Artificial intelligence: a systematic review of methods and applications in hospitality and tourism

Doborjeh, Zohreh Gholami; Hemmington, Nigel; Doborjeh, Maryam Gholami; Kasabov, Nikola

doi:10.1108/ijchm-06-2021-0767

Cited by 119 publications

(86 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The third level of contribution, which we argue returns the highest value, is the application of DL for the development and implementation of novel concepts, business models and product offerings to consumers. Within this level of DL applications, we have seen the development of novel and unique experiences to consumers, for example, AR/VR techniques (Doborjeh et al , 2021; Gaur et al , 2021) and computer-generated imaging (Lee and Madera, 2019; Lin et al , 2021).…”

Section: Resultsmentioning

confidence: 99%

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

Essien

Chukwukelu²

2022

IJCHM

View full text Add to dashboard Cite

Purpose This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research. Design/methodology/approach Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations). Findings Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists. Research limitations/implications Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel. Originality/value To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Essien

Chukwukelu²

2022

IJCHM

View full text Add to dashboard Cite

show abstract

“…Its results confirmed the positive impact of digitalization on firm performance (Chatterjee et al, 2020 ; Ribeiro-Navarrete et al, 2021 ; Zahra, 2021 ), which was particularly visible in the case of our non-family firms. The results showed that digitalization can be one of the core conditions that can lead to increases in firm performance in hospitality organizations where advanced solutions have been adopted (Doborjeh et al, 2022 ; Ivanov & Webster, 2019 ; Salguero & Espinilla, 2018 ). By indicating those dimensions that have not been reflected in the existing EO scholarship and may indeed contribute to a firm’s outcome (namely, flexibility, and digitalization), this study responded to the call that was formulated by Zellweger and Sieger ( 2012 ), who suggested extending the existing EO scales when studying family firms.…”

Section: Discussionmentioning

confidence: 99%

Entrepreneurial behaviors that shape performance in small family and non-family hotels during times of crisis

et al. 2022

View full text Add to dashboard Cite

Recent economic and public health crises have posed important challenges to family businesses – particularly those in the hospitality sector. While sustaining a business, performance becomes critical; there is insufficient knowledge on the use of entrepreneurial behaviors in mitigating the impact of a crisis by family businesses. To help fill this gap, this study explores the configurations of entrepreneurial behaviors that lead to improved performance in small firms under crisis market conditions – particularly, risk-taking, innovativeness, proactiveness, flexibility, and digitalization. This study employs fuzzy-set qualitative comparative analysis (fsQCA). The sample consists of 117 one- and two-star Polish hotels that are comprised of both family and non-family businesses. The data was collected in November and December 2021. The results confirm the core role of risk-taking, proactiveness, and flexibility in increasing the performance of these small firms. However, performance outcomes depend on the configurations of the firms; differences between family and non-family businesses stood out. In family hotels, risk-taking is accompanied by flexibility as a core factor, and digitalization does not play an important role in achieving higher performance. Overall, these results contribute to the literature on organizational entrepreneurship (especially entrepreneurial orientation) as well as family business crisis management in the tourism sector. These findings offer implications for managers by indicating combinations of entrepreneurial behaviors that can help foster business performance.

show abstract

“…The advent of smart restaurant and dining renders as a new phenomenon of interest in foodservice (Peek, 2021). Given the objective stated above, the present research builds the necessary foundation based on three hospitality research areas: service quality (Lin et al , 2020; Wong and Yang, 2020), AI and robots (Doborjeh et al , 2022; Jiang and Wen, 2020; Pillai and Sivathanu, 2020; Fu et al , 2022) and information technology applications in restaurants and other related contexts (Stylos et al , 2021; Law et al , 2022; Mariani and Baggio, 2022). In particular, we seek to synthesize these streams of work to better assess how technologies, especially AI and robots, are infused within the smart restaurants through the lens of service quality.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…In recent years, AI has been applied to various fields, including management for event lodging, travel agencies, supply chains, restaurants and sales and marketing (Davari et al , 2022; Doborjeh et al , 2022; Fu et al , 2022). AI applications in front-line services may focus on assisting or complementing employees doing more routine tasks.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Smart dining, smart restaurant, and smart service quality (SSQ)

Wong

Huang

Lin

et al. 2022

IJCHM

View full text Add to dashboard Cite

Purpose Have you been to a smart restaurant, and how were its services? A common limitation of hospitality studies stems from the lack of research on how service quality is shaped within smart technology. This study aims to fill this literature void not merely to reiterate the importance of technology but also to recast service quality through the lens of information technology. It synthesizes the 5-S model of smart service quality (AKA SSQ) as a new conceptualization of service quality application in smart hospitality contexts such as smart restaurants. Design/methodology/approach This study undertook a qualitative research design based on theoretical synthesis from service quality, information technology and attention restoration. Drawing from online review comments and semistructured interviews from smart restaurants, the authors improvised the SSQ model to identify the essence of smart service in smart dining establishments. Findings “5-S” reflects an extension of the literature to denote a new SSQ abstraction pertinent to s-servicescape, s-assurance, s-responsiveness, s-reliability and s-empathy. A nomological network was posited to better understand the importance of smart design and consequence of SSQ. Research limitations/implications The emergence of smart dining gives rise to smart restaurants, which puts technology at center stage. As consumers are becoming increasingly comfortable with self-service technology, auto-payment and ordering systems and robotic services, technology in foodservice will continue to play an essential role to better serve diners. Geared with advanced innovations and intelligent devices, smart restaurants are now more than mere eateries. It is a trend and a lifestyle. Originality/value This novel SSQ concept adds new nuances to the literature by acknowledging the technological essence in today’s hospitality industry. By integrating smart technology into the service quality paradigm, the authors are able to observe several interesting behaviors exhibited during smart dining, including tech-induced restoration, which opens a new avenue to understand how attention restoration could be attained through immersion in a technologically advanced setting. By synthesizing theoretical essence from service quality, attention restoration and information technology, the authors are able to create a new dialog that should warrant a forum of discussion in future studies.

show abstract

Artificial intelligence: a systematic review of methods and applications in hospitality and tourism

Cited by 119 publications

References 88 publications

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

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

Entrepreneurial behaviors that shape performance in small family and non-family hotels during times of crisis

Smart dining, smart restaurant, and smart service quality (SSQ)

Contact Info

Product

Resources

About