Co-creation of perceived place value by tourism actors in different countries

Abstract

This research examines gaps in the co-creation of value literature to address the need for (i) quantitative research in different visitor settings, (ii) deeper knowledge of the impact of different destination actors on visitor perceptions, and (iii) increased use of mixed methods (including in-situ) to enhance the effectiveness of place branding across destination risk contexts. The study examines how co-creation of place value occurs, the role of tourism actors and the moderating effect of the country risk level. In-situ data were collected over a period of time of six years from active tourists in 12 different countries (n=3,643). A typology of country destinations is established to build a structural equation model using data from 12-countries. Findings indicate that all actors significantly contribute to the place value perceived by tourists (i.e., co-creation), where the country risk level is found to limit the impact of some tourism actors on tourists’ perceived place value while enhancing other actors’ impact and loyalty.

Keywords

destination actors destination image security safety

Introduction

The co-creation of value by multiple and collaborating organizations, stakeholders and individuals has become an important topic for tourism researchers and marketers alike. The study of the co-creation of value encompasses the idea of multiple entities sharing efforts in a social exercise to improve an experience, a product, or a service associated with a brand (Iglesias et al., 2017). This collaboration-based approach to creating value for customers is relevant for the tourism sector since multiple actors in the place ecosystem, including tourists, service providers, and locals, can contribute to perceptions of a place value and the resulting images associated with that destination. Notably, the ever-changing tourist environment over the past 15 years (i.e., digital communications, global pandemic, metaverse, political shifts, etc.) has considerably influenced the tourism sector (Gursoy et al., 2022).

A consequence of the changing nature of the tourism environment (Gursoy et al., 2022) is the importance ascribed to place image. Place images are important to tourism because they are comprised of perceptions which influence the desire and intention to visit the location, even as the country risk level has increased (Bigné et al., 2001; Garanti et al., 2024). This notion of seeking to collaboratively provide value to tourists fits with the service-dominant logic thought revolution that has shifted marketing theory from a transaction-oriented product-based approach towards one defined by the service that fulfills customer needs, including tourists (Gronroos and Voima, 2013). Thus, marketing based on a service-dominant logic directly influences how we view the creation of value by organizations through their interactions with customers (Bettencourt et al., 2014).

Specific to the tourism context where the tourist is the customer, both service-dominant logic and co-creation of value play an important role in understanding how tourists’ needs are satisfied (Shaw et al., 2011) and in implementing tourism marketing activities to enhance place image outcomes for these tourists (Iordanova and Stylidis, 2019). Tourism is a form of services marketing where customers (tourists) experience intangible services and their evaluations are based on perceptions about the destination, its people and its products (Elliot et al., 2011).

As customers, tourists often seek experiences that involve interaction with others either via service providers or informal interactions (e.g., taxi drivers), all of which provide opportunities for the co-creation of value within the modern place ecosystem (Gursoy et al., 2022; Woratschek et al., 2020). The co-creation of value perspective brings a new lens to tourism marketing research particularly in the areas of place image management and place branding, both activities that seek to influence tourist travel choices.

However, the country risk level in a destination can impact a tourist’s decision to travel. For example, Samitas et al. (2018) examined the effects of terrorism on Greece tourist arrivals and found a negative effect over the period from 1977 to 2012. While safety and security represent a salient consideration for tourists, the effects of these perceptions on tourists’ decisions such as arrivals remains an understudied area in the literature and tends to focus on isolated cases (Fourie et al., 2020; Santana-Gallego et al., 2020). In addition, we know little about the impact of the country risk level on tourists’ perception of a place value. Methodologically, data were collected from tourists while physically present at the destination (in-situ data). Although logistically more demanding than post-hoc online surveys, in-situ data collection yields more authentic and contextually valid assessments of destination image, free from recall bias or retrospective rationalization (Iordanova and Stylidis, 2017). This approach, still underutilized in tourism research despite its recognized advantages, strengthens the ecological validity of our findings.

In sum, we suggest that co-creation of value is increasingly and inextricably linked to the image of the place. Various collaborating stakeholders shape both tourists’ evaluative perceptions of a destination (perceived place value) and their behavioral intentions (loyalty). These relationships, in turn, may be influenced by the level of safety and security risks in the destination. This paper builds on the limited existing literature and brings a co-creation perspective to analyze the tourist experience in the broader country context of the destination. Specifically, the study examines the relationships of touristic beliefs about key actors to the perceived co-creative value for places and the sensitivity of these relationships in different and evolving tourism contexts. As such, the purpose of this research is to model the drivers of destination loyalty and tourists’ perception of destination value based on the place ecosystems actors (i.e., tourism actors), and then to compare these effects for countries with a low level of risk to higher levels of risk regarding tourist safety and security. Nadeau et al. (2008) developed the drivers of destination loyalty (i.e., willingness to return, willingness to extend stay, advocacy) and tourists’ perception of destination value (e.g., quality of service, experience, overall satisfaction, etc.) reflecting tourism actors (i.e., destination attraction, destination service organizations, natural environment and residents), which are core to this work. For this research, a tourism actor (or place actor) is used to describe any entity (e.g., restaurant, other tourist, tourism board, etc.) who has influence on the decisions and experiences of the tourist.

Background: Existing literature on perceived place value by tourism actors and role of risk

With the purpose to systematically investigate the significant research linking value co-creation and safety and security in tourism, the three journals “Annals of Tourism Research”, “Tourism Management” and “Journal of Travel Research” were searched for any articles referring to the combinations of the keywords “place branding” or “destination image” and risk via the terms “safety” and “security” since 2010 and up to 2022. As a result, 366 articles were identified. The extensive research suggests that the relationship between safety and place image have been well examined in the top tourism management journals as of 2022. Another 101 articles were found to contain the keywords “value co-creation” or “co-creation of value” and “tourism”. The results are shown in Table 1. To narrow the search, the two literature streams were merged by adding the keywords of the second inquiry to the first. As expected, the word “tourism” resulted in the same number of articles as in the first search, but appending “value co-creation” or “co-creation of value” reduced the output to only nine articles. Based on the literature examined, we can infer that limited research has examined this particular topic.

Table 1.

Results of systematic literature search.

Keywords	Journal	Results
(“place branding” OR “destination image”) AND (“security” OR “safety”)	Annals of Tourism Research	71
	Tourism Management	203
	Journal of Travel Research	92
	Total	366
(“value co-creation” OR “co-creation of value”) AND “tourism”	Annals of Tourism Research	29
	Tourism Management	58
	Journal of Travel Research	14
Total		101
(“place branding” OR “destination image”) AND (“security” OR “safety”) AND (“value co-creation” OR “co-creation of value”)	Annals of Tourism Research	0
	Tourism Management	5
	Journal of Travel Research	4
Total		9

In Table 2, the results of the search are summarized. The articles were compared according to the different analysis criteria, depending on whether the respective criterion played a role in our analysis or not (Y, if the criterion played a role; N, if not). See Table 2.

Table 2.

Analysis of selected articles.

Authors	Main focus	Role of value co-creation	Role of safety and security	1	2	3	4	5	6^a
Chekalina et al. (2018)	Customer-based brand equity is transferred to a tourism destination context to develop a model.	Value-in-use as a new dimension for customer-based brand equity in the tourism destination context.	Safety in the ski area reflects a destination resource but does not play a significant role in the model.	Y	N	N	Y	N	Y
Gkritzali et al. (2018)	The evolution of Athens destination image where an economic recession impacted individual perceptions.	Value co-creation is only mentioned among current research interests of one of the co-authors.	Security as an affective attribute of the destination. Visitors’ perceptions of security changed over time.	Y	N	N	Y	N	N
Hyun and Kim (2020)	To refine and validate a multidimensional destination brand equity scale in the context of tourism.	Call for testing value co-creation between destination marketers and tourists in the Chekalina et al., (2018) model.	Personal safety is an item reflecting destination brand quality.	Y	N	N	Y	N	Y
Stylidis (2020)	The impact of resident interactions with tourists on tourists’ own image formulation is investigated.	The influence of resident-tourist interaction and tourism employee-tourist interaction on image is investigated.	Safety is item of tourists’ interactions with residents.	Y	Y	N	Y	N	N
Angeloni (2016)	Prepaid cards, which can be reloaded by tourists according to need, is investigated.	Prepaid cards are supposed to facilitate value co-creation.	IT security, private security, network security and, cyber security are features of prepaid cards.	Y	N	N	Y	N	N
Ellis et al. (2018)	Five food tourism themes: motivation, culture, authenticity, mgmt. & mktg., and dest. orientation.	Value co-creation is only mentioned once in the conclusions as being inherent to place and associated foods.	Security is only mentioned once about motivation.	Y	N	N	Y	N	N
Filieri et al. (2021)	Destination brand love (DBL) is investigated online posts when a destination has a crisis.	Tourists experiencing destination love are more willing to co-create online and use hashtags to express destination love.	It is only stated that crises are assumed to have negative effects on a destination’s perceived safety.	Y	Y	N	N	N	N
Fu et al. (2019)	Examines dimensions of place attachment on exhibition attendees’ satisfaction.	Value co-creation is mentioned as an antecedent of attendees’ satisfaction.	Previous study is cited that a sense of security in a familiar environment contributes to higher attachment.	Y	N	N	Y	N	N
Leung et al. (2019)	Three sustainable strategies for the sharing economy: interest alignment, collaboration, & social responsibility.	An ecosystem framework of shared economy in tourism is derived and includes a reference to value co-creation.	Safety regulations mentioned as a topic. Safety plays a role in the government sector and in ecosystem relationships.	Y	N	N	Y	N	N
This paper	Investigate different tourism actors contribute to value co-creation with different perceptions of safety and impact on loyalty aspects.	Dimensions of destination image consist of tourism actors’ contribution including without agency and lead to perceived co-created destination value.	Destination value moderated by levels of safety and security of destination have an impact on economically relevant variables (loyalty dimensions).	Y	Y	Y	Y	Y	Y

^aNote. 1. Value co-creation; 2. Impact of tourism actors on value; 3. Impact of multiple tourism actors’ incl. natural environment on value; 4. Safety and security; 5. Impact of safety and security on tourists’ behavior; 6. Impact of value on tourists’ behavior.

Y, if criteria play a role in the analysis; N, if not.

This review of the literature found that the identified articles neither investigate the extent to which different tourism actors contribute to value co-creation in destinations with different perceptions of safety or what influence the perceived value caused by co-creation has on economically relevant behavior in tourism.

Next, we review the broader literature to highlight the importance of place image, the role of actors, especially tourists in co-creation of place value, and the moderating role of a country’s risk level. Figure 1 representing our conceptual model. It is based on our interpretation of the literature discussed below. It is presented prior to the literature review to provide a framework for the sections that follow.

Figure 1.

Conceptual model of the place value co-creation process.

Literature review

The literature examines tourists’ place perceptions through various constructs—place image, product-country image, destination image, and place brands—while co-creation research investigates how value emerges through tourist interactions with destination actors. These streams share a common limitation: image-based constructs capture what tourists associate with a place, not how valuable they judge it to be; co-creation research fragments value into isolated outcomes (satisfaction, loyalty, memorability) rather than integrating them into a holistic assessment. The following review traces both streams to establish the conceptual foundation for place value—an integrative construct capturing tourists’ overall evaluative judgment of a destination’s worth.

Place perceptions and tourism actors

Place image research is a popular topic and is studied under the guise of several related terms, including product-country images, tourism destination image and place brands (Josiassen et al., 2016; Költringer and Dickinger, 2015). Recent work on the topic embraces the changing tourist experience (Gursoy et al., 2022), with, for example, work on the role of digital photo-taking by tourists in-situ or inbound as a method to assess destination image (Xiao et al., 2022) evolving into visual content analysis (Wang et al., 2024). This paper is guided by the understanding that relevant place images represent for tourists the perceptions and associations they hold about a destination based on their experiences with the place (Lai and Li, 2016).

For place branding to occur, there needs to be active management or attempts by actors to influence the place image held by tourists. Anholt (2010) argues that perceptions of places are driven by what is done by the place and its people (e.g., offerings, coordinated experiences) and not by what is said about the place in a promotional campaign. In addition, Josiassen et al. (2016) argue that the image or aspects of it may be shared with others. Therefore, the active management of destination images is accomplished through various actors located in a place. Four actors are prominent in the research on destination images. First, the attraction-based actors are those with an original experience, a built attraction, and ones that are memorable (Költringer and Dickinger, 2015; Nadeau et al., 2008). Second, there are destination service organization actors which provide service to tourists, such as, accommodations, restaurants, and shopping facilities (Nadeau et al., 2008). Third, the natural environment actors represent the physical attributes of a place, scenery, and climate aspects of a place (Költringer and Dickinger, 2015; Lecomptea et al., 2017; Nadeau et al., 2008). Fourth, residents of the place are an important actor because they are responsible for the cultural heritage, social relationships, and character of the people (Költringer and Dickinger, 2015; Lecomptea et al., 2017; Nadeau et al., 2008). These four actors are important for the management and propagation of a place brand.

We focus on these four actor categories because they represent the principal dimensions of place that tourists directly encounter during their destination visit: the built and cultural environment (attractions), the service delivery system (service organizations), the physical landscape (natural environment), and the social milieu (residents). These place-based actors are embedded in the destination and engage tourists in direct, experiential interaction. We do not include destination management organizations (DMOs), as their influence operates primarily behind the scenes—through pre-visit marketing, expectation formation, and coordination—rather than through direct tourist contact during the stay. Similarly, while fellow tourists may shape the experience (e.g., through crowding or atmosphere), they are transient visitors rather than stable, destination-defining actors. Our classification thus captures the actors with whom tourists co-create value through immediate, on-site engagement.

Recently, two detailed literature reviews on place branding by Swain et al. (2024) and Garanti et al. (2024) identified several gaps in our knowledge about place image where future research is needed, including the development of shared and socially friendly practices in tourism marketing in a manner that is sustainable (i.e., sustainable branding) and the resulting impact on the competitiveness of destinations in seeking tourists. This is important from a branding perspective where efforts are extended to shape a public image of a place (Költringer and Dickinger, 2015) for tourists from further away (Dolnicar and Grün, 2017) and in the context of the digital world (Garanti et al., 2024; Swain et al., 2024). Further gaps relevant to this research include (i) the need for quantitative research in different visitor settings, (ii) deeper knowledge of the impact of different destination actors on visitor perceptions, and (iii) the use of mixed methods (including in-situ) to enhance the effectiveness of place branding across various cultural and geographical contexts (Garanti et al., 2024). This research, in part, seeks to address each of these areas via in-situ quantitative data collection using a validated theory-informed instrument (Nadeau et al., 2008) that assess tourists in 12 different countries.

Tourism co-creation of value

The goal of an active place branding strategy and its activities is to generate added value through destination brand capital (Hankinson, 2004). The notion of place brand capital (also called place brand equity) is about leveraging added value associated with the place image onto commercial interests (Pappu and Quester, 2010). In other words, generated incremental value for a place image suggests that there is an impact of the brand image on tourist loyalty. In this paper, tourist loyalty refers to a conative (behavioral-intentional) response toward a destination, reflected in the intention to revisit, extend the stay, and recommend the destination to others.

Further, Költringer & Dickinger (2015) point to research indicating that a strong and positive place image can positively impact the economic wellbeing of its people, encourage investment, create cultural value, increase product sales, provoke emotional attachment and enhance revisit intention.

A formal definition of value co-creation refers to the value created jointly through the service provider and the customer (Prahalad and Ramaswamy, 2004). However, this view of value co-creation limits the basis for value generation to a bi-way interaction and in the pace-based context. Value can be created in the tourism context through tourist interaction with other tourists (Sugathan & Ranjan, 2019), locals, and technology-based sources (Minkiewicz et al., 2014). Where there is a general lack of consensus about co-creation terminology (Nysveen and Pedersen, 2014), a common thread is that co-creation is about the collaborative activities that generate value (Kleinaltenkamp et al., 2012).

In tourism, value is determined by a variety of actors, e.g., local service providers, tourists, residents, the tourist office, etc. Therefore, the service-dominant logic (Vargo and Lusch, 2008) is used here, in which value is co-created by a multitude of actors through resource integration.

We assume that destination management organizations (DMOs) operate engagement platforms to enable and facilitate this resource integration in the form the core of an engagement platform (Breidbach and Brodie 2017). Resource integration means that both the operator of engagement platforms and other actors provide resources to co-create value. In tourism destinations, the DMO orchestrates the different service providers and residents to guarantee a great experience for tourists. Service providers of a destination must collaborate to a certain degree to provide value and to attract tourists although they may be competitors.

Sugathan and Ranjan (2019) suggest that collaboration should be encouraged among tourism actors to facilitate high co-creation situations. The extent of interactions with tourists is important and directly related to the complexity and depth of the resulting perceived co-created value. Reichenberger (2017) found that tourists sharing an experience enhance emotional, entertainment, self-actualization, and practical outcomes. However, the relationship is complicated since the influence may depend on familiarity with the other tourists, the environment, personality, objectives, and partner relationship. Previous research has shown that experience value is a good predictor of some co-created outcomes (Prebensen et al., 2016). Therefore, the co-created outcomes themselves may be inferred as sufficient indicators of the tourist value generated through interaction with tourism actors.

Woratschek et al. (2020) contradict the hitherto widely accepted premise that only actors with agency resources can integrate resources. Agency means that an actor has the power and the will to act. The natural environment does not possess agency and can neither contradict the use of its resources nor does it voluntarily and consciously make its resources available. Companies and tourists use the resources of the natural environment without it being able to object. At the same time, nature brings in resources that strongly influence the economic and social exchange of human actors and can limit co-creation, such as climate change and nuclear accidents. Thus, natural resources play a special role in value co-creation.

Huang and Choi (2019) developed a tourist engagement scale for value co-creation. In their work, they acknowledge the importance of accounting for interaction with multiple actors (i.e., DMOs and service providers). They also recognize the complexity and multi-dimensionality of understanding tourist interactions that include social, service and activity related opportunities. The development of engagement can go beyond interaction in a single slice-of-time instance, where Sarmento and Loureiro (2019) note that there is a requirement for a proactive relationship among the tourist and aspects of the place. In sum, tourists will develop their image of a place based on their own personal experience whether it reaches the threshold of active engagement or not.

It is important to understand the value generated for a destination from tourism co-creation. Prior research shows that tourism co-creation shapes tourists’ attitudes across cognition, affect, and conation (Roth and Diamantopoulos, 2009). There is a breadth of outcomes associated with the three components of attitudes – cognitions, affect and conation (Roth and Diamantopoulos, 2009). In terms of cognitions or beliefs, co-created tourism outcomes studied include trust (Shulga et al., 2018), perceived atmosphere and service-related beliefs (Reichenberger, 2017). From an affect or evaluative perspective, tourism studies regarding co-created value outcomes have included satisfaction (Hollebeek and Rather, 2019), emotional responses and self-actualization (Reichenberger, 2017), service quality (Xu et al., 2018), and memorability (Barnes et al., 2020). Behavioral co-creation of value outcomes in tourist studies include behavioral intention (Huang and Choi, 2019), loyalty (Hollebeek and Rather, 2019), revisit the place (Sugathan & Ranjan, 2019), intentions to recommend (Barnes et al., 2020), and advocacy (Hollebeek and Rather, 2019). Overall, these studies suggest that co-creation is reflected in both in tourists’ overall evaluations and in their future-oriented behavioral tendencies-two outcomes that are particularly relevant for destination performance. Because tourism actors enable and shape co-creation through their resource contributions with tourists, their perceived role should be associated with both evaluative (place value) and conative (loyalty) outcomes.

Perceived place value is conceptualized here as the holistic value judgment that tourists attribute to a place. Unlike destination image, which captures cognitive-affective associations, perceived place value focuses on the overall evaluative assessment. This aligns with Service-Dominant Logic, which posits that value emerges not from discrete transactions but as an emergent property of resource integration across various actors (Vargo and Lusch, 2008) and therefore motivates our modeling of tourism actors as predictors of perceived place value and loyalty intentions.

Risks related to tourism safety and security

Security in the tourism context refers to the desire of tourists to be safe during their travels (Hall et al., 2012). Research has explored several different types of risks to safety and security including terrorism (Samitas et al., 2018), tourism scams (Xu et al., 2021), and aggressive street behavior (Collins and Millar, 2019). The types of safety and security risks are typically categorized into broad themes reflecting their underlying nature. For example, Hall et al. (2012) examined the subject through the lens of terrorism, crime, food safety, and disasters. They point out that health, social and environmental concerns have entered the discourse. Recent measurement development for perceived safety at destinations reflects four elements consisting of humans (safety assessments and perceptions of individual behavior), facilities and equipment, environments (natural and socio-cultural), and management (policies and actions and related aspects at the organizational or managerial levels) (Xie et al., 2020).

Risk related to the safety and security of tourists is considered a critical aspect for a destination’s tourism industry and tourist-led growth (Chingarande and Saayman, 2018). Destinations should aim for a positive image rooted in safety and security. The impacts of safety concerns on tourism may also depend upon the individuals’ risk tolerance. In a study of adventure tourists, worry did not play a role in perceived risk and safety behavior for cautious adventure tourists but did influence the relationship of risk attitudes on behaviors for vulnerable and adventurous tourists (Wang et al., 2019). In addition, the perception of risks can depend on the tourist and the context in the home country. For example, Fourie et al. (2020) show that tourists who come from stable and safe countries prefer similar conditions in their tourism destinations while those from unstable or less safe countries can be more tolerant about the safety and security conditions in the destination. Fourie et al. (2020) also note that more information provided to tourists about the safety and security conditions can reduce negative impacts on tourist arrivals. In some cases, information about conflict zones can become a draw for tourists who seek to experience or observe this safety risk or the attraction of heritage sites can moderate the negative effects of safety risks (Santana-Gallego et al., 2020).

While value co-creation and destination risk have been discussed separately, they likely interact in shaping tourist experiences. A CLPR can change how tourists engage with different actor categories in the co-creation process. In high-risk environments, tourists may be less able to explore independently or rely on formal service organizations, shifting co-creation toward interpersonal interactions with residents who provide local knowledge, safety guidance, and culturally mediated experiences. Residents become key “risk navigators” whose trustworthiness and hospitality shape perceived value. The natural environment—as a resource relatively independent of institutional infrastructure—may also retain or gain importance when other tourism systems are perceived as unreliable.

In low-risk destinations, tourists can more fully use structured offerings from attractions and service organizations without safety-related constraints. Co-creation shifts toward transactional service encounters and curated experiences, where service quality and attraction appeal are the primary value drivers. Thus, risk not only influences overall destination evaluation but moderates which actors become central to value co-creation—a proposition formalized in the following hypotheses.

Hypothesis development

Based on Figure 1, developed from our review of the literature (e.g., Költringer and Dickinger, 2015), our framework positions tourism actors (organized under four groups – attractions, destination service organizations, natural environment, and residents) as antecedents of two key outcomes: perceived place value (tourists’ holistic evaluative judgement of the destination) and tourist loyalty (behavioral intentions including willingness to return, willingness extend stay, and recommend). These outcomes are moele in parallel rather than sequentially, as discussed in the method section. Crowther and Donlan (2011) argue that the collaborative nature of events is particularly suitable for the co-creation of value, which is reflected in the framework. They suggest that value creation spaces are abundant during events and include the many possibilities for interaction among relevant actors in a network. This reinforces the previously discussed tourism-based research suggesting a positive correlation between tourism actor collaborations and value co-creation (Sugathan & Ranjan, 2019).

Within the tourism perspective, there are multiple actors of relevance for understanding the co-creation of place value including residents, tourist service providers, and the natural environment (Lecomptea et al., 2017). Of course, tourists also play an important role when visiting a place by purchasing services and animating local attractions (Malone et al., 2018). All these actors are relevant for the engagement platform in tourism and the interaction opportunities for co-creating value. The use of data directly from tourists in multiple places over time responds to the gap identified by Garanti et al. (2024). Therefore, this research seeks to test the following hypotheses:

Tourism actors (destination attractions (H1a), destination service organizations (H1b), natural environment (H1c), and residents (H1d)) positively contribute to tourists’ perceived place value.

Whilst there is little research at the intersection of risks for safety and security on tourism decisions to travel to a destination (Santana-Gallego et al., 2020) and as noted in our review of the literature (see Table 2), we do expect actor groups to differ in their relationship with perceived value based on the country risk level. George (2003) reported a positive destination image but noted a safety weakness pertaining to public transit services and going out at night. This indicates that service organizations may contribute positively to perceived value when the destination has lower risk. Fourie et al. (2020) suggests that those coming from less safe countries have a higher risk tolerance so destination management actors and service organization may be better positioned in low-risk destinations to contribute to perceived value. But, in high-risk destinations, the environment and its people may represent a significant draw to the destination and its perceived value (Santana-Gallego et al., 2020). This leads to additional hypotheses:

Destination attractions (H2a) and destination service organizations (H2b) contribute more to perceived place value in low risk destinations as compared to higher risk destinations.

The natural environment (H3a) and residents (H3b) contribute more to perceived place value in high risk destinations as compared to lower risk destinations.

From a place branding perspective, the importance of perceived value being leveraged into commercial benefit has been acknowledged (Pappu and Quester, 2010). Josiassen et al. (2016) explicitly note that the intentions to visit (or revisit) a destination is a significant result of a strong and positive place image. In particular, experience with a destination and the resultant perceived value is viewed as a good predictor of co-created outcomes (Prebensen et al., 2016). It is the tourism actors that provide for interactions with tourists and the basis for value co-creation (Huang and Choi, 2019). Therefore, the fourth hypothesis tested is:

There is a positive relationship between the perceptions of tourism actors and the loyalty dimensions (willingness to return (H4a), willingness to extend stay (H4b), willingness to recommend (H4c)) of tourists.

We note that our model examines perceived tourism actors as predictors of both perceived place value (H1) and loyalty dimensions (H4) in parallel, rather than imposing a sequential path from place value to loyalty. This modeling decision reflects theoretical ambiguity regarding causal direction. While traditional attitude theory suggests an ‘evaluation → intention’ sequence, cognitive dissonance theory (Festinger, 1957) supports the reverse: tourists who form loyalty intentions may subsequently adjust their evaluative judgments upward to maintain cognitive consistency. Recent studies examining the value–loyalty relationship frequently embed mediating constructs (e.g., well-being, place attachment) and indicate that the ordering of effects can be context-dependent (Liu et al., 2023; Zhang et al., 2022). With cross-sectional data and without these mediators, we opted to assess the direct contribution of tourism actors to both constructs separately and therefore refrain from imposing a directional path between place value and loyalty.

Method

In seeking to represent a set of place brands, this study draws from data collected from active tourists in 12 different countries (n = 3643) over a 5-year period from 2005 to 2010. The locations include Kathmandu, Nepal (2005); Helsinki, Finland (2007); Paris, France (2007); Beijing, China (2008); Seoul, South Korea (2009); Rodney Bay, St. Lucia (2008); Bangkok, Thailand (2009); Kingston, Jamaica (2009); Osaka, Japan (2009); Mendoza, Argentina (2009); Cape Town, South Africa (2010); and Nairobi, Kenya (2010). These places were selected as part of a research program that sought to sample from a variety of different tourist settings based on built and natural environments of different types.

While these indices postdate our data collection period (2005–2010), the underlying factors they capture—geopolitical stability, institutional quality, infrastructure, and public safety—tend to exhibit relative persistence at the national level. To assess the robustness of our classification, we examined GPI score trajectories across the full timespan. South Africa, for example, had GPI scores ranging from 2.436 to 2.528 during our data collection period (2009–2010) and scores of 2.503 to 2.616 when the indices were published (2019–2020). Over the entire 12-year period (2009–2020), South Africa’s score fluctuated within a narrow band of 0.18 points (minimum: 2.436 in 2012; maximum: 2.616 in 2019), while consistently remaining well above low-risk countries such as Finland (∼1.3), Japan (∼1.5), and France (∼1.9). This stability suggests that our categorical distinction between low-risk and higher-risk destinations is robust despite the temporal gap, and that countries would have received the same classification regardless of which year’s index was applied. Nevertheless, we acknowledge this limitation, and the risk classification should be interpreted as a structural proxy rather than a precise temporal match. Furthermore, in-situ data collection across 12 countries using a validated instrument represents a methodological contribution that addresses a recognized gap in tourism research (Garanti et al., 2024). The fact that data was collected in-situ (i.e., tourists in tourist environments via intercept surveys) that can be considered contemporary tourism environments, relevant and generalizable to today’s tourism environments.

The list of countries was selected for their variety of tourist contexts and situations. For example, the South African data was collected around the 2010 FIFA World Cup, a global mega-sporting event, while the Nepal data was collected from tourists in the Himalayan region that is home to Mount Everest, the world’s highest point.

In each instance of data collection, we sought to collect 300 completed surveys via a street-intercept questionnaire. The survey tests a model of tourism attitudes (beliefs, evaluations, conation) within the broader context of country images derived from Product Country Image (PCI) and Tourist Destination Image (TDI) research. Perceived place value is operationalized through five indicators: service quality, overall satisfaction, destination rating, country experience, and country rating. These indicators are measured reflectively—each is hypothesized to reflect the latent construct of perceived place value. We acknowledge that including satisfaction departs from traditional models positioning satisfaction as a value outcome; however, this distinction derives from Goods-Dominant Logic rather than the Service-Dominant Logic underpinning our study (Grönroos and Voima, 2013). In a reflective measurement model, the adequacy of any indicator is empirically assessed: if satisfaction did not reflect the same underlying construct as the other indicators, this would manifest in poor factor loadings or convergent validity. The confirmatory factor analysis confirms adequate loadings and construct reliability (CR = .85), providing empirical support for satisfaction as a valid indicator of perceived place value in this context. These indicators are deliberately aggregated and evaluatively framed, capturing not isolated co-creation outcomes but the cumulative value judgment resulting from interactions with multiple destination actors.

The street interviews were conducted with international tourists in the locations mentioned at selected attractions known for high tourist density. These attractions include squares, temples, churches, event locations, facilities, and other high traffic areas. Attractions included only ‘active’ tourist locations and did not include passive ones (e.g., airports, train stations, etc.) where a tourist might just be ‘passing through’. To ensure sample independence, every third tourist from unique groups was approached in these public places and immediately subsequent tourists were only approached when the interview was declined. The resulting useable samples were Argentina (300), China (288), Finland (312), France (316), Jamaica (276), Japan (291), Kenya (300), South Korea (300), Nepal (307), Saint Lucia (313), South Africa (332), and Thailand (308).

Questionnaires were administered in English as well as different languages, depending on the destination, including French, Chinese, Finnish, and others. The questionnaire was common across each of the 12 countries, with a few minor additions/variations in some cases (e.g., questions about the Olympic Games in the 2008 China study). The instrument used was the same for all instances of data collection and was developed with measures that have been used in previous research and the design is discussed in the published article by Nadeau et al. (2008), which built and tested the scales based on PCI and TDI. The final instrument contained approximately 30 scales regarding respondents’ views about the destination and about another 30 scales to measure views about the people and their country. Five-point scales (1 = low/poor, 5 = high/good) were used. The concept of risk was assessed independently from the survey ex post facto and is based on two secondary sources. Specifically, we used and report in the results section the International SOS Foundation’s (2020) risk outlook study and the Institute for Economics and Peace’s (2019a) global peace index to assess a country’s risk level.

Results

Based on data assessed using SPSS version 26 for missing values and construct reliabilities, we used confirmatory factor analysis in LISREL to test the measurement models. Five measurement models have been identified in Table 3. Besides the perceived place value (PlaVal; e.g., service quality, overall satisfaction, overall evaluation), one measurement model specifies the destination attractions (DestAtt) and another one the destination service organizations (DSOs). Destination attractions represent the attractiveness of activities and cultural experiences (e.g., variety of activities, originality of experience), whereas destination service organizations must consider the places of interest during a tourism visit (e.g., accommodation, restaurants, sport facilities). Natural environment has been summarized as contributing actor in the NaEn measurement model (e.g., scenery, climate). Another contributing actor are the residents represented in the measurement model Resi (e.g., friendliness, trustworthiness). All standardized loadings in Table 3 are significant (P < .01) and construct reliabilities (DestAtt = .83; DSOs = .80; NaEn = .71; Resi = .88; PlaVal = .85) suggest adequate scales (Table 4).

Table 3.

CFA of measurement models: Standardized loadings, t-values, construct reliabilities.

Variables and items		Stand. loadings	Construct reliabilities
Destination attractions (DestAtt)			.83
Variety	Variety of activities	.71 (38.98)
Original	Originality of experience	.75 (42.32)
CultInte	Culturally interesting	.73 (41.04)
Memorabi	Memorability of experience	.75 (38.30)
Attracti	Tourist attractions	.62 (34.30)
Destination service organizations (DSOs)			.80
EaseArou	Ease of getting around	.59 (33.97)
EaseFind	Ease of finding places of interest	.70 (41.95)
Accom	Accommodation	.56 (28.95)
Restaura	Selection of restaurants	.63 (35.71)
Nightlif	Nightlife/Entertainment	.60 (34.31)
Shopping	Shopping facilities	.62 (35.93)
Sport	Sport facilities	.51 (28.13)
Natural environment (NaEn)			.71
AttraSce	Attractive scenery	.82 (42.24)
Wilderne	Wilderness	.58 (31.62)
Climate	Climate	.60 (31.44)
Residents (Resi)			.88
Like	Likeability of people	.71 (38.24)
Friend	Friendliness	.71 (39.74)
Help	Helpful	.82 (50.99)
Courteou	Courteous	.82 (54.65)
Trust	Trustworthiness	.71 (45.98)
Honest	Honest	.70 (44.82)
Place value (PlaVal)			.85
QualServ	Quality of service	.60 (30.27)
Satis	Overall satisfaction	.77 (42.38)
OverRaDe	Overall, how would you rate visiting this country as a tourist destination?	.80 (46.17)
ExpeRate	Relative to your expectations, your experience in this country is…?	.77 (41.91)
OverRaCo	Overall, how would you rate this country?	.66 (38.28)

Note. t-values are reported in parentheses.

Table 4.

Path coefficients for overall sample and subsamples (peace/risk).

	Overall (N = 3120)	High safety/low risk⁺ (N = 1357)	Low safety/medium-high risk (N = 1510)
	Coeff.	Coeff.	Coeff.
H2a: DestAtt → place value	.23*	.34*	.16*
H2b: DSOs → place value	.30*	.32*	.29*
H3a: NaEn → place value	.29*	.19*	.35*
H3b: Resi → place value	.28*	.25*	.30*
R² place value	.86	.92	.78

Note. *p < .05; Coeff. = Path coefficient; R² = Squared multiple correlation; ⁺excl. St. Lucia.

Hypotheses H1a-d, H2a-b, and H3a-b were tested in separate structural equation models (SEMs), while H4a-c were tested using multiple regression analyses. Figure 2 shows the path coefficients of the SEM testing the contributions of the different actors to the perceived place value (PlaVal). The SEM provides a reasonable level of model fit: RMSEA = .077 (90% confidence interval = .075; .078), NNFI = .878, CFI = .892, SRMR = .056. (Hu and Bentler, 1999), and all actors significantly contribute to the perceived place value. The SEM supports H1a-d concerning the co-creation of perceived place value by different actors in tourism.

Figure 2.

Sem. Note. *P < .05.

The literature highlights that travel safety and security risks can pertain to the individual safety (Collins and Millar, 2019; Wang et al., 2019) or the broader security of the destination (Fourie et al., 2020; Samitas et al., 2018). To obtain a proxy measure of a country risk, we used the International SOS Foundation’s (2020) risk outlook study and the Institute for Economics and Peace’s (2019b) global peace index, which are two of the most comprehensive and widely accepted studies of risk at the country level. Based on these studies, we coded countries in our dataset with both a high level of peace and a low level of risk. These countries are Argentina, Finland, France, Japan and Korea. Other countries, i.e., China, Jamaica, Kenya, Nepal, South Africa and Thailand are considered to have a low level of peace and a medium to high level of risk.

St. Lucia, while in our dataset, has not been evaluated for level of peace within the global peace index study (Institute for Economics and Peace, 2019b) and has, therefore, been excluded from this analysis. The results of the separate SEMs support H2a-b and H3a-b. DestAtt and DSOs contribute more to perceived place value in low risk destinations (β_DMAs = .34; β_DSOs = .32) compared to higher risk destinations (β_DMAs = .16; β_DSOs = .29). NaEn and Resi contribute more to perceived place value in high risk destinations (β_NaEn = .35; β_Resi = .30) compared to lower risk destinations (β_NaEn = .19; β_Resi = .25).

In a final step, we performed multiple regression analyses to examine the effects of the different actors on the loyalty dimensions of tourists. The results of the regression analyses in Table 5 indicate a positive relationship between the different actors (destination attractions, destination service organizations, natural environment, and residents) and the loyalty dimensions of tourists. Therefore, H4a-c are supported.

Table 5.

Regression analyses examining the effects of actors on loyalty dimensions of tourists.

		Coeff.	SE	β	t
DV = willingness to return	Constant	4.15	.01		296.71
	DestAtt	.23	.02	.24	11.22
	DSOs	.19	.02	.19	10.12
	NaEn	.16	.02	.16	8.61
	Resi	.18	.02	.18	10.62
	R² = .37; F(4, 3162) = 473.00; p < .01.
DV = willingness to extend stay	Constant	3.75	.02		194.61
	DestAtt	.15	.03	.13	5.39
	DSOs	.19	.03	.16	7.66
	NaEn	.20	.02	.17	8.06
	Resi	.23	.02	.20	10.18
	R² = .27; F(4, 2830) = 265.21; p < .01.
DV = willingness to recommend	Constant	4.31	.01		387.08
	DestAtt	.32	.02	.36	19.39
	DSOs	.12	.02	.14	8.37
	NaEn	.17	.01	.19	11.82
	Resi	.17	.01	.19	12.59
	R² = .51; F(4, 3153) = 830.37; p < .01.

Table 6 provides a summary of hypotheses and results. All are supported.

Table 6.

Summary of hypotheses and results.

	Supported?
Tourism actors’ impact on place value
H1a: Destination attractions positively contribute to tourists’ perceived place value.	Yes
H1b: Destination service organizations positively contribute to tourists’ perceived place value.	Yes
H1c: The natural environment positively contributes to tourists’ perceived place value.	Yes
H1d: Residents positively contribute to tourists’ perceived place value.	Yes
Moderating role of risk
H2a: Destination attractions contribute more to perceived place value in low risk destinations as compared to higher risk destinations.	Yes
H2b: Destination service organizations contribute more to perceived place value in low risk destinations as compared to higher risk destinations.	Yes
H3a: The natural environment contributes more to perceived place value in high risk destinations as compared to lower risk destinations.	Yes
H3b: Residents contribute more to perceived place value in high risk destinations as compared to lower risk destinations.	Yes
Tourism actors’ impact on tourist loyalty
H4a: There is a positive relationship between the perceptions of tourism actors and the willingness to return of tourists.	Yes
H4b: There is a positive relationship between the perceptions of tourism actors and the willingness to extend stay of tourists.	Yes
H4c: There is a positive relationship between the perceptions of tourism actors and the willingness to recommend of tourists.	Yes

Discussion

The results of this research are based on extensive empirical study in multiple countries over many years, in an effort to sample from a variety of places of different tourism contexts (natural, built, etc.). This conceptualization of five models builds on previous literature (e.g., Költringer and Dickinger, 2015) that considered a three-construct characterization of products, place, and people.

The SEM model analysis of the four hypotheses found that all the actors in the place ecosystem significantly contribute to the place value perceived by tourists, supporting the co-creation of perceived place value by different actors in tourism. This is in line with past research (e.g., Collins and Millar, 2019; Fourie et al., 2020). Additionally, these results show that destination attractions and DSOs contribute more to perceived place value in low risk destinations versus higher risk ones, while the natural environment and residents contribute more to perceived place value in high risk destinations versus lower risk ones. This finding is important because this is the first study to leverage data from in-situ tourists in multiple countries (i.e., places) to assess the dual role of a country risk level on the relationships between tourism actors and tourist’s perceived value of a place. In the final empirical phase of the research, the regression analysis found a positive relationship between the different actors and the loyalty dimensions of tourists. This is in line with other research that has previously found a positive relationship exists between tourism actors and tourist loyalty (e.g., Barnes et al., 2020; Hollebeek and Rather, 2019; Shulga et al., 2018; Sugathan & Ranjan, 2019).

Theoretical implications

This research sought to deepen our understanding of value co-creation in tourism as it relates to relevant tourism actors involved in the process and the impact of co-created value on tourists’ perceived place value as well as tourists’ loyalty. The research makes a particular contribution by investigating tourist value co-creation using a large sample based on data collection in 12 different countries. While this research is focused on the theoretical contributions, these contributions rely on the empirical strength of our research methodology. In doing so, this study answers the call to study brand image empirically in varied contexts (Garanti et al., 2024). The work responds to a series of identified gaps in the literature via employing, in-situ, a validated, theory-informed (PCI, TDI) instrument in 12 different countries of tourists.

First, the examination of different actors contributing to the co-creation of value reinforces previous research indicating that the interaction of multiple actors is an important part of the process (Huang and Choi, 2019). Specifically, Huang & Choi (2019) recognize the complexity of tourist interactions that included social, service and activity situations. The current research supports this perspective by finding that beliefs about the destination attractions, destination service organizations, natural environment and residents together significantly contribute to the co-creation of perceived place value.

Second, the co-creation of place value is sensitive to the country context. The examination of different countries on the basis of peace and risk represents a contribution to the literature, as the role of risk had not been explicitly studied over a diverse set of places (countries). The current research found that the relationship of the four actors to tourists’ perceived place value changes significantly based on the dimension of peace and risk. For low risk countries, destination attractions and destination service organizations contribute more to the co-created destination value than the natural environment and residents. For high risk countries, the natural environment and residents play a larger role in the formation of place value than a low risk context. This is an important finding because it suggests that some actors can compensate for potential weakness of other actors in certain risk situations. For instance, tourists might choose to visit a destination with unique natural characteristics and welcoming residents, and ignore risks related to safety and security. Destinations could therefore tailor their marketing messages to emphasize these aspects over destination attractions. These findings contribute to the extensive research on risks in tourism that has leveraged protection motivation theory (Rogers, 1975, 1983). PMT posits that individuals respond to threats through threat appraisal and coping appraisal processes. For instance, research on health-related risks and tourism suggest that tourists will be actively involved to seek more information when there are increased health risks (e.g., COVID-19 pandemic), and they rely on tourism actors like DMOs to provide this information (Alhemimah, 2023).

From this perspective, our findings could reflect similar coping processes: residents functioning as ‘risk navigators’ and natural environment offering value independent of institutional vulnerabilities. However, alternative explanations are equally plausible: tourists choosing high-risk destinations may self-select for authenticity-seeking motivations; formal tourism infrastructure tends to be less developed in such contexts; and engaging with locals in challenging environments may generate distinctive experiential value independent of risk mitigation. The precise mechanisms underlying our risk-moderated effects remain an open question for future research employing direct measures of threat appraisal, coping appraisal, and tourist motivations.

Third, the actors contributing to co-created place value have a positive relationship with the value outcome of tourist loyalty. The research demonstrates the importance of these actors on future behavior intentions supporting the direction of existing literature (e.g., Barnes et al., 2020; Hollebeek and Rather, 2019; Shulga et al., 2018; Sugathan & Ranjan, 2019). The current research found a positive relationship between the different actors (destination attractions, destination service organizations, natural environment, and residents) and loyalty dimensions of tourists (willingness to return, extend stay, and recommend). The results also showed some sensitivity of the relationship depending on the specific value outcome. For instance, destination attractions have the strongest relationship for willingness to return and to recommend. However, the residents had the strongest relationship for the willingness to extend the tourists’ stay. These results are important because the influence of key tourism actors on the value outcomes is now explicitly identified.

Managerial implications

Specific to practitioners, the results of this research provide a number of important directions. First, much like any consumer facing marketing, the importance of both (i) stakeholders and (ii) market segmentation are evident in seeking to build tourist loyalty. In the case of stakeholders, for each specific organization, a clear articulation of both the actors and the customers (tourists) is essential on a country-by-country basis, with all the destination actors clearly identified, strategized and considered. A particular emphasis on understanding the tourists is suggested to practitioners, including a deep segmentation model, to clearly understand the segments, their needs and wants, and their tourism destination beliefs. Such an understanding, at a country level, will provide a clear identification of all contributors to value co-creation.

Second, the co-creation of destination value is found to be very sensitive to the country context. Thus, a very country-oriented focus is recommended to tourism marketers, particularly those within global firms who may not have expertise and research at the level of the country in all places where they operate. The dimensions of place value, a country risk level are all recommended to be included here, with low risk countries, as well as high risk countries particularly included. As the results noted, if the tourism marketer can identify and address weaknesses in a certain aspect of their market, they will be more successful regarding tourist loyalty.

Finally, the results emphasize the need to engage all of the actors contributing to co-created place value, including building specific plans to engage each of the different actors (i.e., destination attractions, destination service organizations, natural environment, and residents). Given that each actor was found to have different views on place value, a unique and well-researched plan (or approach) to each of the four is recommended.

Limitations

While the empirical basis for the current research represents a real strength due to the sampling in 12 different countries, an examination of the main tourism actors and their influence on place value and its outcomes could be conducted in additional contexts. The current research examines the dimension of a country risk level, there may be other important dimensions to consider for future research. Identifying additional dimensions might be best suited to a qualitative study interviewing the different key actors and tourists visiting a destination. While we argue that the directionality of structural relationships is theoretically robust, we acknowledge that effect magnitudes may differ in post-2010 tourism contexts. The replication of our structural model with contemporary data is therefore identified as a priority for future research.

Furthermore, our parallel modeling of place value and loyalty as outcomes of tourism actors does not test whether place value mediates the actor–loyalty relationship. Disentangling this relationship—including the causal direction and potential mediating mechanisms such as well-being or place attachment (cf. Liu et al., 2023; Zhang et al., 2022)—would require longitudinal or experimental designs. In addition, the study of the relationship between risk and loyalty could also be considered in future research.

Moreover, our operationalization of perceived place value captures holistic evaluative judgments rather than disaggregated value dimensions (functional, emotional, social). While this approach is consistent with SDL’s premise that value is phenomenologically determined as an integrated experience, future research employing multi-dimensional value scales (e.g., Sweeney and Soutar, 2001) could examine how specific actor interactions contribute to particular value facets. While the objective of the study (place value typology) is not time-sensitive, we recognize that the timeframe of the data collection 2005 to 2010 is a limitation. Relatedly, the risk indices used to classify destinations (GPI 2019, International SOS, 2020) postdate our survey data. Although our robustness checks indicate that country classifications remained stable over the intervening period, future research should ideally employ contemporaneous risk measures. Additional data collection in other environments is recommended for future research to further extend our understanding of co-creation of value in in-situ tourism contexts.

The objective of this study was to examine how actors in a place ecosystem influence tourists’ perception of the place value and their loyalty, under specific consideration of perceived safety and security risks in tourism. Results from a multi-country survey over more than 5 years show that tourism actors all play a critical role in enhancing tourists’ perceptions of the value of the place they are visiting and their loyalty. This positive role is even more important in low-risk countries. Exploring the role of risk for tourist perceptions of a place requires a comparison of perceptions over varied contexts. While the research design used in this study creates implementation challenges, we hope that the richness of the results will emulate future research with similar research designs.

Footnotes

ORCID iD

Norm O’Reilly

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Author biographies

John Nadeau is Dean and Professor at Cape Breton University. He specializes in tourism marketing research.

Herbert Woratschek is Professor at the University of Bayreuth. Most recently, he was Chair in Marketing & Service Management.

Norm O’Reilly is Dean and Professor at the College of Business, University of New England in Maine, USA. His area of research is sport business.

Tim Ströbel is Professor at the University of Bayreuth. He holds the Professorship in Marketing and Sport Management.

Nadège Levallet is an Assistant Professor at the University of Maine’s Maine Business School. Her research interests include strategy and management information systems.

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