A Systematic Review of Chatbots: Classification, Development, and Their Impact on Tourism

Recently, we have observed a noticeable evolution and growing use and incorporation of chatbots on websites, mobile and social networking apps. A chatbot is a computer program that exhibits a capacity to converse quite naturally with users in a way that resembles a human dialogue. Examples of chatbots can be found in several areas, including education, commerce, and tourism. The use of Artificial Intelligence (AI) and its sub-fields, such as Machine Learning, Deep Learning, and Natural Language Processing (NLP), is increasing across all business sectors. One of the most advanced applications of this technology is the chatbot, which is particularly beneficial due to the quick response times and its simplicity. Nevertheless, although studies on chatbots exist in tourism, academic research covering their adoption, technological evolution, and impact on this sector is still relatively sparse. Therefore, this study aims to provide a comprehensive overview of chatbots and their effect on tourism. First, we provide a new classification of chatbots based on specific criteria. Second, we explore the conceptual architecture of chatbots and their key components. Third, this study aims to assess and contrast the main existing tools for developing chatbots, classifying them and highlighting their key advantages and disadvantages. Fourth, this study aims to examine the integration of chatbots in the tourism sector by identifying their key applications in the industry over the past decade. Additionally, it seeks to analyze the impact of chatbots on the various functionalities outlined in the 6A framework for tourism. To achieve this, a thorough search will be conducted using five prominent databases - Scopus, ACM, IEEE Xplore, Springer Link, and Web of Science - covering the period from 2013 to 2023. For this study, 1155 academic publications were reviewed after applying a systematic review protocol including purpose, research questions, keywords, digital libraries, search strings, and inclusion and exclusion criteria. Only 31 were identified to be primary studies.