Guest Editorial

Flavián, Carlos; Casaló, Luis V.; Wang, Dan

doi:10.1108/ijchm-10-2021-1234

Cited by 2 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…employees’ attitudes toward robot colleagues) (Diaz et al , 2023) or marketing-oriented (e.g. customers’ willingness to interact with service robots) (Flavián et al , 2021).…”

Section: Potential Themes For Artificial Intelligence-assisted Mindfu...mentioning

confidence: 99%

“…The special issue of Artificial Intelligence in Hospitality and Tourism published by The International Journal of Contemporary Hospitality Management (Flavi an et al, 2021) showcases a recent trend of investigating AI in hospitality and tourism research. Existing AI studies cover issues on customers' and employees' acceptance of and interactions with AI applications (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…service delivery) and timings (e.g. the COVID-19 pandemic) suitable for AI applications (Flavián et al , 2021). Although hospitality and tourism experiences have been recognized as ideal opportunities for improving mental recovery and well-being (Chen et al , 2022; Uysal et al , 2016), existing AI studies that investigate how AI can be used to support human mindfulness in tourism, hospitality and events are still limited.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence-assisted mindfulness in tourism, hospitality, and events

Wang

Uysal

2023

IJCHM

View full text Add to dashboard Cite

Purpose Following the increasing trend of artificial intelligence (AI) research in hospitality literature, this critical reflection paper aims to identify AI-assisted mindfulness as a critical yet under-investigated issue and to contribute feasible directions for future research. Design/methodology/approach The authors first conceptualize a framework explaining the effects of mindfulness design in AI interventions on improving human mindfulness. The authors then identify opportunities for interventions in AI-assisted mindfulness for the tourism, hospitality and events industries. Finally, the authors propose potential themes for AI-assisted mindfulness research. Findings This study contributes three major conceptual works. First, we conceptualize a framework of AI-assisted mindfulness, showcasing that the scope of AI-assisted mindfulness spans from AI interventions to state mindfulness and then to trait mindfulness. Second, the authors offer two approaches to strategic thinking, one from mindfulness (i.e. mindfulness-focused niche markets and activities) and one from AI applications (i.e. AI-facilitated devices and platforms), to identify opportunities for AI-assisted mindfulness interventions. Third, for both management- and marketing-oriented AI-assisted mindfulness research, the authors propose 18 themes. Research limitations/implications This critical reflection paper offers directions for future knowledge creation in AI-assisted mindfulness in the tourism, hospitality and events industries. Originality/value To the best of the authors’ knowledge, this critical reflection paper serves as the first in hospitality and tourism literature to systematically propose the research issue of AI-assisted mindfulness, offering directions and themes for future research.

show abstract

“…employees’ attitudes toward robot colleagues) (Diaz et al , 2023) or marketing-oriented (e.g. customers’ willingness to interact with service robots) (Flavián et al , 2021).…”

Section: Potential Themes For Artificial Intelligence-assisted Mindfu...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence-assisted mindfulness in tourism, hospitality, and events

Wang

Uysal

2023

IJCHM

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) has been widely applied in the tourism and hospitality industries to offer effective operations and services, facilitate technology-assisted functions, cope with increasingly high labor costs and implement sanitation tasks during the COVID-19 pandemic (Buhalis et al , 2023; Flavián et al , 2021). Recently, AI-based service robots have been adopted by hotels, restaurants and theme parks (Ma et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Cross-segment validation of customer support for AI-based service robots at luxury, fine-dining, casual, and quick-service restaurants

Wang

Papastathopoulos

2023

IJCHM

View full text Add to dashboard Cite

Purpose With the trend of adopting and studying artificial intelligence (AI) service robots at restaurants, the authors’ understanding of how customers perceive robots differently across restaurant segments remains limited. Therefore, building upon expectancy theory, this study aims to propose a trust-based mechanism to explain customers’ support for AI-based service robots. Design/methodology/approach For cross-segment validation, data were collected from online survey participants under the scenarios of experiencing AI service robots in luxury (n = 428), fine-dining (n = 420), casual (n = 409) and quick-service (n = 410) restaurant scenarios. Findings In all four segments, trust in technology increased willingness to accept AI service robots, which was then positively related to customers’ support for AI-based service robots. Meanwhile, customers’ AI performance expectancy mediated the relationship between trust in technology and willingness to accept AI service robots. On the other hand, at luxury, fine-dining and casual restaurants, males perceived a stronger positive relationship between trust in technology and AI performance expectancy. No generational differences were found in the four restaurant segments between trust in technology and AI performance expectancy. Originality/value To the best of the authors’ knowledge, this study is one of the first attempts in hospitality research to examine cross-segment validation of customers’ responses to AI-based service robots in the luxury, fine-dining, casual and quick-service restaurant segments.

show abstract

Guest Editorial

Cited by 2 publications

References 28 publications

Artificial intelligence-assisted mindfulness in tourism, hospitality, and events

Artificial intelligence-assisted mindfulness in tourism, hospitality, and events

Cross-segment validation of customer support for AI-based service robots at luxury, fine-dining, casual, and quick-service restaurants

Contact Info

Product

Resources

About