INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 726
A Bibliometric Analysis of Scopus Literature (1987–2024) On Smart
Technologies in Hospitality: From Artificial Intelligence to
Augmented Reality.
Madhavaraman M, Dr. Jitender Kumar, Avinash Adhupiya
Department of Tourism & Hotel Management, Central University of Haryana
DOI: https://doi.org/10.51244/IJRSI.2025.1210000063
Received: 20 October 2025; Accepted: 27 October 2025; Published: 03 November 2025
ABSTRACT
This study aims to explore the research landscape involving smart technologies in the hospitality and hotel
industry employing a bibliometric analysis of publications from 1987 to 2024. In the paper, the researchers aim
to focus on technologies like Artificial Intelligence, Machine Learning, Internet of Things, Augmented Reality,
Virtual Reality, and robotics, identification of the key trends, most productive authors and journals, as well as
the emerging themes, while highlighting research gaps for future exploration.
This study employed bibliometric analysis, collecting data from 1987 to 2024 from the Scopus database. This
study analyzes publication types, collaborative patterns, subject areas, citation impacts, leading authors,
affiliations, countries, and publication sources. It examines research trends and keyword co-occurrences and
creates a thematic map to visualize key themes in the field.
This bibliometric analysis of 176 publications (1988–2024) reveals a surge post-2018, peaking at 70 papers in
2024 with 3,218 citations (average 18.3 per paper). Authorship is collaborative (3.3 authors/paper; 582 authors).
Top authors include Bowen, Morosan, and Yu. Leading institutions are Amity University and the University of
South Florida. Countries: India leads in output, while the USA leads in impact. Major journals are IJHM and
Electronic Markets.
The study was confined to articles in the English language, primarily published in chosen subject areas and
Scopus-indexed journals, potentially excluding relevant non-English or non-indexed research. The study
captures quantitative patterns and may overlook the depth of qualitative insights and their related real-world
applications. The study has been conducted on articles published between 1987 and 2024
This study presents a unique bibliometric analysis, shedding light on key research trends in various innovative
technologies within the hospitality industry. It offers insightful perspectives on collaborative research patterns,
institutional contributions, and the growth and evolution of innovative technology-related themes in hospitality
research.
Keywords: Artificial Intelligence, Machine Learning, Internet of Things, Augmented Reality, Virtual Reality,
Robotics, Humanoids, hospitality, bibliometric analysis
INTRODUCTION
Significant technological advancements have marked the beginning of the digital era. Rapidly growing
technology has redrawn the way people perceive things and enabled easy access to everything (Buhalis et al.,
2019). Advanced technologies are being introduced regularly and have made a deep penetration into industrial
environments (Liu & Hung, 2020).
The hotel industry has incorporated various technological tools, including smart sensors, chatbots, service robots,
and AI-embedded equipment, into its operations (Singh et al., 2022).
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Modern technologies, including artificial intelligence, machine learning, robotics, humanoids, and the Internet
of Things, are transforming the hospitality industry by providing enhanced guest experiences while also
improving operational efficiency. While research on these technologies in hospitality has grown across business,
sociology, and economics, a systematic analysis is needed to identify key trends, contributors, and gaps.
Bibliometric analysis offers a valuable means of mapping this evolving scholarly landscape and informing future
research and practice.
Through customization, the hospitality sector has adopted artificial intelligence to enhance the guest experience.
The study of guest data to provide custom, customer-centric hospitality services according to personal
preferences and behavior patterns has been made possible with the introduction of AI technology (Yang et al.,
2021). Utilizing modified operations, data-driven decision-making, and personalized guest experiences (Kaur et
al., 2024), the Internet of Things, machine learning, and similar technologies are revolutionizing the hospitality
sector. The IoT presents an opportunity for the travel and hospitality industries to enhance customer service,
increase productivity, and offer more personalized services (Car et al., 2019).
Kabadayi et al. (2019) conceptualize the smart service experience in hospitality and tourism, where intelligent
use of data and technology enables the provision of unique and personalized services.
Chi et al. (2020) identify seven major themes in their study, including current AI technology, levels of AI, AI
agents, human and AI service encounters, theoretical frameworks of AI acceptance, reasons for adopting AI, and
potential challenges.
Goel et al. (2021) conducted a review of the factors influencing customer adoption of AI-based services. Their
findings revealed that usefulness perceived, trust, and ease of use are the critical factors. It has been said that
understanding customer psychology is crucial for the successful implementation of this approach. Zhu et al.
(2023) provide a review of customer acceptance and the alleged ethical issues associated with AI technology
within the tourism and hospitality sector, and examine customer acceptance and perceptions of robots in the
service sector. Their findings highlighted the importance of ethical design, transparency, and emotional
intelligence in fostering trust and a positive attitude toward adoption.
Kumar et al. (2021) in their study explain how AI and service robots are utilised in improving customer service
through more personalized, efficient, and automated services. They point out that these technologies shall emerge
as essential components of future hospitality operations.
Ivanov et al. (2019) provided a brief overview of robotics and its adoption trends, highlighting its key
applications across front-office and back-office functions. It gave a comprehensive review of research on robotic
technology in travel, tourism, and hospitality, identifying the key research areas. McCartney and McCartney
(2020) proposed a model for incorporating service robots, providing a foundation for understanding how robots
can be gradually and systematically integrated into hospitality operations. This framework addresses both
dimensions of operational design and customer interaction.
Bowen and Morosan (2018) looked ahead to 2030, predicting that robots might comprise approximately 25
percent of the hospitality staff. The study highlights the operational benefits and the social as well as ethical
implications, which may include potential resistance from staff and guests.
Gaur et al. (2020) provided a strategic research framework to aid the exploration of integrating AI and robotics
in the hospitality industry. It was suggested that these technologies had the potential to impact staff roles, enhance
the customer engagement process, and introduce innovation in the services provided to customers.
Mercan et al. (2020) discuss how Internet of Things technologies are aiding automation, increased energy
efficiency, and personalization. The key point to highlight is that the hospitality industry has been taking
significant steps in acquiring innovative technologies, improving, and revolutionizing the quality of service.
Buhalis and Moldavska (2021) highlight the increasing use of voice assistants (VAs) in the hotel and tourism
sectors. The study explains how these voice assistant systems facilitated contactless, unique, and personalized
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interactions, which proved to be of immense value during the onset of the COVID-19 pandemic, enabling
enterprises to maintain continuity in their services while ensuring enhanced safety for guests.
Nayyar et al. (2018) studied the applications of virtual reality (VR) and augmented reality (AR) in tourism. Their
study has revealed that any immersive experiences, such as virtual hotel tours and AR-guided navigation,
enhance customer engagement and become key factors affecting travel decisions.
Osadare et al. (2021) investigated the application of Artificial Intelligence and other modern technologies in
hospitality management to enhance customer satisfaction and improve decision-making.
Given the steady integration of these advanced technologies and their significant impact on hospitality operations
and guest experiences, it becomes crucial to target research questions that can guide future scholarly inquiry and
practical implementation.
The research questions are as follows:
1. What is the current research status in the field of smart technologies in the hospitality/hotel sector
2. Who are the most active researchers, affiliations, and countries in smart technologies in the
hospitality/hotel sector
3. What are the trending topics in this field?
METHODS
Search strategy
Smart technologies in the hospitality industry encompass a vast academic horizon. The articles are primarily
scattered over several disciplines. Therefore, the search string was formed in this research by limiting the search
to article titles, which enhanced the relevance of the data obtained by focusing on publications where the key
topics were relevant and matched the study purpose. This approach aided in reducing irrelevant records and
improving data quality. (Donthu et al., 2021). The search string was formed using keywords that include various
smart technologies that have gained traction in the hospitality segment over the last decade, as well as terms
associated with the hospitality and hotel sectors. Only English articles published in journals and reached the final
publication stage were shortlisted for the study.
The following search string has been framed to perform the search query:
(TITLE ((“hospitality management" OR "hotel management" OR "hospitality industry" OR "hotel industry" OR
"hospitality sector”) AND (“smart technology"OR"artificial intelligence" OR "augmented reality" OR "AR "OR
"virtual reality" OR "VR" OR "AI" OR "robot*" OR "humanoid" OR "machine learning" OR "ML" OR "internet
of things" OR "IoT”))
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Figure I. Flow diagram of the search strategy(Moher et al., 2009; Zakaria et al., 2021).
Data cleaning, harmonization, and analysis
A total of 211 records were initially identified using the keyword, as per the search strategy. Later, the search
filters were applied, thereby eliminating articles irrelevant to the context. As a result, 35 irrelevant documents
were removed. Preliminary screening and duplicate data analysis yielded no results, leaving 176 documents for
further analysis. These documents were assessed for relevance by the researchers through a careful study of the
article titles and cross-verification of their abstracts to ensure relevance to the study. These 176 papers were
found relevant and selected for bibliometric analysis. This comprehensive screening process, which included
duplicate removal, ensured the careful selection of articles.
Bibliometric measures
Any bibliometric analysis includes the performance analysis of authors, institutions, and countries. It consists of
a productivity analysis of journals, descriptive qualitative analysis that includes citation metrics, and bibliometric
mapping, which covers the relationships between different domains. Here, the researchers have included
analyses of annual productivity, prolific authors, institutions, countries, journals, most cited articles, and thematic
analysis using Bibliometrix(.Aria & Cuccurullo, 2017) and VOSviewer tools(Van Eck & Waltman, 2010)
PRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only
Topic
Smart Technologies in Hospitality & Hotel
management
Scope & Coverage
Search Field: Article Title
Timeframe:1987-2024
Language: English
Source type: Journal, Conference Proceeding, Book,
Book Series
Document type: Article, Conference Paper, Book
Chapter, Review
Publication Stage: Final
Keyword & Search string
TITLE ( ( "hospitality management" OR "hotel
management" OR "hospitality industry" OR "hotel
industry" OR "hospitality sector" ) AND ( "smart
technology"OR"artificial intelligence" OR "augmented
reality" OR "AR "OR "virtual reality" OR "VR" OR "AI"
OR "robot*" OR "humanoid" OR "machine learning" OR
"ML" OR "internet of things" OR "IoT" ) )
Date extracted
05th May 2025
Records identified &
screened
n=211
Records removed
n=35
Duplicate(n=0)
Irrelevant = (n=35)
Records included for
bibliometric analysis
n=176
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RESULTS
Document profiles
Table I presents the essential details of the bibliometric analysis conducted to examine the scope, collaboration
patterns, and citation impact of publications on the topic over 38 years, from 1988 to 2024. The data, comprising
176 publications, were analysed using biblioMagika® (Ahmi, 2024), yielding valuable insights into the
development and scholarly influence of research within the studied domain. Over the studied time period, 176
academic documents were published, depicting a sustained yet moderate research output. The publication data
shows an increasing trend toward collaborative authorship, along with an average of 3.31 authors per publication.
Five hundred eighty-two unique contributors have been identified, indicating a multidisciplinary and networked
research environment.
The studied dataset has received a total of 3,218 citations, with 129 papers, which is nearly 73%, that have
received at least one citation. It has to be noted that he average citation per paper stands at 18.28. The citation
per cited paper is 24.95, proving that a significant portion of the literature is generating a substantial academic
impact. It is observed that citations have accumulated over time, with an annual citation rate of 89.39. The
analysis of impact indices reveals that the h-index is 29, indicating that a minimum of 29 publications have
received 29 citations. A g-index of 53 shows the concentration of citations among top-performing papers, where
Table I: Citation Metrics
Main Information Data
Publication Years 1988 - 2024
Total Publications 176
Citable Year 38
Number of Contributing Authors 582
Number of Cited Papers 129
Total Citations 3218
Citation per Paper 18.28
Citation per Cited Paper 24.95
Citation per Year 89.39
Citation per Author 5.53
Author per Paper 3.31
Citation sum within h-Core 2,986
h-index 29
g-index 53
m-index 0.763
Source: Generated by the author(s) using
biblioMagika® (Ahmi, 2024)
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the top 53 papers as a whole account for nearly 2,809 citations. The m-index, obtained by dividing the h-index
by the number of citable years, was found to be 0.763, reflecting a moderate and consistent level of scholarly
impact over time.
Table II presents the breakdown of document types within the dataset, showing that journal articles constitute
the most significant proportion of publications (41.48%), indicating a strong emphasis on peer-reviewed
dissemination. Conference papers, which account for 31.82% of the output, depict vibrant and active
participation in academic conferences, as well as a healthy tendency toward the prompt communication of
ongoing research. Book chapters account for 22.73% of the publications, demonstrating a notable contribution
to edited volumes and thematic compilations, often associated with interdisciplinary or foundational work. It is
observed that review articles are relatively underrepresented, comprising only 3.98% of the total works, pointing
to a gap in the existing literature and a potential for future contributions to academic knowledge in the field.
Table III: Subject Area
Subject Area TP %
Computer Science 97 0.55
Business, Management and Accounting 94 0.53
Engineering 44 0.25
Social Sciences 44 0.25
Economics, Econometrics and Finance 38 0.22
Decision Sciences 28 0.16
Environmental Science 12 0.07
Mathematics 11 0.06
Energy 10 0.06
Medicine 7 0.04
Biochemistry, Genetics and Molecular Biology 6 0.03
Table II: Document type
Document Type TP %
Article 73 0.41
Conference
Paper 56 0.32
Book Chapter 40 0.23
Review 7 0.04
Source: Generated by the author(s)
using biblioMagika® (Ahmi, 2024)
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Agricultural and Biological Sciences 5 0.03
Physics and Astronomy 5 0.03
Earth and Planetary Sciences 4 0.02
Chemical Engineering 3 0.02
Psychology 3 0.02
Arts and Humanities 2 0.01
Neuroscience 2 0.01
Health Professions 1 0.01
Materials Science 1 0.01
Multidisciplinary 1 0.01
Note(s): The percentages exceed 100% because some publications
are categorized under multiple subject areas
Source(s): Generated by the author(s) using BiblioMagika
Table III indicates that the dominant subject areas are Computer Science (97 publications, 55.11%), Business,
Management and Accounting (94 publications, 53.41%), Engineering (44 publications, 25.00%), and Social
Sciences (44 publications, 25.00%). Economics, Econometrics and Finance (38 publications, 21.59%), Decision
Sciences (28 publications, 15.91%), Environmental Science (12 publications, 6.82%), and Mathematics (11
publications, 6.25%), Energy (10 publications, 5.68%) and Medicine (7 publications, 3.98%). This analysis
highlights the multidisciplinary nature of the research, with a strong focus on technology, business, and social
sciences subject domains.
Publication Trends
Table IV: Publication by year
Year TP NCA NCP TC C/P C/CP h g m
1988 1 1 1 1 1.00 1.00 1 1 0.026
2011 1 2 1 10 10.00 10.00 1 1 0.067
2016 1 2 1 25 25.00 25.00 1 1 0.100
2017 1 3 1 2 2.00 2.00 1 1 0.111
2018 4 10 4 401 100.25 100.25 3 4 0.375
2019 10 26 10 365 36.50 36.50 8 10 1.143
2020 13 42 13 908 69.85 69.85 11 13 1.833
2021 14 39 12 365 26.07 30.42 7 14 1.400
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2022 24 101 24 433 18.04 18.04 10 20 2.500
2023 37 117 26 379 10.24 14.58 11 19 3.667
2024 70 239 36 329 4.70 9.14 10 17 5.000
Total 176 582 129 3218 18.28 24.95 29 53 0.763
Note: TP=total number of publications; NCA=number of contributing authors; NCP=number of cited
publications; TC=total citations; C/P=average citations per publication; C/CP=average citations per cited
publication; h=h-index; g=g-index, m=m-index.
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)
From the analysis of annual research contributions and bibliometric data from 1988 to 2024, as depicted in Table
IV, it was found that from the initial phase (1988-2017), publication output remained limited. However, the
situation has changed since 2018, as there has been a modest increase in output (4 publications) and a significant
citation impact, totalling 401 citations, which translates to an average of 100.25 citations per publication —the
highest in the dataset. The following years display continuity in this upward trend in productivity and influence:
2019 (10 publications, 365 citations), 2020 (13 publications, 908 citations), and 2021 (14 publications, 365
citations), thus revealing a phase of intense scholarly involvement. 2024 marks the highest volume of research
output, with nearly 70 publications. The citation impact was relatively lower, at 329 citations (C/P = 4.70), which
may be attributed to the citation lag, a common phenomenon for recent publications.
The sharp rise in both publication volume and citation metrics after 2018 may be attributed to the increasing
popularity of these technologies in public life. Figure II highlights Publication by Year and Citation.
Figure II: Publication by year & citation
Source: Generated by the author(s) using Bibliomagika
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Publications by authors
Table V: Most Productive Author
Full Name
Current
Affiliation Country TP NCP TC C/P C/CP h g m
Bowen, John
University
of Houston United States 1 1 214 214 214 1 1 0.125
Morosan,
Cristian
University
of Houston United States 1 1 214 214 214 1 1 0.125
Yu, Chung-En
Salzburg
University
of Applied
Sciences Austria 1 1 208 208 208 1 1 0.167
Nam, Kichan
American
University
of Sharjah
United Arab
Emirates 1 1 200 200 200 1 1 0.200
Dutt,
Christopher S.
The
Emirates
Academy of
Hospitality
Management
Dubai
United Arab
Emirates 1 1 200 200 200 1 1 0.200
Chathoth,
Prakash
American
University
of Sharjah
United Arab
Emirates 1 1 200 200 200 1 1 0.200
Daghfous,
Abdelkader
American
University
of Sharjah
United Arab
Emirates 1 1 200 200 200 1 1 0.200
Khan, M. Sajid
American
University
of Sharjah
United Arab
Emirates 1 1 200 200 200 1 1 0.200
Nayyar, Anand
Duy Tan
University Viet Nam 1 1 160 160 160 1 1 0.125
Mahapatra,
Bandana
SOA
University India 1 1 160 160 160 1 1 0.125
Le, DacNhuong
Haiphong
University Viet Nam 1 1 160 160 160 1 1 0.125
Suseendran, G.
VELS
Institute of
Science India 1 1 160 160 160 1 1 0.125
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Reis, João
Aveiro
University Portugal 2 2 201 100.5 100.5 2 2 0.333
Melão, Nuno
Polytechnic
Institute of
Viseu Portugal 1 1 146 146 146 1 1 0.167
Salvadorinho,
Juliana
Aveiro
University Portugal 2 2 201 100.5 100.5 2 2 0.333
Soares, Barbara
Aveiro
University Portugal 2 2 201 100.5 100.5 2 2 0.333
Note: TP=total number of publications; NCA=number of contribution authors; NCP=number of
cited publications; TC=total citations; C/P=average citations per publication; C/CP=average
citations per cited publication; h=h-index; g=g-index, m=m-index.
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)
Table V here, presents the most productive authors based on bibliometric indicators such as total publications
(TP), number of cited publications (NCP), total citations (TC), average citations per publication (C/P), average
citations per cited publication (C/CP), and author impact indices including the h-index, g-index, and m-index.
It is noted that Bowen and Morosan, from the University of Houston (United States), have received the highest
citation count (TC = 214) with a single publication, underscoring their substantial individual academic impact
(C/P = 214). Similarly, Yu (Austria) and several authors from the United Arab Emirates, including Nam, Dutt,
Chathoth, Daghfous, and Khan, have received 200 or more citations from a single publication, indicating high-
impact contributions. Authors such as Reis, Salvadorinho, and Soares from Aveiro University (Portugal) have
shown sustained academic contribution with two publications each and an m-index of 0.333—the highest among
all listed researchers. The data highlights the prominence of authors from the United States, the United Arab
Emirates, and Portugal in producing high-impact publications from within the studied subject area.
Publications by institutions
Table VI: Most Productive Institutions
Institution Name Country TP NCA NCP TC C/P C/CP h g m
Amity University India 8 15 5 28 3.50 5.60 3 5 0.750
Graphic Era Deemed to Be
University India 5 9 5 51 10.20 10.20 4 5 1.000
Chandigarh University India 4 18 2 11 2.75 5.50 2 3 0.500
University of South Florida
United
States 4 7 4 121 30.25 30.25 4 4 1.000
Lovely Professional University India 3 8 3 4 1.33 1.33 1 2 0.500
Kyung Hee University
South
Korea 3 8 3 110 36.67 36.67 3 3 0.429
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Eastern Mediterranean
University Turkey 3 3 3 165 55.00 55.00 3 3 0.500
Sharda University India 3 4 1 50 16.67 50.00 1 3 0.500
Bahria University Pakistan 3 3 2 6 2.00 3.00 2 2 0.667
Sejong University
South
Korea 3 6 3 76 25.33 25.33 3 3 0.500
Polytechnic Institute of Cávado
and Ave Portugal 3 7 3 5 1.67 1.67 2 2 0.333
Porto Polytechnic Portugal 3 6 3 5 1.67 1.67 2 2 0.333
University of the Punjab Pakistan 3 4 3 18 6.00 6.00 2 3 1.000
Neapolis University Pafos Cyprus 3 4 2 11 3.67 5.50 2 3 0.286
University of Education Pakistan 2 2 2 17 8.50 8.50 2 2 1.000
Note: TP=total number of publications; NCA=number of contribution authors; NCP=number of
cited publications; TC=total citations; C/P=average citations per publication; C/CP=average
citations per cited publication; h=h-index; g=g-index, m=m-index.
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)
Table VI provides insights into the most productive institutions contributing to academic output in the study
area. It is worth noting that Indian institutions dominate in terms of publication output, with Amity University
leading the way with eight publications, followed by Graphic Era Deemed to Be University and Chandigarh
University from the country. However, when considering citation impact, institutions from other countries
emerge as academically more impactful. The University of South Florida (USA) and Eastern Mediterranean
University (Turkey) are noted to have the highest average citations per publication (C/P = 30.25 and 55.00,
respectively), showing the significance and academic reach of their research contributions. Similarly, Kyung
Hee University and Sejong University (South Korea) demonstrated strong performance in citation metrics, with
C/P values of 36.67 and 25.33, respectively. Graphic Era Deemed to Be University and the University of the
Punjab (Pakistan) stand tall with a high m-index of 1.000, demonstrating a significant scholarly impact that has
evolved. Institutions from Portugal and Cyprus have contributed multiple publications, and their citation metrics
were comparatively modest. Here, the study reveals that a notable concentration of publication activity is in the
Asian continent, particularly India. Institutions from the United States, Turkey, and South Korea produced
research works with a superior citation impact, indicating a higher influence within the scholarly community.
Publications by countries
Table VII: Top 20 most productive countries
Country TP NCA NCP TC C/P C/CP h g m
India 56 166 36 536 9.57 14.89 11 23 1.375
China 28 60 22 532 19.00 24.18 11 23 1.833
United States 20 42 20 846 42.30 42.30 12 20 0.316
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Pakistan 11 26 11 222 20.18 20.18 6 11 1.500
Turkey 10 18 7 188 18.80 26.86 4 10 0.571
Malaysia 9 19 6 32 3.56 5.33 4 5 1.000
United Arab Emirates 8 26 5 220 27.50 44.00 3 8 0.600
Portugal 7 28 6 208 29.71 34.67 2 7 0.222
Indonesia 7 22 4 29 4.14 7.25 3 5 0.750
South Korea 6 16 6 186 31.00 31.00 6 6 0.857
United Kingdom 6 9 6 272 45.33 45.33 5 6 0.714
Greece 5 10 3 20 4.00 6.67 3 4 0.429
Italy 5 15 3 82 16.40 27.33 3 5 0.600
Cyprus 4 5 3 43 10.75 14.33 3 4 0.429
Viet Nam 3 6 2 181 60.33 90.50 2 3 0.250
Spain 3 6 3 137 45.67 45.67 3 3 0.200
Bangladesh 2 6 2 19 9.50 9.50 2 2 0.333
Serbia 2 9 2 7 3.50 3.50 1 2 0.500
Egypt 2 6 1 23 11.50 23.00 1 2 0.500
Israel 2 2 2 53 26.50 26.50 2 2 0.500
Note: TP=total number of publications; NCA=number of contribution authors; NCP=number
of cited publications; TC=total citations; C/P=average citations per publication;
C/CP=average citations per cited publication; h=h-index; g=g-index, m=m-index.
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)
Table VII presents the bibliometric analysis of the top 20 most productive countries in the studied subject area,
based on various bibliometric indicators. It is found that India leads the list with the highest number of
publications (TP = 56), followed by China (TP = 28) and then by the United States (TP = 20). Further study
reveals that despite India and China dominating in the volume of research, the United States has the highest
research impact, owing to its greater total citations (TC = 846) and average citations per publication (C/P =
42.30). indicating high citations and high influence. Countries such as Vietnam, Spain, and the United Kingdom
also demonstrate strong citation performance, underscoring their research contributions. Vietnam has recorded
the highest average citations per publication (C/P = 60.33) and per cited publication (C/CP = 90.50), after having
only three publications. Countries like Malaysia, Indonesia, and Greece, to the contrary, despite contributing a
moderate number of publications, have been able to earn lower citation metrics (e.g., Malaysia's C/P = 3.56 and
C/CP = 5.33), thus testifying to their limited global visibility and academic impact.
Interestingly, smaller research economies, such as Israel, Cyprus, and Bangladesh, despite having fewer total
outputs, still manage to secure moderate citation counts and maintain a presence through consistent h- and g-
index values, suggesting focused yet credible contributions to niche areas—Figure III highlights Worldwide
scientific production on smart technology research related to hospitality and tourism.
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Figure III: Worldwide scientific production
Source: Generated by the author(s) from iipmaps.com
Publications by source titles
Table VIII:Most Productive Source Title
Source Title TP NCA NCP TC C/P C/CP h g m
International Journal of
Hospitality Management 7 34 7 307 43.86 43.86 6 7 1.000
Lecture Notes in Networks and
Systems 6 25 4 23 3.83 5.75 3 4 0.750
International Journal of
Contemporary Hospitality
Management 5 16 5 188 37.60 37.60 4 5 0.667
Smart Innovation, Systems and
Technologies 5 21 3 5 1.00 1.67 2 2 0.333
Springer Proceedings in Business
and Economics 4 9 4 27 6.75 6.75 3 4 0.429
Impact of AI and Tech-Driven
Solutions in Hospitality and
Tourism 4 11 4 10 2.50 2.50 2 3 1.000
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The Role of Artificial Intelligence
in Regenerative Tourism and
Green Destinations 3 13 3 7 2.33 2.33 2 2 1.000
Artificial Intelligence for Smart
Technology in the Hospitality and
Tourism Industry 3 8 0 0 0.00 0.00 0 0 0.000
Integrating AI-Driven
Technologies Into Service
Marketing 2 6 1 2 1.00 2.00 1 1 0.500
Geojournal of Tourism and
Geosites 2 5 1 23 11.50 23.00 1 2 0.333
European Journal of Tourism
Research 2 5 1 26 13.00 26.00 1 2 0.143
Technology in Society 2 10 2 233 116.50 116.50 2 2 0.333
Tourism and Hospitality
Management 2 9 1 12 6.00 12.00 1 2 0.500
ACM International Conference
Proceeding Series 2 4 2 3 1.50 1.50 1 1 0.167
Electronic Markets 2 8 2 265 132.50 132.50 2 2 0.400
Note: TP=total number of publications; NCA=number of contributing authors; NCP=number
of cited publications; TC=total citations; C/P=average citations per publication; C/CP=average
citations per cited publication; h=h-index; g=g-index; m=m-index.
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)
Table VIII provides insights into the most productive source titles in smart technology research within the
hospitality and tourism domain, revealing important information on the academic impact of the publications.
The International Journal of Hospitality Management stands out as the most prominent source, with the highest
number of total publications (TP = 7), along with a strong citation record (TC = 307), averaging 43.86 citations
per publication (C/P), and also matched by its citations per cited publication (C/CP). It showed robust h-index
(6), g-index (7), and m-index (1.000) values, thus revealing a consistent scholarly influence and a sustained
research presence. Similarly, the International Journal of Contemporary Hospitality Management (TP = 5)
exhibits a high impact, with a C/P of 37.60, thereby affirming the dominance of established hospitality-focused
journals in shaping the academic knowledge base. Furthermore, interdisciplinary journals such as Technology in
Society and Electronic Markets, although having only two publications each, exhibited remarkably high citation
averages, underlining the credible reach and relevance of cross-disciplinary research in technology and society
studies applicable to hospitality contexts. It is observed that advanced niche publication sources, such as "The
Impact of AI and Tech-Driven Solutions in Hospitality and Tourism" and "The Role of Artificial Intelligence in
Regenerative Tourism and Green Destinations," are seen to have an emerging presence with modest citation
metrics, revealing a developing but potentially impactful research area. To summarize, the data suggest that the
volume of academic publications contributes to the focus on artificial intelligence and digital transformation
within research, as well as the journal's reputation and the interdisciplinarity of the publication venue. Thus,
underlining the notion that impactful research in hospitality and tourism increasingly benefits from integration
with comprehensive and more exhaustive technological and societal discussions.
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Highly cited documents
Table IX. Top 15 highly cited articles
No. Author(s) Title Source Title TC C/Y
1
Bowen J.; Morosan
C. (2018)
Beware hospitality
industry: the robots are
coming
Worldwide Hospitality
and Tourism Themes 214 26.75
2 Yu C.-E. (2020)
Humanlike robots as
employees in the hotel
industry: Thematic
content analysis of
online reviews
Journal of Hospitality
Marketing and
Management 208 34.67
3
Nam K.; Dutt C.S.;
Chathoth P.;
Daghfous A.; Khan
M.S. (2021)
The adoption of
artificial intelligence and
robotics in the hotel
industry: prospects and
challenges Electronic Markets 200 40.00
4
Nayyar A.;
Mahapatra B.; Le
D.; Suseendran G.
(2018)
Virtual Reality (VR) &
Augmented Reality
(AR) technologies for
tourism and hospitality
industry
International Journal of
Engineering and
Technology(UAE) 160 20.00
5
Reis J.; Melão N.;
Salvadorinho J.;
Soares B.; Rosete
A. (2020)
Service robots in the
hospitality industry: The
case of Henn-na hotel,
Japan Technology in Society 146 24.33
6
Xu S.; Stienmetz J.;
Ashton M. (2020)
How will service robots
redefine leadership in
hotel management? A
Delphi approach
International Journal of
Contemporary
Hospitality Management 116 19.33
7
Leung X.Y.; Lyu J.;
Bai B. (2020)
A fad or the future?
Examining the
effectiveness of virtual
reality advertising in the
hotel industry
International Journal of
Hospitality Management 111 18.50
8
Cain L.N.; Thomas
J.H.; Alonso M., Jr.
(2019)
From sci-fi to sci-fact:
the state of robotics and
AI in the hospitality
industry
Journal of Hospitality
and Tourism
Technology 100 14.29
9
Martinez-Torres
M.R.; Toral S.L.
(2019)
A machine learning
approach for the
identification of the
deceptive reviews in the
hospitality sector using
Tourism Management 91 13.00
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Page 741
unique attributes and
sentiment orientation
10
Khaliq A.; Waqas
A.; Nisar Q.A.;
Haider S.; Asghar
Z. (2022)
Application of AI and
robotics in hospitality
sector: A resource gain
and resource loss
perspective Technology in Society 87 21.75
11
Ruel H.; Njoku E.
(2020)
AI redefining the
hospitality industry
Journal of Tourism
Futures 79 13.17
12
Zhong L.; Zhang
X.; Rong J.; Chan
H.K.; Xiao J.; Kong
H. (2020)
Construction and
empirical research on
acceptance model of
service robots applied in
hotel industry
Industrial Management
and Data Systems 72 12.00
13
Yang L.; Henthorne
T.L.; George B.
(2019)
Artificial intelligence
and robotics technology
in the hospitality
industry: Current
applications and future
trends
Digital Transformation
in Business and Society:
Theory and Cases 70 10.00
14
Saydam M.B.; Arici
H.E.; Koseoglu
M.A. (2022)
How does the tourism
and hospitality industry
use artificial
intelligence? A review
of empirical studies and
future research agenda
Journal of Hospitality
Marketing and
Management 68 17.00
15
Mingotto E.;
Montaguti F.;
Tamma M. (2021)
Challenges in re-
designing operations and
jobs to embody AI and
robotics in services.
Findings from a case in
the hospitality industry Electronic Markets 65 13.00
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)
The growing integration of artificial intelligence (AI), robotics, and immersive technologies in the hospitality
industry is reflected in the most highly cited scholarly articles of recent years. As shown in Table X, there has
been a concentrated and significant scholarly effort to examine the technological transformation phase of service
delivery, customer interaction, and standard operational processes in the hospitality and service sectors.
Thematically, these articles explore the use of humanoid robots as hotel staff, the application of AI for enhancing
managerial and operational efficiency, and the utilization of technologies such as virtual and augmented reality
to improve guest experiences. Notably, Nam et al. (2021) is seen as the most influential work in terms of citations
per year, a testament to the industry's heightened interest in the prospects and challenges of AI and robotics.
Journals such as the Journal of Hospitality Marketing and Management, Technology in Society, and Electronic
Markets frequently appear, highlighting their significant role in disseminating research at the intersection of
hospitality and emerging technologies. Moreover, newer studies, such as those by Saydam et al. (2022) and
Khaliq et al. (2022), provide empirical insights and theoretical perspectives on AI acceptance and resource
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Page 742
dynamics, indicating a shift toward more sophisticated yet practical frameworks. In brief, the literature suggests
a transition phase from conceptual speculation to applied research, particularly in the domains of customer
service automation, workforce redesign, and strategic technology adoption within the hospitality sector.
Keywords Co-occurrence analysis
Figure IV: Keyword co-occurrence network visualization using VOSviewer.
Source: Authors’ work
Figure IV displays the keyword network visualization of the keywords used by 61 authors, with different clusters
color-coded. The size of each node (keyword) represents its importance, or the occurrence of the connections
(edges) between nodes represents the dependence or total link strength (TLS) between keywords. The network
clusters of the keyword occurrences with a cluster focus are shown in Table X
Table X. Keywords clusters
Cluster
No. Keywords Keywords Cluster Theme / Focus
1
artificial intelligence, emotions, service
automation, service robots, technology
adoption, tourism, tourism and hospitality 7
AI, Emotions &
Automation in Tourism
2
big data, digital transformation, hotel
industry, internet of things, machine
learning, smart hotel 6
Smart Hotels, IoT & Data
Transformation
3
automation, customer satisfaction, robotics,
robots, service quality 5
Service Robotics &
Automation
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4
behavioral intention, customer, hospitality,
hotels, sustainability 5
Hospitality Experience &
Sustainable Practices
5
augmented reality, bibliometric analysis,
hotel management, technology, virtual
reality 5
Immersive Tech &
Research Trends
Source(s): Authors’ own work
For a comprehensive understanding of the thematic structure of scholarly research in brilliant technology-linked
hospitality, the researchers performed a keyword co-occurrence analysis, which yielded six unique clusters, each
reflecting a core area of academic focus within the intersection of hospitality, tourism, and emerging
technologies. The derived clusters are found to represent both mature domains and emerging research areas.
Here, Table 11 tabulates the keyword groups, the number of terms per cluster, and their thematic focus.
Cluster 1 draws attention to Artificial Intelligence and Automation in Tourism, including keywords such as
artificial intelligence, emotions, service robots, and technology adoption. It highlights the integration of AI to
improve service delivery, personalization, and guest experience in the tourism and hospitality sectors.
Cluster 2: centres around Smart Hospitality and IoT, and is represented by terms like big data, digital
transformation, machine learning, and smart hotels. The key emphasis here is on technological infrastructure and
data-enabled decision-making, which ultimately improve operational efficiency and customer satisfaction.
Cluster 3: deals with Service Robotics as well as Automation, including main keywords like robotics,
automation, service quality, and robots, suggesting a growing interest towards replacing or augmenting human
labour with robotic solutions to streamline service processes and reduce costs.
Cluster 4: gives signifacat importance to Customer Experience and Sustainability, with keywords like behavioral
intention, hospitality, customer, and sustainability. It reflects research on sustainable practices and customer-
centric strategies within hospitality environments.
Cluster 5 explores Immersive Technologies and Analytical Tools, including augmented reality, virtual reality,
bibliometric analysis, and technology. This cluster demonstrates the use of immersive tools to enhance guest
experiences and data analysis methods for mapping research trends.
Here, the clusters reflect a dynamic and interdisciplinary research horizon, wherein technological innovations
seem to merge with service management, user psychology, and sustainability. As the hospitality industry enters
its digital evolution era, future research must address integration challenges, ethical considerations, and the long-
term impact of emerging technologies on both customer experience and business performance.
Thematic map
To gain insights into the conceptual structure of the research area, a thematic map, Figure V, was generated based
on two metrics: centrality, which represents the importance of the theme, and density, which reflects the
development of the theme. The map generated classifies themes primarily into four quadrants, where each
quadrant represents a different role in the academic landscape.
Motor Themes represent the core focus areas of the field. Themes such as hospitality, sales, machine learning,
artificial intelligence, and predictive analytics show potentially strong integration of innovative technologies into
the hospitality sector. Such areas are most likely to remain influential and might continue to progress towards
advanced service automation and customer personalization.
Niche Themes, such as employment and technology adoption, are often specialized and more likely to be region-
or context-specific. Though it has progressed to a great extent, the limited global relevance suggests potential
for integration into broader theoretical frameworks or comparative studies.
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Basic Themes, such as hotels, the hotel industry, and the Internet of Things, are serving as a foundation for the
research area. Despite their key role, these areas lack maturity, often exhibiting a need for formal empirical
exploration and theoretical expansion, particularly through the interdisciplinary perspectives linked to hospitality
infrastructure and digital transformation.
Emerging or Declining Themes, which encompass the internet, customer interactions, and resource valuation,
often represent legacy topics that are losing academic traction or new areas that are awaiting further scholarly
attention. Monitoring their trajectory in future analyses will help clarify their status.
It is worth noting that the themes situated at the centre of the thematic map—such as the tourism and hospitality
industries, sustainable development, and case studies—are in development and are transitional. They often hold
and exhibit potential to grow and might transform themselves into motor or basic themes with continued
research.
The thematic map thus suggests several pathways for future research. Researchers should attempt to deepen their
research into foundational but underdeveloped topics (e.g., IoT in hotels), expand the main themes through cross-
disciplinary interactions (e.g., AI and customer behavior), and reinvigorate research on declining themes by
adopting a modern approach (e.g., redefining customer interaction in digital environments). Additionally,
exploring transitional themes like sustainability within tourism can lead to impactful, forward-looking
contributions.
Figure V: Thematic map of the author’s keywords using Biblioshiny.
Source: Authors’ work
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FINDINGS, DISCUSSION, AND CONCLUSION
Bibliometric findings
This comprehensive bibliometric review encompassed 176 scholarly works published between 1988 and 2024,
reflecting a steady increase in research output, particularly from 2018 onward, with a peak in 2024 accounting
for 70 publications. These works have garnered nearly 3,218 total citations, averaging 18.28 citations per
publication, indicating a growing academic influence. Journal articles comprised the largest share (41.48%),
followed by conference papers (31.82%) and book chapters (22.73%). Research in the subject area was found to
be interdisciplinary, with subjects such as Computer Science (55.11%) and Business, Management, and
Accounting (53.41%) leading, but also extending into Engineering, Social Sciences, and Decision Sciences.
The analysis highlighted 582 contributing authors, with a positive and collaborative authorship trend (3.31
authors per paper). The major contributors in the area included John Bowen, Cristian Morosan, and Chung-En
Yu, whose individual papers had received more than 200 citations. A study revealed that the most productive
institutions include Amity University, Graphic Era Deemed to Be University, and the University of South
Florida; at the same time, journals such as the International Journal of Hospitality Management and Electronic
Markets featured prominently. A Keyword co-occurrence analysis conducted had resulted in six distinct thematic
clusters: (1) technology adoption and behavioural intention, (2) customer experience and emotional AI, (3)
innovative technologies aligned with sustainability goals, (4) immersive technologies like AR/VR, (5) digital
transformation of traditional service models, and (6) AI and strategic innovation in hospitality.
To conclude, a thematic map was developed, classifying themes by centrality and density, thus providing insights
into their maturity and need. Motor Themes, which include machine learning, artificial intelligence, and
predictive analytics, were well-developed and are central to the field. Niche Themes identified, such as
employment and technology adoption, are mature enough but are specific to the context. Basic Themes, inclusive
of terms such as hotels, the hotel industry, and IoT, form the foundation of research but are in dire need of
theoretical expansion. Emerging or Declining Themes, such as internet use, customer interaction, and resource
valuation, remain transitional, requiring close monitoring for future relevance.
The findings from this study highlight a dynamic evolution in scholarly discourse surrounding AI and smart
technologies in the hospitality industry. The rise in volume and citation of publications since 2018 shows the
shift driven by the practical integration of service robots, smart hotels, and digital platforms. The prominence of
fields like computer science and business management confirms a convergence of technical innovation with
strategic and experiential concerns. The clustering of keywords suggests that while certain areas (e.g., technology
adoption and automation) are well-established, others (e.g., immersive technologies and sustainability) are still
emerging and thus require refinement. The keyword co-occurrence analysis provides five thematic clusters
interpreting AI’s transformational role in hospitality.
The thematic map developed provides a useful conceptual lens in interpreting the current state of research in the
particular study area. The maturity of Motor Themes is a testament to the robust research activity and the
subject’s practical relevance. However, the existence of underdeveloped Basic Themes and transitional
Emerging Themes shows the areas where future research is needed. Topics such as IoT, customer interaction,
and sustainability lend themselves to deeper exploration. They would have significant impacts on the field with
the right and focused academic attention in these areas.
Thus, this comprehensive analytical study has demonstrated that AI, innovative technologies, and digital
innovations are reshaping the landscape of hospitality and tourism both theoretically and practically. The
literature has been rapidly growing in volume, interdisciplinary scope, and academic influence, with foundational
themes such as technology adoption and customer satisfaction being extended into new frontiers, including
captivating environments, emotional intelligence, and sustainability. Although progress has been made in some
areas, many themes remain underdeveloped or in a transitional phase, underscoring the need for more
comprehensive theoretical integration and empirical validation.
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Major studies and prominent institutions are now shaping the current research area, and the field continues to
diversify both geographically and thematically. The rise in cross-disciplinary collaboration, which spans
computer science and engineering, as well as social sciences and management, brings the field to the meeting
junction of technological advancement and human-centric service innovation.
Implications
For the researchers in the field, these keyword clusters and thematic maps offer a future direction for scientific
research. The underexplored and promising areas include the ethical and psychological aspects of AI adoption,
the environmental impact of smart hospitality infrastructures, and the growth and evolution of customer
interactions in digital environments. The gaps, particularly those found in evolving clusters and declining areas,
provide opportunities for future academic contributions.
For key industry players, this study will identify strategically viable areas for investment and development.
Intelligent automation, personalization engines, and sustainable tech solutions are emerging as key players,
ensuring a competitive advantage. Emotional AI and immersive interfaces will redefine customer engagement
and loyalty.
For stakeholders in the education sector, the study's results serve as a guide, suggesting the need to revise
hospitality curricula to include topics such as AI ethics, digital transformation, and interdisciplinary problem-
solving. These competencies will play a crucial role in preparing professionals who will navigate and lead a
tech-driven hospitality ecosystem.
For stakeholders involved in the policymaking process, this work provides valuable guidance on policy
formulation regulating AI integration in service industries. The issues concerning data security, algorithmic
fairness, job losses faced by existing workers, and sustainability should be addressed through proactive
governance informed by ongoing academic research
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Further Reading
1. Ahmi, A. (2024), “biblioMagika”, available at: https://aidi-ahmi.com/index.php/bibliomagika \
2. Ahmi, A. (2023). OpenRefine: An approachable tool for cleaning and harmonizing bibliographical data.
AIP Conference Proceedings. 27TH INTERNATIONAL MEETING OF THERMOPHYSICS 2022,
Dalešice, Czech Republic. https://doi.org/10.1063/5.0164724
Declaration of AI‑Assisted Writing
During the preparation of this research manuscript, the authors utilized ChatGPT (OpenAI GPT-4) to have a
deeper analysis of tables. The obtained information was carefully studied and played a key role in shaping the
authors' insights. The explanation of tables is the authors' work after studying this information. This has ensured
the accuracy of analysis and added academic value to the work.
Additionally, Grammarly, an AI Tool, was used for grammar checks, spelling, and punctuation, with its primary
function being language refinement.