Keywords

11.1 Introduction

Tourism in mountain destinations in Romania has great potential since it brings tangible benefits to local communities. It can stimulate local economic growth because of its contribution to job creation and gross domestic product, its characteristic of being a source of income, and its complementarity with other economic activities. It can also contribute to rejuvenating food systems and local traditions. According to Baba et al. (2020), through the last years, tourism in mountain destinations holds the second place in the preferences of the tourists from Romania (Baba et al., 2020). The brittleness of the cultural and natural heritage that is fundamental to mountain regions influences the quality and competitiveness of tourism services provided in mountains.

The purposes for selecting an accommodation unit in a mountain resort (i.e., a hotel) are influenced by the motive of the traveler’s visit: picturesque beauty, sports attractions, business tourism, recreational tourism, or rich cultural heritage. Despite the aim of the visit, the tourist’ length of stay (LOS) has significant importance for any mountain tourist destination. Since the 1970s, when LOS analysis in the field of hospitality and tourism was highlighted in the literature (Mak et al., 1977), different researchers have examined the concept of LOS from various perspectives to identify the major factors influencing the LOS in different touristic destinations.

According to the authors’ expertise, previous research studies related to tourism and hospitality fields in Romania have not focused on LOS in mountain destinations. This study is the first emphasizing how tourists’ nationality and typology influence the average LOS of tourists in Poiana Brasov Mountain resort for full-service hotels. Also, it investigates how much the winter months and the seasons of the year affect the typical duration of stay. Thus, the present study supplies relevant analyses of tourism literature for mountain destinations.

As favorite mountain destinations in Romania, it can underline the following: Poiana Brasov, Predeal, Paltinis, Lake Balea, Sinaia, Ranca, and Vatra Dornei. Of these, in terms of value for money, the most beautiful landscapes, quality and access to the slope, and food options Poiana Brasov rank first, with Predeal and Sinaia coming next, according to the study available online on the website of “Adevarul” newspaper—Economic section.

Poiana Brasov is one of the biggest and most luxurious mountain resorts in Romania, being also one of the main snowy and well-preserved mountain areas in Eastern Europe. It is located close to Brasov, at the base of a very well-known massif in Romania, Postavarul, and is one of the most visited towns in Transylvania due to its bohemian atmosphere and baroque architecture. Sinaia is situated in the historical region of Muntenia, in Prahova County. Known as “the Carpathian Pearl,” this town and mountain resort is well known for the rumored medicinal properties of its mineral springs and its natural beauty. Its main attractions are alpine activities (hiking and skiing), a seventeenth-century monastery (Sinaia Monastery) and beautiful castles (Peles and Pelisor Castle). Predeal City, located in the Prahova Valley, is situated at the highest altitude in Romania. It is mountainous: the Bucegi Mountains to the southwest, the Piatra Mare Mountains to the north, and the Postavarul Massif to the northwest. It is popular for the woods around it, which have rich and diverse fauna and many different forest animals.

The present paper analysis how the following two factors, nationality and tourists’ typology, influence the LOS for the four-star hotel tourists in the most popular Romanian Mountain resort. It identifies the typical duration of stay for the full-service hotels in these mountain resorts and investigates how much the winter months and the seasons of the year affect the typical duration of stay. The reason for this research is motivated by the fact that in Romania, mountain tourism in the main resorts represents a subject not studied by many researchers up to now, regardless of its relevance at the national level. Observing this is one of the first scientific efforts in Romania for mountain destinations.

In terms of accommodation units in the main mountain tourist destination in Romania, Poiana Brasov, the below table highlights the classification of the hotels from this resort, based on OTA’s database (Booking.com March 2023) (Table 11.1).

Table 11.1 Number of hotels in 2021 and 2022 in Poiana Brasov resort depending on the level of classification
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The present paper starts with a section that highlights a literature review related to LOS. Further, it is emphasized the research methodology that has been conducted, and finally, the main findings and the research conclusions of this subject are revealed, discussed, and correlated with its objectives.

11.2 Literature Review

Over time, the number of publications analyzing the LOS increased a lot mainly to its significance for tourist destinations. Therefore, the concept of LOS has been defined in many ways. For example, Atsız et al. (2020) defined it as “the total nights spent by tourists who accommodated at least one night-time in a single destination and who used a commercial type of accommodation.” Many researchers have studied this concept from different contexts (Aguilar & Díaz, 2019; Atsız et al., 2020; Thrane, 2016).

The length of stay is an important metric for hotels because it directly impacts the revenue generated by the property. The longer a guest stays, the more revenue the hotel can generate from room rates, food and beverage sales, and other auxiliary services. Additionally, longer stays can lead to higher guest satisfaction and potentially lead to repeat business in the future. Hotels also use length of stay data to forecast demand, optimize pricing strategies, and plan staffing and inventory needs. Understanding guest length of stay can help hotels better manage their resources and improve their overall profitability.

Different determinants of length of stay can be underlined: economic variables (consumer’s income and the prices of goods and services), past traveling experiences, distance (a rational tourist is seeking to achieve a higher utility from any trip and, therefore, will weigh the ratio between the variable cost and the fixed cost), social-demographic variables (tourists having different ages accommodate different number of days), trip characteristics, etc. The existing literature underlines most of all the following determinants of LOS: travel or motive reason (Oliveira-Santos et al., 2015; Rodriguez et al., 2018), tourist typology (Gokovali et al., 2007), previous experience in the destination (Gokovali et al., 2007; Machado, 2010; Menezes, 2008), destination’s attractiveness or image (Machado, 2010), social factors (Boto-Garcia et al., 2018; Gomes de Menezes et al., 2008; Porut et al., 2021), personal characteristics (i.e., education, gender, age, nationality, and revenue), travel features (i.e., travel cost, season of travel, aim of trip, how the trip was organized, and mode of transport), and destination attributes (i.e., climate, cultural attributes, nature, quality of service, satisfaction, and loyalty) (Rodríguez et al., 2018). The majority of the studies conclude that the tourists’ LOS is influenced in a high proportion by sociodemographic characteristics.

The existing literature highlighted that according to Hofstede’s theory, tourists from more collectivistic cultures (Japan, China, Korea) tend to prefer a shorter LOS at the destination in comparison with individualistic tourists (American, Australian, British). In the process of explaining the differences in the length of stay in terms of nationality, besides cultural dimensions, travel distance emerged as a key factor in this respect (Thrane & Farstad, 2012). Thus, when the travel distance implies a higher effort, then it was observed a higher LOS. This comes in correlation with the tourists’ nationality and explains different behavior.

The research processes reveal that length of stay has many determinants, but in time, other variables like socioeconomic characteristics, personal habits or activities, previous visits or accommodation facilities, and information have been investigated. According to tourists’ age, literature suggested that seniors differ in their behavior concerning the length of stay. Some of the scholars acknowledged that younger tourists tended to stay for shorter durations than older ones (Martínez-Garcia & Raya, 2008, Machado, 2010, Salmasi et al., 2012). But others found contrary pieces of evidence (Barros & Machado, 2010). In their study, Esiyok et al. (2017) explained a difference in the length of stay between tourists of different ages revealing two main age groups: working age (45–65) and retirement age (+66). For the interval 58–77 years old, the age influences negatively the LOS, and these results highlighted the senior’s market diversity in tourism.

Owing to the profound economic consequences considered for the length of stay, many researchers investigated the effect of it on the tourists’ expenditure per day. In these regards, even if there are no conclusive results, seemed that tourists who stay for fewer days in a destination have higher expenditure than their counterparts who spend more time at it (Esiyok et al., 2017; Thrane & Farstad, 2012).

The present research incentive results from the fact that, so far, mountain tourism in Romania has not been studied by a lot of researchers even if its importance at the national level is undoubted. This study reveals the tourists’ behavior in Poiana Brasov, a well-known and traditional mountain resort in Romania. It is relevant research meant to be a useful tool for the development of new strategies of tourism at Poiana Brasov.

11.3 Research Methodology

To achieve the main objectives of the present research, the investigated population is given by foreign and Romanian tourists accommodated in Poiana Brasov, benefiting from four-star hotel services. In this study, the hotel represents the sampling unit, while the foreign and resident tourists represent the observation unit. The research’s main findings will enhance the whole studied population, that is, to the level of all tourists who visited Poiana Brasov and were accommodated in the 4-star hotels, during the analyzed period [2021; 2023]. In total, the number of tourists included in the research was 4386 tourists. It was selected to incorporate in the sample those hotels, at the level of which, the number of ratings was the highest, respectively, whose rating averages were dispersed. The data were processed using the SPSS program.

To establish the level of structural and statistical relevance, it was designed and used an equivalent of the sampling base intended to provide a probabilistic sample. The following procedure for building the sample and the sample base was used:

  • Time span: 01.2021–02.2023.

  • The data was collected between 01.03.2023 and 21.03.2023, from the platform Booking.com.

  • The following variables were used in order to collect the data:

    1. (1)

      Number of stars: four-star hotels.

    2. (2)

      Tourists’ typology. Were used the following categories: groups of friends, families, individual travelers, respectively couples.

    3. (3)

      The year in which the tourists accommodated in a hotel in Poiana Brasov resort: 2021, 2022 or the beginning of 2023.

    4. (4)

      LOS (days).

    5. (5)

      The nationality of the tourists. Were identified the following main groups: tourists from Romania, Germany, Hungary, Israel, France, and Italy. Tourists arriving from the Moldova, England, USA, Austria, Sweden, Spain, Bulgaria, Poland, Belgium, Denmark, Switzerland, Turkey, Czech Republic, Russia, Greece, Netherlands, Portugal, Ukraine, Finland, and Sweden were considered “other nationalities”.

    6. (6)

      The month in which the tourists were accommodated in Poiana Brasov.

To validate the sample, a test to compare the differences between the percentages was employed. Only one practically possible variable was considered, namely, the number of hotels according to the level of compliance.

11.4 Findings and Results

The primary goals of the analysis were to determine if the independent factors had a substantial impact on the length of stay in the mountain area and to determine the degree to which they might have had such an impact. The analysis’s findings were used to support these conclusions. The study’s findings are arranged according to the following format: the descriptive statistics are presented in the first section, followed by a discussion of the findings of the ANOVA analysis, and finally, the simple linear regression model and a multifactorial regression model are presented and discussed. The following conclusions are reached based on the primary objectives of the research as they were advanced.

A1. Descriptive Statistics

Important characteristics for all the variables have been found for the database based on the descriptive statistics that have been summarized in the Tables. The average length of stay of the tourists analyzed in this study is one of the study’s initial findings. It demonstrates that most visitors stay for two nights (the average number of nights is 2.11). As seen in Fig. 11.1, the database’s distribution of consumers by length of stay is very evenly split between stays of one night, two nights, and three nights or longer. A stay of at least three nights, which in our instance amounts to just 27%, becomes advantageous from the perspective of hotel room running costs. However, most visitors are more focused on weekend tourism in the mountain region, according to the statistics.

Fig. 11.1
A pie chart of number of overnights spent at hotel unit. 1, 35.32 percent, 2, 36.55 percent, 3, 17.44 percent, 4, 6.25 percent, 5, 2.60 percent, and more than 5, 1.85 percent.

Number of overnights spent at hotel unit. (Source: Authors’ calculations)

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In terms of nationality, the majority of Poiana Brasov’s full-service hotels welcome Romanian visitors and therefore cater to domestic travelers. About 18% of all tourists are from outside the country, with Israeli visitors accounting for a sizable share of it (Fig. 11.2).

Fig. 11.2
A pie chart for nationalities percentage for the sample. Romanian, 80.37 percent, Hungarian, 0.41 percent, German, 1.03 percent, Italian, 1.73 percent, French, 0.80 percent, Israel, 4.01 percent, and Other, 11.65 percent.

Nationality percentage for the sample. (Source: Authors’ calculations)

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Concerning the average LOS of Romanian and foreign tourists in Poiana Brasov, in the analyzed period, data shows that the highest value was registered for Romanian tourists (3.525) and for international tourists just 861. The most significant nonresident tourist markets are from Israel, Italy, Germany, France, and Hungary.

The second independent variable examined in the study pertains to the typology of tourists. The analysis of the sample revealed that families constitute the largest proportion of guests for hotels in Poiana Brasov, followed by couples. However, the presence of groups and solo travelers is quite low. This pattern can be linked to the fact that mountainous regions—in particular, the Poiana Brasov resort—serve as important wintertime travel hubs for the ski tourism industry. Families become an important target market because of this context (Fig. 11.3).

Fig. 11.3
A pie chart for typologies weight of the sample’s guests. Family, 50.14 percent, Couple, 40.52 percent, Group, 5.34 percent, Solo, 4.01 percent.

Typologies weight of the sample’s guests. (Source: Authors’ calculations)

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Moreover, this result is understandable with family’s preference for mountain destinations, as considering the congestion and pollution and the alert rhythm of city life, more and more families prefer to go on holidays in a quiet and peaceful destination. Furthermore, with the health threat generated by the COVID−19 pandemic, one can easily understand the respondents’ orientation toward nature-based tourism, less frequented, remote, and quiet areas.

In contrast, group travelers are less frequently seen staying at full-service hotels, primarily due to the high tariffs associated with such establishments. These rates may not align with the diverse demands typically exhibited by group tourists. On the other hand, it is uncommon to see solo travelers exploring the mountainous area.

Furthermore, when examining the period of guests’ stays in the Poiana Brasov resort, it was found that the majority of stays occur during the peak seasons of winter (December to February) and summer (June to August). This aligns with the resort’s popularity as a destination for skiing and outdoor activities during these periods. However, it is crucial to acknowledge that there is a significant proportion of tourists who choose to visit during the off-season (the remained months).

The presence of tourists during the off-season presents a significant opportunity for the region’s hotel development goals. It suggests that there is a chance to draw and accommodate tourists outside of the typical peak seasons. This can be done by providing distinctive and alluring experiences that make use of Poiana Brasov’s natural beauty and attractions outside of the winter and summer months (Fig. 11.4).

Fig. 11.4
A pie chart of tourists’ frequency according to season period. Season, 60.26 percent, Out of the season, 39.74 percent.

Tourists’ frequency according to season period. (Source: Authors’ calculations)

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The last variable considers to be studied about the LOS was the accommodation facilities, which was associated with the room type comfort. The data sample indicates that most visitors selected standard rooms, indicating that they were content with the full-service hotel’s typical amenities (Fig. 11.5).

Fig. 11.5
A pie chart of room type comfort weight. Budget, 73.58 percent, Superior, 26.42 percent.

Room type comfort weight. (Source: Authors’ calculations)

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A2. Multifactorial ANOVA

The initial hypothesis focused on how the independent factors may affect how many nights clients stayed at the full-service hotels. To obtain an understanding of guest behavior and preferences, it is important to comprehend how these factors and their interplay affect the length of stay (LOS). A multifactorial ANOVA was done for the variables of nationality, tourist typology, season’s period, and room type to test these connections, and results are presented in Table 11.2.

Table 11.2 The influence of the factors on LOS (nights)
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According to the results, all factors have a substantial impact on the number of nights because their p-values are less than the significance level (0.05), providing strong evidence that the null hypothesis is false. This indicates that in influencing the dependent variable, which is the length of stay of guests in the current context, the nationality of the guests, the type of tourist, the season, and the type of room booked are all significant considerations.

Further analysis, such as post hoc tests, would be necessary to determine the specific levels of each factor that are significantly different from each other in terms of their effect on the length of stay. However, the significant main effects suggest that interventions targeting these factors (such as marketing strategies, room pricing, or service offerings) could be effective in influencing the length of stay of guests in the given context.

A3. Simple and Multiple Regression Models

To explore the relationship between the independent variables and LOS, both simple and multiple regression models were employed in this study. The objective was to understand the impact and significance of each independent variable on the dependent variable, as well as to assess the overall predictive power of the model. The dependent variable, LOS, was measured in terms of the number of overnights spent in hotels.

In the simple regression model, dummy variables were applied to determine whether tourists could be classified based on their geographical origin. The model comprised five dummy variables representing Romania’s primary geographical markets: Hungary, Germany, Italy, France, Israel, and other countries (see Table 11.3). The domestic market group acted as the control group in the regression model (Tables 11.4 and 11.5).

Table 11.3 Dummy variables for the simple regression model
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Table 11.4 Results for the multiple regression model
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Table 11.5 The influence of the dummy variables on LOS (nights)
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The regression results show that the independent variables DN1, DN2, DN3, and DN4 are not statistically significant predictors of the dependent variable LOS since their p-values are higher than the customary cutoff of 0.05. The only DNs that are statistically significant predictors of the number of nights spent in full-service hotels are DNs 5 and 6. A one-unit increase in any of these variables determines an increase in the LOS of 0.631046 or 0.22926, respectively, according to the coefficients of DN5 and DN6.

In spite of this, the ANOVA table shows that the model as a whole is statistically significant because the F-p-value ratio is less than 0.05. This indicates a significant impact on the dependent variable from at least one of the independent factors. The independent factors in the model only partially explain the variance in LOS, as seen by the R-squared value of 1.47937%. The corrected R-squared value, which takes into account how many independent variables are included in the model, is a little lower at 1.34438%.

The results show that Romanian visitors favor shorter stays in mountain hotels, which is in line with earlier research (Statista Research Department, 2023) (Pop, 2014), which discovered an average of 2.2 overnights for hotels generally. The LOS for hotels appears to be slightly higher as compared to earlier data for Brasov Mountain areas, indicating a minor increase from 1.93 in 2016 (Grigoras et al., 2018). According to our research, staying in hotels as a Romanian guest is related to a short LOS.

By applying multiple regression analysis, we seek to identify the key factors that influence the dependent variable, quantify their individual contributions, and explore the strength and direction of these relationships. The objective is to provide a comprehensive understanding of the factors that influence the dependent variable, facilitate prediction and forecasting, and inform decision-making processes in the relevant field.

Additional dummy variables were included in the multiple regression model to examine the impact of guest typology, such as family, couple, group, or solo travelers, on LOS (see Table 11.4). Furthermore, the model included a dummy variable to represent the season when visitors visit the mountain area. Finally, the type of room was considered, as it is a significant factor influencing the comfort of accommodation that guests expect, and the model included dummies for superior and standard rooms (Tables 11.6, 11.7 and 11.8).

Table 11.6 Dummy variables for the multiple regression model
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Table 11.7 Results for the multiple regression model
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Table 11.8 The influence of the dummy variables on LOS (nights)
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The length of stay (LOS) at hotels, which is the dependent variable, is thought to be significantly influenced by several independent variables, according to the results of the multiple regression model.

The DN variable, which represents the nationality of the visitor, has a positive correlation of 0.244679, indicating that visitors from nations other than Romania tend to remain in hotels for longer periods of time.

Couples typically remain in hotels for a shorter time, according to the DT1 variable, which represents couples, which has a negative coefficient of −0.348903. Similar to the DT1 variable, the DT2 variable, which represents groups, and the DT3 variable, which represents lone travelers, both have negative coefficients, indicating that these groups typically have shorter stays than families do.

The off-season period is represented by the SP variable, which has a negative coefficient of −0.362326, indicating that visitors typically stay for fewer nights at this time.

Last but not least, the DRT variable, which represents the type of room, has a negative coefficient of −0.103026, which suggests that guests who choose standard rooms typically stay for shorter periods than those who prefer better comfort rooms.

The results of the analysis of variance (ANOVA) demonstrate a significant model fit overall, which means that all of these factors together have a significant impact on the number of nights, as shown by the low p-value of 0.0000. Approximately 5.34% of the total variability in the dependent variable is explained by the model. The model may have weak predictive ability, as evidenced by the adjusted R-squared value, which is significantly lower at 5.21% and accounts for the degrees of freedom (Tables 11.9 and 11.10).

Table 11.9 The influence of the typology of Romanian guests on room type. (Romanian (DN = 0))
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Table 11.10 The influence of the typology of foreign guests on room type. (Other nationalities (DN = 1))
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11.5 Conclusions

The present research highlights the factors influencing the length of stay (LOS) at Romanian mountain hotels from Poiana Brasov, the main mountain resort in Romania. It is concluded that the typology of guests plays an important role, the primary contributors to hotel occupancy being families and couples. This key finding of the study emphasizes the necessity for hotels to explicitly target these guest categories with their services and amenities in order to meet their wants and preferences.

Moreover, it is found that seasonality is another main factor that influences the LOS, with winter (December to February) and summer (June to August) being the peak seasons for guests. However, it is important to note that there is still a substantial number of tourists during the off-season periods (the rest of the year). This is a significant sign for hotel managers since it emphasizes the opportunity to capitalize on the potential of attracting guests during these times. As useful methods to achieve this target can be underlined the following: offering attractive promotions, packages, or unique experiences that can entice visitors to extend their stays.

The findings of the research also reveal the significant impact of nationality, typology, seasonality, and room type on the length of stay. Romanian guests tend to have shorter stays compared to foreign tourists, indicating the need to focus to increase the LOS of domestic visitors. Similarly with Thrane and Farstad (2012), our results confirm that when the travel distance implies a higher effort, then it was observed a higher LOS.

Additionally, family guests tend to have longer stayed compared to other typologies, suggesting that hotels can focus on creating family-friendly environments and activities to encourage extended stays. The results from this study show significant differences in length of stay for the different nationalities and typologies of tourists and complete the results from other studies analyzing this concept from different contexts (Thrane & Farstad, 2012; Esiyok et al., 2017; Gokovali et al., 2007).

Furthermore, the study reveals that superior rooms attract guests with longer stays compared to standard rooms. This information can guide hotel managers in optimizing their room inventory and pricing strategies to maximize revenue and enhance the overall guest experience.

In conclusion, the findings imply that hotel managers can utilize this regression model to pinpoint the variables affecting the number of nights their clients stay. Hotel managers can put specific plans in place to lengthen stays and raise occupancy rates by knowing which types of customers have shorter stays and which seasons of the year are related to shorter stays. For instance, to entice couples or lone travelers to stay longer during the off-season, hotels may offer special deals. In a similar vein, hotels can think about providing incentives to customers who book higher-comfort rooms and extend their stay. Hotels may raise client happiness, increase occupancy rates, and eventually improve financial performance by putting in place focused marketing initiatives, delivering alluring packages, and offering first-rate services and amenities.

Even though this study adds significantly to the scientific literature related to LOS, it still has several limitations. The first restriction is based on a geographic study, which includes the resort hotels in Poiana Brasov. The results should be compared to other resorts in the vicinity even if this one is a good representation of the lodging market in Romania’s mountainous region. The study, which is thought to have been focused on a different market niche for hotels, solely looked at the behavior of OTA travelers (data utilized in the study came from the Booking.com site).

To further improve the current findings, more research is required. The comparison of four-star hotels and three-star hotels in this resort, on the one hand, and the replication of the methodology for the other two major mountain resorts in Romania, Sinaia, and Predeal, on the other, could constitute an important future study path. Analyzing the impact of destination qualities (such as safety and security, climate, wildlife, sea, or gastronomy) on the length of stay could be another significant future research field. In addition, more research is required to determine whether tourist spending or sociodemographic factors, that is, education level and age or gender influence the average LOS in the Poiana Brasov resort.

Additionally, it could be worthwhile to look at study analyses relating to the tactics used by mountain hotel management to battle not only seasonality but also the new pandemic setting. It is helpful to compare the procedures used by Romanian hotels to those used by other hotels on a global scale. By putting good practices into practice, when necessary, Romanian hotel management may benefit from being able to react to the new pandemic setting more effectively.