Chatbot Design and Implementation: Towards an Operational Model for Chatbots

Skuridin, Alexander and Wynn, Martin G ORCID: 0000-0001-7619-6079 (2024) Chatbot Design and Implementation: Towards an Operational Model for Chatbots. Information, 15 (4). art 226. doi:10.3390/info15040226

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Abstract

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and a model is developed and validated to support the planning and implementation of chatbot projects. The presented model can serve as an exemplary guide for researchers and practitioners working in this field. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience in chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Chatbots; Digital transformation; Customer service; TOE framework; Artificial intelligence; Machine learning; Project management; Agile; Minimum viable product; Large language models
Subjects:	T Technology > T Technology (General)
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Martin Wynn
Date Deposited:	23 Apr 2024 11:11
Last Modified:	29 Apr 2024 11:00
URI:	https://eprints.glos.ac.uk/id/eprint/13973

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