Reverse Market Segmentation With Personas

Ketamo, Harri; Kiili, Kristian; Alajääski, Jarkko

doi:10.5220/0002781300630068

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data-driven personas were proposed by [12,23], for example, based on clickstreams [23,24] or statistical data [12,23]. Machine learning methods, more specifically K-means clustering, are used to build personas [2,18,19,25].…”

Section: A Persona Lensmentioning

confidence: 99%

Chatbot Technology Use and Acceptance Using Educational Personas

Amer jid Almahri,

Bell,

Gulzar

2024

Informatics

View full text Add to dashboard Cite

Chatbots are computer programs that mimic human conversation using text or voice or both. Users’ acceptance of chatbots is highly influenced by their persona. Users develop a sense of familiarity with chatbots as they use them, so they become more approachable, and this encourages them to interact with the chatbots more readily by fostering favorable opinions of the technology. In this study, we examine the moderating effects of persona traits on students’ acceptance and use of chatbot technology at higher educational institutions in the UK. We use an Extended Unified Theory of Acceptance and Use of Technology (Extended UTAUT2). Through a self-administrated survey using a questionnaire, data were collected from 431 undergraduate and postgraduate computer science students. This study employed a Likert scale to measure the variables associated with chatbot acceptance. To evaluate the gathered data, Structural Equation Modelling (SEM) coupled with multi-group analysis (MGA) using SmartPLS3 were used. The estimated Cronbach’s alpha highlighted the accuracy and legitimacy of the findings. The results showed that the emerging factors that influence students’ adoption and use of chatbot technology were habit, effort expectancy, and performance expectancy. Additionally, it was discovered that the Extended UTAUT2 model did not require grades or educational level to moderate the correlations. These results are important for improving user experience and they have implications for academics, researchers, and organizations, especially in the context of native chatbots.

show abstract

Section: A Persona Lensmentioning

confidence: 99%

Chatbot Technology Use and Acceptance Using Educational Personas

Amer jid Almahri,

Bell,

Gulzar

2024

Informatics

View full text Add to dashboard Cite

show abstract

Applications of Machine Learning in Education

Almahri

Bell

Arzoky

2021

Machine Learning Approaches for Improvising Modern Learning Systems

View full text Add to dashboard Cite

This research aims to explore how to enhance student engagement in higher education institutions using novel chatbots. This study's principal research methodology is design science research, which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This chapter focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, precisely a k-means clustering technique. Data analysis is conducted using two datasets. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically k-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations.

show abstract

Reverse Market Segmentation With Personas

Cited by 2 publications

References 4 publications

Chatbot Technology Use and Acceptance Using Educational Personas

Chatbot Technology Use and Acceptance Using Educational Personas

Applications of Machine Learning in Education

Contact Info

Product

Resources

About