Factors Influencing Travel Package Prices
Gorazd Sedmak
University of Primorska, Faculty of Tourism Studies - Turistica gorazd.sedmak@fts.upr.si
Tina Kociper
University of Primorska, Faculty of Tourism Studies - Turistica tina.kociper@fts.upr.si
The aim of this paper is to define the factors influencing travel package prices offered by Slovenian tour operators and the impact of each factor. A hedonic pricing model for package travels offered on the Internet was developed, and implicit prices of different package features were estimated via the employment of hedonic regression. Previously, similar research was done only for restricted geographical areas, using data obtained from tour operators' brochures. In the present research, the model was built on the basis of web-accessible information, while different travel destinations throughout the world were taken into consideration. The type of destination and its character were determined to influence package travel pricing. The results also highlight that those packages bearing more cultural experience potential can be sold at higher prices.
Keywords: hedonic price theory, web offered travel, regression analysis, Slovenia
Introduction
Package travel or a package tour is an inclusive form of travel organized by intermediaries, usually tour operators. Although it is a compound of at least two goods or services, it is marketed as one distinct and separate product (Buhalis, 2000). Such a bundle can include transportation, accommodation, entrance tickets, meals, etc., or any other combination of goods consumed by tourists. As a rule, a buyer does not have an insight into the price structure of package travel, thus in a decision-making process a direct comparison between different packages (with different services included) is not possible. In this article, the focus is on package travel marketed via the Internet.
In the previous 20 years, the Internet has dramatically changed the buying behaviour and decision-making process of travellers, specifically the ways in which they search for travel information and
purchase vacation products (Chiam, Soutar, & Yeo, 2009; European Travel Commision, 2011; Hyde & Decrop, 2011; Tanford, Erdem, & Baloglu, 2011).
"The mass adoption of the internet and digital networks is transforming the way in which consumers share and manage information among themselves and corporations, presenting an interactive platform where consumers can build meaningful dialogue with tourism providers - challenging the top-down approach of "we market you" or "telling and selling" to a bottom-up experience whereby the consumer can become a more active participant in the overall experience as opposed to a passive receiver of information and products" (Moutinho, 2011).
Understandably, the roles of travel agencies, classic forms of advertising and information channels
have subsided considerably. Instead, on-line offers, interactive portals, virtual shows, direct booking and purchase options have expanded considerably. Travel is already the largest online retail category and its advantage over the traditional travel agencies is continually growing (Kim & Gang, 2009). This expansion is fuelled by the increasing number of people with high-speed internet access, improved technologies and growing confidence in on-line purchasing (Kim, Bojanic, & Warnick, 2009). During the phase of travel planning/organization, Slovene citizens used the internet in almost one third of cases (31%) (SORS, 2012). This was higher for longer trips abroad (43%). In 60% of cases, they used the Internet merely as a source of information, while 40% booked accommodation and/or other services through the web.
The internet has caused the so-called disinterme-diation in the field of travel arrangements (Davidson & Rogers, 2006); it provides potential tourists a direct communication with tour operators and a comprehensive overview of different products from an extensive number of suppliers at relatively low transaction costs. Moreover, a comparison of prices, for tourists as well as for tour operators, is facilitated, and communication among the users (through ratings and forums) is easier. The market is thus becoming significantly more transparent and competitive (O'Connor in Frew 2004; Kim, Bojanic, & Warnick, 2009; Wen, 2009). The likelihood of misleading offers or deviations from "normal" value for money are minimized. It can be presumed that discrepancies in prices between different packages actually derive from different contents included in the packages and their characteristics.
The field of package travel is marked by differentiated products. Differentiation can derive from product contents, characteristics, time, place or circumstances of consumption, or from the circumstances in which the product is bought (Ricard Rigall-I-Tor-rent & Fluvià, 2011). When a product is purchased, what is actually being bought is a set of characteristics (Sard, 2006). Although some of these characteristics/factors may be intangible, they vary from one product or seller to another, and buyers at least partially consider them in the selection process. (Anderson, de Palm, & Thisse, 1992). According to Caves and Williamson (1985), a product is differentiated if any basis exists to make it possible to distinguish it
from similar products of other sellers; the basis for this can be real or imaginary. Theoretical conditions of differentiation are that buyers perceive products within a particular group of products as relatively close substitutes, but as weak substitutes for products from outside the group. At the same time, products from within the group should not be perfect substitutes; i.e., every seller should deal with a down-sloped demand curve. As in the case of package travels, a potential tourist chooses from a variety of different packages that can satisfy his/her need to spend some leisure time away from home. If package travels are seen as bundles of goods and services consisting of different combinations of characteristics, it is possible to ascribe implicit prices to these characteristics through hedonic price modelling.
Hedonic Prices
Product prices and the quantities of characteristics embodied in products enable setting hedonic or implicit prices for each characteristic. The hedonic price theory is formulated as a problem of space equilibrium, in which the aggregate implicit prices of characteristics lead buyers and customers to individual points in the space of characteristics (Rosen, 1974).
The theory is based on the attributive theory of demand, which presupposes that product characteristics can be described as points in a co-ordinate system. It assumes that the price of a product is a function of its immanent utility-bearing characteristics or attributes (Thrane, 2005). The hedonic function is given by: P = h(z),
where P denotes the prices of a cross-section of goods (i.e. one price for each variety or model; in our case, the package tour) available in a given period, while the matrix "z" denotes a bundle of characteristics for each package tour. The basic model of hedonic theory presupposes a state of competitive equilibrium. The economic behaviour of buyers and sellers of heterogeneous products is defined by sets of demand and supply functions for characteristics. These demand and supply functions are the result of the buyers' and sellers' optimizations. Each type of product from a group of differentiated products is fully described by a vector of objectively measurable characteristics. Due to product differentiation, buyers are given the opportunity to choose from among different insep-
arable bundles of characteristics. A price p(z)=p(z... z ) is set for each point in space and leads sellers and buyers to preferred bundles of characteristics (Silver, 1996). Riera (2000) suggests that hedonic regression is also a suitable method for placing value on different environmental attributes that can be applied to valuations of non-priced tourism resources. Indeed, in the presented research, variables denoting the type of destination in terms of their main attractions have introduced.
Hedonic prices models have been investigated before in the field of tourism and hospitality. Espin-et, Saez, Coenders, and Fluvia (2003) studied the implicit prices of hotel attributes; Juaneda, Maria Raya, and Sastre (2011) used the hedonic price model to explain the pricing of the time and location of a stay at a hotel or self-catering apartment; Falvey, Fried, and Richards (1992), Gunawardana and Havrila (1993) and Sedmak, Mihalič, and Rogelj (2004) dealt with implicit prices in restaurants. Thrane (2005) proposed two models for sun-and-beach package tours to the Canary Islands offered by Norwegian tour operators; Sinclair, Clewer, and Pack (1993) researched packages to Malaga; Laesser and Crouch (2006) employed hedonic pricing in a segmentation of visitors to Australia. However, in previous studies of package tours, only restricted geographical areas were taken into consideration and all data were obtained from brochures (Aguilo, Alegre, & Sard, 2003; Haroutuni-an, Mitsis, & Pashardes, 2005; Papatheodorou, 2002; Thrane, 2005). However, considering the actual situation of the travel market, the increasing role of the internet and the enormous amount of choices tourists have, their decisions are highly unpredictable (Zahra & Ryan, 2007). Doyle (2002) defines product as "anything that a firm offers to satisfy the needs or wants of customers". While wants are narrow and particular, needs are basic requirements that individuals wish to satisfy. Previous studies seem to neglect that, owing to simplified accessibility of information, package travels to different destinations are nowadays closer substitutes than they used to be. They can actually be perceived as one differentiated product satisfying the same need to spend some time away from home. Indeed, Kotler, Bowen, and Makens (2006) claim product differentiation can occur via physical attributes, services, location, image or personnel; all of these elements as a rule differ between
destinations. Although some research was recently done using web-accessible data (e.g. Fleischer (2011) used the internet to analyse the differences in room prices by comparing the rooms having a view of the Mediterranean Sea to those without such a view) to the knowledge of the authors of this paper, no hedon-ic model for package travel using this source of information has been proposed thus far. Moreover, studies limited to only very similar and close destinations do not render possible the estimation of individual characteristics, such as type of destination, country, macro-location, etc.
The aim of the present research was to fill these gaps. We decided to identify the information package tour features available on the tour operators' web pages upon which tourists decide where to travel and estimate their implicit prices. Therefore, the main contributions of this paper are: employment of a comparable and much fuller set of information available on the net instead of brochures, and the holistic approach that takes into consideration all available target destinations instead of a limited geographical area, thus proposing a general hedonic model for package travels. These were investigated for the case of Slovenia as an outbound destination. Our rationale was that the packages offered on-line are essentially perceived by potential tourists as one differentiated product.
Research Methodology
The data gathering was carried out in spring 2011 by well-trained research trainees (students) of the University of Primorska, Turistica - Faculty of Tourism Studies. Every trainee was asked to randomly, without discrimination, choose a certain amount of web available packages offered by Slovene tour operators. They were encouraged to search for a variety of different companies' offers and destinations. With this approach, we believe that the nearest proximity to randomness of the sample was attained. The authors were present and available to help the trainees' throughout the process. This was especially valuable to achieve a relative uniformity of judgements in which, for example, the type of destination or the main attraction were determined.
Although an exhaustive literature review and examination of previous similar studies was performed before the conceptualization of the present
research, the final selection of variables was predominantly made on the basis of a preliminary survey of information on organized travels, holidays, tours, etc., available online. Bearing in mind that the decision-making of tourists buying packages on line is based almost exclusively on information available on the Internet, it was considered reasonable that only this information be taken into consideration for the modelling.
Earlier hedonic models intensively focused on just one or few like destinations, thus tour operators companies and hotel characteristics, such as star rating, presence of lifts, bar, sport facilities, swimming pool, etc., represented the majority of independent variables (Aguilo, Alegre, & Riera, 2001; Sinclair, Clewer, & Pack, 1993; Thrane, 2005; Israeli, 2002). Destination characteristics were only implicitly included through the variables "Distance to beach", "Distance to shopping areas", "Proximity to a population centre", "Picturesque spot", etc. In our case, after the overview of web-offered packages, all the published or indirectly attainable information available on the majority of Slovene tour operators' websites were included in the model. All prices were calculated for one night, the remoteness of the destination was reckoned from Ljubljana, the capital of Slovenia. Since not only seaside destinations were dealt with, in line with previous studies in which the distance to the beach was one of the independent variables, the variable "distance to the main attraction" was introduced. It was left to the person entering the data to judge what the main attraction (beach, town
centre, etc.) was in each case. Similarly, the classification of each destination into one or more categories of destination type was left to their judgement, according to the information available on the website and their general knowledge. Beforehand, however, students were instructed by the authors who helped them throughout the work. The three options were: 3S destination, destination with important cultural attractions, and destination with important natural attractions. One destination could have more than one attribute. According to Juaneda et al. (2011), time is an important factor affecting price. Pricing this component provides information on the effect of the seasonality, which is a crucial field in tourism studies. The high season was defined as from June 15th to September 15th, i.e. the period when the vast majority of Slovene tourists go on their annual holidays.
In order to keep the number of explanatory variables to a practical number, two "umbrella variables" were introduced: "Hotel equipment exceeding standards of the category" and "Extra contents offered free of charge" (event tickets, sauna, excursions, etc.) for some of the hotel characteristics judged as being of marginal importance or that are place/destination specific, such as free air conditioning, WiFi, Jacuzzi, children's play-room, deck chairs, pool/billiard, etc. With such a definition of variables, multicollineari-ty with a hotel category was also diminished to a certain degree as well. Thus, the variables included in the study were (see Table 1).
Table 1 Variables Included in the Research
Variable	Description	Expected sign
PRICE	Price per person per night	/
OVERNIGHTS	Total no. of overnights	-
FIRST	First minute or last minute offer	-
STARS	Category of the hotel (1-5 stars)	+
COMFORT	Hotel equipment exceeding standards of the category	+
PLANE	Flight fare included in the price	+
BUS	Bus fare included in the price	+
COUNTRY	Destination country	?
REMOTE	Remoteness of destination	+
Variable	Description	Expected sign
BREAKFAST	Only breakfast included in the price	+
HALF_B	Half board	+
FULL_B	Full board	+
GUIDE	Organized guidance	+
EXTRA	Extra contents offered free of charge	+
ATTRACTION	Distance to the main attraction (beach, town centre, etc.)	-
SSS	Sea, sand and sun destination	-
NAT	Destination with natural attractions	?
CUL	Destination with cultural attractions	+
HIGH	Travel in the high season	+
TO	Tour operator company	?
MIN	Minimum no. of travellers
While other variables' expected signs are self-explanatory, for the variables SSS and CUL hypothesized directions on the relation between dependent and independent variables were assigned on the basis of previous research and a literature review. Traditional 3S destinations tend, due to tough competition in this segment and their mature stage in their life-cycle, to be relatively low priced (Agarwal, 1999; Apostolopoulos & Somez, 2001; Montemagro, 2001; Tsaur, Lin, & Lin, 2006), while cultural destinations seem to have higher profit margins through differentiation and perceived high value of cultural attractions (Richards, Goedhart, & Herrijgers, 2001).
Six variables were of the scale type (while all the rest were dummy variables, coded "1" if a characteristic/element was present in the offer and "0" if not. As in Thrane's (2005) research, accommodation star rating was included as a quasi-scale variable. The research encompassed organized travels to 77 different countries. These were offered by 23 different tour operators. However, 80.5% of all sample data was obtained from the four largest suppliers. Only those four tour operators representing more than 5% of the sample were included as independent explanatory variables in the model. Small operators were implicitly incorporated in the model as a base. Sard (2006) claims that the differentiated prices are not merely
a result of contents and characteristics of packages but also negotiations with hoteliers, and that prices are also dependent on the market power that the tour operators have in their markets of origin. Destination countries were then divided into five groups: Slovenia (SLO), neighbouring countries (NEIG), other non-Mediterranean European countries (EUR), Mediterranean countries (MED) and other/overseas countries (OVER).
The final sample comprised 1125 different travel packages. In Table 2, the sample structure and the mean values are presented. The average price of the package for one person was €124.03 per night, the mean distance of travel from Ljubljana 2262.61 kilometres and the average number of overnights 5.48. Prices of the four large suppliers were on average higher (mean: €133.06) than those of the small ones (mean: €121.85).
Table 2 Mean Values and Structure of the Sample
Scale variables (unit)	N	Mean	Std. deviation
PRICE (€)	1125	124.03	108.95
OVERNIGHTS	1121	5.48	3.42
STARS	1035	3.41	0.69
REMOTE (km)	1103	2262.61	3218.19
ATTRACTION(m)	849	130.12	789.00
MIN (persons)	526	19.39	14.42
Dichotomous variables			
COMFORT	1085	60%	
PLANE	1122	56%	
BUS	1122	63%	
BREAKFAST	1123	27%	
HALF_B	1123	52%	
FULL_B	1123	13%	
GUIDE	1123	56%	
EXTRA	1125	41%	
SSS	1125	49%	
NAT	1125	50%	
CUL	1125	55%	
HIGH	1101	52%	
FIRST	1125	9%	
SLO	1125	14%	
NEIG	1125	20%	
EUR	1125	36%	
MED	1125	8%	
OVER	1125	19%	
TO_1	1125	32%	
TO_2	1125	32%	
TO_3	1125	9.5%	
TO_4	1125	7%	
The structure of destination countries is not com- ists' trips. SORS (2011) only publishes the structure of parable with the actual structure of Slovenian tour- all the so-called "longer private trips" regardless of
whether they are organized individually or by tour operators. According to these data, a quarter of trips are made within the borders of the country, approximately half of the trips are to neighbouring countries (mostly Croatia), and only 4% of the trips are outside Europe. The rest of trips, approximately one fifth, are to other European countries.
Analysis
Ordinary least square regression (OLS) was used to estimate the hedonic function. P<0.05 was taken for the critical statistical significance value. As suggested by Gujarati (1995) and Papatheodorou (2002), in the case of linked bundles of the dummy variables (for example destination countries), one of them was dropped out of the model and constitutes the base or the benchmark against which the remaining coefficients are interpreted, which presupposes that other characteristics remain the same (the ceteris paribus condition). These base variables are: "no food included", "no transportation included in the price", "destination without important natural, cultural or 3S attractions" and "small tour operators".
In the case of dichotomous variables in which less than 10% of the sample fall into one group, they were excluded from further analysis. These were: FIRST, MED, ATTRACTION, TO_3 and TO_4. The variable REMOTE was shown to be highly correlated with OVER (r=0.87) and significantly with PLANE (r=o.5i) and was also excluded from the model. MIN was also left out of the model due to the high portion of missing data and unclear presentation of the data
published by different operators. Among the remaining variables, no problematic multi-collinearity was detected (max VIF = 2.77).
Both the log-lin and lin-lin functional forms were used in the simple OLS regression. As the former demonstrated greater explanatory power, only the results of this functional form are presented. Estimated coefficients of such a functional form are interpreted as "the percentage change in the dependent variable associated with a one unit increase in the independent variable" (Thrane, 2005).
After the first regression, the model was narrowed. According to the "from general to specific" approach (Campos, Ericsson and Hendry, 2005; Hendry, Leamer and Poirier, 1990) seven explanatory variables (BUS, OVERNIGHTS, COMFORT, HALF_B, EXTRA, TO_2 and HIGH) with coefficients having a sign contrary to what was expected or those with t-statistics absolutely less than 1 were excluded from the model.
Results and Discussion
In Table 3, the results of the final regression are presented. Two explanatory variables (NAT and TO_1) are not statistically significant; therefore, they are not included in the model. The final model can thus formally be written as:
logPRICE = CONSTANT + Bt STARS + B2 PLANE + B NEIG + B EUR + B OVER + B BREAKFAST +
345	6
B GUIDE + B„ SSS + B CUL + e
7	89
Table 3 Regression Coefficients
B	Std. Error	t	Sig.
(Constant)	3.351	0.104	32.228	0.000
STARS	0.151	0.024	6.193	0.000
PLANE	0.460	0.041	11.224	0.000
NEIG	0.278	0.054	5.181	0.000
EUR	0.221	0.049	4.500	0.000
OVER	0.792	0.062	12.752	0.000
BREAKFAST	0.123	0.038	3.207	0.001
GUIDE	0.198	0.043	4.589	0.000
SSS	-0.115	0.039	-2.924	0.004
B	Std. Error	t	Sig.
NAT	0.047	0.036	1.291	0.197
CUL	0.104	0.041	2.512	0.012
TO_i	-0.045	0.036	-1.261	0.208
R2= 0.531; R2adj= 0.526
The model was able to explain 53% of the variance. The Ramsey RESET test showed that the model is not mis-specified (t= 1.639; sig = 0.101) and the Breusch-Pagan test proved the absence of heteroske-dasticity (F= 1.362; sig = 0.185).
The results are to be interpreted in the following manner: e.g. if the package tour price includes airfare, the package is (under the ceteris paribus condition) on average 46% more expensive than the package tour with no travel fare included in the price. Or, for the case of a negative coefficient, if the destination of the package tour is a 3S resort, the price is expected to be (again, ceteris paribus) 11.5% lower than if the destination had no outstanding natural, cultural or 3S attractions.
The formal categorization of the accommodation contributes 15.1% to the price with every additional star. The quality of accommodation is, therefore, far from being trivial. If breakfast is included in the package, a 12.3% higher price is the average, which is, interestingly, practically the same figure as that found by Thrane (2005). The findings, in the part that is comparable to the previous studies, also confirm the results obtained by Aguilo, Alegre and Riera (2001), who ascertained hotel category, type of board and location explain to a considerable extent the distribution of prices.
In contrast, there are variables that were not included in previous models. As can be concluded from Table 3, the most important factor influencing the package travel prices is the overseas/other destination of the trip (OVER), which increases the price in comparison to destinations in Slovenia by almost 80%. This result is not surprising, because transportation cost accounts for an important portion in the cost structure of the package travel (Aguilo, Alegre, & Riera, 2001; Lickorish & Jenkins, 1997). Specifically, this variable undoubtedly also incorporate the effect of the destination remoteness. Moreover, some other problems and organizational costs are con-
nected with the remote destinations: different language and necessity of translations, local income agency provisions or local representative costs, communication and insurance costs, visits of tour operator representatives in the destination in the phase of product formation, risk of unstable exchange rates, etc. (Čavlek, 2002; Haroutunian, Mitsis, & Pashard-es, 2005). However, the lack of knowledge and experience of tourists with such destinations make them willing to pay this extra money (Williams, 1996). Similarly, flight as a transportation mode considerably increases the price (46%). The next two factors raising the package price by more than 20% in comparison to domestic destinations are also destinations of travel as well: neighbouring countries (27.8%) and other non-Mediterranean European countries (22.1%). As these two groups are rather heterogeneous in terms of the countries included, it is difficult to interpret the coefficient difference in favour of neighbouring countries. If the package includes organized guidance, the price is expected to be 19.8% higher than without it. This result confirms the importance of this service component in the overall tourism experience (Huang, Hsu, & Chan, 2010) and is in line with the findings that the learning and acquisition of knowledge constitutes a valuable part of it, especially for cultural tourists (Richards, 2002).
As expected, 3S destinations negatively influence the package price (11.5%). Lower prices in this destination segment are the result of a loss of uniqueness, ecological degradation, poor product quality, negative image as well as an overdependence of these destinations on failing markets over the last 30 years (Agarwal, 1999). Moreover, the emergence of new overseas destinations and increased interest in previously neglected rural and urban destinations has made the competitive struggle for traditionally in-tra-regional European tourists more severe (Sedmak & Mihalič, 2008). Connected to these trends, higher prices (10.4%) of the destinations with distinct cultural attractions (CUL) were also expected. Many
radical changes occurred on the demand side. The so-called "new" tourists (Poon, 1998) that reject inflexible and inauthentic products developed for mass tourism show increased interest in indigenous heritage and tradition (Gale, 2005). The up-market especially started to demand more customized products and experiences within local cultures in environmentally "responsible" destinations (Carey, Goun-tas, & Gilbert, 1997). Unlike 3S destinations, cultural destinations do not need to compete primarily with prices as (authentic) culture is a place-specific category; thus, a high degree of differentiation allows them to attain higher prices.
There are two additional contributions of the study. Firstly, the Internet was used as source of information because no hedonic model for package travel had thus far used this source; nowadays, the internet represents the primary source of information for tourists (Chiam, Soutar, & Yeo, 2009; European Travel Commision, 2011; Tanford, Erdem, & Baloglu, 2011; SORS, 2012) due to the instant accessibility and high levels of accurate and current information, thus making it a more reliable source than brochures. Secondly, prior studies were limited to very narrow geographical areas and did not enable the estimation of individual characteristics of implicit prices, such as the type of destination, macro-location, etc.
The main limitation of the model and the research is connected to the unexplained part of the variance. As Abratea, Fraquellia, and Viglia (2012) claim for the case of hotels, but we believe is true also for destinations, price differentiation is the result of many factors: different quality and types of services provided, different physical attributes of accommodation, the reputation and brand of the hotel, travel agent or airline (Chiam, Soutar, & Yeo, 2009), and site-specific attributes, such as local attractions, climate, beach, etc.
Of course, it is impossible to include all this specific information into the model even though some of it might be accessible on the web in individual cases. Here, it should be noted that the ascription of certain destination features, e.g. "destination having distinct natural attractions" or the choice of the "main attraction" (for the variable "Distance to the main attraction") was done to a great extent on a subjective basis. Although students were encouraged to discuss uncertain cases with the coordinators/authors, the fact
is the knowledge and perceptions certainly vary between them, which might influence the reliability of results. Another possible limitation of the study is that some prices might differ from actual prices in equilibrium due to reasons such as negotiations, "decoy" prices, etc. Furthermore, the (changeable) fash-ionableness of destinations (Shaw & Williams, 2004), which is very difficult to assess, is probably a significant factor influencing the prices. Finally, despite the identified multi-collinearity among the variables being within the acceptable boundaries, it probably did affect the results to some extent.
In spite of these limitations, bearing in mind the growing importance of the web in organized travel commerce, we believe that a significant contribution to the body of knowledge has been made by this research, because the introduced model presents a sound basis for understanding the price structure of package travels offered on-line for different destinations.
Practical Implications
Price has frequently been cited as a key decision criterion in the purchase of travel products (Coulter, 2001). Despite the differences in mean values of prices between small and large tour operators, the present research showed that price differentiation is not significantly influenced by tour operator brands for the case of Slovenia. Differences are rather a consequence of different operational focuses (destinations and mode of transportation selection, etc.). It is beyond the purposes of this article to search for the answer to the question of whether branding is not as important in the organized travel business or that there are simply no significant differences between the Slovene tour operators in their branding efficiency.
Transportation costs remain the main challenge for package tours providers. Those firms that will be able to organize cheaper transportation per passenger will benefit the most. In times when trends towards individualization and custom-made experiences are rising sharply, this is an extremely difficult task.
The results implicitly confirm the importance of culture and authenticity in tourism, which are expected to gain further significance in the future (Sedmak & Mihalič, 2008), and the declining appeal of the 3S destinations. Significant differences in prices
between 3S and cultural destinations (22%), as well as the importance of organized guidance, usually connected with cultural contents, indicate that packages bearing more cultural experience potential can count on higher prices.
References
Abrate, G., Fraquelli, G., & Viglia, G. (2012). DynaF mic pricing strategies: Evidence from European hotels. International Journal of Hospitality Management, 31(1), 160-168. Agarwal, S. (1999). Restructuring and local economic development: Implications for seaside resort regeneration in Southwest Britain. Tourism Management, 20(4): 511-522. Aguilo, E., Alegre, J., & Sard, M. (2003). Examining the market structure of the German and UK tour operating industries through an analysis of package holiday prices. Tourism Economics, 9(3), 133150.
Aguilo, P.M., Alegre, J., & Riera, A. (2001). Determinants of the price of German tourist packages on the island of Malorca. Tourism Economics, 7(1), 59-74.
Anderson, P. S., de Palma, A., & Thisse, J.-F. (1992). Discrete choice theory of product differentiation. Cambridge, England: The MIT Press. Apostolopoulos, Y., & Sonmez, S. (2001) Greek tourism on the brink: Eestructuring or stagnation and decline? In Y. Apostolopoulos, P. Loukissas, & L. Leontidou (Eds.), Mediterranean tourism: Facets of socioeconomic development and cultural change (pp. 72-86). London, England: Routledge. Buhalis, D. (2000). Package tour. In J. Jafari (Ed.), Encyclopedia of tourism (pp. 423-424). London, England: Routledge. Campos, J., Ericsson, N. R., & Hendry, D. F. (2005). General-to-specific modeling: An overview and selected bibliography (International Finance Discussion Working Paper No. 838). Washington, D.C.: The Federal Reserve Board. Carey, S., Gountas, Y., & Gilbert, D. (1997). Tour operators and destination sustainability. Tourism Management, 18(7), 425-431. Caves, E. R., & Williamson, J. P. (1985). What is product differentiation, really? The Journal of Industrial Economics, 34(2), 113-132.
Chiam, M., Soutar, G., & Yeo, A., (2009). Online and off-line travel packages preferences: A conjoint analysis. International Journal of Tourism Research, 11(1), 31-40.
Coulter, K. S. (2001). Decreasing price sensitivity involving physical product inventory: A yield management application. Journal of Product & Brand Management, 10(5), 301-317.
Čavlek, N. (2002). The influence of vertically integrated travel concerns on education and training in tourism. In B. Vukonić & N. Čavlek, (Eds.), Rethinking of education and training for tourism (pp. 411-418). Zagreb, Slovenia: University of Zagreb.
Davidson, R., & Rogers, T. (2006). Marketing destinations and venues for conferences, conventions and business events. Oxford, England: Butterworth Heinemann.
Doyle, P. (2002). Marketing Management and Strategy. Harlow: Prentice Hall
Espinet, J. M., Saez, M., Coenders, G., & Fluvia, M. (2003). Effect on prices of the attributes of holiday hotels: A hedonic prices approach. Tourism Economics, 9 (2), 165-177.
European Travel Commision. (2011). Retrieved from http://www.newmediatrendwatch.com/ world-overview/91-online-travel-market?for-mat=pdf
Falvey, R. E., Fried, R. O., & Richards, B. (1992). An hedonic guide to New Orleans restaurants. Quarterly Review of Economics and Finance, 32 (1), 123133.
Fleischer, A. (2011). A room with a view - A valuation of the Mediterranean Sea view. Tourism Management, 33(3), 598-602.
Gale, T. (2005). Modernism, post-modernism and the decline of British seaside resorts as long holiday destinations: A case study of Rhyl, North Wales. Tourism Geographies, 7(1), 86-112.
Gujarati, N. D. (1995). Basic econometrics. New York, NY: McGraw-Hill Book Company.
Gunawardana, J. P., & Havrila, I. I. (1993). An analysis of restaurant characteristics and meal prices in Melbourne, Australia. Tourism Economics,
2(1), 79-93.
Haroutunian, S., Mitsis, P., & Pashardes, P. (2005). Using brochure information for the hedonic
analysis of holiday packages. Tourism Economics, 11(1), 69-84.
Hendry, D. F., Learner, E. E., & Poirier, D. J. (1990). The ET dialogue: A conversation on econometric methodology. Econometric Theory, 6(2), 171-261.
Huang, S., Hsu, C. H. C., & Chan, A. (2010). Tour guide performance and tourist satisfaction: A study of the package tours in Shanghai. Journal of Hospitality & Tourism Research, 34(1), 3-33.
Hyde, K. F., & Decrop, A. (2011). New perspectives on vacation decision making. International Journal of Culture, Tourism and Hospitality Research, 5(2), 103-111.
Israeli, A. A. (2002). Star rating and corporate affiliation: their influence on room price and performance of hotels in Israel. Hospitality Management, 21(4), 405-424.
Juaneda, C., Maria Raya, J., & Sastre, F. (2011). Pricing the time and location of a stay at a hotel or apartment. Tourism Economics, 17(2): 321-338.
Kim, J., Bojanic, D. C., & Warnick, R. B. (2009). Price bundling and travel product pricing practices used by online channels of distribution. Journal of Travel Research, 47(4), 403-412.
Kim, Y. G., and Gang, L. (2009). Customer satisfaction with and loyalty towards online travel products: A transaction cost economics perspective. Tourism Economics, 15(4), 825-846.
Kotler, P., Bowen, J., & Makens, J. (2006): Marketing for hospitality and tourism. Upper Saddle River, NJ: Prentice Hall.
Laesser, C., & Crouch, G. I. (2006). Segmenting markets by travel expenditure patterns: The case of international visitors to Australia. Journal of Travel Research, 44(4), 397-406.
Lickorish, J. K., & Jenkins, L. C. (1997). An introduction to travel and tourism. Oxford, London, England: Butterworth Heinemann.
Montemagro, G. (2001). The challenge of Mezzogiorno. In Y. Apostolopoulos, P. Loukissas, & L. Lo-entidou (Eds.), Mediterranean tourism (pp. 5263). London, England: Routledge.
Moutinho, L. (2011). Strategic management in tourism. Cambridge, England: Cambridge University Press.
O'Connor, P., & Frew, A. J. (2004) An evaluation methodology for hotel electronic channels of dis-
tribution. International Journal of Hospitality Management, 23(2), 179-199.
Papatheodorou, A. (2002). Exploring competitiveness in Mediterranean resorts. Tourism Economics, 8(2), 133-150.
Poon, A. (1998). Tourism, technology and competitive strategies. Oxon, England: CABI Publishing.
Ricard Rigall-I-Torrent, R., & Fluvià, M. (2011). Managing tourism products and destinations embedding public good components: A hedonic approach. Tourism Management, 32(2), 244-255.
Richards, G., Goedhart, S., & Herrijgers, C. (2001). The cultural attraction distribution system. In G. Richards (Ed.), Cultural attractions and European tourism (pp. 71-89). Oxon, England: CAB International.
Riera, A. (2000). Environmental valuation. In J. Jafa-ri (Ed.), Encyclopedia of tourism (p. 200). London, England: Routledge.
Rosen, S. (1974). Hedonic prices and implicit markets. Journal of Political Economy, 82(1), 34-55.
Sard, M., (2006). Analysis of package holiday prices in the Balearic Islands. Retrieved from http://dea. uib.es/digitalAssets/136/136601_w19.pdf
Sedmak, G., Mihalič, T., & Rogelj, R. (2004). What affects restaurant prices in tourism destinations? The case of the Slovenian coastal area. Tourism, 52(3), 255-266.
Silver, M. (1996). Quality, prices and hedonics. International Journal of the Economics of Business, 3(3), 351-365.
Sinclair, M. T., Clewer, A., & Pack, A. (1993). Hedon-ic prices and the marketing of package holidays: The case of tourism resorts in Malaga. In: G. Ash-worth & B. Goodall (Eds.), Marketing tourism places (pp. 85-103). London, England: Routledge.
Thrane, C. (2005). Hedonic price models and sun-and-beach package tours: The norvegian case. Journal of Travel Research, 43(3), 302-308.
Richards, G. (2002). Tourism attraction systems: Exploring cultural behavior. Annals of Tourism Research, 29(4), 1048-1064.
Sedmak, G., & Mihalič, T. (2008). Authenticity in mature seaside resorts. Annals of Tourism Research, 35(4), 1007-1031.
Shaw, G., & Williams, M. A. (2004). Tourism and tourism spaces. London, England: Sage.
Statistical Office of the Republic of Slovenia - SORS.
(2011).	Tourism travels of domestic population, Slovenia, 3rd quarter 2010 - final data. Retrieved from http://www.stat.si/eng/novica_prikazi.asa px?id=3650
Statistical Office of the Republic of Slovenia - SORS.
(2012).	Tourism travels of domestic population, Slovenia, 3rd quarter 2011 - final data. Retrieved from http://www.stat.si/eng/novica_prikazi.as-px?id=4428
Tanford, S., Erdem, M., & Baloglu, S. (2011). Price transparency of bundled vacation packages. Journal of Hospitality & Tourism Research, 35(2), 213234.
Tsaur, S., Lin, Y., & Lin, J. (2006). Evaluating ecotour-ism sustainability from the integrated perspec-
tive of resource, community and tourism. Tourism management, 27(4), 640-653.
Wen I., (2009) Factors affecting the online travel buying decision: a review. International Journal of Contemporary Hospitality Management, 21(6),
752-765.
Williams, A. M. (1996). Mass tourism and international tour companies. In M. Barke, J. Tonner, & M. T. Newton (Eds.), Tourism in Spain-Critical Issues (pp. 119-135). Wallingford, England: CAB International.
Zahra, A., & Ryan, C. (2007). From chaos to cohesion - Complexity in tourism structures: An analysis of New Zealand's regional tourism organizations. Tourism Management, 28 (3): 854-862.