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The task to Technology view of text-based Chatbot Utilization and Performance: Quantitative study
Linnaeus University, Faculty of Technology, Department of Informatics.
2022 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Chatbots are very widely used nowadays. However, much of the research on Chatbots have had a technology focus or has been limited to studies of adoption. To take advantage of the potential associated with chatbots, research that addresses the issues online users face when interacting with such programs is needed. The study described in this paper used the task-to technology fit theory to address the question of how individual characteristics and task/technology requirements influence the performance and utilization of chatbots. This paper used the quantitative methodology over two sets of data collected independently from two different populations. The first dataset of 100 respondents was obtained firstly through a structured questionnaire administered at Linnaeus University Campus in Växjö. The respondents are students in the university who use chatbots regularly. A second dataset was also collected from 20 participants through a practical test experiment with three different chatbots (Eliza, Rose, and Watson). The result and the data were then recorded through an online interview via the zoom application. The two datasets were analyzed quantitatively using comparative factor analysis with the aid of Smart PLS software. While few variables provided little support for the claims, the majority of the variables show strong support for the importance of task–technology fit, as a measure of chatbot utilization and performance based on individual characteristics as well as the task/technology requirements.  

Place, publisher, year, edition, pages
2022. , p. 68
Series
Linnaeus University Dissertations
Series
Master's Thesis
Keywords [en]
Chatbots, Conversational agents, Task to Technology Fit, Partial Least Squares, and Structural Equation Modelling
National Category
Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-112921OAI: oai:DiVA.org:lnu-112921DiVA, id: diva2:1659083
Subject / course
Informatics
Educational program
Master Programme in Information Systems, 120 credits
Presentation
2021-10-06, Zoom, Online, 13:40 (English)
Supervisors
Examiners
Available from: 2022-05-23 Created: 2022-05-18 Last updated: 2022-05-23Bibliographically approved

Open Access in DiVA

Master's Thesis- Ifasanya SubuloyeOgunjobi(2596 kB)194 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf