The hotel industry is characterized by a wide range of services provided to customers. Companies within this sector are not homogeneous and generic strategies adopted indiscriminately by firms can generate different results according to the company’s context (Becker & Olsen, 1995). Hotels present a high complexity of their operations, involving several operations like accommodation, food, beverage, event management, and transportation that require a high degree of coordination and skilled staff. However, firms have high fixed costs and often face seasonal, fluctuate, and uncertain demand (Mullins, 1993).

Hotel companies are critical for the tourism industry as they provide a set of services needed to maintain tourist flow. Tourists are expected to be provided with accommodation, food, transportation, and a set of related services. The hotel industry is also relevant in supporting economic development due to its effect on job creation and supporting trade and commerce (UNWTO, 2024). For instance, in the European Union (EU), which is a major tourist destination with four member states among the top ten world destinations, 2.92 billion nights were spent in 2023, similar to values recorded before the COVID-19 pandemic, which contributes to the overall growth of the sector (Eurostat, 2024).

Demand fluctuations are among the major threats for hotel companies since they are substantially influenced by external and internal factors affecting the industry. Crises such as the United States (US) September 11, 2001, terrorist attack, the 2007-08 global financial crisis, (Cohen, 2012; Papatheodorou et al., 2010), or the COVID-19 pandemic (Gössling et al., 2021; Ioannides & Gyimóthy, 2020) showed the industry reliance on tourist demand.

Nevertheless, companies can address, at least partially, the effect on demand due to internal factors like the price level and service quality. A customer’s decision to stay in a hotel is influenced by several factors, including the accommodation price, purchasing power, which depends on economic cycles, price fairness (Heo & Lee, 2011), competition (Chattopadhyay & Mitra, 2019), hotels’ characteristics and proximity to attractions like beaches (Espinet et al., 2003), hotel features, customer experience, and competition (Sánchez-Pérez et al., 2019), location (Chan & Wong, 2006; Zhang et al., 2011), rating (Rigall-I-Torrent et al., 2011), and perceptions of service quality and value (Ye et al., 2012), among other aspects.

Service quality is mostly dependent on firms’ decision-making once they can manage how to position themselves. Service quality can be addressed from several dimensions or determinants of service quality including reliability, courtesy, communication, responsiveness, credibility, security, competence, knowledge of customer, physical evidence of service, courtesy, communication, and competence (Parasuraman et al., 1985). However, the way and the strength of how each determinant affects customer’s assessment of service quality varies, some being associated with dissatisfaction while others being satisfiers (Johnston, 1995). The former includes intangible aspects of service, like commitment, care, courtesy, friendliness, and attentiveness and the latter usually includes tangible or systemic aspects of service, like security, reliability, functionality, cleanliness, integrity, and aesthetics (Johnston, 1997). Likewise, service quality has been identified as an antecedent of customer satisfaction and as a source of purchasing intention (Cronin & Taylor, 1992), which is recognized as a major determinant of positive word-of-mouth, loyalty, and customer retention (Bearden & Teel, 1983). However, customers’ service expectation and perceived service also affect customer satisfaction (Grönroos, 1984) and customer needs vary, which means customer perceptions of service quality varies (Garvin, 1984).

Service quality is also highly related to a hotel’s guest experience. Among the underlying dimensions of guest experience in hotels, previous research found several similar aspects to determinants of customer satisfaction, including environment, accessibility, driving benefit and incentive (Knutson et al., 2009). Similarly, customer experience of quality involves factors such as emotional related experiences, staff-customer contact, customer-customer contact, learning, lifestyle, guest safety, and atmospherics, which are a strong predictor of client satisfaction, perceived value, and brand loyalty (Alnawas & Hemsley-Brown, 2019), warm and welcoming staff, comfortable rooms, location near tourist attractions, and food (Sthapit, 2019), physical environment and human interactions (Walls et al., 2011), ambiance, escapism, personalization, and convenience, all positively affecting positive word-of-mouth and customer retention (Shahid & Paul, 2022).

Consequently, hotel customer experience and service quality are highly related, showing significant evidence that service quality in hotels enhances customer satisfaction, by positively influencing customer experiential value (Kang et al., 2004; Oh, 1999; Wu & Liang, 2009). Following this reasoning and the perceived role of service quality in hotel customer experience and customer satisfaction (or dissatisfaction), this paper seeks to evaluate the existing literature on customer satisfaction and hotel customer experience, and how these themes interact within hotel industry, using bibliometric methods. To conduct the research, an approach based on a thematic analysis and an analysis of co-citations is employed. Previous research on these topics has been mostly focused on one of these themes, customer experience (Kim & So, 2022; Rehman et al., 2022) on the one hand, and the link between service quality and customer satisfaction (Shyju et al., 2023) on the other hand. Furthermore, while this study employs bibliometric methods incorporating thematic analysis and co-citation analysis, which allow for a comprehensive mapping of the intellectual structure of the research field, identifying key themes and the interrelations between service quality, customer experience, and satisfaction in the hotel industry, other studies have conducted systematic reviews focusing on customer satisfaction and sentiment analysis (Ameur et al., 2023; Castanha et al., 2023).

This paper is outlined as follows, the next section addresses the research methodology, section three presents the obtained results, and section four delves into the results discussion, implications, and main conclusions.

Bibliometric methods are useful to evaluate and understand the evolution of a research stream as portrayed in scientific literature (Hood & Wilson, 2001). This type of analysis seeks to assemble and interpret statistics regarding written communications, to uncover trends, and assess their development (Pritchard, 1969; Raisig, 1962). Therefore, it helps researchers in literature analysis to portray the evolution of the scientific field without analytical bias (Zupic & Čater, 2015) further depicting the research stream evolution. Bibliometric analyses have two main scopes: performance analysis, based on counting citations, and science mapping, seeking to represent the evolution of the research field (Cobo et al., 2011; Noyons et al., 1999).

This study conducts a thematic analysis and an analysis of co-citations. It seeks to understand how customer satisfaction and hotel customer experience research streams evolved in scientific literature. Data was obtained through a search on the Web of Science (WoS) database on March 11, 2024. Document type “article”, written in English language, containing the words “hotel” and “experience” and “satisfaction” within the document’s author keywords, keyword plus, title, or abstract, published in all available years until the end of 2023, were searched. The exact query was TS=(“hotel” AND (“experience” AND “satisfaction”)), “index date”: “1900-01-01” to “2024-03-11”, “Refined By”: “NOT Publication Years: 2024”, “Document Types: Article”, and “Languages: English”. Results returned 644 documents. The WoS database was chosen due to its relevance to the scientific community and reliability as a source of data. Furthermore, the use of a single database as a source of data enables the use of standardized data (Donthu et al., 2021).

The thematic approach uses a co-word analysis to assess the course and evolution of themes in published literature (He, 1999). This type of analysis is an automated content analysis technique to assess the corpus of texts and it is based on the co-occurrence of pairs of words, aggregated into networks, to study the evolution of the research field (Callon et al., 1983).

The co-citation analysis is a quantitative method that evaluates the frequency at which authors or documents are cited together. This method enables the classification of scientific literature into smaller thematic groups, including documents addressing similar and specific problems. According to this technique, two documents are considered co-cited when both are cited together by a third document, even in the absence of a direct citation between them (Marshakova-Shaikevich, 1973; Small, 1973). Data was computed using R Studio software (R Core Team, 2023) and results were extracted using the bibliometric package “bibliometrix” (Aria & Cuccurullo, 2017).

As mentioned in section 2, data was collected from the WoS database on March 11, 2024, and 644 articles were retrieved for analysis. Table 1 presents the descriptive statistics about data.

The thematic analysis is conducted through a co-word analysis. This method aggregates groups of words to uncover connections among themes. This approach enables the detection of clusters of interrelated terms that correspond to centres of interest in a research stream (Callon et al., 1991). These relevant areas are organized in diagrams structured around concepts of centrality and density (Callon et al., 1991; Cobo et al., 2011).

According to Cobo et al. (2011), thematic analysis involves the use of strategic maps to understand the dynamics of a research field. These maps aggregate the constructs of co-words according to concepts of density and centrality to visualise and assess the development of various research themes, according to four quadrants. Clusters included within the upper-right quadrant are labelled as “motor themes”, presenting high centrality and high density; clusters included within the lower-right quadrant are labelled as “basic themes” and show high centrality and low density; clusters included within the upper-left quadrant are labelled as “niche themes” and present low centrality, and high density; and clusters included in the lower-left quadrant are labelled as “emerging or declining themes”, presenting low centrality and low density.

To conduct the thematic analysis, data was split into three time slices, the first one representing themes resulting from the production output over the period 1999-2009, the second one representing the themes resulting from the analysis of documents published over the period 2010-2019, and the third one, representing themes extracted from documents published over the period 2020-2023. Documents were split to analyse periods of one decade, although the first document published fulfilling the search criteria was published in 1999. Hence, these papers were included within the first period. Additionally, the third period (2020-2023) sought to evaluate if and how the COVID-19 pandemic affected the research field.

Table 2 shows themes extracted from the first analysed period based on a set of 16 articles published over the above-mentioned period (1999-2009).

Results concerning the first analysed period (Table 2) show three clusters: attitudes, experience, and model. Analysing the frequency of words within each cluster enables one to identify patterns and relationships that may reveal insights about the research themes.

The experience cluster occupies on a central position, suggesting it is well-established within the literature. This cluster includes words like experience, dimensions, and physical surroundings, suggesting that, according to literature, the nature of customer experience in hotels was focused on the service provided and also on the tangible aspects such as the physical environment. This evidence may also suggest that customer experience encompasses various factors that contribute to build a decision regarding a guest’s stay in a hotel. Cluster model is identified as a motor theme. This cluster presents a strong interconnectivity with further themes. The term model occurs more frequently, followed by a set of words including service, customer perceptions, quality, customer satisfaction, impact, market orientation, and satisfaction. These terms may imply an emphasis on understanding and predicting customer satisfaction and service quality in hotels and a focus on how hotels align customer and market needs to improve customer satisfaction, as showed by the emphasis on market orientation. The cluster attitudes is labelled as an emerging or declining theme and is represented by only one word, which may indicate that it can only be assessed over a longitudinal perspective within the scope of the following period.

The second analysed period refers to the 2010-2019 time slice and includes 13 clusters as shown in Table 3. Over this period, 256 articles fulfilling the search criteria were published.

Results (Table 3) show 13 clusters involving various aspects of the hotel industry customer experience and customer satisfaction, qualified as basic themes or niche themes and one centred theme.

The quadrant qualified as basic themes includes six clusters. The cluster job satisfaction includes terms such as mediating role, turnover, motivation, and organizational commitment, which show a close relationship with factors affecting employee’s satisfaction. Cluster performance includes terms like management, commitment, and engagement, suggesting a focus on organizational performance, the role of employee involvement and management strategies. Additionally, this cluster also includes terms such as green hotel and corporate social responsibility, which may reflect the increasing relevance of sustainable practices and their impact on hotel business performance. Cluster intentions includes terms such as online, customers, and moderating role. On the one hand, results point towards customer intentions research. On the other hand, terms like recovery and responses may indicate a growing interest in understanding how companies respond to service failure and manage customer intentions. Cluster satisfaction includes the largest number of terms. Among them, experience, impact, quality, and loyalty appear as the most relevant, suggesting a wide range of factors affecting and contributing to customer satisfaction, from physical environments to service quality. Cluster behavioural intentions includes terms such as co-creation, brand equity, and utilitarian, reflecting factors that affect customer behavioural intentions. It further suggests an interest on the effects of brand perception and the usefulness aspects of hospitality services. Word-of-mouth (WOM) represents the remaining cluster classified as basic theme. This theme encompasses terms like online reviews and user-generated content, emphasising the relevance of eWOM and its impact on the hotel industry.

The quadrant labelled as niche themes includes 6 clusters. Among them, cluster resources include terms such as burnout, conflict, and conservation, which suggests a focus on employee well-being and human resources management in the hotel industry on the one hand, and on the effect of the industry on the environment on the other hand. Cluster strategies involve terms like behavioural, conceptual-model, and service innovation, suggesting an emphasis on innovative approaches to providing services in the hotel industry and on the methodological approaches of research. Cluster access involves terms such as attitudes, cost, and travel, implying a focus on how cost structures affect the hotel industry and how companies positioning affect guest attitude. Cluster arousal includes terms like e-loyalty, personality, and music. Together, these themes seem to address emotional and psychological aspects of customer experience and their implications for customer loyalty. Cluster value dimensions seem to emphasise the experiential value of hotels and how it affects sales and customer behaviour. Cluster perceived justice includes terms such as consumer responses and moderating. These themes seem to imply, on the one hand, customers’ perceptions of fairness of service and, the other hand, the methodological aspects of the scientific research output, particularly the moderating effects.

The time frame 2010-2019 also included a centred theme, labelled as services, involving terms like big data, purchase, and sharing economy, which may emphasize a focus on the evolving nature of the hotel industry, over a period of technological advances and changing consumer behaviours.

The third analysed timeframe represents the period 2020-2023, encompassing a period of high turbulence for the hotel industry due to the COVID-19 pandemic. This period was also characterized by a higher scientific research output, comprising a total of 354 articles included in the database, addressing hotel customer experience and customer satisfaction. Table 4 presents themes resulting from the thematic analysis of the 2020-2023 period.

Results from the third analysed period (Table 4) show a consolidation of the research stream themes in five clusters, with clusters performance and satisfaction qualified as basic themes. Cluster performance involves a wide range of terms, including antecedents, consequences, and mediating role. It further includes resources and gender, suggesting an emphasis on the measurement of the effects among variables involving customer satisfaction and hotel customer experience. Additionally, these terms may also suggest an emphasis on methodological aspects regarding the research output. Furthermore, the cluster satisfaction also emerged as a basic theme. This theme is similar to customer satisfaction (a centred theme), although it represents a broader cluster, including terms like experience, tourism, hospitality, loyalty, perceptions, behaviour, perceived value, emotions, determinants, physical-environment, and price, suggesting that may have been given an emphasis on the determinants of customer satisfaction and its impact on loyalty and customer behaviour.

Clusters job-satisfaction and e-commerce represent niche themes. The former signals a focus on the well-being of employees within the industry, as indicated by terms such as work engagement, stress, burnout, and family conflict. Furthermore, perceived organizational support and workplace highlight organizational factors that contribute to job satisfaction. Cluster e-commerce indicates a concentrated focus. This cluster includes terms such as big data, purchase intention, and business intelligence, reflecting an interest in how e-commerce platforms are leveraged in the hotel and tourism industries, and how analytics are employed to understand and predict consumer purchasing behaviours.

Cluster customer satisfaction is represented as a centred theme and includes terms such as service quality, WOM, online reviews, and social media, which are prominent. However, it also includes sharing economy and big data, indicating an emphasis on online platforms and data analytics to understand customer satisfaction and demand predictions. Additionally, the relevance of value co-creation and sentiment analysis, highlights an emphasis in the literature on qualitative aspects of customer feedback and their implications for service improvement.

Regarding the co-citation network analysis, results were computed, and three clusters were identified. This analysis helps to identify the knowledge base of the research field and its intellectual structure, the latter involving the analysis of to the set of articles cited by the current research, disciplinary composition, influential research topics, and the pattern of interrelationships within the examined scientific domain. To compute data and provide a broader and clean representation of the intellectual structure the co-citation analysis was limited to a cut off of 50 documents, as suggested by Zupic and Čater (2015), which were included in the analysis. Three clusters emerged from the analysis.

Figure 1 represents the cluster 1 co-citation network.

Following the obtained results from cluster 1, documents were analysed to evaluate the scope and nature of each one. Table 5 represents the documents included in the cluster 1 co-citation network.

Based on the documents in cluster 1 (Table 5) , there is a clear focus on understanding how online reviews, as a form of eWOM, influence customers’ decisions (Litvin et al., 2008; Serra Cantallops & Salvi, 2014; Sparks & Browning, 2011; Vermeulen & Seegers, 2009; Xu & Li, 2016; Zhou et al., 2014). These papers underscore the critical role of eWOM in shaping potential customers’ decisions, trust, and intentions to book, and highlight the transition from traditional WOM to eWOM. Furthermore, the consensus is that online reviews are a critical source of information for consumers in their purchasing decisions, in industries where products are intangible and experiences cannot be evaluated beforehand. These papers also suggest that both positive and negative reviews significantly influence customer satisfaction and the decision making process (Guo et al., 2017)

The cluster 2 co-citation network is represented in Figure 2.

Table 6 presents the list of documents included in the cluster 2 co-citation network, showing the document reference and main subject.

Based on documents included in cluster 2 (Table 6), several main interconnected ideas are highlighted: service quality and satisfaction measurement (Cronin et al., 2000; Cronin & Taylor, 1992; Parasuraman et al., 1988, 1985; Ryu et al., 2012); methodological approaches, including the use of SEM and statistical analysis in customer satisfaction, service quality, and customer hotel experience research (Anderson & Gerbing, 1988; Bagozzi & Yi, 1988; Chin, 1998; Fornell & Larker, 1981; Hair et al., 2011, 2010; Henseler et al., 2015; Kline, 2005), moderation and mediation effects (Baron & Kenny, 1986), and method bias (Podsakoff et al., 2003); physical environment’s impact on hotel customer experience (Bitner, 1990, 1992; Mehrabian & Russell, 1974; Wu & Liang, 2009); customer behaviour and behavioural intentions and their relationship with satisfaction, loyalty, and experiences on customer behaviour (Barsky & Nash, 2002; Brakus et al., 2009; Han et al., 2011; Holbrook & Hirschman, 1982; Kandampully & Suhartanto, 2000; Mano & Oliver, 1993; Oh et al., 2007; Oliver, 1980, 1999; Pine & Gilmore, 1998; Schmitt, 1999; Zeithaml et al., 1996); experiential marketing and experience economy (Andrew Walls Fevzi Okumus & Kwun, 2011; Chen & Chen, 2010; Hosany & Witham, 2010; Otto & Ritchie, 1996).

Concerning cluster 3, Figure 3 presents the results and shows a cluster including solely two documents.

Table 7 identifies the documents references and the main subject included in the cluster 3 co-citation network.

Cluster 3 (Table 7) is represented by studies involving determinants of guest satisfaction, how they relate to customers behaviour and are affected by hotel’s traits (Choi & Chu, 2001), including budget hotels (Ren et al., 2016).

NECE and this work are supported by FCT - Fundação para a Ciência e Tecnologia, I.P. by project reference UIDB/04630/2020 and DOI identifier 10.54499/UIDP/04630/2020.

Conceptualization, Carlos Sampaio and Mónica Régio; Methodology, Carlos Sampaio; Data Collection, Carlos Sampaio; Data Analysis, Carlos Sampaio and Mónica Régio; Writing - Original Draft Preparation, Carlos Sampaio and Mónica Régio; Writing - Review and Editing, Carlos Sampaio and Mónica Régio; Supervision, Carlos Sampaio and Mónica Régio.