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

Bernard J. Jansen et al.

Pacific Asia Journal of the Association for Information Systems2022https://doi.org/10.17705/1pais.14301article
AJG 2
Weight
0.62

What the paper says

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.

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https://doi.org/https://doi.org/10.17705/1pais.14301

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@article{bernard2022,
  title        = {{How to Create Personas: Three Persona Creation Methodologies with Implications for Practical Employment}},
  author       = {Bernard J. Jansen et al.},
  journal      = {Pacific Asia Journal of the Association for Information Systems},
  year         = {2022},
  doi          = {https://doi.org/https://doi.org/10.17705/1pais.14301},
}

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Evidence weight

0.62

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.68 × 0.4 = 0.27
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.