2022

Mixed Methods Personas: Strengths and Weaknesses

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. In this post, we describe the Mixed Methods method and discuss the strengths and weaknesses of this methodology for persona […]

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Quantitative Personas: Strengths and Weaknesses

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. In this post, we describe the Quantitative method and discuss the strengths and weaknesses of this methodology for persona development.

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Gender-Inclusive HCI

What is Gender and Inclusive HCI Design? User-Sensitive Inclusive Design is a concept that embraces designing for marginalized groups of people and considering different types of users. This includes considering users’ gender while designing or evaluating software, websites, or other digital technology. Empirical research has shown gender differences in software and other digital technology use.

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Creating More Personas Improves Representation of Demographically Diverse Populations: Implications Towards Interactive Persona Systems

Personas represent distinct user types. However, while online user data can be demographically and behaviorally heterogeneous, most studies generate less than ten personas, regardless of how heterogeneous the data is. Because all persona creation efforts need to assign a number of personas to create, assigning this number evokes a fundamental question, How many personas to

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Intentionally Biasing User Representation?: Investigating the Pros and Cons of Removing Toxic Quotes from Social Media Personas

Algorithmically generated personas can help organizations understand their social media audiences. However, when using algorithms to create personas from social media user data, the resulting personas may contain toxic quotes that negatively affect content creators’ perceptions of the personas. To address this issue, we have implemented toxicity detection in an algorithmic persona generation system capable

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