Generative ai, exploring use cases by inbound tourism firms and tourists in japan, 2024
The inbound tourism industry in Japan is anticipated to grow at a rapid pace, Artificial Intellig... more The inbound tourism industry in Japan is anticipated to grow at a rapid pace, Artificial Intelligence (AI) is also expected to develop exponentially. Literature suggests that the tourism industry worldwide tends to be slow at adopting technology. Similarly, Japan has in recent years been slower in adopting technology and innovation compared to other countries. In order for Japan’s inbound tourism industry to remain globally competitive and be able to match their visitor’s potential technological related expectations inbound tourism firms should look at adopting AI into their operations.
This research paper investigates the current use of Generative AI tools by professionals and tourists within Japan's inbound tourism industry, offering practical suggestions for adopting AI in inbound tourism operations. The study explores the motivations and sentiments of tourism professionals regarding AI adoption and how these tools enhance their operations. Utilizing theories such as Diffusion of Innovation and the Technology Acceptance Model, the research employs qualitative analysis through semi-structured interviews with professionals and web scraping of tourist experiences to demonstrate the potential of Generative AI. The study also examines biases in AI outputs, including gender, ethnic/racial, and age biases.
The findings from the literature review and data analysis indicate that the primary barriers to AI adoption include a lack of social proof and digital aptitude and that Generative AI tools can significantly enhance operational efficiency and add value to the industry.
In conclusion, to mitigate these barriers, the paper offers practical examples of current AI tool usage, potential use cases, a roadmap for AI adoption, and a proposed curriculum for industry workshops or seminars. Additionally, it highlights important bias considerations for professionals in the industry.
Generative ai, exploring use cases by inbound tourism firms and tourists in japan, 2024
The inbound tourism industry in Japan is anticipated to grow at a rapid pace, Artificial Intellig... more The inbound tourism industry in Japan is anticipated to grow at a rapid pace, Artificial Intelligence (AI) is also expected to develop exponentially. Literature suggests that the tourism industry worldwide tends to be slow at adopting technology. Similarly, Japan has in recent years been slower in adopting technology and innovation compared to other countries. In order for Japan’s inbound tourism industry to remain globally competitive and be able to match their visitor’s potential technological related expectations inbound tourism firms should look at adopting AI into their operations.
This research paper investigates the current use of Generative AI tools by professionals and tourists within Japan's inbound tourism industry, offering practical suggestions for adopting AI in inbound tourism operations. The study explores the motivations and sentiments of tourism professionals regarding AI adoption and how these tools enhance their operations. Utilizing theories such as Diffusion of Innovation and the Technology Acceptance Model, the research employs qualitative analysis through semi-structured interviews with professionals and web scraping of tourist experiences to demonstrate the potential of Generative AI. The study also examines biases in AI outputs, including gender, ethnic/racial, and age biases.
The findings from the literature review and data analysis indicate that the primary barriers to AI adoption include a lack of social proof and digital aptitude and that Generative AI tools can significantly enhance operational efficiency and add value to the industry.
In conclusion, to mitigate these barriers, the paper offers practical examples of current AI tool usage, potential use cases, a roadmap for AI adoption, and a proposed curriculum for industry workshops or seminars. Additionally, it highlights important bias considerations for professionals in the industry.
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Thesis Chapters by Anna Ayvazyan
This research paper investigates the current use of Generative AI tools by professionals and tourists within Japan's inbound tourism industry, offering practical suggestions for adopting AI in inbound tourism operations. The study explores the motivations and sentiments of tourism professionals regarding AI adoption and how these tools enhance their operations. Utilizing theories such as Diffusion of Innovation and the Technology Acceptance Model, the research employs qualitative analysis through semi-structured interviews with professionals and web scraping of tourist experiences to demonstrate the potential of Generative AI. The study also examines biases in AI outputs, including gender, ethnic/racial, and age biases.
The findings from the literature review and data analysis indicate that the primary barriers to AI adoption include a lack of social proof and digital aptitude and that Generative AI tools can significantly enhance operational efficiency and add value to the industry.
In conclusion, to mitigate these barriers, the paper offers practical examples of current AI tool usage, potential use cases, a roadmap for AI adoption, and a proposed curriculum for industry workshops or seminars. Additionally, it highlights important bias considerations for professionals in the industry.
This research paper investigates the current use of Generative AI tools by professionals and tourists within Japan's inbound tourism industry, offering practical suggestions for adopting AI in inbound tourism operations. The study explores the motivations and sentiments of tourism professionals regarding AI adoption and how these tools enhance their operations. Utilizing theories such as Diffusion of Innovation and the Technology Acceptance Model, the research employs qualitative analysis through semi-structured interviews with professionals and web scraping of tourist experiences to demonstrate the potential of Generative AI. The study also examines biases in AI outputs, including gender, ethnic/racial, and age biases.
The findings from the literature review and data analysis indicate that the primary barriers to AI adoption include a lack of social proof and digital aptitude and that Generative AI tools can significantly enhance operational efficiency and add value to the industry.
In conclusion, to mitigate these barriers, the paper offers practical examples of current AI tool usage, potential use cases, a roadmap for AI adoption, and a proposed curriculum for industry workshops or seminars. Additionally, it highlights important bias considerations for professionals in the industry.