Customer Experience Management in the Tourism Sector: Insights from a Bibliometric and Thematic Analysis

Abstract

The growing importance of customer experience management (CEM) in the tourism sector has led to a proliferation of research interests in satisfaction enhancement, loyalty, and value co-creation. This study proposes a systematic and exhaustive thematic and bibliometric analysis of 3874 articles on CEM in the tourism industry published in the Scopus database between 1979 and 2024. Following the guidelines of the PRISMA protocol, the study uses Bibliometrix (version 4.4.1) in R and VOSviewer (version 1.6.20) to map publication trends, author networks, thematic and chronological evolution, and influential contributions. A qualitative content analysis of the most cited works, guided by grounded theory, revealed the main antecedents, consequences, mediators, and moderators of customer experience management. This analysis is embodied in the proposal of a conceptual model that illustrates the dynamic relationship between these elements and provides the basis for future research for theoretical enrichment and empirical validation. The results offer actionable insights for academics and industry practitioners alike, with the aim of promoting authentic and memorable tourism experiences.

1. Introduction

In light of the prevailing socio-economic, environmental, and technological challenges, a paradigm shift in thinking is imperative to steer the research agenda on experience and to redefine managerial practices in the economy and society (Agapito & Sigala, 2024). Despite the growing emphasis on visitor experience in the tourism industry, there is a noticeable absence of this priority in academic research (Radic et al., 2024). The integration of phenomenology into tourism research, founded on a robust philosophical basis, has been instrumental in enhancing the rigor and depth of studies, thereby contributing to a more nuanced understanding of the concept (Gillovic et al., 2021; Godovykh & Tasci, 2020).

Notwithstanding these substantial advancements, a number of methodological and conceptual lacunae persist within the domain of customer experience management (Hwang & Seo, 2016). A considerable body of extant literature on tourism experience has acknowledged the paramount importance of meticulous attention to customer experience management in comprehending the dynamic nature of the tourism sector (H. Kim & So, 2022). The present study employs a systematic review and bibliometric analysis of the extant literature on CEM in the tourism sector to address this research gap. This paper aims to enhance the extant body of knowledge on customer experience management in the tourism sector by addressing the research gaps identified. The PRISMA protocol has been adopted to ensure the reproducibility and validity of results.

In order to capture both quantitative and qualitative dimensions, the study conducts a bibliometric analysis, employing recognized tools such as Bibliometrix and VOSviewer to identify the most influential themes, authors, and publications in the field of customer experience. In parallel, a grounded theory approach is utilized to conduct a more in-depth analysis of the results, thereby fostering a thorough approach and reinforcing the scientific rigor of data interpretation to identify the key antecedents, outcomes, mediators, and moderators of customer experience in tourism settings. The objective of this study is to synthesize extant knowledge on customer experience management in the tourism sector, identify conceptual gaps, and propose innovative avenues of research for future studies.

To this end, the present study will be divided into three sections in addition to the introduction and conclusion. A conceptual framework is employed to identify the various dimensions of tourism experience management. This is followed by the methodology section, which illustrates the protocol for data collection and analysis. The subsequent section will present the primary results and discussion of the bibliometric and thematic analysis, as well as directions for future research.

2. Conceptual Framework

2.1. Theoretical Foundations of the Tourism Experience

The concept of customer experience in the tourism industry was initiated primarily in the 1970s with the seminal work of Cohen (1979), who critiqued the superficial nature of earlier studies and proposed a taxonomy of modes of tourist experience, including recreational, entertaining, experiential, experimental, and existential categories. These modes are predicated on the meaning tourists attribute to their experience and their relationship to cultural or spiritual references. Building on Pine and Gilmore’s (1998) reflections on the experience economy, Baum (2006) highlights the need for a re-evaluation of traditional models of hospitality competence, emphasizing that success in this field depends as much on emotional and experiential intelligence as on technical skills. Oh et al. (2007) further elaborate on this notion by underscoring the significance of experience dimensions, including entertainment, education, escapism, and esthetics, in enhancing marketing strategies and elevating customer satisfaction (Hosany & Witham, 2010).

The application of the principles of the experience economy enables tourism stakeholders to create more engaging and memorable experiences that not only attract visitors but also foster a deeper appreciation of cultural heritage (Hayes & MacLeod, 2007) and contribute to the economic value of tourism (S. Lee et al., 2024). In this regard, Buonincontri and Marasco (2017) have proposed the integration of smart technologies to enhance these dimensions further.

Customer experience management (CXM), a concept derived from the field of relationship marketing, aims to optimize the customer experience throughout the tourism journey. This objective is pursued through collaboration between marketing, operations, design, and human resources (Kandampully et al., 2018; Sharples, 2019). Castañeda et al. (2019) underscore the pivotal role of engaging, user-tailored features in enhancing satisfaction and engagement. Memorable experiences, including involvement, hedonism, and local culture, have positively influenced tourists’ revisit intention and word-of-mouth (Sthapit et al., 2019). These experiences also translate to wellness tourism, where the integration of hedonic and eudemonic dimensions contributes to the creation of meaningful and sustainable experiences (Knobloch et al., 2017; Voigt et al., 2010; X. Zhang et al., 2024).

2.2. Customer Experience Management (CEM) and Authenticity

Customer experience management is predicated on understanding internal and external attributions, the former comprising tourist skills and efforts and the latter comprising environmental factors. This understanding is essential for optimizing expectations and minimizing dissatisfaction (Jackson et al., 1996). As proposed by Andereck (1997), the theory of human territoriality underscores the significance of social and psychological dynamics in enhancing the quality of tourist experiences.

The “new tourist” seeks enriching and authentic experiences through the integration of cultural and gastronomic heritage (Lu et al., 2015). N. Wang (1999) rethinks the concept of tourist authenticity by emphasizing individual and existential experience rather than mere object authenticity, while McIntosh and Prentice (1999) identify three key cognitive processes: assimilation, cognitive perception, and retroactive association.

2.3. Emerging Technologies and Experience Transformation

The advent of contemporary technologies and media has rendered modern tourism experiences increasingly intricate (Jansson, 2007). In this context, storytelling emerges as a significant tool for positively influencing destination-related emotions and associations, particularly in the context of responsible tourism (Caruana et al., 2014). Consequently, tourism industry actors are compelled to adopt approaches tailored to the creative class, favoring the co-creation of experiences and utilizing emerging technologies (Neuhofer et al., 2014).

Thanks to the integration of technology into the tourism experience, the boundaries between physical and digital in the traveler’s journey have been redefined (Aarabe et al., 2025b). Among the various examples, virtual reality (VR) and augmented reality (AR) have enriched the tourism experience by offering immersive interactions, virtual tours, and tailored contextual information (Cranmer et al., 2020; Wong et al., 2023b). These technologies diversify tourism offerings, facilitate virtual exploration of destinations prior to physical visits, and enhance the management of cultural heritage (Garau, 2014; Han et al., 2018; Jefferson et al., 2020).

In this way, big data, algorithms, and mobile applications have emerged as pivotal facilitators of personalization in tourism experiences through targeted recommendations and direct interaction between destinations and visitors (Tussyadiah & Wang, 2016). The Internet of Things (IoT) and the aggregation of data have been instrumental in optimizing tourism resources, effectively managing accommodation capacities, and ensuring the provision of real-time information to tourists (Liu, 2022). Smart tourism is based on informativeness, interactivity, and personalization to create enriched and sustainable experiences tailored to visitors’ needs (Aarabe et al., 2024; Jeong & Shin, 2020; Tsang & Au, 2024). Concurrently, gamification transforms interactions into engaging and rewarding experiences (Xu et al., 2017), while social networks enhance experience sharing and electronic word-of-mouth (Semrad & Rivera, 2018; Tham et al., 2013).

Artificial intelligence (AI) technologies, including generative solutions like ChatGPT, offer personalized support throughout the journey. These technologies facilitate planning by offering customized recommendations, autonomous guided tours, and continuous assistance, contributing to a more seamless and enriched user experience (Wong et al., 2023a). Big data and artificial intelligence (AI) have also been shown to facilitate enhanced strategic decision-making processes for tourism management professionals, enabling the generation of precise visitor behavior analyses (Alla et al., 2022; Martín et al., 2018; Vecchio et al., 2018). The integration of physical and digital dimensions, “phygital experience”, has emerged as a transformative force in the tourism sector at every stage of the travel journey (Ballina et al., 2019; Godovykh & Tasci, 2020). This paradigm shift, further fueled by the advent of tourism 4.0 and metaverses, is in response to an escalating demand for immersive, autonomous, and personalized experiences (Pencarelli, 2020).

2.4. Co-Creation, Emotions, and the Quest for Unique Experiences

Customers play a significant role in the co-creation of experiences within the tourism sector. This paradigm transcends the mere promotion of destinations, emphasizing the creation and connection of unique experiences (Rihova et al., 2015). This co-creation process, facilitated by Information and Communication Technologies (ICT), is characterized by the presence of key elements, including dialogue, access, transparency, and a mutual understanding of the risks and benefits involved (Pawłowska-Legwand, 2020; Prahalad & Ramaswamy, 2004). The multidimensional and cumulative tourism experience is characterized by its emotional and intangible benefits that enhance visitor satisfaction and revisit intentions (Cole, 2004).

In terms of marketing, it is essential to integrate both the co-production and affective aspects of experiential landscapes (Mossberg, 2007). Affective judgments, influenced by specific travel attributes, play a crucial role in the overall perception of experience (Powell et al., 2012). In a market where experiences take precedence over products, tourism must respond to consumers’ quest for unique (Binkhorst & Dekker, 2009) and memorable (Buhalis & Foerste, 2015) experiences. For instance, wonder, characterized by dimensions such as the human–nature relationship, personal transformation, and goal clarification (Zhao et al., 2024), enriches co-creation processes by engaging tourists. Finally, intercultural communication has been shown to transform tourism experiences into opportunities for learning and personal growth (Aarabe et al., 2025a; Steiner & Reisinger, 2004).

2.5. Transformative Effects of the COVID-19 Pandemic

The pandemic has increased consumers’ awareness of their unconscious behaviors and purchasing choices, encouraging a re-evaluation towards more sustainable tourism (Stankov et al., 2020). Furthermore, it has disrupted the continuity of the tourism experience, thereby unveiling the intricacies of emotions experienced during travel, including happiness, fear, frustration, tension, and relief (Munar & Doering, 2022).

The influence of social pressures and risk assessments has prompted modifying tourist behaviors and adjusting their expectations in the face of the pandemic (Mayer & Coelho, 2021). This crisis has also been an opportunity to reinvent the tourism sector, notably by integrating more digital technologies and promoting e-tourism (Raza et al., 2021). Pandemic fatigue, the pursuit of safety, and the desire for social connection have collectively influenced how tourism experiences are co-created. The concept of tourism experience has evolved over time (Figure 1).

3. Materials and Methods

The PRISMA protocol was developed to assist researchers in identifying systematic literature review and meta-analysis elements (Moher et al., 2009). Numerous researchers have extensively adopted this protocol due to its efficacy in enhancing the comprehensiveness and reproducibility of results obtained in tourism and hospitality customer experience research (Chauhan et al., 2022; Fritz et al., 2005; H. Kim & So, 2022; Pahlevan Sharif et al., 2019a, 2019b). The PRISMA protocol guidelines generally comprise three stages. The first stage involves identifying searches through the optimal and strategic combination of main keywords. The second stage involves selecting according to eligibility criteria (inclusion and exclusion). The third stage involves including the corpus intended for quantitative and qualitative analysis (Page et al., 2021).

Bibliometric analysis involves consulting bibliographic references in various databases, including Scopus, Wos, and Google Scholar. Scopus and Wos are regarded by numerous scholars as the most pertinent databases for conducting bibliometric analysis, particularly within the domain of tourism and hospitality (Archambault et al., 2009; Baas et al., 2020; Sánchez et al., 2017). A considerable number of studies have used Scopus for bibliometric analyses in the tourism and hospitality sector, with a particular focus on customer experience. This recourse is explained by the database’s capacity to provide researchers with extensive coverage, facilitating the identification of themes, authors, and publications pertinent to the subject (H. Kim & So, 2022; Arici et al., 2022). Despite the limitations reported by some studies regarding the use of single databases (Kumar et al., 2024), Scopus remains a popular choice. This is primarily due to its indexing of a significant number of journals specializing in tourism and hospitality (Baas et al., 2020). Furthermore, Scopus provides a representative and homogeneous corpus for the analysis of research trends in customer experience management in the tourism sector. The exclusive utilization of Scopus guarantees the uniformity and coherence of thematic and semantic analysis, facilitating the interpretation of metadata (Pranckutė, 2021).

A search equation was formulated in the Scopus database to collect references related to customer experience management for potential systematic and bibliometric analysis. The search equation combined the main keywords (“Customer Experience Management” OR “CEM” OR “Experience Economy” OR “Tourist Experience” OR “Tourism Experience”) AND (“Tourism” OR “Tourism Industry” OR “Tourism Sector” OR “Hospitality”) (D. Kim & Kim, 2017). The database identified a total of 5814 references of all types. Several authors posit that papers published in journals are the most relevant due to the scientific rigor provided by editors and reading and review committees (Gutiérrez-Nieto & Serrano-Cinca, 2019; Salleh et al., 2023; Vong et al., 2021). In alignment with this perspective, the present analysis is constrained to journal articles (n = 4065). The concept of “experience” has emerged over the years in the field of tourism and hospitality, according to the diversity and multidisciplinarity of approaches in previous discussions (Cohen, 1979).

To ensure the relevance and objectivity of our corpus, articles written in languages other than English were excluded, leaving us with 3874 articles written in English. The resulting corpus was exported from the Scopus database both in CSV format for potential purification with Bibliometrix for data cleaning before use and in RIS format for reference management with Zotero. The corpus of 3874 articles will be subjected to bibliometric analysis using bibliometrix based on the R language (Aria & Cuccurullo, 2017) and VOSviewer based on Java programming (Van Eck & Waltman, 2010). Then, the 25 most cited articles will be subjected to in-depth qualitative analysis. As illustrated in Figure 2 and detailed in the checklist, the PRISMA guidelines have been employed to guide the systematic review process (Page et al., 2021).

4. Results

This section can be divided into subsections. It will give a short and exact description of the results of the experiment, how they are understood, and the conclusions that can be drawn from them.

4.1. Main Information

Table 1. Overview.

Description	Results
Main information about data
Timespan	1979:2024
Sources (journals, books, etc.)	758
Documents	3874
Annual growth rate %	3.97
Document average age	5.77
Average citations per doc	37.05
References	190,640
DOCUMENT CONTENTS
Keywords plus (ID)	3725
Author’s keywords (DE)	9352
AUTHORS
Authors	6892
Authors of single-authored docs	616
AUTHORS COLLABORATION
Single-authored docs	726
Co-authors per doc	2.74
International co-authorships %	26.2
DOCUMENT TYPES
Article	3874

Source: Bibliometrix.

provides an overview of the corpus studied, which includes 3874 articles spread over 758 sources and covering a period from 1979 to 2024. A total of 6892 authors contributed to CEM research in the field of tourism, 726 of whom produced papers without any collaboration with other authors, giving an average of 2.74 authors per paper. The rate of international collaboration among authors was 26.2%.

Table 1 offers a comprehensive overview of the corpus studied, providing a general description of the 3874 analyzed articles. This is followed by a presentation of the evolution of publications over time, which is intended to facilitate comprehension of the trend of scientific contributions in the field under study.

4.2. Number of Publications per Year

As illustrated in Figure 3, there has been a marked upward trend in the number of publications concerning customer experience management in tourism from 1979 to 2024. On average, there has been a 3.97% annual increase in publications. Most of these contributions were published after 2010, with only 9.5% published before that year.

While the breakdown of contributions by year of publication offers valuable insights into publication trends, it also highlights the importance accorded to the subject by the scientific community. However, it would be beneficial for researchers to identify the primary sources of publication. The subsequent section will thus present the primary journals in the domain of customer experience management.

4.3. Publications by Journal

As illustrated in

Table 2. Top 10 most influential sources.

Source	h_index	g_index	m_index	TC	NP	PY_start
Annals of tourism research	73	156	1.659	24,444	170	1981
Tourism management	69	130	2.029	17,167	151	1991
Journal of travel research	55	111	1.279	12,386	122	1982
Current issues in tourism	40	63	1.6	4739	153	2000
Journal of sustainable tourism	40	67	1.25	4524	69	1993
Tourism management perspectives	36	58	2.769	3537	87	2012
Journal of travel and tourism marketing	35	55	1.346	3783	55	1999
Journal of destination marketing and management	31	61	2.583	3794	62	2013
Sustainability (switzerland)	30	45	3.333	3129	176	2016
Tourism geographies	29	58	1.115	3428	65	1999

Source: Bibliometrix.

, the top ten most influential sources in the field of CEM in tourism are as follows: “ANNALS OF TOURISM RESEARCH”, the longest-running journal in the field, is in the number one position with 170 publications and 24,444 citations, and the highest impact factor. In second place by impact factor is “TOURISM MANAGEMENT”, with 151 publications and 17,167 citations. The journal “Sustainability (Switzerland)” is the newest in the field, with 176 publications, and is in ninth position.

A focus on the primary authors in the domain of CEM facilitates a complementary analysis of journals and the chronological evolution of publications. This approach enables researchers in the field to identify pillars for potential collaborations, discern approaches, currents, and schools, and deepen or broaden the subject where appropriate.

4.4. Prolific Authors

The ensuing table (

Table 3. Authors’ local impact.

Author	h_index	g_index	m_index	TC	NP	PY_start
Zhang, Y.	10	19	1.429	398	32	2018
Wang, Y.	13	26	0.813	720	31	2009
Sthapit, E.	13	27	1.625	993	27	2017
Li, Y.	9	25	0.36	813	25	2000
Kastenholz, E.	14	24	0.7	1339	24	2005
Zhang, H.	9	20	1.125	722	20	2017
Kim, S.	15	19	1	970	19	2010
Wang, J.	7	15	1.167	247	19	2019
Pearce, P.L.	15	18	0.375	906	18	1985
Scott, N.	14	17	0.875	1155	17	2009

Source: Bibliometrix.

) illustrates the top 10 most prolific authors in the domain of CEM in tourism. ZHANG Y occupies the preeminent position with 32 publications and 398 citations since 2018, followed by WANG Y, who initiated his research in 2009 and has accumulated 720 citations in 31 publications. KASTENHOLZ E has received the highest total citations (1339) of 24 publications since 2005, followed by SCOTT N with 1155 citations on 17 publications since 2009.

Following a thorough examination of the documents based on their year of publication, source, and author, the subsequent sections will undertake a content analysis. This analysis will entail the examination of author co-citations, keyword co-occurrence, and clustering. This preliminary analysis aims to establish the foundations for an in-depth qualitative investigation.

4.5. Co-Citation Network

The co-citation network offers insights into the similarities between journals, authors, or references (Boyack & Klavans, 2010; Hoffman & Holbrook, 1993). The citation of two references in a given work indicates the presence of similarities and commonalities. As illustrated in Figure 4, the co-citation network of the 361 references that attained a threshold of 20 citations out of a total of 188,727 reveals notable interconnections.

The 361 items in the database are divided into six clusters, which collectively generate 27,314 links. The utilization of distinct colors signifies the differentiation of clusters of quotations that are frequently cited in conjunction with the extant literature. The network exhibits a relatively dense central structure accompanied by peripheral branches. While the clusters are well-defined, they exhibit substantial interconnections. Oh et al. (2007), and N. Wang (1999), emerge as prominent central nodes, with 325 and 328 links to the various clusters.

The red cluster encompasses studies that address transformations and authenticity in tourism. Cohen (1979) is a foundational study within this cluster, as it initiated the early work that advanced the phenomenological examination of the tourism experience. N. Wang (1999) proposed a conceptual clarification of the meaning of authenticity in the tourism experience, while Zhou et al. (2013) explored the impact of tourists’ attitudes on the perception of authenticity. Uriely (2005) identified conceptual developments in the study of the tourist experience.

The yellow cluster is based on the memorable and cultural aspects of the tourism experience. Sthapit (2017) proposed a conceptual framework for the memorable gastronomic experience, “MFE”. Seyfi et al. (2020) developed a theoretical model of memorable experiences in cultural tourism by identifying key factors such as authenticity, engagement, cultural exchange, culinary attraction, and service quality. Chen and Rahman (2018) have examined how visitor engagement and cultural interactions influence memorable experiences and visitor loyalty in cultural tourism. Concurrently, Cheng and Lu (2013) and H. Zhang et al. (2018) sought to ascertain the causal link between perceived image (country and destination image), memorable tourism experiences (MTEs), and revisit intention in the context of international tourism. In a separate study, Chandralal et al. (2015) identified the constituent elements of memorable tourism experiences (MTEs).

The blue cluster is centered on the nexus between marketing approaches and the tourism experience. Mossberg (2007) presented two frameworks for analyzing tourism experiences: one focusing on co-production and the other on factors influencing the tourism experience, such as environment, staff, other visitors, and theme. To develop and validate a measurement scale, Oh et al. (2007) adapted Pine and Gilmore’s four dimensions of experience to accommodate tourism contexts. This endeavor was undertaken to analyze and improve experience economy practices in the tourism sector. Similarly, Prahalad and Ramaswamy (2004) elucidated the paradox of consumer satisfaction by demonstrating the transition of value creation from a product-centric approach to a personalized experience approach. In this paradigm, consumers collaborate with companies to co-create value through direct interaction.

The purple cluster centers on technological aspects of tourism. Boes et al. (2016) and Gretzel et al. (2015a) explored the essential intelligence components in the context of smart cities and tourism destinations. J. Lee et al. (2024) investigated the effects of virtual reality and social media on heritage and heritage tourism sites. Munar and Jacobsen (2014) explored the motivations behind sharing tourism experiences on social media. Guttentag (2010) explored the applications and implications of virtual reality in tourism.

The light blue cluster centers on the evolution from traditional dark tourism to experience-based tourism. Biran et al. (2011) and Stone (2012) examined the symbolic meanings and visitor motivations as predictors of a broader heritage experience beyond traditional dark tourism. Agapito (2020) explored the importance of the senses in the design of the tourism experience.

4.6. Keyword Co-Occurrence Analysis

Keyword co-occurrence analysis generates a network of main issues and their associations, thereby illustrating the conceptual structure of the research domain (Sedighi, 2016). Figure 5 shows a keyword co-occurrence mapping derived from the titles and abstracts of the corpus examined using binary counting. A total of 1728 terms out of 57,610 met the threshold of 10 occurrences. Subsequently, a relevance score is calculated for each term to select the 60% most relevant terms (Van Eck & Waltman, 2010). The 1037 items were distributed across seven clusters, resulting in the formation of 88,326 links.

A thorough examination of the network in question discloses the preeminence of concepts about tourism and experience, which occupy a pivotal role within the network. Concomitantly, peripheral concepts are linked to the primary themes, establishing a multifaceted, nuanced network. The red cluster, for instance, encompasses terms lexically linked to the social and cultural facets of tourism, including “heritage” (Halewood & Hannam, 2001; Nguyen et al., 2024; Poria et al., 2009; Taheri et al., 2020), “society” (Butler & Szromek, 2019), “space” (Aleksandrova, 2020; Aranburu et al., 2016; Birenboim, 2016; Cohen, 2017), and “meaning” (Agapito et al., 2014; Picard, 2013), indicating an orientation towards the study of cultural meanings and experiences of places with high heritage value, including “dark tourism” (Ashworth & Isaac, 2015; Stone, 2012), suggesting an interest in authenticity, collective memory, and social norms in the creation of meaningful tourism experiences.

The blue cluster addresses issues related to sustainability. In the context of sustainable tourism and natural resource management, terms such as “biodiversity” and “national park” are pertinent (AlAli et al., 2024; Byström & Müller, 2014; Catibog-Sinha, 2010; Schmidt, 2006). The term “COVID” signifies the repercussions of the COVID-19 pandemic on-site visitation and the management of tourist flows in parks and destinations. The term “public initiative; UNESCO” indicates the interest in conservation policies and initiatives to preserve heritage sites (Feng et al., 2024; Koo et al., 2019; Ord & Behr, 2023). Gastronomy constitutes an element of tourist appeal in heritage-oriented tourist destinations (Pappas et al., 2022; Sio et al., 2024; Yoo et al., 2022).

The green cluster, including “tourist satisfaction”, “behavioral intention”, and “memorable tourism experience”, is related to tourists’ memorable experience and the factors that influence their satisfaction and behavioral intention (Angeloni, 2023; Kladou et al., 2022; H. Zhang et al., 2018).

Conversely, the purple cluster centers on the applications of emerging technologies in tourism and the profound impact of these innovations on the tourism experience. The term “technology” encompasses the use of technology to enhance the tourism experience (Neuhofer et al., 2014). The terms “virtual reality” and “augmented reality” emphasize the importance of immersive technologies in enhancing the tourism experience (Bec et al., 2021; Jefferson et al., 2020; Jiang et al., 2023; Lim et al., 2024). The term “algorithm” underscores the significance of artificial intelligence (AI) and data analysis in personalizing and recommending travel experiences (Jaelani et al., 2024). The concept of “network” underscores the role of social networks and digital platforms in the collaborative creation of the tourism experience (Haddouche & Salomone, 2018; Kavoura & Katsoni, 2013; Latifah & Setyowardhani, 2020; Munar & Jacobsen, 2014). The concept of “experimental result” reveals that several empirical or experimental studies have been carried out on the application of these technologies in the context of tourism (Huang et al., 2023).

The yellow cluster is oriented toward specific forms of tourism, such as “wine tourism” (Alebaki et al., 2022; Alonso, 2011) and “food tourism” (An et al., 2024; Andrinos et al., 2022), indicating an interest in sensory and cultural experiences. The light blue cluster addresses tourism experiences focused on “heritage tourism”, oriented towards visiting cultural and historical sites (Adie, 2020; Feng et al., 2024). These experiences draw on the concept of “nostalgia”, which is defined as the desire to reconnect with the past (Ali, 2015; Bhogal et al., 2024).

To satisfy the need for an exhaustive temporal analysis, Figure 6 maps the evolution of research on customer experience concepts and terms in the tourism sector over the last three decades, covering almost all the publications in our corpus.

A meticulous examination of thematic transitions illuminates the diverse advancements in research on customer experience in tourism. The period preceding 2010, distinguished by a paucity of publications, witnessed the emergence of fundamental concepts such as “visitor experience”, “ecotourism”, and “marketing”. This particular focus on these concepts in this subject demonstrates the significance of specific forms of tourism, such as ecotourism and cultural tourism, as fundamental components of the tourism experience. Concepts such as “gender” and “destination attractiveness” illustrate the presence of multiple interconnected components that can be used to characterize the customer experience. The period from 2011 to 2020 is characterized by the emergence of novel forms of tourism, including “sustainable tourism”, “volunteer tourism”, “dark tourism”, and “adventure tourism”. This period also incorporates the social dimensions of “well-being” and the “sharing economy”, indicating the evolution of the tourism experience through the integration of holistic and societal perspectives. The final period is characterized by a marked acceleration in thematic evolution, with the predominance of technological considerations, such as “technology”, “virtual reality”, and “data mining”, as well as environmental considerations, including “sustainability” and “climate change”. This period also encompasses quantifiable aspects of the tourism experience, such as “memorable experience” and “satisfaction”. It is important to note the emergence of new trends in the sharing of experiences on online platforms such as “Tripadvisor”.

This evolution demonstrates the systematic progression of the field of tourism experience research, transitioning from fundamental concepts to more specialized and contemporary theories. This progression is influenced by technological advancements and shifting societal concerns, reflecting the dynamic nature of the field.

4.7. Clustering by Coupling

Figure 7 shows a clustering analysis based on document coupling to identify distinct themes in the literature on the subject. Clusters are labeled according to the authors’ keywords. The vertical “impact” axis represents the influence of concepts in the scientific literature, while the horizontal “centrality” axis shows the importance of the concept in the field studied. Each color represents a thematic cluster made up of documents or concepts with similarities (Aria & Cuccurullo, 2017).

The red cluster, which consists of the terms “memorable tourism experience-s-; satisfaction”, exhibits a higher degree of centrality and impact. This finding suggests that these terms play a significant role in the scientific literature and are associated with other concepts. Among the most frequently cited references in this cluster is H. Zhang et al. (2018), with a Normalized Local Citation Score of 20.69, who proposed a model for analyzing the relationship between perceived image, memorable tourism experience, and intention to revisit, followed by Chen and Rahman (2018) who examined the relationship between visitor engagement, cultural contact, memorable tourism experience, and destination loyalty. These references were followed by Rasoolimanesh et al. (2021), who investigated the relationship between the dimensions of memorable tourist experience in managing tourist behavioral intention with the mediating role of satisfaction and other authors showing the importance of customer experience as a lever of tourist satisfaction and loyalty.

The blue cluster, which comprises the terms “Tourist Experience”, “Experience Economy”, and “Co-creation”, exhibits an average level of impact and centrality. The most frequently cited references in this cluster are Hosany and Gilbert (2010), who empirically investigated the dimensions of tourists’ emotional experiences in hedonic destinations; Zatori et al. (2018), who analyzed how service providers can enhance the memorable and authentic tourist experience on-site, in the context of tourist tours; and Knobloch et al. (2017), who explored the nature of individual experiences in terms of emotions, meanings, and well-being. Other research suggested a move towards collaborative tourism experience design, where visitors can play an active role in creating their own experience.

The green cluster, which includes the terms “authenticity”, “tourist experience”, and “existential authenticity”, exhibits a comparatively modest degree of impact and centrality. Authors such as Quan and Wang (2004) endeavored to construct a conceptual model that integrates the peak experience and consumer experience dimension into a structural and interdependent whole for food experiences in tourism. Magrizos et al. (2020) explored the stages of the transformation process among volunteer tourists and examined the impact of the authenticity and immersion of their experiences on this transformation. Lu et al. (2015) attempted to examine how perceived authenticity, tourist involvement, and destination image influence tourist experience and satisfaction. Many other authors have contributed to enriching the debate on profound, immersive cultural experiences and interaction in tourist destinations.

The preceding Section 4.1, Section 4.2, Section 4.3, Section 4.4, Section 4.5, Section 4.6 and Section 4.7 offer a structured bibliometric analysis of emerging publication trends, influential journals, prolific authors, and central concepts in research on customer experience management in the tourism sector. The results of the quantitative analysis form the foundation for the subsequent in-depth qualitative analysis in the following section.

4.8. Qualitative Analysis

The transition between bibliometric and qualitative analysis constitutes a rigorous methodological progression, thereby enhancing the depth of our study (Costa et al., 2023). Keyword co-occurrence analysis and chronological evolution (3.6) and clustering by coupling (3.7) reveal the multidimensional and interconnected nature of the customer experience in tourism. These analyses underscore the significance of pivotal concepts such as “tourism satisfaction, memorable experience, authenticity, and co-creation”. These concepts are intricately linked, and a comprehensive understanding of the phenomenon necessitates an in-depth examination of its multifaceted nature. The clusters examined suggest that the customer experience in the tourism sector cannot be comprehended in isolation; instead, it must be analyzed holistically and dynamically, integrating its antecedents (e.g., service quality, environment), its consequences (e.g., satisfaction, loyalty), and the intermediary mechanisms (mediators and moderators) that influence this relationship. This conceptual structure, which emerges from the bibliometric analysis, directly guides our qualitative analysis framework.

By employing the grounded theory framework as an analytical paradigm, this comprehensive thematic content analysis facilitates the exploration of specific phenomena through inductive reasoning, thereby generating an in-depth theoretical understanding of the multifaceted dimensions of customer experience in the tourism sector. This approach is further substantiated by the purposive sampling technique, which encompassed titles, abstracts, and keywords from 3874 articles (Corbin & Strauss, 1990). The integration of a descriptive bibliometric analysis with a systematic qualitative analysis is essential for a comprehensive understanding of customer experience management in the tourism sector (H. Kim & So, 2022). The in-depth analysis will be conducted on a reduced sample of 25 of the most-cited articles. The selection of our corpus is based on a rigorous methodology combining several complementary criteria. Firstly, the total number of normalized citations per year (“TC per year”) was taken into account instead of the total number of citations (

Table 4. Most global cited documents.

Paper	Total Citations	TC per Year	Normalized TC
BUHALIS D, 2020, TOUR REV	624	124.80	17.65
ZENG Z, 2020, TOUR GEOGR	434	86.80	12.27
IOANNIDES D, 2020, TOUR GEOGR	430	86.00	12.16
TUNG VWS, 2011, ANN TOUR RES	1003	71.64	11.66
PENCARELLI T, 2020, INF TECHNOL TOUR	355	71.00	10.04
PRAYAG G, 2017, J TRAVEL RES	566	70.75	11.29
MUNAR AM, 2014, TOUR MANAGE	758	68.91	9.94
ZHANG H, 2018, J DESTIN MARK MANAGE	452	64.57	9.30
OH H, 2007, J TRAVEL RES	1125	62.50	8.46
CHEN H, 2018, TOUR MANAGE PERSPECT	405	57.86	8.33
SIMS R, 2009, J SUSTAINABLE TOUR	876	54.75	9.38
BOGICEVIC V, 2019, TOUR MANAGE	315	52.50	10.13
KIM J-H, 2018, J TRAVEL RES	367	52.43	7.55
COHEN E, 1988, ANN TOUR RES	1938	52.38	2.91
QUAN S, 2004, TOUR MANAGE	1090	51.90	8.04
LEE H, 2020, INF MANAGE	252	50.40	7.13
KIM H, 2022, INT J HOSP MANAGE	151	50.33	12.10
GALLARZA MG, 2006, TOUR MANAGE	952	50.11	7.27
GRETZEL U, 2015, COMPUT HUM BEHAV	494	49.40	7.79
RASOOLIMANESH SM, 2022, TOUR REV	140	46.67	11.22
JEONG M, 2020, J TRAVEL RES	232	46.40	6.56
KIM J-H, 2014, TOUR MANAGE	488	44.36	6.40
WANG D, 2016, J TRAVEL RES	390	43.33	8.08
XU F, 2017, TOUR MANAGE	326	40.75	6.50
NEUHOFER B, 2014, INT J TOUR RES	446	40.55	5.85

Source: Bibliometrix.

). This was performed in order to capture a fair balance between old (Cohen, 1988) and new publications (Buhalis, 2020). This corpus is reaching a level of conceptual and thematic saturation (Saunders et al., 2018). The present sample encompasses the primary dimensions identified in the aforementioned bibliometric analysis, namely memorable experiences, authenticity, and technological integration. Additionally, it exhibits temporal diversity, covering the period from 1988 to 2022. Valtakoski (2020) asserts that thematic analysis within the framework of grounded theory necessitates a depth of analysis and richness of data rather than a large number of articles. The subsequent table illustrates the most-cited references, arranged according to the total number of citations per year.

In accordance with the definitive corpus, which comprises articles that have attained a substantial level of impact within the domain of customer experience management in the tourism sector (H. Kim & So, 2022), this section proffers a synthesis of the pivotal antecedents, consequences, moderators, and mediators of customer experience management (

Table A1. Synthesis of Key Studies.

References	Antecedents of CEM	Consequences of CEM	Mediators	Moderators
Buhalis (2020)	Emerging technologies Consumer expectations Smart environments	Satisfaction and engagement Innovative services Accessibility and inclusivity	Value co-creation Interconnectivity and interoperability	Organizational leadership Technological adaptation Digital skills Data privacy and security norms
Zeng et al. (2020)	Pandemic conditions (COVID-19) Robotics and AI advancements	Efficiency and safety Consumer perceptions and expectations Evolution toward ‘High-Tech’	Technology acceptance and adoption Service design and quality	Societal and cultural attitudes Privacy and confidentiality
Ioannides and Gyimóthy (2020)	Pandemic-driven changes	Decline of mass tourism Rise of niche tourism Reduced carbon footprint Redefinition of travel perception	Government interventions Virtual technologies Eco-responsible behavior	Tourist behavior Climate change perception Sector resilience
Tung and Ritchie (2011)	Preparation and anticipation Emotional and sensory perceptions Cognitive engagement Psychological memory-related factors	Increased satisfaction Loyalty and intention to return Positive word-of-mouth (WOM) Destination management	Positive affect Perceived service quality Emotional engagement	Cultural context Environmental conditions
Pencarelli (2020)	Digital technologies Consumer behavior and expectations Smart infrastructure	Tourist satisfaction Loyalty and advocacy Value co-creation	Technology integration Engagement platforms	Cultural and environmental sensitivity Technology adoption Sustainability practices
(Prayag et al., 2017)	Tourists’ emotional experiences Destination image Expectations	Satisfaction Behavioral intentions Memorable experiences	Perceived overall image Satisfaction	Repeat vs. first-time visitors Tourist demographics Cultural and contextual variations
(Munar & Jacobsen, 2014)	Motivations for sharing content: altruisme, engagement community involvement, hedonism and emotion Technological Affordances: sharing content on platforms Cultural and demographic factors	Enhanced social capital Improved destination perception Increased engagement with platforms	Type of content Platform features	Type of social media platform Generational differences Destination type
(H. Zhang et al., 2018)	Perceived Image: country and destination Destination attributes Emotional and cognitive factors	Memorable tourism experiences (MTEs) Revisit intention Destination competitiveness	Memorable tourism experiences (MTEs)	Tourist culture Destination familiarity Experience context
Oh et al. (2007)	High functional quality of products/services (influential factor) Search for authenticity and novelty (preconditions) Tourist motivation: authenticity, intrinsic benefits (independent variables) Interaction with local hosts	Customer satisfaction (direct result) Positive memories (indirect result) Business success in B2B (organizational impact)	Emotional arousal or excitement Interaction with locals (intermediate process) Memories of the experience (transmission mechanism)	Contextual variable: form of accommodation and hotel consensus Customer demographics (debated influence) Geographic location of the B&B (disputed control variable)
Chen and Rahman (2018)	Visitor engagement Cultural motivation	Loyalty (“MTEs influence revisit intentions and WOM”) Satisfaction (“Satisfaction alone may not ensure loyalty”)	Cultural contact (“fully mediates engagement and MTE”)	Cultural variations (“Tourists’ desires for authentic experiences vary”)
Sims (2009)	Tourists’ desire for authenticity (CEM influencing factor) Alternative food networks Preconditions: availability of authentic local products and collaboration between producers and restaurateurs Identified independent variable: cultural authenticity of local food products Triggering factor: tourist behavior and expectations	Enhanced tourism experience: cultural immersion and local economic support Satisfaction Promotion of sustainable tourism Support for the local economy Environmental impact	Engagement of local producers: connecting food and culture	Tourists’ prior experiences and knowledge Contextual factors: regional differences Control variable: types of restaurants and producers
Bogicevic et al. (2019)	Immersive technologies in tourism marketing Preview interactivity	Enhanced tourist brand experience	Sense of presence Mental imagery	Type of technology Individual user preferences
J.-H. Kim (2018)	Memorable tourism experiences (MTEs) Destination image	Loyalty behavior: intention to revisit Word-of-mouth (WOM) Improved destination image Organizational performance	Satisfaction	Contextual factors (e.g., type of destination) Tourist expectations
Cohen (1988)	Commodification of cultural products The need to meet tourists’ demand for “authentic” experience	Transformation of the meanings of cultural products for local communities Tourists’ perception of authenticity, even for commodified products	Depth of tourist experience sought by the visitor	Tourists’ varying expectations regarding authenticity The role of play and participation in the acceptance of commodified cultural products
Quan and Wang (2004)	Growing importance of the experience economy Tourists’ demand for high-quality services, especially gastronomy	Economic impacts of tourism consumption	Integration of everyday experiences into tourism experiences	Specific context: type of destination, service quality
H. Lee et al. (2020)	Educational experience Entertainment experience Use of emerging technologies (e.g., VR)	Visit intention Customer satisfaction and loyalty Increased interest in museums	Immersion mechanisms (e.g., escapism and esthetics) that transform initial experiences into a complete virtual experience	Contextual factors (e.g., type of museum, VR accessibility)
H. Kim and So (2022)	Emergence of the experience economy (Pine & Gilmore, 1998): CEM as a theoretical framework Variability of customer interactions (employees, technology, other customers) Understanding customer preferences to design positive experiences	Increased customer satisfaction and loyalty Improved organizational performance	Multichannel interaction (physical and digital) Experience co-creation	Complexity of interactions and customer expectations Digital technologies can either enhance or diminish customer experience impact
Gallarza and Saura (2006)	Value dimensions: quality, price, emotion, social Understanding customer needs and expectations Perceived value dimensions Students’ past travel experiences	Satisfaction Loyalty Reputation enhancement Increased recommendations and positive feedback	Satisfaction as a mediator between perceived value and loyalty Perceived quality and benefits Ongoing evaluation of travel experiences	Cultural and economic context Service level and engagement of travel providers Demographics: age, gender, frequency of travel
Gretzel et al. (2015b)	Smart technologies, digital infrastructures, smart business networks Consumer needs for personalized experiences	Value creation Enriched tourism experiences Increased competitiveness and customer satisfaction Personalized experiences boosting satisfaction and loyalty Revenue growth and reputation enhancement	Information sharing Co-creation of value Smart technologies facilitating interaction and personalization	Technological context and level of smart tech adoption Availability of digital infrastructure and government support Cultural differences, local regulations Technological development, data privacy policies
Rasoolimanesh et al. (2021)	Hedonism Novelty Local culture Refreshment Meaningfulness Involvement Knowledge	Revisit intention WOM and eWOM intention	Tourist satisfaction
Jeong and Shin (2020)	Accessibility, informativeness, interactivity, and personalization of Smart Tourism Technologies (STTs) Customer satisfaction	Memorable experience Revisit intention	Satisfaction and memorable experience mediate the effect of STTs on revisit intention	Security and privacy Smart destinations require secure technological environments to optimize STT effectiveness
J.-H. Kim (2014)	Tangible attributes (e.g., infrastructure, accessibility) Intangible attributes (e.g., local culture, hospitality)	Customer satisfaction and loyalty Competitive advantages for the destination Enhanced destination image	On-site experiences and interaction play a critical role in transmitting the effects of destination attributes	Contextual factors such as destination competitiveness Cultural differences between visitors and residents affecting the strength of relationships
D. Wang et al. (2016)	Attitudes toward technology and skills acquired in daily life are key factors influencing the adoption of smartphones in the tourism context	Connectivity and the transposition of digital routines deeply modify the tourism experience (de-exoticization, better decision-making, enhanced social connections).	The processes of appropriation and integration of digital routines serve as key mechanisms explaining the impact of smartphones	Social, technological, and personal contexts influence the intensity of the observed effects
(Xu et al., 2017)	Growing popularity of persuasive technologies (e.g., Serious Games) Rapid development of mobile devices and immersive technologies Increasing adoption of gamification in various sectors (education, health, tourism) Intrinsic and extrinsic motivation of users (e.g., badges, rewards, autonomy, competence)	Increased satisfaction and loyalty of tourists towards the destination Increased engagement and co-creation of tourism experiences Better organizational performance through customer engagement and employee training	Hedonic function of games enhancing user engagement Specific game mechanisms (badges, points, autonomy, etc.) facilitating engagement	Technological context: availability of mobile devices and tourism applications Game design and relevance of gamification mechanisms
(Neuhofer et al., 2014)	Trigger factors: Technological advances in ICT, trends in experiential tourism Independent variables: Adoption of new technologies (virtual reality, mobile apps) Robust technological infrastructure Consumers’ digital skills	Direct outcomes: Transformation in the way tourists interact with destinations (engagement) Indirect outcomes: Creation of new business models Increased customer engagement Strengthened loyalty through personalization	Interaction between users and technologies Digital storytelling (immersive storytelling)	Social acceptance of technologies by tourists Economic accessibility of digital tools

). This synthesis facilitates the establishment of a robust theoretical framework and an appreciation for the causal relationships between the primary construct and its associated constructs (Figure 8).

This model delineates the primary antecedents, consequences, mediators, and moderators of customer experience management (CEM), a construct derived from a comprehensive analysis of the 25 most cited and top-rated articles in the field. A thorough examination of each element is presented in the subsequent sections, underpinned by a qualitative analysis. This analysis aims to elucidate the influence of antecedents on CEM (Section 4.8.1), assess the associated impacts of these practices (Section 4.8.2), and analyze the pivotal roles played by mediating (Section 4.8.3) and moderating (Section 4.8.4) variables. This analysis aims to delve deeper into the mechanisms underlying the dynamics of CEM while reinforcing the relevance of the proposed conceptual model.

4.8.1. Antecedents of CEM

The antecedents of customer experience management (CEM) in the tourism and hospitality sectors encompass the factors, conditions, and processes that facilitate the design, adoption, and implementation of customer-centric strategies (Hwang & Seo, 2016). These elements serve as a foundation for developing memorable, engaging, and loyal customer experiences (Rasoolimanesh et al., 2021). An expanded review of the extant literature reveals factors that precede and influence the implementation of CEM, including organizational, technological, environmental, and factors relating to changing consumer behaviors and attitudes (Kandampully et al., 2018).

The development of customer experience management has been identified as being influenced by emerging technologies, such as digital platforms, smart devices, and immersive technologies. The importance of infrastructure and technological innovations such as VR, AI, and IoT in facilitating the development of intelligent, personalized, and interactive experiences, which, in turn, improve customer engagement and operational capabilities, has been demonstrated by Buhalis (2020) and Neuhofer et al. (2014). The process of integrating and adopting these technological advances is a crucial element to consider. In this regard, D. Wang et al. (2016) posit that the ubiquity of smartphone use and habitual engagement with digital technologies enhance decision-making processes and mitigate experiential barriers. Conversely, Xu et al. (2017) underscore the efficacy of gamification and persuasive technologies as instruments to augment user engagement. Other studies have consistently demonstrated that technologies are true enablers of customer experience management, showing their potential to transform tourist interactions with a particular focus on technology acceptance (Neuhofer et al., 2014) and barriers related to confidentiality and privacy (Zeng et al., 2020).

Customer expectations, needs, emotions, attitudes, and behaviors exert a profound influence on the development of customer experience management strategies. Authenticity and emotional spontaneity have been identified as key factors in the creation of sensual tourism experiences (Tung & Ritchie, 2011). Conversely, Rasoolimanesh et al. (2021) identify hedonism and engagement as predictors of satisfaction and intention to revisit. Concurrently, studies by Chen and Rahman (2018) and Sims (2009) demonstrate the growing role of authenticity and cultural immersion, particularly in the context of gastronomic tourism and tourist destinations. The universality of these antecedents corroborates the findings of several studies elucidating contingent nuances with regional values (Cohen, 1988; Prayag et al., 2017). However, Cohen (1988) presents a contradictory perspective, asserting that motivations may vary in their universality depending on expectations and cultural and demographic factors.

In the context of organizational and environmental factors, Buhalis (2020) underscored the significance of intelligent infrastructures in enabling the design of customer-centric services. Neuhofer et al. (2014) identified technological readiness and organizational capabilities as prerequisites. Furthermore, Ioannides and Gyimóthy (2020) have examined how the advent of the novel Coronavirus (COVID-19) accelerated the adoption of digital technologies and redefined the tourism sector, with the concomitant emergence of new business models to meet sustainability requirements and satisfy new market niches. It is evident that organizational and environmental factors play a significant role in determining CEM success, but this influence is dependent on technological adaptability and readiness. However, it is important to note that this overreliance on technology is at the expense of human contact (Prayag et al., 2017).

4.8.2. Outcomes of CEM

The consequences of customer experience management (CEM) in the tourism sector are diverse and multifaceted, including the direct and indirect results of CEM implementation, impacts on commercial and organizational performance, effects on customer satisfaction and loyalty, and financial and non-financial spin-offs. These elements collectively demonstrate the strategic and operational importance of CEM strategies.

In the context of customer-related consequences, studies by Gallarza and Saura (2006) and Pencarelli (2020) have underscored the pivotal role of satisfaction as an integral component of CEM consequences, thereby facilitating its impact on loyalty and positive word-of-mouth. For instance, J.-H. Kim (2018) has identified satisfaction as a mediator of behavioral intention. Jeong and Shin (2020) and Rasoolimanesh et al. (2021) have revealed that memorable tourism experiences (MTEs) have a considerable impact on behavioral intention in terms of revisit and word-of-mouth, particularly in the tourism context. This satisfaction is influenced by specific contextual elements, such as destination familiarity (H. Zhang et al., 2018) and service quality (Oh et al., 2007).

Regarding business performance, J.-H. Kim (2014) asserts that effective customer experience management enhances destination branding, competitive positioning, and revenue generation. Conversely, Gretzel et al. (2015b) emphasize the significance of co-creating value and enhancing customer retention. Zeng et al. (2020) elucidate the prospective benefits of AI and robotics adoption in tourism during the pandemic, emphasizing efficiency and safety.

In addition to its impact on customers and companies, CEM has a considerable environmental impact. Ioannides and Gyimóthy (2020) and Sims (2009) have demonstrated the positive impact of CEM on the environment, particularly in terms of reducing the carbon footprint associated with tourism and assisting the local economy. Conversely, Munar and Jacobsen’s (2014) study centered on social capital, emphasizing the indirect advantages of content sharing and interaction on social media concerning social cohesion and community engagement.

4.8.3. CEM Mediators

Customer experience management mediators facilitate the comprehension of the mechanisms through which causes influence consequences, how effects are transmitted, and the intermediate processes that mediate the cause–effect relationship.

Customer engagement plays a pivotal role in the nexus between the antecedents and consequences of CEM. According to Chen and Rahman (2018) and J.-H. Kim (2018), cultural engagement serves as a mediator between the memorable tourism experience and the intention to revisit, as well as word-of-mouth, satisfaction, and loyalty. Consequently, Gallarza and Saura’s (2006) seminal work posits that satisfaction, while being a consequence of CEM, functions as a pivotal mediator between perceived value and loyalty, in addition to perceived quality and the benefits received. This notion is further elaborated by Prayag et al. (2017), who underscore the notion that the emotional and cognitive alignment fostered by positive experiences can be significantly amplified by satisfaction.

4.8.4. CEM Moderators

Moderators influence the intensity and strength of relationships within the CEM framework, incorporating conditions that mitigate and reinforce effects, corresponding contextual factors, and control variables.

CEM relationships are moderated by factors related to the environment, technology, and customer characteristics (Gretzel et al., 2015b; Zeng et al., 2020). These factors encompass technology adoption levels and the presence of digital infrastructures, which have been identified as technological preparations that moderate CEM relationships (Gallarza & Saura, 2006). Additionally, Cohen posits that additional factors, including age, frequency of travel, technology fit, and other demographic characteristics of the tourist, can influence the variability of expectations of authenticity (Cohen, 1988).

4.9. Agenda for Future Research

A review of the extant literature reveals an understanding of the antecedents, consequences, mediators, and moderators of customer experience management. However, the literature also identifies several opportunities to provoke future debate. These opportunities include aspects related to technology and sustainability, as well as experiential and methodological aspects.

Tourism 4.0 represents a metamorphosis of the sector through the convergence of smart tourism and the advancement of new technologies. Future research endeavors must prioritize understanding how smart destinations can effectively integrate human and social technological resources to enhance the tourism experience (Pencarelli, 2020). To leverage the potential of smart destinations, it is imperative to adopt a collaborative approach that integrates marketing, big data, urban planning, and destination governance and management systems. The manner in which collaborative systems integrating service providers, tourists, and the local community enable experiences to be enriched in both physical and digital contexts warrants particular attention (Pencarelli, 2020).

Digital technologies have permeated all facets of the travel experience, including the initial stages of conception, planning, reservation, execution, and the dissemination of experiences. However, it is imperative to examine the mechanisms that ensure a harmonious balance between technological advancement and environmental sustainability (Pencarelli, 2020). This “high-tech” and “high-touch” balance underscores the significance of human interaction in the co-creation of value (Pencarelli, 2020). It is, therefore, imperative to explore solutions that enhance the quality of life and social value of tourists and residents while mitigating potential risks to authenticity (Pencarelli, 2020). Furthermore, social media and content creation have gained significant traction in the field of tourism research. Future research endeavors should explore the motivating factors behind different types of content on various platforms (Munar & Jacobsen, 2014). Factors influencing the willingness to share experiences on social networks, as well as lurker behavior and their influence on tourism dynamics, are also of interest (Munar & Jacobsen, 2014).

The study of memorable tourist experiences, as they relate to psychological and cognitive perspectives, necessitates in-depth investigation. Future research can explore how tourists evaluate their memorable experiences in relation to outcomes such as satisfaction and behavioral intention (e.g., intention to revisit and word-of-mouth recommendation) (Tung & Ritchie, 2011). Empirical validation is necessary to substantiate this relationship (Tung & Ritchie, 2011).

A novel research direction has emerged that focuses on the real-time examination of tourist emotions, employing physiological and technological methodologies (Prayag et al., 2017). This approach aims to enhance the decision-making process among tourists by leveraging insights into their perceptions and the interplay with their emotional experiences (Prayag et al., 2017). It is crucial to acknowledge the integration of cultural and geographical contexts to mitigate potential biases in the generalization of results. To address the disparity between the generalizability and depth of the results obtained, it is imperative to adopt a methodology based on a representative sample in terms of cultural, geographical, and demographic variety (Prayag et al., 2017; H. Zhang et al., 2018) as well as qualitative data (Tung & Ritchie, 2011).

Despite the comprehensive approach of the review, which encompasses the diverse domains of tourism, including hospitality, it is imperative to assess the applicability of the customer experience management framework across these various tourism subsectors (Hwang & Seo, 2016). A comprehensive sector analysis, which considers the particularities inherent to each subsector, is imperative to attain a more nuanced comprehension of the formation process, evaluation, and impact of each sector on the tourism experience. Indeed, the factors influencing the customer experience in the hotel industry may differ from those in transport or catering (Kandampully et al., 2018). A sector-based approach facilitates a rethinking of theoretical reflections and the relevance of the practical implications of customer experience management strategies for managers operating in distinct segments of the tourism ecosystem.

5. Conclusions

This study makes a significant contribution to the ever-evolving literature on customer experience management in the tourism sector, offering a meticulous and methodologically robust analysis of the field. A systematic analysis was conducted by examining a corpus of 3874 articles published on the Scopus database in accordance with the PRISMA guidelines. This analysis integrates bibliometric performance analysis and scientific mapping with grounded theory as a methodological approach to systematic qualitative data analysis. The objective of this conceptual integration is twofold: first, to elucidate the intellectual structure and thematic and conceptual evolution of the subject under study, and second, to deepen the analysis through a qualitative study.

The results of this research offer two notable observations. Firstly, the bibliometric analysis enabled the identification of prolific authors and institutions, as well as the collaborative networks that have significantly influenced the development of the field. Secondly, the study revealed a remarkable transition in thematic priorities. Initially, the focus was on satisfaction and service quality. However, the focus subsequently shifted to co-creation, authenticity, and the adoption of emerging technologies to enhance the experience. A qualitative content analysis was conducted to identify the main antecedents (e.g., technological, consumer, organizational, and environmental factors), consequences (e.g., customer, business, and operational outcomes), mediators (e.g., perceived value), and moderators (e.g., cultural and contextual factors) of CEM. These elements are integrated into a conceptual model that provides the structural foundations for the theoretical and empirical development of research on the subject.

However, as with all academic research, this study has certain limitations. The corpus analyzed comes exclusively from the SCOPUS database, which means that some works published in other scientific databases may not have been considered. Additionally, the dynamic nature of the concept of customer experience poses a challenge in establishing a unified and standardized definition, as researchers frequently employ varying terms and conceptual frameworks, complicating the comparison and integration of findings across studies.

The results of this study offer a foundation for future research in several areas. For example, the results could be used to explore specific aspects of the customer experience, such as sustainability, social responsibility, the impact of emerging technologies, and co-creation. Additionally, it would be beneficial to expand the scope of the subject through longitudinal studies or geographical, cultural, ideological, subsectorial, and ethnic comparisons. This approach would contribute to a more comprehensive understanding of the customer experience in diverse contexts.