Segmentation by Image Attributes in Island Marine Protected Areas: The Galapagos Islands, Ecuador

Abstract

The image attributes of a tourist destination are the elements that make up the perception that visitors have about a place. Segmenting by image attributes is establishing subgroups of tourists, differentiating them by the way they perceive the image of the destination. The present study in a marine protected area aimed to (i) identify image attributes, (ii) establish segments based on image attributes, (iii) determine the relationship between image segments, satisfaction, and behavioral loyalty, and (iv) ascertain the socio-demographic characteristics of image segments in insular marine protected areas. This study was conducted in the Galapagos Islands of Ecuador, a marine protected Pacific Ocean area declared a World Heritage Site. A total of 407 surveys were collected in situ. The data were interpreted using factor analysis techniques and non-hierarchical K-means cluster analysis. The results show four image attributes in marine protected areas: Staff Attention, Tourist Facilities, Nature and People, and Cultural Attractions. Likewise, three segments based on image attributes were identified: the Passive segment, with low scores overall; Nature, with high scores only in attributes related to nature; and the Want It All segment, with high scores in all image attributes. Among these groups, the Want it All segment demonstrates the highest satisfaction and loyalty levels. The results will serve as management guidelines for marine protected area administrators and contribute to academic literature.

1. Introduction

Researching the image of a destination is vital for several reasons. Firstly, the perception of a destination impacts how tourists choose destinations, services, and products, as well as how they make decisions [1]. Additionally, understanding the reputation of the tourist site could help in its strategic branding [2]. Similarly, comprehending tourists’ choices of places, satisfaction with their experiences at the site, and intention to return involves understanding the destination [3,4,5]. According to Lai and Li [6], the image of a tourist destination is composed of tourists’ impressions, perceptions, ideas, beliefs, and representations of that place as a result of their experiences. According to Michael et al. [7], tourists’ ideas, feelings, visions, and intentions about this type of location shape their perception of coastal places. In this regard, some experts in the field affirm that there are two different types of images people have of destinations (cognitive and affective images), while others added a third one (conative image). For instance, Michael et al. [7], regarding the perspective of Emirates visitors in Australia, identified three different forms of destination images (cognitive, emotional, and conative).

On the other hand, other researchers have reduced the variety of objective images to only two types. For example, Almeida-García et al. [8] identified two types of images after studying the destination image of the coastal Spanish city of Málaga. These are the cognitive or perceived image (related to tourists’ perceptions and thoughts about that particular location) and the affective or projected image (the attraction component in the preferred location).

The focus of this study is centered on destination image segmentation. Therefore, it is essential first to understand the general meaning of demand segmentation to ensure a better understanding of its significance concerning destination image. Segmentation involves grouping consumer groups into more manageable and smaller markets, endowing them with distinctive identifying characteristics [9]. In the tourism sector, segmentation refers to dividing visitors into groups with similar characteristics based on certain factors such as socio-demographic traits, economic status, and travel experience to a specific destination [10]. Based on this information, managers and marketing specialists can offer activities and services that cater to the needs and preferences of those tourists [11]. There are various ways to segment destinations based on visitors’ motivation and experience [11]. Another method that has gained popularity recently is destination image segmentation, which involves categorizing travelers based on the qualities of a destination’s image [12]. In this sense, segmenting by image attributes is identifying subgroups of tourists by the way they perceive the image attributes of a destination. Image attributes being the elements that make up the perception that visitors have about a place. The image of a tourist destination is a key factor in visitors’ decisions to visit a place. This image is formed from the beliefs, ideas, feelings, and expectations of tourists.

Due to their unique qualities and the experiences they can offer visitors compared to other destinations, coastal and marine tourism, including island destinations, with their natural and cultural attractions, facilities, services, and activities, are attracting growing interest from travelers and marketing specialists. The significance of coastal and marine tourism also stems from the fact that these sectors can be lucrative and provide visitors with cutting-edge recreational opportunities. These activities include participating in water sports, ecotourism, marine life observation, involvement in community events, and enjoying local gastronomy [13]. In this context, coastal destinations are places where tourists can engage in sea-based activities such as swimming, surfing, sunbathing, and other leisure, recreational, and sports activities along ocean, lake, or river beaches. Additionally, the services and facilities that support coastal tourism should be close to the coast [14]. Marine protected areas, a component of the coastal and marine tourism sector, have recently experienced significant growth and have become trendy vacation spots for individuals seeking solitude and tranquility in nature. According to regulations that organize activities and uses in public and private sectors, such as fishing, marine protected areas are coastal and oceanic areas distinguished by unique flora and fauna. They are subject to regulations ensuring conservation and sustainability in that area [15].

The Galápagos Islands, located in Ecuador, are one of the world’s most recognized marine protected areas due to their endemic flora and fauna. They comprise a group of islands on the west coast of Ecuador in the Pacific Ocean, with 8010 km² of total area that encompasses seven main islands (Isabela, Santa Cruz, Fernandina, Santiago, San Cristóbal, Floreana, and Marchena) along with 14 other islands. The Galápagos Marine Reserve and the Galápagos National Park are protected regions within these islands. This archipelago’s extraordinary flora and fauna make it a valuable place for scientific research and stand as a symbol of its significance. The Galápagos Islands are among the most protected islands globally, following UNESCO’s declaration as a World Heritage Site in 1978. Visitors to this place can engage in various coastal and marine activities such as swimming, diving, snorkeling, and observing marine life like fish, sharks, and sea lions on volcanic rocks. Moreover, they can shop at local stores, purchase locally-made products, taste seafood cuisine, and learn about local traditions [16].

The impact of cognitive and conative image segmentation on satisfaction and its prediction of loyalty towards a destination remains relatively unexplored, especially in coastal and maritime destinations, making it challenging to understand this potential relationship [13]. Except for some research focused on sun-and-beach destinations such as Mauritius Island [12], Liuqiu Island in Taiwan [11], and Acapulco, Mexico [17], there is limited investigation in this area. The Galápagos Islands remain the most popular domestic tourist destination in Ecuador and the most renowned tourist destination in Latin America and the world due to their outstanding offerings for visitors, endemic flora and fauna, and tourist attractions. Hence, the significance of this place as a tourist destination, with its potential for coastal development and its flora and fauna, makes scientific research pertinent because it possesses the necessary qualities to contribute to academic literature. Few studies have been conducted on cognitive and conative image segmentation in insular marine protected areas and the relationship of demand segments with satisfaction and loyalty in this field. The sustainable growth of these sites to meet visitor preferences requires knowledge about the segmentation of cognitive and conative images of a place and their link to satisfaction and loyalty (return to the destination). Therefore, considering these information gaps in the academic literature about tourist demand, the objectives of this research are as follows: (i) identify image attributes of marine protected areas; (ii) establish segments based on image attributes in insular marine protected areas; (iii) determine the relationship between image segments with satisfaction and behavioral loyalty in insular marine protected areas; and (iv) ascertain the socio-demographic characteristics of image segments in insular marine protected areas.

2. Literature Review

2.1. The Cognitive and Conative Image in Island Marine Protected Areas

Studying how travelers perceive a destination is crucial for understanding travel behavior and decision-making processes [18,19,20]. According to Esper and Rateike [21], the definition of a destination’s image comprises ideas, thoughts, beliefs, and feelings developed around the destination over time. It represents visitors’ overall perceptual impression of a site, influencing their attitude towards the area and the decision-making process during travel. In a coastal and marine destination, this perception is based on an evaluation of coastal area features such as facilities, services, the landscape and natural attractions of the beach, the climate and weather at the beach, a clean and pristine environment, and the programs and activities offered [22]. On the other hand, marine protected areas are geographical zones characterized by high biodiversity in terms of plants, animals, soil, and the overall environment [23]. These areas are lawfully protected to preserve their inhabitants, unique ecological wealth, and sea-based environment [15]. Based on the above, it can be inferred that the image of a marine protected area is defined as visitors’ perception of environmental characteristics such as fauna and flora, cultural and recreational activities, and provided services.

According to Prayag and Ryan [24] and Stylidis et al. [25], a destination’s image can be categorized in two ways: in terms of qualities or as holistic components. The destination image is divided into two or three dimensions based on its attributes: (1) Cognitive Image, which represents a person’s general perceptions of the place, where the characteristics of a location form a mental image in the individual’s mind; (2) Affective Image refers to a person’s emotions and feelings towards the place; and (3) Conative Image, the mental representation of the location based on cognitive (e.g., providing a family with high-quality and secure housing) and affective images (e.g., offering a pleasant experience) [24,26,27,28]. Additionally, it is highlighted that the conative image is linked to the recommendation and return to the destination, depending on the successful fulfillment of the traveler’s needs [29]. Concerning holistic images, these encompass the overall feeling and ambiance of the country. In other words, holistic elements constitute the general image of a destination [30].

2.2. Image Attributes in Island Marine Protected Areas

A coastal area’s cognitive, affective, and conative image comprises a series of characteristics [22]. In other words, these are visitors’ opinions about a place’s characteristics that can impact its reputation [31]. Natural and environmental attractions, cultural and historical attractions, entertainment and leisure activities, infrastructure, accommodations, accessibility, and ambiance are some mentioned characteristics or factors in previous studies [32,33,34]. Studies have shown that perceived values (i.e., going to the beach makes a person feel better and provides pleasure) and service quality (i.e., beach cleanliness, accommodation quality, food quality, availability, and quality of transportation) are related [22]. According to a different study on beach destinations in Bangladesh, tourists’ perception of the place’s risk impacts the coastal area’s image [35]. Lam-González et al. [36] focused on visitors to Canarias Islands (Spain) and Cabo Verde, and found five destination criteria or aspects: good environmental management and less polluted waterways, security and socio-political stability, peaceful and exotic fishing experience, outstanding cultural value and activities, and good weather, beaches, and tourist services. Similarly, Akgün et al. [37] established that the cognitive image of Istanbul was a multidimensional construction composed of attractions, infrastructure, atmosphere, and value variables. In a coastal and maritime destination (Acapulco, Mexico), six attributive elements of cognitive image were recently identified by Carvache-Franco et al. [38]: staff attention, tourist infrastructure, cultural activities, service quality, natural and aquatic activities, and entertainment.

The explanation provided makes it clear that the perception of a destination differs depending on the destination’s characteristics. Coastal locations and areas of insular marine protected areas currently lack image attribute characteristics. In light of the previous explanation, the following research question is proposed: RQ1: What are the image attributes of insular marine protected areas?

2.3. Image Segmentation in Island Marine Protected Areas

In coastal and marine areas, tourist segmentation is essential for classifying tourists into homogeneous groups based on different variables such as motivations, activities, and spending patterns [10]. Studying the segmentation of image attributes is vital to comprehend the characteristics of each image attribute segment and cater to tourists’ needs based on these segment features. Furthermore, this could help understand tourists’ behavior concerning their satisfaction with the quality of service provided, the organization, the travel program, and their intention to recommend the place and return to the destination [10].

Despite the importance of segmenting tourists in coastal and marine areas based on destination image, there have been few studies conducted on this aspect, as most research has examined demand segmentation based on motivation and recreational experience [11,38,39]. Prayag’s [12] study on visitor satisfaction segmentation at Mauritius beaches using cognitive images is one of the currently available studies. The academic identified three visitor groups: Group I visitors were usually moderately dissatisfied or dissatisfied with their satisfaction levels. Group II focused on travelers who were mainly satisfied or slightly satisfied with the destination’s attractions. Group III included tourists disappointed with their trip to Mauritius and the assistance they received from hotel staff. Elements that enhance the intention to return and recommend a tourist destination, which could be used as segmentation criteria, involve the natural environment, reputation, and people’s friendliness.

Additionally, Agapito et al. [40] identified two segments with opposite tendencies in their study on demand segmentation based on Lagos’ image characteristics in the Portuguese Algarve region. Tourists in Segment II have a less positive perception of the place in terms of its “interesting cultural heritage”, “interesting cultural events”, and “good value for money”. Segment VII: Regarding the qualities of “interesting cultural heritage”, “good value for money”, and “good sports facilities”, this group has a more positive perception of the place. Finally, Sánchez-Rivero and Pulido-Fernández [41] investigated demand segmentation based on image attributes in Andalucía, Spain, famous for coastal tourism. The academics identified the following groups: Class 1: Quality perceivers who tend to evaluate the tourist image excellently except for the price-quality relationship. Class 2: Relaxed contingent perceivers with high satisfaction with the price-quality relationship and aspects of attention and treatment. Class 3: Tourist service perceivers with a null evaluation of tranquility and cleanliness as aspects of the image. Class 4: Global destination perceivers have the worst image of the destination. Class 5: Perceivers for whom everything is fine, including those with the highest image of destination aspects. Finally, Carvache-Franco et al. [38] found three distinct groups in Acapulco (Mexico): “Want it All” tourists who are satisfied with all image attributes; “Coastal activity seekers”, tourists with a higher level of satisfaction with natural, aquatic, and cultural activities; and “Passive tourists”, who had lower levels of satisfaction compared to the other groups.

The previous discussion highlighted the scarcity of studies on image segmentation in coastal and marine tourist destinations, resulting in a gap in this research field. This limitation hinders the development of a clear destination image and the segmentation of tourists in marine protected areas, making it challenging for marketers and managers to provide services, programs, and activities these tourists prefer. Therefore, it is crucial to delve deeper into demand segmentation based on the destination’s image in marine protected areas. This investigation will contribute to a better understanding of these tourists’ needs and their image-based segmentation, ultimately ensuring an increase in the number of tourists visiting the area. Consequently, we present our second research question: RQ2: What image-based segments are in marine protected areas?

2.4. Relationship Between Image Demand Segments with Satisfaction and Loyalty in Marine Protected Areas

Marketers and destination managers are dedicated to ensuring tourists’ happiness with their travel experience to guarantee their satisfaction and loyalty (recommendation and return to the destination), which can impact the destination image and tourism revenue [16]. Based on a detailed literature analysis, few studies have examined image demand segments concerning satisfaction and behavioral loyalty in coastal and marine areas. Therefore, thoroughly examining the potential relationship between these constructs is essential. Understanding the satisfaction and loyalty of visitors to a destination concerning image demand segmentation is considered beneficial for the area’s development. Furthermore, it can improve the attributes of a destination’s image to meet the needs and desires of visitors, thereby ensuring their satisfaction and, on the other hand, loyalty to the place [12].

The study by Sánchez-Rivero and Pulido-Fernández [41] identified five tourist groups in the Andalusia region, Spain, each with different levels of satisfaction and destination perception. Tourists in Class 1 and Class 2 differ in happiness and goal perception. Class 1 tourists express high pleasure with cleanliness and tranquility, while Class 2 tourists show satisfaction with attention and service. Class 3 values peace and tranquility in the inland cities of Andalusia. Travelers in Class 4 have the lowest destination image and the highest dissatisfaction, whereas Class 5 travelers have the highest levels of both satisfaction and dissatisfaction. In another study by Prayag [12], visitor satisfaction with the beaches of Mauritius was segmented into two groups using cognitive images. Cluster I included visitors traveling for reasons other than vacations, such as visiting friends, family, and newlyweds, and Cluster 2 included those traveling on a holiday package. It was found that visitors in Cluster 2 were the most dissatisfied with the image attributes. Regarding loyalty, that study showed that Cluster I has the highest proportion of visitors who are less likely to recommend and return to the destination. Cluster II has the most visitors likely to revisit and recommend the destination.

The previous discussion highlighted the limited studies on the relationship between image-based demand segments, satisfaction, and loyalty in coastal and marine destinations. Furthermore, the results from available studies are inconclusive and warrant further investigation. It is important to note that research in this area has not explored insular marine protected areas. Therefore, we propose our third research question: RQ3: What is the relationship between image-based segments’ satisfaction and loyalty in insular marine protected areas?

2.5. Relationship of the Socio-Demographic Aspects of the Segments by Image in Island Marine Protected Areas

Regarding the relationship between socio-demographic aspects of image and demand segmentation in coastal and marine areas, a study exists in this area, leaving a knowledge gap to comprehend this topic thoroughly. Prayag’s study [12] on the island of Mauritius identified three visitor groups based on nationality and marital status. For instance, most British and French visitors belonged to Cluster 2, while Cluster 3 comprised German visitors. Most married or partnered visitors belonged to Clusters 2 and 1, which requires further exploration of other socio-demographic characteristics to understand diverse visitor needs and preferences better, helping marketers in destination planning.

It has also been demonstrated that there is a gap in the literature concerning the relationship between socio-demographic aspects of image and demand segmentation in coastal and marine areas. This difficulty in understanding the varying needs of visitors based on socio-demographic characteristics such as age and marital status is crucial in marketing planning and promotional activities to encompass all visitor groups and cater to their diverse needs. Due to the limited literature on this subject, our fourth research question arises: RQ4: What are the socio-demographic characteristics of image segments in insular marine protected areas?

3. Methodology

3.1. Study Area

The Galápagos Islands are located in the Pacific Ocean, constituting an archipelago of volcanic origin with a total surface area of 8010 km² and a population of over 25,000. This Ecuadorian region comprises 13 main islands, over 100 islets, and a marine reserve established in 1998 that covers around 143 km². Strict conservation regulations govern tourism within marine protected areas to preserve biodiversity. Cromwell currents, the warm Panama current, and the cold Humboldt current have made this territory home to more than 2000 animal species. (Figure 1).

Within the main permitted tourist activities lies the observation of flora and fauna, including diving, kayaking, and snorkeling. The variety of fauna present varies depending on the season; however, the most characteristic species within the reserve are sharks, giant tortoises, penguins, sea lions, hammerhead sharks, dolphins, and around 325 species of fish. Surfing in more than 20 spots across the archipelago is also possible, with Punta Piqueros, Lobería, and Tongo Reef being highly recommended. Lastly, experiential fishing is carried out on a boat with a licensed operator who ensures that safety standards and biodiversity protection are met. These same standards apply to swimming activities within the marine reserve, as authorized personnel are also necessary.

3.2. Survey, Data Gathering, and Analysis

The present study is part of the Project that was ethically approved by the Polytechnic University of Ecuador ESPOL, Code: CIEC-16-2015. Informed consent was requested in writing at the beginning of the questionnaire. A questionnaire was developed based on the reviewed literature to achieve the goals of this study. A pilot test of 20 questionnaires was carried out to analyze the ease of response of the respondents. The data collection instrument consisted of three parts. The first section analyzed socio-demographic aspects and comprised 8 closed-ended questions derived from Lee et al.’s [11] study. The second part of the questionnaire examined image attributes and comprised 22 items adapted from Prayag’s [12] study. This section utilized a 5-point Likert scale (where 1 was “not very important” and 5 was “very important”). The Cronbach’s Alpha coefficient for the attribute items was equal to 0.93 (close to 1), indicating that the instrument used to analyze image attributes was reliable in obtaining stable and consistent measurements.

The studied population consisted of national and foreign tourists over 18 years old. The sample was collected at Mann Beach on San Cristobal Island in the Galapagos Archipelago in Ecuador during January and February 2019. Data were collected using convenience sampling to select respondents with self-administered questionnaires. A market research company distributed these questionnaires. The questionnaires were distributed in different places on Mann Beach to tourists who were willing to answer the questions. Interviewers approached tourists while they were resting. A total of 407 valid questionnaires were obtained, filled out by the respondents, with a margin of error of +/− 5%, a confidence level of 95%, and a 50% variation. This sample size was appropriate because a larger sample size does not reduce the sampling error beyond SPSS software, version 26. This study used factorial analysis as a variable reduction technique for fewer factors. The image attributes question was composed of 22 items adapted from Prayag’s study [12]. The varimax rotation method was used to arrange factorial loads, and the Kaiser criterion was used to find eigenvalues greater than 1. Another technique used was the k-means clustering method, which was used to find segments according to the image attributes of an insular marine protected area. K-means segmentation was used to find the segments differentiated by the mean. However, the Kruskal–Wallis test was used as another method to test, not find, relevant differences between the means. However, the Kruskal–Wallis test finds significant differences between groups, but does not find which groups are different. To do this, the Mann–Whitney U test was used to check the differences between segment 1 and 2, between segment 2 and 3, and between segment 1 and 3, and in this way know where these differences were found. The Chi-squared test was another technique used to understand the different segments significantly related to other variables such as satisfaction, loyalty, and various socio-demographic variables. SPSS software, version 26 was also used to apply this technique. The methods used can be adapted to the different marine protected areas, due to the characteristics and similarities presented by the different typologies of marine protected areas.

4. Results

4.1. Sample Profile

It was not possible to compare national tourists with foreigners, due to the inequality in size between the two groups. The sample of tourists consisted of 55.8% women and 44% men. Most tourists, 59.2%, were single, and 30% were married. About 42.1% of tourists were between 21 and 30 years old, while 35% were between 31 and 40. About 62.9% of tourists had a university-level education, and 21.6% had a postgraduate/master’s level education. About 30.7% of tourists were employed in the private sector, 25.8% were students, and 15.7% were entrepreneurs/business people. About 26.3% of tourists traveled alone, and 26% with a partner. Regarding income, 27.3% of tourists earned between USD 1001 and USD 1500, while 22.6% earned between USD 501 and USD 1000 monthly. Regarding spending, 33.7% of tourists spent between USD 50.01 and USD 100, while 29.5% spent between USD 101.01 and USD 150 (

Table 1. Socio-demographic variables.

Variable	Categories	Percentage
Gender	Male	44.0
	Female	55.8
Marital Status	Single	59.2
	Married	30.0
	Other	8.8
Age	Less than 20 years old	5.7
	21–30	42.1
	31–40	35.0
	41–50	10.8
	61–60	4.4
	More than 61 years old	2.0
Education Level	Primary	2.7
	Secondary	12.8
	University	62.9
	Postgraduate/Master/Ph.D.	21.6
Occupation	Student	25.8
	Researcher/Scientist	4.7
	Businessman	15.7
	Private Employee	30.7
	Public Employee	14.3
	Pensioner	2.9
	Unemployed	2.0
	Other	3.9
With whom do you travel	Alone	26.3
	With family	17.7
	With friends	18.4
	With partner	26.0
	Other	11.3
Income level	Less than USD 500	10.8
	From USD 501 to USD 1000	22.6
	From USD 1001 to USD 1500	27.3
	From USD 1501 to USD 2000	12.5
	From USD 2001 to USD 2500	9.8
	From USD 2501 to USD 3000	7.9
	More than USD 3000	9.1
Average daily expense	Less than USD 50	10.8
	USD 50.01–USD 100	33.7
	USD 101.01–USD 150	29.5
	USD 150.01–USD 200	15.0
	USD 200.01–USD 250	5.7
	More than USD 250	5.4

).

4.2. Image Attributes of an Island Marine Protected Area

A factor analysis was conducted to condense the information into a smaller number of factors. Four factors explained the image attributes. The varimax rotation method was used to sort the factorial loads into high and low ones. The Kaiser criterion was used to find the appropriate number of factors with eigenvalues greater than 1. The Cronbach’s Alpha of the factors ranged from 0.87 to 0.93, close to 1, indicating a robust internal consistency in each factor. Factor loads ranged from 0.50 to 0.88, above Hair et al.’s [42] suggested value of 0.50. The KMO index was 0.92, indicating adequacy for conducting the Factor Analysis. Additionally, Bartlett’s Sphericity Test was significant, justifying the use of Factor Analysis. See

Table 2. Image attributes of an island marine protected area.

Variable	Staff Attention	Tourist Facilities	Nature and People	Cultural Attractions
Kindness of hotel employees	0.875
Attitude of hotel employees towards family	0.856
Welcome and respect received from hotel employees	0.833
Courtesy of hotel employees	0.816
Hotel employees’ attention to my needs	0.809
Signaling	0.510
Store facilities for shopping		0.765
Variety of restaurants and bars		0.757
Local gastronomy		0.738
Accessibility to get to the destination		0.708
As a family vacation spot		0.705
Local transportation		0.651
Landscapes and natural attractions			0.827
Natural environment			0.781
Weather and climate			0.708
Kindness of people			0.702
Tourist crowd			0.507
Exoticism of the location			0.500
Cultural diversity of the location				0.846
Cultural and historical attractions				0.795
The towns and city				0.787
Variety and quality of accommodation				0.618
Cronbach’s Alpha	0.931	0.882	0.855	0.866
Eigenvalue	9.731	2.157	1.722	1.422
Variance explained (%)	44.231	9.805	7.828	6.466
Cumulative variance explained (%)	44.231	54.036	61.863	68.329

.

According to the results in Table 2, the first dimension of image attributes was labeled “Staff Attention”, as it was associated with hotel employees’ friendliness, attitude, courtesy, and attention. This dimension accounted for 44.23% of the explained variance. The second factor of image attributes was named “Tourist Facilities”, which is linked to store facilities, the variety of establishments, and accessibility to reach the destination. This dimension accounted for 9.81% of the explained variance. The third dimension of image attributes, “Nature and People”, was related to landscapes, natural environment, attractions, and local people and tourists. This factor contributed to 7.83% of the explained variance. The fourth factor of image attributes was labeled “Cultural Attractions”, which is associated with cultural and historical attractions. This factor accounted for 6.47% of the explained variance. These results address our first research question, RQ1: What are the image attributes in insular marine protected areas?

4.3. Segmentation of Image Attributes of an Island Marine Protected Area

A non-hierarchical K-means segmentation was performed to find the segments according to the image attributes of an insular marine protected area. See

Table 3. Segmentation of image attributes of an island marine protected area.

Variable	Passive	Nature	Want It All	Kruskal–Wallis	Sig.	Mann–Whitney U
Kindness of people	2.55	3.57	4.61	165.72	0.000	All
Natural environment	2.73	3.77	4.7	152.55	0.000	All
Landscapes and natural attractions	3.41	4.18	4.59	50.190	0.000	All
Weather and climate	3.14	3.79	4.55	102.62	0.000	All
Tourist crowd	3.07	3.46	4.36	106.29	0.000	All
Cultural and historical attractions	2.97	3.55	4.39	104.87	0.000	All
Cultural diversity of the location	2.90	3.31	4.35	113.25	0.000	All
Towns and city	2.62	3.22	4.32	136.82	0.000	All
Variety and quality of accommodation	2.77	3.24	4.36	145.42	0.000	All
Local transportation	2.56	3.18	4.03	99.33	0.000	All
Store facilities for shopping	2.48	3.40	4.12	112.12	0.000	All
As a family vacation spot	2.51	4.07	4.52	151.17	0.000	All
Local gastronomy	2.44	3.74	4.4	148.18	0.000	All
Variety of restaurants and bars	2.25	3.59	4.33	166.44	0.000	All
Accessibility to get to the destination	2.46	3.67	4.34	139.96	0.000	All
Exoticism of the location	3.18	4	4.55	86.650	0.000	All
Signaling	2.86	3.51	4.45	137.36	0.000	All
Hotel employees’ attention to my needs	3.21	3.37	4.55	144.93	0.000	All except 1–2
Courtesy of hotel employees	3.08	3.26	4.56	154.63	0.000	All except 1–2
Kindness of hotel employees	3.45	3.38	4.61	141.36	0.000	All except 1–2
Attitude of hotel employees towards family	3.35	3.37	4.61	137.36	0.000	All except 1–2
Welcome and respect received from hotel employees	3.31	3.36	4.61	142.89	0.000	All except 1–2

.

According to the results in Table 3, the three segments showed significant differences in their means (significant Kruskal–Wallis Test). The Mann–Whitney U test was employed, revealing differences in the means and determining where these differences lay. The first identified segment was labeled “Passives”, as it constituted a group with low scores in all image attributes analyzed in this study. The second segment was termed “Nature” since it was related solely to attributes associated with nature, such as landscapes, natural attractions, and the natural environment. The third identified segment was named “Want it All”, consisting of tourists who obtained high scores in all image attributes analyzed in this study.

These results address our second research question, RQ2: What segments are based on image attributes in insular marine protected areas?

4.4. Segments by Image and Satisfaction and Loyalty Variables of an Island Marine Protected Area

Pearson’s Chi-squared coefficient has been used to analyze the relationship between the segments and other variables, such as satisfaction and loyalty in an insular marine protected area. See

Table 4. Segments by image and satisfaction and loyalty variables of an island marine protected area.

Variables	Passive	Nature	Want It All	Chi-Squared	Sig.
Overall satisfaction	3.41	4.07	4.61	126,602	0.00
I intend to come back to this destination	3.10	3.54	4.31	77,695	0.00
I intend to recommend this destination	3.38	4.18	4.72	128,363	0.00
I will say positive things when I talk about this marine protected area	3.80	4.32	4.75	88,975	0.00

.

According to the results in Table 4, the three segments had a significant relationship (p < 0.05) with satisfaction and loyalty variables (intentions to return, to recommend, and to speak positively about the destination). The “Want it all” segment exhibited the highest level of satisfaction and loyalty compared to the other segments. Meanwhile, the “Nature” segment also achieved high scores (though not as high as the previous segment) regarding satisfaction and loyalty. On the contrary, the “Passives” segment scored the lowest regarding satisfaction and loyalty. Therefore, improving most image attributes is necessary to enhance satisfaction and loyalty among the “Want it all” and “Nature” segments. These segments, “Want it all” and “Nature”, showed the highest levels of satisfaction and loyalty (intentions to return, to recommend, and to speak positively among tourists) in insular marine protected areas. These results address our third research question, RQ3: What is the relationship between image segments and satisfaction and loyalty in insular marine protected areas?

4.5. Image Segments and Socio-Demographic Variables

Pearson’s Chi-squared coefficient has been used to analyze the relationship between the segments and socio-demographic variables in an insular marine protected area. See

Table 5. Image segments and socio-demographic variables.

Variable	Categories	Passive	Nature	Want It All	Chi-Squared	Sig.
Occupation	Student	25.4%	27.5%	25%	30.843	0.000
	Researcher/Scientist	2.8%	3.3%	5.7%
	Businessman	8.5%	8.8%	20.5%
	Private Employee	45.1%	33%	25.8%
	Public Employee	14.1%	19.8%	12.3%
	Pensioner		1.1%	4.5%
	Unemployed	4.2%	2.2%	1.2%
	Other		4.4%	4.9%
With whom do you travel	Alone	43.7%	29.7%	20.1%	28.148	0.000
	With family	5.6%	18.7%	20.9%
	With friends	19.7%	12.1%	20.5%
	With partner	21.1%	22.0%	29.1%
	Other	9.9%	17.6%	9.4%
How many days does the stay last in this island marine protected area?	1 day	1.4%	5.5%	2.5%	23.264	0.000
	2 days and 1 night	26.8%	12.1%	13.9%
	3 days and 2 nights	29.6%	22.0%	20.9%
	4 days and 3 nights	12.7%	22.0%	14.8%
	5 days and 4 nights	26.8%	38.5%	47.1%
	More than 5 days	2.8%		0.8%

.

According to the results from Table 5, the three segments had a significant relationship (p < 0.05) with the analyzed socioeconomic variables. Concerning the segment named “Passives”, a high percentage were private employees (45%). A significant proportion traveled alone (43.7%), others with their partners (21.1%), or friends (19.7%). Some tourists stayed in the insular marine protected area for 3 days (29.6%). On the other hand, the “Nature” segment mainly consisted of students (27.5%) and public employees (19.8%). They traveled alone (29.7%), with their partners (22%), or with their families (18.7%). A good percentage of tourists in this group stayed in the insular marine protected area for 5 days (38.5%). In contrast, tourists who belonged to the “Want it All” segment were predominantly private employees (25.8%) and business owners (20.5%). They traveled more often with their partners (29.1%), with their families (20.9%), friends (20.5%), or alone (20.1%). A high percentage stayed in the insular marine protected area for 5 days (47.1%).

5. Discussion

The first objective of this research was to identify the image attributes of marine protected areas. Addressing RQ1, the findings revealed four dimensions of image attributes: Staff Attention, Tourist Facilities, Nature and People, and Cultural Attractions. Our Staff Attention factor was found by Lam-González et al. [36] to be “Staff attention”. Authors Carvache-Franco et al. [38] found a similar dimension called service quality. Our Tourist Facilities dimension was similarly found by Carvache-Franco et al. [38] as “tourist infrastructure”. Akgün et al. [37] found a factor called “infrastructure”. However, our third image attribute, Nature and People, has not been found by other authors. The closest matches were termed “Natural and Aquatic Activities” by Carvache-Franco et al. [38], and authors Chiu et al. [31] found a factor called “Natural and Environmental Attractions”. Our cultural attractions factor was similarly found to be “cultural and historical attractions” by Chiu et al. [31]. Akgün et al. [37] found the factor generally to be “attractions”, and Carvache-Franco et al. [38] named the dimension “cultural activities”. There are various criteria for image attributes in coastal and marine destinations. However, a set of image attribute dimensions in destinations within marine protected areas has not been identified. In this sense, this study contributes to the academic literature by identifying the dimensions of image attributes, establishing that there are four factors in marine protected areas: Personnel Attention, Tourist Facilities, Nature and People, and Cultural Attractions. These factors constituting cognitive image revolve around service, facilities, and natural and cultural destination attractions. It has also been found that image-related attributes in marine protected areas are multidimensional. Due to variations in the characteristics that drive different visitors to visit different islands with marine protected areas, this study may help understand why various attribute dimensions are linked to the image formed by tourists in the area.

This study aims to establish segments based on image attributes in insular marine protected areas as a second objective. In response to RQ2, the findings demonstrate three segments: The “passive” segment, with low scores in image attributes; the “Nature” segment, with high scores only in attributes related to nature; and the “Want it All” segment, with high scores in all image attributes. These three segments, based on image attribute dimensions, have not been previously studied and bear little resemblance to those found by Prayag [12], which are related to environmental nature, reputation, and kindness of people. According to Carvache-Franco et al. [38], the “Want it All” group and “Passive” tourists resemble each other, but the scholars did not find the Nature segment. The contribution of this study to scientific literature lies in identifying three segments differentiated by the image of destinations within insular marine protected areas. As we can see, due to being insular protected areas, a group of “Nature” tourists embark on their trips attracted by the cognitive image of the islands’ endemic flora and fauna.

This study also aimed to determine the relationship between image segments and satisfaction and behavioral loyalty in insular marine protected areas as a third objective. In response to RQ3, the results found that the “Want it All” segment had the highest levels of satisfaction and loyalty, followed by the “Nature” segment. These findings demonstrate that the conative image mainly influences tourists seeking various activities in insular marine protected areas to achieve higher satisfaction and loyalty when visiting the destination. No studies have been found relating image segments to satisfaction and loyalty; therefore, these results contribute to the academic literature.

As a fourth objective, our study aimed to determine the socio-demographic characteristics of image segments in insular marine protected areas. In response to RQ4, the results indicate that the “Passive” tourist segment comprised a high percentage of private employees who traveled alone and stayed for 3 days. Meanwhile, the “Nature” segment mainly consisted of students who traveled alone and stayed for 5 days. On the other hand, tourists from the “Want it All” segment were mostly private employees and business owners, who traveled more with their partners or family and stayed for 5 days, which indicates that the segment seeks various activities. The “Want it All” group travels more with friends and family and stays longer at the destination. It has been identified that the segmented groups based on image possess distinct socio-demographic characteristics, contributing to the literature due to the scarce studies on this topic.

Regarding tourist arrivals to the Galapagos Islands, official figures from the Galapagos Tourism Observatory [43] establish that in 2023, 329,477 tourists arrived, 57.38% being foreigners and 42.62% nationals. In 2023, the arrivals of the year before the pandemic, which received 271,238 tourists in 2019, have been exceeded. It is difficult to define a volume of tourism that is “sustainable”, because it depends fundamentally on the management capacity of the protected areas, provision of services on inhabited islands and, above all, the control service quarantine, without which the transportation of cargo and people entails the entry and dispersal of more and more invasive species [44].

As practical implications for enhancing the cognitive image of a marine protected area like the Galapagos Islands, administrators could develop management plans that include guidelines to increase satisfaction with the image attributes. Tourism sector employees could undergo training in customer service to improve Staff Attention. Enhancing the attributes of Tourist Facilities could involve upgrading hotels and restaurants by creating recreational areas and implementing necessary services to accommodate tourist activities. Workshops for observing flora and fauna and other activities related to visiting local communities could be implemented to attract more attention to Nature and People. Games during tours might also be introduced to facilitate interaction between tourists and recreational activities. Guides could receive training on heritage and history to elevate the importance of Cultural Attractions. Additionally, specialized tourist packages centered around cultural sites could be developed.

Regarding the identified segments, engaging “Passive” tourists in recreational activities and community interactions would be necessary to enhance their interest as part of the cognitive image. For the “Nature” segment, activities related to water sports and visits to places with endemic flora and fauna could be created. Concerning tourists falling under the “Want it All” category, it would be necessary to involve them in packages offering a variety of activities to maintain the satisfaction and loyalty that currently constitute part of the conative image in protected marine areas. Regarding the segments based on image attributes, there could be consideration for developing packages tailored for private employees, who comprise the most significant number within the Passive group. Therefore, increasing the conative image among this segment of tourists could be crucial.

6. Conclusions

The destination image has become an increasingly important and growing area of research in recent years, since understanding the destination image can help comprehend how tourists choose destinations, services, and products when making decisions. It also aids in understanding tourists’ choices, satisfaction, and intention to return to the destination. A tourist’s destination image comprises their impressions, ideas, beliefs, perceptions, and representations of that place due to their visiting experiences. Some experts in the field stated that people hold two different types of images about destinations (cognitive and affective images), while others added a third type (conative images).

Ecological diversifications characterize marine protected areas in terms of environment, fauna, and flora. The purpose of protecting these unique coastal areas is to limit fishing and allow research to increase the effectiveness of environmental protection. Therefore, these marine protected areas can attract many tourists to visit them to see their endangered animals and plants and enjoy the peaceful atmosphere.

The present study conducted in the Galapagos Islands of Ecuador aimed to perform an attribute segmentation related to the image applied to insular marine protected areas to obtain results that contribute to the literature related to this subject and the industry. After applying multivariate statistics, a process was carried out that initially involved identifying image attributes, determining segments to find the relationship between satisfaction and behavioral loyalty, and identifying socio-demographic differentiations among these demand groups.

Four image attributes were found in the marine protected areas: Staff Attention, Tourist Facilities, Nature and People, and Cultural Attractions. Similarly, three image attribute segments were identified in the insular marine protected areas, including the “Passive” segment, with only low scores in the image attributes; the “Nature” segment, with high scores only in attributes related to nature; and the “Want it All” segment, with high scores in all image attributes. Regarding behavioral loyalty analysis among these groups, the “Want it All” segment showed the highest satisfaction and loyalty levels, followed by the “Nature” segment. Concerning significant differentiation in socio-demographic aspects among the segments, this study indicates differences in occupation, travel companionship, and length of stay at the destination.

As theoretical implications, this study identifies four dimensions of image attributes related to cognitive images that other scholars had not collectively found in destinations declared as marine protected areas. It also identified three image segments that other scholars had not found. As part of the conative image, it analyzed mostly satisfied and loyal groups, which had not been studied previously. This study also examined the socio-demographic differences among the identified segments. All of these aspects contribute to the academic literature on image areas in marine protected areas. Regarding practical implications, this study provides guidelines for managers of marine protected areas and offers information for tourist service providers, enabling them to design products according to the identified demand.

This study’s main limitation was the sample collection timing, as the demand could vary due to the increase in tourists during high seasons of the year. Another limitation was the convenience sampling method, which relied on tourists willing to participate in this study. Finally, as a future line of research, a study related to the relationship between image segments and environmentally responsible behavior in marine protected areas is recommended.