Abstract
AbstractPhotographic images help customers perceive product information more accurately and clearly. A customer’s perception of a particular product also influences their decision to purchase it. In the context of a hotel, guests evaluate digital hotel photos online during their booking decision process. While a large body of research has contributed to the understanding of how hotel online digital images shape hotel customer behaviour, little is known about the aesthetics, content, and composition of hotel images and their effects on booking decisions. In addition, previous research has routinely been criticised for having methodological limitations. These studies have routinely used surveys and experiments to explore how hotel pictures affect customer perception of the hotel and his/her booking intentions. Unlike prior studies, this research scopes a determination of the ‘selling’ properties pertinent to the hotel’s digital images placed online on the hotel-themed websites with the application of the latest technologies pursuant to visual data mining, processing and analysis. This study employed Google’s Inception v3 neural network as an AI solution for embedding and classifying hotel photo images with the further application of logistic regression and fuzzy cognitive mapping method. The results of the present study determined the hotel picture properties that may engender positive customer perception of the hotel and sequentially can precipitate hotel booking. The revealed ‘selling’ hotel image properties comprise (a) light and time of the photo shooting, (b) image colour scheme, (c) human presence, and (d) shooting angle. This study suggests a set of practical recommendations to hotel marketers to develop ‘selling’ photo images that generate hotel bookings online. The completed research is one of the first in the nascent literature stream in AI-powered computer vision solutions studies to determine the effects of photo aesthetics on online hotel bookings.
Publisher
Springer Science and Business Media LLC
Subject
General Economics, Econometrics and Finance,General Psychology,General Social Sciences,General Arts and Humanities,General Business, Management and Accounting
Reference74 articles.
1. Aksoy S, Ozbuk MY (2017) Multiple criteria decision making in hotel location: does it relate to postpurchase consumer evaluations? Tour Manag Perspect 22:73–81
2. Amin M, Ryu K, Cobanoglu C, Nizam A (2021) Determinants of online hotel booking intentions: website quality, social presence, affective commitment, and e-trust. J Hosp Mark Manag 30(7):845–870
3. Andrade M (2012) El uso de Internet como fuente de información turística: propuesta metodológica para el análisis de su importancia en la creación de la imagen del destino. Pap Turis 52:44–62
4. Arora AS, Arora A (2017) WYSIWYG—seeing is believing: consumer responses to levels of design newness, product innovativeness, and the role of country-of-origin. J Int Consum Mark 29(3):135–161
5. Aziz O, Musngi M, Park EJ, Mori G, Robinovitch SN (2017) A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med Biol Eng Comput 55(1):45–55
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