Rethinking Agritourism Marketing in Africa: A Look Through the Lens of Artificial Intelligence

Tendai Shelton Muwani (), Njodzi Ranganai, Chipo Katsande, Gracious Mutipforo and Prosper Tafadzwa Denhere
Additional contact information
Tendai Shelton Muwani: Manicaland State University of Applied Sciences
Njodzi Ranganai: Manicaland State University of Applied Sciences
Chipo Katsande: Manicaland State University of Applied Sciences
Gracious Mutipforo: Manicaland State University of Applied Sciences
Prosper Tafadzwa Denhere: Manicaland State University of Applied Sciences

Chapter Chapter 3 in Agritourism Marketing in Africa, 2025, pp 79-102 from Springer

Abstract: Abstract Traditional agritourism marketing strategies in Africa often struggle to cater to the diverse needs of tourists and promote sustainable practices. Artificial Intelligence (AI) enhances agritourism marketing strategies by providing valuable insights, optimizing decision-making processes, and enabling personalized experiences. The chapter explores the potential of Artificial Intelligence (AI) to personalize the agritourism experience and foster environmentally conscious tourism. The agritourism sector in Africa holds immense potential for economic growth and rural development. However, current marketing approaches often lack personalization, failing to target tourists with specific interests and preferences effectively. Additionally, promoting sustainable practices within agritourism remains a challenge. The book chapter’s task is to respond to the following research questions: how can AI be leveraged to personalize agritourism marketing strategies in Africa? Can AI-powered tools encourage sustainable practices within the agritourism sector, and what are the potential benefits and challenges of using AI for agritourism marketing in Africa? The significance of this research is to interact with and address the SDGs (Sustainable Development Goals) with a focus on using digital technology in agritourism by ensuring sustainable innovation, securing continuous production patterns, and enhancing collaboration for sustainable tourism development, which is critical for human development. The chapter addresses a critical gap in understanding how AI can revolutionize agritourism marketing in Africa. By personalizing the tourist experience and promoting sustainable practices, AI has the potential to enhance the economic and environmental impact of agritourism. A systematic literature review for information deemed relevant to the subject under inquiry was conducted. AI frameworks such as recommender systems and sentiment analysis will be explored for their applicability in personalizing marketing strategies. The chapter reveals valuable insights into how AI can personalize agritourism marketing in Africa. The findings may suggest AI-powered solutions for tailoring recommendations to tourists, showcasing sustainable practices of agritourism businesses, and optimizing marketing campaigns based on real-time data and tourist sentiments. By examining the potential of AI technologies in agritourism marketing, this research seeks to unlock new possibilities for personalized and sustainable marketing strategies that can drive the growth and success of agritourism ventures in Africa.

Keywords: Artificial Intelligence technologies; Agritourism marketing; Tourist; Sustainable practices of agritourism (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-031-78682-2_3

Ordering information: This item can be ordered from
http://www.springer.com/9783031786822

DOI: 10.1007/978-3-031-78682-2_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().