest that when using interactive persona systems, the number of personas can be increased from the conventionally suggested ‘less than ten’, without significant negative effects on user perceptions or task performance, and with the positive effects of increasing engagement with the personas, having a more diverse representation of the end-user population, as well as users accessing personas from more varied demographic groups for a design task. Using the interactive persona system, users adjusted their information processing style by spending less time on each persona when presented with fifteen personas, while still absorbing a similar amount of information than with five personas, implying that more efficient information processing strategies are applied with more personas. The results highlight the importance of designing interactive persona systems to support users’ browsing of more personas.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Feature Papers "Age-Friendly Cities & Communities: State of the Art and Future Perspectives"

The "Age-Friendly Cities & Communities: States of the Art and Future Perspectives" publication presents contemporary, innovative, and insightful narratives, debates, and frameworks based on an international collection of papers from scholars spanning the fields of gerontology, social sciences, architecture, computer science, and gerontechnology. This extensive collection of papers aims to move the narrative and debates forward in this interdisciplinary field of age-friendly cities and communities