The Effect of Value-Creation on Consumer-Based Destination Brand Equity

Abstract

The present work examines the competitive strategies of tourist destinations and proposes that value-creation among tourists during their entire experience of a destination (before, during, and after their stay) is an antecedent of increased destination brand equity. This value-creation is conceptualized and measured from the service-dominant logic perspective. The research objective is achieved by (a) identifying the dimensions of customer-based destination brand equity and tourist value-creation; (b) validating the scales generated for the measurement of both variables; and (c) proposing a model that captures the antecedent effect of value-creation on customer-based destination brand equity. The findings reveal that value-creation is an antecedent by which the customer perceives greater destination brand equity. The results of the study make a contribution to the specialized literature on tourism and service-dominant logic and offer interesting implications for the professional domain.

Keywords

competitive advantage destination brand equity customer-based destination brand equity service-dominant logic value-creation

Introduction

Destinations are recognized as a primary unit of analysis in tourism research as they constitute a fundamental aspect of the tourist experience (Mistilis, Buhalis, and Gretzel 2014; Pike and Page 2014). They have a variety of stakeholders with an interest in effectively managing the tourist experience, to the benefit of all participants in the destination (Cvelbar et al. 2016; Line and Wang 2015).

These participants include (a) the public agencies responsible for managing tourism resources, (b) suppliers of services (such as accommodation establishments, restaurants, and leisure facilities) and (c) other tourists already at the destination or who have prior experience of it (Line and Wang 2015) (these three types of participants being referred to collectively in this paper as ASTs¹). Through their combined actions these participants are capable of generating appeal among the new tourists who opt to visit the destination in question (Pike and Page 2014).

As these ASTs pertain to the same destination, it is helpful for there to be an organization charged with supporting the coordination of their respective activities (Mistilis, Buhalis, and Gretzel 2014; Pike and Page 2014). The literature recognizes destination marketing organizations (DMOs) as being responsible for the management of destinations, providing leadership and direction for the multifaceted tourism system (Mistilis, Buhalis, and Gretzel 2014). Pike and Page (2014) suggest that the essential goal of all DMOs is sustained destination competitiveness, and that attaining this requires the cultivation of resources that can create competitive advantage (Zehrer, Smeral, and Hallmann 2016). One of the most important resources is that of brand-building for the destination.

The branding literature proposes that the model of customer-based brand equity (CBBE), developed by Aaker (1991, 1996) and Keller (1993, 2003), offers destination marketers a performance instrument with which to evaluate and measure customer perceptions of a destination brand (Pike and Bianchi 2013). In this sense, measuring the effectiveness of destination brand equity (DBE) is increasingly attracting attention in the tourism field, as reflected in the empirical works of, for example, Boo, Busser, and Baloglu (2009); Konečnik-Ruzzier and Gartner (2007); and Pike et al. (2010). Although recent studies have attempted to measure the effectiveness of destination brands from the customer-based destination brand equity perspective (CBDBE), there remains a lack of empirical evidence evaluating the applicability of CBBE in the tourism context, and a lack of agreement on the effective measurement of destination brands (e.g., Boo, Busser, and Baloglu 2009; Pike et al. 2010).

Against this backdrop, certain key works in the literature (e.g., Bianchi and Pike 2014) point to the value of examining in more depth the effect of new antecedents of CBDBE. Elsewhere, the work of Ferns and Walls (2012) proposes a model that considers the effect of enduring travel involvement on DBE. Similarly, Pike (2009) identifies several gaps related to the influence of tourist destination management on DBE, which would be helpful to measure from the perspective of the tourist’s experience of the tourist destination.

The customer’s experience can be captured using the variable “value-creation,” based on service-dominant logic (SDL) (Vargo and Lusch 2004, 2006, 2008). A central element in value-creation is that of the interactions between the different players in the market and their respective customers, as these form the basis of the latter’s experience of the service (Grönroos and Voima 2013). Therefore, in the tourism context, to determine the value created it is helpful to consider the interactions between new tourists and ASTs throughout the entire consumption process—that is, in the predestination visit phase, during their visit (stay) at the destination, and in the postvisit phase (e.g., Jun, Vogt, and Mackay 2007; Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012; Prayag et al. 2015).

Given the implications of value-creation for customer behavior, the literature has undertaken some empirical applications on the concept, although it is worth noting two important issues that need to be addressed. The empirical studies conducted to date may be characterized thus: (a) it seems helpful, to approach the empirical study of the value-creation process by considering the different forms that value can take (such as value-in-exchange, value-in-use, and value-in-context, all of which are captured in service-dominant logic [SDL]; see, e.g., Grönroos and Voima 2013); and (b) it is of interest to capture the interactions between customers and the different participants that potentially intervene in the entire value-creation process. These may include other customers and other suppliers of information or resources above and beyond service suppliers in the strictest sense. In the context of a destination visited by a tourist, this relates to the ASTs that have an established relationship with the destination (e.g., Pike and Page 2014).

As the interactions between the tourist and the ASTs throughout their experience of the destination form the basis of value-creation (e.g., Grönroos and Voima 2013; Heinonen et al. 2010), it can be assumed that tourists’ value-creation has an impact on their evaluation of destination branding (Merz, He, and Vargo 2009; Mohd-Any, Winklhofer, and Ennew 2014). This is a matter of particular interest for the SDL literature (e.g., Merz, He, and Vargo 2009; Vargo and Lusch 2008) and for tourist destinations (e.g., Merz, He, and Vargo 2009; Pike 2009).

The overarching aim of the present work is therefore to better understand the effect of the customer’s value-creation on their evaluation of the brand of the destination where they have stayed. This aim requires the study to (a) identify the dimensions for CBDBE and value-creation for the customer; (b) validate the scales generated to measure both CBDBE and value-creation; and (c) propose and validate a model that successfully captures the antecedent effect of value-creation on CBDBE.

This research enables the literature to advance in several ways: the effectiveness of CBDBE is demonstrated; a measurement for value-creation in the tourism sector is proposed; and the importance of value-creation as an antecedent of the customer’s evaluation of a destination’s brand is demonstrated.

Theoretical Background

Customer-Based Destination Brand Equity

Brand equity (BE) is the most common term used to represent brand performance and is measured in terms of a financial value on the corporate balance sheet (Pike 2010). CBBE, developed by Aaker (1991, 1996) and Keller (1993, 2003) provides an alternative to the financial accounting perspective. According to Keller (1993), CBBE can be conceptualized as “the differential effect of brand knowledge on customer response to the marketing of the brand” (p. 2). CBBE includes brand beliefs and attitudes encompassing the perceived benefits of a given brand (Keller 1993).

There are only a few works that focus on the measurement of CBDBE (Table 1). Of these, the majority take the following to be dimensions of CBBE: (a) brand awareness; (b) brand quality; (c) brand image; and (d) brand loyalty (Chen and Myagmarsuren 2010; Gartner and Konečnik-Ruzzier 2011; Im et al. 2012; Kladou and Kehagias 2014; Konečnik-Ruzzier and Gartner 2007; Pike 2007, 2009, 2010; Pike et al. 2010; Pike and Scott 2009; Zavattaro, Daspit, and Adams 2015). Other works add brand value to these dimensions (Bianchi, Pike, and I. Lings 2014; Boo, Busser, and Baloglu 2009; Pike and Bianchi 2013). These studies often propose relationships between the different dimensions of CBBE (Boo, Busser, and Baloglu 2009, Chen and Myagmarsuren 2010; Kladou and Kehagias 2014; Pike and Bianchi 2013; Pike et al. 2010), and it is only in the work of Im et al. (2012) that the overall brand equity (OBE) proposed by Yoo and Donthu (2001) is used.

Table 1.

Empirical Evidence on Measures CBDBE.

Author/Year	Dimensions CBBE	Country
Bianchi, Pike, and Lings (2014)	Brand associations Brand loyalty Brand quality Brand salience Brand value	Australia
Boo, Busser, and Baloglu (2009)	Brand awareness Brand experience (Brand image, Brand quality) Brand loyalty Brand value	Las Vegas and Atlantic City
Chen and Myagmarsuren (2010)	Brand awareness Brand image Brand quality	Mongolia
Gartner and Konečnik-Ruzzier (2011)	Brand awareness Brand image Brand loyalty Brand quality	Slovenia
Im et al. (2012)	Brand associations Brand awareness Brand image Brand loyalty	Korea
Kladou and Kehagias (2014)	Brand assets Brand associations Brand awareness Brand loyalty Brand quality	Roma (Italia)
Konečnik-Ruzzier and Gartner (2007)	Destination awareness Destination image Destination loyalty Destination quality	Slovenia
Pike (2007) Pike (2009) Pike (2010)	Brand associations Brand loyalty Brand resonance Brand salience	Australia
Pike and Bianchi (2013)	Brand image Brand loyalty Brand quality Brand salience Brand value	Austria
Pike et al. (2010)	Brand associations Brand loyalty Brand resonance Brand salience	Australia
Konečnik-Ruzzier and Gartner (2007)	Brand awareness Brand loyalty Brand image Brand quality	Slovenia and Austria
Zavattaro, Daspit, and Adams (2015)	Brand associations Brand awareness Brand loyalty Perceived brand quality	Southern U.S. state

Source: Own elaboration.

Brand awareness represents the strength of the brand’s presence in the mind of the target audience, along a continuum (Aaker 1996). Awareness is essential to brand equity because it is the first step in building and increasing brand value (Gartner and Konečnik-Ruzzier 2011). In a tourism context, a tourist needs to know of a place, in some context, before they can even consider it as a potential destination (Gartner and Konečnik-Ruzzier 2011). Destination brand awareness (DBA) has been found to play an important role in the traveler’s destination choice (Chon 1992; Um and Crompton 1990) and is considered an important dimension of DBE (e.g., Bianchi, Pike, and Lings 2014; Zavattaro, Daspit, and Adams 2015).

In the literature, brand quality has been used interchangeably with perceived quality (Aaker 1991; Zeithaml 1988). Perceived quality is defined as the “perception of the overall quality or superiority of a product or service relative to relevant alternatives and with respect to its intended purpose” (Keller 2003, 238). Perceived quality, then, is another important dimension of BE (Aaker 1991; Pappu, Quester, and Cooksey 2005). In conceptualizing a DBE model, perceived quality is one of the constructs frequently used by tourism researchers (e.g., Bianchi, Pike, and Lings 2014; Zavattaro, Daspit, and Adams 2015).

Destination brand quality (DBQ), therefore, refers to perceptions of quality of the facilities and nonphysical aspects of the destination (Pike and Bianchi 2013). Previous research finds that among the elements of perceived quality, those relating to the environment and service infrastructure should be included (Williams, Gill, and Chura 2004).

Image is the brand dimension that has received the most attention in the academic literature (Gartner and Konečnik-Ruzzier 2011), with several different perspectives being proposed to define brand image (Lai and Xiang 2015). Of these, the definition chosen for the present work is that of brand image as the reasoned or emotional perceptions customers attach to specific brands (Dobni and Zinkhan 1990; Keller 2003).

Destination brand image (DBI) is regarded as an important dimension of DBE (e.g., Bianchi, Pike, and Lings 2014; Zavattaro, Daspit, and Adams 2015). Although destination image research is well established in the tourism literature, Dobni and Zinkhan (1990) argued that there were numerous definitions of brand image in the literature, which initially may cause confusion about the most suitable scale to use.

Destination brand value (DBV) is considered a principal dimension of BE (Bianchi, Pike, and Lings 2014; Pike and Bianchi 2013). The value of a service refers to the benefits customers believe they receive from it, relative to the costs associated with its consumption (McDougall and Levesque 2000). Zeithaml and Bitner (2000) suggest that service value is an overall evaluation of a service’s utility, based on customers’ perceptions of what is received at what price.

Meanwhile brand loyalty has been defined as the attachment a customer has to a brand (Aaker 1991), and the ability to create customer loyalty is one of the goals of brand management (Boo, Busser, and Baloglu 2009). Although attracting new customers is essential, it is more desirable and much less expensive to retain current customers (Reichheld, Markey, and Hopton 2000). Keller (2003) operationalized brand loyalty as the main source of CBBE. Brand equity is based on customer brand loyalty (Aaker 1991).

Several authors have used brand loyalty as a dimension of DBE (e.g., Bianchi, Pike, and Lings 2014; Zavattaro, Daspit, and Adams 2015). Although loyalty has long been an important research area in tourism (Baloglu 2001, 2002), there is no consensus as to the definition of destination brand loyalty within the concept of DBE (Boo, Busser, and Baloglu 2009). Commonly, brand loyalty is viewed as a composite measure, integrating both behavioral and attitudinal dimensions of loyalty (Boo, Busser, and Baloglu 2009; Konečnik-Ruzzier and Gartner 2007; Pike 2010). In the present study, as in other works on DBE, the attitudinal loyalty of BE is taken as a measure of future travel preference or intention to visit (Im et al. 2012; Pike and Bianchi 2013).

Moving on from the dimensions of CBDBE, the OBE scale was developed by Yoo, Donthu, and Lee (2000), primarily to evaluate the convergent validity of the multidimensional BE scale—including brand awareness, perceived quality, brand associations, and brand loyalty. Yoo and Donthu’s (2001) multidimensional BE and OBE scales were assessed by Washburn and Plank (2002) to examine their robustness. The results showed consistent correlation between the scales. Im et al. (2012) adapted these scales for Korea as a tourist destination.

In light of the literature review, the present work seeks to test whether the CBBE of the destination visited by the customer has five dimensions, specifically: destination brand awareness, destination brand quality, destination brand image, destination brand loyalty, and destination brand value. It is therefore proposed that

Hypothesis 1: CBDBE has five dimensions: destination brand awareness, destination brand quality, destination brand image, destination brand loyalty, and destination brand value.

Value-Creation from the SDL Perspective

SDL holds that customers play a fundamental role in the value-creation process during their consumption experience (e.g., Grönroos 2011; Helkkula, Kelleher, and Pihlström 2012; Karpen, Bove, and Lukas 2012; Mohd-Any, Winklhofer, and Ennew 2014). SDL describes service as the main purpose of exchange, highlights that service ultimately needs to be experienced by the customer, and provides a theoretical understanding of how firms, customers, and other market players contribute to value-creation. Several researchers argue that customers create value independently, but with the support of the supplier (Payne, Storbacka, and Frow 2007). For this reason, taking the approach of Grönroos (2008), the term value-creation is used in the present work when referring to the customers’ role, and value cocreation when referring to the suppliers’ role. Moreover, as Heinonen et al. (2010) point out, a reverse perspective on value-creation may be required: rather than focusing on how customers can be engaged in cocreating with the supplier, suppliers should rather focus on developing more of a direct presence in their customers’ lives. This service-centered perspective on value-creation emphasizes interaction between customer and supplier as central to value-creation (Grönroos 2011; Grönroos and Ravald 2011; Grönroos and Voima 2013). It is through interactions that the value is created and experienced (Salomonson, Åberg, and Allwood 2012). It is therefore useful to analyze the process of value-creation from this new perspective as it enables the activities of ASTs to be geared to tourists, and this, in turn, generates greater value during their consumption experience.

There are few studies providing empirical evidence on the role of customers in the process of value-creation during the consumption experience (Table 2). The works noted in Table 2 demonstrate the significant interest in the literature regarding this area applied to the tourism context, as covered in the works of Grissemann and Stokburger-Sauer (2012), Polo-Peña, Frías-Jamilena, and Rodríguez-Molina (2014), Shaw, Bailey, and Williams (2011) or Søresen and Jensen (2015). Furthermore, with the exception of the latter two studies that address the process of value-creation from a qualitative viewpoint, the majority of the empirical works have focused exclusively on value cocreation (Andreu, Sánchez, and Mele 2010; Grissemann and Stokburger-Sauer 2012; Hsiao, Lee, and Chen 2015; Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2014; Siltaloppi and Nenonen 2013). These studies highlight the importance of the interaction between customers and suppliers, although the empirical applications only capture the interactions that arise between the customer and one single supplier. These factors point to the need to make advances in the study of value-creation among customers during the consumption process, and specifically in the tourism sphere.

Table 2.

Empirical Studies Focusing on Value-Creation Process.

Author/Year	Objective	Sphere of Application	Characteristics of the Empirical Application	Contribution
Works focusing on the analysis of value cocreation
Andreu, Sánchez, and Mele (2010)	To compare customers’ and suppliers’ perspectives on the value cocreation process	- Retail sector- Spain	Qualitative:- Face-to-face interviews with customers and retailers- Case studies on Spanish furniture supply chains	Examines the resources, processes, and practices used by the firm during B2C interaction.Extends the knowledge base concerning customer knowledge and customer integration
Grissemann and Stokburger-Sauer (2012)	To provide a conceptual model and empirical evidence of the effect of company support on value cocreation and on business outcomes	- Tourism sector (family-run travel agencies specializing in organizing travel for Austrian students and young consumers who were traveling to Italy and Spain)- Dyadic data collection: employee and customer- Austria	Quantitative:- Convenience sampling method- 185 customers- Structural equation models	The resource aimed at value cocreation within the firm is termed “company support.”Company support covers varying levels (low, medium, and high) of communication, customer service, and interaction with the customerValue cocreation has significant and positive effects on satisfaction, expenditure, and loyalty toward the firm.
Hsiao, Lee, and Chen (2015)	To ascertain the effects of servant leadership on customer value cocreation	- Hotel industry (considering a cross-level analysis including organizations, employees, and customers)- Taiwan	Quantitative:- Hierarchical linear models considering three levels: organization, employee, and customer	This study adopted hierarchical linear models to conduct a cross-level analysis on the relationships among organizations, employees, and customers.Servant leadership can systematically stimulate customer value cocreation. This relationship is mediated by the variables “positive psychological capital” and “service-oriented organizational citizenship behavior.”
Polo-Peña, Frías-Jamilena, and Rodríguez-Molina (2014)	The work examines the firm’s capabilities in the context of its B2C interactions, focusing on information and communications technology as a particular driver of value cocreation.	- Small service (rural tourism) enterprises- Spain	Quantitative:- The sample consists of service firms and their customers- Quota sampling method- Multilevel structural equation models	The findings indicate that information and communications technology capabilities have a direct effect on value cocreation, as does value cocreation on perceived value and loyalty.
Works focusing on the analysis of value cocreation
Siltaloppi and Nenonen (2013)	To identify and elaborate on the possible roles firms and their customers enact in the service provision/value-creation process	- Finnish residential real estate industry	Qualitative:- Multiple case studies based on qualitative interview data	This research presents an empirical analysis and interpretation of the service coproduction/value cocreation process.In particular, the results display different depths of interaction between firms and their customers in coproducing and cocreating value, which suggests that it is not necessarily meaningful to consider everything as cocreation from an empirical perspective.
Yi and Gong (2013)	Validate a customer value cocreation behavior scale referring to the interaction between the customer and the service delivered by frontline personnel	- The point at which service-delivery involves interaction between frontline personnel and the customer	Quantitative:- Convenience sampling method- Undergraduate and graduate student customers- Structural equation models	This work validates a value cocreation scale referring to the customer’s interaction with the service delivered by frontline personnel. This construct is multidimensional and hierarchical, and the scale comprises two second-order dimensions: customer participation behavior (which refers to information search, information sharing, responsible behavior, and personal interaction) and customer citizenship behavior (which refers to feedback, advocacy, helping, and tolerance).
Works focusing on the analysis of value-creation processes
Shaw, Bailey, and Williams (2011)	To demonstrate the critical importance of value-creation processes which involve the customer as cocreator of value in the tourism sector	- Tourism sector (national independent hotels, medium-sized enterprises)- United Kingdom	Qualitative:- Case studies- Face-to-face interviews with managers, directors, and MDs of the hotels	It is shown that the processes of B2C value cocreation are vital to achieving innovation in the sector.It is identified that further advances are called for in studying key areas of knowledge such as (a) the competitive advantage that can be achieved by means of B2C value cocreation; and (b) how ICT not only changes the scope of B2C interactions but also the power relationships between firm and customer.
Salomonson, Åberg, and Allwood (2012)	To generate a deeper understanding of how customer service representatives’ communication skills when liaising with customers support customers’ value creation	A Swedish company in the building and construction industry, and subsidiary to a large, multinational corporation.	Qualitative:- Naturally occurring data in the form of conversations between customer service representatives and customers	This study demonstrates three communication skills that are essential in supporting customers’ value-creation: attentiveness, perceptiveness, and responsiveness.
Works focusing on the analysis of value-creation processes
Søresen and Jensen (2015)	To identify if tourism service encounters (between customers and staff) are transformed into “experience encounters” by integrating them into the tourism experience to which they are related, creating added experiential value for tourists and increasing the creation of knowledge about users	- Retro design boutique hotel	Qualitative:- Case studies- Experiment	The limitations of service encounters in tourism regarding their potential for knowledge development and value creation are emphasized. It is argued that changing service encounters into experience encounters can overcome these barriers and bring new potential for knowledge development, innovation, and value-creation in tourism.

Source: Own elaboration.

According to SDL, value-creation may only be achieved by the customer to the extent to which they experience the service in question (Vargo and Lusch 2004, 2006, 2008). It comprises affective value, cognitive value, and behavioral value (Salomonson, Åberg, and Allwood 2012; Mohd-Any, Winklhofer, and Ennew 2014). Affective value elements refer to the customer’s feelings or affective state, cognitive value refers to rational processes such as attention, information-processing and problem-solving, and behavioral value is concerned with action that stems from the interaction, such as decision making (Edvardsson et al. 2013; Saarijärvi, Kannan, and Kuusela 2013).

Value-creation responds to the development of a complex process comprising the concepts of value-in-exchange, value cocreation, value-in-use, value-in-context, and interactions (Figure 1). Again according to this SDL perspective on value-creation, suppliers cannot create value but can only offer value propositions (value-in-exchange) and then collaboratively cocreate value with customers (Vargo and Lusch 2004, 2006, 2008). As stated earlier, it is through the interactions that value is created, given that service is exchanged, consumed, and produced, as well as knowledge being generated and services codesigned and cocreated (Salomonson, Åberg, and Allwood 2012). Hence, the nature of value-in-use is the extent to which a customer feels better-off (positive value) or worse-off (negative value) as a result of their experiences relating to consumption (Grönroos and Voima 2013). Value-in-use not only accumulates from past and current experience but also can be imagined or previously intended or subsequently evaluated (Helkkula, Kelleher, and Pihlström 2012).

Figure 1.

The value-creation process.

Value has been recognized as being created in context (Chandler and Vargo 2011; Vargo 2008), for example, in social situations (Edvardsson et al. 2013). Users’ accumulated experiences (individual and social) of resources, processes (and/or their outcomes), and contexts are at the heart of value-creation (Epp and Price 2011). Within this process, it is essential that customers and suppliers interact. Interactions are situations in which the parties are involved in each other’s practices (Grönroos and Ravald 2011). The basis of interaction is a physical, virtual, or mental contact, such that suppliers create opportunities to engage with their customers’ experiences and practices and thereby influence their flow and outcomes (Grönroos and Voima 2013).

Finally, it is worth highlighting the dynamic nature of value-creation, which alters over the course of the consumption experience (Grönroos and Voima 2013). In the tourism context, it is useful to take into account the three stages of the consumption process for a tourism stay: the previsit stage (in which the tourist recognizes the need for the trip, accesses information from different ASTs, and plans their stay, including making the relevant bookings); the visit itself (when the tourist “consumes” their stay); and the postvisit phase (referring to the tourist’s evaluation of the experience once they have consumed their stay) (Jun, Vogt, and Mackay 2007; Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2012; Prayag et al. 2015). Hence, it is helpful to study value-creation across all three stages (Figure 2).

Figure 2.

Stages of consumption for the tourism experience and value-creation.

In light of the literature review, it is proposed that value-creation for a tourism stay is a complex process that should ideally take into account the following: (a) the interactions between the tourist and the ASTs involved in the consumption process for the tourism stay, which enable value to be generated for the tourist (of an affective, cognitive, and behavioral nature) and which form the basis for the development of different types of value-in-exchange, value cocreation, value-in-use, and value-in-context); and (b) value-creation is generated throughout the entire consumption experience, making it necessary to include value-creation in the previsit, visit, and postvisit stages. It is therefore proposed that

Hypothesis 2: Value-creation for the tourist’s experience of a destination includes three dimensions: value-creation in the previsit stage; value-creation during the visit; and value-creation during the postvisit stage.

SDL holds that brands must be understood from the customer’s perspective and are the result of “relationship partners” (customers, suppliers, and other participants) (Brodie, Glynn, and Little 2006; Merz, He, and Vargo 2009; Vargo and Lusch 2004, 2008). This perspective is in line with Aaker’s framework, which suggests that customer perceptions of a brand can be formed and influenced by any contact they may have with that brand (Aaker 1996; Fournier 1998). As a result, customers are considered to be active BE creators via their interactions with suppliers and other participants (Brodie, Glynn, and Little 2006; Merz, He, and Vargo 2009).

The interactions between customers, suppliers, and other participants are embraced within the value-creation concept (Grönroos and Voima 2013; Heinonen et al. 2010). Thus, the customer’s value-creation plays an important role in brand-formation (Brodie, Glynn, and Little 2006) and therefore is likely to influence evaluations of BE (Merz, He, and Vargo 2009). This issue is particularly relevant in the context of tourist destinations. Achieving greater DBE is comparable to the generation of competitive advantage for the tourist destination (Pike 2009). The literature review shows that there are few works to date in the tourist destination context that contribute to understanding the antecedents of DBE (Pike 2009), with the exception of the work of Ferns and Walls (2012), which proposes a model addressing the effect of enduring travel involvement on DBE. The literature has not analyzed the effects of value-creation on DBE and whether it constitutes an antecedent of DBE. Understanding this latter facet would be of tremendous value to the literature on SDL (Merz, He, and Vargo 2009; Vargo and Lusch 2004, 2008) and tourist destinations (e.g., Merz, He, and Vargo 2009; Pike 2009), as it would help to better gear the activities of ASTs toward tourists and thus enable the latter to generate greater value in their experience of the destination, which would ultimately translate into greater DBE. The following is therefore proposed:

Hypothesis 3: Value-creation in the tourist’s experience of a destination has a positive and significant effect on CBDBE.

Methodology

This study used all the steps proposed by Churchill (1979), including a qualitative study, on the basis of which the content of the dimensions and the items relating to each dimension of value-creation for the experience of the tourist destination (VCETD) were determined. The literature review provided the basis for determining the items for CBDBE measurement. A pretest was conducted, which enabled the measurement scale of CBDBE and VCETD to be refined; and an empirical study was undertaken in which the CBDBE and VCETD measurement instruments were validated and the proposed hypotheses tested.

Qualitative Study: Generation and Selection of the Scale Items

A comprehensive literature review identified the nature of the construct under study and the dimensions that should be considered for the VCETD and CBDBE scales. First, for the VCETD scale, the literature review focused on SDL and the value-creation process, together with the specific area of tourism. It covered tourist destinations, tourist behavior and tourists’ perceptions and evaluations of their destination experience. Out of this review came an initial selection of items.

From this first selection phase, 26 items were identified and classified in line with the dimensions to which each one belonged. Each of the dimensions was shown to be relevant, according to the literature, and there were no indications that any additional dimensions should be taken into account.

Meanwhile, a judging panel comprising experts was created, including a group of researchers experienced in the fields of both value and tourism (one professor and three lecturers, all from Spanish universities). As stressed by Hardesty and Bearden (2004), expert judgments can contribute to correctly defining a construct. The methodology applied by Ouellet (2007) and Vandecasteele and Geuens (2011) was used here. This procedure yielded 15 items (Table 3).

Table 3.

Items Used in the VCETD Scale.

Dimension	Item
	Value-creation for the experience of the tourist destination (VCETD)
Value-creation in previsit phase (VCPRE)	VCPRE1. Exchanging information with agents at the destination and/or other tourists enabled me to save time when planning my stay.VCPRE2. Exchanging information with agents at the destination and/or other tourists enabled me to tailor-make my stay.VCPRE3. Exchanging information with agents at the destination and/or other tourists enabled me to find a tourism offer at a good price.VCPRE4. Exchanging information with agents at the destination and/or other tourists enabled me to plan my stay from start to finish.VCPRE5. Exchanging information via social networks to plan my stay was good fun.
Value-creation during the visit (VCVISIT)	VCVISIT1. Exchanging information with agents at the destination, the local population, and/or other tourists has enabled me to get to know new people to talk to.VCVISIT2. Exchanging information with agents at the destination, the local population and/or other tourists has enabled me to enjoy my stay.VCVISIT3. Exchanging information with agents at the destination and/or other tourists has enabled me to find useful resources for my stay (such as websites, mobile apps, tourist brochures, maps, etc.).VCVISIT4. The information and materials given to me by agents at the destination and/or other tourists have been useful in helping me to address my needs.VCVISIT5. Exchanging information with agents at the destination, the local population and/or other tourists has enabled me to get the most out of my stay.
Value-creation postvisit (VCPOST)	VCPOST1. Exchanging feedback with agents at the destination and/or other tourists will enable me to offer suggestions about the destination.VCPOST2. Exchanging feedback with agents at the destination and/or other tourists will enable me to share my tourism experiences.VCPOST3. Exchanging feedback via social networks will enable me to offer insights regarding my experience to agents at the destination and other tourists.VCPOST4. Exchanging feedback about my experience of the destination will help other tourists design their holidays.VCPOST5. Exchanging feedback will help agents at the destination to extend the range of resources available at the tourist destination.
	Overall value-creation for the experience of the tourist destination (OVCETD)
	OVC1. In general, my interactions with others before, during, and after this trip will have enabled me to have a good personal experience.OVC2. The time and effort I devoted to the planning process have enabled me to design a trip of great value to me.OVC3. The time and effort I have invested during my stay have proved to be of great value to me.OVC4. The time and effort I put into sharing my experiences will be of great value to me.

In addition, 4 other items were included to establish an overall VCETD scale, relating to the emotional, social, and behavioral value experienced by the tourist during the entire process surrounding their stay at the tourist destination (OVC1 to OVC4).

The field research was undertaken between February and March 2014.

Second, with regard to CBDBE, the literature review helped identify the dimensions and items to be used for its measurement (Table 4). Here, five dimensions were identified (DBA, DBQ, DBI, destination brand loyalty [DBL], and DBV), together with an overall measurement (ODBE). The items identified were deemed to capture the CBDBE construct in its entirety, and their wording was checked for clarity.

Table 4.

Items Used in the CBDBE Scale.

Dimension	Item	Empirical studies on those used
Destination brand awareness (DBA)	DBA1. This destination has a good name and reputation.	Boo, Busser, and Baloglu (2009); Kladou and Kehagias (2014); Pike and Bianchi (2013); Pike et al. (2010)
	DBA2. This destination is very famous.	Boo, Busser, and Baloglu (2009); Kladou and Kehagias (2014); Pike and Bianchi (2013); Pike et al. (2010)
	DBA3. The characteristics of this destination come to mind quickly.	Boo, Busser, and Baloglu (2009); Ferns and Walls (2012); Kladou and Kehagias (2014); Konečnik-Ruzzier and Gartner (2007); Pike and Bianchi (2013); Pike et al. (2010)
	DBA4. When I am thinking about having fun, this destination comes to mind immediately.	Boo, Busser, and Baloglu (2009); Kladou and Kehagias (2014); Pike and Bianchi (2013); Pike et al. (2010)
Destination brand quality (DBQ)	DBQ1. This destination provides tourism offerings of consistent quality.	Boo, Busser, and Baloglu (2009)
	DBQ2. This destination provides quality experiences.	Boo, Busser, and Baloglu (2009); Kladou and Kehagias (2014)
	DBQ3. I can expect superior performance from this destination’s offerings.	Boo, Busser, and Baloglu (2009)
	DBQ4. This destination performs better than other similar destinations.	Boo, Busser, and Baloglu (2009)
Destination brand image (DBI)	DBI1. The image that I have of this destination is as good as, or even better than, that of other similar destinations.	García, Gómez, and Molia (2012)
Destination brand image (DBI)	DBI2. The overall image of the destination is very positive.	García, Gómez, and Molia (2012)
Destination brand loyalty (DBL)	DBREC1. I will recommend this destination to anyone who asks for my advice.	Im et al. (2012)
	DBREC2. I will encourage my friends/family to visit this destination.	Boo, Busser, and Baloglu (2009); Ferns and Walls (2012); Im et al. (2012)
	DBREP1. I intend to go back to this destination within the next 5 years.	Im et al. (2012)
	DBREP2. Overall, I feel loyal toward this destination.	Im et al. (2012)
Destination brand value (DBV)	DBV1. This destination has reasonable prices.	Boo, Busser, and Baloglu (2009); Pike and Bianchi (2013)
	DBV2. Considering what I paid for this trip, this destination offers real value for money.	Boo, Busser, and Baloglu (2009); Pike and Bianchi (2013)
	DBV3. The costs of visiting this destination are a bargain compared to the benefits I received.	Boo, Busser, and Baloglu (2009); Pike and Bianchi (2013)
	DBV4. This destination is economical.	Boo, Busser, and Baloglu (2009)
	DBV5. This destination is a good deal.	Boo, Busser, and Baloglu (2009)
Overall destination brand equity (ODBE)	ODBE1. It makes sense to choose this destination rather than another one even if they are similar.	Im et al. (2012)
	ODBE2. Even if there is another destination with the same characteristics, I prefer this one.	Im et al. (2012)
	ODBE3. If there is another destination as good as this one, I still prefer this one.	Im et al. (2012)
	ODBE4. If there is another destination, no different to this one, it seems more intelligent to choose the destination I’m in now.	Im et al. (2012)

Testing of the VCETD and CBDBE scales was conducted in the pretest.

Pretest: Refining the Scales Relating to Customer-Based Destination Brand Equity and Value-Creation for the Experience of the Tourist Destination

This pretest stage involved an initial gathering of quantitative data and an evaluation of the items included in the measurement scales. From this, refined scales were obtained. The structured questionnaire included a brief clarification that the questions referred to the different participants in the destination with whom the tourist may have had contact before, during, or after their stay. More specifically, it was explained that the questions referred to interactions with (a) the public agencies responsible for managing tourism resources at the destination; (b) suppliers of services (such as accommodation establishments, restaurants, and leisure facilities); and (c) other tourists at the destination or with prior experience of it. Examples were provided to aid comprehension. There were 15 items for the VCETD scale; 4 for the OVCETD scale; 19 for the CBDBE scale; and 4 for the ODBE scale (Table 4). These were selected on the basis of the literature review, as explained in the Measures section.

Interviews were carried out in March 2014. A convenience sample was obtained to ensure each interviewee fulfilled two requirements: that he/she had personally organized and undertaken a trip to a tourist destination in the previous six months; and that they had had contact with different participants in the destination (the ASTs) in each of the different stages of consumption (i.e., before, during, and after their stay). A total of 33 valid interviews were carried out.

The final sample comprised 54.50% men and 45.50% women. Ninety-four percent of the sample were aged 18–29 years, and all were educated to the postgraduate level. The majority mainly traveled with their family, followed by friends and, finally, their partner.

In this pretest it was identified that some of the items were a little difficult to understand, but that all the items presented a correct correlation with the items belonging to the same dimension. To refine and improve understanding of some of the more problematic items, a panel of experts was formed, made up of four senior lecturers familiar with value-creation research and the tourism sector, and their contributions resulted in an improved VCETD scale and items, as reflected in Table 3.

Empirical Study

Population

The Spanish tourist population was taken as the basis of the empirical research. Spain is considered to be one of the most representative and significant tourist destinations, and is currently the third most visited country in the world (UNWTO 2015). British tourists represent the principal market for arrivals to Spain (Frontur 2012).

The sample was generated by applied quota sampling as this technique provides a sample structure similar to that of the population. The sample was obtained at the end of the British tourists’ stay in Spain, ensuring that the tourist experience was recent and complete. The questionnaires were completed via personal interviews in English held at the different tourist destinations visited. The interviewers traveled to different tourism locations and selected those tourists who confirmed they were coming to the end of their stay in Spain. To achieve a representative sample, the regions with the greatest volume of tourists were chosen, specifically: Andalusia, Catalonia, Canary Islands, Balearic Islands, the Autonomous Community of Valencia, the Autonomous Community of Madrid, the Autonomous Community of Castilla y León, and Asturias (Frontur 2012).

A total of 503 valid interviews were conducted. The profile of the respondents was similar to that of the population of foreign tourists visiting Spain (Frontur 2012). There was an almost equal number of females (52.90%) and males (47.10%). Most respondents were aged either 30–44 (30.00%) or 45–65 (30.20%), followed by those under 30 (23.10%), and over 65 (13.70%). The majority of the tourists were employed (56.90%).

Measures

The dependent variable in our research was CBDBE. Each construct in the DB model requires scale items that are destination-specific. Reis and Judd (2000) noted that “the psychometric approach relies on aggregate patterns of data to evaluate a proposed measurement model” (341). Hence, multiple items for each dimension are useful to examine construct validation and check the consistency level of a respondent’s self-report of each dimension. Multiple items were used to measure each dimension of DBA, DBQ, DBI, DBL, DBV, and ODBE. To measure brand equity, scales validated by the literature were used (Table 4). To measure VCETD, the scale proposed in the present work was adopted (see Table 3), following the same procedure as described for the pretest.

There was no evidence of common method bias (according to Harman’s single-factor test), as the proposed model achieved a very poor fit (χ²_SB = 3501.43; df = 464; p = 0.00; root mean square error of approximation [RMSEA] = 0.12; standardized root mean square residual [SRMR] = 0.13; comparative fit index [CFI] = 0.59).

Results

To test the proposed hypotheses, the CBDBE and VCETD scales were validated (hypotheses 1 and 2) using confirmatory factor analysis (CFA) and maximum likelihood. Once the scales were found to be valid and reliable, the relationship between CBDBE and VCETD was estimated (hypothesis 3). To this end, structural equation modeling (SEM) was used, together with the maximum likelihood (ML) estimation method. The theory behind this procedure is that the data under analysis should follow a multivariate normal distribution. If this assumption is not fulfilled, the standard errors of the estimated parameters need to be corrected, along with the global fit statistics. There are several approaches to achieving this, one of the most commonly used being that developed by Satorra and Bentler (1994). In our case, the asymmetry and kurtosis omnibus test proved significant (Om = 6401.16, p = 0.00), as did the multivariate asymmetry and kurtosis tests (b1p = 354.18, p = 0.00; and b2p = 2176.89, p = 0.00), respectively. Therefore, the overall fit statistics and standard errors corrected for nonnormality were used.

The CFA estimated to validate the CBDBE measure is shown in Figure 3. CBDBE is defined as a latent construct comprising five dimensions: DBA, DBQ, DBI, DBL, and DBV. Furthermore, the DBE dimension is defined as a second-order latent variable with two dimensions: intention to recommend the destination visited to others (REC) and intention to repeat the visit to the destination (REP) (Zeithaml, Berry, and Parasuraman 1996). DBE is a higher-order latent variable as the present work seeks to understand the relationship between this variable and VCETD, and because this factor takes into account the covariances between the first-order dimensions (Brown 2006). Finally, to test the convergent validity, a measure of overall DBE (ODBE) was used, similar to that employed by Yoo and Donthu (2001).

Figure 3.

Validation of the CBDBE scale.

The results showed that the overall fit indices for the proposed model achieved values below the level recommended by the literature (Hair et al. 2009), as the individual reliability of two of the items was significantly below 0.50 (DBA4, R² = 0.23; DBQ3, R² = 0.17). Hence, they were eliminated and the model was estimated once again. The revised model showed that the Satorra-Bentler chi-square was statistically significant (χ²_SB = 626.35, df = 181, p = 0.00), although it should be noted that this statistic is dependent on sample size.

In addition, the ECVI of the original model was compared with that of the model with the two low-reliability items removed. The results showed the modified model was preferable (ECVIOR = 1.95 vs. ECVIMOD = 1.53). The single-sample cross-validation index (Browne and Cudeck 1989) was also calculated, again showing that the modified model was superior to the original one (CVIOR = 3.80 vs. CVIMOD = 2.92). Furthermore, other overall fit indices for this model were within the values recommended by the literature (goodness-of-fit index [GFI] = 0.98, adjusted goodness-of-fit index [AGFI] = 0.98, RMSEA = 0.07, SRMR = 0.06, CFI = 0.90, incremental fit index [IFI] = 0.90, Tucker–Lewis index [TLI] = 0.88); hence, it can be said that the modified model adequately reproduces the covariance matrix under observation.

As regards the first-order constructs, all of the nonstandardized coefficients achieved high values significantly different from zero. Similarly, the standardized coefficients were found to be above 0.70, and hence individual reliability registered values over 0.50; the Cronbach’s alpha coefficient (α) (Spearman-Brown’s formula when the scale has just two items), and the composite reliability (CR) index achieved values above, or very close to, 0.70, while that of average variance extracted (AVE) was above 0.50 in all cases. The same applied for the second-order latent variable (DBL) and the third-order construct (CBDBE); hence, it was concluded that the scales used to measure the dimensions of CBDBE were internally consistent (Table 5). Finally, the correlation between CBDBE and ODBE was found to be very high (r = 0.70), indicating convergent validity. Hypothesis 1 therefore receives empirical support.

Table 5.

Estimated Coefficients and Internal Consistency for the CBDBE.

Latent Variables and Items	Coefficient	Z Value	Standardized Coefficient	Internal Consistency
Destination brand awareness (DBA)
DBA1	1.00		0.72	α = 0.77CR = 0.77AVE = 0.53
DBA2	1.18	11.90	0.73
DBA3	1.02	11.03	0.74
Destination brand quality (DBQ)
DBQ1	1.00		0.72	α = 0.77CR = 0.77AVE = 0.53
DBQ2	1.05	16.71	0.75
DBQ4	0.99	14.72	0.71
Destination brand image (DBI)
DBI1	1.00		0.72	r_SB = 0.67CR = 0.67AVE = 0.50
DBI2	0.89	13.02	0.70	r_SB = 0.67CR = 0.67AVE = 0.50
Intention to recommend the destination (DBREC)
DBREC1	1.00		0.82	r_SB = 0.82CR = 0.82AVE = 0.70
DBREC2	1.02	18.95	0.86	r_SB = 0.82CR = 0.82AVE = 0.70
Intention to return to the destination (DBREP)
DBREP1	1.00		0.84	r_SB = 0.70CR = 0.71AVE = 0.56
DBREP2	0.82	11.38	0.65	r_SB = 0.70CR = 0.71AVE = 0.56
Destination brand loyalty (DBL)
DBREC	0.93	12.10	0.86	CR = 0.73AVE = 0.57
DBREP	1.00		0.64	CR = 0.73AVE = 0.57
Destination brand equity (DBE)
DBV1	1.00		0.76	α = 0.86CR = 0.86AVE = 0.55
DBV2	1.03	20.71	0.79
DBV3	0.98	15.47	0.71
DBV4	0.96	16.63	0.73
DBV5	0.94	13.56	0.73
Overall destination brand equity (ODBE)
ODBE1	1.00		0.71	α = 0.87CR = 0.87AVE = 0.63
ODBE2	1.15	20.46	0.78
ODBE3	1.18	17.50	0.86
ODBE4	1.21	15.22	0.81

Note: α = Cronbach’s alpha; r_SB = Spearman-Brown formula; CR = composite reliability; AVE = average variance extracted.

Discriminant validity between the latent variables was tested by estimating the bootstrap confidence interval for the correlations between the first-order latent variables. According to Anderson and Gerbing (1988), discriminant validity can be said to exist if the interval does not include 1. The results shown in Appendix A show that the scales presented discriminant validity.

The CFA proposed to validate the VCETD scale can be seen in Figure 4. VCETD is defined as a latent construct with three dimensions: VCPRE, VCVISIT, and VCPOST. With a view to testing the convergent validity, a measure of overall value-creation for the experience of the tourist destination (OVCETD) was also included.

Figure 4.

Validation of the VCETD scale.

The results indicated that the overall fit of the model was adequate. Despite the Satorra-Bentler chi-square being statistically significant (χ²_SB = 332.14, df = 147, p = 0.00), other overall fit indices of the model were within the limits recommended by the literature (GFI = 0.98, AGFI = 0.97, RMSEA = 0.05, SRMR = 0.04, CFI = 0.95, IFI = 0.95, TLI = 0.94). It can therefore be concluded that the model adequately reproduces the covariance matrix under observation.

The nonstandardized coefficients achieved high values and were significantly different from zero in all cases, both for the first-order latent variables (VCPRE, VCVISIT, and VCPOST) and also for the second-order latent variable (VCETD). Furthermore, the standardized coefficients were above 0.70 in all cases, with the exception of one item relating to value-creation during the previsit phase and another relating to value-creation in the postvisit phase. Internal consistency was thus comfortably above the values recommended by the literature. More specifically, the Cronbach’s alpha coefficient was over, or very close to, 0.90; composite reliability was consistently above 0.90; and in all cases the variance extracted was over 0.60 (Table 6). Finally, the correlation between VCETD and OVCETD was very high (r = 0.78), demonstrating convergent validity. In view of these findings, hypothesis 2 receives empirical support.

Table 6.

Estimated Coefficients and Internal Consistency for VCETD.

Latent Variables and Items	Coefficient	Z Value	Standardized Coefficient	Internal Consistency
Value-creation in previsit phase (VCPRE)
VCPRE1	1.00		0.83	α = 0.90CR = 0.90AVE = 0.65
VCPRE2	1.02	21.01	0.87
VCPRE3	1.03	17.66	0.84
VCPRE4	1.07	20.75	0.86
VCPRE5	0.81	11.07	0.61
Value-creation during the visit (VCVISIT)
VCVISIT1	1.00		0.74	α = 0.91CR = 0.91AVE = 0.67
VCVISIT2	1.09	23.90	0.85
VCVISIT3	1.18	18.96	0.81
VCVISIT4	1.14	19.12	0.84
VCVISIT5	1.12	18.47	0.84
Value-creation postvisit (VCPOST)
VCPOST1	1.00		0.86	α = 0.90CR = 0.90AVE = 0.65
VCPOST2	1.07	30.94	0.87
VCPOST3	0.88	15.91	0.65
VCPOST4	1.05	20.88	0.85
VCPOST5	1.00	19.70	0.79
Value-creation for the experience of the tourist destination (VCETD)
VCPRE	1.00		0.77	α = 0.92CR = 0.91AVE = 0.78
VCVISIT	1.01	13.71	0.91
VCPOST	1.18	15.46	0.97
Overall value-creation for the experience of the tourist destination (OVCETD)
OVC1	1.00		0.83	α = 0.95CR = 0.92AVE = 0.74
OVC2	1.13	25.48	0.86
OVC3	1.08	21.04	0.86
OVC4	1.18	24.76	0.89

Note: α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted.

Appendix A shows that the items used to measure each of the first-order dimensions present discriminant validity, according to the criterion established by Anderson and Gerbing (1988).

To test the third and final hypothesis, a model was proposed in which VCETD influences CBDBE. The overall fit indices showed that the Satorra-Bentler chi-square was significant (χ²_SB = 951.69, df = 452, p = 0.00), although other overall fit indices of the model were within the limits recommended by the literature (GFI = 0.98, AGFI = 0.97, RMSEA = 0.05, SRMR = 0.05, CFI = 0.93, IFI = 0.93, TLI = 0.92). It can therefore be concluded that the model adequately reproduced the covariance matrix under observation. As proposed in hypothesis 3, the relationship between VCETD and DBE was significant (β = 0.29, p = 0.00) and high (β_st = 0.53) (Figure 5); thus, hypothesis 3 also receives empirical support.

Figure 5.

Result of the research model proposed.

Discussion, Conclusions, and Implications

The management of tourist destinations forms the basis for tourists’ consumption of their stays and is a key factor in the generation of sustainable competitive advantage (Line and Wang 2015; Pike, Murdy, and Lings 2011).

The literature finds that achieving a greater level of BE is equivalent to achieving competitive advantage (Pike, Murdy, and Lings 2011; Pike and Page 2014). In the present research, three important challenges in the literature have been identified in relation to the application and development of BE in tourist destinations. These challenges are (a) how to measure and test the effectiveness of CBBE in the context of tourist destinations; (b) how value is created by customers, based on the process they follow to consume their tourist experience; and (c) how to test whether value-creation is an antecedent variable on the basis of which CBDBE can be fostered.

In seeking to address these challenges, the present study offers the following contributions to the literature:

First, the applicability of CBBE in the tourism context and its effectiveness as a measure of DB have been demonstrated, CBBE being underutilized to date for the tourist industry (in relation to tourism service suppliers, e.g., Hsu, Oh, and Assaf 2012) and tourist destinations (e.g., Gartner and Konečnik-Ruzzier 2011; Pike et al. 2010). Specifically, it has been shown that CBBE for tourist destinations can be measured using the following dimensions: DBA, DBI, DBQ, DBV, and DBL. This finding supports those achieved in earlier works (e.g., Bianchi, Pike, and Lings 2014; Pike and Bianchi 2013). The work also demonstrates the convergent validity of the scale, using ODBE as a measure and corroborating the results obtained by Yoo and Donthu (2001). The results of the present work help provide greater robustness in the measurement of CBBE for tourist destinations and respond to the lack of agreement on the effective measurement of destination brands, as noted by authors such as Boo, Busser, and Baloglu (2009) and Pike et al. (2010).

Second, as regards value-creation, the validated scale represents a step forward for the SDL literature (Vargo and Lusch 2004, 2006, 2008) as it captures value-creation entirely as it is conceptualized in this theory (e.g., Grönroos 2011; Grönroos and Voima 2013). Tourist value-creation is reliant on the customer participating and interacting (Prebensen, Kim, and Muzaffer 2015) with the different participants in the destination (Mohd-Any, Winklhofer, and Ennew 2014; Pike, Murdy, and Lings 2011). To date, value-creation has been approached from a more conceptual perspective that identifies and analyzes its nature and composition (e.g., Grönroos 2011; Grönroos and Voima 2013) or by taking an empirical approach but from a partial perspective, examining a single service provider only (e.g., Polo-Peña, Frías-Jamilena, and Rodríguez-Molina 2014; Søresen and Jensen 2015). The present investigation provides deeper insight into value-creation by validating a scale that captures the customer’s consumption experience for the entire tourist experience, in three phases (previsit, visit, and postvisit). Hence, the study takes into account the interactions between the tourist and the different participants in the destination. All three phases have been identified as being of major importance in the tourism context (e.g., Huang and Hsu 2010; Murphy and Chen 2014; Prayag et al. 2015; Xiang et al. 2015). This approach acknowledges that value-creation is a dynamic variable that shifts over time, as reflected in tourism experiences (Graham and Sparks 2012).

Furthermore, the validated value-creation proposed in the present work is the first of its kind to take a quantitative approach and embrace (a) the offer and capacities of the ASTs to make value propositions to tourists (value-in-exchange); (b) value cocreation based on the interactions between tourists and service providers (value cocreation); (c) tourists’ experiences derived from using the offer and resources of the ASTs (value-in-use); and (d) the tourist context (value-in-context).

Finally, the present work makes an interesting contribution in terms of the antecedent effect of value-creation on CBDBE—of importance because the effect has been empirically tested specifically for the tourist destination field. This finding responds to the need for more advanced knowledge on the antecedents of CBDBE as these can contribute to improving DBE and achieve greater levels of competitiveness for tourist destinations by managing each one in a unified approach that brings together all the relevant participants (e.g., Pike and Page 2014). This contribution is of particular note for the literature, considering that—with the exception of the work of Ferns and Walls (2012), which deals with customer involvement—no other studies to date have examined the antecedent variables that may contribute to achieving improved CBDBE.

Professional Implications

The results achieved in the present work are of interest for the professional sector comprising a wide range of public agencies (responsible for managing and developing tourist destinations) and service suppliers.

First, the present work provides a CBDBE scale, which helps to identify the broad areas of activity that those responsible for managing tourist destinations should undertake in order to improve evaluations of destination branding. This variable is of strategic importance for the competitiveness of tourist destinations, not least because it supports the implementation of actions at the destination designed to emphasize its most appealing features and resources so as to improve customer perceptions of its image and quality. This helps provide an incentive to new customers to try out the destination, perceive it to be an attractive option, develop revisit intention and subsequently recommend it to others.

A further contribution of the work is that of the validated value-creation scale, which provides a comprehensive framework for the process experienced by tourists when consuming their destination experience. This framework can help destination managers and other professionals to understand the importance of the interactions between tourists and each of the participants in the destination in the creation of value, given that destinations are complex systems in which these multiple interactions all contribute in their own way to the experience ultimately consumed by the tourist (Bregoli 2012; Mistilis, Buhalis, and Gretzel 2014). The scale developed in the present work reflects the fundamental role of tourist participation in value-creation (Prebensen, Kim, and Muzaffer 2015) and takes a dynamic, longitudinal perspective covering all stages of the consumption of a tourism experience (previsit, visit and postvisit) (Graham and Sparks 2012; Murphy and Chen 2014). In this regard, it is worth highlighting that (a) it is important to pay attention to all of the participants that may potentially interact with the tourist during their consumption of the destination experience, so as to ensure that those interactions are positive and also respond to a shared vision of the destination, and (b) the tourist may interact directly, in dialogue with the staff of the service supplier, public agencies, and other tourists, and they may also be involved in indirect interaction (via websites, blogs, or social networks, for instance)—and these, too, must be taken into account (Mohd-Any, Winklhofer, and Ennew 2014; Xiang et al. 2014, 2015). There is also evidence that the interaction between tourists and ASTs must be drawn upon to understand the needs and requirements of the former and offer them a value proposition that is in line with those needs (value-in-context). Furthermore, from the validated scale it can be inferred that the value created by the tourist includes not only functional content but also—of equal importance to them—affective and emotional content (as previously noted by Hosany et al. 2014). This is a key factor when seeking to ensure that the tourist feels inclined to take up the value proposition on offer and that, ultimately, the entire process leads to value-creation for them.

Finally, the work provides empirical evidence in relation to the antecedent effect of value-creation on CBDBE. This suggests that if tourist destination managers wish to achieve improved levels of DBE, they can do so by implementing a strategy based on the development of value for tourists.

Limitations and Future Lines of Research

In this work, there are certain limitations that need to be considered and that themselves constitute possible lines of research for the future. One such limitation might be the relatively low number of variables employed in the study, although those used were appropriate and adequate for the purpose of the research aims. Future studies could work with a greater number of variables relating to DBE and value-creation and its effect on destination performance, such as the extent of innovation in the tourism destination, the effects on tourist behavior, and the achievement of results.

A further limitation lies in the fact that the work examines the effect of the tourist’s interactions with all of the ASTs collectively on value-creation and subsequently CBDBE. This collective approach limits the results achieved as it is not possible to compare the different contributions to value-creation and CBDBE made by each of the sub-groups under the AST umbrella. Nor has it been possible to analyze the effect of value-creation in a specific phase (previsit, during, or postvisit) on a subsequent phase, as the different ASTs may participate in different ways in each of these three phases. A potential future line of research is therefore proposed, to examine in depth the particular role played by the tourist’s interaction with each of the different types of AST in turn. By taking this approach, it would be possible to identify how each particular collective influences the tourist when consuming his or her destination experience, and to what extent, and the influence each type of AST has in each of the three phases of consumption. In addition, it would facilitate an evaluation of the possible synergies that could be achieved by means of joint activities between the different ASTs to create a positive impact on the experience consumed by the tourist.

It would also be of interest to continue studying the effect of the value created in each of the tourism consumption phases on subsequent phases, and on other key variables of customer behavior, such as tourist satisfaction or loyalty. It would therefore be useful to develop a longitudinal sample to capture the value created following each of the consumption stages (as previously achieved in the work of Graham and Sparks 2012).

Returning to the present work, it would be helpful to improve the reliability of some of the scales by increasing the number of items used to measure the construct in question. This is the case, for example, with the DBI variable, which achieves a borderline level of reliability according to the recommendations of the literature. Similarly, it would be helpful to compare the results of future studies that use VCETD, to retain or eliminate the two items that present standardized coefficients below 0.70.

A further future line of research potentially of interest is the application of the proposed research model to other geographical areas.

Footnotes

Appendix

VCETD Scale			Estimate	Confidence Interval (Lower)	Confidence Interval (Upper)
VCPRE	↔	VCVISIT	0.74	0.66	0.79
VCPRE	↔	VCPOST	0.74	0.69	0.80
VCPRE	↔	OVCETD	0.52	0.41	0.61
VCVISIT	↔	VCPOST	0.87	0.83	0.91
VCVISIT	↔	OVCETD	0.71	0.61	0.79
VCPOST	↔	OVCETD	0.78	0.71	0.83

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: This study was carried out thanks to financing received from the national research project ECO-2012-39217 by the Ministerio de Economía y Competitividad (Spain) and the research project P11 SEJ-8104 of the Junta de Andalucía.

Notes

Author Biographies

Dolores María Frías Jamilena is a senior lecturer in the Department of Marketing and Market Research at the University of Granada. Her research interests are in the areas of online consumer behavioral, tourism and cross-cultural research. She has published in Tourism Management, Journal of Sustainable Tourism, Journal Small Business Management, Entrepreneurship and Regional Development, Internet Research, Online Information Review, The Services Industries Journal, International Journal of Hospitality Management, Cornell Hospitality Quartely, Journal of Travel Research, and other scholarly journals.

Ana Isabel Polo Peña is a senior lecturer in the Department of Marketing and Market Research at the University of Granada. Her research interests are in the areas of consumer behavior, tourist marketing and rural tourism. He has published in Journal of Travel Research, Journal of Sustainable Tourism, Journal Small Business Management, Entrepreneurship and Regional Development, The Services Industries Journal, International Journal of Hospitality Management and other scholarly journals.

Miguel Ángel Rodríguez Molina is a senior lecturer in the Department of Marketing and Market Research at the University of Granada. His research interests are in the areas of consumer behavior, tourism and research methodology. He has published in Journal of Travel Research, Tourism Management, Journal of Sustainable Tourism, Journal Small Business Management, Entrepreneurship and Regional Development, The Services Industries Journal, International Journal of Hospitality Management, Cornell Hospitality Quartely and other scholarly journals.

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