José Antonio Folgado-Fernández¹; Paulo Alexandre Oliveira Duarte²; José Manuel Hernández-Mogollón³

¹University of Extremadura, Department of Financial Economics and Accounting, MARKETUR Research Group, Avda. Virgen del Puerto s/n 10600 Plasencia, Spain, jafolgado@unex.es
²University of Beira Interior, Faculty of Human and Social Sciences, Department of Management and Economics, NECE, Estrada do Sineiro - Edifício Ernesto Cruz. 6200-209 Covilhã, Portugal, pduarte@ubi.pt
³University of Extremadura, Faculty of Management and Tourism, Department of Management and Sociology, MARKETUR Research Group, Avenida de la Universidad s/n 10071 Cáceres, Spain, jmherdez@unex.es

ABSTRACT

Intense competition among tourism destinations has forced tourism managers to use all their available resources in comprehensive strategies to improve their place image, including city managers who are looking to create a distinctive, strong city brand image capable of attracting tourists. The main objective of this paper is to assess the contribution of cultural heritage, events, tourist attractions and infrastructure to the development of cities’ brand image. A sample of Spanish tourists who visited the Spanish town of Plasencia was used to assess the contribution of these factors to city brand image development through partial least squares path modelling. The results show a significant, but differentiated, contribution of the four factors to city brand image, with events offering the lowest contribution. The findings provide tourism managers with new insights into how best to use each resource to build a strong city brand image and improve marketing strategies for their cities.

Keywords: City branding, city image, tourism management, image development.

RESUMEN

La intensa competencia entre destinos turísticos ha obligado a las administraciones a incluir todos los recursos disponibles dentro de una estrategia integral que mejore la imagen de un lugar. Los gestores de la ciudad buscan crear una imagen de marca que actúe como signo distintivo fuerte, capaz de atraer a los turistas. El objetivo principal de este trabajo es evaluar la contribución del patrimonio cultural, los eventos, los atractivos turísticos y las infraestructuras, para el desarrollo de la imagen de marca de ciudad. Se utilizó una muestra de turistas que visitan la ciudad española de Plasencia, con el objetivo de analizar la influencia de estos factores al desarrollo de su marca, a través de un modelo de mínimos cuadrados parciales (PLS). Los resultados muestran una relación significativa, pero diferenciada de los cuatro recursos con la imagen de marca de ciudad, siendo la celebración de eventos la contribución más baja. Los resultados proporcionan a los gestores turísticos nuevas ideas sobre cómo utilizar eficientemente cada recurso del destino en el afán por construir una imagen de marca de ciudad fuerte y mejorar su promoción.

Palabras clave: Marca ciudad, imagen de ciudad, gestión turística, desarrollo de imagen.

City managers must identify the dimensions that affect their cities’ image in order to make strategic decisions and differentiate their cities from competing destinations. Overall, tourists’ experiences with cultural resources, attractions and events taking place in destinations are regarded as crucial to nurturing each place’s tourism value (Getz, 2008). Destination image includes different dimensions, such as strategic planning, competitiveness compared to other places or marketing to potential buyers. Thus, it is important that city managers identify the main factors influencing tourists’ perceptions, as these aspects are key elements in satisfaction and loyalty and they can easily be translated into job creation, new business opportunities and economic benefits (Echtner & Ritchie, 1993).

The main purpose of this research was to conduct an empirical investigation to evaluate and compare the process of brand image formation in a medium-sized city with important cultural resources. The specific objective was to assess the contribution of cultural heritage, tourist attractions, infrastructure and events to the city’s image, from the visitors’ perspective.

The relevance of this research can be summarised by two key aspects. The first is the use of a medium-sized city with a distinct historical, urban heritage. The second is the value – for public and private tourism administrators – of understanding the attributes that can be used to communicate the brand image of destinations and the factors on which these managers need to base their brand awareness strategies to improve their marketing activities.

Cities have become consumer products with identities and values ​​that must be designed and marketed to be successful. This perspective has forced city administrators to apply the same marketing processes and resources used by private companies, taking risks and innovating to attract potential tourists. Currently, the management of tourist places involves the cooperation of a large number of different entities to implement branding strategies, use the perfect combination of components required to identify and differentiate each city and, at the same time, convey a strong, positive image (Ashworth & Kavaratzis, 2009). Destination branding conveys a series of promises and sets up expectations used to induce favourable values and influence consumers to choose particular destinations. In addition, it is expected that a high quality destination brand will involve improvements for both hosts and guests and will contribute to building long lasting relationships with tourists (Bigné, Sánchez & Sánchez, 2001). Moreover, it will bring prestige and help attract a higher level of tourist demand (Morales-Fernández & Lanquar, 2014). To achieve this desired success, cities must develop strong brands to communicate their city image effectively. A city image is built from an interrelated system of perceptual and emotional components that form a single and unified meaning (Stern & Krakover, 1993). Cities also promote their different resources with the aim of diversifying their tourist offer and attracting new market segments (Marrero-Rodríguez & Abdul-Jalbar, 2012). In order to achieve these goals, cities have to develop new attractions apart from their cultural assets (Santos, Ferreira & Costa, 2014), by applying effective marketing and communication tools (Rey, Medina & Rufín, 2013).

Based on a review of the relevant literature, this section presents and analyses the concept of destination brand image and discusses variables that compose a multidimensional construct of city brand image, as well as their relevance and contribution to predicting city images. The dimensions analysed are events, cultural heritage, tourist attractions and infrastructure. This approach is based on Baloglu and McCleary’s (1999) research. These authors proposed a path model with three determinants of image formation from the visitors’ perspective: (1) infrastructure and socioeconomic environment, (2) social factors and (3) natural and cultural resources.

2.1     Concept of destination image and its components

The literature reviewed considers destination images to be dynamic structures involving several dimensions and describes them as the sum of all beliefs and ideas about, and impressions of, places that people have. Thus, destination images can be defined as the set of available perceptions toward places that are developed by tourists (Baloglu & Brinberg, 1997), based on what they are told and on their own experience. Analyses of city images are central to tourism research as these provide insights into the key variables used in segmentation of target markets and the behavioural motivations of individuals in choice processes (Bigné et al., 2001). 

The majority of studies assert that image, as a multidimensional construct, is formed through the combination of cognitive and affective components that are strongly interrelated (Baloglu & Brinberg, 1997). Destination images can be shaped by the selection of elements (both cognitive and affective) used to communicate brand images to target clients. Deciding what the target market will be is essential to the development of place marketing strategies, since specific aspects of a place can be positive for one market segment and negative for another (Baloglu & McCleary, 1999).

The cognitive assessment of destinations comprises knowledge and beliefs generated from a set of attributes, resources and attractions or from generic positioning proposals provided by destinations (Baloglu & McCleary, 1999). The affective dimension corresponds to emotional attachment to places (Russel, Ward & Pratt, 1981). This separation of destination image into affective and cognitive components enables a better assessment and understanding of how people value places (Baloglu & McCleary, 1999). Traditionally, the cognitive component consists of four dimensions proposed in the literature surveyed: heritage and natural resources, infrastructure, social conditions and the general atmosphere of destinations (Baloglu & McCleary, 1999; Beerli & Martín, 2004; Lin, Morais, Kerstetter & Hou, 2007; San Martín & Rodríguez Del Bosque, 2008; Qu, Kim & Im, 2011) (see Table 1).

When developing their destination images, visitors take into account tangible characteristics (e.g. natural monuments, climate, infrastructure or art) and intangible variables (e.g. comfort, safety, security, cleanliness, pollution, noise and calmness). Successful tourist destinations must possess basic resources, such as accommodations, gastronomy, shopping facilities, attractions, activities and events, natural resources and ease of accessibility, in order to attract visitors (Chi & Qu, 2008).

Following up on Gallarza, Gil & Calderón’s (2002) research, this study’s approach considers two main types of resources: natural and heritage resources and man-made resources. These are considered the most influential in the majority of the literature on destination image. Heritage and natural resources include aspects such as landscapes, cultural activities and events, activities related to nature and local traditions (Beerli & Martín, 2004; San Martín & Rodríguez Del Bosque, 2008; Leco, Pérez, Hernández-Mogollón & Campón, 2013). The man-made resources dimension includes factors such as accommodation facilities’ availability and quality, the convenience and variety of shopping areas, architectural resources and accessibility (i.e. the availability and quality of transportation) (Beerli & Martín, 2004). Other authors also include socioeconomic variables (San Martín & Rodríguez Del Bosque, 2008), which were not included in this study, since the analysis was restricted to a single location.

2.2     Infrastructure

The development of infrastructure in tourism destinations is important to attract visitors and foster positive images of places, and it must always be considered in measurements of destination brand performance. Some authors consider the infrastructure of destinations together with their socioeconomic conditions (San Martín & Rodríguez Del Bosque, 2010). It is also not unusual to see the concept of infrastructure associated with environments, transportation, roads, health services, security and cleanliness, among others (Qu et al., 2011). Others authors limit the scope of their research to infrastructure associated exclusively with tourist attractions in destinations (Beerli & Martín, 2004). We have taken the approach of separating tourist attractions from infrastructure because we find that they serve different objectives and they are regarded differently by tourists.

2.3     Tourist attractions

Tourist attractions available in destinations can be the most decisive component of tourists’ choices and the most appealing reason to travel, depending on their level of attractiveness (Swarbrooke, 2001). The literature reviewed, notably, showed no general consensus on the definition and classification of tourist attractions. For example, Alhemoud and Armstrong (1996) divided tourist attractions into four categories: (1) natural attractions (e.g. landscapes, forests and rivers), (2) historical attractions (e.g. castles, ruins, forts and old buildings), (3) cultural attractions (e.g. churches, museums, architecture, fairs, events and customs) and (4) artificial attractions (e.g. theme parks and buildings). Based on this lack of consensus, we opted to treat these attractions globally, without distinctions of any kind. Therefore, for the purpose of this study, tourist attractions include natural resources and man-made attractions that are not pure events or architectural infrastructure and are permanently available in destinations (Calvo-Mora, Berbel-Pineda, Periáñez & Suárez, 2012).

2.4     Events

Events and event marketing management are recognised as an important area of research by academics and professionals, due to socioeconomic benefits resulting from increased tourism demand and an improved identity and image (Getz, 2008). The particular role of events in the creation and development of positive images of cities through coordinated alignment with city branding strategies was emphasised by Cameron (1989). Events can thus become relevant components of global brand images of destinations and can be included in communication initiatives by city promoters, not only because of financial benefits but also because of the benefits of media coverage and public support of, or agreement with, city brand values (Hankinson, 2004). In his model, Swarbrooke (2001) suggested clustering tourist attractions into four groups but proposed a different scheme of classification: (1) natural attractions, (2) events and festivals and (3) man-made attractions. Other authors, such as Baloglu and McCleary (1999) and Beerli and Martín (2004), include all natural and cultural resources as tourist attractions of destinations, which is not the approach used in this research, as was previously clarified.

2.5     Cultural heritage

Heritage-based tourism is increasing in popularity within tourism due to its ability to develop and maintain sustained flows of tourists throughout the year. Increasingly, regions and cities are developing strategic courses of action aimed at generating steady flows of tourists eager to experience unique places that are highly appreciated by society for their cultural heritage and natural surroundings. This type of tourism is known to attract a special type of tourist, profiled as having a high socioeconomic status, with a particular interest in visiting monuments, attending festivals, learning about local cultures and exploring natural environments in destinations (Kim, Cheng & O’Leary, 2007).

(H1): 

Events have a positive influence on city image.

(H2): 

Cultural heritage has a positive influence on city image.

(H3):

Tourist attractions have a positive influence on city image.

(H4): 

Infrastructure has a positive influence on city image (see Figure 1).

The measurement of destination image was executed by requesting tourists’ opinions and judgment about the attributes that define places. Several techniques were used to perform the evaluation. In all cases, the methodology relied on structured techniques, asking respondents to assess a number of attributes and using a Likert scale or semantic differential (Gartner, 1989; Echtner & Ritchie, 1993; Bigné et al., 2001, Beerli & Martín, 2004) with a defined set of items.

The data were gathered through assisted personal interviews conducted by trained interviewers, lasting between 8 and 10 minutes. Each respondent was asked to assign a score from 0 to 5 on a Likert scale (1 = strongly disagree and 5 = strongly agree) to the variables selected for this study. The data collected evaluated the five constructs under investigation. The questionnaire was divided into six sections: the first five addressed the variables of interest in the proposed model, and the remaining section collected general information about respondents. The scales were adapted from scales already used and tested in other studies, specifically from Boo, Busser and Baloglu (2009); Qu et al. (2011); Echtner and Ritchie (1993); Russel et al. (1981); Stern and Krakover (1993); Walmsley and Jenkins (1993) and Baloglu and McCleary (1999). A total of 503 responses were obtained, which resulted in a final sample of 471 valid responses.

To assess the predictive power of the proposed conceptual model, a partial least squares (PLS) path analysis, using SmartPLS 2.0 M3 (Ringle, Wende & Will, 2005), was performed. PLS path modelling is a structural equation modelling technique that can simultaneously test the measurement model (i.e. relationships between indicators or manifest variables and their corresponding constructs or latent variables) – also called the outer model – and the structural model (i.e. relationships between constructs) – also called the inner model. The choice of PLS in this study is due to the study’s causal predictive nature (Jöreskog & Wold, 1982) and the specific objective of exploring the topic in order to try to find a better understanding of factors capable of predicting city image, with a focus on maximising the explained variance of the target construct (i.e. city image)

The first step in testing the conceptual model was assessing the accuracy of the measurement model to ensure that the measures used are valid and that they adequately reflect underlying theoretical constructs. The strength of the measurement or outer model for constructs with formative measures was assessed by looking at the magnitude and statistical significance of items’ weight. It was also crucial to assess multicollinearity among items. The quality of the measures could be assessed based on the results shown in Table 3.

Only three items were not significant at the 0.95 level (based on t(470), two-tailed tests). To assess multicollinearity, a set of linear regressions with the indicators were conducted with SPSS 20 to obtain collinearity statistics (i.e. tolerance and variance inflation factors (VIF)). The maximum VIF obtained was 1.603, indicating the absence of multicollinearity. Since the two non-significant items presented sizable weights, no multicollinearity was found and, as the study is of an exploratory nature, we decided to keep all three items. For reflective indicators, the quality of measures was assessed by examining individual item reliability and internal consistency (see Table 4), as well as discriminant validity.

Based on the guidelines provided by Hair, Anderson, Tatham and Black (1998), who suggested 0.707 as a benchmark for individual item loadings, the measurement model reveals adequate individual reliability and internal consistency, since the Cronbach’s alpha and composite reliability index from Fornell and Larcker (1981) both exceeded the 0.707 benchmark. The significance of loadings was checked with a bootstrap procedure (with 5,000 subsamples) to obtain t-statistic values. All loadings were significant at the 0.999 level (based on t(470), two-tailed tests).

Discriminant validity indicates the extent to which a given construct is different from all other latent constructs. This was assessed by comparing the square root of the average variance extracted (AVE) (diagonal value) with correlations with other constructs. The results suggest adequate convergent and discriminant validity. After establishing the validity of the measures, we could evaluate the structural model that represents the relationships between constructs or latent variables hypothesised in the conceptual model. Figure 2 provides a graphic representation of the results.

Since the primary objective of PLS is prediction, the goodness-of-fit of a theoretical model is established by the strength of each structural path (the hypotheses) and the combined predictiveness (R²) of its endogenous constructs (Chin, 1998). Our model has a R² of 0.451, which means that 45.1% of the variance of city image is explained by the four proposed constructs. In PLS, the hypotheses are tested by examining path coefficients and their significance levels. Bootstrapping (with 5,000 re-samples) was performed to obtain estimates of t-statistic values to examine the statistical significance of path coefficients. The results show that only the events construct is not significant at the 0.05 level. All other path coefficients are significant at the 0.001 level (see Table 5).

The literature review sought to study factors that contribute to city image and showed that city image exerts an important influence on tourists’ destination selection. The model proposed in this study evaluated the contribution of four major city resources to city image formation, based on previous studies in the field of tourism. Many studies suggest that events are an important element in image formation and that they should be considered not only in terms of their direct contributions to city tourism income but also as a long-term investment that creates positive images in tourists’ minds. Based on this study’s results, this claim is questionable. Thus, a major contribution of the current study is to question this perception by showing that events are the weakest element in city image development, among the aspects considered. The results highlight the significantly differentiated contributions of three categories of city resources to city image.

The first and most important category is cultural heritage, consisting of built, cultural and social attractions (e.g. lifestyle and local traditions, exotic local customs, gastronomy-related heritage, language and social interactions). The second category includes all tourist attractions (e.g. parks, flora and fauna, interesting tourist sites and spectacular natural scenery), religious or sacred places to visit, recreation and entertainment facilities and special services and infrastructure for doing and watching certain sports (e.g. skiing schools, sailing and golf clubs and stadiums). The third category includes the general infrastructure that allows tourists to arrive at, and enjoy, attractions and the facilities and services available at destinations that allow tourists to settle in comfortably and enjoy tourist activities.

If cities want to compete in the tourism marketplace, they must have a strong image, supported not only by past events but also grounded in cultural and physical heritage and in easily available, enduring natural resources, infrastructure and services. All these factors help determine how well cities build and sustain strong images for tourists, which are capable of influencing potential tourists’ perceptions and expectations. Although this research has important implications for strategic image management and can help design and implement marketing programmes to create and enhance tourism destination images, other factors must be explored in order to maximise the predictive power of this model. Moreover, this study’s insights are valuable to decision makers in defining cities’ short and long-term strategies, including investment allocation for city resources, tourism policies and city marketing strategies.

Based on a supply and demand structure, it is important for the entire tourism sector to engage in brand management and communication. This is crucial to the roles of local and regional administrations due to their decisional power and influence (Fresneda & Lobo, 2014).

In this context, the model proposed and evaluated in this paper can be used as a strategic tool to determine the best marketing mix for destinations, enabling those responsible to choose and implement the most appropriate initiatives. The results suggest that the adoption of a mixed approach encompassing assets of several available resources is more relevant than investing in events alone. Upon further reflection, this is not surprising. The short duration of events makes them harder to remember in the long term. That is why they should be associated with permanent resources at destinations, in order to reinforce the retention and later recall of the event experience. As a cross-sectional study with a non-probabilistic sample, these findings impose limitations on the interpretation of results, which need to be considered when assessing conclusions.

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Article history:
Received: 23 January 2014
Accepted: 30 October 2014