Keywords

1 Introduction

Today, the sharing economy, also known as the collaborative economy or peer-to-peer (p2p) economy (Krok, 2019), acts as a disruptive force and a crucial ally in the transformation of tourism, providing tourists options beyond traditional tourism services it provides. Tourist destinations require a wide range of services to support tourists’ experiences among which are those related to aquatic leisure and boat rentals, in particular. In recent years, yacht charter has experienced a significant increase, in socializing navigation and becoming essential for the present and future of coastal tourism, being the tourism with the highest spending in some destinations qualified as a premium product that can contribute to maintaining tourist activity, increase average tourist spending, and change the image of destinations (Alcover et al., 2011).

Andalusia has a privileged geographical location in south Spain, 910 km of coastline, 45 marinas, and an exceptional climate, which makes it an ideal destination for nautical tourism. In 2022, more than 30 million tourists visited it, which represents an inter-annual variation of 53.6% (IECA, 2023). The yachting sector offers many new opportunities and developments that Andalusia has integrated to continue ensuring a tourist service with high added value.

In this context, like the accommodation industry, the activity of p2p platforms for the intermediation of tourist services contributes to the expansion of tourist boat rentals and makes a wider range of products available to users. Due to the great diversity of products hosted on these platforms, it is necessary to dedicate efforts to their analysis and characterization to better understand them. The objective is to determine patterns to group products and facilitate the study of their offer and consumption behavior, providing values of the rates of the offered and chartered vessels that will be used as predictors to make tailor-made decisions, according to the type of product and destination.

2 Theoretical Framework

There is no universally agreed definition of the sharing economy (Höfner & Rosegger, 2022). We will adopt the definition established by the European Commission in 2016, which refers to “commercial models where activities are facilitated by collaborative platforms that create an open market for the temporary use of goods or services, often provided by individuals” (EC, 2016). Undoubtedly, the collaborative economy interests companies in its ability to create economic opportunities for all parties involved (Hamari et al., 2016). This has fuelled research on the sharing economy to examine how it has transformed specific sectors, as well as new opportunities and challenges brought about by it. In this sense, some studies analyze key sectors of the sharing economy in Europe (Vaughan & Daverio, 2016; Akbari et al., 2022), with the transportation sector being the most influential in terms of revenue. This sector enables the connection of drivers and passengers to save on travel costs and share rental vehicles (De Miguel-Molina et al., 2021; Ghorbani et al., 2023; Nerinckx, 2016; Akbari et al. 2020). On the other hand, the largest sector in terms of total transaction value is the peer-to-peer accommodation sector, which provides access to various rental modalities and includes rental platforms, vacation rentals, and home exchanges (Dredge et al., 2016; Milone et al., 2023).

Focusing on the tourism sector, several reasons are established that highlight the importance and growth of the collaborative economy, providing benefits for both companies and consumers. Among others, the following are fundamental: greater access to accommodation and tourism services, income generation for the local communities where these shared services are located, and the promotion of sustainable tourism (Dredge & Gyimóthy, 2017; Gössling & Michaei Hall, 2019; Hossain, 2020; Vila-Lopez & Küster-Boluda, 2022).

Yacht charter is an important subsector of the tourism industry in general. European Commission (2017), estimates that the global recreational boat rental market is expected to grow by 7.1% per year until 2026. Applying this growth rate assumes that the boat rental market could double in size by 2026. Unlike the rest of tourist activities, nautical tourism is recovering faster and better than expected. Yacht charter has experienced an increase of more than 80% in the years after covid19 (Click&Boat, 2023), becoming an important asset with a high economic return for the regions where they operate (Luković, 2012), being a tourist option that is gaining more and more prominence as a trend among tourists whom they choose the Spanish coasts. The main motivations of the nautical tourist in normal conditions are to enjoy the most beautiful beaches in the country, relax and live an original experience on board (Nexotur, 2023). In addition, the offer through p2p platforms has triggered access to these products and more and more marinas are promoting this rental, also producing a significant increase in the fleet of rental boats.

Even though, in the literature, extensive research has been conducted on digital business models for hotel reservations (Dredge et al., 2016; Zentner et al., 2022), little attention has been given to other areas of the tourism industry, such as yacht rentals (Seraphin and Maingi, 2023; Wilhelms et al., 2017). This justifies the convenience of conducting studies on this sector. For this, we consider it necessary to start by determining the characterization and behavior of the offer and consumption of these products hosted on the p2p platform.

This study focuses on a leading p2p yacht charter platform, Click&Boat, founded in 2013 and based in France. This company focused on expanding its international market and has experienced fast growth by acquiring the main competitors in France (Sailsharing in 2016, Oceans Evasion in 2019), Germany (Scansail in 2020), and Spain (Nautal in 2020). It has become the largest boat rental service in Europe, operating in more than 50 countries and 600 destinations, being considered the Airbnb of the sea. With the acquisition of Nautal, Click&Boat reinforces its position in Spain, considered one of its most important markets.

It connects boat owners with individuals interested in renting boats for recreational use. It operates similarly to other collaborative economy models. Boat owners can register on the platform and offer their boats for rent, setting their rates, conditions, and availability. The platform offers a wide range of rental options with options that adapt to all budgets and needs, allowing users to choose the boats, in the different Andalusian marinas distributed along the coast.

3 Methodology

3.1 Data Collection

Automated web scraping techniques were used to collect web data from the Click&Boat website applying a filter to Andalusian provinces. The dataset consists of information from 365 offered nautical products in March 2023 and 11 attributes (Province, location, boat, captain, license, fuel, passengers, deposit, price, beds, and users). Apart from these variables, we also incorporate the binary variable Rented (yes/no) that defines whether the boat was ever chartered depending on whether the user variable had a value or not.

The nautical charter dataset, with the total of products offered, presents the following descriptive characteristics:

The vast majority of the boat is offered in Malaga province (52.60%) followed by Cadiz (21.64%) and Huelva (14.79%). In the range of boats available, mostly launches and sailboats are offered with 66,58% and 23,29% respectively. The highlight of the charter offer is the skipper onboard service (67.67%), no license requirements (63.84%), and no fuel supply (73.15%). Regarding capacity, the most common offer is boats with capacity for a large number of people (more than six less than thirteen) (65.75%) instead of smaller boats (less than seven people) (34.25%), usually doing not have beds. Finally, almost 45% of charters do not require a deposit. The price per day depends on the type of boat but dataset analysis led us to conclude that the cheapest option are water scooters and sailboats, this last with a median price similar to launches and versus yachts as the most expensive option.

3.2 Clustering Yacht Charter

Clustering is a widely used statistical tool in determining a data set that groups items that are close enough to each other and far enough from other items (Rokach & Maimon, 2005), i.e., has the highest intra-class similarity and minimal inter-class similarity. The clustering technique has been extensively studied in many fields such as pattern recognition, customer segmentation similarity search, and trend analysis of products (Akay & Yüksel, 2018; Lan et al., 2023). These groups are useful for exploring data, identifying anomalies, and making predictions (Sun et al., 2008; Van Steenbergen & Mes, 2020). This method is an unsupervised machine-learning technique used to identify groups of data objects based on similar characteristics (Asensio et al., 2022; Nedyalkova et al., 2021; Rojas-Torres et al., 2022). Useful patterns may be extracted by analyzing each cluster. For this reason, clustering with an R language package software is utilized for grouping the boat rental offer. The scripts were created using the R programming language, which was set up in the RStudio environment, to accomplish the goal mentioned in this study (R Core Team, 2021; RStudio Team, 2020). The tidyverse and cluster libraries have also been used to clean the data, estimates, and graphic representations (Maechler et al., 2022; Wickham et al., 2019). In this paper, a classification of the behavior of the offer of boats for rent is given. We provide visually attractive groups to predict the preferences of the nautical offer of the users from an analyzed data set. In this way, we offer a detailed inspection of the data collected to facilitate the understanding of their behavior regarding the possibility of being rented.

Clustering mixed-type data is important for the areas such as knowledge discovery and machine learning (Li et al. 2022) although it is difficult for applying traditional clustering algorithms directly to these kinds of data. Our boat dataset contains both numeric and categorical attributes where traditional clustering (k-means or hierarchical) is not valid. We have used Gower´s distance during the clustering process, which allows giving weight for each variable as a combination of absolute distance and 0–1 distance (Gower, 1971). This means that the distance calculated for two individuals includes mixed attributes (d(i,j)). The Partitioning Around Medoids (PAM) algorithm was selected to group data points around the most centrally located object of the cluster (medoid) with a minimum sum of distances to other points rather than using the mean point as the center of a cluster as k-means method. Once the optimal number of clusters is identified (e.g. k value), the first medoid is assigned as the data point that has the smallest distance to all other data points, so it is in the center of the data set. The subsequent medoid is introduced such that the total distance of each data point to their nearest medoid is reduced. All subsequent medoids in the building phase follow a similar cycle. Then, different data points are tested as medoids, and if a different point reduces the total within-cluster distance the PAM method swaps the medoid with that point (Botyarov & Miller, 2022). All possible combinations in the given data set are tested, therefore only one solution is possible.

The optimal number of clusters is selected by silhouette width method, best used with the PAM method, which measures the similarity between each point in a cluster and compares it to the closest point in the neighboring cluster. The value K = 2 corresponds to the optimal number of clusters for the given data set using the silhouette width. It means that segmenting the data set into 2 groups maximizes the similarity within the groups and the difference between the groups. However, using k = 2 produces useful results.

4 Results

After applying the k-medoids machine learning algorithm for clustering our boat datasets, two clusters with 157 and 209 objects respectively were obtained. This section presents an analysis of cluster characteristics to help define the type of boats offered and give light on the nautical charter to increase the success of boat-marketed. Furthermore, it is so significant to provide key information regarding the proportion offered and chartered boats by location (associated with a marina). It would lead to establishing a suited offer to meet the particular needs of tourists in different places from the Andalusian region.

Firstly, the proportion of boats ever were chartered or not by cluster is 68.15% and 31.85% respectively in cluster 1 and 54.32% and 45.67% in cluster 2. This data is relevant to get knowledge of how the characteristics of each cluster affect to chartered rate.

Subsequently, we graph the variables by cluster to find differences between them and to be able to recognize discriminatory variables and construct patterns in the boats offered in the two clusters (see Fig. 1).

Fig. 1
5 horizontal and 3 vertical bar graphs, and a box plot with error bars present the composition of clusters 1 and 2 by various categories. Cluster 2 has higher values for most categories including province, boat, and deposit. Cluster 1 has higher values for those including beds and passengers.

Representation of the composition of each cluster

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Cluster 1 presents less diversity of types of boats. It is mainly formed by launches (85.35%), although it also contains sailboats (10.83%) and other boats (3.81%). The services and equipment are linked to charter boats without skippers intended for people who own a boat license, with a maximum capacity of 4–6 passengers on board (64%), normally without beds (89.17%) or fuel included in the trip (87.26%). The fleet of boats is characterized by low prices per day that usually require a deposit. The boats chartered rate belong this cluster is 68.15%.

Cluster 2 involves a lower chartered rate than Cluster 1 (54.32%). Mostly, it contains more sophisticated boats such as yachts, sailboats, catamarans, or launches with skippers included (100%). It is common in this group, boats provide capacity on board for more passengers, in addition to being equipped with beds more frequently. Thus, it includes higher-priced boats in which a deposit is not normally required and there is optionality regarding the inclusion of fuel.

Once patterns had been identified, it can be helpful to have a better understanding of what yacht charter offer and, as such, to be able to determine the matching between boats offered and demand for complements or services for users, who are mostly tourists spending their holidays in coastal areas. The data obtained can enable a suitable product for the growth and development of the sector. In addition, being able to predict the most appropriate type of chartered boat according to the location, based on machine learning technique, is a relevant contribution of this paper.

For this, an in-depth and detailed analysis by location is required to prevent general diagnoses and achieve a positive impact based on the boat-chartered needs of each area. To do this, we compute and compare the proportion of boats available (total offer) and the rate of boats ever chartered, specifically by cluster and location. Using the variable Rented, previously defined, and the segmentation by clustering obtained, we calculate the proportion of boats chartered in each location. Table 1 shows a comparison between the total available (offer) and total chartered (consume), detailing the cluster and the location. We observe imbalances in some places caused by:

Table 1 Proportion of boats that have ever been chartered, by cluster and location
Full size table

  • Higher interest in Cluster 1 boats.

In the marinas of the towns of Garrucha, El Puerto de Santa María, San Roque, Ayamonte, El Rompido, Isla Cristina, Punta Umbría, Benalmádena, or Nerja, the rental of boats grouped in Cluster 1 is more likely.

  • Higher interest in Cluster 2 boats.

In Almería, Carboneras, Cádiz, Sotogrande, Fuengirola, Málaga, and Marbella, the probability of renting a boat classified in cluster 2 is higher.

  • Lack of interest in cluster products

There are marinas where boats from two patterns defined in clusters 1 and 2 are offered, but the probability of success of being rented is low or null for one of them. These are the cases of the marinas of Almería and Carboneras, where cluster 1-boats have not ever been chartered. All charter transactions have been with cluster 2-boats.

The opposite occurs in the marinas of San Roque, Isla Cristina, Mazagón, or Torre del Mar, where cluster-2-boats have uninteresting in that area.

For their part, the boats offered in Almerimar are not successful in the market, such as boats in Isla Canela or Sanlucar de Guadiana.

In addition, by province, we observe more concentration of cluster-2-boats in Cadiz and Malaga, perhaps according to the purchasing power of tourists from those areas. Nevertheless, Malaga diversifies its offer more than Cadiz, with an acceptable demand for both products. However, Huelva and Almeria have the strongest presence of cluster-1-boats. Despite this, boat rentals in Cluster 2 are more successful in Almeria. For its part, Huelva, like Malaga, shows a high acceptance of rentals in both clusters.

5 Conclusions

Nautical tourism has recorded one of the highest development rates in the tourism industry. Tourism values water resources and activities related to marine leisure. The yacht charter provides a nautical experience that can be enjoyed by anyone, regardless of their purchasing power, breaking with the thought associated exclusively with high purchasing power people. In this way, the nautical charter is established as an asset for local tourism.

Spain ranks fifth among the top ten countries with the largest offer of boats on Click&Boat. This boat rental platform confirms a growing trend in bookings by Spanish users and offers an increasingly complete offer that responds to the plans and budgets of boaters. The expansion of this offer along the Spanish coast and, more specifically, along the Andalusian coast, is destined to play an important role in the economy and the tourist development of Andalusia. The enclaves such as those in the province of Malaga, which concentrate the largest offer of the community, stand out.

Searching for patterns of yacht charter product offerings has yielded good results by identifying two groups. The first with simpler boats and the second with more complete and sophisticated boats. In the aggregate data for Andalusia, we find that the yacht charter offer has a slight tendency towards cluster-2-boats. However, the chartered boat rate in Andalusia is higher for cluster-1. This can be visualized in Cadiz province, where cluster-1-boats are accepted by demand but the offer is low. In this sense, it would be advisable to expand the supply of more affordable boats to tourists with basic equipment to meet the profile of the boats assigned to cluster-1.

However, interesting findings emerged when the cluster-provided segmentation is performed in each locality or municipality, based solely on the rate of ever-chartered vessels compared to the total offer. As for the groups obtained, significant differences are observed depending on the locality, probably conditioned by the type of tourism in the area. This study contributes to a better understanding of the behavior of the nautical charter on the p2p platform by providing a characterization of the nautical products that affect the consumption trends of tourists in each location. Likewise, the providers obtain information on the profile of the most accepted or most needed boats in the town where they wish to moor their boats. In this way, it will be possible to improve the boat-chartering offer, the visitor experience, and the tourist development of the different localities in Andalusia.

While data-driven boat rental management is offered as a solution to improve promotional strategy design, we also saw some limitations in our case study. These are mainly related to the particular context studied. At the same time, it becomes an opportunity to expand it in future research.