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Table of Contents

    Topic review

    Benefits of Online Tourism Sources

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    Definition

    Online sources of information are a matter of special interest in tourism research. In particular, they are key elements in the formation of destination image. The purpose of this paper is to examine the relationship between online sources of information and destination image and to analyze the mediating role of motivation to co-create in that relationship. A research model was developed, and hypotheses were tested on data collected from 394 usable responses about the World Heritage city of Cuenca (Spain). The results show that online commercial sources have a direct positive impact on the conative, affective and cognitive dimensions of the tourist image, in this order. Additionally, this study supports the view that motivation to co-create mediates the relationship between online information sources and destination image. Finally, motivation to co-create was also found to have a positive and direct impact, in this order, on conative, cognitive and affective image. The main value of our research is that it underlines the essential influence of motivation to co-create in the relationship between online information sources and destination image. This study also provides a critical review of the existing literature by positing a conceptual theoretical framework that links three types of online sources of information (social media sources, online commercial sources and online non-commercial sources) and destination image. 

    1. Introduction

    Tourists have traditionally relied on travel agencies, tour operators, brochures, travel guides, friends and family when planning a trip [1][2]. However, the proliferation and the development of online sources of information has drastically changed this paradigm. In addition to providing consumers with access to book and purchase a wide range of tourism products and services [3], online information sources have significantly transformed the way consumers gather information, make decisions and give their opinions about purchases [4][5].
    In the tourism industry, this means that many tourists use online information sources to carry out activities such as choosing a destination or booking a table in a restaurant or a hotel room [6][7]. Moreover, the expansion of these online environments has allowed nearly anyone to publish information about a destination, and for travelers to check and share this information in real time. As a result, people’s decisions to visit and recommend a place are increasingly shaped by comments, ideas, photographs and videos that others upload to these sources [8].
    In order to leverage these developments, companies use online information sources to develop direct relationships with travelers in the different phases of their journey. This process—known as value co-creation or the co-creation experience [9][10]—is a matter of special interest in the literature [11][12][13] and for the broader tourism sector. Specifically, co-creation has been posited as a necessary condition for competitiveness, due to the significant changes in tourism behavior [14] and a paradigm shift in how the tourism industry creates and offers experiences [15]. As a result of the use of these sources, the way a destination image is created has now changed, requiring a reinterpretation of who participates in the image-formation process, and how [16]. Thus, although Destination Marketing Organizations (DMOs) still play a significant role in the image process, this work is shared by other agents, including tourists. As a result of this sharing process, the image of a destination is now co-created [16].
    Despite the growing number of articles about destination marketing [17], few have analyzed value co-creation in the tourism sector and its implications for companies [16][17][18]. On the one hand, many studies have analyzed the impact of the Internet and the mainstream media on destination image [2][19], and, on the other hand, the academic literature also recognizes online information sources as an important tool for enacting co-creation activities [20][21][22][23][24]. There is less research, however, on how co-creation generates value in the tourist experience [16] and the mechanisms underlying the relationship between online information sources and destination image. In this sense, few studies have analyzed how and why tourists engage in co-creation activities by using different types of online sources of information [25], while others have shown that the images perceived by tourists do not usually coincide with the images projected by providers and DMOs [26]. Few are the studies that treat online information sources as useful platforms for companies to strengthen the participation of users in the construction of the image [27]. Furthermore, it is crucial to know which sources are more important for individuals when making decisions about their travel plans [26].
    Therefore, in order to cover this research gap, this article proposes to broaden the relationship between online information sources and destination image by incorporating the motivation to co-create as a mediating variable in the relationship that has not been examined in prior research. Examining why consumers engage in co-creation activities can guide companies to develop effective communication strategies with consumers in a way that creates superior value for themselves and for the company or destination itself [28]. Specifically, the study of motivation to co-create lacks sufficient understanding in the context of the destination image. Companies and promoters in charge of promoting a tourist destination typically use images that are not consistent with reality and provide little information about the destination [29]. In this sense, motivating consumers to share their experiences and perceptions about the destination can lead to a clearer and more coherent image being transmitted to other users, encouraging other travelers to visit the place. Most researchers have thus far been more concerned with understanding the effect of destination image on tourist behavior than with determining what influences the image [30]. Motivation is considered a key concept to understand consumer behavior in tourism and in the process of choosing a destination [31], and, in turn, the image of the destination is strongly related to motivation [32].
    These main aims of this study were as follows: (1) We aimed to identify the influence of online information sources on destination image. We established a theoretical classification of online information sources (into social media sources, online commercial sources and online non-commercial sources) and considered three dimensions of destination image (cognitive, affective and conative). Online information sources refer to how important the source is for the individual to search for tourist information, and thus we intended to better understand “which” source has the strongest impact on destination image. (2) We aimed to determine the mediating role of motivation to co-create in the relationship between online information sources and destination image. Thus, the greater the importance of the source for the individual, the more motivated he/she will be to co-create. (3) We aimed to study the relationship between online information sources and motivation to co-create. Lastly, (4) we aimed to examine the effect of motivation to co-create on destination image.

    2. Analysis on Results

    2.1. Measurement Model

    Table 1 and Table 2 show the reliability and validity for all the constructs. Specifically, Table 1 reports the FIV, weights, t-test results, p-values and confidence intervals for the formative constructs, while Table 2 reports the individual, construct reliability and convergent validity (average variance extracted, AVE) for the reflective constructs.
    Table 1. Formative constructs and their respective items.
    Scheme 95. FIV Weights Student’s t-Test p-Value 95% Confidence
    Interval
    OnlineSource1
    (Generic social networks)
    1.093 0.410 * 1.759 0.039 0.008–0.745
    OnlineSource2
    (Tourism blogs)
    1.076 0.547 ** 3.341 0.000 0.298–0.820
    Online commercial sources FIV Weights Student’s t-test p-Value 95% Confidence
    Interval
    OnlineSource3
    (Official website of the city and province)
    1.043 0.502 *** 7.341 0.000 0.588–0.919
    OnlineSource4
    (Reservation websites with user ratings)
    1.131 0.540ns 0.951 0.171 −0.133–0.437
    OnlineSource5
    (Tourist providers’ websites)
    1.088 0.454 *** 3.187 0.001 0.214–0.679
    Online non-commercial sources FIV Weights Student’s t-test p-Value 95% Confidence
    Interval
    OnlineSource6
    (Maps)
    1.064 0.592 *** 2.843 0.002 0.193–0.911
    OnlineSource7
    (Pictures and media websites)
    1.237 0.598 ** 2.540 0.006 0.298–0.528
    Notes: *** p < 0.001 (t(4999) = 3.10); ** p < 0.01: (t(4999) = 2.33); * p < 0.05 (t(4999) = 1.65); ns = not significant.
    Table 2. Reflective constructs and their corresponding items.
        Construct Reliability AVE
    Loading Cronbach’s
    Alpha
    Dijkstra–Henseler’s ρA Composite
    Reliability
     
    Cognitive image   0.931 0.934 0.940 0.596
    Cogni5. Good reputation of the destination 0.698
    Cogni6. Natural environment without pollution 0.748
    Cogni7. Relaxed environment 0.806
    Cogni8. Safe place to travel 0.777
    Cogni9. Accessibility from accommodation 0.566
    Cogni10. Family-oriented destination 0.753
    Cogni11. Good quality-price relationship 0.783
    Cogni12. Satisfactory customer service 0.671
    Cogni13. Interesting activities 0.693
    Cogni15. Interesting cultural attractions 0.706
    Cogni16. Interesting historical monuments and important events 0.734
    Cogni17. Opportunities for cycling, climbing and other sports 0.670
    Cogni18. Good weather 0.592
    Cogni19. Magnificent landscapes 0.753
    Cogni20. Beautiful nature 0.755
    Affective image   0.878 0.878 0.904 0.514
    Afect1. Nice 0.756
    Afect2. Relaxing 0.668
    Afect3. Pretty 0.748
    Afect4. Exciting 0.695
    Afect6. Awake 0.719
    Afect8. Animated 0.749
    Afect9. Friendly 0.749
    Afect11. Interesting 0.788
    Conative image   0.924 0.926 0.938 0.656
    Conat1. It has always been a dream destination to visit at some point in my life. 0.817
    Conat2. I think it is an appropriate vacation option. 0.756
    Conat3. It helps enhance my knowledge about certain subjects (for example, history or geography). 0.675
    Conat4. I have always considered it a personal goal to have a vacation in the city. 0.836
    Conat5. It is a personal need of mine that has to be fulfilled. 0.833
    Conat6. I have always had a permanent desire to visit it. 0.869
    Conat7. It has positive attributes that improve my personality. 0.821
    Conat8. It makes me believe that my holidays are the best reward or gift that I can give myself. 0.858
    According to the findings on our formative online sources of information constructs (Table 1), all items had significant weights to build the social media source construct (SMS). Regarding the online commercial source construct (OCS), all items also showed significant weights, with the only exception of OnlineSource5 (Reservation websites with user ratings). However, the t-value was positive and close to 1, so we followed Reference [33] and decided to keep this item. Finally, because all FIV values are below 3.3, we can affirm that our formative constructs are free of multicollinearity problems, and thus they all help build their corresponding formative constructs (SMS, OCS and ONCS).
    With regard to findings on the measurement of our reflective variables, Table 2 indicates that most of the individual items achieved good reliability, with item loadings almost always exceeding the desired threshold of 0.707 [34]. Five items of cognitive image (cogni1, cogni2, cogni3, cogni4 and cogni14) and three items of affective image (afect5, afect7 and afect10) showed very low values (below the minimum required threshold of 0.55 [35]. We ultimately decided to remove those items, following previous recommendations [33]. In terms of construct reliability, the Cronbach’s alphas and Dijkstra–Henseler’s composite reliabilities (ρA) [36] were above 0.70 (Table 2), as recommended [33]. All the constructs also had convergent validity, as the AVE for each reflective variable was greater than 0.50 (Table 2) [33]. Finally, Table 3, which captures the correlations across all our research variables, shows that the heterotrait–monotrait (HTMT) values fell below the most restrictive threshold of 0.85 and were significantly different from 1 [37], thus confirming discriminant validity between each pair of variables. Discriminant validity was also met; according to the Fornell–Larcker criterion [38], the square roots of AVE for each variable were greater than the correlation of each variable with the others, as is required [33] (see Table 3).
    Table 3. Descriptive statistics, correlation matrix and square roots of AVE for the reflective constructs.
      Mean SD 1 2 3 4 5 6 7
    1. Social media sources 3.18 0.95 --- n.a. n.a. n.a. n.a. n.a. n.a.
    2. Online commercial sources 2.96 0.83 0.540 ** ---- n.a. n.a. n.a. n.a. n.a.
    3. Online non-commercial sources 3.03 0.98 0.352 ** 0.420 ** ---- n.a. n.a. n.a. n.a.
    4. Motivation to co-create 3.33 1.23 0.226 ** 0.292 ** 0.181 ** 0.82 0.552
    [0.46;0.65]
    0.559
    [0.44;0.65]
    0.611
    [0.52;0.68]
    5. Affective image 4.01 0.67 0.225 ** 0.170 ** 0.270 ** 0.389 ** 0.72 0.840
    [0.79;0.89]
    0.679
    [0.61;0.74]
    6. Cognitive image 4.18 0.63 0.165 ** 0.150 ** 0.243 ** 0.406 ** 0.607 ** 0.77 0.677
    [0.63;0.72]
    7. Conative image 3.38 1.01 0.182 ** 0.123 * 0.272 ** 0.435 ** 0.752 ** 0.624 ** 0.81
    Notes: Values in bold on the diagonal are square roots of AVE (variance shared between the constructs and their measures). Off-diagonal elements below the diagonal are correlations between the constructs; ** p < 0.01, * p < 0.05 (two-tailed test). Off-diagonal elements above the diagonal are the heterotrait–monotrait ratios of correlations (HTMT), and their corresponding confidence intervals at the 95% significance level. SD = standard deviation; n.a., non-applicable.
     
    Regarding the measurement of our second-order construct, “motivation to co-create”, we followed recommendations by Reference [33] and built latent variable scores of first-order constructs, such as “Autonomy”, “Competence” and “Enjoyment” (Table 4). These items were adapted from References [10][39]. Ref. [39] considers that people involve themselves in creative activities because they seek experiences that give them feelings of competence, autonomy and enjoyment of the task, while Reference [10] shows that people with an interest in participating in creative activities are looking for pleasant, autonomous and competent co-creation experiences. More specifically, participants were asked about their motives for participating in co-creation activities related to the destination. We measured “autonomy” by using the item, “I am able to help improve the image of [Cuenca]”; “competence” was measured as “My participation in co-creation activities enhances my knowledge about [Cuenca]”; and “enjoyment” was measured as “The experience is a lot of fun and I am having a good time participating in co-creation activities”.
    Table 4. Motivation to co-create. Measurement model.
      Construct Reliability Convergent Validity
    Second-Order Construct First-Order Construct Weight Loading Composite Reliability Cronbach’s
    Alpha
    Dijkstra–Henseler’s ρA AVE
    Motivation to
    co-create
    Competence 0.495 *** 0.922 0.852 0.746 0.856 0.666
    Autonomy 0.465 *** 0.909
    Enjoyment 0.213 *** 0.569
    Notes: AVE = average extracted variance.
    Table 4 shows the weights, loadings, reliability and convergent validity (AVE) for the construct. The first-order indicators that underlie the second-order construct achieved adequate loading values (i.e., in excess of 0.55, although enjoyment has a low value). In terms of reliability, they achieved adequate values for composite reliability, Cronbach’s Alpha and the Dijkstra–Henseler ratio (rho_A). Lastly, this construct met the convergent validity criterion, as the AVE exceeded the minimum 0.5 threshold.

    2.2. Hypothesis Testing

    Table 5 contains the findings related to our hypotheses. The results show that social media sources did not relate to any component of the destination image, contrary to our predictions, and thus H1 (H1a, H1b and H1c) could not be accepted.
    Table 5. Hypothesis validation (H1–H4).
    Hypothesis   Original Sample Student’s t-Test p-Value Supported
    H1a Social media sources -> Cognitive image 0.033 0.371 0.355 No
    H1b Social media sources -> Affective image 0.001 0.016 0.494 No
    H1c Social media sources -> Conative image −0.068 1.120 0.131 No
    H2a Online commercial sources -> Cognitive image 0.147 ** 2.416 0.008 Yes
    H2b Online commercial sources -> Affective image 0.156 ** 2.826 0.002 Yes
    H2c Online commercial sources -> Conative image 0.178 *** 3.478 0.000 Yes
    H3a Online non-commercial sources -> Cognitive image −0.010 0.116 0.454 No
    H3b Online non-commercial sources -> Affective image 0.068 0.951 0.171 No
    H3c Online non-commercial sources -> Conative image 0.047 0.943 0.173 No
    H4a Motivation to co-create -> Cognitive image 0.450 *** 8.163 0.000 Yes
    H4b Motivation to co-create -> Affective image 0.427 ** 7.832 0.000 Yes
    H4c Motivation to co-create -> Conative image 0.494 *** 11.852 0.000 Yes
    Notes: *** p < 0.001 (t(4999) = 3.10); ** p < 0.01: (t(4999) = = 2.33); * p< 0.05 (t(4999) = 1.65); * p < 0.10 (t(4999) = 1.282).
    With regard to H2, the results confirm that online commercial sources (H2a, H2b and H2c) positively influence cognitive, affective and conative destination image. Therefore, the participants’ perceived image of the destination improved when the information about the destination came from sources such as official marketing campaigns. This result is consistent with the findings of (i) Reference [40], who found that travel agency staff acted as a commercial source with a positive influence on one of the cognitive image factors considered; (ii) Reference [41], who observed how certain US tour operators were having a major impact on the image of Russia as a US tourist destination, contributing to the positioning of the US as a primarily historical and cultural destination; (iii) Reference [42], who detected that commercial sources of information were having an impact on the perceived image of Mauritius as a vacation destination; and Reference [43], who found that commercial agents were one of the market forces that had dominated the process of forming the image of Yanyu (East China), helping to communicate an image of freedom, leisure and romance.
    Regarding H3, the results also revealed that this hypothesis could not be accepted. Thus, contrary to expectations, we did not find that online non-commercial sources influenced the perceived image, so H3a, H3b and H3c could not be accepted.
    Although neither H1 nor H3 could be accepted, the results obtained are consistent to some extent with those obtained by previous research. For example, although Reference [40] detected a certain influence of social media sources and online non-commercial sources information in the image (in particular, the cognitive image), this influence was quite small, since it only significantly influenced some components of the cognitive image.
    For H4, we found that motivation to co-create positively influenced destination image. In particular, Table 5 and Figure 1 show that motivation to co-create positively influences cognitive image (β = 0.450, p < 0.001), affective image (β = 0.427, p < 0.001) and conative image (β = 0.494, p < 0.001). Thus, H4 (and therefore H4a, H4b and H4c) could be accepted.
    Figure 1. Proposed research model.
    In this regard, it is worth highlighting that the three categories of online information sources showed a significant relationship with the motivation to co-create. Social media sources were the most influential, followed by online commercial sources and online non-commercial sources.
    Finally, with regard to H5, all our results suggest that motivation to co-create exerts a significant mediating effect on the relationship between online information sources (SMS, OCS and ONCS) and the image of the tourist destination (cognitive, affective and conative). In fact, the bias-corrected and accelerated (BCA) bootstrap method (with 5000 repetitions) revealed a significant indirect effect of social media sources, online commercial sources and online non-commercial sources on cognitive, affective and conative image (see Table 6). These results suggest that motivation to co-create does indeed mediate the relationship between informational online sources and destination images, in support of H5a, H5b and H5c; H6a, H6b and H6c; and H7a, H7b and H7c. With regard to H6a, H6b and H6c, the indirect effects of online commercial sources coexist with the significant direct effect of online commercial sources on cognitive (H2a), affective (H2b) and conative (H2c) destination image. Therefore, we can conclude that motivation to co-create partially mediates that particular relationship(s). Finally, albeit at a low level (p < 0.10), the indirect effects of online non-commercial sources on all three destination images are significant, thus suggesting the existence of such a mediation effect of motivation to co-create on the relationship between online non-commercial sources and destination image. Thus, we can also give support to H7a, H7b and H7c (Table 6).
    Table 6. Mediation hypothesis validation (H5–H7): direct, indirect, total effects and explained variance.
    Effects on Dependent Variables Direct Effects
    (t-Value)
    Indirect Effects
    (Hypothesis Support)
    Total Effects
    Cognitive Image
    (R2 = 0.255)
       
    Motivation to co-create 0.450 *** (8.20) --- 0.450
    Online non-commercial sources −0.010 ns (0.12) 0.042 (Yes) 0.032
    Social media sources 0.033 ns (0.37) 0.069 * (Yes) 0.102
    Online commercial sources 0.147 **(2.40) 0.051 * (Yes) 0.198
    Affective Image
    (R2 = 0.254)
       
    Motivation to co-create 0.427 *** (7,77) --- 0.427
    Online non-commercial sources 0.068 ns (0.94) 0.040 (Yes) 0.108
    Social media sources 0.001 ns (0.02) 0.065 * (Yes) 0.066
    Online commercial sources 0.156 **(2.80) 0.049 * (Yes) 0.205
    Conative Image
    (R2 = 0.299)
       
    Motivation to co-create 0.494 *** (11.98) --- 0.494
    Online non-commercial sources 0.047 ns (0.93) 0.046 (Yes) 0.093
    Social media sources −0.068 ns (1.12) 0.076 * (Yes) 0.008
    Online commercial sources 0.178 ***(3.50) 0.057 * (Yes) 0.235
    Notes: *** p < 0.001 (t(4999) = 3.10); ** p < 0.01: (t(4999) = = 2.33); * p < 0.05 (t(4999) = 1.65);  p < 0.10 (t(4999) = 1.282); ns = not significant.
     
    Table 7 is helpful to understand the quality of such mediation effects of motivation to co-create. For the mediation in the relationships between online sources of information and cognitive image, the data in the table show that the mediated model (R2mediated model = 0.255) triples the variance explained of cognitive image compared to an unmediated model (R2unmediated model = 0.083; ΔR2 = 0.172), which indicates that the mediation effect is medium in size (f2 = 0.23) [44] (Table 7). Of a similar size is the mediation effect of motivation to co-create in accounting for affective image (see Table 7); in this case, data show that the mediated model (R2mediated model = 0.254) has nearly triple the variance explained compared to an unmediated model (R2unmediated model = 0.097; ΔR2 = 0.157), indicating that the mediation effect is medium in size (f2 = 0.21) [44] (Table 7). Finally, regarding the mediation effect of motivation to co-create in the relationship between online sources of information and conative image (see Table 7), the results show that the mediated model (R2mediated model = 0.299) also triples the variance explained of conative image compared to an unmediated model (R2 unmediated model = 0.072; ΔR2 = 0.227), which indicates that the mediation effect is also medium in size (f2 = 0.32); [44].
    Table 7. Mediation effect size of motivation to co-create.
    Independent–Dependent Variable Relationship Variance Explained Size of the
    Mediation Effect
      Unmediated Relationship Mediated Relationship ΔVariance
    explained
    (f2)
    Online sources of information—cognitive image 0.083 0.255 0.172 0.23
    (medium effect)
      Unmediated relationship Mediated Relationship ΔVariance
    explained
     
    Online sources of information—affective image 0.097 0.254 0.157 0.21
    (medium effect)
      Unmediated
    relationship
    Mediated
    Relationship
    ΔVariance explained  
    Online sources of information—conative image 0.072 0.299 0.227 0.32
    (medium effect)
    Notes: f2 = (R2 included – R2 excluded)/(1 − R2 included); effect sizes of f2 ≥ 0.02, ≥ 0.15 and ≥ 0.35 are small, moderate and large, respectively [45].
     
    Finally, regarding the model’s explanatory power, the R2 and Q2 (predictive relevance of the endogenous variable) yielded satisfactory values (Table 7). The R2 adjusted values were 0.255 for cognitive image, 0.254 for affective image and 0.299 for conative image. The R2 adjusted value for motivation to co-create was low (R2 adjusted = 0.084), but the abovementioned R2 adjusted values show the model has substantial power to explain these main dependent variables [33]. Interestingly, the Stone–Geisser blindfolding sample reuse technique, with an omission distance of 7, revealed Q2 values larger than zero for all the cases. This indicates that the model we tested has good predictive power for motivation to co-create (Q2 = 0.043), cognitive image (Q2 = 0.128), affective image (Q2 = 0.122) and general conative image (Q2 = 0.185) [33].

    3. Current Insights

    The main purpose of this research was to contribute to the marketing literature by analyzing the relationship between online sources of information, co-creation and destination image. More specifically, beyond the direct relationship between the importance of online sources of information and destination image, the current study analyzed the mediating effect of motivation to co-create in the relationship between online sources of information and destination image, responding to recent calls to analyze the role of motivation in the image of the destination [46][47].
    Our findings reveal a significant indirect effect of social media sources, online commercial sources and online non-commercial sources on cognitive, affective and conative image, through motivation to co-create. The mediation value was also significant. It reveals that tourists contribute more effectively to the formation of the image when they are motivated. This study calls on tourist companies and destination managers to consider incentives to promote motivation among consumers to engage in value co-creation through informational online sources. In this process, consumers participate in the building of the destination image, so companies can develop their marketing strategies to motivate consumers to promote positive aspects of the destination. As a result, value co-creation may reinforce the image projected and perceived by other users and make tourists prefer to visit and get to know that destination instead of other competing destinations. These results are consistent with recent studies that show how co-creation has a positive effect on the image of the destination. For example, it has been found that the co-creation experience has a positive and significant impact on the cognitive and affective image of the destination through UGC platforms [48]. Authors have also shown that the publications shared by users themselves on Instagram are the most influential in promoting the attractiveness of the destination and attracting other travelers [49].
    The results additionally showed that the three categories of online sources of information were also directly related to motivation to co-create. The greater the perceived importance of the online source among users, the greater is the motivation to co-create. Social media sources, such as the recommendations of family and friends, exerted the greatest influence on such motivation, followed by online commercial sources and online non-commercial sources. These results substantiate previous findings [8][50] indicating that word-of-mouth is the most effective and accurate communication channel for tourists, particularly when the source is friends and family.
    Moreover, the findings reveal a positive relationship between the importance of online commercial sources for the individual and all image dimensions, albeit in the following order: conative, affective and cognitive image. These findings highlight the importance of the information provided by public officials, sources and private companies through their website. Thus, based on the results obtained, the official website of the destination and tourist reservation portals are the online source considered most important by users to search for tourist information about Cuenca. This conclusion is consistent with the findings of previous studies that indicate a greater influence of induced sources on the image of the destination [8][51]. In particular, the results indicate that online commercial sources have a significant positive impact on conative image. In contrast to previous studies, we have been unable to verify the influence of social media sources and online non-commercial sources on the destination image. These results are consistent with the findings of a number of papers, however. For example, although Reference [40] detected a certain influence of social media sources and online non-commercial sources on image (in particular, cognitive image), this influence was quite small, influencing only some components of the cognitive image. Sociodemographic characteristics lead tourist provider websites to be more important for middle-aged users (45–64 years), in contrast to younger people, who report not using this type of social media or using it very little. Thus, while younger generations are influenced by the opinions of other users on platforms such as TripAdvisor or Booking, retired people prefer to obtain information directly from the official source. A possible explanation is that older people may have greater knowledge and experience in life and are thus are more likely to want to be careful with what they communicate and transmit to other people.
    Lastly, the results also showed that the effect of motivation to co-create was greatest on conative image, followed by cognitive image and affective image. These results are consistent with the works of References [52][53], who showed that conative image plays a fundamental role in the decision and/or recommendation to visit a destination [52][53][54], and with Reference [50], who found that the psychological motivation of travelers positively impacts on cognitive and affective image. Regarding the motivation to co-create, 48% of respondents consider that their main motivation is to contribute to improving the image of Cuenca, while 46% consider it is to inform and advise other tourists about the activities and places to visit in Cuenca. If we consider conative image, the respondents said that Cuenca constitutes a dream destination to visit at some point (39.3%) and an adequate vacation option (34.5%). For affective image, users highlight that Cuenca is “pleasant”, “relaxing”, “pretty” and “interesting”. If the cognitive image is considered, Cuenca is a destination of nature (71.8%), artistic–cultural interest (56.3%) and mountains (55.8%), with a relaxed and safe environment, important monuments and opportunities for sports.

    The entry is from 10.3390/jtaer16060115

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