Integration of artificial intelligence in hospitality management: A comprehensive literature review

Resumo

Objectives | The rise of digital technologies has greatly impacted the global hospitality industry. Factors like a shortage of workers in the sector and the COVID-19 pandemic have made digitalization even more crucial: helping in managing staff efficiently and meeting customer needs like online orders and contactless payments. Despite the growing use of restaurant management systems (RMS), they are often not fully utilized. They miss out on important aspects of management like planning and organizing tasks. Artificial intelligence (AI) has the potential to address these gaps by controlling costs, boosting productivity, and enhancing customer satisfaction at an organizational level. While integrating digital solutions, can offer many benefits, it's essential to recognize and address any resistance that might arise during implementation. This resistance may be rooted in diverse factors, yet understanding them and taking appropriate steps can help businesses overcome it and successfully adopt digital technologies. Thus, our study seeks to contribute to the body of knowledge on artificial intelligence’s utilization in restaurant management by synthesizing the most recent literature.

Methodology | In the initial stage of our ongoing research series, a systematic literature review (SLR) has been undertaken to explore questions previously examined within the subject matter while simultaneously identifying gaps in literature. This comprehensive literature review followed a four-step methodology: conceptualization, screening for relevance, screening for eligibility, and ultimately, full-text data analysis. Commencing with systematic inquiries across academic search engines and databases (Web of Science, Scopus), we employed a set of keywords to procure appropriate scholarly papers.

Main Results and Contributions | AI technology presents a multitude of advantages for restaurants, encompassing heightened operational efficiency, diminished labor costs, elevated customer service standards, and augmented revenue streams. Kumar et al. (2021) have discerned numerous opportunities for AI and machine learning applications within the food industry, spanning from the optimization of supply chain logistics and enhanced food safety measures to the refinement of product development processes. Furthermore, Lee et al. (2021) have developed a predictive model, fortified by AI-powered big data analytics, to assist in managing restaurant reviews, thereby empowering customers to make more informed dining decisions. AI-driven chatbots and voice assistants hold the potential to automate routine operational tasks such as order processing, payment handling, and reservation management, culminating in substantial cost savings for restaurants and accelerated service delivery. Additionally, AI technology can harness customer data for the purpose of crafting personalized marketing initiatives and menu offerings, thereby bolstering customer loyalty and augmenting revenue streams.

Despite its promising benefits, AI deployment within the restaurant industry confronts a spectrum of challenges that necessitate careful scrutiny. Nozawa et al. (2021) have observed that consumer responses to AI utilization exhibit variations when comparing luxury and non-luxury dining establishments, with concerns centered on issues of data privacy and the potential erosion of human interaction. Moreover, issues concerning data security and privacy emerge, as AI mandates substantial user data for optimal functioning. Naumov (2019) has underscored the complex nature of utilization of robots and AI on service quality and hospitality experience, demanding careful consideration. Blöcher and Alt (2020) highlighted the need for specialized technical expertise to develop and maintain AI systems, which encompasses the financial considerations associated with its implementation.

Limitations | Literature reviews serve as a valuable method for synthesizing existing literature within a specific domain, however, they have certain limitations. A foremost constraint lies in the potential for publication bias, where studies with statistically significant results tend to dominate the literature, while those with null or negative findings are often left out. Moreover, despite extensive search efforts, the potential exists for the omission of some relevant studies. It can be attributed to several factors, including limitations in the chosen search terms, access constraints, or the existence of studies published in languages not covered within the review's scope.

Conclusions | The integration of AI within the restaurant industry presents a multifaceted tapestry of advantages and challenges, exerting a transformative influence on hospitality management. However, with consideration and continuous developmental initiatives, digital technology stands ready to revolutionize the sector, ushering in benefits such as amplified productivity, heightened customer satisfaction, and augmented revenue streams. The research underscores a diverse array of opportunities for further AI development and application, thereby emphasizing the substantial future potential of AI within the restaurant industry. Notwithstanding the obstacles that necessitate surmounting, including the costs associated with implementation, technical expertise deficits, and concerns related to data security, AI holds a promising future within the restaurant sector.

References

Blöcher, K. & Alt, R. (2020). AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry. Electronic Markets, 31, 529–551. DOI: 10.1007/s12525-020-00443-2

Kumar, I., Rawat, J., Mohd, N. & Husain, S. (2021). Opportunities of Artificial Intelligence and Machine Learning in the Food Industry. Journal of Food Quality, 2021, 1–10. DOI: 10.1155/2021/4535567

Nozawa, C., Togawa, T., Velasco, C. & Motoki, K. (2021). Consumer responses to the use of artificial intelligence in luxury and non-luxury restaurants. Food Quality and Preference, 96, 104436. DOI: 10.1016/j.foodqual.2021.104436

Naumov, N. (2019). The impact of robots, artificial intelligence, and service automation on service quality and service experience in hospitality. Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality, DOI:10.1108/978-1-78756-687-320191007

Lee, M., Kwon, W., & Back, K. (2021). Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making. International Journal of Contemporary Hospitality Management, 33(6), 2117-2136. DOI:10.1108/IJCHM-06-2020-0587