Transport modes and tourism seasonality in Italy: By air or by road?

Abstract

From the late 1990s, the European air transport deregulation has led to the increasing use of the plane to reach tourist destinations. This article investigates the impact of tourists’ changed traveling habits on Italy’s seasonal tourism demand pattern. The role of transportation on seasonality, indeed, has been almost neglected in empirical research. We analyze international monthly arrivals by transport modes from 1997 to 2018, and we use the Gini index as a measure of seasonality. The Gini’s decomposition allows us to evaluate the effects of the growth in the use of each transport mode (cars, planes, ships, trains) on seasonality. A beta regression model confirms that an increment in the proportion of tourist arrivals by airplane determines a reduction in Italy’s overall seasonality of international tourism demand. These results might be a starting point for policy makers in developing strategies to counteract seasonality.

Keywords

beta regression model Gini index international tourism flows seasonality transport modes

Introduction

Tourism is not a single cohesive industry but impacts many economic activities; among the services involved in the tourism experience, transport services play an important role. Mobility decisions characterize all phases of tourism experience. Transport systems connect tourists from their country of origin to destination regions and are also crucial for inter- and intra-destination movements. The choice of transport modes used to reach a destination inevitably impacts the destination’s transport mode choice.

The relationship between tourism and transports has been widely studied from different points of view. The effects on the development of tourist destinations (i.e. Khadaroo and Seetanah, 2008; Prideaux, 2000; Pagliara et al., 2015), the relationship between tourist intra- and inter-destination movement patterns and transport modes (see among others Masiero and Zoltan, 2013), the factors behind the choice of transport mode at a tourist destination (Hough and Hassanien, 2010), the environmental impact of the transport choices of tourists (e.g. Gössling and Peeters, 2007; Peeters et al., 2007), the impact of specific innovations and the effects of the deregulation of the air transport (see among others Graham, 2006; Pagliara et al., 2015; Rey et al., 2011), the identification of tourist typologies associated with transport modes used at destinations (Aguilò et al., 2012) are only some among the issues discussed by a relevant part of the literature.

Despite the wide existing literature on transport and tourism, the effects of the transport mode used to reach a destination on the seasonal tourism demand pattern have still received too little attention. This issue should deserve more consideration in the literature both for the relevance of the phenomenon of seasonality and because a clear change in the traveling habits of tourists emerged in the last decades, mainly due to the deregulation of the air transport industry from the late 1990s in Europe (Pender and Baum, 2000). The increasing competition in air transport has opened new routes and has contributed to dropping airfares, making the use of planes affordable to many people, not only wealthy people. In turn, in the measure in which the plane has become a substitute for the other transport modes, air transport competition has affected the airline companies’ strategies and has also contributed to changing tourists’ traveling habits. Since the availability of means of transport may influence the feasible destinations (Pellegrini and Scagnolari, 2019), the great change in the supply side has increased the accessibility to some destinations expanding the basket of tourists’ choices. Accordingly, the demand side of the market has been affected; the increasing number of tourists moving by plane is mainly due to both the lower fares applied by airlines and the augmented accessibility to old and new destinations.

This article aims to investigate the effects of the changed traveling habits of tourists on seasonality of demand. The underlying hypothesis is that a different seasonal pattern for each transport mode used to reach a destination could exist. In other words, if tourists traveling by road tend to be more seasonal compared to those who travel by air, the increment in the use of air transport should have some effect on the seasonal distribution of tourist flows. Although there is a consolidated theoretical framework about the causes of seasonality, empirical research is still inadequate. We are aware that several factors contribute to determining the intra-year distribution of tourism demand; however, the transport mode’s role in reaching the destination has been neglected. Based on the abovementioned hypothesis, we contribute to the tourism economics literature by investigating whether the tourists’ mutated traveling habits contributed to reduce or, more in general, to modify the seasonal pattern of tourism demand. The analysis uses a Bank of Italy database about foreign tourists reaching Italy from January 1997 to December 2017.

The article is organized as follows: in the second section, the literature related to seasonality and its main causes is reviewed. In the third section, the data at hand are described, and the statistical methodology is discussed. Results are highlighted in the fourth section, and we conclude with a short discussion.

Seasonality of tourism demand

Seasonality is one of the most remarkable features of tourism; it affects most of the worldwide tourist destinations. Seasonality of tourism demand at a destination can be defined as an uneven distribution of tourists in a certain period—generally throughout a year—characterized by recurrent stability (e.g. Bar-On, 1999; Butler, 2001; Hartmann, 1986). Although it is often analyzed on an annual basis (e.g. Cuccia and Rizzo, 2011; Lundtorp et al., 2001; Ruggieri, 2015), other kinds of seasonality can be observed, such as weekly or daily variations (Rossellò and Sansò, 2017; Sainaghi et al., 2019; Vergori, 2017).

Seasonality has several economic implications (see Allcock, 1994; Baum and Lundtorp, 2001; Chen and Pearce, 2012; Coshall et al., 2015; Grobelna and Skrzeszewska, 2019, among others), generally perceived as negative. The efficient use of available facilities is hampered by the fact that firms are forced to overuse their facilities during the peak season and to underuse them during the off-peak season. This has some effects on the job market, contributing to creating temporary employments. Furthermore, higher prices for products and services are charged during the peak season, affecting both tourists and residents. The concentration of tourists during a given period of the year causes several social and environmental problems too. Congestion of resorts and tourist destinations, waste management, and traffic are the most relevant issues dealt with by scholars. It is worth noting that, according to a part of the literature (e.g., Andriotis, 2005; Hartmann, 1986; Smith, 2003), seasonality may also have some positive effects on residents, who, during the off-peak season, take a break from tourists.

The analysis of the causes of the phenomenon is at the core of the strategies carried out to overcome the economic, social, and environmental consequences of the uneven intra-year distribution of tourist flows. The literature (see among others Baron, 1975; Baum and Hagen, 1999; Butler, 1994; Butler and Mao, 1997; Coshall et al., 2015; Ferrante et al., 2018; Frechtling, 1996; Lundtorp et al., 2001) has highlighted a mix of causes which directly or indirectly impact on both the demand and the supply side of the tourism at a given destination. That is, seasonality seems to be determined both by attributes of the tourist destination – such as climatic conditions, events, cultural heritage, provision of services, accessibility – and by factors concerning the demand side, namely, tourists and their countries of origin – such as inertia, changing tastes, and institutionalized holidays. Thus, the interaction between many economic and noneconomic factors defines the seasonal pattern that characterizes each tourist destination. Such patterns are dynamic (Rossellό Nadal et al., 2004), tend to change over time, and both slow and abrupt changes can occur (Hartmann, 1986).

Appropriate policy measures can contribute to modifying a destination’s tourism seasonal pattern according to the stakeholders’ needs. Among the most-cited approaches to counteract seasonality, we can cite the organization of events and festivals during the off-peak season, both market and product diversifications, structural changes, price and tax incentives on a temporal basis (e.g. Barros and Sousa, 2019; Baum and Hagen, 1999; Butler, 2001; Coshall et al., 2015; Vergori and Arima, 2020; Weaver and Oppermann, 2000). These and other similar strategies aim to lengthen the main season and encourage tourism out of the peak season.

However, in many areas, despite intensive efforts by both the industry and governments, seasonality persists, hence suggesting that the problem is more complex than generally thought. A more in-depth analysis of the causes is necessary to define more effective counter-seasonal policies (Ferrante et al., 2018). Recently, also Duro and Turrion-Prats (2019: 40) have highlighted that “although researchers may have identified the causes of seasonality […] greater efforts should be made to establish a more comprehensive theoretical framework. It is also necessary to corroborate this theoretical framework with empirical research.”

Transportation and tourism seasonality

The role of transport infrastructures for the development of tourist destinations has been emphasized in the literature (i.e. Khadaroo and Seetanah, 2008; Pagliara et al., 2015; Prideaux, 2000). More efficient transport options strengthen the links between tourism generating regions and tourist destinations, and from an economic viewpoint, they allow demand (tourists) to match supply (providers of tourism services). Furthermore, travel costs are among the most common determinants used in estimating tourism demand (e.g. Barry and O’Hagan, 1972; Cho, 2010; Crouch, 1994; Lim, 1999; Loeb, 1982; Smeral, 1988; Song and Li, 2008). More in general, the less expensive the journey is, the most attractive the destination is.

Among the causes of seasonality, the main literature (e.g. Bar-On, 1975; Baum and Hagen, 1999; Butler, 1994; Butler and Mao, 1997; Coshall et al., 2015; Frechtling, 1996; Lundtorp et al., 2001) cites the accessibility to a destination, for example, when transportation links are not assured throughout the year. However, to the best of our knowledge, the few empirical studies about the impact of transport modes used to reach a destination on seasonality focus on low-cost carriers (LCCs) (Chung and Wang, 2011; Donzelli, 2010; Graham and Dennis, 2010; Pulina and Cortès-Jimènez, 2010). The introduction of LCCs in air transport, indeed, has contributed to both changing the traveling habits of tourists and opening new tourist markets. In fact, it is self-evident that distances traveled tend to be a function of the amount of available time (Hinch and Jackson, 2000); thus, the lower fares applied by LCCs have allowed an increasing number of tourists to use the faster air transport, saving both time and money. In turn, the tendency to take breaks in more than one season (Lohmann and Duval, 2011) is boosted by the new travel habits.

The abovementioned studies deal exclusively with air transport, and they agree that LCCs have had a positive impact on the growth of tourist flows. Regarding the impact on seasonality, they get mixed results. A more even intra-year distribution of tourists emerges from Donzelli’s analysis of air traffic flows in three Southern Italy airports. This result is confirmed by Pulina and Cortès-Jimènez (2010) for Alghero (an Italian tourist destination). On the other hand, there seems to be no reduction in seasonality in Korea (Chung and Whang, 2011) or Malta (Graham and Dennis, 2010). These last two results are, probably, because the seasonal patterns of tourism demand have been compared with reference to years that were too close to each other, neglecting the fact that the effects on seasonality take a longer time to emerge.

More empirical research should be performed to understand better the dynamics through which the seasonal variations of tourism demand can be explained. Bearing in mind that there could be different seasonal distributions of tourist flows for each transport mode used, it is worth considering tourists’ behavior according to all possible transport modes that could be used to reach a destination.

Database and methodology

This article is based on the data collected by the Bank of Italy. Since 1997, a sample survey about international tourism has been conducted by the Italian monetary institute. It is based on interviews done with foreigners that take a trip to Italy. The survey has been conducted by administrating questionnaires at more than 60 international borders (roads, ports, airports, and railways). The interviews are taken at the end of the trip. About 130,000 travelers are interviewed every year (see the Manual and the Questionnaire available on the website of the Bank of Italy: www.bancaditalia.it).

In the following sections, we analyze the data related to foreign tourists who visit Italy. A first selection of the data has been made according to the reason for the trip; thus, we can focus exclusively on tourists. In fact, the data allow us to distinguish between business trips and personal trips. Within the group of those interviewed who travel for personal reasons, different alternative purposes are considered, such as tourism, study, visit relatives and friends, and so on.

To achieve our aim, the interviewed people who traveled for tourism from January 1997 to December 2017 have been considered according to the transport mode used to reach Italy. Four monthly time series concerning tourists traveling by air, rail, road, and sea were our analysis’s focus.

Data description

The time series of monthly arrivals by transport modes are represented in Figures 1 and 2. In two different graphs, we have shown arrivals by air and road (Figure 1) and by sea and railway (Figure 2), respectively.

Figure 1.

Monthly tourist arrivals by air and road (in thousands).

Figure 2.

Monthly tourist arrivals by sea and railway (in thousands).

The greater number of tourists reach Italy by road and air, while just a minor number of tourists choose the other two transport modes. The importance of air transport has decisively increased in the period under scrutiny, whereas a clear trend in the use of both road transport (Figure 1) and sea (Figure 2) is not as evident. Although foreign tourists visiting Italy come mainly from neighboring countries, the use of the train was reduced. It is worth noting that more than 80% of foreign tourists come from European countries. In 1997, about 10% of the total European tourists reached Italy by plane, while in 2018, they were about 44%. That is, foreign tourists coming from Europe have definitely increased the plane’s use and reduced other transport modes to reach Italy. For example, Spanish, English, French, and Dutch have made greater air transport use and increased their relative importance among foreign tourists.

A seasonal pattern characterized by a peak during the summer season emerges for each time series during the period under investigation. The peak months are July and August. Seasonal swings and their variation need a fuller analysis that will be carried out in the next sections according to the following methodology.

Methodology

How to measure seasonality is still an open problem in the literature. There is no overall consensus about the different indices proposed (see among the others De Cantis et al., 2011; Duro, 2016). The Gini coefficient, Theil indexes, and the coefficient of variation are three of the most widely used inequality indices. Since none can be considered overcoming the others, the choice generally depends on the specific case study and the research goals. To analyze the impact of different transport modes on the general level of seasonal concentration, we rely on the Gini index’s decomposition. This index has been applied extensively in several contexts, and it has been firstly proposed to analyze tourism in Wanhill (1980). In recent works of Coshall et al. (2015), Fernandez-Morales and Cisneros-Martínez (2015), Fernandez-Morales et al. (2016), and Fernandez-Morales and Cisneros-Martínez (2019), the Gini index has been used as an indicator of the seasonal concentration of tourism demand. The Gini index allows us to study the annual seasonal concentration of a tourist series: High values highlight high monthly concentration, while it assumes almost null values when seasonal patterns are uniformly distributed during the year.

There are many different approaches to the calculation of the Gini index. In our work, we use the covariance approach. Accordingly, the Gini index of each annual data set Y, with distribution function F(Y) and mean µ_Y is calculated using:

G_{Y} = \frac{2 Cov (Y, F (Y))}{μ_{Y}} .

There are several methodological challenges about this decomposition, mainly discussed in the field of wealth index and inequality. Following Giorgi (2011), we use the marginal decomposition of the Gini index. In the context of economic inequality, this decomposition is known as the decomposition by income sources or by factor components. This marginal decomposition, recently used in Fernandez-Morales et al. (2016), allows us to analyze the impact of different transport modes over the general level of seasonal concentration. The Gini index of an annual set of the tourism flow, Y, can be additively decomposed into K market segments, which, in our context, correspond to the different transport modes (Y, X₁, X₂,…X_K). The Gini index of Y (G_Y) can be decomposed as a weighted average of three components: the Gini index of segment k, G_k, the market share of segment k, S_k, and the Gini correlation between segment k and the total demand, Γ_k (for simplicity we replaced the notation Γ_kY). In other words, the Gini index may be expressed as:

G_{Y} = \sum_{k = 1}^{K} S_{k} Γ_{k Y} G_{k} .

Following Fernandez-Morales et al. (2016), we also consider the marginal effect of a change in any of the components of the series over the total Gini index defined as:

{RME}_{k} = \frac{S_{k} Γ_{k} G_{k}}{G_{Y}} - S_{k} .

The RME _k of segment k measures the relative increment (when positive) or decrement (when negative) of the overall Gini index associated with that segment’s growth. In other words, it can be interpreted as an indicator of the potential impact of the kth segment on seasonal concentration.

Indeed, segments (here termed “favorable tourists”) with negative RME _k will be those prone to counteract seasonality, as they tend to reduce the overall Gini index.

The indices described above allow us to evaluate the variability of the phenomenon’s seasonality from a descriptive perspective. A further advance is devoted to the inferential analysis of such an index to verify whether any significant differences between Gini indices in different years occur. With this aim, we build confidence intervals. Since closed-form distribution for the Gini index cannot be defined and, in particular, we cannot derive its variance analytically, then we build bootstrap confidence intervals. The bootstrap technique has been proposed by Efron et al. (1979). It relies on the idea of evaluating the properties of one estimator, for example, the variance, using samples, with replacement, of the observed sample, and it allows to determine approximate confidence interval. Considering two populations, not overlapping intervals define a statistically significant difference between the two populations’ means. To further examine the transport modes’ quantitative importance as a seasonality source in tourism demand, we also propose a statistical model. The linear regression model, in particular, is commonly used in many fields of applied statistics. We assume that the error terms are distributed as a Gaussian distribution with null mean and unknown variance in the classical linear regression model. Such a parametric assumption is particularly useful when the sample size is small. However, this model is not appropriate in the context we will analyze, where the response variable, the Gini index, is restricted to the unitary interval.

Hence, inference based on the normality assumption can be misleading. To overcome this problem, Ferrari and Cribari-Neto (2004) proposed the beta regression model for continuous variates that assume values in the standard unit interval, for example, rates, proportions, or concentration indices. In their model, the regression parameters are interpretable in terms of the mean of y (the variable of interest), and the model is naturally heteroskedastic, and it easily accommodates asymmetries.

More formally, let y₁, y₂,…, y_n be independent random variables, and each y_t follows a beta density with mean µ_t and dispersion parameter ϕ.

The regression model can be written as:

g (μ t) = \sum_{i = 1}^{k} x_{t i} β_{i},

where β = (β₁,…, β_k)^T is a vector of unknown regression parameters, x_t₁,…, x_t_k are the fixed covariates, and g() is a monotonic and double differentiable link function over (0, 1). We use a logistic function as a link function. The β coefficients give the additional increase or decrease in the log odds of the response variable. Estimations and inferences are performed using the maximum likelihood method and the Newton–Raphson algorithm implemented in the R function betareg (Cribari-Neto and Zeileis, 2010).

We use the annual Gini index as a response variable, and we model it as the function of the annual proportion of tourists who reached Italy by air, train, and ship. The proportion of tourists by car is fixed as a corner in the standard corner point parameterization. Pseudo R² and analysis of residuals have been used to validate the proposed model.

Results

According to each transport mode, the proportion of travelers is shown in Figure 3 for the years from 1997 to 2017. It clearly highlights that the car is still the most preferred transportation mode for tourists visiting Italy. However, the proportion of tourists reaching Italy by car significantly decreases during the years, from 75% in 1997 to 47% in 2017. On the other hand, although the proportion of travelers using trains and ships seems almost stable during the years, airplanes’ use shows a significant increment passing from 17% in 1997 to 49.6% in 2017, becoming the most widely used transportation mode.

Figure 3.

Proportion of travelers according to transport modes.

The overall seasonality of tourism demand has been evaluated using the Gini index. Figure 4 shows the bootstrapped confidence intervals for the Gini index computed for each year from 1997 to 2007.

Figure 4.

Bootstrapped confidence intervals for the Gini index.

Since none of the intervals contains the null values, bootstrapped confidence intervals reveal that the index resulted significantly higher than 0 in all years, highlighting the seasonality of tourism demand. The figure also reveals a significant mean reduction of the index since intervals corresponding to the first 6 years do not overlap substantially with the intervals in the last years. Moreover, the intervals’ size decreases with the years as well as the variability, showing an important decreasing trend. According to Figures 3 and 4, we hypothesized that the increment in using the airplane as transport mode might impact the decreasing trend of the seasonality.

Figure 5 shows the Gini index from 1997 to 2017, distinguishing each transportation mode. As expected, ships are strongly related to the season as their Gini index is larger than the same index computed for other transport modes. The second most seasonal tourists are those who reached Italy by car. The Gini indices’ smallest values correspond to the airplane, whose variability seems to be less affected by seasons than other transport modes. To further support this point, we accomplished the Gini decomposition as in equation (3), where the k markets in our case are the four transport modes used by tourists to reach Italy.

Figure 5.

Gini indexes.

From Figure 6, it emerges that the RME _k values for both ships and trains are almost zero, while a positive marginal effect is highlighted for the car. As we have already pointed out in the previous section, the RME _k allows us to evaluate each transport mode’s potential impact on the Gini index change. Thus, tourists traveling by car seem to contribute to a more uneven intra-year distribution of tourist flows. On the other hand, tourists traveling by plane exhibit negative RME _k values, confirming that traveling by plane can be a tool for counteracting the seasonality, as they tend to reduce the overall Gini index.

Figure 6.

RME _k values.

To further evaluate the role played by transport modes used to reach a destination as a cause of seasonality of tourism demand, we fit a beta regression model having as dependent variable the Gini index computed at each year and as explanatory variable the proportion of tourists who visited Italy according to the different transport modes (see equation (4)). We use the car as a corner point, as in the standard analysis of variance parameterization. Table 1 presents the estimates and the corresponding p values. The model highlights that the only significant reduction of the seasonality may be ascribed to the airplane’s use. In our model, when the proportion of tourists traveling by airplane increases compared to the proportion of tourists traveling by car, a statistically significant reduction in the Gini index occurs. To verify that the model’s assumptions are valid for the considered data, we analyze the residuals of the model. Figure 7 plots different residuals (Espinheira et al., 2008) against linear predictor and a half-normal residual plot with simulated envelope. Clearly, the four residuals suggest a satisfactory fit of the beta regression model, in agreement with the overall fit index, pseudo R², equal to 0.69.

Table 1.

Estimates of the beta regression model (corner point = car).

	Estimates	p Values
Intercept	−0.397	0.221
Airplane	−1.379	0.013
Train	−0.393	0.884
Ship	−2.4298	0.463

Figure 7

Diagnostic tools: Standardized weighted residuals versus observation number; standardized weighted residuals versus linear predictor; predicted versus observed values; half normal plot of residuals.

To further validate our model results, we performed the augmented Dickey–Fuller unit root test (Dickey and Fuller, 1979): it resulted in a p value equal to 0.205, allowing us to reject the null hypothesis of a unit root. We also performed seasonal unit root tests based on the Canova and Hansen (CH) test statistic for the null hypothesis of a stable seasonal pattern (CH, 1995): also the CH test highlights a stable seasonal pattern resulting in p values all larger than 0.05 (ranging from 0.111 to 0.257).

From an inferential point of view, the proposed model confirms the results obtained by analyzing the Gini index’s decomposition, showing that airplanes’ use substantially reduced the seasonality of the tourist flows.

Conclusions

As it is widely recognized and recently well-documented (Batista e Silva et al., 2018), the summer months tend to be the most popular season for almost every region in Europe. In the literature, the seasonal pattern of tourism demand has been attributed to many causes that can be roughly divided into two groups involving natural and institutionalized factors, respectively. As far as we know, although the accessibility to a tourist destination has been mentioned among the causes of seasonality, it has never been studied, jointly considering the role played by different transport modes.

The results of our study provide interesting food for thought. Given that, whatever the transport mode used, foreign tourists in Italy are in any case unevenly distributed during the year, we surmise that the intensity of the phenomenon varies according to the transport mode chosen to reach the destination. Thus, notwithstanding summer is the most preferred season also by tourists who travel by air, the plane’s choice seems to allow tourists to be less conditioned by the seasons than those who choose other transport modes. These results dovetail with the effects of the deregulation of the air transport industry in Europe. In Italy, LCCs carried about 48.8% of total foreign tourists in 2017, while they were about 9.1% in 2004 (www.enac.it). LCCs’ lower fares allowed more and more tourists to use air transport, enabling tourists to travel longer distances in lesser time. Consequently, tourists are not tied to travel during the period in which they have more free time, contributing to spread tourism demand more evenly during the year.

Cars and airplanes are both the most used transport modes to reach Italy. Although international tourist flows tend to be concentrated during the summer, tourists traveling by air tend to be less seasonal than those who travel by car. Since the seasonality of tourism demand depends on many causes, the transport mode choice alone cannot completely change the underlying pattern. However, as it emerges from our analysis, it can mainly impact the intensity of the phenomenon.

These results may broaden the strategies to manage the uneven distribution of tourist flows according to a tourist destination’s needs. When the aim is to reduce the intensity of the phenomenon, policy makers should consider that an (up to now neglected) relationship between transport modes and seasonality exists next to the commonly used strategies. Policy makers could provide price incentives to encourage air companies to increase the number of flights during the periods out of peak. Furthermore, they could try to foster the use of trains. Although the train’s use has been gradually reduced in the period analyzed, it is the less seasonal transport mode after the air.

Unfortunately, in the case of Italy, the two most used transport modes to reach Italian destinations are also the most polluting ones. Since policy makers should foster a kind of tourism that is both less seasonal and less polluting, the analysis of the relationship between seasonal patterns and transport modes should be useful to define the strategies that can be adopted to reach both those goals. This open issue deserves to be adequately dealt with.

The analysis performed is based on a single aggregate time series related to Italy as a tourist destination. Of course, there are several destinations in Italy, each with its own peculiarities and tourist attractions. When we deal with Italy as a whole, we neglect to consider the hundreds of different realities that Italy actually is. This is a weakness of this article, mainly due to data availability. Notwithstanding, the analysis gets the goal to highlight the impact of the transport mode used to reach a destination on the intra-year distribution of tourism demand, up to now neglected. This kind of analysis paves the way for further research based on weekly arrivals other than more disaggregated data, compatibly with data availability. Future research could also concern the effect of travelers’ transport choices on the spatial distribution of tourist flows.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Anna Serena Vergori

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