Motivational Factors for Participation in Domestic Marine Tourism: The Case of Marine Protected Areas of Dar es Salaam Coast, Tanzania

Domestic tourism has rapidly become one of the better options for enhancing social, economic, and environmental development in many countries. For a country to benefit from this option, the participation of citizens in tourism activities is highly encouraged. Several efforts have been made by the government of Tanzania to encourage its citizens to participate in tourism. Despite these efforts, there is still an absence of knowledge of what motivates citizens to visit Marine Protected Areas (MPAs). The study was conducted in marine park reserves, namely Bongoyo, Mbudya, and Fungu Yasini-all located in the Indian Ocean in the Dar es Salaam region of Tanzania (Marine Park & Reserves, n.d.). The study adopted an exploratory research design to explore the factors that visitors consider important for participating in marine tourism. The study used a sample size of 60 respondents. The respondents were gathered using the convenience sampling technique. Data has been collected using questionnaires and telephone interviews and analyzed using the Statistical Package for Social Science (SPSS) v26. The partial least square structural equation model (PLS-SEM) was used to explore the statistical relationships between destination attributes and visitors’ psychological needs for their satisfaction. Findings indicate that psychological needs have a strong relationship with destination attributes. The destination attributes have a significant impact on satisfaction. The study revealed that resting and relaxation, pride, and novelty are the psychological needs that significantly influence satisfaction. Likewise, destination attributes that were mentioned by respondents as important were scenic beauty, clean water, cruising environment, and staff hospitality. It is recommended that service providers understand and match visitors’ psychological needs with destination attributes for their target markets and accordingly design the provision of products and services at affordable rates.

Introduction

Tourism is one of the world’s most rapidly expanding industries. It is an important source of foreign currency for many countries. Tourism contributes 10.3% of the global gross domestic product (GDP) (WTTC, 2020). It is also known to be one of the world’s top job creators, contributing 9.9% of global employment (UNWTO, n.d.). In Tanzania, tourism contributes 17.5% of GDP, and it is one of the major sources of employment opportunities (Gupta, 2020; Ubwani, 2022; UNWTO, n.d.). In countries like China, Malaysia, Philippines, Rwanda, and Ivory Coast, Domestic tourism continues to be the most popular type of tourism that stimulates economic growth, creates employment opportunities, and contributes to the development of other sectors (UNWTO, n.d.). Given its importance, UNWTO (n.d.) has been emphasizing the use of domestic tourism to boost economic revival in destinations after the COVID-19 pandemic. Marine tourism is a form of tourism that is currently growing fast (Tegar & Gurning, 2018). It has been projected that by 2030, Marine tourism will be one of the most significant tourism segments in the ocean economy (WTTC, 2020). The most popular countries for marine tourism include Indonesia, Malaysia, the Caribbean, Australia, and the Galapagos (Brumbaugh & Patil, 2017). Marine tourism is a temporary movement of people from their usual environments to the marine environment to participate in marine recreation, adventure, or leisure activities (Liet al., 2022). It is a type of tourism that is totally connected to and depends on the sea, ocean, or any other marine resources (Tegar & Gurning, 2018). For many decades, MPAs have become important tools for the conservation of coastal ecosystems (Haliket al., 2018). Based on IUCN’s definition, MPAs are areas of sea or ocean especially committed to preserving and protecting biological diversity. They benefit from protective and conservation solutions for natural areas designed according to predefined management objectives (ICCA Consortium, 2017). In Tanzania, MPAs include marine parks such as Mafia Island and Mnazi Bay Coelecantha, located in the Mtwara region, and Tanga Coelacanth, located in the Tanga region (Marine Park & Reserves, n.d.). Others include Fungu Yasini, Mbudya, Bongoyo, Pangavini, Makatumbe, and Sinda in the Dar es Salaam region. In the Mafia reserves, there are Shungu Mbili, Barakuni, and Nyororo; in Tanga reserves, there are Kwale, Mwewe, Ulenge, Kirui, and Maziwe. In Zanzibar, there are the Chumbe and Mnemba Islands (Marine Park & Reserves, n.d.). All these MPAs have the potential to attract tourists, but traditionally, the government of Tanzania has used them solely for the conservation of marine ecosystems (Katikiro, 2016).

However, the Tanzania tourism policy of 1999 stimulated the growth of sustainable and high-quality tourism that is economically, socially, culturally, and ecologically accepted in MPAs. As such, the policy suggests that the management of MPAs needs to include both conservation and tourism (Anderson, 2010; Kara & Mkwizu, 2020; Lwoga, 2011; Mkwizu, 2019). To make this new requirement happen, prior planning of MPAs to ensure the services match tourists’ needs is important. Understanding factors that motivate tourists to travel and their relationship with their satisfaction is important in predicting and planning for future travel of tourists to MPAs. This knowledge is specifically important in formulating tourism products and destination marketing strategies. Therefore, this study focuses on understanding travel motivation and dissatisfaction factors for tourists traveling to Mbudya, Fungu Yasini, and Bongoyo marine parks. Most tourism experts and theorists regard motivation as a significant concept in planning and marketing tourism activities (Bayih & Singh, 2020; Hwanget al., 2020; Lohchabet al., 2017; Seyidov & Adomaitienė, 2017; Wonget al., 2017). Çelik and Dedeoğlu (2019) and Lemmetyinenet al. (2016) have applied motivation theories to understand tourists’ choice of destinations and, in turn, helped in deciding the levels of individual satisfaction. Yan and Halpenny (2019) used motivation findings in forecasting tourists’ involvement in leisure pursuits. Similarly, Cavagnaro and Staffieri (2015) used motivation for identifying travel patterns, while Changet al. (2015). As applied, it is to understand tourists’ travel decisions and behaviors.

Several investigations have been carried out in Tanzania on the motivation of visitors for domestic tourism (Kanza, 2013; Kara & Mkwizu, 2020; Matolo & Salia, 2017; Melita, 2015; Mloziet al., 2012). Among these studies, marine tourism has not received enough attention compared to terrestrial tourism, such as wildlife tourism. In addition, the number of domestic tourists to different MPA attractions within the country is not at the same as that of international tourists (Kara & Mkwizu, 2020; Melubo, 2020). However, no studies have shown why tourists/visitors are attracted to MPAs and their level of satisfaction in Dar es Salaam. In fact, both motivation and satisfaction create the aptitude for understanding tourist behaviors in the tourism industry. Thus, it is meaningful to study tourists’ motivation and satisfaction within MPAs, as motivation is a driving factor for tourists to visit a destination, and fulfillment is seen as a significant success factor for marketing a destination (Kusdibyo & Setiawati, 2021). Furthermore, the motivation and happiness of visitors in MPAs may differ from those of other tourist attractions, such as national parks and heritage sites.

Deriving from the identified research gap, this study seeks to understand how motivation factors influence participation and satisfaction within MPAs. The study makes two contributions: theoretically, it expands our current knowledge of motivation and satisfaction in MPAs, and practically, it provides MPA supervisors or managers with a practical tool for promoting their destination based on tested items on tourist motivation and satisfaction.

Theoretical Framework

The consumer behavior theory is the underlying theory for this study. The theory is underpinned by the consumers’ utility/satisfaction maximization. It helps to explain why tourists opt to go to a particular destination and what activities to participate in therein (Chenet al., 2014). It helps to enhance our understanding of how visitors/tourists decide on the use of their limited resources, time, money, and effort multiple needs and wants they must attain the desired products or services to satisfy themselves (Chaharsoughi & Yasory, 2012). Scholars have consistently emphasized that the psychological needs (push) and destination attributes (pull) model is a commonly recognized theoretical framework often used in various tourism studies to understand tourists’ motivation to travel and participate in tourism activities (Antara & Prameswari, 2018; Arowosafeet al., 2022; Kumaret al., 2019; Said & Maryono, 2018). When an individual selects a specific place for visitation based on the features available in a destination, the driving forces for this choice are regarded as destination attributes (Antara & Prameswari, 2018).

Methodology

Study Area

The study was conducted in marine reserves, namely Bongoyo, Mbudya, and Fungu Yasini-all located in the Indian Ocean in the Dar es Salaam region as shown in Fig. 1. They are among the oldest islands on the coast of Dar es Salaam (Marine Park & Reserves, n.d.). Bongoyo lies between 06°43′12″ S and 38°16″00″ E and is about 8 km north of Dar es Salaam. It has sand beaches (beaches I and II), coral reefs, seagrass beds, rocky shores, lagoons (shark lagoon), and algal beds. Mbudya lies between 06°40′–06°40′.5 S and 39°15 E to the northwest, about 3 km offshore, within easy reach of the hotels on the North coast of Dar es Salaam. According to Marine Park and Reserves (n.d.), the island has the ruin of a German outpost and a tomb thought to be of the descendant of Prophet Muhammad, the founder of Islam. The tombs have generated myths and cultural values as the destination attributes that people from different parts of the country visit the area to pay homage to and make sacrifices for cleansing and fortune-making purposes. Besides, Fungu Yasini is an island of sandbank seen only during low tides, lying between 06°36′00″ S and 39°14″30″ E. These islands offer fabulous snorkeling and diving opportunities. The islands also have various tourist attractions, such as museums, animal zoos, and old German buildings. The three Islands are uninhabited (Marine Park & Reserves, n.d.).

Fig. 1. Map of study sites showing MPAs (Source: modified from Juliuset al., 2016).

Research Design

An exploratory research design was employed in this study. The design involves either studying a relatively unstudied area or extending the existing studies especially when the study fails to answer the questions accurately enough (Creswell, 2014). This study approach is supported by Nieswiadomy (2008), as the method is used when knowledge about a topic is limited. It is a useful research method in laying the foundation of in-depth research.

Sampling and Data Collection

The study used a convenience sampling method in recruiting the respondents. Convenience sampling is a non-probability sampling method that includes participants who are easily or conveniently ready to answer the interview questions (Baniya & Paudel, 2016; Boulayet al., 2013; Creswell, 2014). By using convenience sampling, the researcher managed to select the respondents. The study was conducted from January to February 2021 in the Mbudya, Bongoyo, and Fungu Yasini MPAs. To minimize bias, visitors were diligently approached, and those who were willing to participate were given questionnaires (Edgar & Manz, 2017). Both primary and secondary data were collected in this study. Primary data was collected using questionnaire interviews, telephone interviews, and observation methods. Questionnaire interviews involved domestic visitors who mainly came from within and outside Dar es Salaam. Telephone interviews were conducted using the same questionnaire to visitors who were not comfortable filling out the questionnaire while at the beach due to different reasons, including lack of time, hangover, fear of being detached from an enjoyable activity, and an excitement to enjoy the scenery or beach view. In addition, direct observation was used where photos were taken to complement information gathered by other methods and to provide a better understanding of the real situation on the ground. This includes pictures taken of products and services offered in the parks, such as food and beverage, accommodations, and attractions.

Sample Size

The study involved a total of 60 visitors of whom. 40 visitors responded to the questions directly in the field, and 20 respondents responded to the interview questions through their mobile phones in later days. Although the sample looks small, it is within the acceptable range; as stated by Sekaran and Bougie (2016), the rule of thumb for determining sample size states that the sample size of more than 30 and less than 500 respondents is appropriate for exploratory research. The study applied convenience sampling along with purposive sampling techniques. Purposive sampling was used to select the three MPAs covered by the study, namely, Mbudya, Bongoyo, and Fungu Yasini.

Data Processing and Analysis

The collected data was cleaned by observing the missing items. After this check, the data were coded by assigning special values to the responses, such as 1 for females and 0 for males. Before the actual analysis, the data were summarized by showing the descriptive statistics and verified before analysis to see which model would fit in the study. Data analysis was done using SPSS v26, and smart PLS-SEM v 3.3.2 was used to check for the validity and reliability of the data. PLS-SEM was specifically used to evaluate the suitability of the proposed model in testing the relationships among visitors’ needs, destination attributes, and satisfaction factors (Bidoet al., 2014). PLS-SEM is considered a suitable technique for small-sample analysis (Bidoet al., 2014; Richteret al., 2015; Ringleet al., 2020). Descriptive statistics such as frequencies and percentages were computed for the socio-demographic characteristics of respondents.

PLS-SEM was implemented in a two-step process: the first step involved the Confirmatory Composite Analysis (CCA) to develop the measurement model, and the second stage was used to test if the model fits. According to Hairet al. (2017), the CCA involves defining and identifying item dependability, construct validity, and construct reliability. The structural model also assisted in evaluating the multicollinearity, path coefficient, and predictive value of various variables, which are destination attributes (pull factors), psychological needs (push factors), and satisfaction. (Hairet al., 2019, 2020). This model is considered suitable for exploratory research (Ahmedet al., 2018; Do Valle & Assaker, 2016; Küçükerginet al., 2021). However, SEM consists of two components: structural and measurement models. According to Jöreskog (2005, 2021), the general form of the structural model, when it contains both endogenous as well as exogenous latent constructs, is specified as follows: (1)η=βη+Γξ+ζwhere:

η and ξ are unobserved continuous vectors of underlying latent constructs,

β is a matrix of regression coefficients among endogenous variables η with zeros in the diagonal, and (I-β) is non-singular,

Γ is a coefficient matrix that quantifies the direct effect of exogenous latent variables on dependent variables (η),

ζ is a random vector of error terms.

Furthermore, the model implies that the latent variables η and ξ are not immediately observable, but the y and x vectors, which are consequences (or indicators) of these constructs, show the following relationships: (2)y=Λyη+ε

(3)x=Λxξ+δwhere, respectively, y and x are observable vectors of latent indicators.

The measurement model’s error terms are represented by the vectors ε and δ, which are associated with the coefficient vectors, and Λy and Λx, which are factor loadings.

Results and Discussion

Socio-Demographic Characteristics of Respondents

The analysis of demographic characteristics was conducted to provide important information about the respondents, which is considered to have influenced their motivation to visit MPAs. Results in Table I show that more than half (56.7%) of the respondents represented females and 43.3% were males. Moreover, 45% of the respondents were between 18 and 28 years old, 36.7% were between 29 and 34, and 18.3% were 35 years old and above. With regard to education, results in Table I reveal that the majority (80%) of respondents had a higher level of education (degree), 20.0% of visitors had attained a secondary education level, while none of the respondents had completed only primary school. With regard to marital status, two-thirds (66.7%) of the respondents were single as they had free time to explore and visit the MPAs, whereas 30.0% of visitors were married couples with minimal time to visit the destination due to having different family responsibilities, only a few (3.3%) were divorced.

Table I. Tourists’ Demographic Characteristics of Tourists (N = 60)

Demographic characteristic	Category	Frequency	Percentage
Gender	Male	26	43.3
	Female	34	56.7
Age group	18–28	27	45.0
	29–34	22	36.7
	35+	11	18.3
Education level	Secondary school	12	20.0
	Higher learning	48	80.0
Marital status	Single	40	66.7
	Married	18	30.0
	Divorced	2	3.3
Family size	Less than 3	50	83.3
	4–7	7	11.7
	More than 8	3	5.0
Occupation	Employed	18	30
	Self-employed	26	43.3
	Unemployment	16	26.7

Furthermore, the results in Table I show that over two-fifths (43.3%) of visitors were self-employed, whereas 30% were employed and 26.7% were unemployed in the private sector. However, it was revealed that (83.3%) of the visitors had a family size of less than 3 visitors, whereas (11.7%) indicated a size of 4 to 7 visitors, and (5%) of the visitors had more than 8 family members while visiting the MPAs.

Model Measurement

Validity

In measuring the validity of the data, the Fornell-Larcker test was used under the criterion that the values of the loading ranging from 0.6 to 0.7 are considered valid for the model estimation (Fornell & Larcker, 1981; Purnami & Suryawardani, 2018). Table II shows the obtained correlations between destination attributes and psychological needs to be 0.742, which satisfies the criterion. Similarly, the correlation between destination attributes, psychological needs, and satisfaction was |0.732| and |0.752|. Therefore, the obtained loadings on destination attributes, psychological needs, and satisfaction were valid for model estimation (Fornell & Larcker, 1981; Kusdibyo & Setiawati, 2021). The data analysis indicates that this study satisfies all measurement requirements.

Table II. Fornell-Larcker Criterion

Model validity	Destination attributes	Psychological needs	Satisfaction
Destination attributes	1
Psychological needs	0.742	1
Satisfaction	−0.732	−0.752	1

Reliability

Table III shows the reliability, which refers to how precision is obtained and produces precision levels (Dijkstra & Henseler, 2015). In assessing the reliability of the model, the Rho_A value was used as a good indicator of reliability compared to other measures like 1 Cronbach’s alpha (CA) and composite reliability (CR) due to the fact that many indicators were used in each latent variable. On the data output report, the measures are reflected side-by-side, with Rho_A between CA and CR (Dijkstra & Henseler, 2015). This is helpful to determine if the value is good in between the CA and CR values. The Rule of Rho is that a value above 0.7 or above 70% is an acceptable and reliable latent variable (Dijkstra & Henseler, 2015). It can be explained that the destination attributes variable Rho_A value is 1.000, which is equal to 100%, the psychological needs variable is 0.748, which indicates 74% of the variance, and the Rho_A value of satisfaction variable is 0.912, which indicates that 91% can be explained by the psychological needs and destination attributes factors. This implies that there is reliability in all factors as they are above the acceptable value based on Rho_A (Fornell & Larcker, 1981).

Table III. Cronbach Alpha, Rho_A, Composite Reliability, and Average Variance Extracted (AVE)

Latent variables	Cronbach’s alpha	Composite reliability (Rho_A)	Average variance extracted (AVE)
Destination attributes	–	1	–
Psychological needs	−0.518	0.748	0.304
Satisfaction	0.884	0.912	0.813

Goodness of Fit

The model’s goodness of fit (GoF) is the measure of how the obtained results fit the acceptable range of the standard model with respect to the residuals or error at its confidence interval. This study used a confidence interval of 95%. Dijkstra and Henseler (2015) state that for a correct adjustment of the estimated model reference indices, Standardised Roots Mean Square Residual (SRMR) and Normal Fit Index (NFI) are considered and must have values lower than 95% or 99% (Henseleret al., 2016) of bootstrap respectively. Table IV shows the results of GoF of estimated models using structural equation models. In measuring GoF, the model was tested through SRMR and NFI. The SRMR value is 0.095, whereas it is perfectly fit if the value is less than 0.10 (Henseleret al., 2015; Hu & Bentler, 1999). Thus, it means any number that is approaching 0 is a good fit and is practicable. However, the NFI of 0.573 indicates that SEM is viable because the value had a range of value > 0.5; it is moderate as it was between 0.5 and 0.8, whereas the highest was not more than 0.9 (Bentler & Bonett, 1980; Lohmöller, 2013). Therefore, it can be concluded that the research model was feasible to use to test the research questions.

Table IV. Goodness of Fit (GoF) Research Model Test Results

GoF index	GoF criterion	Estimated model	Decision
SRMR	SRMR < 0.10	0.095	Acceptable
NFI	The greater value of NFI > 0.9	0.573	Moderate acceptability

Structural Equation Model

Motivation Drivers for Tourism in MPAs as Determined by the Structural Equation Model (SEM)

In this study, SEM was intended to facilitate the interpretation related to the influence of psychological needs and destination attributes on satisfaction, whereby the model involved three latent variables, namely the psychological needs (push) variable, which is reflected through six indicators that are pride, novelty, escaping daily routine, attain recognition, presence of celebrity and resting and relaxation, the destination attributes (pull) factor variable reflected by 12 indicators which are scenic beauty, clean water, walking along the beach sports (volleyball), cruising, swimming, safety, quality of services, accessibility, adventurous, staff hospitality and prices and the satisfaction variable is reflected by three indicators. The SEM that was performed after going through the bootstrapping process is presented in Fig. 2. However, SEM analyzed how psychological needs and destination attributes affect tourist satisfaction in MPAs whereas it reveals what drives tourists/visitors to visit MPAs and how the experiences impact satisfaction as illustrated in Fig 2:

Fig. 2. Bootstrap model for significance of psychological needs (push), destination attributes (pull), and satisfaction variables.

The numbers in the brackets represent p values, and the numbers out of the brackets represent outer loadings/outer weight.

Relationship between Motivation and Satisfaction

Results from the study, as shown in Table V, show that all relationships were significant, Whereas the relationship between psychological needs and destination attributes was significant at 99.9% confidence levels or a p-value of 0.001, except for the relationship between psychological needs and satisfaction (p-value of 0.007) and the association between psychological needs, destination attributes and satisfaction has (p-value = 0.028) that was supported by a 95% confidence level and the association between destination attributes and satisfaction with confidence interval of 90%. The relationship with the greatest loadings was the relationship between psychological needs and Destination attributes (T-statistic = 13.854). However, it is essential to identify which motives influenced the satisfaction of the tourists when they participated in the MPAs (Roldán Nogueraset al., 2021).

Table V. Relationship Between Motivation and Satisfaction

Latent variable	Original sample	Standard deviation	t	p
Destination attributes -> Satisfaction	−0.383	0.156	2.455	0.014
Psychological needs -> Destination attributes	0.739	0.053	13.854	<0.001
Psychological needs -> Satisfaction	−0.458	0.169	2.704	0.007
Psychological needs -> Destination attributes -> Satisfaction	−0.283	0.129	2.198	0.028

However, the association between Destination attributes and Satisfaction was significant, but the relationship decreased by 38% (−0.383), implying that destination attributes played a great role in creating tourist satisfaction in MPAs (Chiuet al., 2016). Destination attribute factors are visible aspects of the destination, allowing visitors to visually identify the destination and judge its suitability as a place to visit. This finding supports many empirical works in the tourism industry, such as Devesaet al. (2010) and Wonget al. (2017).

The association between psychological needs and destination attributes is highly significant, and the relationship between the two variables increases by 74% (0.739). This result is backed up by the theory that psychological requirements come before destination attributes (Dann, 1981; Wonget al., 2017). The finding is consistent with the assumption that once travelers decide to travel, they will then consider the destination attributes of a destination that attract them (Wonget al., 2017). Therefore, destination attributes are common attributes that correspond effectively to the motivators of psychological needs (Bayih & Singh, 2020). The association between psychological needs and satisfaction decreases by 46% (−0.458) despite the fact that the association is moderately significant. This infers that visitors’ psychological needs are important in achieving satisfaction in MPAs. The result shows that the MPAs have fulfilled tourists’ psychological needs (Wonget al., 2017). However, the relationship between visitors’ needs, destination attributes, and satisfaction decreased by 28%, with a moderate significance of 28%. The findings reveal that tourists’ psychological needs are more dominant in influencing tourists’ travel to MPAs than destination attribute motivation. This suggests the likelihood that a tourist can decide to visit a destination even without complete information about it. The results support efforts to better understand tourists/visitors’ psychological needs and destination attributes in order to boost tourist satisfaction inside MPAs.

Psychological Needs Influencing Motivation

Table VI shows that resting and relaxation are highly significant, with an average increase of 83%. This finding is consistent with Van Vuuren and Slabbert (2011) research, which also identified rest and relaxation as one of the psychological need factors influencing visitors visiting MPAs, with an average increase of 60%. With specific reference to visitors, relaxation was found to be one of the crucial motivations (Baniya & Paudel, 2016; Bui & Jolliffe, 2011; Kanagaraj & Bindu, 2013).

Table VI. Influence of Psychological Needs Factors on Satisfaction

Latent variables	Original sample	Standard deviation	t	p
Resting and relaxation	0.606	0.093	6.503	<0.001
Pride	−0.300	0.064	4.682	<0.001
Novelty	0.293	0.078	3.743	<0.001
Presence of celebrities	0.175	0.091	1.914	0.056
Attaining recognition	−0.077	0.100	0.771	0.441
Escaping daily routine	0.062	0.105	0.590	0.555

According to Van Vuuren and Slabbert (2011), MPAs should maintain calm and relaxing environments that provide the chance to ease the pressures of visitors’ everyday lives. This entails that if the emotions are highly met, satisfaction is likely to be met (Chenet al., 2014; Kimet al., 2012). Similarly, results show that pride is highly significant, with an average decrease of 30%. At the same time, it was observed that visitors take pride in participating in tourism activities and desire that others know about their expeditions in Mbudya, Bongoyo, and Fungu Yasini. However, pride was also found in (Kanagaraj & Bindu, 2013).

Novelty was also highly significant, with an average increase of 29%. The novelty was identified in Mbudya, Bongoyo, and Fungu Yasini as a common motivational factor for people to visit islands has been seen by different kinds of literature, such as the studies of Yousefi and Marzuki (2015) and Prebensen and Rosengren (2016). Also, the novelty was seen as a component of having memorable tourism experiences and included experiencing something new, different, unique, and once in a lifetime (Geuset al., 2016; Kimet al., 2012; Mitas & Bastiaansen, 2018). Mbudya, Bongoyo, and Fungu Yasini have shown how novelty experiences can trigger strong emotions and also increase memorability (Skavronskayaet al., 2020). However, domestic tourists with a high level of novelty are likely to recommend the experience to others and are likely to revisit the destination again (Vittersøet al., 2017).

Destination Attributes Influencing Motivation

Table VII indicates that scenic beauty is significant, with an average increase of 34%, which means it has a significant influence on attracting visitors. However, it is observed that those viewing scenic beauty by visitors are highly motivated by approaching places that are pleasing to them in their aesthetic view (Jeonget al., 2018; Kimet al., 2012). Therefore, the natural beauty of Mbudya Island is seen as a motivating factor that contributes to attracting visitors. Additionally, many visitors acknowledged that the environment is so cool with fresh, clean air. It has also been seen as a factor that tends to influence satisfaction significantly if the environment is well-maintained or conserved (Shahrivar, 2012). This can lead to a positive experience for visitors (Kama & Karagöz, 2021). Clean water has influenced visitors as well, with an average increase of 36%. This means that MPAs have to try to clean the beach so as to be free from trash and fragments such as soda bottle covers and broken bottle pieces. It was observed in other studies (Goh & Balaji, 2016; Ngahet al., 2021) that by Keeping the beaches clean sustains the beautiful scenery of the islands and promotes pleasant feelings for visitors in MPAs.

Table VII. Influence of Destination Attribute Factors on Satisfaction

Latent variables	Original sample	Standard deviation	t	p
Cruising environment	−0.435	0.163	2.668	0.008
Clean water	0.356	0.145	2.462	0.014
Staff hospitality	−0.304	0.128	2.373	0.018
Scenic beauty	0.335	0.143	2.344	0.019
Swimming	0.248	0.157	1.577	0.115
Adventurous	0.286	0.193	1.484	0.138
Safety	0.142	0.144	0.985	0.324
Walking along the beach	−0.098	0.135	0.725	0.468
Beach sport	−0.087	0.135	0.643	0.520
Quality of service	−0.081	0.136	0.594	0.553
Accessibility	0.066	0.123	0.540	0.589
Prices	0.077	0.149	0.515	0.606

Nevertheless, Table VII shows that cruising is highly significant, with an average decrease of 44%. Cruising has become one of the favorite holiday activities among visitors visiting MPAs (Han & Hyun, 2019). In Mbudya, Bongoyo, and Fungu Yasini, results indicated that the majority of tourists practicing this activity were Tanzanian celebrities who tended to rent a private boat with some services such as music and drinks, though these visitors tended to take their meals in the MPAs restaurant and as well they swam along the beach side of the MPAs. However, staff hospitality was also significant, with an average decrease of 30%. This implies that staff in the MPAs tended to clean the environment so as to give customers a clean environment and comfort. This finding is in line with that of Choi and Joung (2017), who found the same results. This is important as it pays more attention to customer-oriented behavior and internal marketing for the success of business in MPAs.

Satisfaction Factors

Table VIII shows visitors who were satisfied. It was significant, with an average increase of 43%. This means that the majority of visitors were satisfied by 39% with the services being offered in the MPAs. As well this was also influenced by visitors’ chances to revisit the MPAs, which was significant, with an average increase of 35%. However, domestic tourist expectations are fulfilled with an average increase of 31%. Different literature has discussed that tourist expectations should be respected, especially when offering services and products in MPAs (Hasanet al., 2019; Hossainet al., 2015).

Table VIII. Satisfaction Factors

Latent variables	Original sample	Standard deviation	t	p
Expectation fulfillment	0.431	0.030	14.466	<0.001
Revisit again	0.355	0.029	12.035	<0.001
Satisfied	0.318	0.034	9.402	<0.001

Conclusion and Recommendations

Conclusion

The findings obtained in this study lead to a conclusion that psychological needs for the selection of MPAs are escaping daily routine, rest and relaxation, pride, novelty, and the presence of celebrity. If these are improved, there will be an increase in the number of tourists who select Bongoyo, Mbudya, and Fungu Yasini. On the other hand, the destination attributes mapped in the study area are scenic beauty, clean water, walking along the beach, beach sports, cruising, swimming, safety, quality of services, accessibility, staff hospitality, and adventure. From the analysis of the data obtained, it was revealed that there were three psychological needs factors that had a significant relationship with the selection of MPAs, notably resting and relaxation, pride, and novelty. Therefore, any initiative to improve tourist attractions should focus on these important variables so as to be able to attract more visitors and retain available visitors. In this study, scenic beauty, clean water, cruising, and staff hospitality were found to be significant variables in influencing the selection of MPAs. It is, therefore, reinforced that these destination attributes should be considered if public and private sectors wish to improve domestic tourism in the marine sector.

Nevertheless, psychological needs variables have a significant effect on destination attributes motivation variables in comparison to other relationships. As a result, destination managers must pay close attention to each of these features in order to promote tourism destinations. Customer fulfillment must be consistently enhanced because it is a key indicator of customer retention.

Recommendations

It is important to understand that although the study results provide important information for understanding visitors’ motivations that trigger their desire to travel to MPAs, the results also have managerial concerns for destination management due to the following reasons:

Firstly, visitors’ satisfaction is greatly influenced by their psychological needs and destination attributes. Besides, these attributes include fulfilling visitors’ expectations with the authenticity they feel, providing them with proper products and services that can bring satisfaction, and covering general feelings of satisfaction as perceived by tourists. However, would recommend service personnel in the destination should be trained and as well Offer high-quality products and services that will deliver outstanding domestic experiences while diversifying the products that are offered in MPAs.

Secondly, as evidenced by their direct, substantial relation, psychological needs play an important role in driving tourist destination attributes motivation. It is recommended that public awareness campaigns could then be launched to educate the domestic market about the country’s product offerings, particularly in marine tourism. This is to enable visitors to visit MPAs.

Lastly, the psychological needs motivation variables have a greater significant effect on destination attributes compared to other relationships. As a result, destination management must pay close attention to this variable in order to promote the destination, and satisfaction must be consistently enhanced as this variable is a vital indicator for tourists to revisit MPAs. Likewise, Managers can improve tourist satisfaction by increasing both psychological demands and destination attribute motivator elements. The psychological needs and motivational factors can be addressed by introducing campaigns that promote tourists’ motivational factors to visit MPAs. Therefore, understanding the drives of tourists who opt for this type of tourism and its association with satisfaction and consumer behavior would allow better management and planning of MPAs.

Since motivation knowledge enables planners to define tourist behavior and the importance of domestic tourism more clearly in MPAs, it will also reflect in the development of facilities in a destination. It is crucial to understand tourist markets and their needs in a particular area, like MPAs. Additionally, decision-makers and resource creators in the tourism sector ought to consider how to project a favorable image that motivates visitors to visit MPAs. The motivations of their target markets must be understood to design the products and services on offer in a way that is in line with those motivations. This is equally important for tourism businesses, which are service providers. However, currently, the MPAs fall under the Ministry of Fisheries and Livestock (MFL) with coordination from the Ministry of Natural Resources and Tourism (MNRT); it would be proper to place these MPAs under MNRT to manage tourism in MPAs. This is eco-friendly, and it coordinates with the MFL, which will manage the fisheries aspect.

Future research that fills in the gaps left by this study can be based on how the tourist site is perceived by both domestic and foreign visitors. It is equally important to investigate each visitor’s unique motivations for visiting a particular location to evaluate the image in accordance with how visitors see it.