Using deep learning to investigate digital behavior in culinary tourism

Abstract

Purpose The purpose of this study is to gather insights into digital consumer behaviour related to Chinese restaurents by examining visual contents on Tripadvisor platform. Design/methodology/approach Using the deep learning approach, this research assessed consumer-posted online content of dining experiences by implementing image analysis and clustering. Text mining using word cloud analysis revealed the most frequently repeated keywords. Findings First, 4,000 photos of nine Chinese restaurants posted on Tripadvisor’s website were analyzed using image recognition via Inception V3 and Google’s deep learning network; this revealed 12 hierarchical image clusters. Then, an open-questionnaire survey of 125 Chinese respondents investigated consumers’ information needs before visiting a restaurant and after purchasing behavior (motives to share). Practical implications This study contributes to culinary marketing development by introducing a new analysis methodology and demonstrating its application by exploring a wide range of keywords and visual images published on the internet. Originality/value This research extends and contributes to the literature regarding visual user-generated content in culinary tourism.

Deep learning in hospitality and tourism: a research framework agenda for future research

“…DL can provide an automated approach towards understanding consumer, traveller or tourist behaviour, by leveraging usergenerated content especially in the form of social media data (Essien et al, 2021). This was explored in Zhang et al (2020a), via the analysis of the visual content of geo-attached photos (Hasan et al, 2021). In this present study, eight studies in the provide theme applied DL for enhancing convenience of service via automation (see Table 5).…”

“…The study analysed 29,081 photos shared by tourists from Europe, North America and Asia in Hong Kong. Besides, DL can perform image analytics for analysing consumer-posted online content relating to dining experiences (Hasan et al , 2021). In this present study, eight studies in the provide theme applied DL for enhancing convenience of service via automation (see Table 5).…”

“…In the study sample analysed, a total of 31 articles that apply DL for the analysis or evaluation of some tourismrelated variable (refer to Table 6). For example, DL was used to analyse news articles Zhang et al (2019a), hotel reviews and responses Chang et al (2020), as well as tourist behaviour Kim et al (2017); Hasan et al (2021) these articles have also enabled the provision of better services (i.e., in the provide theme), for example Zhang et al (2020b), where DL was used to perform spatiotemporal analysis of tourist experiences.…”

Intention to Revisit Culinary Tourism

“…There is a strong relationship between attitudes and behavior. This shows that the culinary experience in tourism is strongly related to the attitude, psychological, and perception factors of tourists [9]. Culinary experiences are related to moods and emotions that tourists will remember [15].…”

“…Tourists' perceptions of local cuisine will affect attitudes and determine tourist behavioral intentions [9]. Research on culinary tourism is important because foreign tourists who visit a tour will enjoy local cuisine by [6].…”