Holistic approach for selecting chatbot development tools: combining AHP and TOPSIS methodologies
Mohamed Lachgar,
Hamid Hrimech,
Younes Ommane and
My Driss Laannaoui
Journal of Business Analytics, 2025, vol. 8, issue 1, 1-23
Abstract:
The proliferation of chatbot development tools presents a significant challenge in identifying the most suitable ones for specific needs. This study addresses this issue by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to create a comprehensive methodology for selecting chatbot development tools. The study aims to provide a transparent, objective decision-making framework by evaluating tools across multiple dimensions including deployment characteristics, input processing capabilities, dialogue management, response generation, and economic considerations. Utilizing AHP for criteria weighting and TOPSIS for ranking alternatives, the methodology systematically evaluates various tools to identify the optimal choice. Key findings highlight the strengths and weaknesses of different tools, offering insights that guide developers and organizations in choosing the most suitable chatbot development tool aligned with their specific needs and strategic goals. The implications of this methodology are significant, enhancing decision-making processes within technological tool selection and contributing to the fields of artificial intelligence and automated systems development.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/2573234X.2024.2399087 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tjbaxx:v:8:y:2025:i:1:p:1-23
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjba20
DOI: 10.1080/2573234X.2024.2399087
Access Statistics for this article
Journal of Business Analytics is currently edited by Dursan Delen
More articles in Journal of Business Analytics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().