Online Marketing Strategy and Market Segmentation in the Spanish Rural Accommodation Sector

Abstract

The present work tests the effect of the online differentiation strategies employed by rural accommodation enterprises (RAEs) among different segments of the tourist population. More specifically, the following aspects are identified: (1) the different tourist segments that undertake rural tourism; (2) the various online differentiation strategies adopted by RAEs; and (3) the effect of these online differentiation strategies on tourist behavior, by segment (in terms of perceived value). The results reveal that when the main motivation of tourists is to experience the destination itself, it is strategies based on convenience and rural identity that most influence perceived value; when tourists are more motivated by enjoying the services and facilities provided by the RAE, strategies based on convenience and reputation are those with the strongest influence on perceived value; and when tourists are interested in undertaking activity holidays, it is strategies based on convenience, reputation, and rural identity that most influence perceived value.

Keywords

rural tourism segmentation motivation differentiation strategy Internet perceived value

Introduction

Rural tourism is an increasingly important diversification activity in the progress of rural areas (Brandth and Haugen 2011; Galloway, Sanders, and Deakins 2011), in which rural accommodation enterprises (RAEs) are at the heart of sectorial development (Hernández-Maestro and González-Benito 2014). At the same time, with European rural development policy moving away from a protectionist stance and focusing more on the market and global competition (Ward and Lowe 2004), RAEs must begin to adopt strategies for becoming more competitive.

One such strategy may be to focus on designing and promoting offers with high tourist perceived value (PV). It is widely acknowledged throughout the literature that PV has a major influence on consumer behavior (Zeithaml 1988) and, more specifically, on consumer behavior in the rural tourism sector (Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012b). However, in order for firms to create an offer with high PV they must have an in-depth understanding of the market, so as to be able to identify (1) the motivations of the different segments of the tourist population, (2) the most suitable competitive strategy for each segment, and (3) how to make use of the channel they select as an information source to reach their target publics.

While the Internet has become one of the most important sources of information within the tourism field in general (Tanford, Baloglu, and Erdem 2011; Xiang et al., forthcoming; Yacouel and Fleischer 2012), for RAEs in particular the central role played by the Internet in promoting their offer is indisputable (Galloway, Sanders, and Deakins 2011; Herrero and San Martín 2012). The Internet has much to offer RAEs in terms of promoting their respective service/product offers, yet although the online medium enables firms to convey large volumes of information, the user’s ability to process that information is limited (Lee and Lee 2004; Yacouel and Fleischer 2012). This limitation can trigger information overload among users which, in turn, can lead to poor decision-making processes and dysfunctional performance (Lee and Lee 2004). Thus, when firms use the Internet as a means of promoting their offer, it is vital that they select only the most relevant information pertinent to each client group they wish to reach with their message.

For this reason, it is also essential that firms understand the different motivations of their various consumer groups, and in this regard market segmentation is a key technique used for subdividing a heterogeneous market into homogeneous subgroups (Berry, Parasuraman, and Zeithaml 1991). In the case of RAEs, this understanding of consumer motivations may constitute the very basis of their differentiation strategy in the market. It is recognized by the literature that such strategies have a positive effect on consumer behavior (Porter 1980) and, in the rural tourism context, on PV (e.g., Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012a). That said, the literature to date has not analyzed the effect of online differentiation strategies employed by RAEs on tourist behavior (taking into account the different segments of the tourist population) nor has it focused specifically on the Internet as an information source.

In light of the above, the aim of the present work is to test the effect of the online differentiation strategies implemented by RAEs for different tourist segments. In order to fulfill this objective, the present work (1) defines the various online differentiation strategies adopted by RAEs; (2) identifies the different segments of the tourist population that have used the services of RAEs; and (3) tests the effect of these strategies on tourist behavior for each segment of the rural tourism population, focusing on PV as a key variable.

The findings of the present work are of particular interest to both the literature and also to practitioners from the sector as they provide an insight into (1) the effect of online differentiation strategies on tourist behavior, providing greater insights into the antecedents of PV, and (2) the particular kinds of information that should take precedence in terms of RAE website content, depending on which segment of the rural tourist population RAEs wish to appeal to. This contributes to the specialist segmentation literature and also helps RAEs to effectively manage the online content they present to potential customers, while avoiding information overload.

Literature Review

Segmentation of Tourists Who Undertake Rural Tourism

Market segmentation has become a valuable instrument in planning appropriate marketing strategies. It is based on the idea that a market is composed of subgroups of people and that each subgroup will manifest different, specific needs and motivations when defining its preferences (Berry, Parasuraman, and Zeithaml 1991; Mok and Iverson 2000). The purpose of segmentation is to facilitate more cost-effective marketing through the formulation, promotion, and delivery of purpose-designed products that satisfy the identified needs of target groups.

The usefulness of market segmentation in travel literature has long been recognized (e.g., Dolnicar 2004; Dolnicar and Grün 2008; Dolnicar et al. 2012; Dolnicar et al. 2014). A significant number of studies from the literature use different descriptors and discriminating variables to segment markets, including motivations (Cha, McCleary, and Uysal 1995; Loker-Murphy 1996; Madrigal and Kahle 1994; Needham et al. 2011), behavioral characteristics (Formica and Uysal 1998), vacation attributes (Crask 1981), normative evaluations (Needham et al. 2011), price sensitivity (Masiero and Nicolau 2012), product bundles (Oh, Uysal, and Weaver 1995), loss aversion (Nicolau 2012), previous experience (Shani, Reichel, and Croes 2012), and benefits sought by travelers (Gitelson and Kerstetter 1990; Loker-Murphy and Perdue 1992).

In the specific case of the rural tourism sector, the literature includes works that center, above all, on identifying tourist segments in terms of rural areas or villages, principally in the context of visitor motivation (Table 1). An examination of these empirical works identifies that there are few empirical studies on segmentation based on the offer of individual firms. Notable exceptions are the works of Chen, Lin, and Kuo (2013) and Albaladejo-Pina and Díaz-Delfa (2009).

Table 1.

Empirical Works Devoted to Identifying Tourist Segments in the Rural Tourism Sector.

Author(s) and Year	Geographic Scope/Sample	Variable Used for the Purposes of Segmentation	Technique Applied	Outcomes/Findings
Empirical applications based on rural destinations or villages
Loker-Murphy (1996)	Domestic and foreign backpackers in national parks of Australia	Factors were “excitement/adventure” and “meeting local people.”	Cluster analysis	Four segments: achievers, self-developers, socializing/excitement seekers, and escapers/relaxers
Kastenholz, Davis, and Paul (1999)	Rural visitors in northern and central Portugal	Benefits sought	Cluster analysis	Four segments: want-it-all ruralist, independent ruralist, traditional ruralist, and environmental ruralist
Frochot (2005)	Rural visitors in Scott	Motivations for visiting the destination	Explorative factor analysis and cluster analysis	Four segments: actives, relaxers, gazers, and rurals
Park and Yoon (2009)	Visitors to rural villages in Korea	Motivations for visiting the destination	Principal components analysis and cluster analysis	Four segments: family togetherness, passive tourist, want-it-all, and learning and excitement seeker
Devesa, Laguna, and Palacios (2010)	Visitors to the province of Segovia (Spain)	Destination attributes	Cluster analysis	Four segments: visitors looking for tranquillity, rest and contact with nature; cultural visitor; “proximity, gastronomic and nature” visitor; and return visitor
Empirical applications based on RAES
Albaladejo-Pina and Díaz-Delfa (2009)	Visitors to Murcia region (Spain)	Preferences expressed when selecting their rural accommodation	Experimental design in which the option to select a rural accommodation establishment is re-created. Multinomial logit model	The appeal of rural houses among rural tourists in the Murcia region is enhanced, to differing degrees, by traditional buildings specially renovated for this purpose; its location in the countryside; its offering a large number of rooms; its having been awarded the “Q” certificate for quality; and its offering horse-riding facilities.
				There are other attributes that also exert a positive effect on the attractiveness of rural houses for certain segments of tourists only (e.g., the offer of a mini-farm could be appealing to those tourists who travel with children), or the possibility of reserving rooms may attract return tourists.
				Other characteristics can also have a positive or negative effect on the rural house’s appeal, such as sharing a bathroom, its being a new building, its being located within a vegetable garden, or its being rented only as a whole house.
Chen, Lin, and Kuo (2013)	Visitors to Maoli county (Taiwan)	Motivations for choosing B&B accommodation	Principal components analysis and cluster analysis	Four segments: accessibility and socialization seekers, physical utility seekers, trend followers, and novelty and relaxation seekers

Note: RAE = rural accommodation enterprise.

Chen, Lin, and Kuo (2013) identified four clustered segments based on five motivational factors (socialization and learning, relaxation, accessibility, novelty, and physical utility) for B&Bs in Taiwan. Meanwhile Albaladejo-Pina and Díaz-Delfa (2009) identified tourist preferences in the context of rural accommodation in Spain. They demonstrated that, from the tourist’s perspective, the appeal of RAEs lies principally in their natural and cultural surroundings and their intrinsic rural characteristics, combined with other factors associated with the RAE itself such as size or type of building, the quality of the furnishing and the services, and the activities it offers in the rural setting. Information about such preferences in terms of the attributes of the RAE can therefore be highly significant in helping such firms to develop and promote their offer. Within this context, then, the present work seeks to identify the principal motivations of tourists who have used the services of RAEs, and to segment the tourists on the basis of these principal motivations (as outlined in the Methodology).

The Online Differentiation Strategies Employed by RAEs, and Their Effect on PV

The concept of differentiation goes back to the seminal work on monopolistic competition by Chamberlin (1933), who highlighted that “a general class of product is differentiated if any significant basis exists for distinguishing the goods of one seller from those of another and leads to a preference for one variety of the product over another” (p. 56).

In general terms, the positive effects of differentiation in protecting firms from the competition have been widely accepted since the work of Porter (1980). In this regard, Porter (1980) popularized the generic strategy of differentiation when a firm creates something tangible or intangible that is perceived as being “unique” by at least one set of customers. Thus, it is the customers’ perceptions that determine the extent of product differentiation.

This premise is also of interest to RAEs, the target public of which mainly comprises people living in nearby cities who undertake short stays, often for only a weekend or day trip (Chen, Lin, and Kuo 2013; Lane 2009). It is these particular characteristics that enable the rural tourism sector, and thus RAEs, to enjoy high revisit rates (Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2013). However, in order for RAEs to benefit fully from such characteristics, it is essential that they develop an offer that is perceived by tourists as being of high value and that is perfectly suited to the features they seek during their visit. In this regard, there are a number of works that endeavor to identify the differentiating factors that contribute to RAEs being able to achieve greater value with their offer (such as the works of Hernández-Maestro and González-Benito 2014; Hernández-Maestro, Muñoz, and Santos 2007; Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012a). However, to date the literature has not analyzed the effect of different online differentiation strategies on the tourist’s PV, distinguishing between each segment of the rural tourist population. This knowledge is believed to be vital for RAEs in helping them to create and promote offers that are uniquely suited to each particular segment.

Early concepts of PV were based on pricing literature (Dodds and Monroe 1985) that used perceived quality and monetary sacrifice as the key components of the PV of a product. The general opinion was that “PV as perceived by consumers represents the relationship between perceived quality and monetary sacrifice perceived upon payment of prices” (Monroe 1992, p. 46; Murphy and Pritchard 1997). However, the vision of PV proposed by Zeithaml (1988, p. 14), namely, as “the overall assessment of the utility of a product based on the perceptions of what is received and what is given,” is the most universally accepted definition (Gallarza and Gil-Saura 2006). This concept of a trade-off between “get” and “give” elements has led to a universal interest in the composite nature of PV (see Babin, Darden, and Griffin 1994; Holbrook 1994; Mathwick, Malhotra, and Rigdon 2001, 2002; Sheth, Newman, and Gross 1991; Woodruff 1997).

Consumer behavior in general has been studied from a rationalist perspective, though attention is increasingly being paid to the affective component (for instance, Al-Sabbahy, Ekinci, and Riley 2004; Petrick 2004a, 2004b; Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012b). The rationalist perspective refers to the functional and economic valuations made by individuals, while the affective dimension is less developed but captures the feelings or emotions generated by products or services.

There is an extensive body of literature recognizing the positive effects of PV on consumer behavior (Zeithaml 1988), within the tourism context (Bradley and Sparks 2012; Gallarza and Gil-Saura 2006; Graham and Sparks 2012; Murphy and Pritchard 1997; Sánchez et al. 2006), in the online context (Tanford, Baloglu, and Erdem 2011), and specifically in the context of the rural tourism sector (Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012b).

Indeed, the importance of PV is such that there is a major endeavor on the part of the literature to identify its antecedents, some prominent examples being brand confidence (Chu 2009), consumer expectations (Terblanche 2006; Wu and Hsing 2006), tourist involvement (Prebensen et al. 2013), tourist motivations (Prebensen et al. 2013), perceived quality (e.g., Graham and Sparks 2012; Kumar and Lim 2008; Liao and Wu 2009), and the use of customization programs (Merle, Chandon, and Roux 2008; Sigala 2006). Focusing on the rural tourism sphere, Polo-Peña, Frías-Jamilena, and Rodríguez-Molina (2012a) identified that the use of autochthonous resources as a differentiation strategy has a positive effect on PV among visitors to rural tourist destinations. In view of this finding, it is of interest to study more deeply the impact of using online differentiation strategies on PV at the individual firm level, taking into account the different segments of the tourist population.

In addition, the information channel that RAEs use to convey their respective offers should also be taken into account. One such channel of vital importance to RAEs is the Internet (Buhalis and Law 2008; Yacouel and Fleischer 2012). The Internet allows RAEs to present themselves in the market, reduce their dependency on intermediaries, directly promote and distribute their services, and compete on a more equal basis with larger businesses (e.g., Kim, Kim, and Shin 2009; San-Martín and Herrero 2012). That said, RAEs need to be aware of the possible effect of information overload among users when using the online channel to promote their offer. An excess of online information has been shown to trigger disorientation among users, especially for those with little experience of the Internet medium (Ahuja and Webster 2001; Bawden and Robinson 2009; Eveland and Dunwoody 2001; Tremayne and Dunwoody 2001). The principal cause of this phenomenon is related to the vast quantity of information available on the Internet (Burke 1997; Chen and Wells 1999; Ducoffe 1996; Frías, Rodríguez, and Castañeda 2008; Wang et al. 2007). In this regard, it is also important to remember that the low cost of information search on the Internet can stimulate the search for information to a greater degree than compared to traditional media (Biswas 2004). As a consequence, considering that the amount of information on any given topic may be very high while, at the same time, the user’s ability to process that information is limited (Miller 1956; Malhotra 1982; Owen 1992), the likelihood of information overload occurring is also very high.

Based on the notion of limited human processing capacity (Bettman 1979; Streufert and Driver 1965), if consumers are provided with too much information at a given time, such that it exceeds their processing limits, overload occurs (Lee and Lee 2004). Information overload has been found to lead to poorer decision-making and to dysfunctional performance (Gao et al. 2012; Lee and Lee 2004). It is therefore essential that RAEs select with great care the online content they intend to use when promoting their offers to the market as this will assist in providing the basis for their differentiation strategy. Above all they should ensure that only that information that is most relevant to their target segments is included in their online communications.

Research Objectives

The principal aim of the present work is to test the effect of the online differentiation strategies employed by RAEs for different tourism segments. In order to fulfill this aim, the work

identifies the online differentiation strategies employed by RAEs when promoting their offer;

identifies the different tourist segments that select an RAE, based on their motivations when using a given RAE for their trip; and

tests the effect of the online differentiation strategies on PV for each tourist segment.

First, two empirical qualitative studies were carried out (as explained in the sections Qualitative Study Design, Qualitative Data Analysis, and Qualitative Study Results). These parts of the research identified (1) the online differentiation strategies employed by RAEs when promoting their offers via the Internet and (2) the main tourist motivations when using an RAE, identified by analyzing the opinions expressed by tourists via an online portal following their trip. On the basis of the results obtained, an empirical quantitative study was then undertaken (outlined in sections Quantitative Study Design and in Findings). This enabled the effect of each differentiation strategy on PV for each tourist segment to be checked (Figure 1).

Figure 1.

Research model proposed: Tourists mainly motivated by (a) visiting the rural tourist, (b) enjoying the RAE’s services and facilities, and (c) undertaking activities in the rural area.

Methodology

Qualitative Study Design

The qualitative studies were based on an inductive approach, by making reference to a series of deductive insights from the literature review. The applied methodology followed the requirements for qualitative data analysis determined by authors such as Flick (2004) for qualitative sampling guidelines, Denzin and Lincoln (2000) for data triangulation (use of RAEs’ websites and use of clients’ opinions), Belk (2006) for undertaking reflexive analysis, and Silverman (2006) for ethics and credibility of analysis.

The selection of cases (units of analysis) for the sample was undertaken by using the most relevant variables found in the literature review for the study phenomenon, as proposed by Miles and Huberman (1994) and Patton (2002). This provides a good level of variation in the codification of the data (Bazeley 2013). This approach to sample selection also finds support among authors including Belk, Wallendorf, and Sherry (1989), Denzin and Lincoln (2000), and Creswell (2003). The final sample is purposive and theoretical, which is quite usual and widely accepted in qualitative research (Creswell 2003).

The initial sample was pared down to ensure that the accommodation establishments chosen all (a) had their own website and (b) appeared on the rural accommodation search site www.booking.com, with comments from guests who had stayed there. Using this filter, the final units of analysis for the study were arrived at.

As regards the number of rural accommodation establishments selected, in line with the recommendations of Kvale (2008) the sample was extended until data saturation was achieved, in other words until the point at which no new information appeared, after two or three additional units of analysis (Bazeley 2013).

In relation to data analysis, the approach taken by the research team for reducing data into thematic units (nodes) followed all of the requirements highlighted by Giorgi (1989) and Kvale (2008) in terms of basic phenomenological principles.

In relation to the fundamentals of qualitative research, respect for widely accepted quality requirements ensured the rigor of the research. In this case, the ethical aspects of the analysis process were taken into account, and the research team took a reflexive approach when reducing the data into thematic units. Minimal inference levels were used, explaining concepts by using the narratives that appeared on RAE websites, for constructing the thematic units of strategies, and client feedback (Giorgi 1997), for generating thematic units of clients’ opinions about the RAEs they had visited. These are alternative concepts for achieving rigor in the research process, in contrast to the language of validity or reliability that is more suited to quantitative research methodologies (Denzin and Lincoln 2000; Rey 2004).

The design for the two qualitative studies was as follows: (1) the first study focused on analyzing the content used by RAEs on their respective websites to attract potential tourists, with a view to identifying the online differentiation strategies employed by these firms when promoting their offer online; and (2) the second qualitative study was based on an analysis of the opinions of tourists who had used the RAEs. These opinions were made available via the various RAE subsites within the travel portal and were used to identify tourists’ principal motivations when choosing an RAE for their trip.

The first qualitative study, then, consisted of analyzing the content of Spanish RAE websites. The researchers undertook a textual and visual analysis of the components of the homepage of each RAE included in the study (Banks 2008). The sample for this first part of the study consisted of 22 RAE websites. In selecting these 22 firms, the aim was to capture the greatest possible diversity, by including different RAE activities (RAEs with extra-hotel activities and those without) and categories (RAEs classified as high category and basic category), as these are the characteristics that have been found to most strongly influence the strategic activities of RAEs (Polo-Peña and Frías-Jamilena 2010). The rationale for this sample design was to achieve richness of data, in light of the objectives of the study (Kvale 2008), with a priori chosen variables (activities and categories) to represent the different types of RAE (Flick 2004).

The content of the RAE homepages (text and images) were imported to NVivo10 CAQDAS software, as a source that could subsequently be codified and analyzed (Bazeley 2007; Rettie et al. 2008). The nodes tree–node structure was created by reducing the text into thematic units (nodes) (Kvale 2008; Richards 2005).

In the second qualitative study, researchers visited a leading travel portal to search for the same RAEs that had previously been analyzed in the first qualitative study. Booking.com was the chosen portal in light of previous works that had used this same website for research on rural accommodation (e.g., Inversini and Masiero 2013; Tsujii et al. 2014) and the fact that it attracts a broad range of user opinions from tourists who have used the services of the accommodation establishments contracted via this portal.

Booking.com uses an online survey that asks users to rate different aspects of the accommodation they used, and includes an open question inviting users to express their negative and positive opinions of their accommodation experience. This feature was of particular interest for the present study and also provides invaluable information for other users in helping them simplify their own decision-making process (Sparks and Browning 2011).

The research team conducted a textual analysis of all the opinions expressed by all of the clients relating to the 22 RAEs under study during the fieldwork. These opinions were imported using the NCapture tool (via Google Chrome) from CAQDAS NVivo 10 software, and codified into thematic units (Bazeley 2007; Rettie et al. 2008). In total, the opinions of 548 clients regarding 22 RAEs were analyzed. As for the first qualitative study, the nodes tree–node structure was created by reducing the text into thematic units (nodes) (Kvale 2008; Richards 2005).

The Booking.com portal also enabled the researchers to identify and record the following client profiles: families with older children (n = 25); families with small children (n = 48); groups of friends (n = 51); young couples (n = 192); mature couples (n = 217); and people traveling alone (n = 15).

Qualitative Data Analysis

The overall analysis of the text and images from RAE websites, and the text relating to tourist opinions drawn from the Booking.com website, was undertaken in three stages (Kvale 2008):

Reducing chunks of text into common meanings, following an initial read-through. This stage involved codifying texts and visual elements of the RAEs’ websites, following the key themes and concepts that appeared in the first step of analysis, and undertaking a more “advanced” reading of the texts and images. The same rationale was applied for opinions expressed by clients about RAEs on the Booking.com portal.

Undertaking a complete read-through of all the imported website content and client opinions, repeated several times.

Extracting literal chunks of text and/or images from the websites, and quotes from client opinions, to illustrate the results.

Qualitative Study Results

With regard to the first qualitative study (to identify the online differentiation strategies adopted by RAEs), the results of the literature review were taken into account, together with the analysis of RAE homepage content (text and images). Three key strategies were identified, labeled by the researchers as “reputation” (154 references), “convenience” (61 references), and “rural identity” (84 references), with reputation being by far the most frequently cited aspect communicated by RAEs to their clients, followed by rural identity and convenience. Table 2 illustrates some of the content from RAE websites and its conversion into the aforementioned thematic units.

Table 2.

Examples of Use of the Strategies of Reputation, Convenience, and Rural Identity Extracted from RAE Websites.

Strategy	Examples Extracted from Analysis of RAE Website Content
Reputation	Information conveyed by the RAE identified as a strategy based on reputation:
	“Hotel recommended by Rusticae.”
	“Exclusive, distinctive services that make our Spa quite unique.”
	“Rated number 1 by Clubrural.”
	“The first rural accommodation in Guadalest to offer the services of a rural hotel.”
	“Winner of the Tripadvisor Certificate of Excellence 2012.”
Convenience	Information conveyed by the RAE identified as a strategy based on convenience:
	“7 nights for the price of 5 in a cottage or farmhouse with FREE SPA. Choose your 7 nights to suit throughout the year, in a unique location. SPA, Finnish sauna, Turkish baths, and heated pool—all FREE.”
	“Transfer service for rural hotels in Alicante to help you arrive safely”
Rural identity	Information conveyed by the RAE identified as a strategy based on the rural identity:
	“The accommodation blends perfectly with the landscape and vegetation, with its crags and the delightful sound of flowing water from the Fuente del Chorrico creating a uniquely wonderful ambience.”
	“At the Parque Natural Fragas do Eume de Monfero, set in a vast estate of meadows, mountains and rivers, you will find a valley that is green all year round thanks to its own micro-climate, protected from the mountains but close to the coast and places of historical interest such as Monfero, Betanzos, Pontedeume, Coruña, Santiago and Lugo. This old country farmstead dating back to the XVII Century is the ideal base for exploring the whole of Galicia.”
	“Iribas, a hamlet of just 60 inhabitants located at the foot of the Aralar mountain range. From here you can visit the San Miguel de Aralar sanctuary or the Mendukilo caves, or go hiking through the Vía Verde del Plazaola or Beigorri park.”

Note: RAE = rural accommodation enterprise.

The majority of the references on RAE homepages were related to the reputation of the firm, such as featuring in quality and/or excellence rankings, being included in a prestigious search engine, or messages designed to convey excellence in services or facilities. These aspects were repeatedly communicated via RAE homepages in the sample and typically featured very heavily as soon as the website was opened. It was thus possible to identify the explicit way in which RAEs communicate their unique features, their use of prestigious brands, and claims that, together, convey a message of outstanding service to their potential clients.

The elements relating to convenience did not appear in the website text, but rather formed part of the visual elements, for example, specific promotions, or the facility to make reservations with the establishment or contact it for queries. In both cases, it was necessary to go one step further by clicking on a specific option, such as “contact us” or “reservations.”

Lastly, RAEs typically promote additional activities that can be undertaken in the local area and that have a distinctly “rural” flavor. This additional offer is covered in both the text and the visual elements of RAEs’ homepages, with significant emphasis being placed on the scope offered by the rural setting and rural identity for visitors to participate in a range of activities. RAEs typically offer packages that include activities based on the rural identity, and make these explicit on their websites. In addition, on occasion this identity is used to emphasize the uniqueness of the RAE offer and underline the reputation of RAEs.

On the basis of the website content analysis undertaken, the research team established the three central strands of the investigation that would form the basis for the items to be used to measure each of the online differentiation strategies (i.e., reputation, convenience, and rural identity) implemented by RAEs. These were then combined with the practices outlined in the literature relating to similar contexts (such as Cánoves et al. 2004; Hernández-Maestro, Muñoz, and Santos 2007; Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012a, 2012b, 2013; Vázquez-Casielles, Suárez-Álvarez, and Díaz-Martín 2005) and with previous works that have measured similar constructs in contexts not dissimilar to that of RAEs (such as Doney and Cannon 1997; Ganesan 1994; Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012a; Vázquez-Casielles, Suárez-Álvarez, and Díaz-Martín 2005) to complete the proposed research model (Figure 1) and propose a list of items to measure each differentiation strategy (see appendix).

With regard to the second qualitative study (to identify the tourists’ principal motivations when using the services of an RAE), opinions from customers were taken from the travel portal website and their frequency by subject area measured. The aspect most frequently emphasized by tourists was that of “RAE services and facilities” (767 references expressed by 521 tourists), appearing in 95% of all positive opinions expressed by tourists. When tourists expressed negative opinions, these too referred to this aspect in their entirety; that is, no negative comments were registered for any other category. Another aspect attracting a significant number of references was “visiting the tourist destination” (197 references). By contrast, the aspect least emphasized by tourists was “activities in the rural area” (such as trekking or canoeing), with just 57 references.

Quantitative Study Design

The sample

The Spanish national population relating to rural accommodation demand was taken as the basis of the empirical research. Spain is considered to be among the leading countries internationally in the field of tourism and hospitality, in which RAEs occupy an important position (INE 2012).

The sample was generated by applied quota sampling as this technique provides a sample structure similar to that of the population. Quotas were established in respect of the demand in each Spanish region (according to the distribution of tourist arrivals to each region; see Table 3; INE 2012). The sample was obtained as soon as respondents had consumed the services provided by the RAEs, ensuring that the customer experience was recent. To establish the sample, RAEs from each region were asked to collaborate by distributing questionnaires randomly among their customers on their departure from the establishment. The number of questionnaires distributed by each RAE was based on the demand identified for each corresponding region (Table 3). The questionnaires were completed by the customers and then sent directly back to the researchers.

Table 3.

Sample Distribution for Spanish Rural Tourism Demand.

Spanish Regions	Number of Tourists Participating from Each Spanish Region
Andalucía	68
Aragón	38
Asturias (Principado de)	39
Cantabria	45
Castilla y León	118
Castilla La Mancha	42
Cataluña	70
Comunitat Valenciana	32
Extremadura	25
Galicia	39
Madrid (C. de)	24
Murcia	14
Navarra (C. Foral de)	26
País Vasco	32
Rioja (La)	20
Total	632

Some 632 valid questionnaires were returned and classified into three groups, depending on whether the respondent expressed a preference for (1) visiting the tourist destination (260 tourists), (2) enjoying the facilities at the RAE (140 tourists), or (3) undertaking activities in the rural environment (232 tourists).

The profile of the respondents can be seen in Table 4, and is similar to those used in other tourism studies (e.g., Hernández-Maestro, Muñoz, and Santos 2007; Herrero and San Martín 2012). All were Spanish residents, and there was an almost equal number of females (54.80%) and males (45.20%). Most respondents were either under 29 years of age (41.20%) or between 30 and 44 (32.20%), and were employed (53.80%). In addition, most (92.6%) had a college education or a graduate degree. Income was medium-to-high, with 78.5% of the sample earning a net monthly income of over €1,500. A minority (11.4%) lived alone. Some 77.8% had a daily budget for their travels of between €35 and €80; and most respondents traveled in the company of their partner (42.6%), their friends (38.1%), or their family (18.8%).

Table 4.

Descriptive Characteristics of the Sample for Spanish Rural Tourism Demand.

	Sample		Group 1 (n = 140)		Group 2 (n = 260)		Group 3 (n = 232)
Variable	n	%	n	%	n	%	n	%
Gender
Male	300	47.47	57	40.7	121	46.5	122	52.6
Female	332	52.53	83	59.3	139	53.5	110	47.4
Age (years)
18–29	276	43.67	91	65	137	52.70	48	20.07
30–39	203	32.12	49	35	76	29.20	78	33.6
40–49	115	18.20	0	0	34	9.60	81	34.9
≥50	38	6.01	0	0	13	8.50	25	11.43
Household composition (no. of members)
1	72	11.40	22	15.7	30	11.5	20	8.6
2	153	24.20	37	26.4	50	19.2	66	28.4
3	152	24.00	35	25	66	25.4	51	22
4	168	26.60	30	21.4	73	28.1	65	28
≥5	87	13.80	16	11.4	41	15.8	30	12.9
Household net monthly disposable income (€)
≤600	0	0	0	0	0	0	0	0
601–1200	54	8.50	12	8.6	32	12.3	10	4.3
1,201–1,500	82	13.00	21	15	31	11.9	30	12.9
1,501–1,800	84	13.30	20	14.3	42	16.2	22	9.5
1,801–2,100	102	16.10	30	21.4	39	15	33	14.2
2,101–3,000	178	28.20	38	27.1	61	23.5	79	34.1
3,001–5,000	117	18.50	18	12.9	50	19.2	49	21.1
≥5,000	15	2.40	1	0.7	5	1.9	9	3.9
Education level
No qualifications	8	1.30	0	0	3	1.2	5	2.2
Secondary	39	6.20	0	0	14	5.4	25	10.8
A-levels/vocational training	244	38.60	68	48.6	93	35.8	83	35.8
University/postgraduation	341	54.00	72	51.40	150	57.7	119	51.3
Daily holiday budget per person per day (€)
<25	3	0.5	1	0.7	1	0.4	1	0.45
26–35	28	4.4	8	5.7	14	5.4	6	2.6
36–45	71	11.2	10	7.1	51	19.6	10	4.3
46–55	135	21.4	37	26.4	49	18.8	49	21.1
56–65	181	28.6	50	35.7	65	25.0	66	28.45
66–80	105	16.6	24	17.1	45	17.3	36	15.5
81–95	51	8.1	4	2.9	14	5.4	33	14.2
96–110	34	5.4	6	4.3	15	5.8	13	5.6
≥110	24	3.8	1	0.7	6	2.3	18	7.8
Travel companions
Alone	3	0.5	0	0	3	1.2	0	0
Partner	269	42.6	73	52.15	78	30.0	118	50.9
Family	119	18.8	1	0.7	38	14.6	80	34.5
Friends	241	38.1	66	47.15	141	54.2	34	14.6

Note: Group 1: tourists mainly motivated by visiting the rural tourist destination; group 2: tourists mainly motivated by enjoying the RAE’s services and facilities; group 3: tourists mainly motivated by undertaking activities in the rural area.

Measurement instrument

PV was measured using the scale proposed by Zeithaml (1988). This scale has previously been used in the service industry (e.g., Cronin, Brady, and Hult 2000) and in the tourism sector (e.g., Gallarza and Gil-Saura 2006).

To measure each of the online differentiation strategies (Reputation, Convenience, and Rural Identity), the results of the qualitative study were used, in conjunction with previous works from the literature that have measured similar constructs in contexts not dissimilar to that of RAEs. Reputation was measured using three items referring to the positive image of the RAE in terms of its client service (items RE1 to RE3; see appendix). This scale was based on the works of Anderson and Weitz (1992), Ganesan (1994), Doney and Cannon (1997), and Vázquez-Casielles, Suárez-Álvarez, and Díaz-Martín (2005).

Convenience was measured using three items referring to the usefulness of the directions provided to reach the RTE, efficient time management, and monetary price (items CO1 to CO3). Lastly, Rural Identity was measured using two items referring to heightening the value of the rural environment as part of the RAE’s offer (items RI1 and RI2; see appendix). These scales were based on the work of Polo-Peña, Frías-Jamilena, and Rodríguez-Molina (2012a), which was undertaken in the context of rural tourism.

The questionnaire captured tourist opinions by means of a 7-point Likert-type scale, on which 1 equaled “totally disagree” and 7 equaled “totally agree” (see appendix).

Findings

Figure 2 shows that Reputation, Convenience, Rural Identity, and “PV” were first-order constructs, and highlights the moderating effect of tourists’ preferences.

Figure 2.

Outline of results from the proposed research model.

To calculate the moderating effect of the tourists’ preferences on the online differentiation strategies employed by RAEs, a multigroup structural equation modeling (SEM) analysis was undertaken (with factorial invariance) in which (1) the researchers differentiated between each of the three segments of tourists depending on whether they were mainly motivated by (a) visiting the tourist destination, (b) enjoying the services and facilities provided by the RAE, or (c) undertaking activities in the rural environment and (2) the effect of Reputation, Convenience, and Rural identity on PV was included.

To this end, first the psychometric properties of the proposed model were estimated and evaluated. Since the test of multivariate normality of the variables included in the proposed model was significant, it was deemed necessary to undertake the estimation by using the maximum likelihood method combined with the bootstrap method (Yuan and Hayashi 2003). In this case, a valid reference was the value of normed chi-square, which gave a value of 3.16—within the limits recommended by the literature. As regards the overall fit of the model, the goodness-of-fit index presented a value of 0.90, which matched the reference value recommended by the literature, while the root mean square error of approximation value was 0.06, slightly above that recommended in the literature (Figure 2). The incremental fit measurements comparative fit index (0.91) and incremental fit index (0.91) were also acceptable as they presented values above the threshold indicated by the literature. These results for the total and incremental fit indices indicated that the fit of the model could be considered acceptable (Figure 2).

In order to verify that all the items used were adequate measures of their respective latent constructs, their convergent and discriminant validity, together with their reliability, had to be confirmed (Devlin, Dong, and Brown 1993). Table 5 shows the measures, the SDs and the bivariate correlations of the items in the research model. In all cases it can be observed that the correlation between the items that form part of the same dimension is greater than that found between those from different dimensions, which is a necessary condition for demonstrating the existence of convergent and discriminant validity. Meanwhile all of the coefficients related to each latent variable (with its respective indicators) were statistically significant. Furthermore, the composite reliability and variance extracted were close to, or above, the reference value, at 0.70 and 0.50 respectively (Hair et al. 2008, pp. 649-51) (Table 6). Only Convenience presented variance extracted values below the 0.50 threshold. These results revealed that a greater effort was required to improve the variance extracted in the Convenience variable. Nevertheless it must be remembered that the literature shows that quantifying the sacrifices included in Convenience is a complex task (Cronin, Brady, and Hult 2000; Gallarza and Gil-Saura 2006; Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012b). Often this means that the results obtained when measuring the sacrifices are poorer than the reference parameters established by the literature (Cronin, Brady, and Hult 2000). This appears to be the case here.

Table 5.

Means (Standard Deviations) and Bivariate Correlations of the Items in the Research Model.

	REP1	REP2	REP3	CON1	CON2	CON3	DI1	DI2	PV1	PV2	PV3
Group 1. Tourists mainly motivated by visiting the rural tourist destination
REP1	6.12 (0.75)
REP2	0.516**	6.11 (0.76)
REP3	0.462**	0.556**	6.26 (0.80)
CON1	0.122 n.s.	0.226**	0.238**	5.58 (0.95)
CON2	0.105 n.s.	0.226**	0.166*	0.525**	5.54 (0.89)
CON3	0.177*	0.266**	0.162 n.s.	0.329**	0.359**	5.19 (0.96)
DI1	0.057 n.s.	0.230**	0.312**	0.448**	0.285**	0.376**	5.61 (0.96)
DI2	0.211*	0.286**	0.314**	0.328**	0.266**	0.354**	0.783**	5.55 (1.06)
PV1	0.209*	0.199*	0.251**	0.302**	0.377**	0.320**	0.471**	0.398**	6.28 (0.79)
PV2	0.172*	0.212*	0.221**	0.331**	0.435**	0.255**	0.437**	0.396**	0.820**	6.32 (0.75)
PV3	0.117 n.s.	0.145 n.s.	0.165 n.s.	0.328**	0.450**	0.202*	0.378**	0.356**	0.768**	0.773**	6.24 (0.74)
Group 2. Tourists mainly motivated by enjoying the RAE’s services and facilities
	REP1	REP2	REP3	CON1	CON2	CON3	DI1	DI2	PV1	PV2	PV3
REP1	5.80 (0.86)
REP2	0.516**	5.74 (0.94)
REP3	0.490**	0.561**	5.73 (0.92)
CON1	0.111	0.146*	0.212**	5.42 (0.95)
CON2	0.219**	0.255**	0.214**	0.422**	5.45 (0.98)
CON3	0.081n.s.	0.134*	0.253**	0.398**	0.369**	5.35 (0.98)
DI1	0.247**	0.288**	0.299**	0.248**	0.304**	0.251**	5.42 (0.89)
DI2	0.225**	0.303**	0.266**	0.237**	0.346**	0.251**	0.665**	5.40 (0.94)
PV1	0.344**	0.290**	0.242**	0.164**	0.330**	0.228**	0.227**	0.210**	6.14 (0.78)
PV2	0.275**	0.215**	0.278**	0.177**	0.280**	0.242**	0.207**	0.205**	0.727**	6.12 (0.79)
PV3	0.237**	0.202**	0.281**	0.165**	0.283**	0.227**	0.143*	0.176**	0.712**	0.76**	6.08 (0.78)
Group 3. Tourists mainly motivated by undertaking activities in the rural area
	REP1	REP2	REP3	CON1	CON2	CON3	DI1	DI2	PV1	PV2	PV3
REP1	6.12 (0.80)
REP2	0.530**	6.08 (0.81)
REP3	0.444**	0.585**	6.23 (0.79)
CON1	0.194**	0.185**	0.313**	5.70 (0.95)
CON2	0.178**	0.108	0.129*	0.437**	5.74 (0.95)
CON3	0.271**	0.205**	0.178**	0.377**	0.446**	5.48 (1.02)
DI1	0.218**	0.213**	0.323**	0.451**	0.320**	0.398**	5.67 (0.91)
DI2	0.261**	0.204**	0.228**	0.379**	0.357**	0.40**	0.777**	5.71 (0.91)
PV1	0.219**	0.215**	0.232**	0.197**	0.201**	0.169**	0.239**	0.190**	6.32 (0.72)
PV2	0.188**	0.183**	0.213**	0.262**	0.281**	0.243**	0.325**	0.307**	0.716**	6.31 (0.65)
PV3	0.227**	0.222**	0.196**	0.297**	0.272**	0.262**	0.364**	0.326**	0.707**	0.757**	6.18 (0.79)

Note: The diagonal includes the mean (standard deviation) for each item. n.s. = correlation not significant.

p < 0.01 (bilateral); *p < 0.05 (bilateral).

Table 6.

Convergent Validity and Internal Consistency of Scales Used.

Items	Unstandardized Coefficient	Standard Error	Composite Reliability	Average Extracted Variance
REP1 ← Reputation	1.00	–	P_{Group 1} = 0.77	AVE_{Group 1} = 0.54
REP2 ← Reputation	1.24	0.10	P_{Group 2} = 0.77	AVE_{Group 2} = 0.53
REP3 ← Reputation	1.12	0.09	P_{Group 3} = 0.76	AVE_{Group 3} = 0.54
CON1 ← Convenience	1.00	–	P_{Group 1} = 0.68	AVE_{Group 1} = 0.42
CON2 ← Convenience	1.169	0.15	P_{Group 2} = 0.67	AVE_{Group 2} = 0.40
CON3 ← Convenience	0.95	0.09	P_{Group 3} = 0.70	AVE_{Group 3} = 0.44
RI1 ← Rural Identity	1.00	–	P_{Group 1} = 0.87	AVE_{Group 1} = 0.78
RI2 ← Rural Identity	0.94	0.16	P_{Group 2} = 0.80	AVE_{Group 2} = 0.68
RI2 ← Rural Identity	0.94	0.16	P_{Group 3} = 0.88	AVE_{Group 3} = 0.77
PV1 ← PV	1.00	–	P_{Group 1} = 0.88	AVE_{Group 1} = 0.71
PV2 ← PV	1.01	0.05	P_{Group 2} = 0.89	AVE_{Group 2} = 0.72
PV3 ← PV	1.03	0.05	P_{Group 3} = 0.91	AVE_{Group 3} = 0.77

Note: p = composite reliability; AVE = average variance extracted; RAE = rural accommodation enterprise. Group 1: tourists mainly motivated by visiting the rural tourist destination; Group 2: tourists mainly motivated by enjoying the RAE’s services and facilities; Group 3: tourists mainly motivated by undertaking activities in the rural area.

Meanwhile the dimensions considered in the model achieved discriminant validity, as in all cases the correlation between them did not exceed 0.90 (Kline 2011, p. 72), nor did their confidence interval include 1 (Steenkamp and van Trijp 1991) (Table 7).

Table 7.

Discriminant Validity.

Dimensions	Correlation and Confidence Interval
Reputation—Convenience	Group 1: 0.40 (0.23, 0.55); p = 0.01
	Group 2: 0.41 (0.25, 0.55); p = 0.01
	Group 3: 0.40 (0.19, 0.61); p = 0.01
Reputation—Rural Identity	Group 1: 0.37 (0.22, 0.49); p = 0.01
	Group 2: 0.47 (0.29, 0.61); p = 0.01
	Group 3: 0.36 (0.16, 0.53); p = 0.01
Reputation—Perceived Value	Group 1: 0.33 (0.18, 0.46); p = 0.01
	Group 2: 0.41 (0.26, 0.55); p = 0.01
	Group 3: 0.30 (0.07, 0.51); p = 0.01
Convenience—Rural Identity	Group 1: 0.67 (0.56, 0.78); p = 0.01
	Group 2: 0.54 (0.39, 0.67); p = 0.01
	Group 3: 0.59 (0.38, 0.75); p = 0.01
Convenience—Perceived Value	Group 1: 0.45 (0.30, 0.61); p = 0.01
	Group 2: 0.44 (0.30, 0.57); p = 0.01
	Group 3: 0.58 (0.39, 0.77); p = 0.01
Rural identity—Perceived Value	Group 1: 0.40 (0.29, 0.51); p = 0.01
	Group 2: 0.28 (0.15, 0.40); p = 0.01
	Group 3: 0.52 (0.39, 0.65); p = 0.01

In general, the results obtained for nonstandardized coefficients, composite reliability, variance extracted, and correlation between dimensions (Tables 5 and 6) indicated that the set of first-order dimensions proposed to measure the constructs provided an adequate level of reliability and convergent and discriminant validity. The correlations between PV, on the one hand, and Reputation (0.23), Convenience (0.33), and Rural Identity (0.20), on the other, were significant for the sample overall, demonstrating criterion validity (Malhotra 2008, pp. 286–87).

Next, as outlined in the proposed model, the effects of Reputation, Convenience, and Rural Identity on PV for each of the three tourist segments were analyzed. Figure 2 shows the standardized coefficients, the confidence interval, and the p value. On the basis of these results, the following aspects are to be noted:

For the group of tourists mainly motivated by visiting the tourist destination, it was found that Convenience and Rural Identity exerted a statistically significant effect on PV. More specifically, it was found that the effect detected for Convenience was 0.43 (with a confidence interval of between 0.22 and 0.64 and a p <0.01) and that of Rural Identity was 0.36 (with a confidence interval of between 0.18 and 0.53 and a p <0.01). However, Reputation had no significant influence on PV (with a standardized coefficient of 0.09 and a confidence interval of between −0.11 and 0.32). These results suggest that when an RAE is aiming to attract tourists principally motivated by visiting the destination itself, the offer it promotes should clearly emphasize the convenience aspect and the rural identity, as these are the aspects that have a significant influence on PV. In this case, information regarding the reputation of the RAE is quite secondary.

For the group of tourists mainly motivated by enjoying the services and facilities offered by the RAE, it was found that Convenience and Reputation exerted a statistically significant effect on PV, with the two achieving a very similar value for their standardized coefficients (0.33 and 0.32, respectively, with a confidence interval of between 0.14 and 0.49 and a p <0.01 for Convenience, and a confidence interval of between 0.13 and 0.50 and a p <0.01 for Reputation). In this case, Rural Identity had no significant influence on PV (with a standardized coefficient of 0.05 and a confidence interval of between −0.10 and 0.21). These results demonstrate that when an RAE wishes to reach tourists mainly motivated by its services and facilities, its communication strategy should emphasize the Convenience and Reputation aspects, as these two components were found to have a significant influence on PV, while it is not necessary to refer to the Rural Identity.

Finally, for those tourists principally motivated by undertaking rural activities, it was found that Reputation, Convenience, and Rural Identity all exerted a statistically significant effect on PV. The effect detected for Convenience was quite marked (with a standardized coefficient of 0.27, a confidence interval of between 0.09 and 0.49, and a p <0.01), followed by the effect of Rural Identity (with a standardized coefficient of 0.24, a confidence interval of between 0.08 and 0.39, and a p <0.01), and lastly followed by Reputation (with a standardized coefficient of 0.19, a confidence interval of between 0.03 and 0.34, and a p equal to 0.018). These results demonstrate that if an RAE wishes to convey an offer of the highest PV, it should make reference to Reputation, Convenience, and Rural Identity when addressing its communication to tourists mainly motivated by carrying out rural activities.

Theoretical Conclusions

The main contribution of the present study lies in demonstrating the effect of the online differentiation strategies used by RAEs for each segment of the rural tourist population. This contribution is of particular interest given that, although the Internet has entirely changed how the tourism offer is commercialized and there are numerous works on the use of the Internet as an information source for the tourism sector, there remain several areas where greater knowledge is required as to its effective use (Mohd-Any, Winklhofer, and Ennew, forthcoming; Xiang et al., forthcoming). For example, a key question is how firms can make best use of this medium to communicate their offer to tourists (Filieri and McLeay 2014), and, further, how this might be achieved in areas of particular interest such as rural tourism (Santana-Jiménez et al., forthcoming). By addressing this question (the main objective of the research) the present study makes the following related theoretical contributions in the areas of segmentation, online content management to avoid information overload, and PV formation.

Identification of the rural tourist segments: Despite the extensive body of literature devoted to identifying tourist segments, to date no works have determined such segments in terms of tourists’ motivations when consuming an RAE offer. By means of a qualitative study, the present work identifies three key tourist segments, thanks to which it is possible to specify the different competitive strategies at play and thus better adapt the RAE offer to rural tourists whose main motivation is to (1) visit the tourist destination, (2) enjoy the services and facilities of the RAE, or (3) undertake rural activities. These results are in line with those achieved in earlier studies that also established—for other tourism offers—tourist segmentation on the basis of motivations (e.g., Cha, McCleary, and Uysal 1995; Madrigal and Kahle 1994; Needham et al. 2011).

Identification of the online differentiation strategies employed by RAEs when promoting their offer via the Internet: By means of a qualitative analysis of RAE homepage content and images, the present work identifies the following RAE strategies, based on (1) reputation, (2) convenience, and/or (3) rural identity. The quantitative study then validates the measurement scales for each of these strategies. These results make a contribution to the knowledge base regarding the effects of Internet use on tourists (Mohd-Any, Winklhofer, and Ennew, forthcoming); and finally

Identification of the most appropriate online differentiation strategies for each tourist segment: This was achieved by examining the effect of each strategy on the PV of the offer of the RAE for each tourist segment. This point is of particular importance as, on the one hand, it revealed that the online communication strategies were an antecedent of PV and, on the other hand, it helps to delimit the volume of information aimed at each tourist segment and thus avoid the problems associated with information overload. Furthermore, these findings respond to the growing interest in the literature regarding the effect of Internet use on tourist PV formation (Mohd-Any, Winklhofer, and Ennew, forthcoming; Xiang et al., forthcoming). The present work highlights the need for the RAE to identify only that information which will be of greatest relevance to include on its website, depending on the particular segment of the tourist population it wishes to appeal to.

Implications for the Professional Sector

There are good-practice lessons that can be drawn from this work, both for privately owned RAEs and for public agencies charged with developing the sector. First, one significant contribution of the present work is that it facilitates understanding of the use of online differentiation strategies among RAEs.

It is of major value to owner-managers of RAEs to understand the different tourist segments that have been established in the present work, on the basis of tourist motivations for their rural trip. According to Mansfeld (1992), an individual’s behavior in relation to travel is influenced by a great many motives. Tourists seek to satisfy not just one single need but several at once (Baloglu and Uysal 1996). It is precisely this that gives rise to the difficulty in distinguishing their motives individually, and in identifying the relative importance of each motive in choosing one option over another. There are also the dominating motives in a hierarchy pertaining to a particular moment in time (Crompton 1979).

However, according to Simon (1955), the human brain does not always try to obtain a rational solution to a problem by considering all the alternatives and optimizing, since it has a limited analytical capability. Hence, when confronted with a complex problem, the “limited” human brain satisfies rather than optimizes (Kumar and Subramaniam 1997). Thus, the decision maker deconstructs the problem and the environment into stable subsystems. Only a small set of critical variables are monitored and the final decision is made by a sequential process based on heuristics.

According to Kumar and Subramaniam (1997) and Pan and Tse (2000), there are many types of (tourism) offers, some of which are more similar than others. This scenario becomes even more difficult for the tourist because of problems of information quality (as some tourists use information that is subject to the usual problems of data integrity and reliability associated with the sources), which leads to a hierarchical decision-making process. Many tourists do not have the time or the resources to collect extensive information on destinations, and they may use the hierarchical strategy for their destination choice to reduce uncertainty to a manageable level. Hence, if there are too many factors to be considered, and if obtaining reliable and accurate information is too difficult and expensive, individuals will use a simplified structure for the decision problem (Kumar and Subramaniam 1997; Nicolau and Más 2008).

As a result, the arrival at a choice of an offer-type may follow a simplified process to reduce the uncertainty and complexity in the task of decision-making; tourists may define an evaluation criterion and refer to this factor when evaluating the different tourism offers (Kumar and Subramaniam 1997; Nicolau and Más 2008; Pan and Tse 2000). This is the scenario that features in the present work, namely that in view of the multiple motives that the tourist may wish to satisfy during their stay, when it comes to selecting an RAE they may follow a simplifying process by only taking into account certain key factors that act as an initial filter to reduce the number of alternatives.

Hence it is of value to understand the principal motivations that drive tourists when selecting their RAE. Although tourist segmentation is covered by the literature, to date segmentation based on the key motivations of tourists when selecting rural accommodation has not been addressed. The present work establishes three segments of rural tourists on the basis of their motivation to (1) visit the tourist destination, (2) enjoy the services and facilities provided by the RAE, and/or (3) undertake rural activities. These results are helpful to professionals from the sector as they highlight the key themes that RAEs must include on their websites and convey to rural tourists in their communication strategies.

More specifically, it is useful for owner-managers of RAEs to understand how to make effective use of their websites when seeking to commercialize their tourism offers. Owner-managers must pay particular attention to the information they need to convey to tourists depending on the tourism product they wish to promote. By being selective about the information they use on their websites, they will not only avoid the potential for information overload among tourists but will also be able to use their resources more efficiently by focusing on the information that is most relevant to each tourist segment.

In this regard, the present work identifies the communication strategies that prove most effective for each tourist segment. It is demonstrated that when the RAE designs and communicates an offer aimed at tourists who are mainly interested in visiting the tourist destination, it is the combination of communication strategies based on rural identity and convenience that will work most effectively for this particular segment, as these two themes are found to influence the PV of the offer. This means that RAEs must highlight throughout their website content (text and images) messages that link the firm with the indigenous resources of the destination and the uniquely rural nature of the offer. They should also make special mention of the accessibility of the accommodation, their efficiency at making online bookings, and the suitability of the room rate.

Meanwhile, if the RAE is seeking to commercialize an offer aimed at tourists who are motivated above all by the services and facilities offered by the firm, the combination of strategies based on reputation and convenience is that which will have the most impact on PV. In this case, the RAE should include on its website texts and images alluding to its track record in service delivery, characterized by excellence, the swift resolution of any problems that may arise during a visitor’s stay at the accommodation, and the satisfaction of past clients. This should be accompanied by effective management of online reservations, suitable information regarding the RAE’s location, and appropriate pricing.

Finally, if the RAE is aiming to commercialize an offer based on undertaking activities in the rural environment, it should use a combination of all three online differentiation strategies identified (i.e., reputation, convenience, and rural identity), as in this case all three have an influence on tourist PV. Hence, the firm should convey via its website messages pertaining to its recognized track record in this type of offer (with the accompanying message that it will solve any problems that may arise during a tourist’s visit and that its customers are typically very satisfied with the service); the fact that it offers a good location and price, and effective management (i.e., demonstrating that the offer is such that it balances appropriately the resources [time, money, effort] the tourist must sacrifice in order to enjoy their stay); and that it is genuinely integrated within the environment and culture of the rural tourist destination (alluding to the rural ambience and the indigenous resources that visitors can access as a result of staying at the RAE in question).

Finally, the insights provided by the present work will enable RAEs to develop a communication strategy that can be effectively tailored to tourists’ needs and to the motivations they take into account when selecting an RAE for their next trip. Given the effect of online communication strategies on tourist PV, such strategies can ultimately increase the value of the tourist experience.

Limitations and Future Lines of Research

As with all empirical studies, this work has certain limitations that themselves constitute possible lines of research for the future. One such limitation is that only those variables considered to be the most relevant for achieving the study’s objectives were included in the research model. In this regard it would be of interest to include other variables so as to go further toward understanding how RAEs use the Internet as a communication channel, and its effect on tourists. It would therefore be valuable to undertake further study and measurement of the online communication strategies employed by this collective, and to analyze their effect on the different components—functional and affective—of PV, in line with the recommendations of Xiang et al. (forthcoming), proposing a PV scale for the online medium that differentiates between the different functional and affective components of PV. Elsewhere, following the approach proposed by Filieri and McLeay (2014), it would be of interest to advance in the study of the effects of online communication strategies, differentiating between (1) firms’ individual websites and (2) travel portals that provide information on a range of firms. The effect of the use of social media would also be worthy of study when evaluating the effect of online communication strategies on tourists (Filieri and McLeay 2014; Mistilis, Buhalis, and Gretzel 2014). The use of methodologies suitable for achieving a predictive perspective would be of further value. It would also be of interest to study the effects of using online differentiation strategies on RAEs’ financial and market performance.

A further limitation lies in the geographical scope of application of the study. Despite the choice of a geographical area with an extremely strong presence of RAEs, with regard to maximizing the representativeness of the results obtained it would be interesting to explore whether the application of this research model would lead to different conclusions if applied to other geographical areas.

Footnotes

Appendix

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financing was received from research project “Internet, Comercialización Turística y Desarrollo en Andalucía” by the Junta de Andalucía (Spain).

Author Biographies

Ana Isabel Polo Peña is senior lecturer in the Department of Marketing and Market Research at the University of Granada (Spain). Her research area is focused in touristic marketing.

Dolores María Frías Jamilena is senior lecturer in the Department of Marketing and Market Research at the University of Granada (Spain). Her research area is focused in touristic marketing.

Miguel Ángel Rodríguez Molina is senior lecturer in the Department of Marketing and Market Research at the University of Granada (Spain). His research area is focused in touristic marketing.

Juan Miguel Rey Pino is senior lecturer in the Department of Marketing and Market Research at the University of Granada (Spain). His research area is focused in Public and Nonprofit Marketing.

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