Coastal Cities Seen from Loyalty and Their Tourist Motivations: A Study in Lima, Peru

Abstract

The objective of this empirical study is to determine: (a) the underlying variables of the travel motivations related to a coastal city; and (b) the motivational dimensions that predict return, recommendation, and saying positive things about a coastal city as loyalty variables. This project utilized an in situ investigation carried out in Lima, a coastal city located on the Pacific Ocean near Peru with important natural and cultural attractions. The researchers used 381 questionnaires that were analyzed through factor analysis, in addition to the stepwise multiple regression method. Reesults identified six underlying variables or motivational factors: “culture and nature”, “authentic coastal experience”, “novelty and social interaction”, “learning”, “sun and beach”, and “nightlife”. Regarding loyalty, the “novelty and social interaction” dimension is the most important predictor of return and the “authentic coastal experience” dimension is the most important predictor of recommending and saying positive things about a coastal city. To motivate a return, events could be created on the beach to motivate novelty, as well as increase recommendations and the amount of positive things said about the destination; educational and sports activities and workshops could also be created with the community and the coastal environment. Results can be used by firms for preparing information for new customers in order to increase trip intention and improve guides for destination marketing organizations (DMOs).

1. Introduction

Motivations are formed by a set of biological and spiritual needs and desires that lead people to carrying out specific tasks [1]. In relation to tourism, motivations are considered to be a set of needs that involve people in tourist activities [2]. Along these lines, Schuhmann et al. [3] stated that decisions to return to a destination are sensitive to changes in all aspects of coastal and marine quality.

The pandemic has hit the tourism sector in such a way that, on 28 January 2021, the World Tourism Organization (UNWTO) [4] reported that global tourism registered the worst year in its history in 2020, with a 74% drop in international arrivals. Destinations around the world received a billion fewer arrivals in 2020 compared to the previous year due to a dramatic drop in demand and other general travel restrictions. Before this pandemic occurred, it had been predicted that, by 2030, coastal and marine tourism would constitute the largest share of the ocean economy in the world (26%), employing 8.6 million people [5]. Coastal and marine destinations [6] offer a wide range of activities for tourists, such as visits to local communities, water sports, sightings of marine flora and fauna, ecotourism, and local gastronomy. Similarly, coastal tourism includes a wide variety of activities, such as sports, wellness stays, nature and wildlife observation, and volunteer activities and education [7]. Coastal tourism is a marine activity, as both are closely linked since coastal areas are the starting and return points of marine tourism that offer activities such as navigation, diving, cruises, fishing activities, and offshore whale watching [8]. Marine tourism thus encompasses a wide range of activities that take place in the deep oceans, the most frequent being cruising and navigation [9,10].

It is crucial to have empirical results that determine which motivational dimensions predictively influence loyalty in coastal cities. With this standardized information, tourist destination managers and DMOs could design sustainable management guides for the benefit of the controlled development of these sites. On the other hand, companies could create marketing plans to develop products according to demand characteristics.

In this study, we consider the city of Lima, located besides the Pacific Ocean, which is a global coastal tourism destination known as Costa Verde, with 23 beaches along 6 districts. This coastal area is a scene of varied commercial, cultural, natural, and sporting attractions, including global and regional events. There are also musical concerts throughout the year, and an annual gastronomy fair called Mistura.

In this context, studies have been carried out on other forms of tourism as influenced by motivations as predictive variables of loyalty in different types of destinations. Studies in coastal and marine destinations are also scarce, but there are no studies on coastal cities. This shows the existence of a gap in the academic literature; therefore, this study aims to find: (a) the dimensions or underlying variables of travel motivations associated with a coastal city with natural and cultural attractions; and (b) the motivational dimensions that predict return, recommendation, and positive things said about a coastal city as loyalty variables.

Factor analysis is used here as a statistical technique to facilitate the interpretation of motivational variables through a smaller number of variables or underlying factors. Then, step-by-step multiple regression was applied to find the most important motivational factors in returning, recommending, and saying positive things about coastal cities. The findings contribute to the academic literature and function as action guides for tourism destination managers.

To achieve the objectives, this manuscript is structured as follows. Section 2 presents a review of the scientific literature, Section 3 presents the methodology, Section 4 presents the investigation results, Section 5 contains a discussion, and Section 6 t concludes the study.

2. Literature Review

Tourism in coastal cities is perceived as an industry that helps the regional economy since a significant number of tourists visit an area [11]. Coastal resort cities have great ecological, cultural, and economic benefits. Therefore, they are especially attractive [12]. Hence, tourism is a relevant activity with considerable economic benefits for many coastal cities [13].

2.1. Motivations in Coastal Cities

Tourist motivation is considered to be a factor that rationalizes tourist behavior [14], in this sense, Meng et al. [15] defined tourist motivations as a mixture of the needs and desires of tourists that form a tendency to enjoy a tourist attraction or destination.

One of the first studies related to coastal cities is that of Molera and Abaladejo [16], who found the five rural and coastal reasons to travel to Murcia (Spain); these were nature and tranquility, physical and cultural activities, family, travel characteristics, and rural life.

From a profit perspective in marinas, Paker and Vural [17] in Turkey found the following motivational factors: service, prestige, accessibility, tourist attraction, local culture, entertainment, and support elements. Security was the most important element, followed by accessibility and tourism. In analyzing a coastal national park, Carvache-Franco et al. [18] found six motivational factors: self-development, interpersonal relationships and ego defense function, construction of personal relationships, escape, appreciation of nature, and reward. In the field of motivations from the Push and Pull point of view, Jeong [19] in Seoul (South Korea) found two dimensions of push: escape and novelty. Furthermore, two pull factors were found: active marine activities and static marine activities. The results showed that active marine activities should attract more tourists who have novel push motivations and destinations with static marine activities to tourists who have escape motivations. Similarly, Kassean and Gassita [20] analyzed the motivational drive and attraction factors that affect the selection of tourists for a vacation destination determined that rest and relaxation were the strongest motivational driving forces, followed by nostalgia, escape, novelty, and social interaction. The pull factors were the unique climate and climate of Mauritius and the exquisite scenery.

The academics Rid et al. [21] carried out a segmentation by motivations in Gambia, the motivational factors found were heritage and nature, authentic rural experience, learning, sun, and beach. With similar results in a study carried out in Salinas (Ecuador), Carvache-Franco et al. [22] identified six motivational dimensions: authentic coastal experience, heritage and nature, learning, novelty and social interaction, physical activities, and sun and beach. The authors argue that all dimensions are related to the sun and the beach, nature, culture and the social. In a more current investigation, Güzel et al. [23], in Antalya on the Mediterranean coast of southwestern Turkey, identified six factors: curiosity, relaxation, escape, sport and active life, extravagance, and travel flaunting.

Aspects related to motivations and loyalty in the time of COVID-19 have been scarcely studied. In this sense, Chua et al. [24] showed that negative affect as COVID-19 outcome significantly influenced perceived health risk, which in turn induced mental well-being and perceived uncertainty. In addition, Rather [25] examined the impact of fear of COVID-19 and the perceived risk in the associations between social networks, client–brand engagement, and return. This author found that fear of COVID-19 and perceived risk had a significant negative impact on attitude towards travel; however, social media had a significant positive effect. From this perspective, academics, such as Carvache-Franco et al. [26], found two dimensions of coastal and marine tourism “escape,” for the theme “travel” and “sun and beach,” for the themes “beach,” “sea,” and “cruise” in a worldwide study using Twitter tourism hashtags during the COVID-19 pandemic.

In summary, the motivations of coastal cities vary based on the characteristics of each destination, but in previous findings, recurrent motivations, such as novelty, social, sun and beach, nature, and culture, can be found. However, no previous findings are found on what are the motivational dimensions of coastal cities with natural and cultural resources. For this reason, our first research question arises (RQ1): What are the dimensions or underlying variables of the travel motivations associated with a coastal city with natural and cultural attractions?

2.2. Loyalty in Coastal Destinations

The concept of loyalty has been recognized as one of the most important indicators of success in the marketing literature [27,28,29,30]. Similarly, if tourists have had better experiences at a destination, they are more likely to return to the same destination in the future [31,32]. Assaker et al. [33] used the intention to revisit as the main concept and modeled its relationship with the search for novelties, image, and satisfaction. In this sense, it is necessary to conceptualize the branding of the place not only from the perspective of promoting tourism, but also from the perspective of promoting sustainable governance [34].

Among the findings that academics have made on this topic, Goffi et al. [35] indicated that sustainability affects the satisfaction and intention to return of the tourist segments of large-scale coastal packages, these groups being tourists interested in sustainability. Huyen and Nghi [36] on Kien Giang Island, Vietnam, found that the novelty has a positive impact on visitor loyalty in marine and coastal adventure tourism. In Phuket, Thailand, Sangpikul [37] identified that beach attractions and the local community are crucial factors of the tourism experience that affect visitor loyalty to island destinations.

In another study, Prayag [38] predicted behavioral loyalty and highlighted three aspects that improve the intentions to return to a destination: natural environment, reputation, and friendliness of people. Fianto [39] identified that the quality of the service has a significant effect on the intention to revisit a destination. Additionally, the experience of the brand was also found to have a significant influence on the intention to revisit. Additionally, visitor satisfaction had a mediating function of the relationship between the experience of the brand with the intention of visiting it again. Similarly, in a sun and beach destination.

Several previous findings have tried to relate motivations to loyalty in coastal cities, but so far there are no similar results that establish what are the motivations that predict loyalty in these destinations that have a rich nature and culture. The most recurrent are: novelty, social, sun, and beach. Therefore, our second research question arises (RQ2): What are the motivational dimensions that predict return, recommendation, and positive things said about the coastal city as loyalty variables?

3. Methodology

3.1. Study Area

The study was based in Lima city, which is a recommended destination to be visited [40] and has the best business hotel (Swissotel Lima) during the period 2013–2017 and 2019 [41]. In 2014, Lima hosted the United Nations Conference on Climate Change (COP 20) [42]. In 2018, more than 11.8 million trips were made by internal tourists to Lima, which represents 26.1% of total trips nationwide, making Lima the main destination visited. In the same year, of the total of foreign tourists who visited Peru, 72.4% visited Lima, occupying the first place of total inbound visits for tourism. Since the foundation of Lima 485 years ago, the city now has an average of 2.99 m² of ecological area per inhabitant, with some districts having much higher values, such as Santa María del Mar (10.0 m²), La Punta (9.00 m²), San Borja (8.44 m²), and La Molina (7.54 m²).

The Costa Verde region houses the Domos Art convention center, where free cultural activities on gastronomy and art are held throughout the year. It is a space for various sports competitions, such as the 2019 Pan American Games. A highly requested tourist service is Mirabus, which consists of a ride on a bus with tourist guides, explaining the characteristics of the visited beaches and the history of the Costa Verde. A characteristic of the beaches of Lima is that they have gastronomic offerings, among which one can find mainly marine dishes, but there are also meat and pasta offerings, in gastronomic fusion.

Peru, the gastronomic capital [43] and best cultural tourist area [44] in South America, offers tourists various alternatives, whether or not they have a car to get around, as well as places to walk (Figure 1).

3.2. Instrument, Collection of Data and Analyses

In the present study, the authors designed a methodology based on the collection of a sample in situ to achieve the proposed objectives. This was carried out using a questionnaire prepared based on previous academic findings. The questionnaire that was used consisted of 16 questions technically divided and arranged in three parts. The questions arise from the review of the literature. The first part contained 13 closed questions and presented the sociodemographic aspects which were adapted from the study by Lee et al. [45]. The second part contained the motivations with 27 items using a five-point Likert scale (1 being not very important and 5 very important), corresponding to the motivations that tourists have to visit coastal cities. The motivations scale was adapted based on the studies by Rid et al. [21] and Carvache-Franco et al. [22]. A third section that studied loyalty was adapted from the study by Kim and Park [46]. Loyalty variables were analyzed with a 5-point Likert scale, where 1 was unlikely and 5 was very likely. The questionnaire was prepared in Spanish and English.

To analyze the validity and reliability, the judgment of five experts was used to analyze the validity of the questionnaire. They evaluated the items of the questionnaire and contributed to its construction. Likewise, the conceptual equivalence was evaluated, verifying if the words reflected the appropriate meaning according to the context in which they were used. The experiential equivalence was also evaluated, ensuring that the elements should reflect the experience of the visitors. On the other hand, the semantic equivalence was evaluated, verifying that the words should have only one meaning. In addition, to validate the questionnaire, a pilot test of 15 surveys was carried out. The internal consistency analysis was used to analyze the reliability of the questionnaire, using the Cronbach’s Alpha index of the motivations scale, which reached a value of 0.94, which indicated a good internal consistency between the scale items. To minimize biases in the questionnaire, the questions were designed in a clear way, so that they are not technical or difficult to answer, and that they have a precise meaning according to the profile of the visitors who are part of the sample. In addition, the questions of the questionnaire were written in such a way that the options seemed neutral or equally acceptable and not biased to answer in favor of a certain theme of the study.

The population in the study were national and international tourists over 18 years of age. The field of this research was carried out in the coastal city of Lima (Peru) on the weekends of the months of January and February 2020, in the following tourist sites of the main coastal regions: beach area (Las Sombrillas beach and Playa Agua Dulce) and in the vicinity of the Larcomar Shopping Center (located on top of the Costa Verde cliff in the Miraflores district). The quantitative method was chosen because it allows the generalization of the results, based on a representative number of the population. In addition, it ensures advanced statistical analyses that allow finding predictor variables. The interviews were conducted by university students, who received prior training from the authors of this research.

Once the data were collected in the field work, these were organized, tabulated, and analyzed using the program SPSS Version 22. A total of 381 valid questionnaires were obtained, which then became the sample size. There was a representative sample of the population, with a margin +/−5% error, a 95% confidence level, and a 50% variance for the most reliable results. The analysis of this study was performed by its authors of this study.The statistical techniques for data analysis were factor analysis to facilitate the interpretation of motivational variables through a smaller number of variables or underlying factors. Specifically, a Varimax rotation was used to minimize the number of variables that have high loads on each factor. The Kaiser criterion was used to find the number of factors, using eigenvalues greater than 1. The Kaiser–Meyer–Olkin (KMO) index was greater than 0.7, so it was appropriate to perform the factorial analysis. Bartlett’s test was significant (p < 0.05) and it was possible to apply the factor analysis model. Then, in the second stage, the stepwise multiple regression method was implemented to find the most important motivational factors in satisfaction, intentions to return, and recommending coastal cities.

4. Results

For the present study, regarding the sociodemographic and visiting aspects, the sample consisted of national tourists (47%) and international tourists (53%). Most came from South America (81.3%) and Europe (12.1%). Regarding gender, the majority were men (56.8%), followed by women (43.2%). Regarding their marital status, 62.9% were single and 30.33% were married. Regarding age, the largest group was between 31 and 40 years old (35.3%), followed by tourists between 21 and 30 years old (33.9%). Regarding the level of education, the majority had university studies (65.3%), followed by those who had secondary studies (20.5%). Regarding work activity, the majority were private employees (41.1%), followed by tourists who were businessmen (20%). In addition, a large part of the tourists traveled with friends (36.8%) and family (24.7%). Regarding the stay at the destination, most spent three days and two nights. In terms of spending, most tourists wanted to spend less than USD 50 per day (

Table 1. Demographic information.

Demographic Factor	Category	N = 381	%
Origin	National	179	47.1
	Foreign	201	52.9
Origin by continent	North America	20	5.3
	Europe	46	12.1
	South America	309	81.3
	Rest of the world	5	1.3
Gender	Male	216	56.8
	Female	164	43.2
Marital status	Single	239	62.9
	Married	115	30.3
	Other	26	6.8
Age	<20	18	4.7
	21–30	129	33.9
	31–40	134	35.3
	41–50	52	13.7
	51–60	31	8.2
	>60	16	4.2
Level of education	Primary	1	0.3
	Secondary	78	20.5
	University	248	65.3
	Postgraduate/Master’s/PhD	53	13.9
Employment	Student	44	11.6
	Researcher/scientist	16	4.2
	Businessman	76	20.0
	Private employee	156	41.1
	Public employee	40	10.5
	Retired	13	3.4
	Unemployed	18	4.7
	Other	17	4.5
Number of times travelled ever	First time	119	31.3
	2 times	75	19.7
	3 times	73	19.2
	More than 3 times	113	29.7
Travel partners	None	58	15.3
	Family	94	24.7
	Friends	140	36.8
	A partner	69	18.2
	Other	19	5.0
Travel duration	1 day	32	8.4
	2 days–1 night	78	20.5
	3 days–2 nights	101	26.6
	4 days–3 nights	42	11.1
	5 days–4 nights	47	12.4
	More than 5 days	80	21.1
Average daily total expenditures	Less than USD 50	251	66.1
	USD 50–99	88	23.2
	USD 100–149	31	8.2
	USD 150–199	9	2.4
	USD 200–249	1	0.3

).

4.1. Exploratory Factor Analysis

To facilitate the interpretation of the results, a factor analysis was carried out in which six factors were obtained that explain the motivational variables that were used in the present study. For data extraction, principal component analysis was used. In addition, to order the factors into very high or low factor loadings, the Varimax rotation method was used. To find the number of adequate factors, the Kaiser criterion was used; at this point, factors with eigenvalues greater than 1 were taken into account. In addition, a sedimentation graph was used to analyze the number of suitable factors. As shown in Figure 2, the seventh eigenvalues does not cause a slope to respect the other. Therefore, six is the appropriate number of factors.

The six factors that were found represented 67.89% of the total variance, becoming a value suitable for this model. The Cronbach’s Alpha of the factors ranged between 0.90 and 0.71, which indicates that there is a high internal consistency in each of the factors. The factor loadings ranged from 0.53 to 0.93; therefore, all were above the critical value of 0.50 suggested by Hair et al. [47] and Lopez et al. [48]. The KMO index was 0.93, reaching a value very close to 1, indicating a high relationship between the variables and suggesting they are appropriate to perform the factor analysis. In addition, the Barlett’s sphericity test was significant (χ² = 6234.9, p = 0.000), so the use of factor analysis was deemed adequate. The results are shown in

Table 2. Exploratory factor analysis of motivations for visiting.

Variable	Component						Factors
	1	2	3	4	5	6
Importance of Lima’s history & culture	0.788						Culture and nature
Importance of coastal and marine tourism	0.759
Historical attractions experiences	0.752
Experience national parks and marine wildlife sites	0.699
Real culture and traditions experiences	0.696
Importance of tourism in natural areas	0.693
Typical gastronomy of Lima	0.628
Stay among the local population		0.813					Authentic coastal experience
Share interesting experiences with the coastal population		0.740
Access to rural farm goods		0.644
Strong feelings of experiences lived		0.632
Lifestyle of the coastal population of Lima		0.553
Experience related to the coastal landscape of Lima		0.533
Visit family and friends			0.704				Novelty and social interaction
For its tourist attractions			0.638
To know the flora and fauna			0.609
Environmental quality of air, water, and soil			0.593
I want to see the things that I normally do not see			0.582
Security and protection			0.568
Interest in myths and legends				0.772			Learning
Learn traditional languages of Lima				0.746
Interest in coastal handicrafts				0.690
Learn traditional dances				0.648
Importance of swimming					0.819		Sun and beach
Importance of water sports					0.738
Importance of sun-beach tourism					0.545
Nightlife						0.925	Nightlife
Cronbach’s alpha	0.90	0.89	0.83	0.88	0.71	-
Eigenvalue	11.28	1.98	1.81	1.16	1.07	1.03
Variance explained (%)	41.79	7.35	6.69	4.28	3.98	3.80
Cumulative variance explained (%)	41.79	49.14	55.84	60.11	64.09	67.89

.

According to the results of Table 2, the first factor that appears was “Culture and nature” and it was linked to the experience of culture, tradition, and natural sites. This factor included 41.79% of the total variance; due to this, it is considered the most important factor in relation to the others. The second factor was the “Authentic coastal experience” which is related to the exchange of interests and experiences with the coastal population. The third factor was “Novelty and social interaction” which was related to knowing new and attractive things about flora and fauna. The fourth factor was “Learning” which was related to the interest in learning about traditional dances and culture. The fifth factor was “Sun and beach” which was related to sun and beach and water sports. The last factor was “Nightlife”. Results similar to other studies found in the literature. However, this study found a new motivational factor “Nightlife”.

4.2. Confirmatory Factor Analysis (CFA)

A CFA was performed using the maximum probability to establish confidence in the measurement model, which specifies the relationships between the observed variables and the fundamental constructs. See Figure 3.

Regarding the adjustment indices, the CFI is one of the most used relative indices and has the best performance since it ranges between 0 and 1 with a minimum value of 0.9 for a good level of adjustment. For this case, the CFI was 0.86, which indicates a good level of adjustment. In addition, the NFI evaluates the decrease in the statistic x2 of the model with respect to the base model. For a good level of adjustment, a value close to 0.90 should be reached. In the present study, the NFI was 0.83, suggesting an acceptable level of fit. RMSEA refers to the amount of variation not explained by the model by degree of freedom. In general, RMSEA values between 0.05 and 0.08 indicate a reasonable level of adjustment. For this case, the RMSEA indicated an acceptable level of adjustment of 0.08. The figures indicated a good fit of the model. That is, the six factors found in the exploratory factor analysis were confirmed.

Results that answer our first research question (RQ1): What are the dimensions or underlying variables of the travel motivations associated with a coastal city with natural and cultural attractions?

4.3. Relationship between Motivations and Return Intentions

Stepwise multiple regression was used to analyze the most influential predictors in return intentions, which only included the motivational factors that were representative for the model. The regression technique was used because we wanted to know the predictive motivational dimensions with greater intensity in relation to the dependent variable. The results are presented in

Table 3. Relationship of motivations with return intentions (multiple regression).

Variable	Beta	t	Sig.	Tolerance
Novelty and social interaction	0.305	6.392	0.000	1.000
Sun–beach	0.163	3.410	0.001	1.000
Authentic coastal experience	0.132	2.774	0.006	1.000
Learning	0.105	2.201	0.028	1.000
(Constante)		103.985	0.000
Adj. R2	0.139
F statistic	16.258
Sig.	0.000
Durbin–Watson	2.001

.

According to Table 3, 13.9% of the variation is represented by the four motivational factors that predict return intentions. The F test was significant (p < 0.05), indicating a relationship between significant predictors and return intentions. Furthermore, the tolerance values indicated that there was no multicollinearity between the independent variables. Likewise, the Durbin–Watson statistic reached a value between 1.5 and 2.5, so it could be assumed that there was no autocorrelation in the errors. Therefore, it can be mentioned that the model was adequate. The factor “Novelty and social interaction” was the most representative predictor in return intentions (Beta = 0.305, p < 0.05), followed by the factor “Sun and beach” (Beta = 0.163, p < 0.05). This study contributes to the scientific literature by pointing out that “Novelty and social interaction” is the main predictive factor in the return of tourists, followed by “Sun and beach”.

4.4. Relationship of Motivations with the Intentions to Recommend

To analyze the most important predictors in the intentions to recommend the destination, stepwise multiple regression was used, which included only the motivational factors that were significant for the model. The results are presented in

Table 4. Relationship of motivations with the intentions to recommend the destination (multiple regression).

Variable	Beta	t	Sig.	Tolerance
Authentic coastal experience	0.182	3.724	0.000	1.000
Learning	0.176	3.589	0.000	1.000
Novelty–social interaction	0.145	2.962	0.003	1.000
Culture–nature	0.120	2.453	0.015	1.000
(Constante)		127.482	0.000
Adj. R2	0.090
F statistic	10.386
Sig.	0.000
Durbin-Watson	1.682

.

According to Table 4, 9% of the variation was represented by four motivational factors that seemed to influence the intentions to recommend the destination. The F test was significant (p < 0.05), which showed a real relationship between the significant predictors and the intentions to recommend. The tolerance values indicated that there was no multicollinearity between the independent variables. Furthermore, the Durbin–Watson statistic reached a value of 1.68, so it could be assumed that there was no autocorrelation in the errors, making it the appropriate model. The factor “Authentic coastal experience” was the most significant predictor in the intentions to recommend (Beta = 0.182, p < 0.05), followed by the factor “Learning” (Beta = 0.176, p < 0.05). This is similar to the results in other studies. However, this study found “Learning” and “Culture and nature” as predictors of recommendation intentions, thus contributing to the literature.

4.5. Relationship of Motivations with Saying Positive Things about the Destination

To analyze the most important predictors in saying positive things about destiny, stepwise multiple regression was used, which included only the motivational factors that were representative for the model. The results are presented in

Table 5. Relationship of motivations and positive things said about the destination (multiple regression).

Variable	Beta	t	Sig.	Tolerance
Authentic coastal experience	0.249	5.239	0.000	1.000
Culture and nature	0.223	4.690	0.000	1.000
Sun and beach	0.166	3.499	0.001	1.000
Novelty and social interaction	0.111	2.338	0.020	1.000
(Constante)		140.768	0.000
Adj. R2	0.143
F statistic	16.786
Sig.	0.000
Durbin–Watson	1.827

.

According to Table 5, 14.3% of the variation was represented by five motivational factors that seemed to influence saying positive things about the destination. The F test was significant (p < 0.05), which showed a real relationship between significant predictors and saying positive things about the destination. The tolerance values indicated that there was no multicollinearity between the independent variables. Furthermore, the Durbin–Watson statistic reached a value of 1.81, so it could be assumed that there was no autocorrelation in the errors; thus, the model was adequate. The factor “Authentic coastal experience” was the most representative predictor when saying positive things about the destination (Beta = 0.249, p < 0.05), followed by the factor “Culture and nature” (Beta = 0.223, p < 0.05). Similar results were also obtained in other studies. However, this study found “Culture and nature” as a predictor of the intentions of saying positive things about the destination, consequently contributing to the literature.

Figure 4 shows the predictive relationship between motivational dimensions and loyalty variables, such as return, recommendation, and saying positive things about the destination. These results also answer our second research question (RQ2): What are the motivational dimensions that predict return, recommendation and saying positive things about the coastal city as loyalty variables?

5. Discussion

One of the objectives of this study was to find the dimensions or underlying variables of the travel motivations associated with a coastal city. The results of this work show the existence of six important motivational demand factors in the city of Lima, Peru: “Novelty and social interaction”, “Learning”, “Culture and nature”, “Authentic coastal experience”, “Sun and beach”, and “Nightlife”. These results are similar to those of Rid et al. [21], who found four motivational factors: heritage and nature, similar to our “Culture and nature”; authentic rural experience, similar to our “Authentic coastal experience”; learning, analogous to our “Learning”; and sun and beach, very similar to the “Sun and beach” factor in this research. Compared with the results of Carvache-Franco et al. [22], the authentic coastal experience was similar to our “Authentic coastal experience”; heredity and nature were analogous to our “Culture and nature” factor; learning was similar to our “learning” dimension; novelty and social interaction were similar to our “novelty and social interaction” factor; and sun and beach were analogous to the “Sun and Beach” factor in our study. In light of the study by Güzel et al. [23], we find that curiosity is similar to our “Novelty” factor; sport and active life are similar to the “sun and beach” factor in our study; and relaxation is similar to “Escape”. Extravagance was not a factor in this study. Our findings contribute to the academic literature, adding a factor that has been found in a coastal city, which is the motivational factor “Nightlife”.

Another objective of this research was to find the motivational dimensions that predict return, recommendation, and saying positive things about the coastal city as loyalty variables. The findings of this study showed that the factor that most accurately predicted return intentions was “Novelty and social interaction”. Several authors also found the novelty factor as a predictor of the return of a tourist. For example, Huyen and Nghi [36] found it to be novelty, Jeong [19] found it to be escape and novelty, and Kassean and Gassita [20] found it to be novelty and social interaction. Another important predictor found in this research was “Sun and beach”. We obtained results similar to those of Sangpikul [37] whoo identified the attractions of the beach and the coastal community as key factors in the loyalty of tourists to island destinations. Therefore, this study contributes to the scientific literature by providing that “Novelty and social interaction” is the main predictive factor in the return of tourists, followed by the variable “Sun and Beach”.

This study found that the factors that have most accurately predicted the intentions to recommend and say positive things about the destination were “Authentic coastal experience”, “Learning”, and “Culture and nature”. The biggest predictor in recommending and saying positive things about a seaside city is Authentic coastal experience. Similar results were attained by Sangpikul [37], who found that the local community is one of the important factors in loyalty. However, this author’s study did not mention “Learning” and “Culture and nature” as predictors of recommendation intentions and saying positive things. This, therefore, is one of the contributions to this study, which will help coastal cities to strengthen their offerings activities of authentic coastal experience learning, culture, and nature; this encourages tourists to recommend and say positive things about these destinations.

In summary, “Novelty and social interaction”, “Sun and beach”, “Authentic coastal experience”, “Learning”, and “Culture and nature” were found to be important predictors for return, recommendation, and saying positive things from coastal cities. This means that coastal cities must offer innovative services related to sun and beach activities, learning, and culture and nature. In addition, to integrate tourists with the coastal population. These attributes are important to increase intentions to return, recommend, and say positive things of coastal cities.

6. Conclusions

Tourism in coastal cities contributes to the development of the regional economy since a significant number of tourists visit the destination, bringing great ecological, cultural, and economic benefits. The coastal destinations located within the cities allow activities, such as water sports, visits to communities, recreation on the beach, as well as activities in contact with nature and culture. Marine destinations are interrelated with the coasts of cities, allowing tourists to enjoy activities such as watching marine flora and fauna, marine sports, sailing, visits to water parks, among others.

The study identifies and assesses six motivational dimensions in coastal cities: “Novelty and social interaction”, “Learning”, “Culture and nature”, “Authentic coastal experience”, “Sun and beach”, and “Nightlife”. Furthermore, the factors that most accurarely predict return intentions are “Novelty and social interaction” and “Sun and beach”. The factors that most accurately predict the intentions to recommend and say positive things about the destination are “Authentic coastal experience”, “Learnnig”, and “Culture and nature”. The biggest predictor in recommending and saying positive things about a seaside city is “Authentic coastal experience”.

As theoretical implications in relation to motivations, six motivational dimensions have been found. These were not found in a single previous study, but in several studies such as those of Carvache-Franco et al. [22], Güzel et al. [23], and Rid et al. [21], so the motivations are varied. Regarding the prediction of motivations in relation to loyalty, the factor that most accurately predicts return intentions is “Novelty and social interaction”, which is similar to several other studies such as Huyen and Nghi [36], Jeong [19], and Kassean and Gassita [20], as well as “Sun and Beach”, which is similar to the studies by Sangpikul [37]. Similarly, the highest predictor factor for recommending and saying positive things about a coastal city is “Authentic coastal experience”, similar to a study by Sangpikul [37], followed by “Learning”. “Culture and nature” dimensions have not yet been studied in the scientific literature as predictors of recommendation in coastal cities. Thus, this constitutes a significant input to the academic literature. The theoretical contribution of this study was to have found new dimensions, not found by other authors, which predict intentions to return, to recommend, and to say positive things about the destination.

As practical implications related to a coastal city, the following recommendations are made for companies and administrators involved in the development of the destination. Regarding the motivations, this destination must create and develop activities related to the motivations found in the present study, for example, visiting museums and craft sites and walks to increase motivation (“Culture and nature”), implement visits to the community to improve the “Authentic coastal experience” motivation, and increase the offer of water sports to position more motivation “Sun and beach”. To increase motivation surrounding “Novelty and social interaction”, novel activities should be carried out which promote the integration of tourists with their family and friends, group navigable walks, social workshops, familiarization trips, entertainment activities, beach sports, and other activities, which increase relationships and entertainment between friends. To increase motivation surrounding “Learning”, craft and cooking workshops, photography courses, visits to the community to learn about their customs, sighting of flora and fauna, surfing classes, among other activities, should be created. To increase motivation for “Nightlife”, entertainment centers should be implemented in tourist areas, which offer the appropriate services and quality for visitors.

Because the greatest predictor for return is the motivational dimension “Novelty and social interaction” and the greatest predictor of recommending and saying positive things about a coastal city is Auténcia coastal experience, festivals and events are recommended to be held on the beach, involving to the community. This will increase the novelty in the coastal area. Furthermore, workshops could be created for learning water sports, warning of flora and fauna, and typical gastronomy, which will bring novelty to the beach full of culture.

At present times, due to the COVID-19 pandemic, it is necessary for coastal cities to require visitors to wear protective masks and maintain social distancing. Another essential condition is that tourists use alcohol or sanitizing gel to sanitize and wash hands frequently and wash them with soap and water whenever necessary. For this, it is necessary that tourist establishments comply with biosafety regulations. They must also have alcohol on their premises for the use of tourists. It is necessary to have cleaning materials for tourists. Coastal destination managers should implement areas for constant hand cleaning and other facilities needed to protect tourists.

Finally, the main limitation that arose in this study was the temporality with which the sample was taken, since the demand can vary. As a future line of research, it would be interesting to analyze the economic impact of demand in relation to loyalty in coastal and marine destinations. The findings would serve as important information for the literature and as management guides for tourist coastal cities.