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Destination appeal through digitalized comments
Journal of Business Research 101 (2019) 447–453
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Destination appeal through digitalized comments☆ ⁎
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Enrique Bigne, Carla Ruiz , Rafael Curras-Perez Marketing and Market Research Department, University of Valencia, Av. Naranjos s/n, 46022, Valencia, Spain
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Keywords: Tourism destination, TD Tourist generated content, TGC Digital destination image, DDI
Brand choice remains under review in the context of service provision. Drawing on schema theory and persuasion theory, this study tests how, in online reviews, valence (positive vs. negative), content style (general vs. specific), and destination familiarity interact and influence digital destination image (DDI) and intention to visit a tourist destination (TD). We run a 2 × 2 × 2 experimental design using a sample of 1055 TripAdvisor users. Our findings suggest that: (i) Positive (vs. negative) online reviews, specific (vs. general) online reviews, and familiarity with a destination enhance DDI and intention to visit a TD; (ii) the impact of the valence of the review on DDI and intention to visit decreases when the destination is familiar vs. unfamiliar; (iii) the impact of the valence of the review on DDI and intention to visit decreases when the review is specific vs. general.
1. Introduction Brand choice is a critical issue in the context of service provision, especially given the emergence of online social media. There is intense competition among tourist destinations (TDs) (Hultman, Skarmeas, Oghazi, & Beheshti, 2015; Souiden, Ladhari, & Chiadmi, 2017), and tourists often choose online to visit destinations with similar attributes, such as wonderful scenery, beautiful beaches, friendly local communities, and high-quality accommodation. Potential tourists get pre-trip information from online reviews that differentiate one destination from another and form expectations. Prior studies provide empirical evidence that destination image is valuable for understanding tourist preferences, selection processes, and recommendations (Molinillo, Liébana-Cabanillas, Anaya-Sánchez, & Buhalis, 2018; Souiden et al., 2017). As destination image has a decisive influence on tourist behavior, it is very important to develop a positive image. The travel-related content that tourists create and upload on social media is termed “Tourist-Generated Content” (TGC); the tourism literature discusses this in some detail (e.g., Mak, 2017). TGC can both shape the overall image of a destination and raise its profile among prospective tourists and thereby motivate them to travel there (Gavilan, Avello, & Martinez-Navarro, 2018). While a large number of published papers, including meta-analyses, discuss these issues, there is scant research on the impact of product familiarity and the content style of online reviews (e.g., general vs. specific) on brand image and purchase intention in services. In the travel sector, user-generated content (i.e., online reviews)
drives brand choice. TDs are a unique study context because of the variety of consumer experience (Wong & Qi, 2017). This variety might be due to a number of factors, headed by the many possible visit motives, and may include the assorted perspectives that tourists have of a destination's attractions. Given the increasing competition among TDs, key questions are how tourist-generated content affects the decision to visit a destination and how it shapes the digital image of a destination (DDI). TGC is produced by tourists who are generally not rewarded for providing the information and, thus, it is more likely to be perceived as impartial. TGC can be regarded as an “organic” image formation agent in Gartner's (1994) terms; it has higher credibility than other, “induced” agents, such as the information provided by destination marketing organizations. This combination of credibility and accessibility makes TGC a powerful medium for shaping a destination's online image (Mak, 2017). Although TGC influences tourist destination choice behavior, there is comparatively little understanding of this new form of “organic” image formation agent. We explore the impact of TGC on destination image and brand choice. We focus on three key elements: the valence of online reviews, review content style, and the consumer's familiarity with the tourist destination. Our study contributes in two ways to the existing literature on service provision. First, we propose a new approach, based on schema theory and persuasion theory, to analyze the impact of review valence, content style (general vs. specific), and consumer familiarity with the TD, on brand image and visit intention. Although, intuitively, the role of review valence would appear obvious–positive reviews should elicit
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This work was supported by the Ministry of Economy and Competitiveness (Spain) under Grant ECO2014-53837R. Corresponding author. E-mail addresses: [email protected] (E. Bigne), [email protected] (C. Ruiz), [email protected] (R. Curras-Perez).
https://doi.org/10.1016/j.jbusres.2019.01.020 Received 10 June 2018; Received in revised form 9 January 2019; Accepted 11 January 2019 Available online 18 January 2019 0148-2963/ © 2019 Elsevier Inc. All rights reserved.
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H1. Online reviews of familiar (vs. unfamiliar) tourism destinations lead to (a) better digital destination image and (b) higher visit intention.
more positive responses than negative reviews–research on the effects of media coverage on corporate image (Meijer & Kleinnijenhuis, 2006), and on the impact of online reviews on sales (Sorensen & Rasmussen, 2004), demonstrates that negative reviews sometimes elicit positive responses. Furthermore, there are few studies on the influence of the content style of online reviews on DDI and visiting intentions. Destination marketers seek to increase potential tourists' familiarity with destinations to reduce their feelings of uncertainty and risk. Experience affects information processing in several ways; most of the previous studies on the subject focus on how different types of online platforms influence destination image formation (e.g., Molinillo et al., 2018), but they do not explore the interaction effect of TGC and familiarity on digital destination image and visit intention. Familiar brands can be more resilient to online reviews than unfamiliar brands because they have stronger roots in consumer memory (Chatterjee, 2001; Vermeulen & Seegers, 2009). Therefore, the second contribution of this study is the analysis of three interaction effects: content style with familiarity, familiarity with valence, and the impact of content style and valence on DDI and visit intention. These analyses are not addressed in the previous literature and thus now contribute to knowledge of the subject; and they have interesting implications for destination marketing organization managers. The direct effects of online reviews on DDI and visiting intentions may be amplified or mitigated by the interaction effects of the variables. Hence, we argue that familiarity with a tourism destination plays a significant role in the influence of TGC on digital destination image and visit intention. We organize the study as follows. We present first the theoretical background and then develop the conceptual framework and research hypotheses to explain how TGC impacts on brand choice and digital destination image. Next, we run a 2 × 2 × 2 experiment to empirically test our hypotheses with a sample of 1055 heavy-users of TripAdvisor. Then, we discuss the findings, limitations, and opportunities for future research. Finally, we summarize the study and discuss its implications for researchers and practitioners.
Social media have made DDI formation a more dynamic process, with great importance being given to the information available and other users' opinions (Hunter, 2016). Consumers “experience” destinations, and form DDI, without actually visiting locations (Molinillo et al., 2018; Tan & Wu, 2016), through their interaction with multimedia-enhanced websites and social media. Tourism studies show that eWOM, due to its higher level of perceived credibility over induced information sources, has an important influence on destination choice and perceived digital destination image (González-Rodríguez, Martínez-Torres, & Toral, 2016; Molinillo et al., 2018; Sun, Ryan, & Pan, 2015). Comments posted online have a clear impact on DDI formation and brand choice (Sun et al., 2015). Negative valence, defined as unfavorable feelings expressed toward a product or brand, leads to negative brand evaluations and lower purchase intention (Bhandari & Rodgers, 2017). Online reviews significantly impact also on the popularity and sales of experiential products. For instance, Zhang, Ye, Law, and Li (2010) suggest that positive online reviews can greatly improve the popularity of restaurants. Therefore, we posit: H2. Positive (vs. negative) reviews of tourism destinations lead to (a) a better digital destination image and (b) higher visit intention. Two complementary approaches support our next hypothesis. Online reviews are informational cues that facilitate the customer's evaluation of specific attributes of products and services. Recent studies show that the level of detail in a message plays a powerful role in the persuasion process (González-Rodríguez et al., 2016; Hunter, 2016; Mak, 2017). That is, highly-detailed reviews alleviate the customer's uncertainty about product quality and provide confidence in the decision-making process. While the source of information is an important factor in the persuasiveness of online reviews, so too is the content (Sparks, Perkins, & Buckley, 2013). However, content can be either specific or vague. Persuasion theory argues that consumers find specific arguments more persuasive than vague arguments (Sparks et al., 2013); some research finds that better-quality, more detailed explanations are more persuasive (Ajzen, Brown, & Rosenthal, 1996). Therefore, it is reasonable to expect that, in online reviews, specific, relevant information will provide stronger, more persuasive arguments than vague content, which can be considered analogous to a weaker argument. Consumers, according to schema theory, progressively gain knowledge about products and attributes. If online comment is general they consider it insufficient for decision-making and simply store it in their schema. Thereafter, they search for new and different types of information (Sujan & Bettman, 1989). Thus, product positioning based on specifics is more distinctive than overall positioning. The more specific the information about a destination is, the higher will be its effect on image destination and visit intention. Accordingly, we posit that specific reviews are more persuasive than general reviews and, therefore, they will lead to a better image and higher visit intention.
2. Literature review and hypotheses development The prior literature on eWOM demonstrates that eWOM and positive valence have large positive impacts on outcome variables, such as purchase intention, image, and sales. However, tourism research almost totally neglects specific destinations as units of analysis. Tourist destination image (TDI) is defined as the sum of beliefs, ideas and impressions that a person has of a destination (Crompton, 1979, p. 18). Recently, in a review of 45 valid TDI definitions, Lai and Li (2016, p. 10) propose a very elaborate definition: TDI is “a voluntary, multisensory, primarily picture-like, qualia-arousing, conscious, and quasi-perceptual mental (i.e., private, non-spatial, and intentional) experience held by tourists about a destination.” We extend further the definition of perceived destination image by using the term “digital destination image” – “DDI” – to describe the sum of the individual image attributes that make up the tourist experience as manifested in online TGC. Destination familiarity is the visual or mental impression of a destination or tourist experience that can stimulate the consumer's visit intention (Bianchi, Milberg, & Cúneo, 2017; Horng, Liu, Chou, & Tsai, 2012). We explain how consumers establish familiarity based on their cognitive knowledge, structures or schemas. Schemas allow consumers to visualize relationships between stimuli attributes, as they are “conceptual abstractions that mediate between stimuli received by the sense organs and behavioral responses.” (Casson, 1983, p. 430). Consumers encode, decode, categorize, and act according to the schemas they construct (Brewer & Nakamura, 1984). Previous studies suggest that a high level of destination familiarity positively affects image and visit intention (Horng et al., 2012; Milman & Pizam, 1995; Tan & Wu, 2016). Consequently, this study posits the following:
H3. Specific (vs. general) reviews of tourism destinations lead to (a) a better digital destination image and (b) higher visit intention. We now discuss the positive effects of TGC on familiar and unfamiliar tourist destinations. The literature on brand familiarity and new information about a brand is not conclusive (Mariconda & Lurati, 2015). Research on product evaluation shows that perceptions of less familiar products are more easily changed by external sources (Rao & Monroe, 1988), but familiarity mitigates the effect of new information. In a study on WOM, Sundaram and Webster (1999) demonstrate that the evaluation of an unfamiliar brand, in comparison to the evaluation of a familiar brand, is more susceptible to change. Mariconda and Lurati (2015) confirm that, when subjects receive positive new information about a company, the judgment as to its reputation changes more 448
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intention when the review is positive vs. negative is less when the review is specific vs. general.
positively in a low-familiarity group than in a high-familiarity group. They find also that, when negative new information is provided about a company, the judgment as to its reputation changes more negatively in a low familiarity group. Studies show that familiarity has important effects on TD search behavior (Horng et al., 2012). In selecting either familiar or unfamiliar destinations, tourists first use their memories to recall previous experiences for information to guide their decisions. Thus, experiences and memories drive the destination selection process. If a tourist has positive, deeply entrenched memories of a destination, he or she may not need to search for additional information to make a travel decision. Therefore, we posit that the impact of review valence on DDI and visit intention is lower for familiar than for unfamiliar destinations.
3. Methodology 3.1. Experimental design A 2 (valence of the review: positive vs. negative TripAdvisor review) × 2 (content style of the review: general vs. specific review) × 2 (destination familiarity: high vs. low) between-subjects factorial design was used to test the proposed hypotheses. As to the study experimental stimuli, two pretests were undertaken to select the destinations and TripAdvisor reviews. The objective of the first pretest was to choose the tourist destinations. A group of university students (n = 25) indicated their familiarity with 12 destinations (independent of whether they had visited them or not) on a five-point bipolar scale (1 = not known at all/5 = known very well). The 12 destinations were European cities similar in their key touristic resources and positioning. From them, two were chosen: Venice as the better-known (meanVenice = 4.61) and Ljubljana as the lesser-known destination (meanLjubljana = 1.93). The purpose of the second pretest was to choose the TripAdvisor reviews for the experimental stimuli. First, we chose an attraction in both destinations on which to create specific commentaries, The Grand Canal in Venice and Preseren Square in Ljubljana. Next, we designed three TripAdvisor reviews for each scenario; destination (Venice vs. Ljubljana), valence (negative vs. positive), and content style (general vs. specific). In total, we designed 24 reviews. The reviews were adapted from real commentaries posted on TripAdvisor and were validated in terms of content realism and appropriateness of length and readability. A second group of university students (n = 101) ranked their perceptions of valence (1 = negative/7 = positive) and content style (1 = general about the destination/7 = specific about an attraction in the destination) of three reviews from the total of 24. The comments were perceived as significantly different both in valence (meanPositive = 5.95; meanNegative = 1.54; t = 17.54, p < .01) and content style (meanGeneral = 2.82; meanSpecific = 5.32; t = 6.44, p < .01).
H4. The difference between (a) digital destination image and (b) visit intention when the review is positive vs. negative is less when the destination is familiar vs. unfamiliar. We now consider the interaction effects of general versus specific online reviews and familiar versus unfamiliar TDs. According to Bettman's (1978) information processing model, consumers make judgments about the nature and amount of information necessary and sufficient for decision-making. If a tourist believes that the information available on a familiar destination is sufficient, he/she will not need to acquire further information through external searches. In the consideration set model of consumer decision-making (Roberts & Lattin, 1991), familiar brands are more salient to the average consumer than unfamiliar brands. If a brand (destination) is more salient to consumers, the brand's (destination) attributes will also be more salient (Vermeulen & Seegers, 2009). In such cases, exposure to TGC might increase attribute salience to only a limited extent. As a result, specific reviews of familiar TDs will not be as strongly persuasive as specific reviews of unfamiliar TDs. For familiar TDs, the probability of their inclusion in the consumer's awareness set is strong; exposure to reviews will hardly affect this. The persuasive effect of specific online reviews is thus stronger for unfamiliar destinations than for familiar destinations. Therefore, H5. The difference between (a) digital destination image and (b) visit intention when the review is specific vs. general is less when the destination is familiar vs. unfamiliar.
3.2. Data collection, sample and procedure
The next hypothesis deals with the interaction effects of positive versus negative online reviews and general versus specific comments on TDs. A negative comment might have different effects depending on how specific it is. We argue that, if the negative content is specific, its influence will be lower than if it is general. Two complementary streams of research support this view. According to compensatory models, in choosing a brand, consumers make trade-offs between attributes by a type of linear compensation. Some attributes are not taken into account in a non-compensatory decision process (Johnson & Meyer, 1984). This view assumes that, because consumers make limited cognitive effort, only important attributes will be considered (for details, see Hoyer, 1984). Extending this into the social media context, consumers take into account only what is of interest to them when making a decision. If comments are specific, the consumer will be influenced, as H3 predicts. However, if a negative comment is specific, consumers take it into account only if the attribute is relevant to their decision-making process. Consistent with these arguments, general negative comments have a greater impact than specific negative comments. This argument supports Bordalo, Gennaioli, and Shleifer's (2013) concept of salience in consumer choice. An attribute is salient when it stands out from the norm. Intuitively, specific negative comments are not given credence because of their very specificity. Based on these two approaches, we posit that an overall negative comment has a more negative affect than a specific negative comment. Accordingly:
A total of 1055 TripAdvisor users resident in Spain, aged between 35 and 54, participated in the study. The respondents had traveled at least once for leisure/tourism purposes during the previous year. The sample comprised 57% females and 43% males; most had university studies (56%) and were employed (85%). Of the total, 65% had consulted social media sites at least 3–4 times and 57% had posted a review about a tourist destination on TripAdvisor in the previous 6 months. The research is a survey-based experiment with eight different scenarios, the subjects being randomly assigned to one of the experimental conditions. The questionnaire has three parts. The first section includes questions about the subjects' use of TripAdvisor as a social network, among other issues. Second, the subjects are provided with one of the eight scenarios (e.g., in scenario 1 participants read three general-positive TripAdvisor reviews about Venice). Participants were thereafter asked about their image of, and visit intention toward, the destination based on the online comments and some manipulation check questions. The third set of questions covered sociodemographic characteristics. 3.3. Measures To measure the study constructs we employed seven-point Likert scales previously used in the literature. We measured two dependent variables associated with tourist destinations. We examined the
H6. The difference between (a) digital destination image and (b) visit 449
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between the positive (M = 5.42) and negative scenarios (M = 4.62) when the destination is familiar (t (1,510) = 6.97, p < .01); the t-test also shows a significant difference between the positive (M = 4.79) and negative reviews (M = 3.41) when the destination is unfamiliar (t (1,541) = 11.64, p < .01), but the difference in the means between the positive and negative reviews was higher in this instance than when the destination was familiar. Thus, H4a is supported. Again, results from the visit intention ANOVA reveal a significant interaction between familiarity and valence (F = 6.79, p < .01) (see Fig. 1). A comparison ttest shows a significant difference between positive (M = 5.00) and negative reviews (M = 4.41) when the destination is familiar (t (1,510) = 4.37, p < .01); the t-test also shows a significant difference between positive (M = 4.40) and negative reviews (M = 3.31) when the destination is unfamiliar (t (1,541) = 8.20, p < .01); the difference in the means of visit intention between the positive and negative scenarios is higher when the destination is unfamiliar than when the destination is familiar. Thus, H4b is accepted. Regarding H5a, the results from the ANOVAs for destination familiarity and content style indicate that there is no significant impact on overall image (F = 1.68, p > .05) and visit intention (F = 2.11, p > .05), so H5a and H5b are unsupported. Regarding overall image (see Fig. 2), the t-test shows a significant difference between the specific (M = 5.21) and the general scenarios (M = 4.84) when the destination is familiar (t (1,510) = 2.95, p < .01), but not for unfamiliar destinations (MSpecific = 4.19; MGeneral = 4.08; t (1,541) = 1.33, p > .05); the difference in the means between the specific and general scenarios when the destination is familiar, as opposed to unfamiliar, is not statistically significant. Regarding visit intention as a dependent variable (see Fig. 2), again the t-test shows a significant difference between the specific (M = 4.88) and general reviews (M = 4.53) when the destination is familiar (t (1,510) = 2.36, p < .05), but this difference is not significant with the unfamiliar destination (MSpecific = 3.88; MGeneral = 3.82; t (1,541) = 0.62, p > .05). Fig. 2 illustrates these results. Therefore, with low destination familiarity, consumers do not strongly differentiate between specific or general reviews as regards image and visit intention. There are two possible reasons for this. First, as Jacoby, Szybillo, and Busato-Schach (1977) argue, small information units can be integrated into more meaningful higher-order units. Therefore, with unfamiliar brands, the process of inference from a specific review leads to the same outcome as for a general review. Second, as Miller (1956) notes, the amount of information can be understood as “variance.” Thus, if two pieces of information do not differ in their valence, little additional information is delivered, which results in a similar effect on the relevant variables, that is, DDI and visit intention. Finally, the ANOVA results show that the interaction effect between the valence and content style of the reviews is also significant for overall image (F = 23.79, p < .01) (see Fig. 3). The t-test shows a significant difference between positive (M = 5.03) and negative reviews (M = 4.35) when the review is specific (t (1,509) = 5.64, p < .01); the t-test also shows a significant difference between the positive (M = 5.17) and negative reviews (M = 3.64) when the reviews are general (t (1,542) = 12.58, p < .01), the difference in the means of overall image being higher than when the reviews are specific. Thus, H6a is accepted. Similarly, the ANOVA results for visit intention show that the interaction effect between valence and content style is also significant (F = 27.37, p < .01) (see Fig. 3). The means of the positive (M = 4.62) and negative scenarios (M = 4.15) are significantly different (t (1,509) = 3.41, p < .01) when the reviews refer to a specific destination attraction; again, the t-test shows that the difference in the means of visit intention to the destination between the positive (M = 4.78) and negative reviews (M = 3.57) is significant when the review is general (t (1,542) = 9.02, p < .01), this mean difference being higher than for the specific scenario. Thus, H6b is accepted.
perceived overall image of the destination (IMAGE) using an adaptation of the 3-item scale of Nguyen and LeBlanc (2001) (α = 0.93). We assessed visit intention to the destination (VISIT) using a 4-item scale from Gefen, Karahanna, and Straub (2003) (α = 0.96). As all scales exhibited a high degree of reliability, to test the hypotheses we calculated the mean scores of the corresponding items on each scale. 4. Results 4.1. Manipulation checks Manipulation checks were carried out to determine whether treatments related to the destination familiarity, valence, and content style of the TripAdvisor reviews were effective. We used scales similar to those used in the pretest to measure perceived destination familiarity (FA: 1 = not known at all/7 = known very well), perceived valence (VA: 1 = negative/7 = positive) and content style (CS: 1 = general about the destination/7 = specific about an attraction in the destination) of the reviews. The results show that Venice is more familiar to the sample than Ljubljana (FAVenice = 4.36, FALjubljana = 2.58, t = 16.09, p < .01). The valence and content style of the review manipulation was also successful. As regards the review valences, positive conditions are perceived as significantly more positive than the negative scenarios (VAPositive = 5.66, VANegative = 2.27, t = 35.10, p < .01). As to the content style of the reviews, again there are significant differences in the perception of general vs. specific reviews (CSGeneral = 3.62, CSSpecific = 4.40, t = 6.98, p < .01). 4.2. Test of the hypotheses To test the hypotheses, two ANOVAs were conducted, with destination familiarity, valence, and content style as factors and perceived overall image and visit intention as dependent variables. H1 proposes that online reviews of familiar (vs. unfamiliar) tourism destinations lead to (a) higher overall image of the destination and (b) higher visit intention to the destination. The results from the ANOVA show a main effect for destination familiarity for overall image (F = 124.09; p < .01); the reviews of familiar destinations show a significantly more positive image (M = 5.04) than reviews of unfamiliar destinations (M = 4.11), thus supporting H1a. Again, we find a main effect for destination familiarity for visit intention (F = 78.01; p < .01); reviews of familiar destinations influence visit intention (M = 4.72) significantly more than reviews of unfamiliar destinations (M = 3.85), so H1b is supported. H2 posits that positive (vs. negative) TripAdvisor reviews lead to (a) better overall image and (b) higher visit intention. Again, the results from the ANOVA show a main effect for valence on overall image (F = 172.37; p < .01). As predicted, positive reviews lead to better overall image (M = 5.11) than negative reviews (M = 4.03), supporting H2a. Following the same logic, we also find a significant main effect of review valence on visit intention (F = 76.29, p < .01). Positive reviews of the destination generate higher visit intention (M = 4.70) than negative reviews (M = 3.86), supporting H2b. H3 posits that specific (vs. general) TripAdvisor reviews lead to (a) better overall image and (b) higher visit intention. The results from the ANOVA show a main effect for content style on overall image (F = 9.45; p < .01). Specific reviews lead to better overall image (M = 4.71) than general reviews (M = 4.44), supporting H3a. We also find a main effect for content style on visit intention (F = 4.48, p < .05). Reviews with specific commentaries about a destination attraction cause higher visit intention (M = 4.38) than general reviews (M = 4.18), supporting H3b. We found a significant interaction effect between destination familiarity and review valence on the two dependent variables. As posited in H4, results from the overall image ANOVA indicate that there is significant interaction between familiarity and valence (F = 11.52, p < .01) (see Fig. 1). A comparison t-test shows a significant difference 450
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Fig. 1. Interaction effect between familiarity and valence on (a) digital destination image and (b) visit intention.
5. Discussion and conclusions
effects of TGC on familiar and unfamiliar destinations. It seems that schemas previously constructed by users are not easily modified by TGC if the knowledge units are large (as with familiar destinations); (ii) positive TGC impacts more than negative on DDI and visit intention for both familiar and unfamiliar destinations, but the influence of review valence on DDI and visiting intentions is higher for unfamiliar TDs; (iii) specific comments have more influence than general comments, regardless of familiarity with the destination; (iv) the impact of review valence (positive vs. negative) on DDI and visit intention is lower when the online review is specific. When a review is positive, a general review results in better DDI and higher visit intention than a specific review; a general negative online review makes DDI and visit intention lower than a specific negative comment.
5.1. Discussion and theoretical implications This study examines the impact of type of eWOM, valence, and content style on digital destination image and travel intention for familiar and unfamiliar destinations. This differentiation between familiar and unfamiliar destinations raises conceptual and managerial implications. We contribute to the understanding of TGC in service provision by showing that its impact is less clear than previously thought. The study's main theoretical contribution is in the use of schema theory to model and draw conclusions about the impact of TGC on consumer behavior. The study contributes to the literature on destination image by responding to the suggestion of Josiassen, Assaf, Woo, and Kock (2016) that the interaction effects among variables in the destination image model should be analyzed. Our results are largely consistent with persuasion theory (Bettman, 1978), but in a social media context. Our findings suggest that online reviews of familiar destinations have more effect on DDI and visit intention than online reviews of unfamiliar destinations (H1). Furthermore, positive reviews enhance DDI and visit intention (H2); and the evidence supports the impact of specific over general review content for DDI and visit intention, as H3 predicts. Specific content is trustworthier and thus more persuasive than general content. The interaction effect of valence and familiarity shows that DDI and visit intention decrease less when the review is negative and the destination is familiar, than with negative reviews of unfamiliar destinations (H4). Lastly, the interaction effect between content style and valence shows that DDI and visit intention are lower when reviews are generic and negative, as H6 predicts. These findings have several theoretical implications: (i) Schema theory provides a useful framework for understanding the different
5.2. Managerial implications The impact of familiarity with a TD, combined with the valence of online reviews and content style, on brand choice has strategic importance for tourism services providers. First, failure to take into account the combined impact of valence and content type of online reviews on DDI will have serious negative consequences for destination marketing organizations' marketing strategies. Second, in a social media context, destination marketing organizations should recognize that with familiar destinations it is positive and specific online reviews that create a better image of their destinations and increase visit intention. Unfamiliar tourism destinations show a similar pattern, but the impact of TGC on digital destination image and visiting intentions is lower than for familiar destinations. It seems that a minimum threshold of destination awareness is needed for eWOM to have an effective impact. Therefore, we recommend that enhancing destination familiarity should be a priority for destination marketing organizations. This might be achieved through an integrated marketing communications scheme,
Fig. 2. Interaction effect between familiarity and content style (a) digital destination image and (b) visit intention. 451
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Fig. 3. Interaction effect between content style and valence (a) digital destination image and (b) visit intention.
employing a range of social media based sales promotion activities, such as coupon offers, two-for-one sales, games, contests, and attractive price packages. In addition, effective public relations and traditional advertising covering the attractions of the destination might be embedded into the various social media platforms. Third, the differences caused by the posting of general reviews, as opposed to specific reviews, about unfamiliar destinations are not large. Certainly, specific comments might enhance DDI and visiting intentions, but the difference in impact on image of, and visit intention to, unfamiliar destinations is not significant. On the contrary, for familiar destinations the difference is significant. In other words, specific reviews are key drivers of DDI and brand choice for familiar destinations but are not as important for unfamiliar destinations. Fourth, positive comments have similar impact to negative comments regardless of the content style (e.g., general versus specific). However, negative and general comments impact DDI and visit intention to a greater extent than negative and specific comments. Therefore, destination marketing organizations should prioritize their handling of negative and general comments.
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5.3. Limitations and future research lines This study has some limitations that open future research lines. The analysis is limited to text, not images. Although textual TGC can provide rich and authentic information that reflects perceived digital destination image and tourist experiences (Mak, 2017), an emerging area that calls for research is visual eWOM. It would also be interesting to analyze comparatively the influence of eWOM (destination familiarity, valence, and content style) on image and visit intention across different user groups, for example, those more or less active on social networks. This paper focuses on two specific urban destinations. Further studies on other types of tourism destination (rural, beach) would be useful to determine whether different contexts influence the effects of eWOM on digital destination image and visit intention. Lastly, further studies might analyze the strength of the source (see Litterio, Nantes, Larrosa, & Gómez, 2017) and the influence of the intensity of social media use on tourism destination image (Stojanovic, Andreu, & Curras-Perez, 2018). Schema theory might also provide a solid framework for future research on the many possible moderators of online review impact on DDI and visit intention that we have not considered, such as product type (e.g., experiential vs. search goods), cultural differences, gender, age, available budget, source likeability, and rating systems, and so on. References Ajzen, I., Brown, T. C., & Rosenthal, L. H. (1996). Information bias in contingent
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Meijer, M., & Kleinnijenhuis, J. (2006). Issue news and corporate reputation: Applying the theories of agenda setting and issue ownership in the field of business communication. Journal of Communication, 56(3), 543–559. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. Milman, A., & Pizam, A. (1995). The role of awareness and familiarity with a destination: The Central Florida case. Journal of Travel Research, 33(3), 21–27. Molinillo, S., Liébana-Cabanillas, F., Anaya-Sánchez, R., & Buhalis, D. (2018). Destination marketing organizations online platforms: Image and visit intention. Tourism Management, 65, 116–130. Nguyen, N., & Leblanc, G. (2001). Corporate image and corporate reputation in customers' retention decisions in services. Journal of Retailing and Consumer Services, 8(4), 227–236. Rao, A. R., & Monroe, K. B. (1988). The moderating effect of prior knowledge on cue utilization in product evaluations. Journal of Consumer Research, 15(2), 253–264. Roberts, J. H., & Lattin, J. M. (1991). Development and testing of a model of consideration set composition. Journal of Marketing Research, 28(4), 429–440. Sorensen, A., & Rasmussen, S. (2004). Is any publicity good publicity? A note on the impact of book reviews (Working paper)Stanford University. Souiden, N., Ladhari, R., & Chiadmi, N. E. (2017). Destination personality and destination image. Journal of Hospitality and Tourism Management, 32, 54–70. Sparks, B. A., Perkins, H. E., & Buckley, R. (2013). Online travel reviews as persuasive communication: The effects of content style, source, and certification logos on
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Destination appeal through digitalized comments☆ ⁎
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Enrique Bigne, Carla Ruiz , Rafael Curras-Perez Marketing and Market Research Department, University of Valencia, Av. Naranjos s/n, 46022, Valencia, Spain
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A B S T R A C T
Keywords: Tourism destination, TD Tourist generated content, TGC Digital destination image, DDI
Brand choice remains under review in the context of service provision. Drawing on schema theory and persuasion theory, this study tests how, in online reviews, valence (positive vs. negative), content style (general vs. specific), and destination familiarity interact and influence digital destination image (DDI) and intention to visit a tourist destination (TD). We run a 2 × 2 × 2 experimental design using a sample of 1055 TripAdvisor users. Our findings suggest that: (i) Positive (vs. negative) online reviews, specific (vs. general) online reviews, and familiarity with a destination enhance DDI and intention to visit a TD; (ii) the impact of the valence of the review on DDI and intention to visit decreases when the destination is familiar vs. unfamiliar; (iii) the impact of the valence of the review on DDI and intention to visit decreases when the review is specific vs. general.
1. Introduction Brand choice is a critical issue in the context of service provision, especially given the emergence of online social media. There is intense competition among tourist destinations (TDs) (Hultman, Skarmeas, Oghazi, & Beheshti, 2015; Souiden, Ladhari, & Chiadmi, 2017), and tourists often choose online to visit destinations with similar attributes, such as wonderful scenery, beautiful beaches, friendly local communities, and high-quality accommodation. Potential tourists get pre-trip information from online reviews that differentiate one destination from another and form expectations. Prior studies provide empirical evidence that destination image is valuable for understanding tourist preferences, selection processes, and recommendations (Molinillo, Liébana-Cabanillas, Anaya-Sánchez, & Buhalis, 2018; Souiden et al., 2017). As destination image has a decisive influence on tourist behavior, it is very important to develop a positive image. The travel-related content that tourists create and upload on social media is termed “Tourist-Generated Content” (TGC); the tourism literature discusses this in some detail (e.g., Mak, 2017). TGC can both shape the overall image of a destination and raise its profile among prospective tourists and thereby motivate them to travel there (Gavilan, Avello, & Martinez-Navarro, 2018). While a large number of published papers, including meta-analyses, discuss these issues, there is scant research on the impact of product familiarity and the content style of online reviews (e.g., general vs. specific) on brand image and purchase intention in services. In the travel sector, user-generated content (i.e., online reviews)
drives brand choice. TDs are a unique study context because of the variety of consumer experience (Wong & Qi, 2017). This variety might be due to a number of factors, headed by the many possible visit motives, and may include the assorted perspectives that tourists have of a destination's attractions. Given the increasing competition among TDs, key questions are how tourist-generated content affects the decision to visit a destination and how it shapes the digital image of a destination (DDI). TGC is produced by tourists who are generally not rewarded for providing the information and, thus, it is more likely to be perceived as impartial. TGC can be regarded as an “organic” image formation agent in Gartner's (1994) terms; it has higher credibility than other, “induced” agents, such as the information provided by destination marketing organizations. This combination of credibility and accessibility makes TGC a powerful medium for shaping a destination's online image (Mak, 2017). Although TGC influences tourist destination choice behavior, there is comparatively little understanding of this new form of “organic” image formation agent. We explore the impact of TGC on destination image and brand choice. We focus on three key elements: the valence of online reviews, review content style, and the consumer's familiarity with the tourist destination. Our study contributes in two ways to the existing literature on service provision. First, we propose a new approach, based on schema theory and persuasion theory, to analyze the impact of review valence, content style (general vs. specific), and consumer familiarity with the TD, on brand image and visit intention. Although, intuitively, the role of review valence would appear obvious–positive reviews should elicit
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This work was supported by the Ministry of Economy and Competitiveness (Spain) under Grant ECO2014-53837R. Corresponding author. E-mail addresses: [email protected] (E. Bigne), [email protected] (C. Ruiz), [email protected] (R. Curras-Perez).
https://doi.org/10.1016/j.jbusres.2019.01.020 Received 10 June 2018; Received in revised form 9 January 2019; Accepted 11 January 2019 Available online 18 January 2019 0148-2963/ © 2019 Elsevier Inc. All rights reserved.
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H1. Online reviews of familiar (vs. unfamiliar) tourism destinations lead to (a) better digital destination image and (b) higher visit intention.
more positive responses than negative reviews–research on the effects of media coverage on corporate image (Meijer & Kleinnijenhuis, 2006), and on the impact of online reviews on sales (Sorensen & Rasmussen, 2004), demonstrates that negative reviews sometimes elicit positive responses. Furthermore, there are few studies on the influence of the content style of online reviews on DDI and visiting intentions. Destination marketers seek to increase potential tourists' familiarity with destinations to reduce their feelings of uncertainty and risk. Experience affects information processing in several ways; most of the previous studies on the subject focus on how different types of online platforms influence destination image formation (e.g., Molinillo et al., 2018), but they do not explore the interaction effect of TGC and familiarity on digital destination image and visit intention. Familiar brands can be more resilient to online reviews than unfamiliar brands because they have stronger roots in consumer memory (Chatterjee, 2001; Vermeulen & Seegers, 2009). Therefore, the second contribution of this study is the analysis of three interaction effects: content style with familiarity, familiarity with valence, and the impact of content style and valence on DDI and visit intention. These analyses are not addressed in the previous literature and thus now contribute to knowledge of the subject; and they have interesting implications for destination marketing organization managers. The direct effects of online reviews on DDI and visiting intentions may be amplified or mitigated by the interaction effects of the variables. Hence, we argue that familiarity with a tourism destination plays a significant role in the influence of TGC on digital destination image and visit intention. We organize the study as follows. We present first the theoretical background and then develop the conceptual framework and research hypotheses to explain how TGC impacts on brand choice and digital destination image. Next, we run a 2 × 2 × 2 experiment to empirically test our hypotheses with a sample of 1055 heavy-users of TripAdvisor. Then, we discuss the findings, limitations, and opportunities for future research. Finally, we summarize the study and discuss its implications for researchers and practitioners.
Social media have made DDI formation a more dynamic process, with great importance being given to the information available and other users' opinions (Hunter, 2016). Consumers “experience” destinations, and form DDI, without actually visiting locations (Molinillo et al., 2018; Tan & Wu, 2016), through their interaction with multimedia-enhanced websites and social media. Tourism studies show that eWOM, due to its higher level of perceived credibility over induced information sources, has an important influence on destination choice and perceived digital destination image (González-Rodríguez, Martínez-Torres, & Toral, 2016; Molinillo et al., 2018; Sun, Ryan, & Pan, 2015). Comments posted online have a clear impact on DDI formation and brand choice (Sun et al., 2015). Negative valence, defined as unfavorable feelings expressed toward a product or brand, leads to negative brand evaluations and lower purchase intention (Bhandari & Rodgers, 2017). Online reviews significantly impact also on the popularity and sales of experiential products. For instance, Zhang, Ye, Law, and Li (2010) suggest that positive online reviews can greatly improve the popularity of restaurants. Therefore, we posit: H2. Positive (vs. negative) reviews of tourism destinations lead to (a) a better digital destination image and (b) higher visit intention. Two complementary approaches support our next hypothesis. Online reviews are informational cues that facilitate the customer's evaluation of specific attributes of products and services. Recent studies show that the level of detail in a message plays a powerful role in the persuasion process (González-Rodríguez et al., 2016; Hunter, 2016; Mak, 2017). That is, highly-detailed reviews alleviate the customer's uncertainty about product quality and provide confidence in the decision-making process. While the source of information is an important factor in the persuasiveness of online reviews, so too is the content (Sparks, Perkins, & Buckley, 2013). However, content can be either specific or vague. Persuasion theory argues that consumers find specific arguments more persuasive than vague arguments (Sparks et al., 2013); some research finds that better-quality, more detailed explanations are more persuasive (Ajzen, Brown, & Rosenthal, 1996). Therefore, it is reasonable to expect that, in online reviews, specific, relevant information will provide stronger, more persuasive arguments than vague content, which can be considered analogous to a weaker argument. Consumers, according to schema theory, progressively gain knowledge about products and attributes. If online comment is general they consider it insufficient for decision-making and simply store it in their schema. Thereafter, they search for new and different types of information (Sujan & Bettman, 1989). Thus, product positioning based on specifics is more distinctive than overall positioning. The more specific the information about a destination is, the higher will be its effect on image destination and visit intention. Accordingly, we posit that specific reviews are more persuasive than general reviews and, therefore, they will lead to a better image and higher visit intention.
2. Literature review and hypotheses development The prior literature on eWOM demonstrates that eWOM and positive valence have large positive impacts on outcome variables, such as purchase intention, image, and sales. However, tourism research almost totally neglects specific destinations as units of analysis. Tourist destination image (TDI) is defined as the sum of beliefs, ideas and impressions that a person has of a destination (Crompton, 1979, p. 18). Recently, in a review of 45 valid TDI definitions, Lai and Li (2016, p. 10) propose a very elaborate definition: TDI is “a voluntary, multisensory, primarily picture-like, qualia-arousing, conscious, and quasi-perceptual mental (i.e., private, non-spatial, and intentional) experience held by tourists about a destination.” We extend further the definition of perceived destination image by using the term “digital destination image” – “DDI” – to describe the sum of the individual image attributes that make up the tourist experience as manifested in online TGC. Destination familiarity is the visual or mental impression of a destination or tourist experience that can stimulate the consumer's visit intention (Bianchi, Milberg, & Cúneo, 2017; Horng, Liu, Chou, & Tsai, 2012). We explain how consumers establish familiarity based on their cognitive knowledge, structures or schemas. Schemas allow consumers to visualize relationships between stimuli attributes, as they are “conceptual abstractions that mediate between stimuli received by the sense organs and behavioral responses.” (Casson, 1983, p. 430). Consumers encode, decode, categorize, and act according to the schemas they construct (Brewer & Nakamura, 1984). Previous studies suggest that a high level of destination familiarity positively affects image and visit intention (Horng et al., 2012; Milman & Pizam, 1995; Tan & Wu, 2016). Consequently, this study posits the following:
H3. Specific (vs. general) reviews of tourism destinations lead to (a) a better digital destination image and (b) higher visit intention. We now discuss the positive effects of TGC on familiar and unfamiliar tourist destinations. The literature on brand familiarity and new information about a brand is not conclusive (Mariconda & Lurati, 2015). Research on product evaluation shows that perceptions of less familiar products are more easily changed by external sources (Rao & Monroe, 1988), but familiarity mitigates the effect of new information. In a study on WOM, Sundaram and Webster (1999) demonstrate that the evaluation of an unfamiliar brand, in comparison to the evaluation of a familiar brand, is more susceptible to change. Mariconda and Lurati (2015) confirm that, when subjects receive positive new information about a company, the judgment as to its reputation changes more 448
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intention when the review is positive vs. negative is less when the review is specific vs. general.
positively in a low-familiarity group than in a high-familiarity group. They find also that, when negative new information is provided about a company, the judgment as to its reputation changes more negatively in a low familiarity group. Studies show that familiarity has important effects on TD search behavior (Horng et al., 2012). In selecting either familiar or unfamiliar destinations, tourists first use their memories to recall previous experiences for information to guide their decisions. Thus, experiences and memories drive the destination selection process. If a tourist has positive, deeply entrenched memories of a destination, he or she may not need to search for additional information to make a travel decision. Therefore, we posit that the impact of review valence on DDI and visit intention is lower for familiar than for unfamiliar destinations.
3. Methodology 3.1. Experimental design A 2 (valence of the review: positive vs. negative TripAdvisor review) × 2 (content style of the review: general vs. specific review) × 2 (destination familiarity: high vs. low) between-subjects factorial design was used to test the proposed hypotheses. As to the study experimental stimuli, two pretests were undertaken to select the destinations and TripAdvisor reviews. The objective of the first pretest was to choose the tourist destinations. A group of university students (n = 25) indicated their familiarity with 12 destinations (independent of whether they had visited them or not) on a five-point bipolar scale (1 = not known at all/5 = known very well). The 12 destinations were European cities similar in their key touristic resources and positioning. From them, two were chosen: Venice as the better-known (meanVenice = 4.61) and Ljubljana as the lesser-known destination (meanLjubljana = 1.93). The purpose of the second pretest was to choose the TripAdvisor reviews for the experimental stimuli. First, we chose an attraction in both destinations on which to create specific commentaries, The Grand Canal in Venice and Preseren Square in Ljubljana. Next, we designed three TripAdvisor reviews for each scenario; destination (Venice vs. Ljubljana), valence (negative vs. positive), and content style (general vs. specific). In total, we designed 24 reviews. The reviews were adapted from real commentaries posted on TripAdvisor and were validated in terms of content realism and appropriateness of length and readability. A second group of university students (n = 101) ranked their perceptions of valence (1 = negative/7 = positive) and content style (1 = general about the destination/7 = specific about an attraction in the destination) of three reviews from the total of 24. The comments were perceived as significantly different both in valence (meanPositive = 5.95; meanNegative = 1.54; t = 17.54, p < .01) and content style (meanGeneral = 2.82; meanSpecific = 5.32; t = 6.44, p < .01).
H4. The difference between (a) digital destination image and (b) visit intention when the review is positive vs. negative is less when the destination is familiar vs. unfamiliar. We now consider the interaction effects of general versus specific online reviews and familiar versus unfamiliar TDs. According to Bettman's (1978) information processing model, consumers make judgments about the nature and amount of information necessary and sufficient for decision-making. If a tourist believes that the information available on a familiar destination is sufficient, he/she will not need to acquire further information through external searches. In the consideration set model of consumer decision-making (Roberts & Lattin, 1991), familiar brands are more salient to the average consumer than unfamiliar brands. If a brand (destination) is more salient to consumers, the brand's (destination) attributes will also be more salient (Vermeulen & Seegers, 2009). In such cases, exposure to TGC might increase attribute salience to only a limited extent. As a result, specific reviews of familiar TDs will not be as strongly persuasive as specific reviews of unfamiliar TDs. For familiar TDs, the probability of their inclusion in the consumer's awareness set is strong; exposure to reviews will hardly affect this. The persuasive effect of specific online reviews is thus stronger for unfamiliar destinations than for familiar destinations. Therefore, H5. The difference between (a) digital destination image and (b) visit intention when the review is specific vs. general is less when the destination is familiar vs. unfamiliar.
3.2. Data collection, sample and procedure
The next hypothesis deals with the interaction effects of positive versus negative online reviews and general versus specific comments on TDs. A negative comment might have different effects depending on how specific it is. We argue that, if the negative content is specific, its influence will be lower than if it is general. Two complementary streams of research support this view. According to compensatory models, in choosing a brand, consumers make trade-offs between attributes by a type of linear compensation. Some attributes are not taken into account in a non-compensatory decision process (Johnson & Meyer, 1984). This view assumes that, because consumers make limited cognitive effort, only important attributes will be considered (for details, see Hoyer, 1984). Extending this into the social media context, consumers take into account only what is of interest to them when making a decision. If comments are specific, the consumer will be influenced, as H3 predicts. However, if a negative comment is specific, consumers take it into account only if the attribute is relevant to their decision-making process. Consistent with these arguments, general negative comments have a greater impact than specific negative comments. This argument supports Bordalo, Gennaioli, and Shleifer's (2013) concept of salience in consumer choice. An attribute is salient when it stands out from the norm. Intuitively, specific negative comments are not given credence because of their very specificity. Based on these two approaches, we posit that an overall negative comment has a more negative affect than a specific negative comment. Accordingly:
A total of 1055 TripAdvisor users resident in Spain, aged between 35 and 54, participated in the study. The respondents had traveled at least once for leisure/tourism purposes during the previous year. The sample comprised 57% females and 43% males; most had university studies (56%) and were employed (85%). Of the total, 65% had consulted social media sites at least 3–4 times and 57% had posted a review about a tourist destination on TripAdvisor in the previous 6 months. The research is a survey-based experiment with eight different scenarios, the subjects being randomly assigned to one of the experimental conditions. The questionnaire has three parts. The first section includes questions about the subjects' use of TripAdvisor as a social network, among other issues. Second, the subjects are provided with one of the eight scenarios (e.g., in scenario 1 participants read three general-positive TripAdvisor reviews about Venice). Participants were thereafter asked about their image of, and visit intention toward, the destination based on the online comments and some manipulation check questions. The third set of questions covered sociodemographic characteristics. 3.3. Measures To measure the study constructs we employed seven-point Likert scales previously used in the literature. We measured two dependent variables associated with tourist destinations. We examined the
H6. The difference between (a) digital destination image and (b) visit 449
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between the positive (M = 5.42) and negative scenarios (M = 4.62) when the destination is familiar (t (1,510) = 6.97, p < .01); the t-test also shows a significant difference between the positive (M = 4.79) and negative reviews (M = 3.41) when the destination is unfamiliar (t (1,541) = 11.64, p < .01), but the difference in the means between the positive and negative reviews was higher in this instance than when the destination was familiar. Thus, H4a is supported. Again, results from the visit intention ANOVA reveal a significant interaction between familiarity and valence (F = 6.79, p < .01) (see Fig. 1). A comparison ttest shows a significant difference between positive (M = 5.00) and negative reviews (M = 4.41) when the destination is familiar (t (1,510) = 4.37, p < .01); the t-test also shows a significant difference between positive (M = 4.40) and negative reviews (M = 3.31) when the destination is unfamiliar (t (1,541) = 8.20, p < .01); the difference in the means of visit intention between the positive and negative scenarios is higher when the destination is unfamiliar than when the destination is familiar. Thus, H4b is accepted. Regarding H5a, the results from the ANOVAs for destination familiarity and content style indicate that there is no significant impact on overall image (F = 1.68, p > .05) and visit intention (F = 2.11, p > .05), so H5a and H5b are unsupported. Regarding overall image (see Fig. 2), the t-test shows a significant difference between the specific (M = 5.21) and the general scenarios (M = 4.84) when the destination is familiar (t (1,510) = 2.95, p < .01), but not for unfamiliar destinations (MSpecific = 4.19; MGeneral = 4.08; t (1,541) = 1.33, p > .05); the difference in the means between the specific and general scenarios when the destination is familiar, as opposed to unfamiliar, is not statistically significant. Regarding visit intention as a dependent variable (see Fig. 2), again the t-test shows a significant difference between the specific (M = 4.88) and general reviews (M = 4.53) when the destination is familiar (t (1,510) = 2.36, p < .05), but this difference is not significant with the unfamiliar destination (MSpecific = 3.88; MGeneral = 3.82; t (1,541) = 0.62, p > .05). Fig. 2 illustrates these results. Therefore, with low destination familiarity, consumers do not strongly differentiate between specific or general reviews as regards image and visit intention. There are two possible reasons for this. First, as Jacoby, Szybillo, and Busato-Schach (1977) argue, small information units can be integrated into more meaningful higher-order units. Therefore, with unfamiliar brands, the process of inference from a specific review leads to the same outcome as for a general review. Second, as Miller (1956) notes, the amount of information can be understood as “variance.” Thus, if two pieces of information do not differ in their valence, little additional information is delivered, which results in a similar effect on the relevant variables, that is, DDI and visit intention. Finally, the ANOVA results show that the interaction effect between the valence and content style of the reviews is also significant for overall image (F = 23.79, p < .01) (see Fig. 3). The t-test shows a significant difference between positive (M = 5.03) and negative reviews (M = 4.35) when the review is specific (t (1,509) = 5.64, p < .01); the t-test also shows a significant difference between the positive (M = 5.17) and negative reviews (M = 3.64) when the reviews are general (t (1,542) = 12.58, p < .01), the difference in the means of overall image being higher than when the reviews are specific. Thus, H6a is accepted. Similarly, the ANOVA results for visit intention show that the interaction effect between valence and content style is also significant (F = 27.37, p < .01) (see Fig. 3). The means of the positive (M = 4.62) and negative scenarios (M = 4.15) are significantly different (t (1,509) = 3.41, p < .01) when the reviews refer to a specific destination attraction; again, the t-test shows that the difference in the means of visit intention to the destination between the positive (M = 4.78) and negative reviews (M = 3.57) is significant when the review is general (t (1,542) = 9.02, p < .01), this mean difference being higher than for the specific scenario. Thus, H6b is accepted.
perceived overall image of the destination (IMAGE) using an adaptation of the 3-item scale of Nguyen and LeBlanc (2001) (α = 0.93). We assessed visit intention to the destination (VISIT) using a 4-item scale from Gefen, Karahanna, and Straub (2003) (α = 0.96). As all scales exhibited a high degree of reliability, to test the hypotheses we calculated the mean scores of the corresponding items on each scale. 4. Results 4.1. Manipulation checks Manipulation checks were carried out to determine whether treatments related to the destination familiarity, valence, and content style of the TripAdvisor reviews were effective. We used scales similar to those used in the pretest to measure perceived destination familiarity (FA: 1 = not known at all/7 = known very well), perceived valence (VA: 1 = negative/7 = positive) and content style (CS: 1 = general about the destination/7 = specific about an attraction in the destination) of the reviews. The results show that Venice is more familiar to the sample than Ljubljana (FAVenice = 4.36, FALjubljana = 2.58, t = 16.09, p < .01). The valence and content style of the review manipulation was also successful. As regards the review valences, positive conditions are perceived as significantly more positive than the negative scenarios (VAPositive = 5.66, VANegative = 2.27, t = 35.10, p < .01). As to the content style of the reviews, again there are significant differences in the perception of general vs. specific reviews (CSGeneral = 3.62, CSSpecific = 4.40, t = 6.98, p < .01). 4.2. Test of the hypotheses To test the hypotheses, two ANOVAs were conducted, with destination familiarity, valence, and content style as factors and perceived overall image and visit intention as dependent variables. H1 proposes that online reviews of familiar (vs. unfamiliar) tourism destinations lead to (a) higher overall image of the destination and (b) higher visit intention to the destination. The results from the ANOVA show a main effect for destination familiarity for overall image (F = 124.09; p < .01); the reviews of familiar destinations show a significantly more positive image (M = 5.04) than reviews of unfamiliar destinations (M = 4.11), thus supporting H1a. Again, we find a main effect for destination familiarity for visit intention (F = 78.01; p < .01); reviews of familiar destinations influence visit intention (M = 4.72) significantly more than reviews of unfamiliar destinations (M = 3.85), so H1b is supported. H2 posits that positive (vs. negative) TripAdvisor reviews lead to (a) better overall image and (b) higher visit intention. Again, the results from the ANOVA show a main effect for valence on overall image (F = 172.37; p < .01). As predicted, positive reviews lead to better overall image (M = 5.11) than negative reviews (M = 4.03), supporting H2a. Following the same logic, we also find a significant main effect of review valence on visit intention (F = 76.29, p < .01). Positive reviews of the destination generate higher visit intention (M = 4.70) than negative reviews (M = 3.86), supporting H2b. H3 posits that specific (vs. general) TripAdvisor reviews lead to (a) better overall image and (b) higher visit intention. The results from the ANOVA show a main effect for content style on overall image (F = 9.45; p < .01). Specific reviews lead to better overall image (M = 4.71) than general reviews (M = 4.44), supporting H3a. We also find a main effect for content style on visit intention (F = 4.48, p < .05). Reviews with specific commentaries about a destination attraction cause higher visit intention (M = 4.38) than general reviews (M = 4.18), supporting H3b. We found a significant interaction effect between destination familiarity and review valence on the two dependent variables. As posited in H4, results from the overall image ANOVA indicate that there is significant interaction between familiarity and valence (F = 11.52, p < .01) (see Fig. 1). A comparison t-test shows a significant difference 450
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Fig. 1. Interaction effect between familiarity and valence on (a) digital destination image and (b) visit intention.
5. Discussion and conclusions
effects of TGC on familiar and unfamiliar destinations. It seems that schemas previously constructed by users are not easily modified by TGC if the knowledge units are large (as with familiar destinations); (ii) positive TGC impacts more than negative on DDI and visit intention for both familiar and unfamiliar destinations, but the influence of review valence on DDI and visiting intentions is higher for unfamiliar TDs; (iii) specific comments have more influence than general comments, regardless of familiarity with the destination; (iv) the impact of review valence (positive vs. negative) on DDI and visit intention is lower when the online review is specific. When a review is positive, a general review results in better DDI and higher visit intention than a specific review; a general negative online review makes DDI and visit intention lower than a specific negative comment.
5.1. Discussion and theoretical implications This study examines the impact of type of eWOM, valence, and content style on digital destination image and travel intention for familiar and unfamiliar destinations. This differentiation between familiar and unfamiliar destinations raises conceptual and managerial implications. We contribute to the understanding of TGC in service provision by showing that its impact is less clear than previously thought. The study's main theoretical contribution is in the use of schema theory to model and draw conclusions about the impact of TGC on consumer behavior. The study contributes to the literature on destination image by responding to the suggestion of Josiassen, Assaf, Woo, and Kock (2016) that the interaction effects among variables in the destination image model should be analyzed. Our results are largely consistent with persuasion theory (Bettman, 1978), but in a social media context. Our findings suggest that online reviews of familiar destinations have more effect on DDI and visit intention than online reviews of unfamiliar destinations (H1). Furthermore, positive reviews enhance DDI and visit intention (H2); and the evidence supports the impact of specific over general review content for DDI and visit intention, as H3 predicts. Specific content is trustworthier and thus more persuasive than general content. The interaction effect of valence and familiarity shows that DDI and visit intention decrease less when the review is negative and the destination is familiar, than with negative reviews of unfamiliar destinations (H4). Lastly, the interaction effect between content style and valence shows that DDI and visit intention are lower when reviews are generic and negative, as H6 predicts. These findings have several theoretical implications: (i) Schema theory provides a useful framework for understanding the different
5.2. Managerial implications The impact of familiarity with a TD, combined with the valence of online reviews and content style, on brand choice has strategic importance for tourism services providers. First, failure to take into account the combined impact of valence and content type of online reviews on DDI will have serious negative consequences for destination marketing organizations' marketing strategies. Second, in a social media context, destination marketing organizations should recognize that with familiar destinations it is positive and specific online reviews that create a better image of their destinations and increase visit intention. Unfamiliar tourism destinations show a similar pattern, but the impact of TGC on digital destination image and visiting intentions is lower than for familiar destinations. It seems that a minimum threshold of destination awareness is needed for eWOM to have an effective impact. Therefore, we recommend that enhancing destination familiarity should be a priority for destination marketing organizations. This might be achieved through an integrated marketing communications scheme,
Fig. 2. Interaction effect between familiarity and content style (a) digital destination image and (b) visit intention. 451
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Fig. 3. Interaction effect between content style and valence (a) digital destination image and (b) visit intention.
employing a range of social media based sales promotion activities, such as coupon offers, two-for-one sales, games, contests, and attractive price packages. In addition, effective public relations and traditional advertising covering the attractions of the destination might be embedded into the various social media platforms. Third, the differences caused by the posting of general reviews, as opposed to specific reviews, about unfamiliar destinations are not large. Certainly, specific comments might enhance DDI and visiting intentions, but the difference in impact on image of, and visit intention to, unfamiliar destinations is not significant. On the contrary, for familiar destinations the difference is significant. In other words, specific reviews are key drivers of DDI and brand choice for familiar destinations but are not as important for unfamiliar destinations. Fourth, positive comments have similar impact to negative comments regardless of the content style (e.g., general versus specific). However, negative and general comments impact DDI and visit intention to a greater extent than negative and specific comments. Therefore, destination marketing organizations should prioritize their handling of negative and general comments.
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