Exploring Resident–Tourist Interaction and its Impact on Tourists’ Destination Image

Abstract

Although previous research has widely acknowledged the critical role residents play in tourism, limited evidence exists on the impact their interactions with tourists have on tourists’ own image formulation and intention to return/recommend the destination to others. Grounded in the mere exposure and contact theories, this research offers insights into tourists’ destination image formation in light of their interactions with local residents and tourism employees at a destination. Two independent studies were conducted in 2019 to establish the soundness of the model; a preliminary one in the city of Kavala (n = 353) and a follow-up study on the island of Thasos (n = 397), both located in Greece. Findings suggest that interaction between the two parties positively affect cognitive, affective, and conative image, predicting 64% (study 1) and 54% (study 2) of the latter’s variance. Implications to theory and practice along with recommendations for future research are provided.

Keywords

interaction quality mere exposure theory contact theory destination loyalty Greece

Introduction

Destination image is a very well-researched topic in the tourism literature because of the critical role it is known to play across the time span of a trip, affecting destination choice (Heitmann 2011; Tasci and Gartner 2007), satisfaction with the trip (J. H. Kim 2018; Prayag and Ryan 2012) and intention to revisit and/or to recommend to others, both offline and online (Hernández-Mogollón, Duarte, and Folgado-Fernández 2018; Zhang et al. 2014). To facilitate our understanding of destination image formation, a number of theoretical frameworks have been developed (Baloglu and McCleary 1999; Gallarza, Saura, and Garcı́a 2002; Kim and Chen 2016; Stylidis, Shani, and Belhassen 2017), while authors call for additional empirical research and refined frameworks that incorporate new antecedents of image (Ek Styvén, Näppä, and Strandberg 2017; Kislali, Kavaratzis, and Saren 2019). To this end, prior research has acknowledged the critical role local residents play in tourism as an integral part of a destination and its image (Xu, Hui, and Chan 2015), as tourism involves at least some level of interaction with members of the host community (Kirillova, Lehto, and Cai 2015). Some residents also serve as destination ambassadors to potential visitors (Hudson and Hawkins 2006; Stylidis, Sit, and Biran 2016), thereby indirectly affecting tourists’ destination image and future intentional behaviors (e.g., Bigne, Sanchez, and Sanz 2005; Papadimitriou, Kaplanidou, and Apostolopoulou 2018; Walls, Shani, and Rompf 2008).

So far, the link between host–guest interaction and destination image has been largely based on conceptual propositions or indications drawn from recent studies which found that feelings of togetherness and solidarity among the two parties shape tourists’ destination image (Woosnam, Stylidis, and Ivkov 2020); and that interaction with the host community is a significant predictor of change in cross-cultural appreciation (Kirillova, Lehto, and Cai 2015). There is limited evidence, however, on how interaction with local residents and with tourism employees at the destination shape tourists’ destination image. Other studies further questioned the practice of measuring interaction frequency (like in Aleshinloye et al. 2019), while it may actually be the quality of interaction that plays a decisive role for tourists, and called for additional theory-guided quantitative assessment of the interactional experiences between the tourists and residents of the host communities (Kirillova, Lehto, and Cai 2015).

This research aims to contribute to destination image theory and practice by providing a richer understanding of the role perceived quality of interaction with local community plays in the development of tourists’ destination image and behavioral intentions. To achieve its aim, the study seeks to address the following objectives: (a) assess tourists’ level of perceived interaction quality with local residents and tourism employees; (b) establish relationships between such interaction quality and tourists’ cognitive and affective destination image; and (c) establish relationships between such interaction quality and tourists’ conative image (intention to return and to recommend to others). The study applies the mere exposure and contact theories to expand existing frameworks of destination image by incorporating the interaction tourists have with the local community, offering insights for sustainable management and marketing of tourist destinations. These concepts have not been jointly considered in understanding intergroup relations and perceptions within the tourism context in the past. Such knowledge is valuable for local authorities and destination marketers in designing activities that promote valuable contact and interactions between the two parties, targeting to enhance tourists’ destination image and their future behavioral intentions toward the destination.

Literature Review

Destination Image

Boulding (1956, p. 6) defined image in his seminal work as “what we believe to be true, our subjective knowledge” and proposed that image comprises three distinct but hierarchically interrelated components termed cognitive, affective, and conative. Cognitive involves what one knows and thinks about an object or place (Baloglu and McCleary 1999), affective is how one feels about it (Baloglu and Brinberg 1997; Hallmann, Zehrer, and Müller 2015), while conative is how one acts upon this information (Pike and Ryan 2004). In tourism, destination image is often approached as the sum of beliefs, ideas, and impressions people hold of a destination (Crompton 1979), while it is recognized that direct experience with a destination (through visitation) emancipates interactions with people and places (Pearce and Stringer 1991). It is the subjective interpretation of such interactions, along with the subsequently evoked feelings toward the destination and its hosts that shape destination image (Tasci, Gartner, and Cavusgil 2007; Veasna, Wu, and Huang 2013). The action (conative) component of image in tourism is often understood as intention to (re)visit the destination and to recommend it to others (Gartner 1993; Pike and Ryan 2004; Tasci, Gartner, and Cavusgil 2007), which in recent times increasingly takes the form of the tendency to positively/negatively discuss about it through various media such as chatting with friends or sharing content on social media (Kislali, Kavaratzis, and Saren 2019). What is also prominent is that the conative image has often been equated to destination loyalty in the tourism literature (Cai, Feng, and Breiter 2004; Li et al. 2010).

Baloglu and McCleary (1999) were among the first to provide empirical support for the hierarchical relationship (proposed by Gartner 1993) between the cognitive and the affective image in tourism. Along with this line of thought, a number of researchers approached the affective image as largely dependent on the cognitive one (Beerli and Martin 2004; Li et al. 2010; Lin et al. 2007; Stylidis, Shani, and Belhassen 2017). This notion was confirmed, for example, in the work of Agapito, Valle, and Mendes (2013) which found that cognitive image also shapes conative image through its affective counterpart. Qu, Kim, and Im (2011) concluded that both cognitive and affective image shape the conative image of a destination, while this structure was further evidenced in Zhang et al.’s (2014) meta-analysis of 66 studies on this topic. As Zhang et al.’s (2014) study revealed, the affective image, although less studied in the past, appears to have the strongest effect on conative image in comparison to the cognitive image.

On the other hand, few researchers proposed that our first response to a destination is affective and this directs further actions toward a place (Walmsley and Young 1998). This line of reasoning has partially found support in environmental psychology research, in which greater affection was reported to lead to more favorable cognitive evaluations of a place (e.g., Billig 2006; Rollero and Piccoli 2010). Other studies, including Kim and Chen (2016), Hernández-Mogollón, Duarte, and Folgado-Fernández (2018), and Kislali, Kavaratzis, and Saren (2019) rejected the idea that cognitive and affective interact in one way or another, suggesting that conative is simultaneously formed by cognitive and affective components, while for Stylos and his colleagues (2016, Stylos and Bellou 2019), conative image lies at the same level of conceptualization with cognitive and affective. These studies, however, fail to explain the interactions that are known in psychology to exist between cognition and affect. Following, therefore, the initial line of reasoning supported by Baloglu and McCleary (1999) and Agapito, Valle, and Mendes (2013), among others, it is expected that the cognitive image will positively affect the affective image, while both will positively affect the conative image.

Hypothesis 1: Cognitive image positively affects affective image.

Hypothesis 2: Cognitive image positively affects conative image.

Hypothesis 3: Affective image positively affects conative image.

Theoretical Framework: Contact and Mere Exposure Theory

Implicit or explicit in previous works is that the host–guest relationship and the subsequent image and loyalty tourists develop toward a destination seem to be related, among other factors, to their level of exposure to and contact with local residents. Exposure is defined as the extent to which we encounter a stimulus and, following the signal detection theory (Green and Swets 1966), the intensity of a stimulus is one of the key factors that influence its detection. Zajonc (1968, p. 1) argues that “mere repeated exposure of the individual to a stimulus is a sufficient condition for the enhancement of his attitude toward it.” Based on a number of experiments, Zajonc (1968) concluded that subjects exposed to an increasing stimulus rated it more favorably. The value of the theory has been empirically confirmed in many fields including consumer research (Tom et al. 2007) and personal preference and trust (Kwan, Yap, and Chiu 2015). Brand exposure, for instance, was found to improve people’s buying intentions (Tom et al. 2007).

Despite the theory’s wider application in several fields, it has not been largely applied to the context of destination marketing, with one exception. In their study of international tourists visiting Linz, Austria, (Iordanova and Stylidis 2019) reported that the “intensity of the visit,” measured as the volume (frequency) of attractions and events visited or attended had a positive effect on their image of the destination. Iordanova and Stylidis (2019) though did not consider tourists’ interaction with local residents, but only with tangible assets of the destination (i.e., physical attractions), thereby neglecting the vital role of human contact and interaction in shaping destination image.

Research so far seems to indicate that people or objects more frequently encountered are more favorably evaluated (Tom et al. 2007). This could be further explained in sociological terms via the contact theory used to illuminate phenomena like social distance and stereotyping. The contact theory suggests that, under appropriate conditions, meaningful interactions tend to improve intergroup relations (Allport 1954). Contact theory is considered of value in explaining host–guest relations in the tourism context (Aleshinloye et al. 2019; Joo et al. 2018). Its application to tourism suggests that intergroup contact generates positive change in attitude toward the members of the “other” group when the contact takes place under favorable conditions (Aleshinloye et al. 2019; Joo et al. 2018; Pettigrew 1998). Woosnam and his colleagues (e.g., Woosnam, Stylidis, and Ivkov 2020), for example, have documented the solidarity tourists develop with local residents that has come about from interaction on-site (Woosnam and Norman 2010). Yilmaz and Tasci (2015) further supported that perceived social distance between residents and tourists was reduced as a result of contact through direct visitation, relationships with local service providers, and the number of close friendships developed between the two parties.

Similarly, in the context of volunteer tourism, Kirillova, Lehto, and Cai (2015) found that quality of interaction with the host community was the most significant predictor of change in cross-cultural appreciation and intercultural sensitivity. Although interaction (as advocated by the mere exposure and contact theories) between residents and tourists has therefore been recently reported to determine social distance (e.g., Joo et al. 2018), emotional solidarity (e.g., Joo et al. 2018), and/or place attachment (e.g., Aleshinloye et al. 2019), its role in shaping tourists’ destination image is thus far unknown. It is therefore assumed, in line with the contact and the mere exposure theory, that an increased contact and interaction quality with local residents will generate favorable tourist perceptions (cognitive and affective) of the destination and higher degree of destination loyalty (conative image).

Such proposition can be further substantiated by drawing on studies considering residents’ and tourists’ destination image. First, local residents are part of the destination and its image as an interesting and inviting destination (Xu, Hui, and Chan 2015), and a core element of destination branding (Konečnik and Go 2008). There is no cultural experience for tourists without local people (Valek and Williams 2018) and hence “it is nearly impossible to consider a destination without also acknowledging its people as well as the relationship visitors have with such residents” (Woosnam, Stylidis, and Ivkov 2020, p. 917). Walker and Moscardo (2016) noted that such interactions promote tourists’ understanding of local people and of their life, thereby facilitating sustainable development of tourism. It seems that through their contact and interaction with visitors, local residents can influence tourists’ image and experience (Ji and Wall 2015), further contributing to destination differentiation (Agapito, Mendes, and Valle 2010). Residents’ hospitality and receptiveness toward tourists as such is considered a key indicator in the measurement of tourists’ destination image (Echtner and Ritchie 1991; Pike 2002).

Second, local residents, because of their familiarity with the destination, actively serve as information providers who recommend attractions and facilities, especially to their friends and relatives (Shani and Uriely 2012; Simpson and Siguaw 2008), often perceived as “destination ambassadors” (Hudson and Hawkins 2006). Such residents appear keen to spread positive word of mouth (WOM) to others (Ramkissoon and Nunkoo 2011). For example, Stylidis, Shani, and Belhassen (2017) reported that locals who hold favorable images are willing to distribute positive WOM to their friends and relatives, which in the case of Young, Corsun, and Baloglu’s (2007) study in Las Vegas was found to positively influence guests’ activities and expenditure. Studies further exploring the information sources tourists use, concluded that WOM from friends/relatives is the most important agent in shaping image (Baloglu and McCleary 1999). It is thus proposed that the image local residents have of their place is transmitted to tourists through contact and interaction, thereby impacting on the latter’s image and actual experience (Chan and Marafa 2016). Residents’ critical position as image-makers is nowadays further intensified due to their active involvement in various social media platforms (i.e., TripAdvisor), offering local expertise in answering travel related questions, thereby assisting potential and/or current tourists in planning their trips, contributing to value co-creation and destination differentiation (Edwards et al. 2017; Tamajon and Valiente 2017).

Last but not least, through their capacity as tourism employees, some local residents play a key role in visitors’ image of the destination. Employees of tourism businesses, in particular, are among the first locals the tourists meet and their attitudes toward tourists affect the way tourists are treated, thereby influencing their onsite experience and intention to recommendation the destination to others (Pizam, Uriely, and Reichel 2000). Residents though working in tourism, may not necessarily share the same images or motivations to contact tourists like other community members. Studies, for instance, have reported that tourism employees have more favorable images of their place as a tourist destination than tourists (Sternquist-Witter 1985), or other local residents (Stylidis, Belhassen, and Shani 2015), due to the “proud parent syndrome” or as a result of their financial dependence on tourism. Previous research has shown that residents who are dependent on this industry appear to be more positively predisposed toward tourism development projects than residents who do not have a tourism related job (McGehee and Andereck 2004).

Despite the strong theoretical justification for the need to consider local residents in the destination marketing context, there is limited empirical evidence documenting how interaction quality with residents and with tourism employees shape tourists’ destination image and loyalty/conative image. The limited numbers of studies that explored such interactions established their role in minimizing social distance and promoting emotional solidarity, but not destination image per se. This study aims to fill in this gap by offering insights on the effectiveness of such interactions in shaping tourists’ destination image, which is of significance for destination marketing. On the basis of the aforementioned theories and arguments, it is expected that the greater the interaction quality tourists have with local residents and tourism employees, the more positive their cognitive, affective, and conative image (Figure 1).

Hypothesis 4: Interaction quality between local residents and tourists is positively related to tourists’ cognitive image of the destination.

Hypothesis 5: Interaction quality between local residents and tourists is positively related to tourists’ affective image of the destination.

Hypothesis 6: Interaction quality between local residents and tourists is positively related to tourists’ conative image of the destination.

Hypothesis 7: Interaction quality between tourism employees and tourists is positively related to tourists’ cognitive image of the destination.

Hypothesis 8: Interaction quality between tourism employees and tourists is positively related to tourists’ affective image of the destination.

Hypothesis 9: Interaction quality between tourism employees and tourists is positively related to tourists’ conative image of the destination.

Figure 1.

Proposed model.

Research Methodology

Study Context

Two independent studies were conducted in 2019 to test and establish the soundness of the proposed model; a preliminary one in the city of Kavala (n = 353) and a follow-up study on the island of Thasos (n = 397), both located in Greece. The diverse profiles of the two selected study settings and of their visitors provide an excellent opportunity to confirm the validity of the theoretical framework and assess the robustness of the proposed model beyond a single destination.

Study 1—Kavala

Kavala is a seaside city in Northern Greece, with a population of 70,501 inhabitants. The city is located close to the Greek borders with Bulgaria (95 km) and Turkey (189 km). Kavala’s history can be traced back to the seventh century BC and is currently known as the starting point of Christianity in Europe. In the past several years the local council has developed religious and cruise tourism as Kavala is part of the religious tourism route tracking the footsteps of St. Paul. The municipality has also initiated several projects including the conservation of Panagia (Kavala’s old district) and a large beach resort development. Recently, the historical site of Philippi, located on the outskirts of Kavala, was declared a UNESCO world heritage site. Such initiatives are having a significant impact on the volume of tourists the city currently accommodates.

Tourist numbers continue to increase with international tourist overnight stays skyrocketing from 51,998 in 2010 to 222,383 in 2018. The predominance of international tourists are coming from Bulgaria (12%), Turkey (11%), Romania (7%), and Germany (5%) (INSETE Intelligence 2018). Although Kavala has an international airport, the majority of international tourists visit the city by car. The supply side has radically transformed the last few years, fully reflecting the sharing economy era; while hotel (10 units) and licensed rent rooms capacity in Kavala has been virtually unchanged between 2010 and 2018, the properties available in the Booking.com platform increased from 10 (licensed hotels) in 2010 to 650 properties in 2019. Meanwhile, the number of private properties available in the Airbnb.com platform stood at 746 in the Winter of 2020.

Study 2—Thasos Island

Thasos island (population 13,710 inhabitants) is part of the North Aegean Sea, the northernmost major Greek island, and 12th largest by area. Thasos has a very rich history (since 750 bc) and is known for its breath-taking natural environment, including an A-grade collection of beaches, hillsides, fertile vineyards, and olive groves. Its economy traditionally relied on timber, marble quarries, olive oil and honey, and to a lesser extent on tourism. This has radically changed in the past few years, when tourism demand increased by 77% between 2010 and 2018; from 427,555 international tourist overnight stays in 2010 to 758,619 international tourist overnight stays in 2018. Hotel bed capacity also increased by 12% over the same time, from 10,041 beds in 2010 to 11,257 beds in 2018 (INSETE Intelligence 2018). There were 477 properties listed in the Booking.com platform in the Winter of 2020, while the volume of properties available in Airbnb was slightly more than 300.

Overall, the two locations feature distinct characteristics, with Kavala serving as a city break destination, attracting heritage and cultural (religious) tourism, while Thasos has mainly developed as a sun and sand destination targeting the mass tourism market, offering a large number of all-inclusive resorts and direct charter flight connections to its key tourist markets (i.e., Germany, United Kingdom, Romania). Furthermore, the changes reported in the accommodation supply and tourist demand in both locations are likely to generate greater interaction and contact between local residents and tourists, and thus Kavala and Thasos were deemed suitable settings for testing and validating the model proposed in this research, providing greater value to a wider research audience.

Study Instrument

For consistency and comparability, an identical survey was used in both studies conducted in Kavala and Thasos. A questionnaire with three parts was designed to capture the constructs under study, namely, the cognitive, affective, and conative image along with the interaction quality between local residents and tourists, and between tourism employees and tourists. The first section measured tourists’ cognitive, affective, and conative image. Tourists’ on-site image was selected as more accurate in comparison to non-visitors’ imaginations (Tasci 2006). Following established destination image research, a multi-item measure of cognitive image was used to capture the complex and multifaceted nature of the concept (e.g., Beerli and Martin 2004; Chen and Tsai 2007; Chi and Qu 2008; Echtner and Ritchie 1991; Lin et al. 2007; Qu, Kim, and Im 2011; Wang and Hsu 2010). The well-established multi-dimensional scale included 14 items distributed under five dimensions of cognitive image (as also used in previous studies): natural environment (scenery, climate, beaches), amenities (accommodation, restaurants, shops), attractions (activities, heritage, culture/events), accessibility (access, transport), and social environment (friendly, safe, good value). The list of items was presented to two groups of 20 tourists who confirmed their suitability for capturing Kavala’s and Thasos’s image respectively. Following previous studies, a 5-point Likert scale was used from 1 indicating strongly disagree to 5 indicating strongly agree (e.g., Chi and Qu 2008).

Drawing on previous research, affective image was evaluated using four attributes (distressing–relaxing, unpleasant–pleasant, boring–exciting, and sleepy–lively) on a 5-point semantic differential scale (Baloglu and McCleary 1999; Wang and Hsu 2010). Similar to Chi and Qu (2008), Prayag and Ryan (2012), Qu, Kim, and Im (2011), and Moon and Han (2019) conative image was measured through intention to revisit Kavala/Thasos in the next 2 years; say positive things about Kavala/Thasos to friends and relatives as a tourist destination; and encourage friends and relatives to visit Kavala/Thasos, on a scale from 1 (very unlikely) to 5 (very likely).

The second section captured resident–tourist interaction quality occurring at different times via seven items in total that were drawn from studies such as Aleshinloye et al. (2019), Joo et al. (2018), Yilmaz and Tasci (2015), and Kirillova, Lehto, and Cai (2015). Five items were used to estimate interaction quality between residents and tourists (i.e., I have developed friendships with locals; locals gave me recommendations where to dine), while two items captured interaction quality between tourism employees and tourists (i.e., my interaction with employees in tourism is useful/informative). The last section featured personal questions such as gender, age, nationality, and marital status. A pilot study conducted in Spring 2019 using a sample of 40 visitors to Kavala and another 48 to Thasos established the clarity, relevancy, and suitability of the research instrument, supporting the survey’s face validity.

Data Collection

Two independent data collections were conducted in the two study settings throughout the Summer of 2019 using a structured self-administered paper questionnaire distributed to visitors by four experienced multilingual (variety of Greek, English, German, French, and Romanian speakers) research assistants using intercept sampling in key tourist locations. After introducing the research purpose and confirming respondents’ suitability, the research assistants invited subjects to complete the survey. The first study sample comprised adults (older than 18 years) who spent at least one night in Kavala. Tourists were randomly approached in the tourist zone nearby the promenade, where most of Kavala’s hotels, Airbnbs, cafes, and restaurants are located, using a random day/time/site pattern rotating between various days (i.e., weekdays, weekends), time (morning, afternoon, evening), and locations. This sampling approach is in line with previous tourism studies (e.g., Chen and Tsai 2007; Stepchenkova and Li 2013), dictated mainly by the absence of a sampling frame (Prayag and Ryan 2012). The survey initially designed in English was translated by bilingual translators in Greek, German, Romanian, and Bulgarian to reflect the nationalities of the key tourist markets visiting Kavala. Out of the 550 tourists approached, 353 surveys were completed, resulting in a response rate of 64%. Considering that the population of tourists visiting Kavala was estimated at 157,752 in 2018 (INSETE Intelligence 2018), the sample size of 353 is within a 95% confidence level and 5.21% margin of error.

Following the same procedures, only adult tourists who had spent at last one night in Thasos were invited to participate in the second study. For consistency, the same data collection and sampling approach were used. That is, data were collected using self-administered paper questionnaires distributed by the same research team like the study conducted in Kavala. Tourists in Thasos were also approached using the same procedures described before in the main tourist zones of the island (Limenas, Limenaria, and Potos), and in the ferry on their way back to the mainland/airport. Out of the 500 tourists approached, 397 surveys were completed, resulting in a response rate of 79%. This higher response rate as compared to the previous study could potentially be attributed to the spare time respondents had while aboard the ferry (journey duration between 45 and 90 minutes). Given that the population of tourists visiting Thasos stood at 157,999 in 2018 (INSETE Intelligence 2018), the sample size of 397 is within a 95% confidence level and 4.93% margin of error.

Data Analysis

Following Ouyang, Gursoy, and Chen (2019), the proposed model was tested twice using each data set separately. The analysis, in particular, comprised two stages as proposed by Anderson and Gerbing (1988); in the first stage, a confirmatory factor analysis (CFA) was conducted (maximum likelihood estimation) to evaluate the measurement model’s reliability and validity using AMOS v.25. Next, the structural relationships of the model were tested via structural equation modeling (SEM). In line with Hair et al. (2018), several fit indices were used to assess the model fit with the following cut-off criteria: 3 to 1 for the ratio of χ² to the degrees of freedom (CMIN/DF) (Bagozzi and Yi 1988); values greater than 0.90 for the comparative fit index (CFI) and goodness of fit index (GFI); and values less than 0.08 for root mean square error of approximation (RMSEA) and for standardized root mean square residual (SRMR). Convergent validity is established when item loadings are higher than 0.5 and statistically significant (p <.001), and the average variance extracted (AVE) is over the recommended value of 0.50 (Anderson and Gerbing 1988). Discriminant validity is established when the square root of the AVE value is higher than the interconstruct correlations (Hair et al. 2018). Lastly, a multigroup analysis was conducted to assess the invariance of the path relationships in the model between tourists visiting Kavala and Thasos. To test for invariance, all the path estimates in the structural part were constrained to be equal across the two groups. The chi-square difference test between the baseline model (no equality constraint) and the constraint model was used to assess whether constraining the path regression estimates to be equal across the two groups deteriorate the model fit. Partial invariance analysis was applied next to assess the differences between each path among the two groups.

Findings

Respondents’ Profile

Respondents’ Profile—Kavala

Women accounted for 54% and men for 46% of the respondents in the Kavala sample (n = 353). About one-third (31%) of the study’s participants were aged 18–30 years, while 28% were those aged between 51 and 60 years. More than half (54%) of the respondents were married. Roughly 4 of 10 individuals (41%) had visited Kavala in the past. Among respondents, the key nationalities documented were Greek (35%), Bulgarian (13%), Turkish (12%) and Romanian (11%).

Respondents’ Profile—Thasos

The sample (n = 397) of the second study conducted in Thasos comprised slightly more female respondents (56%) than male (44%). About one-third (30%) of respondents were aged 18–30 years, and another third (30%) were those aged 51–60 years. More than half (56%) of the sample population in Thasos were married. Almost 3 of 10 individuals (28%) had visited Thasos in the past. Among respondents, the key nationalities documented were German (24%), English (14%), Bulgarian (15%), Romanian (11%), Greek (10%), and Turkish (4%).

Measurement Model

Given the model’s constructs were captured through a common instrument, Harman’s single-factor test was conducted to eliminate the possibility of common method bias. The total variance explained by a single factor was 33% in Kavala and 31% in Thasos, less than the suggested cut-off (<50%). Further testing for common method variance via the single latent method factor approach (Podsakoff et al. 2003) using AMOS, it was observed that the common variance was 18.5% in Kavala and 21% in Thasos, indicating the absence of common method bias in both samples.

Kavala study

The results in the Kavala sample suggested that the measurement model fits the data well, with a χ² of 992.80 (df = 335, p < .001). The CMIN/DF index was 2.96, which is less than the cut-off value of 3.0. All the measurement model fit indices suggested a relatively good fit to the data: CFI = 0.88, RMSEA = 0.075 (LO90 = 0.069, HI90 = 0.080, PCLOSE = .000), SRMR = 0.066. After an inspection of the item loadings, it was observed that one item (relaxing) of affective image and one item (good value for money) of cognitive image had rather low loadings, and they were thus removed from further analysis. It appears that an urban environment like Kavala is not perceived as relaxing by visitors, as compared to other rural or island destinations. As a result, the measurement model fit indices improved, with a χ² of 768.08 (df = 284, p < .001). The CMIN/DF value of 2.70, the CFI value of 0.91, the RMSEA value of 0.070 (LO90 = 0.064, HI90 = 0.076, PCLOSE = .000), and the SRMR value of 0.060 were all within the recommend criteria, suggesting a good model fit to the data (Hair et al. 2018).

The composite reliability for each construct ranged from 0.70 to 0.92 (Table 1), over the cut-off value of 0.70 (Hair et al. 2018). Construct validity was confirmed by establishing both convergent and discriminant validity. Convergent validity (Table 1) was established since all item loadings were higher than 0.5 and statistically significant (p <.001). Additionally, the AVE values of all constructs ranged from 0.51 to 0.75, exceeding the recommended cut-off value of 0.50 (Anderson and Gerbing 1988). Discriminant validity was verified by comparing the square root of AVE values with the interconstruct correlations (Table 2). All square root AVE values were higher than the interconstruct correlations (Hair et al. 2018).

Table 1.

Measurement Model CFA Results—Kavala.

Constructs/Indicators	ItemLoadings	t-values	Composite Reliability	AVE
Interaction with Residents (IR)			.92	.70
Friendship with locals	.77	16.76
Tips on what to visit	.86	19.84
Recommendations where to dine	.89	20.94
Explained local way of life	.82	18.31
Increased my sense of safety	.84	19.23
Interaction with Employees (IE)			.86	.75
Interaction is pleasant	.87	16.65
Interaction is informative/useful	.86	16.45
Cognitive Image (CI)			.84	.52
CI1 (Natural Environment)	.81	10.38	.77	.53
Scenic beauty	.78	11.06
Pleasant weather	.74	10.77
Nice beaches	.65	–
CI2 (Amenities)	.94	11.66	.76	.51
Quality accommodation	.68	10.35
Appealing cuisine/food	.79	11.72
Variety of shops	.67	–
CI3 (Attractions)	.62	8.79	.81	.58
Variety of activities	.77	11.34
Interesting culture/events	.82	12.44
Interesting heritage sites	.69	-
CI4 (Accessibility)	.52	7.93	.79	.66
Convenient transportation	.76	8.32
Easily accessible	.86	-
CI5 (Social Environment)	.65	8.29	.70	.53
Safe destination	.71	-
Friendly locals	.75	8.23
Affective Image (AI)			.83	.61
Unpleasant–pleasant	.68	13.61
Boring–exciting	.87	19.13
Sleepy–lively	.79	16.75
Conative Image (CONI)			.85	.65
Revisit in the next 2 years	.68	13.76
Say positive things about Kavala	.80	17.57
Encourage friends to visit	.92	21.14

Note: CFA = confirmatory factor analysis; AVE = average variance extracted.

Table 2.

Discriminant Validity—Kavala.

Constructs/Indicators	IR	IE	CI	AI	CONI
Interaction Residents (IR)	.84	.50	.48	.63	.74
Interaction Employees (IE)	.50	.87	.51	.30	.41
Cognitive Image (CI)	.48	.51	.72	.60	.58
Affective Image (AI)	.63	.30	.60	.78	.67
Conative Image (CONI)	.74	.41	.58	.67	.81

Note: Numbers in the diagonal represent square root AVE values; numbers in the off-diagonal represent interconstruct correlations.

Thasos study

CFA was also used to assess the measurement model in the second study (n = 397) using maximum likelihood estimation. The results indicated that the measurement model fits the data well, with a χ² of 846.6 (df = 335, p < .001), CMIN/DF = 2.53, CFI = 0.91, RMSEA = 0.062 (LO90 = 0.057, HI90 = 0.067, PCLOSE = .000), and SRMR = 0.066. However, one item (lively) of affective image and one item (variety of shops) of cognitive image had rather low loadings and were removed from further analysis. As Thasos is rather small in size and provides fewer opportunities for shopping and entertainment, visitors do not seem to associate it with a lively or shopping destination. After removing these two items, the measurement model fit indices improved, with a χ² of 636.0 (df = 284, p < .001), CMIN = 2.24, CFI = 0.93, RMSEA = 0.056 (LO90 = 0.050, HI90 = 0.061, PCLOSE = .000), and SRMR = 0.061, suggesting a good model fit to the data (Hair et al. 2018). Composite reliability exceeded the recommended value of .70, ranging from 0.69 to 0.91 (Table 3). Convergent validity was also established as all item loadings were higher than 0.5 and statistically significant (p <.001) (Table 3). AVE values ranged from 0.50 to 0.78, exceeding the recommended cut-off value of 0.50 (Hair et al. 2018). Discriminant validity was also established as all square root AVE values were higher than the inter-construct correlations (Table 4) (Hair et al. 2018).

Table 3.

Measurement Model CFA Results—Thasos.

Constructs/Indicators	ItemLoadings	t-values	Composite Reliability	AVE
Interaction with Residents (IR)			.90	.65
Friendship with locals	.73	16.39
Tips on what to visit	.80	18.75
Recommendations where to dine	.87	21.22
Explained local way of life	.83	19.59
Increased my sense of safety	.79	18.23
Interaction with Employees (IE)			.84	.72
Interaction is pleasant	.88	19.10
Interaction is informative/useful	.83	17.86
Cognitive Image (CI)			.87	.59
CI1 (natural environment)	.92	11.00	.75	.50
Scenic beauty	.80	10.97
Pleasant weather	.71	10.52
Nice beaches	.59	–
CI2 (amenities)	.88	13.41	.69	.53
Quality accommodation	.71	11.66
Appealing cuisine/food	.74	–
CI3 (Attractions)	.64	10.44	.82	.60
Variety of activities	.71	12.90
Interesting culture/events	.86	15.37
Interesting heritage sites	.75	–
CI4 (Accessibility)	.52	8.24	.78	.63
Convenient transportation	.76	8.65
Easily accessible	.83	–
CI5 (Social Environment)	.80	12.93	.78	.54
Safe destination	.76	–
Friendly locals	.76	13.55
Value for money	.66	11.66
Affective Image (AI)			.75	.51
Unpleasant–pleasant	.73	14.27
Boring–exciting	.82	16.25
Sleepy-lively	.57	10.98
Conative Image (CONI)			.91	.78
Revisit in the next 2 years	.91	23.09
Say positive things about Kavala	.88	21.87
Encourage friends to visit	.86	20.77

Table 4.

Discriminant Validity—Thasos.

Constructs/ indicators	IR	IE	CI	AI	CONI
Interaction Residents (IR)	.81	.45	.42	.29	.41
Interaction Employees (IE)	.45	.85	.65	.45	.55
Cognitive Image (CI)	.42	.65	.72	.49	.69
Affective Image (AI)	.29	.45	.49	.77	.52
Conative Image (CONI)	.41	.55	.69	.52	.88

Note: Numbers in the diagonal represent square root AVE values; numbers in the off-diagonal represent interconstruct correlations.

Structural Model

Kavala Study

Structural equation modeling (ML method) was conducted next to test the hypothesized structural relationships between the study’s constructs. All the fit indices supported the model in study 1: χ² = 768.08 (df = 284 p < .001), CMIN/DF = 2.70, CFI = 0.90, RMSEA = 0.070 (LO90 = 0.064, HI90 = 0.076, PCLOSE = .000), SRMR = 0.060. Given that the model indices indicated adequate fit, the estimates of the structural coefficients were used to examine the hypothesized relationships (Table 5). All hypothesized relationships were significant and in the expected direction, apart from the effect tourists’ interaction with tourism employees had on conative image (hypothesis 9), which was not significant, whereas the link between tourists’ interaction with tourism employees and affective image (hypothesis 8) was significant but not in the predicted direction (negative rather than positive). Overall, cognitive image positively influenced affective (hypothesis 1), and conative image (hypothesis 2), while the affective image also positively shaped the conative image (hypothesis 3). Tourists’ interaction with local residents was found to positively affect cognitive image (hypothesis 4); affective image (hypothesis 5); and conative image (hypothesis 6). Lastly, tourists’ interaction with tourism employees was reported to positively affect cognitive image (hypothesis 7). Table 5 also reports the indirect effects included in the model computed using bootstrapping to generate asymmetric confidence intervals as suggested by Preacher and Hayes (2008) and Ouyang, Gursoy, and Chen (2019). Cognitive image had an indirect positive effect on conative image. Interaction with residents had positive indirect effects on affective image and conative image. Lastly, interaction with employees had positive indirect effects on affective image and conative image. Altogether, interaction with local residents, interaction with tourism employees, and cognitive and affective image were able to estimate 64% of conative image’s variance.

Table 5.

Structural Equation Model Paths—Kavala.

	Hypothesized Path	Direct Effect			Indirect Effect
	Hypothesized Path	Effect	t-value	p value	Effect	p value
Hypothesis 1	Cognitive image → Affective image	.45^a	5.16	<.001	–	–
Hypothesis 2	Cognitive image → Conative image	.22^a	2.93	<.01	.11	<.05
Hypothesis 3	Affective image → Conative image	.24^a	3.28	<.001	–	–
Hypothesis 4	Interaction with residents → Cognitive image	.30^a	4.03	<.001	–	–
Hypothesis 5	Interaction with residents → Affective image	.50^a	7.43	<.001	.13	<.01
Hypothesis 6	Interaction with residents → Conative image	.50^a	7.26	<.001	.22	<.01
Hypothesis 7	Interaction with employees →Cognitive image	.37^a	4.67	<.001	–	–
Hypothesis 8	Interaction with employees → Affective image	–.18^b	–2.66	<.01	.17	<.01
Hypothesis 9	Interaction with employees → Conative image	–.03	–0.49	.620	.08	<.05

Supported hypothesis.

Supported but not in the predicted direction.

Thasos Study

An inspection of the fit indices in the Thasos sample supported the model as well: χ² = 636.03 (df = 284, p < .001), CMIN/DF = 2.24, CFI = 0.93, RMSEA = 0.056 (LO90 = 0.050, HI90 = 0.062, PCLOSE = .000), and SRMR = 0.061. All hypothesized relationships were significant and in the expected direction (Table 6), apart from two: the effect tourists’ interaction with local residents had on affective image (hypothesis 5) and the impact tourists’ interaction with tourism employees had on conative image (hypothesis 9). With regard to the indirect effects tested, cognitive image had an indirect positive effect on conative image. Interaction with residents had positive indirect effects on affective image and conative image. Lastly, interaction with employees had positive indirect effects on affective image and conative image. Altogether, interaction with local residents, interaction with tourism employees, and cognitive and affective image were able to estimate 54% of conative image’s variance in the second study.

Table 6.

Structural Equation Model Paths—Thasos.

	Hypothesized Path	Direct Effect		Indirect Effect
	Hypothesized Path	Effect	t-value	p value	Effect	p value
Hypothesis 1	Cognitive image → Affective image	.33^a	3.45	<.001	–	–
Hypothesis 2	Cognitive image → Conative image	.49^a	6.09	<.001	.07	<.01
Hypothesis 3	Affective image → Conative image	.21^a	3.63	<.001	–	–
Hypothesis 4	Interaction with residents → Cognitive image	.17^a	2.78	<.01	–	–
Hypothesis 5	Interaction with residents → Affective image	.05	0.83	.41	.06	<.05
Hypothesis 6	Interaction with residents → Conative image	.10^a	1.99	<.05	.10	<.01
Hypothesis 7	Interaction with employees →Cognitive image	.57^a	7.48	<.001	–	–
Hypothesis 8	Interaction with employees → Affective image	.21^a	2.50	<.01	.19	<.05
Hypothesis 9	Interaction with employees → Conative image	.10	1.44	.15	.36	<.01

Supported hypothesis.

Multigroup Comparison

Multigroup analysis was conducted last to explore whether the hypothesized relationships vary across the two study settings. The chi-square difference test between the baseline and the constraint model (Δχ² = 284.02, df = 54, p < .001) indicated that constraining the path regression estimates to be equal across the two tourist groups (Kavala, Thasos) deteriorate the model fit. The partial invariance analysis revealed that the following hypothesized relationships are not invariant across the two destinations: hypothesis 1) cognitive image → affective image (Δχ² = 12.28, p < .001); hypothesis 5) interaction with residents → affective image (Δχ² = 12.20, p < .001); hypothesis 6) interaction with residents → conative image (Δχ² = 10.33, p < .001); hypothesis 7) interaction with employees → cognitive image (Δχ² = 10.10, p < .001); and hypothesis 8) interaction with employees → affective image (Δχ² = 9.55, p < .001). The implications of the study’s findings to tourism planning, development, and marketing theory and practice are discussed next.

Discussion and Conclusion

Drawing on the mere exposure and the contact theories, and building on previous works that have either conceptually supported the prominent role local residents play in shaping tourists’ destination image and experience, or established links between frequency of interaction tourists develop with locals and their emotional bonding (Aleshinloye et al. 2019), the aim of this research was to test the capacity of local resident–tourist and tourism employee–tourist interactions to predict tourists’ cognitive, affective, and conative image, an aspect that has been largely ignored thus far. The analysis of the findings collected in two studies, one with 353 tourists who visited Kavala, Greece, and a second one with 397 tourists who visited Thasos Island, Greece, revealed that all nine hypothesis were confirmed, except hypothesis 9 across both studies, and hypothesis 5 in the second study, while in the first study hypothesis 8 was confirmed but in the opposite direction (Figure 2). Though not specifically proposed, the findings also indicate that there were some variations regarding the strength of the hypothesized relationships among the two samples.

Figure 2.

Tested model.

Cognitive and affective image were reported in both studies to predict conative image (hypothesis 1, hypothesis 3). Li et al. (2010), for example, also found that both cognitive and affective image explained the conative image. It also appeared that cognitive image exercised an impact on affective image (hypothesis 2) in both samples. Results as such validate one stream of research supporting the hierarchical nature of the relationships between the three components of image (Agapito, Valle, and Mendes 2013; Stylidis, Shani, and Belhassen 2017), initially proposed by Gartner (1993) in the tourism context. The findings, however, contradict other image conceptualizations proposed, including a single structure (Stylos and Bellou 2019) or a reverse relationship between the affective and cognitive image, whereby the affective is perceived shaping the cognitive image (Rollero and Piccoli 2010). The results as such contribute to current discussions regarding destination image composition along with the order and supremacy of the cognitive and affective image in predicting each other and the conative image. The study also sheds some light on the magnitude of such relationships which might fluctuate depending on the context of the study; in the mass tourism island setting (Thasos), the impact of cognitive on conative appeared to be superior than its affective counterpart, while in the urban-heritage mainland destination (Kavala) the impact of the two on conative was equally balanced.

The relationship between interaction quality and destination image was also substantiated in both study settings. In the first study, resident–tourist interaction exercised a positive effect on tourists’ cognitive (hypothesis 4) and affective image (hypothesis 5). While hypothesis 4 was also confirmed in the second study, the link between resident–tourist interaction and affective image (hypothesis 5) was not substantiated in Thasos, and this relationship seemed to vary among the two destinations. It broadly appears that engaging in tourist activities recommended by locals such as participating in local happenings and/or touring in the old town seem to help tourists develop more favorable perceptions/cognition of the destination. Similarly, increased levels of understanding of local people and of their life led to enhanced destination image (Walker and Moscardo 2016). This is also partially in line with the study of Woosnam, Stylidis, and Ivkov (2020) that established a positive relationship between tourists’ feelings of closeness to residents and destination image. Despite its notable contribution, Woosnam, Stylidis, and Ivkov’s (2020) work largely focused on the emotional aspect of such relationship, termed emotional solidarity, with only partially capturing the quality of interaction between the two parties. Emotional solidarity does not necessarily presuppose physical contact and interaction but can also result through imaginaries, stereotyping, or common cultural backgrounds (Woosnam 2011).

The findings of both studies further indicated that resident–tourist interaction exerted a positive impact on tourists’ conative image, including intention to recommend Kavala/Thasos to their friends and relatives and to return in the future (hypothesis 6). Such interaction promotes cross-cultural understanding and is of critical importance in the construction of a satisfactory tourist experience (Kirillova, Lehto, and Cai 2015), which is often converted into loyalty (J. H. Kim 2018). Tourists across both studies, in particular, agreed that interaction with local residents equipped them with greater knowledge on which spots to visit and where to taste traditional food. They also agreed that such interactions increased their sense of safety and offered them greater understanding of locals’ way of life. Along with the findings of the multigroup analysis, these results imply that tourist activities and itineraries are partially shaped by local residents and the recommendations they offer to tourists, especially in the urban heritage setting (Kavala), whereas in the mass tourism setting (Thasos) where tourists spend most of their time in all-inclusive resorts these interactions appear less prominent. The findings, therefore, extend previous research that failed to recognize the implications such interactions have for tourists’ destination image and future behavioral intentions related to tourism, by empirically confirming the prominence of residents as information providers for visitors (Hudson and Hawkins 2006).

Similarly, tourists’ interaction with tourism employees exercised a positive effect on tourists’ cognitive image (hypothesis 7) across both studies, further highlighting the importance of having hospitable staff working in tourism. Such interaction was also found in the second study (Thasos) to positively shape affective image (hypothesis 8). Notable is also that the effect of interaction on cognitive image was stronger in Thasos than in Kavala. Tourists, especially in Thasos, considered their interactions with tourism employees as pleasant and useful/informative. It appears that tourists in Thasos interact more with tourism employees than with other members of the island community, because of the mass type of tourism that predominates on the island, which helps to explain why employees in tourism are more critical to this type of tourists in shaping their image.

An unexpected finding though, also evidenced in the multi-group analysis, is that interaction with tourism employees had a negative influence on affective image (hypothesis 8) in Kavala; and an insignificant one on conative image (hypothesis 9) across both studies. These results suggest that recommendations provided by people working in the sector are not necessarily always perceived as an organic image agent like those offered by local residents. It seems that some types of tourists are aware that tourism employees’ attitudes and behaviors toward tourists are largely shaped by economic motives because of their higher level of dependability on tourism as compared to other residents (McGehee and Andereck 2004). Tourists, therefore, in the case of Kavala who enjoy plenty of interactions with other residents seem to approach their interactions with tourism staff as an induced image agent (Gartner 1993), often overtly or covertly orchestrated by employees in tourism to promote specific attractions, restaurants or transportation facilities with which they hold mutually beneficial relationships. As an end result, this type of interaction seems to enhance tourists’ knowledge (cognition) of the destination but occasionally fails to generate positive emotions (affect) or to increase the likelihood for tourists to spread positive WOM or to return in the future (conation). While for tourists in Thasos, who have far fewer opportunities to contact and interact with local residents, such interactions with tourism employees seem to have a much larger positive impact on their cognitive and affective image.

Theoretical Contribution

The study makes several contributions to tourism theory; first, this is one of the very few attempts to capture tourists’ interactions with residents and tourism employees, with results stemming from the two studies providing ample support for the validity of the contact and the mere exposure theories in tourism marketing, highlighting another significant but underexplored outcome of human interactions in tourism. An increase in the volume of tourists visiting both destinations has provided additional opportunities for exposure and interaction between the two parties. For tourists, enhanced opportunities for such interaction and intergroup contact facilitate a better understanding of the city and its offerings, and appreciating the host population, thereby helping them to develop and maintain a positive image and future behavioral intentions. Such findings led credence to the mere exposure theory, postulating that frequent exposure to people leads to more favorable evaluations, thereby expanding its application to tourism. Quality of interaction with the host community is also decisive, as the contact theory advocates. Both theories, therefore, supplement each other well in explaining that exposure and contact to one another, under favorable circumstances, can lead to mutual understanding and more positive perceptions of the destination.

Second, previous research has conceptualized resident–tourist interactions through interaction frequency (Aleshinloye et al. 2019) rather than interaction quality, with researchers calling for more theory-guided quantitative assessment of interaction quality between the tourists and residents of the host communities to better understand relationships (Kirillova, Lehto, and Cai 2015). In response to such calls, this research further validated a range of items to facilitate the measurement of interaction quality; and separated tourists’ interactions with residents from those with tourism employees. An understanding of what type of interaction improves relationships “may yield important insights for sustainable management and marketing of a tourist destination” (Joo et al. 2018, p. 251).

Third, previous studies have predominantly focused on the effect such interactions have on emotional solidarity, and/or their role as predictors of residents’ attitudes toward tourism (Aleshinloye et al. 2019; Joo et al. 2018; J. J. Simpson and Simpson 2017); hardly any attention has been given to the link between interaction and image. The current work expands the existing frameworks of destination image by explaining this elusive concept through the interactions tourists have with local residents and tourism employees. Such interactions with residents can be largely considered organic image agents (Gartner 1993), supporting the pivotal role local community plays by being part and parcel of the destination and its image. Lastly, the model supports the tri-compositional and hierarchical nature of destination image (cognition, affect, behavior), contributing to current debates on image structure and interrelationships between its components.

Managerial Contribution

The studies’ findings are critical for local authorities, DMOs, and destination marketers to further comprehend the significant role local residents and tourism employees play separately and jointly in determining tourists’ destination image and their future behavioral intentions toward a destination. In line with the results, when residents are directly involved as information providers in the marketing process of a place, then visitors are more likely to get better insights, develop friendships with locals, and increase their sense of security, thereby positively affecting their destination experience and image. Educational campaigns and events that aim to cultivate residents’ knowledge of local history and customs and foster civic pride can be initiated, followed by internal marketing campaigns that promote interactions with tourists along with the benefits that inbound tourists can bring to the community. Such an example is the educational initiative “I get to know my history, I get to know my city” currently available to local pupils by the municipality of Kavala in collaboration with local schools.

However, as such interactions are not always readily available especially in mass tourism destinations, destination management organizations should strategically orchestrate online and offline interactions by planning activities in which residents participate hand in hand with tourists (Woosnam and Aleshinloye 2013). An example could be free weekly/fortnightly walking tours offered by amateur local tour guides, who will proudly present their neighborhood and narrate its stories. Such practices will also gradually create the impression (to non-visitors) of a destination worth visiting (Moon and Han 2019). At the same time, opportunities to interact with residents should be provided to tourists, further enriched with experiences that cover social and cultural activities (i.e., local celebrations). For instance, it will be a good practice for the municipality of Kavala to initiate during its annual ethnic cultural festival “Cosmopolis” sub-events that facilitate resident–tourist interaction. Deeper understandings of the local culture, traditions, and norms generate positive feelings and experiences that appear to be critical for tourists to revisit a destination (Kirillova, Lehto, and Cai 2015).

Findings also suggest that tourism employees–tourists interaction can also positively contribute to tourists’ affective image development. It appears that training programs aiming to improve the communication and hospitality skills of tourism employees in both locations should become readily available. The results can have critical consequences especially for tourist enclaves or all-inclusive resorts where the vast majority of human interaction takes place between tourists and tourism employees, while contact to local residents is limited. Greater involvement and contact with local communities living in the broader area should be encouraged in such cases like in Thasos through day trips and participation in local events and activities. Lastly, given that local people are the ones who represent a symbolic life in a locality, another key responsibility of local authorities and DMOs is to sustain local culture and traditions and protect the natural and built environment. This is of outmost importance, especially nowadays where phenomena like overtourism dominate the international tourism scene and appear to diminish both residents’ quality of life and tourists’ destination image and satisfaction with a destination (Séraphin, Sheeran, and Pilato 2018).

Limitations and Future Research Directions

Like any other research, this study is vulnerable to some limitations. First, the results of this research are based on two studies conducted separately on tourists who visited destinations in Greece. Perceptions of such individuals and their interactions with local residents can potentially be different from those visiting alternative destinations in other countries; future research needs to continue verifying the established model in different contexts including rural destinations. Second, this research used tourists’ interactions with local residents and tourism employees, excluding other potentially significant factors such as emotional solidarity (Woosnam, Stylidis, and Ivkov 2020). Future research might need to address this by concurrently examining the impact of interactions, emotional solidarity and satisfaction on destination image to shed more light on their relationship. Third, perceptions of destination image can be influenced by previous visitation, political ideology, religion, and/or other cultural factors (Kim, Stylidis, and Oh 2019). To accommodate this diversity, future research should model destination image formation testing also for mediating or moderating variables, differentiating, for example, between first-time versus repeat visitors or various ethnic or religious groups.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Dimitrios Stylidis

Author Biography

Dimitrios Stylidis, PhD is a Senior Lecturer in Tourism at Middlesex University London. His research interests include place image and residents’ attitudes towards tourism. His work has been published in various journals including Tourism Management, Annals of Tourism Research, Journal of Travel Research and Journal of Sustainable Tourism.

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