Service Robots in Hospitality and Tourism Before and During the COVID-19: Bibliometric Analysis and Research Agenda

Abstract

This study presents a comprehensive framework for and viewpoint on the research landscape of service robots within the hospitality and tourism context. Employing a bibliometric analysis, we examined the existing body of research on robots published in both hospitality and tourism and B&M journals. Our findings reveal that, compared to those within the hospitality and tourism context, studies in B&M have explored a broader range of topics and delved deeper into the realm of research on robots. Conversely, research in the hospitality and tourism field has predominantly focused on examining factors and outcomes associated with customer experiences in relation to robot services. Our study highlights the need for future research agendas in the field of service robot research within the hospitality and tourism context. By identifying the research gaps and emerging trends, the present study contributes to the development of this expanding area of study.

Keywords

bibliometric analysis business co-word analysis hospitality and tourism robots

Introduction

For many years, the robotics industry has been led by a group of companies (ABB, Fanuc, KUKA, and Yaskawa) that has commanded roughly 75% of the market. The International Federation of Robotics (2021) predicted that the market for service robots is growing and will have reached a value of $63.8 billion by 2025. Robotic applications have increasingly expanded from the manufacturing industry to the service industry, which has led to the rapid development of service robots (Sun & Wang, 2022). Consequently, service robot–related research has drawn considerable scholarly interest within the hospitality and tourism (H&T) management literature and, more broadly, within that of business and management (B&M; Guerra-Montenegro et al., 2021).

The ongoing pandemic has reinforced the importance of applying this technology in H&T settings, and scholars have been studying it extensively in the academic literature. The COVID-19 pandemic has boosted the service robot market due to high demand (Mordor Intelligence, 2021). The H&T literature includes studies on the pandemic’s influence on the implementation of service robots (Jiang & Wen, 2020). However, most recent studies have focused solely on the current phase, without comparing it to the pre-COVID-19 phase (Farmaki et al., 2020). In spite of extensive research on COVID-19 in the H&T field, little is known about service robots in H&T and B&M during this pandemic.

In B&M, the existing studies have significantly developed our understanding of robots by proposing conceptual frameworks and outlining the drivers of robot development. The H&T literature on robots has extended the foundations and uses of robots derived from mainstream service management (Shin & Perdue, 2022). There have been review studies and a rapidly emerging body of literature on the number of robots within the B&M context (Belanche et al., 2020; McCartney & McCartney, 2020). As the popularity of research on robots continues to grow in the H&T discipline, it is necessary to conduct a review to identify the evolving patterns and prospects of such research. Given the growing importance of service robots both as an H&T focus and a topic of H&T research, the growing interest in both H&T and broader service management raises the need for a review of the study on the topic and an identification of future study avenues (Ivanov et al., 2019; Shin & Perdue, 2022). Therefore, it would be prudent to carry out a bibliometric study of this research domain.

Bibliometric analysis is used to analyze the bibliographic materials of the existing literature to identify patterns, themes, and research trends in a specific area of many different scientific disciplines over a period of time (Zupic & Čater, 2015). It can employ multiple methods, such as citation analysis and co-word analysis. Although bibliometric methods have been widely used in recent years for the examination of H&T studies (e.g., Ávila-Robinson & Wakabayashi, 2018), few service robotics–related studies have been applied with bibliometric analysis in the H&T field (Mejia & Kajikawa, 2017).

This present study involves a reflective review of the existing literature investigating differences in H&T before and after the outbreak of COVID-19 in the field of service robotics. The current study is divided into two subperiods, namely 2010–2019 and 2020–2022, over which it identifies articles published in B&M and H&T journals to analyze the evolution of service robot research themes. In doing so, it sheds light on trends in research on service robotics and outlines a future research agenda. The specific goals of this study are as follows:

(a)To explicate the interorganizational efforts to advance research on robots in the H&T context;

(b)To identify the knowledge structure of the research on H&T robotics and compare the evolution of this research before and during the pandemic;

(c)To acquire insight into future research by comparing the developments in research on robotics in the H&T literature to those in the B&M literature.

This study is organized as follows. The first section discusses the service robot review research and bibliometrics in H&T. This is followed by an explanation of the research methodology utilized to conduct the analysis. The research findings and a discussion are then presented. Finally, the research concludes with a discussion of the limitations and the future research agenda.

Background

Bibliometric Analysis in H&T

Bibliometric analysis has been widely applied in various scientific disciplines, including the H&T field, marketing, accounting, consumer behavior, advertising, education, physics, sociology, and economics. H&T research includes journal ranking and development; research quality; the research productivity of authors, institutions, and countries; research topics; and emerging trends (revenue/operation management, pricing, tourism sustainability, sharing economy, information technology, social media usage, hotel performance, food and gastronomy, social media, strategic management, human resources, service quality, business ethics, sustainable tourism, wine tourism, medical tourism, and tourism crisis and disaster management; Mulet-Forteza et al., 2019; Rodríguez-López et al., 2020).

Bibliometric analysis has been applied to technology, including robotics, in business publications (Leung et al., 2017; Mejia & Kajikawa, 2017; Ruiz-Real et al., 2021). For instance, studies have reviewed robot-related research in the fields of business and the social sciences (e.g., Kaartemo & Helkkula, 2018). For instance, Atzeni et al. (2021) conducted a bibliometric analysis of robots applied to logistics systems, Goeldner et al. (2015) analyzed publications on care robotics, and Mejia and Kajikawa (2017) explored social robotics. However, because few studies to date have applied bibliometrics to the topic of service robots in H&T research, we attempt to fill this gap by utilizing bibliometric analysis as a key analytical tool. Furthermore, by analyzing both H&T and B&M journals, the study aims to provide comprehensive insights into the advancements and future prospects of the study of robotics in these fields. To bridge the existing research gap, our bibliometric study covers an extended publication period and specifically focuses on influential H&T and B&M journals (Leung et al., 2017).

Overview of Research on Service Robots in H&T

Service robots provide a research topic throughout the literature from different points of view within the H&T context. First, previous studies have focused on the role/application/use of service robots (e.g., room service delivery and porter robots, robot receptionists, and robot baristas) in various H&T sectors (Hwang et al., 2021; Odekerken-Schröder et al., 2020; Tuomi et al., 2020). The adoption of and interest in the application of service robots are expected to accelerate for various reasons, such as their increasing efficiency, cost savings, and role in improving the customer service experience.

Second, from the customer perspective, researchers have focused on consumer perceptions of service robots (Ivanov et al., 2020; D. Li et al., 2022; Molinillo et al., 2023). Such research is concentrated on customers’ perceptions of robots; robots’ impact; and the factors influencing customer perceptions, evaluations, and behavior (Guan et al., 2022; Hao et al., 2023; Y. Li & Wang, 2022). Researchers have focused on the antecedents (i.e., service quality, motivations, customers’ preferences and real-world experiences, trust, and customers’ attitudes toward the use of robots) of customers’ perceptions (e.g., Choi et al., 2020; Qiu et al., 2020) and their evaluations of and responses to service robots (e.g., satisfaction, perceptions, use intentions, adoption, acceptance, and sales revenue; e.g., Akdim et al., 2023; Guan et al., 2022; Ladeira et al., 2023; Wang & Li, 2021; S. Zhang et al., 2022). Third, researchers have also investigated customer characteristics (e.g., age, gender, technology readiness, and personality traits) when introducing service robots (e.g., Belanche et al., 2020; Seo, 2022; Wang & Li, 2021).

Finally, studies have investigated employees’ and suppliers’ perceptions of and experiences with collaborating with robots (e.g., Abdelhakim et al., 2023; Ivanov et al., 2020; Parvez et al., 2022; Vatan & Dogan, 2021). Prior studies have also focused on the factors of service encounter characteristics concerning service robots (e.g., failures and complaints, involvement level; Belanche et al., 2020). Additionally, researchers have focused on how robots’ characteristics, features, and aesthetics; robot design factors (e.g., anthropomorphism, human-likeness, and facial features); and the nature of service contexts (e.g., service failures) influence consumers’ intentions to adopt, evaluations of, and use of service robots (Belanche et al., 2020; de Graaf et al., 2019; Fan et al., 2020; Xie & Lei, 2022).

The demand for contactless services has recently increased because of the COVID-19 pandemic. The pandemic has severely affected many industries, including H&T, because people have been forced to avoid human interaction, and has dramatically changed many aspects of H&T businesses. Service robots can be effectively applied to provide physical distancing in the H&T context and have brought substantial efficiency and productivity gains during the COVID-19 pandemic (Zhong et al., 2022). To sum up, most researches on service robots have focused on human perceptions of service robots using the quantitative method (e.g., Kim et al., 2021). An recent studies, because of the COVID-19 pandemic, have explored topics from the perspective of consumers and suppliers (Ye et al., 2022). Research gaps have therefore been identified accordingly.

Methods

Data Collection and Preparation

In this study, we targeted peer-reviewed academic journals for data collection to enhance our understanding of the progress and evolution of research on robots in H&T. On January 19, 2022, we gathered data from the Web of Science (WoS) database, which provides more detailed and comprehensive bibliographic data for citation analysis than other databases, such as Scopus and Google Scholar (Falagas et al., 2008). We also selected WoS because it is a renowned database for research published in top-tier journals (Merigó et al., 2015). We searched for articles using three keywords: robot, robots, and robotics.

To select articles relevant to the H&T discipline, we filtered the article search results using the WoS category “Hospitality, Leisure, Sport & Tourism,” which returned a total of 166 results. However, the journals classified in this WoS category include sports-related journals. Following previous studies (Koseoglu et al., 2022; Park et al., 2022), we excluded the articles published in sports journals, and after acquiring articles by using multiple filtering processes, we manually reviewed them to determine whether they were relevant to robotic applications in the H&T industry. As a result, we retrieved 123 documents published from 2010 to 2022 for data analysis. Most of the documents were regular articles and reviews, and a few were book reviews, editorials, and proceedings. Considering that research on robots is still at a nascent stage, this study included all document types for data analysis.

We also downloaded papers from B&M WoS categories to compare research trends and advances in H&T research as well as to discover research opportunities. We removed the documents published in H&T-related journals from the dataset. Consequently, we gathered 660 documents published from 2010 to 2022. We used the literature on robots in the B&M field only for thematic map analysis.

In this study, we evaluated publications published before and after 2020 to examine key trends in research on robots over time. We chose the year 2020 as a comparison criterion because (a) the number of papers on robots has increased dramatically since 2020 and (b) the COVID-19 epidemic broke out in the same year. Typically, there is a delay from the moment of an occurrence (e.g., the outbreak of COVID-19) to the publication of research results related to that occurrence in academic journals. However, because of the COVID-19 outbreak’s severity and urgency, many journals expedited the peer review and editorial processes to publish COVID-19-related papers. In fact, many papers related to COVID-19 were published in 2020. Hence, in this study, we separated the documents before and after 2020 to consider the impact of COVID-19 along with other specific research topics.

Bibliometric Data Analysis

In this study, we conducted bibliometric analysis with article metadata retrieved from a citation database. Bibliometric analysis is a statistical approach to measuring research performance and its impact and hence can provide “quantitative rigor into the subjective evaluation of literature” (Zupic & Čater, 2015, p. 431). Bibliometrics has two main applications: performance evaluation and mapping analysis (Zupic & Čater, 2015). Performance evaluation is aimed at demonstrating the research performance of researchers, institutions, or countries. Mapping analysis can be conducted with various types of bibliometric data, such as keywords, authors, and citations. This analysis is focused on countries’ and journals’ performance evaluations in terms of studies of robots in the H&T discipline. We selected author keywords, instead of system-generated keywords, for the main analysis, which used mapping analysis.

General Bibliometric Analysis

For both research performance evaluation and mapping analysis, this study used the bibliometrix package in R (Aria & Cuccurullo, 2017). After converting the WoS bibliographic data into R data, we conducted a descriptive analysis. In total, three datasets were generated: (a) one covering all periods (i.e., between 2010 and 2022), (b) one covering the period before 2020 (i.e., between 2010 and 2019), and (c) one covering the period after start of 2020 (i.e., between 2020 and 2022). We used descriptive statistics to find document characteristics, document type, and the number of publications per year. We also identified the countries and journals that had contributed the most to research on robots.

Keyword Network Analysis

With the biblioNetwork function in R, we generated a keyword network based on the co-occurrences of keywords. Our keyword network analysis was grounded in our co-word analysis, which was suggested by Callon et al. (1983). Co-word analysis is used to understanding significant research areas and dynamics by capturing groups of tightly linked keywords (Callon et al., 1991). In the keyword network, each vertex represents a keyword that appears in a manuscript. If two keywords appear together in the same manuscript, a link is drawn between the two keyword vertices. If these keywords frequently appear together in the same document, the connection between these vertices becomes stronger, and their similarity score becomes greater. By repeating this procedure, a keyword network can be drawn for an entire corpus. We drew two keyword networks (i.e., one before and one after 2020) to compare the keyword network structures. To improve the visibility of the keyword networks, we included the top 30 keywords for each network. For the network layout, we used that of Fruchterman–Reingold.

We obtained basic network statistics, namely diameter, average path length, and network density. The diameter indicates the longest path length of all those possible in the keyword network. A keyword network with a large diameter indicates that the literature covers a wide range of subtopic categories (Zhu & Guan, 2013). The average path length is the mean score of the shortest pathways between all possible pairs of vertices (i.e., vertex i to vertex j) in the network. The shorter the average path becomes, the closer two groups of keyword vertices are connected; hence, the groups of keywords are coherently connected (Q.-R. Zhang et al., 2017). Network density refers to the ratio of the number of actual linkages to the total number of possible links in the network (Q.-R. Zhang et al., 2017). The formula for network density is as follows:

D = 2 L / V (V - 1)

(1)

where L is the number of links and V is the number of vertices. A high density indicates a strong correlation among the vertices.

Three centrality scores (i.e., degree, eigenvector, and betweenness centralities) were obtained to identify the keyword vertices that played an important role in connecting other keywords. A high degree of centrality indicates that a node is connected to many other nodes. High eigenvector centrality implies that a node is connected with influential nodes. When a node connects to other nodes that have distinct characteristics, its betweenness centrality is high. In other words, nodes with high betweenness centrality scores serve as bridges connecting other nodes in different subcategories.

Based on the similarities and associations among the nodes, subgroups of keywords can be identified. In other words, a group of keywords that are strongly connected can be clustered. Out of the various options for clustering approaches, we applied the optimal clustering algorithm, which creates clusters with the maximal modularity score.

Thematic Map Analysis

We implemented thematic map analysis using the thematicMap function. Similar to the keyword network analysis, we used two thematic maps: one from before 2020 and one from after 2020. The concept of a thematic map was grounded on Cobo et al. (2011). Keywords that are strongly connected to one another can be clustered, and these clusters represent research subtopics. In thematic map analysis, the research subtopic clusters are placed in a quadrant graph that has the following axes of two-dimensional systems: centrality and density (Supplemental Figure 1). In the context of thematic map analysis, centrality refers to the degree of interconnectedness or the prominence of a research subtopic cluster within the network. Density, on the other hand, represents the level of cohesion or interconnectedness within a research subtopic cluster.

Centrality indicates the external strength of a connection between keyword clusters. High centrality scores indicate that a topic has received considerable attention from researchers and can be extended to similar topics (Callon et al., 1991). Centrality is calculated as follows:

Centrality = 10 \times (\sum e_{kh}),

(2)

where k is a keyword in the target cluster and h is a keyword in other clusters.

Density indicates the internal strength of the connection among all keywords in the cluster. A high density score implies a high degree of progression for a particular research subtopic cluster (Callon et al., 1991), and density is calculated as follows:

Density = 100 (\sum e_{ij} / w),

(3)

where i and j indicate the list of keywords in a cluster and w represents the total number of keywords in it.

To locate the research subtopic clusters in the four quadrants, we began by measuring the clusters’ density and centrality scores. We then ranked those scores to generate relevance and centrality scores. Finally, we used the relevance score medians as thresholds to map out the clusters. Quadrant I includes Motor topics, with centrality and density scores above the threshold. Clusters belonging to the Motor quadrant featured stable connections with other research subtopics and relatively high frequencies of keyword(s) that became mature topics. Quadrant II includes Niche topics and has centrality scores below the threshold but density scores above it. The clusters in this quadrant were mature but isolated from other research subtopics. For Quadrant III, called Emerging or Declining, both centrality and density scores were below the threshold, and the clusters that belonged to this quadrant were neither mature nor connected with other topics (yet). We labeled Quadrant IV topics as Basic, and these topics had centrality scores above the threshold and density scores below it. Clusters belonging to the Basic quadrant had become central topics in the discipline but had room for internal maturity.

Results

Overview of Past Studies of RobotsBetween 2010 and 2022

Table 1 displays general information about the literature on robots. Between 2010 and 2022, 123 research articles on robots were published in 38 H&T journals. On average, each paper was cited 18.86 times. Most of the papers were regular articles (80.5%) and reviews (7.3%). Before 2019, there were only a few articles on robots, but this number climbed in 2019 and peaked in 2020, demonstrating that robot service is an emerging issue in the H&T discipline. As shown in Table 1 and Supplemental Figure 2, China and the United States were the most prolific countries for robot literature production, followed by Bulgaria, the United Kingdom, and Spain. Similar results were found in terms of total citation counts; the papers published by Chinese institutions received the most citations, followed by those from the United States, Bulgaria, the United Kingdom, and Australia. Many studies of robots were conducted by China and the United States, but collaboration among countries was also prevalent.

Table 1.

H&T Robot Literature Descriptive Statistics.

Categories	Article frequency
Document characteristics
Number of documents	123
Number of journals	38
Average citations per document	18.86
Document type
Article (including early access)	99
Review	9
Book review	3
Editorial material	6
Proceedings	6
Year of publication
2010	1
2012	2
2017	4
2018	6
2019	17
2020	44
2021	35
2022 (including early access)	14
Corresponding author’s country (top 10 countries)
China	31
USA	26
Bulgaria	12
United Kingdom	11
Spain	8
Australia	5
Korea	4
Turkey	4
Austria	3
Cyprus	2
Total citations per country (top 10 countries)	Total citations
China	722
USA	557
Bulgaria	283
United Kingdom	195
Australia	106
Finland	106
Austria	83
France	66
Turkey	65
Korea	41

Keywords by Publication Source

When authors submit their papers, they are required to choose relevant keywords that reflect significant research areas. Figure 1 visually presents the author keywords that appeared most frequently (e.g., artificial intelligence, robots, service robots, COVID-19, and anthropomorphism) in studies of robots as well as the journals that published articles containing these author keywords. Our analysis of the relationship between keywords and journals revealed the notable occurrence of articles focusing on robot study subtopics published in influential H&T journals.

Figure 1.

Field plots of publication year, publication source, and author keywords.

The total outgoing flow count (TOC) from the publication year (PY) to the publication source (SO) indicates how many studies of robots were published each year. We drew TOC from PY to SO based on the top five journals that had published the most studies of robots, and this information illustrates the changes in the number of published articles over time. In general, TOC to SO was highest in 2020 and 2021, suggesting that studies of robots were published more frequently after 2020 than before. This trend was consistent across all five journals. The International Journal of Contemporary Hospitality Management (IJCHM) published the most research on robots, with 23 such articles, followed by the International Journal of Hospitality Management (IJHM), with 15 articles; the Annals of Tourism Research (ATR) and the Journal of Hospitality Marketing and Management (JHMM), with 10 articles each; and Tourism Review (TR), with seven articles. Thus, hospitality-oriented journals, such as IJCHM and IJHM, published many studies of robots, particularly after 2020.

We drew the total incoming flow count (TIC) from SO to author keywords (DE) to demonstrate the publication trends for each journal. The following keywords appeared frequently in the literature on robots published in the top five journals: artificial intelligence (26 times), robots (14 times), service robots (13 times), COVID-19 (five times), and anthropomorphism (four times).

Specifically, artificial intelligence appeared seven times in IJCHM, six times in IJHM, seven times in ATR, four times in JHMM, and twice in TR, indicating that this keyword had been frequently used by both hospitality- and tourism-oriented journals. The keyword robots appeared eight times in IJCHM, twice in IJHM, and four times in JHMM. Service robot appeared four times in IJCHM, five times in IJHM, twice in ATR, and one time each in JHMM and TR. Robots and service robots were used more frequently in hospitality-oriented journals than in tourism-oriented journals. The absolute frequencies of COVID-19 and anthropomorphism tended to be high among hospitality-oriented journals, but this was because the number of studies of robots was higher among hospitality-oriented journals (IJCHM, IJHM, and JHMM) than among tourism-oriented journals (ATR and TR).

Frequent Keywords and Changes in Keywords

Figure 2 presents the top 10 keywords that frequently appeared in the literature on robots over all periods (on the left side) and the keyword frequency evolution from before 2020 to after 2020 (on the right side). Artificial intelligence was most frequently used in studies of robots, followed by service robots, anthropomorphism, COVID-19, hospitality, human–robot interaction, and service automation. This result shows that many studies of robots concerned the use of robots in the service context.

Figure 2.

Most frequent keywords.

The right side of Figure 2 shows the top keywords from before 2020 and after 2020 as well as how these keywords have changed. Generic concepts, such as robotics or robots, were frequently utilized as author keywords in 2020. However, the prevalence of service robots and anthropomorphism suggests that several studies recognized robots’ utility in the service context and examined anthropomorphism as a crucial quality of service robots before 2020.

The lines connecting the keywords before 2020 and after 2020 indicate that the generic terms robotics and robots evolved into human–robot interaction, artificial intelligence, and COVID-19. The high frequency of the keyword human–robot interaction indicates that many researchers of H&T were interested in this study area. After 2020, terms related to the contextual background of research on robots, such as AI (artificial intelligence) and COVID-19, appeared, suggesting that researchers’ interest in the use of robots in service contexts had become more specific. For example, Jabeen et al. (2022) considered the role of robotics in developing a theoretical framework for deploying AI in the H&T industry. Grounded in the interactive technology acceptance model, Go et al. (2020) aimed to discover key factors determining customers’ AI robot experiences. Previous studies have acknowledged that implementing AI is inevitable for delivering better services to customers and sustainably developing the industry. However, the current focus of robot research has been mostly on internal and external customers rather than on other stakeholders (e.g., society and government) and specific technological concerns, such as the development of AI systems or algorithms (Loureiro et al., 2021).

Keyword Networks

We constructed two keyword networks based on the co-occurrences of author keywords in the robot literature before and after 2020 (Figure 3). The vertices in the networks represent the top 30 frequently used author keywords. The keywords that frequently appeared together in the same manuscript were densely linked and clustered together.

Figure 3.

Author keyword networks.

In terms of general statistics, the network density of the keyword network prior to 2020 was 0.102, whereas that after 2020 was 0.074, showing that the keyword network prior to 2020 was denser than it was after 2020. One possible reason for the network being denser before 2020 than after 2020 is because author keywords became more complex and diverse during the latter period. The average path length of the keyword network before 2020 was 2.422, whereas it became 2.251 after 2020, indicating that the latter keyword network had a shorter average path length. The average path length can be reduced by the short paths connecting the intermediary keywords between each keyword. In summary, the general keyword network statistics suggest that the keyword network after 2020 is more complex than that before 2020, although the groups of keywords are strongly linked via intermediate keywords.

Table 2 shows the centrality scores of the top 30 author keyword network vertices before and after 2020 to demonstrate their relative importance. We found five clusters in the keyword networks before 2020. Based on the keywords, we labeled the first cluster robots in the hospitality industry. The keyword hospitality had the highest centrality scores (degree: 0.276, eigenvector: 0.704, betweenness: 0.128), followed by information technology (degree: 0.224, eigenvector: 0.444, betweenness: 0.076) and technology acceptance model, behavioral intentions, and consumer acceptance. Cluster 1 suggests that much research conducted prior to 2020 focused on consumer acceptance of IT and its implications for behavioral intentions in the hospitality context, based on the theoretical foundation of the technology acceptance model.

Table 2.

Centrality Scores of Vertices in Author Keyword Networks.

2011–2019					2020–2022
Cluster	Vertex	Centrality scores			Cluster	Vertex	Centrality scores
		Degree	Eigen-vector	Between-ness			Degree	Eigen-vector	Between-ness
Cluster 1: Robots inhospitality	hospitality	0.276	0.704	0.128	Cluster 1: Robot variables and models	service	0.274	0.697	0.049
	information technology	0.224	0.444	0.076		moderating role	0.261	0.652	0.038
	technology acceptancemodel	0.173	0.356	0.015		user acceptance	0.257	0.593	0.052
	behavioral intentions	0.092	0.199	0.000		information technology	0.248	0.560	0.054
	consumer acceptance	0.092	0.199	0.000		intentions	0.230	0.641	0.033
Cluster 2: Technologyadoption	technology	0.367	1.000	0.121		model	0.217	0.596	0.024
	quality	0.245	0.708	0.035		antecedents	0.150	0.357	0.012
	adoption	0.204	0.400	0.063		automation	0.142	0.457	0.007
	acceptance	0.184	0.504	0.013	Cluster 2: Robots in tourism	tourism	0.434	1.000	0.105
	information	0.173	0.533	0.024		satisfaction	0.350	0.792	0.078
	intentions	0.122	0.304	0.013		quality	0.332	0.853	0.057
	behavior	0.092	0.273	0.000		impact	0.296	0.651	0.062
	computer	0.092	0.273	0.000		trust	0.243	0.608	0.040
Cluster 3: Robots intourism	tourism	0.316	0.784	0.169		anthropomorphism	0.226	0.627	0.021
	internet	0.235	0.525	0.149		word of mouth	0.155	0.451	0.010
	attitudes	0.194	0.467	0.082		customer satisfaction	0.150	0.436	0.009
	perceptions	0.173	0.571	0.019		artificial intelligence	0.150	0.435	0.012
	determinants	0.163	0.200	0.025		technologyacceptance model	0.128	0.438	0.004
	experience	0.153	0.438	0.053	Cluster 3: Robots in hospitality	hospitality	0.389	0.923	0.089
Cluster 4: Businessimpact	impact	0.163	0.377	0.039		experience	0.301	0.761	0.049
	business	0.143	0.143	0.016		acceptance	0.261	0.733	0.036
	reviews	0.133	0.396	0.009		technology	0.257	0.680	0.031
	communication	0.092	0.313	0.000		attitudes	0.226	0.543	0.052
Cluster 5: Implicationsof robot services	future	0.286	0.759	0.078		future	0.195	0.503	0.032
	service	0.235	0.679	0.046		behavior	0.164	0.416	0.036
	anthropomorphism	0.224	0.516	0.038		cocreation	0.155	0.407	0.018
	challenges	0.173	0.512	0.022		social robots	0.146	0.442	0.009
	management	0.153	0.510	0.010		framework	0.146	0.489	0.007
	animacy	0.112	0.424	0.001		employees	0.142	0.456	0.007
	agents	0.092	0.211	0.000		appearance	0.137	0.476	0.004

We labeled the second cluster technology adoption. In this cluster, the terms with high centrality scores included technology (degree: 0.367, eigenvector: 1.000, betweenness: 0.121), quality (degree: 0.245, eigenvector: 0.708, betweenness: 0.035), and adoption (degree: 0.204, eigenvector: 0.400, betweenness: 0.063). The first and second clusters had similar keywords, such as intentions, behavior, and acceptance, but different perspectives. Whereas the first cluster concerned customer experiences in the hospitality industry, the second concerned industrial technological innovation and adoption.

We labeled the third cluster robots in tourism. In this cluster, tourism had the highest centrality scores (degree: 0.316, eigenvector: 0.874, betweenness: 0.169), and keywords related to tourists’ experiences and outcomes appeared frequently. These keywords imply that the factors influencing tourists’ attitudes, perceptions, and experiences were examined. As shown in the first and third clusters, the keywords were grouped based on the study context (i.e., whether it mainly involved hospitality or tourism).

We labeled the fourth cluster business impact because the keywords in this cluster (i.e., impact, business, reviews, and communication) mainly related to the effects of communications in the online sphere or online reviews on B&M. Finally, we labeled the fifth cluster implications of robot services. This cluster included terms indicating future research directions (e.g., future) and industrial implications (e.g., challenges and management).

From the studies of robots conducted after 2020, we identified three keyword clusters: robot variables and models, robots in tourism, and robots in hospitality. Although the number of clusters was lower than that from before 2020, the keywords after 2020 were more diverse. We labeled the first cluster after 2020 as robot variables and models. The keyword with the highest centrality scores was service (degree: 0.274, eigenvector: 0.697, betweenness: 0.049), indicating that service robots or the use of robots for better service is an important topic in this cluster. More importantly, terms related to theory building and model testing (e.g., moderating role, model, and antecedents) also had high centrality scores. This result implies that papers published after 2020 considered various factors in relation to robots when building empirical models.

Similar to the keyword network before 2020, that after 2020 generated clusters focused on either the tourism or the hospitality context. The second cluster was centered on the keyword tourism, which had the highest centrality score, whereas the third cluster was centered on the keyword hospitality, which also had the highest centrality score. We labeled the second cluster from after 2020 IT in tourism because the keyword tourism had the highest centrality, followed by other keywords related to robotic service experiences. Most keywords were related to tourists’ experiences and outcomes, such as satisfaction, quality, impact, trust, and word of mouth. The technology acceptance model was still an important theoretical ground after 2020, as shown in the keywords. We labeled the third cluster from after 2020 IT in hospitality. In the third cluster related to the hospitality industry, the role of employees and the value cocreation process were emphasized in addition to customer experiences.

Thematic Maps of H&T Literature on Robots

As a result of the thematic map analysis, we drew research subthemes in the four quadrants (Figure 4). From the author keywords before 2020, we discovered one topic, robotics, in the Motor quadrant because of its high relevance, centrality, and density scores, indicating that this topic had relatively strong associations with other research subtopics and developed topical maturity between 2010 and 2019. This finding implies that prior to 2020, interest in robotics in H&T research was heightened, and the concept of robotics was formulated.

Figure 4.

Research topic cluster classifications: H&T disciplines.

Among the papers published prior to 2020, three topics, robots, service robots, and anthropomorphism, were classified as Basic themes with high centrality and low density scores. That is, these topics were strongly connected with other research subtopics, but they had room for internal improvement to become mature research topics. Many studies published during this period embraced notions connected to service robots and anthropomorphism, although these features should be further investigated for conceptualization.

Finally, we found embodiment to be a Niche topic because of its low centrality scores and high density score prior to 2020. For instance, two papers published before 2020 (i.e., Tung and Au, 2018; Tung and Law, 2017) used embodiment as an author keyword. Tung and Law (2017) defined embodiment in the robot context as “the connection among the control (brain), body and environment” (p. 2502) and proposed it to be a key determinant of service robot experiences. During this period (prior to 2020), H&T researchers explored and assessed vital factors influencing robot experiences.

Between 2020 and 2022, the number of research subtopic clusters and keywords associated with each cluster increased and became more diverse than before 2020. We classified four topics—COVID-19, hotels, service encounters, and chatbots—as Motor themes. These topics were related to human–robot interaction. For example, the high frequency and centrality scores of chatbot themes imply that many studies have examined AI-powered chatbots in the H&T settings (Leung & Wen, 2020; Pillai & Sivathanu, 2020). These studies, however, were customer-oriented, focusing on customer perceptions of and behavioral intentions toward robots. Another important keyword with a high frequency and centrality was COVID-19. The author keywords associated with the COVID-19 theme were automation, physical distancing, and robotization. However, the relevance density for COVID-19 was lower than that of other keywords belonging to the Motor theme, which may be because COVID-19 is a relatively new phenomenon, whereas other research subtopics had already been studied before 2020.

The following topics belonged to the Basic theme, which had high centrality and low density scores: artificial intelligence, hospitality, anthropomorphism, autonomous robots, perceived value, hotel, robots artificial intelligence, and physical appearance. In comparison to customer-centric research, investigations into back-end activities for technological development (e.g., AI and automation) necessitate more focus from researchers for the notion to be fully understood and advanced (Table 3).

Table 3.

Detailed Topic Cluster Information: Frequency, Centrality, Density, and Author Keywords.

Period	Researchsubtopic clusters	Quadrantclassification	Frequency	Relevance centrality	Relevancedensity	Author keywords
2011–2019	Robotics	Motor	5	14	14	robotics (5)
	Robots	Basic	16	15	5	robots (6), artificial intelligence (4), hospitality(4), and service automation (2)
	Service robots	Basic	14	13	1	service robots (5), tourism (4), robonomics (3), andperceived appropriateness (2)
	Anthropomorphism	Basic	4	12	6	anthropomorphism (2) and human–robot interaction (2)
	Embodiment	Niche	2	6	13	embodiment (2)
2020–2022	COVID-19	Motor	35	26	14	COVID-19 (11), robots (11), automation (4), tourism(4), physical distancing (3), and robotization (2)
	Hotels	Motor	13	25	28	hotels (4), customer attitudes (3), robots (2), adoption(2), and service quality (2)
	Service encounter	Motor	7	22	27	service encounter (3), hospitality management (2),and restaurants (2)
	Chatbot	Motor	6	23	15	chatbot (4) and intelligent automation (2)
	Artificial intelligence	Basic	66	28	5	artificial intelligence(19), service robots(17), human–robot interaction(7), service automation(4), e-wom(3), acceptance(2), big data(2), intention to use robots(2), online reviews(2), and operations research (2)
	Hospitality	Basic	17	27	4	hospitality (6), robotics (5), cocreation (2), lego^®serious play^® (2), trust (2)
	Anthropomorphism	Basic	13	24	12	anthropomorphism (9), service failure (2), and social presence (2)
	Autonomous robots	Basic	10	21	9	autonomous robots (2), machine learning (2), technology (2), technology acceptance(2), and tourism industry (2)
	Perceived value	Basic	6	19	13	perceived value (3) and service robot (3)
	Hotel	Basic	4	16	2	hotel (2) and new technologies (2)
	Robots artificial intelligence	Basic	2	20	10.5	robots artificial intelligence (2)
	Physical appearance	Basic	2	18	10.5	physical appearance (2)

Thematic Maps of B&M Literature on Robots

Figure 5 illustrates the major research subtopic clusters of the B&M literature and the classifications of these clusters. Compared to the H&T literature from the same period, more diversified research topics were discussed in B&M journals. The study themes uncovered in the literature on robots in H&T were addressed about 10 years ago in B&M research. Furthermore, several challenges that had not previously been addressed in H&T were addressed in B&M studies, notably after 2020.

Figure 5.

Research topic cluster classifications: B&M disciplines.

Prior to 2010, the main topics in the B&M literature were manufacturing and scheduling. Various types of robots (e.g., mobile robots and cooking robots) were also studied before 2010. Prior to 2010, the human–robot interaction topic was well established, but it had been studied independently from other research subtopics. Whereas human–robot interaction was a key research theme in the H&T literature on robots between 2010 and 2019, this topic had first been seen a decade earlier in the B&M literature.

During the second period in the B&M literature (between 2010 and 2019), robotic applications for industrial innovation, such as digitalization (Motor), optimization (Motor), service innovation (Motor), and open innovation (Motor), were actively studied. In this period, the Niche quadrant features topics related to customer perspectives, such as consumer brand identification and customer satisfaction. Back-end tasks, programming, and modeling-related topics (e.g., deep learning and sequencing) for AI development were found to belong to the Niche quadrant. The low relevance (centrality) scores indicate that these topics tended to be isolated from other topics, but their relatively high centrality scores indicate that they were well established. Whereas particular customer-centric and modeling-related topics had been developed in the B&M literature on robots between 2010 and 2019, these topics were actively considered in the H&T literature in the following decade. Between 2010 and 2019, information technology–related topics (e.g., neural networks and robotic process automation [RPA] technologies) were included in the Basic quadrant. The Basic quadrant also contained the concept of establishing a new business environment fueled by entrepreneurship and (human–robot) collaboration.

Various forms of robots (e.g., cyborgs and social robots) were intensively discussed and clustered within the Motor quadrant during the third period, between 2020 and 2022. The Motor quadrant also contained topics related to the major waves of industrial robotics, such as digitization and personalization. Consistent with the research before 2020, topics related to robot service and automation were actively studied in 2020 and 2022.

Discussion and Implications

Robotics has emerged as a prominent topic in the H&T industry, driven by global contextual factors such as the fourth industrial revolution and rapid technical progress (Belanche et al., 2020; McCartney & McCartney, 2020; Osei et al., 2020). The recent health crisis brought about by the COVID-19 pandemic has also triggered changes in the robot research landscape because the pandemic has exacerbated perceived threats to human services and increased customers’ desire for robotic services (Kim et al., 2021; Zeng et al., 2020). Nevertheless, previous research on robots in H&T is still limited, and only a few systematic review studies have been conducted to uncover the current status of research on robots and discover its key aspects (Shin, 2022). To fill this research gap, we implemented a bibliometric analysis of research on robots in H&T and B&M. This study is differentiated from previous review studies of robots in two ways: (a) It synthesizes the evolution of research on robots over time and (b) assesses the state of research on robots in H&T in comparison with B&M.

The main contribution of this study is its analysis of the evolution of topical trends in studies of robots over time. In the area of H&T, there has been a noticeable increase in the diversity and depth of topics explored in studies of robots after 2020, particularly in the post-pandemic period. Conversely, in B&M, studies of robots have been actively conducted throughout the three decades, with the main areas of focus shifting in response to industrial and social developments and demands. Later in the study, more specific discussions regarding the topics in both H&T and B&M are addressed in detail.

This study also compared the studies of robots published in the area of H&T with those in the B&M field. Compared to the H&T research, studies in B&M explored a wider range of topics in greater depth within the realm of research on robots. In contrast, research in the H&T field primarily focused on factors and outcomes related to customer experiences with robot services. This observation is supported by the high frequency of keywords such as physical appearance, anthropomorphism, and perceived values. Additionally, specific applications of service robots in the H&T field were limited, with a particular emphasis on chatbots. Discussion surrounding technological progress that has facilitated the development of advanced service robots in the back-end was comparatively sparse.

On the other hand, more diverse topics were addressed in B&M research. The integration of robot services has the potential to significantly enhance operational efficiency in general management across industries. By leveraging automation and robotic systems, processes can be streamlined, human errors minimized, and overall productivity increased (Vrontis et al., 2022). As a result, organizations can achieve cost savings and improved efficiency in various operational aspects. In line with these aspects, this study uncovers specific applications and the potential of robots, such as autonomous mobile robots, cooking robots, care robotics, service robots, and social robots, in different areas and in different periods. This finding highlights how the utilization of robot services has evolved over time, with an initial focus on manufacturing applications in the early stages of studies of robots and a shift toward more socially interactive and personalized services in recent years. This demonstrates the dynamic nature of robot service usage and the expansion of its potential in meeting diverse industry needs.

Previous studies also explored the collaborative role of human forces in conjunction with robots (Gervasi et al., 2020; Lv et al., 2021). Rather than seeking to replace human workers, robot services can serve as a valuable augmentation to the existing workforce. The frequency of keywords such as human–robot collaboration or human factors in the analysis of this study reflects the attention given to these aspects and underscores the significance of understanding and optimizing interaction between humans and robots. This collaborative approach not only allows for the development of new skillsets among employees but also enables a more efficient allocation of human resources.

The fourth industrial revolution is characterized by dynamic shifts in consumer expectations and preferences. To meet these evolving demands and deliver futuristic and technologically advanced solutions, industries have increasingly integrated robot services (Luo et al., 2021). Notably, the findings of this study demonstrate that studies conducted since 2010 emphasized the significance of robot services in consumers’ brand identification and the customization of services to enhance customer experiences and service quality. These investigations highlight the growing importance of incorporating robot services as a means to cater to changing consumer needs and elevate overall service delivery.

Lastly, previous studies on robots extensively covered the technological advancements and innovation that have facilitated the integration of robot services. The development of technology has played a crucial role in making the incorporation of robot services feasible. Significant advancements in AI and key technological domains, including image processing, neural networks, deep learning, and machine learning, emerged as important keywords within the research, highlighting their pivotal role in supporting the necessary technological progress required for robot services. These findings underline the significance of ongoing technological developments in enabling the successful implementation of robot services across various industries.

The present study contributes to the academic literature are as follows. First, we investigated cumulative efforts regarding research on robots in H&T. This study’s findings confirm researchers’ continued and growing interest in robots, particularly after 2020. This tendency is reflected in prominent journals from H&T, although preferred research subtopics may differ across journals. For example, AI was mentioned frequently in both fields, but service robots were addressed in hospitality journals more often than in tourism journals. This study also reveals that robot research was dominated by China and the United States, with many other countries actively cooperating.

Second, this study explored the salient research subtopics on robots and changes in the knowledge structure before and after 2020. We found changes in the robot keyword from before COVID-19 to after COVID-19, with more specific keywords appearing after 2020. This implies that researchers considered more specific robot attributes and contextual factors to establish a more robust theoretical framework. The study context (i.e., hospitality or tourism) was the main factor in clustering author keywords. Notably, H&T researchers may emphasize different aspects of robots. This study also revealed that the progress of research subtopics on robots may vary because some topics appear mature and serve as the foundation for subsequent studies, whereas others require more collaborative initiatives for their development.

Finally, this research provided a simultaneous consideration and separate analyses of both the H&T and B&M fields. The findings from the latter enable H&T scholars to compare research on robots within the two literature streams and acquire insights to utilize in future research. For instance, research topics that were recently recognized in the H&T literature had already been studied in B&M. We also discovered that H&T research tended to focus on events and issues concerning robot service encounters and their consequences, while B&M literature considered back-end activities to develop robot performance. Our findings will enable H&T researchers to explore unique research subtopics that have not been or have rarely been considered in the B&M literature.

This study can also contribute to the H&T industry. Our findings on the evolution of research on robots can assist managers who are considering incorporating robots into their operations in learning about their crucial characteristics and outcomes in the field. This study reveals that implementing robots in the hotel business, in particular, is tied not only to consumer experiences but also to those of employees, such as employee morale, technology acceptance, and job replacement (Bowen & Morosan, 2018; McCartney & McCartney, 2020; Song et al., 2022). Consequently, this study’s findings may be useful in considering numerous aspects before making corporate decisions about robot implementation. In addition to the factors considered in the H&T field, managers should consider the issues addressed in other industries. Business journals have recently focused on algorithm development (e.g., machine learning and optimization), methods to provide optimized and personalized services, various types of robots, and digitalization. Developing back-end technologies, in particular, is crucial for providing services that match industry-specific demands. However, comparable attempts have yet to be made in the H&T literature. By comparing the growth of research on robots in the H&T literature with that in the B&M literature, managers can prepare for the future of robot services and digitalization in operations.

The current study has a few limitations, and we suggest future research that will address them. Although this study provides insights into the key research topics and their evolution over time, future researches could focus on a more in-depth exploration of the specific benefits and challenges encountered by industries during the fourth industrial revolution when adopting robot services. By delving deeper into these aspects, researchers can provide a more comprehensive understanding of the implications and dynamics of integrating robots in different industry contexts. This can include examining how the integration of robot services impacts job roles, skill requirements, and overall industry transformation. This study compared the adoption and impact of robot services between the fields of H&T and management, but these can also be compared across more diverse sectors, such as healthcare, manufacturing, retail, and transportation. This can provide insights into industry-specific challenges, best practices, and potential synergies in the implementation of robot services.

Regarding data sources and methodological aspects, future studies should consider expanding beyond peer-reviewed journal articles to include a broader range of sources. This could include major conference papers and proceedings, industry reports, newspaper articles, e-articles, opinion papers, monographs, reviews, dissertations and theses, gray literature, industry reports, books, book chapters, and other relevant publication sources. Incorporating this range of sources will provide a more comprehensive understanding of the industrial and social perspectives related to robot services in different contexts. Whereas the present study included only English-language publications, future studies may consider articles published in other languages. Because the current study used articles published in the WoS database, future research could consider articles from other data sources (e.g., EBSCO, Google Scholar, non-indexed journals, Science Direct, and Scopus). Finally, future research could incorporate other research techniques, such as qualitative interviews, case studies, or mixed-method approaches, to obtain deeper knowledge and more meaningful insights into the implications and challenges of integrating robot services.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440241258281 – Supplemental material for Service Robots in Hospitality and Tourism Before and During theCOVID-19: Bibliometric Analysisand Research Agenda

Supplemental material, sj-docx-1-sgo-10.1177_21582440241258281 for Service Robots in Hospitality and Tourism Before and During theCOVID-19: Bibliometric Analysisand Research Agenda by Eunhye Park and Sung-Bum Kim in SAGE Open

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Inha University provided financial support to conduct this study.

ORCID iD

Sung-Bum Kim

Supplemental Material

Supplemental material for this article is available online.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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