Scholarly Publications - Tourism and Hotel Management

Permanent URI for this collectionhttps://hdl.handle.net/11693/115507

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Browsing Scholarly Publications - Tourism and Hotel Management by Author "Ali, Seyid Amjad"

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    AI-generated future: what awaits tourism and hospitality with AI-based deep learning technologies?
    (2024-05-04) Collins, Ayşe; Ali, Seyid Amjad; Yılmaz, Semih; Berezina, K; Nixon, L; Tuomi, A
    AI-based technologies are taking the world by storm - rapidly changing the course of many industries from arts to education, healthcare to entertainment, and even areas of life we are yet to discover [1-4]. The application of AI-based technologies is also emerging in travel and tourism industries [5, 6], but remains underexplored as a research area [7-9] when specific and feasible AI applications are considered. This study describes and appraises several emerging AI-based deep learning technologies that are un(der)utilized in tourism fields but promise high utility in the future. Furthermore, potential application areas of these technologies within the context of tourism are detailed. Possible research routes and methodologies to investigate the functionality of AI-based applications are also outlined.
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    Coming to terms with the digital natives: understanding the marketing sensitivities of Genzers as hospitality consumers
    (2024-05-04) Yılmaz, Semih; Collins, Ayşe; Ali, Seyid Amjad; Berezina, K; Nixon, L; Tuomi, A
    As "digital natives", GenZ is set apart from previous generations in terms of its online connectedness. Even though this generation is expected to be the prevailing customer base around the world by 2026, there is a noticeable lack of studies on GenZ's consumer characteristics within the hospitality context. This study investigates the marketing-related factors affecting GenZ's accommodation decisions as well as their consumer sensitivities to contemporary constructs such as brand uniqueness, social media presentability, sustainability consciousness, and cancel culture.
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    Evaluating higher education performance via machine learning during disruptive times: a case of applied education in Türkiye
    (Wiley, 2024-12) Yılmaz, Semih Sait; Collins, Ayşe; Ali, Seyid Amjad
    In response to the COVID-19 pandemic, an abrupt wave of digitisation and online migration swept the higher education institutions around the globe. In the aftermath of this digital transformation which endures as the legacy of the pandemic, what lacks in knowledge is how effective the anti-COVID measures were in maintaining quality education. Using machine learning to analyse student grades as a proxy for educational standards, this study investigates and demonstrates the evaluative potential of machine learning (vs. traditional statistics) with respect to not only crisis responses in education but also applied studies such as Information Systems and Tourism. Main implication of this study is the analytical utility of machine learning even when educational data are irregular and small. However, incorporating accurate and meaningful data points into the existing online educational systems is crucial to leverage this utility of machine learning.