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Article

Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts

by
Yinai Zhong
1,
Angellie Williady
2,
Narariya Dita Handani
3,4 and
Hak-Seon Kim
5,*
1
Department of Global Hospitality Management, Kyungsung University, Busan 48434, Republic of Korea
2
Department of Global Business, Kyungsung University, Busan 48434, Republic of Korea
3
School of Global Studies, Kyungsung University, Busan 48434, Republic of Korea
4
Department of Management, Faculty of Economics and Business, Universitas Wijaya Kusuma Surabaya, Surabaya 60225, Indonesia
5
School of Hospitality & Tourism Management, Kyungsung University, Busan 48434, Republic of Korea
*
Author to whom correspondence should be addressed.
Tour. Hosp. 2024, 5(4), 996-1011; https://doi.org/10.3390/tourhosp5040056
Submission received: 10 September 2024 / Revised: 11 October 2024 / Accepted: 17 October 2024 / Published: 22 October 2024
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Abstract

:
This research aims to explore the relationship between customer satisfaction and various extracted factors at dive resorts in the Red Sea, Egypt, utilizing a hybrid methodology of descriptive and diagnostic analytics applied to online review data. Employing techniques such as KH coder for text analysis, exploratory factor analysis (EFA), and linear regression, this study systematically identifies key elements that influence customer satisfaction. Findings reveal that activities related to diving and marine life markedly enhance guest satisfaction, underscoring the critical role these aspects play in the overall appeal of Egyptian coastal tourism. Conversely, areas such as dining and amenities were identified as needing improvement. The originality of this study lies in its application of big data analytics to dissect and understand customer feedback in a sector-specific context, providing strategic insights for the sustainable advancement of coastal tourism in Egypt. By focusing on dive resorts, this research highlights their integral role in coastal tourism and offers a model for leveraging online customer reviews to enhance service quality and promote sustainable practices within the tourism industry, contributing to the overall growth and sustainability of coastal tourism.

1. Introduction

Marine diving tourism, a global phenomenon, is a multi-billion-USD industry with over 6600 diving centers and resorts in 186 countries or territories, as reported by the Professional Association of Diving Instructors [1]. As a major component of the ocean economy, it attracts between 8.9 and 13.6 million diving tourists globally each year, supporting up to 124,000 jobs [2]. This sector has grown rapidly, mirroring broader trends in coastal tourism. However, the high market demand and rapid development have created challenges in maintaining service quality.
The Egyptian Red Sea, renowned for its rich natural and cultural resources, is a premier diving destination that significantly contributes to Egypt’s tourism strategy [3]. Seventy-five percent of Egypt’s tourism activities are leisure-oriented, focusing on the Sinai Peninsula and the Red Sea, generating substantial revenue from its approximately 1386 km coastline [4]. The region’s vibrant coral reefs attract millions of tourists, leading to significant ‘blue tourism’ development since the early 1990s, including a surge in hotels and accommodation facilities. The hospitality industry in Egypt has undergone significant transformation, particularly in the context of seaside-oriented tourism. Major coastal cities like Sharm El Sheikh, Hurghada, and Marsa Alam collectively account for over 110,000 rooms, nearly 60% of the country’s total room capacity [5].
Despite the pandemic, Egypt’s tourism sector continues to grow and show resilience. In 2023, Egypt’s tourism revenues grew by 8% year-on-year to USD 13.2 billion, with tourist arrivals increasing by 27.4% to 14.91 million [6]. Tourists spent an average of USD 93 per day and stayed for 9.5 nights. Egypt expects to attract 18 million tourists in 2024. The country also aims to boost annual tourism revenues from USD 12 billion to USD 30 billion over the next three years [7]. The Egyptian Red Sea coast, as a major tourist destination, attracts millions of tourists annually, generating billions of USD in foreign exchange and creating numerous job opportunities [8]. However, the rapid expansion of coastal tourism has led to challenges such as environmental degradation, coral reef damage, and coastal pollution, threatening sustainability [9]. Overcrowding and resource strain complicate destination management [5]. To enhance coastal tourism destination management, dive resorts, as a crucial part of coastal tourism, play a key role in promoting sustainable development by improving service quality and customer satisfaction.
Digitization has made social media and internet search engines key sources of travel information, with about half of prospective tourists using social media to plan their vacations [10]. Big data analytics in hospitality facilitates extensive review analysis, providing insights into customer preferences and satisfaction to inform sustainable strategic decisions [11].
While many studies link customer ratings to satisfaction in hotels [11,12,13], specific research on coastal hotels, particularly dive resorts, is limited. Given the multifaceted nature of coastal tourism, focusing on dive resorts is crucial to enhance understanding and improve customer satisfaction. Existing research on coastal tourism primarily addresses environmentally sustainable tourism [14,15], climate change [16,17], and market segmentation [18,19]. There is a significant gap in research on how dive resorts enhance customer satisfaction and achieve sustainable development in coastal tourism, highlighting the necessity for in-depth analysis and empirical studies in this field.
Therefore, this study aims to investigate the relationship between the extracted factors and customer satisfaction at dive resorts through a combination of descriptive and diagnostic analytics applied to online review data. This research provides valuable insights for the sustainable development of Egypt’s Red Sea coastal tourism. Specifically, this study employs KH coder for text analysis and exploratory factor analysis (EFA), followed by linear regression analysis to determine the significant factors affecting customer satisfaction. The findings will enable dive resort operators to identify key attributes that can enhance customer satisfaction, improve operational efficiency, inform marketing strategies, and contribute to the success of coastal tourism in a highly competitive tourism industry. In conclusion, this study attempts to address the following research questions by exploring knowledge and insights from online customer reviews of dive resorts:
  • What key terms in online reviews best capture customers’ experiences at dive resorts, and what are the connections between these terms?
  • Which underlying factors influencing customer experiences at dive resorts can be identified through online reviews?
  • How do the identified factors impact overall customer satisfaction at dive resorts, and to what extent does each factor contribute?

2. Theoretical Background

2.1. The Definition and Relationship of Coastal Tourism and Dive Resorts

Coastal tourism, as defined by the United Nations World Tourism Organization (UNWTO), includes a variety of land-based activities such as swimming, surfing, sunbathing, and other leisure, recreation, and sports activities that take place along the shores of seas, lakes, or rivers. The essential element is the proximity to the coastline, which supports the development of related services and facilities like hotels, restaurants, and marinas [20]. This diverse array of recreation and leisure activities attracts tourists to coastal areas, fostering economic growth and the development of local infrastructure [21].
Dive resorts are specialized businesses that integrate accommodation with diving activities, providing scuba diving services for both certified and non-certified divers through associations such as SDI (Scuba Diving International), SSI (Scuba Schools International), and PADI [22]. Unlike conventional accommodation, dive resorts feature on-site dive centers where guests can rent equipment, arrange boat trips to dive sites, and enroll in scuba diving courses conducted by trained staff or instructors [23]. In addition to diving services, these resorts offer a range of amenities and leisure activities, such as swimming pools, spas, beach access, restaurants, bars, and organized social events [22]. Dive resorts serve as central hubs for diving enthusiasts, providing comprehensive services to ensure a comfortable and immersive holiday experience.
Dive resorts significantly contribute to facilitating coastal tourism. The relationship between coastal tourism and dive resorts is symbiotic. Coastal tourism provides the overarching framework within which dive resorts operate, as a thriving coastal tourism industry creates a conducive environment for their success. Dive resorts, in turn, enhance the appeal of coastal destinations by offering unique underwater experiences that attract specific interest groups, thereby driving infrastructure development and promoting environmental conservation efforts [24]. This mutual enhancement is essential for the sustainable growth of both sectors. Addressing environmental challenges such as degradation and pollution through sustainable practices is crucial to ensuring the long-term viability of coastal tourism and dive resorts [25]. Therefore, comprehensive research focused on the specific impacts and potential of dive resorts within coastal tourism is necessary for understanding and maximizing their benefits to local economies and communities.

2.2. The Definition and Relationship of Customer Satisfaction and Online Reviews

Satisfaction depends on individual expectations and subjective perceptions of service performance, with consumers evaluating service providers based on personal experiences [26,27]. Research indicates that satisfied customers are more likely to become loyal patrons, with a crucial impact on profitability and continuity of the organizations [28]. Furthermore, positive word-of-mouth recommendations from satisfied customers can attract new clients and enhance brand reputation [29].
Customer satisfaction in tourism is influenced by various factors, such as the quality of products or services [30] and perceived value [31]. High-quality offerings and a sense of fair value for money are fundamental to increasing customer satisfaction. Additionally, interactions with employees play a significant role in shaping customer experiences [32]. Other factors such as convenience, reliability, and responsiveness to complaints or queries also impact customer satisfaction [33].
In tourism, online reviews and sentiments play a key role in shaping tourists’ decisions as people increasingly rely on the Internet for information [34]. These reviews, which typically consist of written comments or ratings, are available on websites, social media, and online marketplaces [12]. Online reviews can significantly influence potential buyers’ perceptions of brands and their purchase decisions. Positive reviews can enhance a brand’s reputation and credibility [35], while negative reviews may deter potential customers from engaging with a particular product or service [36].
In a digitally driven world, customer satisfaction and online reviews have become crucial factors in determining the success of a business. In the tourism industry, these reviews serve as a medium for sharing experiences and recommending destinations [37]. The proliferation of social media and online review sites has empowered customers, significantly influencing company reputations through feedback.
The relationship between customer satisfaction and online reviews is multifaceted. Ratings offer a quick quantitative measure of satisfaction, while reviews provide qualitative insights into specific experiences. Together, they offer a fuller picture by quantifying satisfaction levels and explaining the reasons behind them, helping businesses identify areas of strength and improvement. In many studies, review ratings are directly regarded as indicators of customer satisfaction [38,39].

2.3. What Is Big Data Analysis?

Digitalization has accelerated the demand for ecosystems in the tourism and hospitality industries to restructure business models in a data-driven direction to foster value creation and innovation [40]. The amount of data generated continues to grow exponentially, making it a critical field in recent years [41]. Big data analysis refers to extracting valuable insights from large and complex datasets [42]. Tourism and hospitality researchers are increasingly adopting big data approaches to retrieving, collecting, analyzing, reporting, and visualizing data [43]. Big Data encompasses huge datasets that challenge traditional data processing tools in management, processing, and analysis. These datasets originate from numerous sources and are distinguished by three key characteristics: volume (the sheer amount of data), velocity (the rapid rate at which data are generated and processed), and variety (the range of data types, including structured, semi-structured, and unstructured data) [42]. It involves various techniques such as data mining, machine learning, and statistical modeling [44].
In the context of the hotel industry, it involves analyzing vast amounts of customer-related information collected through various sources such as online bookings, social media interactions, loyalty programs, and customer feedback. By analyzing customer preferences and behavior patterns derived from big data analytics, hotels can offer personalized services tailored to individual customers’ needs [13].
Based on previous studies, big data analytics encompasses four main types: descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics focuses on analyzing past data to understand what has happened. Diagnostic analytics digs deeper into data to determine the causes of past events. Predictive analytics employs statistical models and machine learning to forecast future outcomes. Lastly, prescriptive analytics suggests actions and advises on potential outcomes to achieve specific objectives. These analytic types offer a comprehensive approach to data analysis, from understanding past events to planning future actions [45,46].

3. Methodology

3.1. Study Design

The research process was designed in two steps: data collection and data analysis, as shown in Figure 1. The purpose of the analysis was to examine the relationship between the extracted factors and customer satisfaction of dive resorts by analyzing the online review data.

3.2. Data Collection

This research is based on an exploratory study of online reviews from nine of the best Red Sea dive resorts by PADI recommended in 2022 [47]. The nine dive resorts are Reef Oasis Blue Bay Resort, Umbi Sharks Bay Diving Village, Reef Oasis Beach Resort, Maritim Jolie Ville Resort & Casino Sharm El Sheikh, Cataract Layalina Resort, Desert Rose Resort, Marsa Shagra Village, Abu Dabbi Lodge, and Hilton Marsa Alam Nubian Resort.
The online reviews were collected from Google Maps using Outscraper 2024, an online API data mining software, as used in previous research [48]. The collected data included multilingual online review texts, numerical ratings, and additional information such as user names, upload times, emotion icons, and photographs. Due to the pandemic, there has been an increasing interest in new activities and attractions, leading to a rise in the popularity of various leisure activities [49]. With that, this study collected a total of 18,428 online reviews from November 2020 to November 2023, which were posted by customers within the last three years.
For data pre-processing, from a total of 18,428 reviews, around 25% of reviews without any text were removed, and the remaining 75% of the reviews, including review text and ratings, were retained for analysis. These reviews removed irrelevant information such as Google ID, review link, review ID, author ID, author image, and other non-essential elements. The language of each review was identified using automatic language detection tools. Non-English reviews were translated into English using translation services to transform unstructured data into a structured format. KH Coder, a text-mining tool, was then used to eliminate stop words for more meaningful results. Consequently, after data pre-processing, 13,905 online textual reviews were analyzed.
Copyright and data privacy requirements: As per the relevant local legislation in South Korea, ethical approval and informed consent were not required for this study. All necessary steps were taken to ensure compliance with copyright and privacy requirements. The online reviews were collected from Google Maps, and we followed the guidelines of Google Maps. The data utilized in this research fully adhered to the applicable legal and ethical standards, and no personally identifiable information was disclosed.

3.3. Data Analysis

First, the descriptive analytics process in this study utilized KH Coder to extract the top 100 frequent words, group similar appearance patterns together, and visualize the relations between these words. KH Coder 3.0 is a software designed for organizing, analyzing, and understanding digitized text data through computer coding and quantitative analysis techniques [50]. Co-occurrence network analysis is employed to explore and visualize the relationships between entities within a dataset based on their simultaneous occurrence in the same context, such as a sentence, paragraph, or document. A co-occurrence network was created with the review ratings, and sentiment groups are visualized to identify distinctive words in each group [51].
Second, for the process of diagnostic analytics, exploratory factor analysis (EFA) and linear regression analysis were carried out using SPSS 26.0. EFA is a data reduction technique used to simplify complex customer review data and to understand the variables contributing to overall customer satisfaction [52]. Linear regression indicates the strength and direction of the relationship between each independent variable and overall satisfaction scores [11]. The analysis helps to understand which factors from the online reviews significantly contribute to or influence customer satisfaction.

4. Results

4.1. Descriptive Analytics

The data collection includes review ratings and review text for the analysis. Ratings from Google Maps serve as a crucial metric for gauging customer satisfaction. Each review is assigned a score on a scale from ‘1’ to ‘5’, with ‘1’ indicating the least satisfied reviewer and ‘5’ representing the most satisfied reviewer [53,54]. This numerical representation provides a straightforward method to assess and quantify overall satisfaction levels based on customer feedback.
The distribution of the star ratings, which follows a J-shape, is shown in Figure 2. The review rating frequency indicates that the dive resort has received predominantly positive feedback. The average satisfaction score is 4.6 out of 5, with the majority of customers (79.3%) awarding a 5-star rating. Negative reviews are minimal, with only 2.8% and 1.6% of customers giving 1-star and 2-star ratings, respectively. Ratings of 3 stars and 4 stars are also relatively low, indicating that while most customers are highly satisfied, a few have experienced minor issues. This distribution suggests high overall customer satisfaction and implies that the resorts are likely to maintain a strong positive reputation. However, it is important to pay attention to the lower ratings to identify potential areas for improvement.

4.1.1. Word Frequency Analysis

The analysis of the top 100 frequent words is laid out as shown in Table 1. The word ‘hotel’ emerges as the most frequently mentioned, with 9049 occurrences, and ‘resort’ (1316) indicates that accommodation is a principal factor in customers’ experiences. This indicates that resorts serve as a foundation for the coastal tourism experience. Substantial mentions of ‘room’ (4267) and ‘beach’ (3785) suggest that the quality of lodging and proximity to the beach are paramount to coastal tourism customers. Dining (‘food’, ‘restaurant’) and customer service (‘staff’, ‘service’, ‘friendly’) are crucial to customer experiences.
Notably, ‘diving’ (1257), the main activity associated with the Red Sea coastal tourism offering, ranks highly in frequency, emphasizing its significance in the Red Sea coastal tourism and reaffirming the resorts’ importance as a dive-related destination. The mention of ‘sea’, ‘reef’, ‘water’, ‘fish’, and ‘coral’ underscores the importance of the marine environment to the customers’ overall coastal tourism experience. Words such as ‘animation’, ‘entertainment’, ‘show’, and ‘activity’ reflect the leisure activities offered by the resorts and their significance in customer satisfaction. While most of the frequent words are positive, terms such as ‘problem’, ‘bad’, ‘old’, and ‘small’ highlight areas of customer concern, suggesting room for improvement in certain aspects of the resort experience.

4.1.2. Co-Occurrence Network Analysis

The co-occurrence network derived from the analysis of online reviews, depicted in Figure 3, illustrates the multifaceted and interconnected nature of the tourist experience in coastal tourism. Key themes identified include ‘hotel’, ‘beach’, ‘room’, ‘food’, ‘staff’, ‘service’, and ‘clean’. These terms form central nodes with the highest frequency and co-occurrence with other terms, highlighting their importance to many customers.
This network shows that coastal tourism is holistic, with various interlinked elements contributing to tourists’ satisfaction. Terms like ‘coral’, ‘reef’, ‘sea’, ‘fish’, and ‘diving’ underscore the significance of marine life and diving activities in the Red Sea region. These elements work synergistically with other aspects to enhance the coastal tourism experience.
Positive words like ‘beautiful’, ‘excellent’, and ‘friendly’ highlight the crucial role of the natural environment and quality customer service. Additionally, terms related to meals, such as ‘breakfast’, ‘lunch’, ‘dinner’, ‘restaurant’, and ‘food’, emphasize the importance of food services in the overall resort experience.
The analysis reveals that every aspect, from accommodation and food to the marine environment and customer service, is interconnected and indispensable. Each element supports the others, creating a comprehensive and satisfying coastal tourism experience. This holistic approach ensures that any weakness in one area could impact overall satisfaction, highlighting the need for an integrated and high-quality offering across all facets of the tourist experience.

4.1.3. Co-Occurrence Network Analysis with Rating

The co-occurrence rating figure, which can be found in Figure 4, shows that the Red Sea coastal tourism reviews indicate a diverse range of experiences, with ratings from 1 (most disappointed) to 5 (most satisfied). The data point towards a polarity in customer experiences, with significant words associated with high and low ratings.
The highest satisfaction ratings (5) co-occur with words like ‘wonderful’, ‘super’, ‘best’, ‘delicious’, ‘excellent’, ‘beautiful’, ‘friendly’, ‘great’, ‘amazing’, and ‘vacation’, alongside central coastal tourism-related terms such as ‘resort’, ‘diving’, ‘reef’, ‘beach’, ‘sea’, and ‘hotel’. The second-highest satisfaction rating (4) is associated with ‘good’, ‘service’, ‘place’, ‘food’, and ‘staff’, indicating that these aspects are satisfactory to customers but may not reach the highest levels of acclaim.
Neutral experiences (3) show with words like ‘bar’, ‘restaurant’, and ‘room’, where a customer feels neither particularly satisfied nor dissatisfied. Lower satisfaction levels (2) include words like ‘money’ and ‘poor’, suggesting issues with value for money or unmet expectations. The lowest rating (1) is associated with highly negative terms such as ‘terrible’, ‘dirty’, ‘bad’, ‘old’, and ‘reception’, indicating areas of critical concern for some customers.

4.2. Diagnostic Analytics

4.2.1. Exploratory Factor Analysis

Exploratory factor analysis (EFA) was conducted to explore the underlying structure among the keywords extracted from online reviews of a dive resort. The objective was to reduce the dimensionality of the dataset by grouping variables into factors based on their correlations [55]. Initially, 34 keywords extracted from word frequency analysis and co-occurrence network analysis were utilized for conducting EFA using SPSS 26.0. The criteria for inclusion in the final model were factor loadings of 0.4 and above with eigenvalues greater than 1.0, ensuring that each factor significantly contributes to explaining variance within the data. Any factors that loaded into two variables concomitantly were removed to maintain clear factor interpretation.
The results, as presented in Table 2, show the Kaiser–Meyer–Olkin (KMO) measure stands at 0.807, surpassing the commonly recommended threshold of 0.6, corroborating the adequacy of the data for factor analysis [56]. Bartlett’s test of sphericity confirms the intercorrelations within the dataset with a chi-square value of 39,292.512 and a significance level below 0.001, indicating that the variables have enough in common to justify the use of EFA. This provides evidence against the data being an identity matrix and supports its suitability for the applied factor analysis.
Finally, based on the EFA results, nine words were eliminated since they did not pass the factor loading standard of 0.4. As a result, 25 words from the seven factors accounting for 44.528% of all variances were used as independent variables to derive key factors reflecting customer experience. This study measured reliability through the Cronbach’s alpha value. Cronbach’s alpha was derived from SPSS to assess the reliability of the factors derived from the online reviews. This value indicates the internal consistency of the items within each factor, demonstrating that the factors are reliable and meet the accepted criteria for reliability analysis. The Cronbach’s alpha value in this study shows 0.730, greater than 0.7, indicating good internal consistency [57].
The factors identified encompass various aspects of the resort experience, including ‘Resort’, ‘Diving’, ‘Amenities’, ‘Outdoor Space’, ‘Entertainment’, ‘Dining’, and ‘Service’.

4.2.2. Linear Regression Analysis

Following the EFA, seven factors were used as independent variables in a linear regression model to assess their impact on customer rating, also called ‘customer satisfaction’ in this study. The results of linear regression analysis are shown in Table 3. Various factors were assessed for their impact on overall customer satisfaction ratings for a resort, with the model indicating a baseline satisfaction rating of 4.6.
Five factors were found to be statistically significant in satisfaction. However, only one factor showed a positive effect on satisfaction. The diving (β = 0.026, p < 0.05) factor indicated that diving experiences enhance customer satisfaction. The other four factors, including resort (β = −0.033, p < 0.001), amenities (β = −0.089, p < 0.001), outdoor space (β = −0.019, p < 0.05), and dining (β = −0.111, p < 0.001) factors, had negative effects on satisfaction, suggesting that aspects within these categories might detract from customers’ satisfaction levels. Particularly, dining and amenities had a significant negative impact, hinting at the need for improvements in the resort’s breakfast, dinner, and swimming pool aspects to boost satisfaction. In addition, entertainment (β = 0.016, p = 0.064) and service factor (β = 0.013, p = 0.126) were not statistically significant, although they had a positive coefficient.
The results highlight the complexity of customer satisfaction and guide targeted improvement strategies, identifying key areas for further investigation. The regression equation based on standardized β is
(CS = 4.637 − 0.033 F1 + 0.026 F2 − 0.089 F3 − 0.019 F4 − 0.111 F6)

5. Discussion

In the digital era, big data revolutionizes the coastal tourism industry by analyzing online reviews to understand customer behavior and preferences, improving operational efficiency, customer satisfaction, and competitive advantage, leading to loyalty and growth. This study applies analytics to online customer reviews, utilizing descriptive and diagnostic analytics to explore the key factors that reflect the customer experience at dive resorts and their relationship with customer satisfaction.
In the descriptive analytics phase using KH Coder, frequently occurring terms like ‘hotel’, ‘room’, ‘food’, ‘staff’, and ‘service’ in textual reviews were analyzed to visualize their connections and impact on ratings. This analysis confirmed previous findings, highlighting their importance on customer experiences [13,58]. The data reflect that dive resorts in the Red Sea are not merely places of stay but pivotal to the holistic coastal tourism experience. The frequent occurrence of ‘diving’ along with ‘reef’, ‘sea’, ‘water’, and ‘fish’ underscores the centrality of diving activities to the coastal tourism offering in the Red Sea. This aligns with the study of Spalding et al. [59], which emphasized coral reefs as major draws for dive tourists, suggesting that the emphasis on marine life can enhance marketing and conservation efforts.
Consistent with previous studies [60,61], positive descriptors such as ‘good’, ‘great’, ‘delicious’, ‘excellent’, and ‘friendly’ show customer satisfaction. Conversely, terms like ‘dirty’, ‘terrible’, ‘worst’, ‘same’, ‘old’, ‘bad’, and ‘poor’ reflect areas where expectations are not met [62]. This dualism is a reminder of the constant need for renovation and attention to detail in service provision and a theme echoed in the study of Jeong and Oh [63], who advocate for continuous quality improvement in the hospitality industry to improve satisfaction. Big data analysis of customer reviews can effectively identify specific areas that businesses need to focus on or improve [64].
The diagnostic analytics phase, utilizing SPSS 26.0, identifies key components of the resort experience impacting customer satisfaction. ‘Diving’ is highlighted as a distinct factor with a positive correlation to customer satisfaction. This reflects the central value proposition of the dive resort and suggests that high-quality diving experiences are integral to customer contentment. Reviews such as ‘The beach is nearby with turtles and corals accessible in diving’ and ‘The best thing about the hotel is the location’. There is a coral reef on the hotel’s own beach. The diving and marine vegetation is outstanding’ underline this.
‘Entertainment’ and ‘service’ factors show a positive correlation with customer satisfaction but are not statistically significant in the model. This suggests that while better entertainment and service tend to increase satisfaction, the relationship is not strong enough to be conclusive [65]. Customer comments indicate satisfaction with these aspects, but issues in other areas, like outdated accommodations, negatively impact overall satisfaction. For example, comments include the following: ‘Activities are offered. What is boring is, unfortunately, the show every evening’, ‘Cool animators but the animation programme is weak’, and ‘Impeccable service but a little dated room’. This highlights the interconnected nature of customer satisfaction.
Conversely, factors such as ‘resort’, ‘amenities’, ‘outdoor space’, and ‘dining’ negatively correlate with customer satisfaction, highlighting gaps between expectations and current offerings. These areas need improvement, particularly staff attitude, room cleanliness, food menu, swimming pool environment, green areas, evening entertainment, and overall service level. Negative feedback includes comments like ‘rooms not good and old’, ‘poor quality of food’, ‘loud party music at the beach and pool’, ‘small and dirty swimming pool’, ‘no green spaces’, and ‘food is almost the same from breakfast to dinner’.
These findings highlight the intricate relationship between resort features and customer satisfaction in coastal tourism along the Egyptian Red Sea. Management should prioritize improving areas of dissatisfaction, such as ‘dining’ and ‘amenities’, while maintaining the strengths identified in positive reviews, like diving activities and service quality. Investments guided by guest feedback can significantly enhance overall satisfaction and the resort’s reputation.
Protecting the coastal environment, a priority outlined in Egyptian government policies and initiatives like the Egypt Environmental Policy Program (EEPP), is essential for maintaining the area’s appeal to tourists [66]. Sustainable tourism practices should balance environmental preservation with the provision of necessary infrastructure and comfortable accommodations, as these are crucial for tourist satisfaction. This approach aligns with the goals of preserving the natural beauty and biodiversity of the Red Sea, which are significant attractions for visitors. By fostering both environmental sustainability and high tourist satisfaction, resorts can contribute to the sustainable advancement of Egypt’s coastal tourism industry.

6. Conclusions

6.1. Implications

This study makes a significant contribution to the theoretical literature on dive resorts by underscoring the necessity to expand traditional service quality models to incorporate unique elements pertinent to dive resorts, such as the quality of dive experiences and the preservation of natural reefs. The findings suggest that specific terms like ‘reef’ and ‘fish’ represent niche aspects of service quality that are not adequately captured in generic models [67,68]. This observation supports the theoretical proposition that environmental factors play a crucial role in influencing consumer satisfaction with coastal tourism. The condition of natural resources, such as coral reefs, is intrinsically linked to the satisfaction derived from diving experiences, indicating a theoretical linkage between environmental sustainability and service quality [69]. By integrating these unique elements into service quality models, dive resorts can enhance their offerings and better meet the specific needs of coastal tourism tourists, thereby promoting both customer satisfaction and environmental conservation.
Moreover, customer feedback expressing appreciation for natural beauty and ecological preservation indicates that environmental sustainability should be a core component of theoretical models in coastal tourism. Expanding these models to include sustainability metrics can guide resorts in aligning their services with eco-conscious consumer expectations. The balance between exploitation and conservation in service provision can offer a new dimension to these models [70]. Finally, this research demonstrates the value of integrating big data from online reviews into the theoretical framework of consumer satisfaction. It highlights how consumer-generated data can provide nuanced insights into customer expectations and experiences [71].
This study not only contributes theoretically but also to field experts. The first implication for the dive resort operators and managers is that the high frequency of diving-related terms in the reviews (e.g., ‘diving’, ‘reef’, ‘fish’) highlights the central role of diving in customer satisfaction. Resort management should ensure the provision of high-quality diving experiences, including the upkeep of equipment, the availability of skilled dive instructors, and the preservation of local reefs. To further enhance customer experiences, managers might consider leveraging partnerships with local conservation groups to emphasize the resort’s commitment to environmental stewardship. Secondly, the negative coefficient for dining in the regression analysis suggests room for improvement. Managerial efforts could be directed towards diversifying the menu, ensuring food quality, providing good dining experiences that meet customers’ expectations, and seeking a premium resort experience [72]. Third, the analysis points to amenities like ‘swimming’, ‘pool’, and ‘green’ areas as significant factors. Investments should be made to maintain and improve these facilities. In addition, the area’s natural beauty (e.g., ‘beach’, ‘sea’) is a key attraction, underscoring the need for environmental stewardship and sustainable practices [73].
Fourth, with ‘service’ emerging as a frequently mentioned term, exceptional service is vital to customer perceptions. Management should prioritize staff training, with a focus on customer engagement and service excellence, to maintain high levels of customer satisfaction [74]. Fifth, cleanliness has emerged as a significant concern in both the frequency and factor analysis. Regular maintenance and ensuring high standards of cleanliness should be a priority to keep customer satisfaction levels high [75,76]. Leveraging technology, such as automated cleaning systems or mobile apps that allow guests to report cleanliness issues directly, can help maintain and monitor cleanliness more efficiently.
Sixth, terms like ‘entertainment’ and ‘show’ have been identified in the factor analysis. Although the β in the regression analysis is insignificant, this should not diminish their value. Enhancing these offerings could serve as a distinct way to set the resort apart from competitors and elevate the customer experience [77,78]. Seventh, the continuous analysis of big data should be a top priority for managers, as it offers valuable insights for operational and marketing strategies and aids in predicting customer needs [79,80]. Swiftly addressing negative feedback is a critical aspect of maintaining customer satisfaction.

6.2. Limitations and Future Studies

While this study offers valuable insights into coastal tourism, it also has certain limitations that future research should consider. First, the findings from dive resorts in the Red Sea area of Egypt may not be generalizable to other dive resorts with different environmental conditions, management practices, or customer demographics. Secondly, this study relies exclusively on online reviews, which may not represent the full spectrum of customer experiences. Online feedback often skews towards extreme views, either highly positive or highly negative, potentially omitting moderate perspectives that could provide a more balanced understanding.

Author Contributions

Conceptualization, Y.Z., A.W., N.D.H. and H.-S.K.; data collection, Y.Z. and A.W.; data analysis and interpretation, Y.Z., A.W., N.D.H. and H.-S.K.; writing—original draft, Y.Z. and A.W.; writing—review and editing, H.-S.K. and N.D.H.; Final approval of the version to be published, H.-S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A2A03052622).

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the data used were publicly available. The authors did not set up a research protocol and conducted the survey by themselves.

Informed Consent Statement

Informed consent was waived due to the data used were publicly available. The authors did not set up a research protocol and conducted the survey by themselves.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. 2021 Worldwide Corporate Statistics. Available online: https://www.padi.com/sites/default/files/documents/2021-02/2021%20PADI%20Worldwide%20Statistics.pdf (accessed on 18 November 2023).
  2. Schuhbauer, A.; Favoretto, F.; Wang, T.; Aburto-Oropeza, O.; Sala, E.; Millage, K.; Cabral, R.; Sumaila, U.R.; Lucrezi, S.; Hsu, A.; et al. Global Economic Impact of Scuba Dive Tourism. Res. Sq. 2023, 1, 1–18. [Google Scholar] [CrossRef]
  3. Kabil, M.; AbdAlmoity, E.A.; Csobán, K.; Dávid, L.D. Tourism Centres Efficiency as Spatial Unites for Applying Blue Economy Approach: A Case Study of the Southern Red Sea Region, Egypt. PLoS ONE 2022, 17, e268047. [Google Scholar] [CrossRef] [PubMed]
  4. Hilmi, N.; Safa, A.; Scientifique De Monaco, C.; Reynaud, S.; Allemand, D. Coral Reefs and Tourism in Egypt’s Red Sea. Top. Middle East. N. Afr. Econ. 2012, 14, 416–434. [Google Scholar]
  5. Ghoneim, S.A.; Abdellatif, W.A. A Customized Indicator-Based Tool to Assess Resiliency of Egyptian Coastal Cities: Case Study of Red Sea Cities. Civ. Eng. Archit. 2022, 10, 2554–2571. [Google Scholar] [CrossRef]
  6. Edmund Bower. Tourists Keep Flocking to Egypt as Gaza Conflict Rumbles on|AGBI. Available online: https://www.agbi.com/tourism/2024/05/tourists-egypt-visitor-numbers-rise/ (accessed on 28 August 2024).
  7. Egypt’s Tourism Revenues Grew by 8 Percent to $13.2 Billion in 2023|UNION OF ARAB CHAMBERS. Available online: https://uac-org.org/en/News/details/5982/ (accessed on 28 August 2024).
  8. Mehanna, S.F. Egyptian Marine Fisheries and Its Sustainability. In Sustainable Fish Production and Processing; Academic Press: Cambridge, MA, USA, 2022; pp. 111–140. [Google Scholar] [CrossRef]
  9. Olive, R. Swimming and Surfing in Ocean Ecologies: Encounter and Vulnerability in Nature-Based Sport and Physical Activity. Leis. Stud. 2023, 42, 679–692. [Google Scholar] [CrossRef]
  10. Marine-Roig, E. Destination Image Semiotics: Evidence from Asian and European Upscale Hospitality Services. Tour. Hosp. 2024, 5, 472–488. [Google Scholar] [CrossRef]
  11. Aakash, A.; Gupta Aggarwal, A. Assessment of Hotel Performance and Guest Satisfaction through EWOM: Big Data for Better Insights. Int. J. Hosp. Tour. Adm. 2022, 23, 317–346. [Google Scholar] [CrossRef]
  12. Filieri, R.; Galati, F.; Raguseo, E. The Impact of Service Attributes and Category on EWOM Helpfulness: An Investigation of Extremely Negative and Positive Ratings Using Latent Semantic Analytics and Regression Analysis. Comput. Hum. Behav. 2021, 114, 106527–106534. [Google Scholar] [CrossRef]
  13. Padma, P.; Ahn, J. Guest Satisfaction & Dissatisfaction in Luxury Hotels: An Application of Big Data. Int. J. Hosp. Manag. 2020, 84, 102318. [Google Scholar] [CrossRef]
  14. Lukoseviciute, G.; Panagopoulos, T. Management Priorities from Tourists’ Perspectives and Beach Quality Assessment as Tools to Support Sustainable Coastal Tourism. Ocean Coast. Manag. 2021, 208, 105646. [Google Scholar] [CrossRef]
  15. Liu, J.; An, K.; Jang, S.S. A Model of Tourists’ Civilized Behaviors: Toward Sustainable Coastal Tourism in China. J. Destin. Mark. Manag. 2020, 16, 100437–100444. [Google Scholar] [CrossRef]
  16. Arabadzhyan, A.; Figini, P.; García, C.; González, M.M.; Lam-González, Y.E.; León, C.J. Climate Change, Coastal Tourism, and Impact Chains–a Literature Review. Curr. Issues Tour. 2021, 24, 2233–2268. [Google Scholar] [CrossRef]
  17. Kubo, T.; Uryu, S.; Yamano, H.; Tsuge, T.; Yamakita, T.; Shirayama, Y. Mobile Phone Network Data Reveal Nationwide Economic Value of Coastal Tourism under Climate Change. Tour. Manag. 2020, 77, 104010–104018. [Google Scholar] [CrossRef]
  18. Pafi, M.; Flannery, W.; Murtagh, B. Coastal Tourism, Market Segmentation and Contested Landscapes. Mar. Policy 2020, 121, 104189–104496. [Google Scholar] [CrossRef]
  19. Carvache-Franco, W.; Carvache-Franco, M.; Carvache-Franco, O.; Hernández-Lara, A.B. Motivation and Segmentation of the Demand for Coastal and Marine Destinations. Tour. Manag. Perspect. 2020, 34, 100661–100669. [Google Scholar] [CrossRef]
  20. Glossary of Tourism Terms|UNWTO. Available online: https://www.unwto.org/glossary-tourism-terms (accessed on 28 August 2024).
  21. Wheaton, B.; Waiti, J.; Cosgriff, M.; Burrows, L. Coastal Blue Space and Wellbeing Research: Looking beyond Western Tides. Leis. Stud. 2020, 39, 83–95. [Google Scholar] [CrossRef]
  22. Marfai, M.A.; Ahmada, B.; Mutaqin, B.; Windayati, R. Dive Resort Mapping and Network Analysis: Water Resources Management in Pemuteran Coastal Area, Bali—Indonesia. Geogr. Tech. 2020, 15, 106–116. [Google Scholar] [CrossRef]
  23. Aygün, Y.; Tüfekçi, Ş. Leisure and SCUBA Diving Safety Tips during and after COVID-19. Cogent. Soc. Sci. 2020, 6, 1809805. [Google Scholar] [CrossRef]
  24. Dimmock, K.; Musa, G. Scuba Diving Tourism System: A Framework for Collaborative Management and Sustainability. Mar. Policy 2015, 54, 52–58. [Google Scholar] [CrossRef]
  25. Honey, M.; Krantz, D. Global Trends in Coastal Tourism; Center on Ecotourism and Sustainable Development: Washington, DC, USA, 2007. [Google Scholar]
  26. Méndez-Aparicio, M.D.; Jiménez-Zarco, A.; Izquierdo-Yusta, A.; Blazquez-Resino, J.J. Customer Experience and Satisfaction in Private Insurance Web Areas. Front. Psychol. 2020, 11, 581659–581663. [Google Scholar] [CrossRef]
  27. Cetin, G. Experience vs Quality: Predicting Satisfaction and Loyalty in Services. Serv. Ind. J. 2020, 40, 1167–1182. [Google Scholar] [CrossRef]
  28. Jiménez-Medina, P.; Navarro-Azorín, J.M.; Cubillas-Para, C.; Artal-Tur, A. What Safety and Security Measures Really Matter in the Post-COVID Recovery of the Hospitality Industry? An Analysis of the Visitor’s Intention to Return in Spain. Tour. Hosp. 2022, 3, 606–617. [Google Scholar] [CrossRef]
  29. Meilatinova, N. Social Commerce: Factors Affecting Customer Repurchase and Word-of-Mouth Intentions. Int. J. Inf. Manag. 2021, 57, 102300–102316. [Google Scholar] [CrossRef]
  30. Cronin, J.; Taylor, S. Measuring Service Quality: A Reexamination and Extension. J. Mark. 1992, 56, 55–68. [Google Scholar] [CrossRef]
  31. Suttikun, C.; Meeprom, S. Examining the Effect of Perceived Quality of Authentic Souvenir Product, Perceived Value, and Satisfaction on Customer Loyalty. Cogent Bus. Manag. 2021, 8, 1976468. [Google Scholar] [CrossRef]
  32. Xie, L.; Guan, X.; He, Y.; Huan, T.C. Wellness Tourism: Customer-Perceived Value on Customer Engagement. Tour. Rev. 2021, 77, 859–876. [Google Scholar] [CrossRef]
  33. Jun, M.; Yang, Z.; Kim, D.S. Customers’ Perceptions of Online Retailing Service Quality and Their Satisfaction. Int. J. Qual. Reliab. Manag. 2004, 21, 817–840. [Google Scholar] [CrossRef]
  34. Santos, A.; Perinotto, A.; Soares, J.R.R.; Mondo, T.S.; Cembranel, P. Expressing the Experience: An Analysis of Airbnb Customer Sentiments. Tour. Hosp. 2022, 3, 685–705. [Google Scholar] [CrossRef]
  35. Bhatti, M.A.; Alawad, W.A. Online Reviews and Brand Reputation Management in the Digital Landscape: Exploring Moderating Role of Product Type. Transnatl. Mark. J. 2023, 11, 40–59. [Google Scholar]
  36. Floyd, K.; Freling, R.; Alhoqail, S.; Cho, H.Y.; Freling, T. How Online Product Reviews Affect Retail Sales: A Meta-Analysis. J. Retail. 2014, 90, 217–232. [Google Scholar] [CrossRef]
  37. Wang, T.L.; Tran, P.T.K.; Tran, V.T. Destination Perceived Quality, Tourist Satisfaction and Word-of-Mouth. Tour. Rev. 2017, 72, 392–410. [Google Scholar] [CrossRef]
  38. Joung, J.; Kim, H. Interpretable Machine Learning-Based Approach for Customer Segmentation for New Product Development from Online Product Reviews. Int. J. Inf. Manag. 2023, 70, 102641–102649. [Google Scholar] [CrossRef]
  39. Naumzik, C.; Feuerriegel, S.; Weinmann, M. I Will Survive: Predicting Business Failures from Customer Ratings. Mark. Sci. 2022, 41, 188–207. [Google Scholar] [CrossRef]
  40. Troisi, O.; Visvizi, A.; Grimaldi, M. Digitalizing Business Models in Hospitality Ecosystems: Toward Data-Driven Innovation. Eur. J. Innov. Manag. 2023, 26, 242–277. [Google Scholar] [CrossRef]
  41. Tao, S.; Kim, H.S. Online Customer Reviews: Insights from the Coffee Shops Industry and the Moderating Effect of Business Types. Tour. Rev. 2022, 77, 1349–1364. [Google Scholar] [CrossRef]
  42. Naeem, M.; Jamal, T.; Diaz-Martinez, J.; Butt, S.A.; Montesano, N.; Tariq, M.I.; De-la-Hoz-Franco, E.; De-La-Hoz-Valdiris, E. Trends and Future Perspective Challenges in Big Data. Smart Innov. Syst. Technol. 2022, 253, 309–325. [Google Scholar] [CrossRef]
  43. Mariani, M. Big Data and Analytics in Tourism and Hospitality: A Perspective Article. Tour. Rev. 2020, 75, 299–303. [Google Scholar] [CrossRef]
  44. Ghasemaghaei, M. Understanding the impact of big data on firm performance: The necessity of conceptually differentiating among big data characteristics. Int. J. Inf. Manag. 2021, 57, 102055. [Google Scholar] [CrossRef]
  45. Shafiq, M.; Tian, Z.; Bashir, A.K.; Jolfaei, A.; Yu, X. Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: A Survey. Sustain. Cities Soc. 2020, 60, 102177. [Google Scholar] [CrossRef]
  46. Ahmed, I.; Ahmad, M.; Jeon, G.; Piccialli, F. A Framework for Pandemic Prediction Using Big Data Analytics. Big Data Res. 2021, 25, 100190. [Google Scholar] [CrossRef]
  47. Koukaras, P.; Tjortjis, C. Social Media Analytics, Types and Methodology. In Learning and Analytics in Intelligent Systems; Tsihrintzis, G.A., Virvou, M., Jain, L.C., Eds.; Springer: Cham, Switzerland, 2019; Volume 1. [Google Scholar] [CrossRef]
  48. Russell, C. Best Red Sea Diving Resorts. Available online: https://blog.padi.com/best-red-sea-diving-resorts/ (accessed on 19 March 2024).
  49. Park, J.Y.; Mistur, E.; Kim, D.; Mo, Y.; Hoefer, R. Toward Human-Centric Urban Infrastructure: Text Mining for Social Media Data to Identify the Public Perception of COVID-19 Policy in Transportation Hubs. Sustain. Cities Soc. 2022, 76, 103524. [Google Scholar] [CrossRef] [PubMed]
  50. Takiguchi, Y.; Matsui, M.; Kikutani, M.; Ebina, K. The Relationship between Leisure Activities and Mental Health: The Impact of Resilience and COVID-19. Appl. Psychol. Health Well-Being 2023, 15, 133–151. [Google Scholar] [CrossRef] [PubMed]
  51. Cioban, S.; Lazăr, A.R.; Bacter, C.; Hatos, A. Adolescent Deviance and Cyber-Deviance. A Systematic Literature Review. Front. Psychol. 2021, 12, 748006. [Google Scholar] [CrossRef] [PubMed]
  52. Wu, H.; Zhou, Q.; Nie, R.; Cao, J. Effective Metric Learning with Co-Occurrence Embedding for Collaborative Recommendations. Neural Netw. 2020, 124, 308–318. [Google Scholar] [CrossRef] [PubMed]
  53. Tien, N.H.; Anh, N.; Dung, H.; On, P.; Anh, V.; Dat, N.; Tam, B. Factors Impacting Customer Satisfaction at Vietcombank in Vietnam. Himal. J. Econ. Bus. Manag. 2021, 2, 44–51. [Google Scholar] [CrossRef]
  54. Ban, H.-J.; Kim, H.-S. Semantic Network Analysis of Hotel Package through the Big Data. Cul. Sci. Hosp. Res. 2019, 25, 110–119. [Google Scholar] [CrossRef]
  55. Mathayomchan, B.; Taecharungroj, V. “How Was Your Meal?” Examining Customer Experience Using Google Maps Reviews. Int. J. Hosp. Manag. 2020, 90, 102641. [Google Scholar] [CrossRef]
  56. Maskey, R.; Fei, J.; Nguyen, H.O. Use of Exploratory Factor Analysis in Maritime Research. Asian J. Shipp. Logist. 2018, 34, 208–209. [Google Scholar] [CrossRef]
  57. Kaiser, H.F. An Index of Factorial Simplicity. Psychometrika 1974, 39, 31–36. [Google Scholar] [CrossRef]
  58. Dias, J.G. Environmental Sustainability Measurement in the Travel & Tourism Competitiveness Index: An Empirical Analysis of Its Reliability. Ecol. Indic. 2017, 73, 589–596. [Google Scholar] [CrossRef]
  59. He, W.; Tian, X.; Tao, R.; Zhang, W.; Yan, G.; Akula, V. Application of Social Media Analytics: A Case of Analyzing Online Hotel Reviews. Online Inf. Rev. 2017, 41, 921–935. [Google Scholar] [CrossRef]
  60. Spalding, M.; Burke, L.; Wood, S.A.; Ashpole, J.; Hutchison, J.; zu Ermgassen, P. Mapping the Global Value and Distribution of Coral Reef Tourism. Mar. Policy 2017, 82, 104–113. [Google Scholar] [CrossRef]
  61. Akhtar, N.; Zubair, N.; Kumar, A.; Ahmad, T. Aspect Based Sentiment Oriented Summarization of Hotel Reviews. Procedia Comput. Sci. 2017, 115, 563–571. [Google Scholar] [CrossRef]
  62. Kaveski Peres, C.; Pacheco Paladini, E. Exploring the Attributes of Hotel Service Quality in Florianópolis-SC, Brazil: An Analysis of TripAdvisor Reviews. Cogent Bus. Manag. 2021, 8, 1926211. [Google Scholar] [CrossRef]
  63. Vassilikopoulou, A.; Kamenidou, I.; Priporas, C.V. Negative Airbnb Reviews: An Aspect-Based Sentiment Analysis Approach. EuroMed J. Bus. 2022, 19, 191–207. [Google Scholar] [CrossRef]
  64. Jeong, M.; Oh, H. Quality Function Deployment: An Extended Framework for Service Quality and Customer Satisfaction in the Hospitality Industry. Int. J. Hosp. Manag. 1998, 17, 375–390. [Google Scholar] [CrossRef]
  65. Quang, T.D.; Dang Vo, N.M.; Van Nguyen, H.; Thi Nguyen, Q.X.; Ting, H.; Vo-Thanh, T. Understanding Tourists’ Experiences at War Heritage Sites in Ho Chi Minh City, Vietnam: A Netnographic Analysis of TripAdvisor Reviews. Leis. Stud. 2024, 43, 624–643. [Google Scholar] [CrossRef]
  66. Balinado, J.R.; Prasetyo, Y.T.; Young, M.N.; Persada, S.F.; Miraja, B.A.; Perwira Redi, A.A.N. The Effect of Service Quality on Customer Satisfaction in an Automotive After-Sales Service. J. Open Innov. Technol. Mark. Complex. 2021, 7, 116. [Google Scholar] [CrossRef]
  67. Tabet, L.; Fanning, L. Integrated coastal zone management under authoritarian rule: An evaluation framework of coastal governance in Egypt. Ocean Coast. Manag. 2012, 61, 1–9. [Google Scholar] [CrossRef]
  68. Bonin, M.C.; Almany, G.R.; Jones, G.P. Contrasting Effects of Habitat Loss and Fragmentation on Coral-Associated Reef Fishes. Ecology 2011, 92, 1503–1512. [Google Scholar] [CrossRef]
  69. Bridge, T.; Beaman, R.; Done, T.; Webster, J. Predicting the Location and Spatial Extent of Submerged Coral Reef Habitat in the Great Barrier Reef World Heritage Area, Australia. PLoS ONE 2012, 7, e48203. [Google Scholar] [CrossRef] [PubMed]
  70. Coghlan, A. Facilitating Reef Tourism Management through an Innovative Importance-Performance Analysis Method. Tour. Manag. 2012, 33, 767–775. [Google Scholar] [CrossRef]
  71. Phelan, A.; Ruhanen, L.; Mair, J. Ecosystem services approach for community-based ecotourism: Towards an equitable and sustainable blue economy. J. Sustain. Tour. 2020, 28, 1665–1685. [Google Scholar] [CrossRef]
  72. Kietzmann, J.; Paschen, J.; Treen, E. Artificial Intelligence in Advertising: How Marketers Can Leverage Artificial Intelligence along the Consumer Journey. J. Advert. Res. 2018, 58, 263–267. [Google Scholar] [CrossRef]
  73. Han, H.; Hyun, S.S. Impact of Hotel-Restaurant Image and Quality of Physical-Environment, Service, and Food on Satisfaction and Intention. Int. J. Hosp. Manag. 2017, 63, 82–92. [Google Scholar] [CrossRef]
  74. Macdonald, C.; Turffs, D.; McEntee, K.; Elliot, J.; Wester, J. The Relationship between Tourism and the Environment in Florida, USA: A Media Content Analysis. Ann. Tour. Res. Empir. Insights 2023, 4, 100092. [Google Scholar] [CrossRef]
  75. Abdelhamied, H.H.S. The Impact of Training Activities on Quality of Service, Customer Satisfaction and Behavioral Intention. J. Tour. Hosp. Manag. 2019, 7, 135–148. [Google Scholar] [CrossRef]
  76. Handani, N.D.; Williady, A.; Kim, H.-S. An Analysis of Customer Textual Reviews and Satisfaction at Luxury Hotels in Singapore’s Marina Bay Area (SG-Clean-Certified Hotels). Sustainability 2022, 14, 9382. [Google Scholar] [CrossRef]
  77. Syaqirah, Z.N.; Faizurrahman, Z.P. Managing Customer Retention of Hotel Industry in Malaysia. Procedia Soc. Behav. Sci. 2014, 130, 379–389. [Google Scholar] [CrossRef]
  78. Zhang, X.; Kim, H.-S. Customer Experience and Satisfaction of Disneyland Hotel through Big Data Analysis of Online Customer Reviews. Sustainability 2021, 13, 12699. [Google Scholar] [CrossRef]
  79. Riswanto, A.L.; Kim, S.; Kim, H.-S. Analyzing Online Reviews to Uncover Customer Satisfaction Factors in Indian Cultural Tourism Destinations. Behav. Sci. 2023, 13, 923. [Google Scholar] [CrossRef] [PubMed]
  80. Costa, G.; Glinia, E.; Goudas, M.; Antoniou, P. Recreational Services in Resort Hotels: Customer Satisfaction Aspects. J. Sport Tour. 2004, 9, 117–126. [Google Scholar] [CrossRef]
Tourismhosp 05 00056 g001
Figure 1. Research flow (source: authors).
Figure 1. Research flow (source: authors).
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Figure 2. Distribution of overall satisfaction review ratings of customers.
Figure 2. Distribution of overall satisfaction review ratings of customers.
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Figure 3. Co-occurrence network result.
Figure 3. Co-occurrence network result.
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Figure 4. Co-occurrence network analysis with rating.
Figure 4. Co-occurrence network analysis with rating.
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Table 1. The top 100 frequent words.
Table 1. The top 100 frequent words.
WordsFreq.WordsFreq.WordsFreq.WordsFreq.
hotel9049diving1257guest609problem427
good4984child1169year608week426
room4267vacation1089experience577kitchen415
food3882super1059special574dinner412
beach3785water951old564fun411
staff3743amazing919bad551variety403
beautiful3591team913star544price399
service2827evening909small539coral395
nice2526delicious886quality538minute393
restaurant2433fish861swimming523polite387
great2329animator822employee520fantastic385
place2274tasty795comfortable517waiter373
pool2232helpful768entertainment516choice371
excellent2050number751perfect496bed365
friendly2026first747big490taste363
animation1990cool730different473free354
clean1975area719guy471nothing353
sea1912reception706view466several351
reef1698rest677pleasant462dish350
wonderful1540drink668show458towel350
bar1517level640same454green349
territory1460family629activity446Italian345
large1396stay626huge441better342
best1392holiday621facility434cleaning337
resort1316location615night432breakfast335
Table 2. Results of exploratory factor analysis.
Table 2. Results of exploratory factor analysis.
FactorWordFactor LoadingEigenvalueCumulative VarianceCronbach’s α
Resort (F1)friendly0.6722.0707.9610.730
staff0.629
clean0.487
room0.449
great0.437
nice0.432
food0.428
Diving (F2)coral0.7341.96215.509
reef0.727
fish0.586
diving0.427
beautiful0.415
Amenities (F3)swimming0.8041.65521.876
pool0.750
Outdoor Space (F4)green0.7281.59428.006
area0.681
large0.516
Entertainment (F5)show0.7081.54533.948
evening0.691
animation0.559
Dining (F6)breakfast0.8001.48339.651
dinner0.782
Service (F7)service0.6961.26844.528
excellent0.596
level0.479
Notes. KMO: 0.807; Bartlett chi-square (p): 39,292.512 (p < 0.001).
Table 3. Results of linear regression analysis.
Table 3. Results of linear regression analysis.
ModelUnstandardized CoefficientsStandardized CoefficientstSig.
βStd. Errorβ
(Constant)4.6370.007 641.962<0.001
Resort (F1)−0.0290.007−0.033−3.993<0.001
Diving (F2)0.0220.0070.0263.0700.002
Amenities (F3)−0.0760.007−0.089−10.573<0.001
Outdoor Space (F4)−0.0160.007−0.019−2.2740.023
Entertainment (F5)0.0130.0070.0161.8510.064
Dining (F6)−0.0960.007−0.111−13.250<0.001
Service (F7)0.0110.0070.0131.5300.126
Notes. Dependent variable: customer satisfaction.
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Zhong, Y.; Williady, A.; Handani, N.D.; Kim, H.-S. Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts. Tour. Hosp. 2024, 5, 996-1011. https://doi.org/10.3390/tourhosp5040056

AMA Style

Zhong Y, Williady A, Handani ND, Kim H-S. Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts. Tourism and Hospitality. 2024; 5(4):996-1011. https://doi.org/10.3390/tourhosp5040056

Chicago/Turabian Style

Zhong, Yinai, Angellie Williady, Narariya Dita Handani, and Hak-Seon Kim. 2024. "Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts" Tourism and Hospitality 5, no. 4: 996-1011. https://doi.org/10.3390/tourhosp5040056

APA Style

Zhong, Y., Williady, A., Handani, N. D., & Kim, H. -S. (2024). Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts. Tourism and Hospitality, 5(4), 996-1011. https://doi.org/10.3390/tourhosp5040056

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