Persona Design

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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Personas for your non-customers – the Edge, the Refuse, and Unknown non-customers

Personas can be a force multiplier in reaching your three categories of non-customers – the Edge Non-customers, the Refuse Non-customers, and Unknown Non-customers. Algorithmically-Generated Personas are great for humanized representation of your existing customers presented in understandable chunks of information. These data-driven personas are great for team communication while creating content and designing systems, along

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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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Toxic Text in Personas: An Experiment on User Perceptions

When algorithms create personas from social media data, the personas can become noxious via automatically including toxic comments. To investigate how users perceive such personas, we conducted a 2 × 2 user experiment with 496 participants that showed participants toxic and non-toxic versions of data-driven personas. We found that participants gave higher credibility, likability, empathy,

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