social media tourism articles

ORIGINAL RESEARCH article

Effects of social media tourism information quality on destination travel intention: mediation effect of self-congruity and trust.

• 1 School of Logistics, Transportation and Tourism, Jiangsu Vocational College of Finance and Economics, Huaian, Jiangsu, China
• 2 College of Business, Gachon University, Seongnam, Republic of Korea

The asymmetry of tourism information makes social media an important information source. Previous research has been conducted on the influence of tourist-generated content on tourism consumption behavior, but few studies have concentrated on the mechanism of tourism information quality on consumers’ travel intention in the social media environment. Adopting the Elaboration Likelihood Model, this paper aims to investigate how the information quality of social media affects consumers’ travel intention rationally and emotionally and the moderation effect of tourists’ prior knowledge. The empirical results indicate that the quality of social media information positively affects travel intention and self-congruity and trust mediates the relationship between the quality of social media information and travel intention. Moreover, this study identified that tourists’ prior knowledge negatively modifies the relationship between information quality and self-congruity in line with the proposed hypotheses. The research explores the influence mechanism of tourist-generated content quality on consumers’ travel intention, which benefits destination management and content marketing.

1 Introduction

Information is the basis of decision-making, and any decision-making is based on the collection, analysis, and evaluation of information, so is the decision-making behavior of tourism consumers. As the pyramid of Data, Information, Knowledge, and Wisdom (DIKW) puts forward, data is the source of information, information is the cornerstone of knowledge, knowledge is the basis and condition of wisdom, and wisdom is the application and productive use of knowledge ( Rowley, 2007 ). On the whole, from data to wisdom, it is a process of continuous processing of data, which is a spiral process and a process of data generating value. With the arrival of the big data era, massive data has penetrated into every industry and field and become an important factor of production. In the Web 2.0 environment, consumers’ travel decision-making behavior relies more on the management and utilization of data and information than ever before. Through social networks, blogs, etc., dormant data on the Web flows in two directions (between users and data providers), making it easier for users to participate in and expand information and transform it into knowledge. However, the availability of information and the creation of knowledge/wisdom do not grow at the same speed ( Malik et al., 2018 ). That makes it more urgent to study how information can be more efficiently transformed into knowledge and wisdom, i.e., how this information is processed through their cognitive perspective to drive the decision-making process.

Social media has greatly improved consumers’ ability to acquire information and knowledge about public events, products, and services ( Bertot et al., 2010 ). In addition, social media improves information exchange, reduces uncertainty, and brings users a sense of belonging ( Zehrer and Grabmüller, 2012 ), fundamentally changing individual travel plans and consumption patterns of travel and leisure ( Hudson and Thal, 2013 ). Therefore, when consumers search for online tourism information, social media has become the essential way ( Xiang and Gretzel, 2010 ) and a prominent place of creating, distributing, and marketing content that is unique to the users ( Sin et al., 2020 ). Even in the middle of the COVID-19 pandemic, the rising UGC content related to tourism impacts numerous consumers to travel ( Flores-Ruiz et al., 2021 ). Hanafiah et al. (2022) documented that social media still plays a vital role in influencing travel intention during the COVID-19 pandemic.

Accordingly, the influence of social media travel information on consumer behavior has become a hot topic in academic research. Some studies focus on adopting social media tourism information and use the technology acceptance model to explain the motivation or influencing factors of potential tourists’ adoption of social media information ( Chung et al., 2015 ; Cheunkamon et al., 2020 ). It is suggested that potential tourists are more inclined to use the contents of social media with similar interests to themselves when making travel plans ( Ayeh et al., 2013 ). The Elaboration Likelihood Model (ELM) documented that potential tourists’ adoption of online review information is influenced by the dual path factors of the central and peripheral path ( Filieri and McLeay, 2014 ). Some scholars focused on social media tourism information or e-word-of-mouth influencing tourists’ decision-making behaviors (such as hotel and destination choices). Chung and Han (2017) used the ELM model to verify the persuasive effect of social media tourism information on tourism decision-making. Kapoor et al. (2022) proposed that information quality positively impacts hotel stay intention.

Notably, the ELM theory is a classical framework for interpreting the influence of social media tourism information or TGC on tourist behavior. However, the ELM framework only focuses on the direct influence of persuasion factors on consumer behavior without considering the psychological transformation process of potential tourists. Many existing studies use consumers’ attitudes or perceived destination impressions as mediators ( Yadav et al., 2021 ; Kapoor et al., 2022 ), while few studies proposed psychological factors behind the changes in tourists’ attitudes.

Social media has given consumers a novel experience. Compared with the promotional content of businesses, the emotional evaluation of destinations and their perceived credibility of tourist-generated content (TGC) may play a greater role in consumers’ decision-making process ( Iordanova and Stainton, 2019 ).

Tourist-generated content is not only the explicit content created, published, and shared by users but also includes implicit content, such as user identity, status, relationship, and reputation. This implicit content acts as symbolic clues to stimulate consumers’ association and associate typical tourist images of destinations with their personality characteristics. As a result, the destination image becomes an available resource for self-expression ( Elliott and Wattanasuwan, 1998 ) and an extension of self ( Belk, 1988 ). Morand et al. (2021) also highlight the influence of tourism ambassadors as destination image inducers within the online realm. In other words, in the social media environment, destination symbolism significantly changes tourists’ attitudes. So far, few studies combine the symbolic meaning of destination with the above-mentioned dual-path persuade model.

To compensate for the deficiency, this study proposes an integrated rational and emotional decision path to explain how tourism information on social media affects consumers’ travel intention, focusing on explaining the psychological mechanism behind consumers’ emotional decision path. Furthermore, considering that consumer product knowledge is an important factor influencing information processing ability ( Petty et al., 1997 ), this study also examines the moderating effect of tourists’ prior knowledge in different decision-making paths.

2 Literature review and hypothesis development

2.1 elaboration likelihood model.

The ELM is one of the most frequently used frameworks in information processing studies and a persuasion model. According to this model, there are two paths to persuade consumers to form and change their attitudes: the core and peripheral paths ( Petty et al., 1983 ). The central path refers to consumers’ comprehensive thinking and analysis of information, forming or changing their attitudes toward products. In contrast to the central path, the peripheral path means that consumers change their attitudes through peripheral clues or implicit hints, which are simple rules or information shortcuts such as brand image and source attractiveness that consumers use to assess a recommendation rather than evaluating the quality of the arguments used by a source ( Petty and Cacioppo, 1986 ).

This study uses social media tourism information quality as an explanatory variable to construct a dual decision-making path model. The core path of ELM corresponds to the rational decision-making path of tourists, which is reflected in that consumers get the perception of destination and generate travel intention through in-depth reasoning and thinking of tourism information itself, that is, the direct influence of tourism information quality on consumers’ travel intention. The peripheral path corresponds to the emotional path, which includes two clues. First, the symbolic meaning of the destination gives consumers the possibility of self-construction. When the self-image of the viewer matches the image of the publisher, the destination becomes a source for individual self-expression, which can easily arouse the viewer’s resonance. The second clue is the trust or emotional experience the tourism destination brings to tourists, especially when consumers cannot form the impression of the destination from the perspective of rational cognition. The feeling or emotional experience brought by tourism information has become the key factor for tourists to make decisions.

2.2 Tourism information quality on social media

The immateriality and simultaneity of production and consumption of tourism products determine that tourists usually search for information in order to reduce risks and uncertainties when making travel decisions. Tourists create and share destination tourism information through various social media platforms (blogs and microblogs, content or virtual communities, and social networks) ( Tsiakali, 2015 ) and produce a large number of user-generated content (UGC). Social media is turned into a collection of tourist destination images ( Luo and Zhong, 2015 ), which influence tourists’ cognition and choice of destination ( Nezakati et al., 2015 ). More and more consumers take tourism information on social media as an essential reference when choosing destinations ( Chung et al., 2015 ). In this study, tourism information is defined as “tourist-generated content (TGC) including texts, pictures, and videos about tourist destinations on social media platforms.” TGC or the shared memorable tourism experiences are both cognitive and emotional ( Kim et al., 2012 ), and are inseparable from tourists’ behavioral engagements ( Servidio and Ruffolo, 2016 ). In other words, the more tourists participate in the activities, the better they can retrieve the memories ( Coudounaris and Sthapit, 2017 ) and present them on social platforms. Furthermore, the distinctiveness of tourists’ memorable tourism experiences is crucial for destination management and marketing ( Wearing and Foley, 2017 ). By classifying city attractions, Yu et al. (2021) proposed to identify the unique patterns of attractions to recognize what can be a memorable cue or stimuli of tourists’ shared memorable experiences on social media.

As Yeap et al. (2014) stated, information quality is “how the provided information is useful for the consumer.” Information quality is a strong predictor of the credibility of information sources and website quality ( Filieri et al., 2015 ), indicating that the quality of information content itself is the core factor in persuading consumers. Our study follows this argument and takes information quality as the independent variable. Different scholars put forward their own opinions on the measurement of information quality. For example, based on the characteristics of the information content itself, the quality of content can be measured from the four indicators of relevance, understandability, adequacy, and objectivity ( Park et al., 2007 ), or authenticity, authority, and relevance ( Wang, 1998 ), value ( Filieri and McLeay, 2014 ), accuracy and completeness ( Zhang et al., 2014 ), richness and usefulness ( Bovee, 2004 ).

The marketing value of information quality is that it has a significant impact on consumers’ willingness to adopt information and purchase decisions. The quality and characteristics of online information will affect tourists’ decision-making. For example, information accuracy, relevance, and timeliness will affect tourists’ adoption behavior of online comment information ( Filieri and McLeay, 2014 ). Positive UGC can stimulate consumers to produce both emotional (motivation and pleasure) and cognitive responses (perceived information quality), form direct behavioral responses (information sharing and direct purchase), and potential behavioral responses (future purchase intention and brand commitment), respectively ( Kim and Johnson, 2016 ).

2.3 Travel intention

Behavioral intention usually refers to an individual’s possibility or attitude tendency to take action on an activity or object ( Smith, 2004 ). Purchase intention is also considered the most effective predictor of consumer purchase behavior ( Morwitz and Schmittlein, 1992 ). The intention is used to predict various consumer behaviors, including travel decision-making behaviors, and researchers can learn how individuals will act from their behavioral intentions ( Sheeran, 2002 ). Travel intention is the main driving force for tourists to travel to destinations ( Woodside and Lysonski, 1989 ). It can predict tourists’ travel behavior and is the tendency of individuals’ expectations, plans, or intentions on whether their future behavior will be carried out ( Lam and Hsu, 2006 ).

Consumers’ impression of products or services is formed through processing various information sources. If the information content is perceived to be complete, accurate, relevant, and authentic, it is easy for consumers to pay attention to and deeply process it and form a rational cognition and attitude toward the destination. The higher the quality of online reviews, the stronger the purchase intention of consumers ( Park et al., 2007 ). High-quality information enables users to understand specific products or services better, gain support, and be able to make better decisions ( Kim et al., 2017 ). Many scholars have empirically tested that online travel reviews significantly positively impact consumers’ booking intentions ( Lu et al., 2013 ; Sparks et al., 2013 ; Torres et al., 2015 ; Zhao et al., 2015 ). Lata and Rana (2021) also verified that information quality is a predictor for online hotel booking intentions. Based on the above literature, this study presumes H1.

H1 Social media tourism information quality positively impacts consumers’ travel intention.

2.4 Self-congruity

Self-congruity stems from one of the core constructs of social psychology: self-concept. Self-concept can be understood as self-image. It is an individual’s comprehensive evaluation of his own behavior, ability, values, and other aspects. It is a subjective perception and cannot be directly observed. “Protecting, maintaining, and promoting one’s self-concept or symbolic self is one of the most basic goals of human behavior” ( Onkvisit and Shaw, 1987 ). Self-congruity is an extension of self-concept, also known as self-concept congruity or self-image congruity. Sirgy et al. (1991) proposed that self-congruity refers to “the degree of matching or consistency between the symbolic image of a product/brand and the self-image of customers.” In tourism, self-congruity refers to matching tourists’ self-image and typical image.

The degree of consistency between consumers’ self-concept and product user image will affect consumers’ attitudes toward products ( Sirgy, 1982 ). The symbolic meaning of a brand can explain this: all social behaviors have symbolic meaning, and consumers can show their public image and construct their desired identity by using a brand ( Helgeson and Supphellen, 2004 ). Symbolic consumption stems from socialized human behavior–human beings constantly construct, maintain, promote, transform, and express their “self” in social behavior ( Elliott and Wattanasuwan, 1998 ; Escalas and Bettman, 2005 ). Therefore, consumption is a universal behavior of human beings, and brands become a resource for people to obtain symbolic meaning in the consumption process. Consuming a certain brand and being associated with a brand image becomes a means for consumers to construct, transform, and express themselves in daily life ( McCracken, 1987 ). Thus, the symbolic meaning of a brand is actually a projection of consumers’ self-concept of the brand. For instance, the perceived luxuriousness of a coffee shop leads to high self-congruity, and thus increasing customers’ willingness to pay a price premium ( Li et al., 2022 ).

Like brand images, destination images are also symbolic. Chon (1992) was the first to introduce self-congruity into the field of tourism, and he found that the higher the degree of self-congruity of tourists is, the more satisfied they are with the destination. People identify with brands or businesses that help define or reinforce, improve or enhance, and communicate their self-concept to others or society. This identification significantly impacts attitudes and behaviors such as purchasing intention, recommendation intention, price sensitivity, and loyalty ( Bhattacharya and Sen, 2003 ). Ahn et al. (2013) proposed that self-congruity affects tourists’ destination choice behavior. Egota et al. (2022) verified that self-congruity directly impacts destination satisfaction, engagement, and expectations.

Consumers are more attracted to the information posted by like-minded publishers. It is obvious that when consumers read other users’ reviews or content, they look for similarities with their preferences and profiles ( Tsiakali, 2015 ). Users on the Internet are more likely to collect information that supports their worldview, exclude different information, and build polarized communities around shared narratives ( Yu and Ko, 2021 ). Furthermore, viewers always process self-related information and then deal with unrelated information ( Fast and Tiedens, 2010 ). This is because the highly relevant information is easier to notice and recognize, helping maintain a consistent self-image. Based on the literature, the following hypotheses are driven:

H2 The quality of social media tourism information positively affects self-congruity.

H3 Self-congruity plays a mediating role in the effect of information quality on travel intention.

Trust is the confident and positive expectation of an individual to another individual or organization in social communication under the circumstance of risk ( Moorman et al., 1992 ). Mayer et al. (1995) proposed that trust is composed of the trustor’s perception of the trustee’s competence, benevolence, and integrity, which indicates the willingness of the individual to bear risks in the transaction and reflects the individual’s cognition of the transaction risks. In the field of tourism, trust is a kind of confidence, belief, and expectation that consumers hold in the tourism destination, and they are willing to believe that the tourism destination has the ability and can meet the needs of consumers in tourism as promised.

Because of the asymmetry of tourism information, consumers cannot experience the quality of tourism products before arriving at the destination. In order to obtain more accurate destination perception, consumers tend to obtain information through more reliable channels. The content shared by users of social media is mostly from consumers’ own experiences rather than business publicity. Due to its non-trading attribute and open access, UGC is regarded as more objective and fair ( Ridings et al., 2002 ), which provides important decision-making reference for consumers to search for tourism information. Compared with promotional materials provided by tourist boards and commercial enterprises, the credibility of UGC is higher, and the perceived credibility of the destination may play a greater role in the consumer decision-making process ( Iordanova and Stainton, 2019 ). Although travel-related UGC is more reliable than information created or uploaded by official tourism organizations ( Fotis et al., 2012 ), there are cases where the user is concerned about their trust in the reliability of online travel reviews as the sources can modify and misuse in various ways ( Fan et al., 2018 ). Sparks et al. (2013) found that user-generated and detailed information is an important clue to trust. Mahat and Hanafiah (2020) documented that information’s accuracy, reliability, confidentiality, and privacy lead consumers to trust information sources. In the social media environment, trust significantly impacts purchase intention ( Hajli, 2014 ). Trust is vital for online tourism marketing because it increases the interest in purchase behavior ( Li et al., 2020 ). Based on the literature, the following hypotheses are driven:

H4 The quality of social media tourism information positively affects trust.

H5 Trust plays a mediating role in the effect of information quality on destination tourism intention.

Tourists have a high degree of trust in destinations because of their similar characteristics, and also have a high sense of identity with destinations that help define, strengthen, and improve their self-concept and reduce the inconsistency between ideal and reality. Self-image consistency will promote consumers’ attachment to the product, induce consumers’ emotional commitment to the brand, improve the relationship between consumers and the brand, and show a kind of emotional trust ( Kressmann et al., 2006 ). The higher the consistency between consumers and information publishers is, the higher the trust of consumers in users’ published content, which shows that self-congruity is the clue of trust ( Ayeh et al., 2013 ). Thus, the hypothesis is proposed:

H6 Trust is mediating in the relationship between self-congruity and travel intention.

2.6 Prior knowledge

Prior knowledge is also known as consumer knowledge or consumer expertise, which refers to the relevant knowledge and experience consumers can rely on when choosing products to solve specific consumption problems ( Mitchell and Dacin, 1996 ). Consumer knowledge is divided into familiarity and expertise ( Jacoby et al., 1986 ). As per knowledge hierarchy (DIKW), knowledge is derived from information but is not a subset of information. It is the information that is “understood,” associated with specific situations, and can guide “how” actions. The knowledge can be available in different formats, but analyzing, understanding, and categorizing it requires extra attention to convert it to wisdom ( Malik et al., 2018 ). In the era of information explosion, knowledge eliminates the false and preserves the true, eliminating the coarse and preserving the fine. Knowledge makes information useful and can solve the “how to” problem for a specific recipient in a specific environment, improving the efficiency and quality of work. At the same time, the accumulation and application of knowledge play a very important role in enlightening wisdom and leading the future.

The level of consumers’ knowledge affects how they collect and use information, ultimately affecting their evaluation, purchase, and use of products ( Cordell, 1997 ). As an embodiment of cognitive ability, the knowledge level greatly influences information processing and decision-making ( Alba and Hutchinson, 2000 ).

According to the ELM theory, individual attitude change has central and peripheral pathways, and the ability to process information affects individuals to adopt central or peripheral pathways ( Petty et al., 1983 ). Consumer knowledge is an important factor influencing information processing ability ( Petty et al., 1997 ). Tourists with a high level of prior knowledge have adequate processing and utilization of information, and a more accurate understanding of the meaning of information. They are more inclined to choose the central approach for fine processing and in-depth analysis of information, forming or changing their attitudes to things based on evaluating the quality of the information itself. While tourists with low prior knowledge are less capable of thinking about a message, they are inclined to use peripheral cues (such as emotional stimulation, preference for information expression methods, etc.) to evaluate a message ( Petty et al., 1997 ).

It has been proved that consumers’ prior knowledge is a very important moderating variable in their information processing ( Roehm and Sternthal, 2001 ). Tourists with higher prior knowledge have stronger cognitive needs and are more willing to obtain information before making travel decisions ( Teichmann, 2011 ). Furthermore, consumers with more prior knowledge tend to adopt rational analysis and seldom evaluate products by peripheral cues ( Ratchford, 2001 ), while novice tourists are more inclined to make use of peripheral information or relatively simple clues (such as tourist images of destination and emotional stimulus) for information evaluation ( Sirgy and Su, 2000 ; Beerli et al., 2007 ). Kumi and Limayem (2012) advocated that high-expertise consumers are more likely to rely on perceived content quality to make the decision, while individuals with low expertise are more likely to rely on contextual factors. That is, tourists’ prior knowledge can strengthen the possibility of tourists’ rational information processing and weaken the possibility of tourists’ emotional information processing. Therefore, the following assumptions are put forward:

H7 Tourists’ prior knowledge positively regulates the impact of tourism information quality on tourism intention.

H8 Tourists’ prior knowledge negatively regulates the impact of information quality on self-congruity.

H9 Tourists’ prior knowledge negatively regulates the impact of information quality on trust.

Based on the above analysis, the conceptual model ( Figure 1 ) is as follows.

Figure 1. Research model.

3 Research method

3.1 data collection.

In order to enhance the reliability of the research results, this study released and collected questionnaires on Credamo. 1 Credamo platform is a professional survey platform with samples and a strict credit investigation system, and it has provided scientific research and education data services for teachers and students in more than 2,000 colleges and universities around the world, including MIT, New York University, Hong Kong University of science and technology, Peking University, Tsinghua University. The questionnaire was released and collected in March 2022, and each sample was paid 3 Chinese Yuan (CNY). To ensure the accuracy and quality of the results, respondents need to meet two conditions: they are social media users and have browsed TGC. Therefore, at the beginning of the survey, we gave the concept of TGC and corresponding examples and asked respondents to recall their recent experience of browsing the tourism destination information on social media. As a result, a total of 530 samples were collected, and 399 valid ones were left after excluding invalid questionnaires.

Among the valid samples, the proportion of men and women is relatively balanced, accounting for 42.9% (men) and 57.1% (women), respectively. The age group of 29–39 accounts for the largest (54.6%), followed by the age group of 18–28 (36.1%), which is in line with the younger characteristics of social media users. More than 89% of the respondents have a bachelor’s degree or above. They have a good understanding and decision-making ability to ensure data accuracy. Those with incomes between 5,001–15,000 yuan accounted for 64.7 percent of the total, which is consistent with the income level of Chinese residents. In short, from the perspective of demographic characteristics, the sample is well-representative (see Table 1 ).

Table 1. Demographic characteristics ( n = 399).

3.2 Measurement design

To ensure content validity, we use relatively mature scales when measuring variables and make necessary adjustments according to the context of the study. Likert 5 scoring method was adopted (1 = “strongly disagree” and 5 = “strongly agree”). The questionnaire mainly includes three parts: (1) tourists’ preference for social media, (2) key variables, and (3) demographic characteristics. An eight-item scale ( Wang, 1998 ; Bovee, 2004 ; Park et al., 2007 ; Zhang et al., 2014 ) operationalized information quality. Self-congruity mainly refers to Sirgy et al. (1997) , including four items; Four items ( McAllister, 1995 ) which were utilized to measure trust. A four-item scale ( Kerstetter and Cho, 2004 ; Füller et al., 2008 ) was used to measure prior knowledge. Finally, four items ( Smith, 2004 ) were used to assess travel intentions (see Table 2 ).

Table 2. Construct reliability and convergent validity.

3.3 Reliability and validity

The reliability of each construct was measured with Cronbach’s Alpha coefficient. As shown in Table 2 , the values of all factors were above the recommended threshold of 0.7 based on George and Mallery’s (2003) criterion. The reference scales in this study were all previous mature scales, and confirmatory factor analysis (CFA) was used to test the measurement model. Factor loadings exceeding the recommended 0.5 ( Hair et al., 2013 ) were accepted. The measurement model indices were all within recommended thresholds ( x 2 /df = 1.385, normed fit index (NFI) = 0.947, goodness of fit index (GFI) = 0.958, standardized root mean squared residual (SRMR) = 0.035, and root mean square error of approximation (RMSEA) = 0.031), indicating that the measurement model achieved acceptable fit ( Browne and Cudeck, 1992 ). To ensure construct validity, we CFA analysis followed by a calculation of average variance (AVE) and composite reliability (CR) to assess the convergent validity of the measurement model. The validity results showed that the average variance (AVE) values exceeded 0.5, and CR values greater than the threshold of 0.7 (see Table 2 ) recommended by Fornell and Larcker (1981) .

Furthermore, we evaluate discriminant validity using the square root of AVE. As shown in Table 3 , the square root of the AVE values of each construct was greater than the correlations between pairs of latent variables, indicating that the discriminant validity was satisfactory.

Table 3. Correlation and discriminant validity.

4 Hypothesis testing

4.1 main effect test.

Firstly, regression analysis was performed to test the effect of tourism information quality on travel intention. The analysis results ( Table 4 ) show that the quality of social media tourism information has a significant positive impact on tourism intention ( b = 0.372, p -value < 0.001), indicating that the main effect is significant supporting H1.

Table 4. Main effect and mediating effect.

4.2 Mediating effect test

The test results ( Table 4 ) show that information quality has a significant positive impact on self-congruity ( b = 0.526, p -value < 0.001), and self-congruity plays a positive role in affecting travel intention ( b = 0.198, p -value < 0.001) thus the data supports H2 and H3. Similarly, information quality has a significant positive impact on trust ( b = 0.150, p -value < 0.001), and trust plays a positive role in affecting travel intention ( b = 0.296, p -value < 0.001), supporting H4 and H5. Furthermore, the impact of tourism information quality on tourism intention is still significant ( b = 0.211, p -value < 0.001), indicating that self-congruity and trust partially mediate between information quality and travel intention. Moreover, self-congruity had a significant positive effect on trust ( b = 0.083, p -value < 0.01), both self-congruity ( b = 0.198, p -value < 0.001), and trust ( b = 0.296, p -value < 0.001) had significant positive influences on travel intention, indicating that trust acts as a partial intermediary between self-congruity and travel intention supporting H6. In addition, this study examined the mediating effect size using the bootstrap method ( Hayes and Rockwood, 2017 ). According to the result in Table 5 , the bootstrap 95% confidence interval (CI) did not contain 0, indicating the mediating effect was significant, and the total indirect effect accounts for nearly half of the total effect (43.5%).

Table 5. Mediating effect test.

4.3 Moderating effect test

A series of regression analyses using PROCESS ( Hayes and Rockwood, 2017 ) was conducted to test the moderating effect further. We test the moderating effect of tourists’ prior knowledge on the relationship between information quality and tourism intention. The results in Table 6 show that the interaction term has a significant negative impact (β = −0.234, p -value < 0.001) on tourism intention rather than a positive role mentioned in Hypothesis 7. Next, spotlight analysis was used to test the moderating effect further. Based on one standard deviation of the average value of tourists’ prior knowledge, tourists were divided into tourists with high (M + 1SD) prior knowledge (PK) and low (M − 1SD) PK for simple slope analysis ( Figure 2 ). The results show that compared with tourists with low PK (Simple Slope = 0.569, p -value < 0.001), tourists with high PK (Simple Slope = 0.135, p -value < 0.05) are less possibly influenced by tourism information on the social media platform, which means that the more knowledge they have about destinations, the less they rely on information when making travel decisions.

Table 6. Moderating effect test.

Figure 2. Interaction of information quality (IQ) and prior knowledge (PK) on travel intention (TI).

Then a moderated mediation model ( Hayes and Rockwood, 2017 ) was conducted to test whether the mediating effect would be weakened or strengthened when the level of moderating variable was changed. Table 6 shows that the interaction of information quality and prior knowledge has a negative effect on self-congruity ( b = −0.176, p -value < 0.05). The interaction effects at different levels of prior knowledge (the mean of prior knowledge ± 1 SD) were further examined, and it turned out that the relationship between information quality and self-congruity is stronger when prior knowledge is low ( b = 0.615, p < 0.001) rather than high ( b = 0.290, p -value < 0.01; Figure 3 ). Table 7 shows the conditional indirect effect of information quality on travel intention through self-congruity at different levels of the moderating variable prior knowledge (M ± 1SD). The indirect effect was strong for lower PK group [β = 0.108, 95% CI: (0.051, 0.185)] and was weak for higher PK group [ b = 0.051, 95% CI: (0.017, 0.110)]. Thus, Hypothesis 8 was supported. As the interaction items of information quality and prior knowledge did not significantly influence trust ( b = −0.052, p -value > 0.05), rejecting H9.

Figure 3. Interaction of information quality (IQ) and prior knowledge (PK) on self-congruity (SC).

Table 7. Conditional indirect effects of information quality (IQ) on travel intention (TI) via self-congruity (SC) at levels of prior knowledge (PK).

In addition, we examined the conditional direct effect of information quality on travel intention after adding two mediator variables. Overall, the interaction of information quality and prior knowledge has a significant negative effect ( b = −0.176, p -value < 0.05) on travel intention ( Table 6 ), while this effect is mainly reflected in the group with low prior knowledge ( b = 0.397, p -value < 0.001). However, for tourists with a high prior knowledge level (M + 1SD), the impact of tourism information quality on travel intention is no longer significant ( b = 0.047, p -value > 0.05).

5 Discussion, implications, and limitations and future work

5.1 discussion.

Integrating rational and emotional perspectives, this study explores tourism information quality’s direct and indirect impacts on consumers’ travel intentions. In addition, the moderating role of prior knowledge in the influence of information quality on rational and emotional decision-making paths is tested.

The empirical results show that consumers’ processing of tourism information and making tourism decisions are the results of both rationality and sensibility, and self-congruity and trust play a mediating role in emotional decision-making. The results align with Levy’s (1959) assertion that consumers are not functionally oriented and the symbolic meaning of products largely influences their behavior. It also proves that, as Sirgy and Su (2000) proposed, the more consistent tourists’ self-concept with the image of the destination, the more positive they will be toward the destination and have the intention to visit.

It is found that tourists’ prior knowledge negatively moderates the direct impact of social media tourism information quality on tourism intention. Compared with expert tourists, novice tourists’ tourism decisions are more dependent on tourism information content. It is contrary to the view proposed by Teichmann (2011) that the higher the level of professional knowledge of tourists, the more inclined they are to obtain information from outside before making tourism decisions. The possible reason is that consumers’ familiarity with the destination is inversely related to their willingness to use the platform ( Abd Aziz et al., 2010 ). Tourists with a high level of prior knowledge have rich destination knowledge or personal experience, so they can better identify the information about the destination on the social media platform, and their attitude toward the destination no longer depends too much on the information contained on the media platform.

The analysis results also show that factors such as travel knowledge and previous travel experience have a negative moderating effect on the relationship between information quality and self-congruity, consistent with Beerli et al. (2007) . Novice travelers rely more on relatively simple information cues, such as the image of a destination’s visitors. The information shared by typical tourists is transformed into a symbolic and emotional information clue, which stimulates the novice tourists to carry out the association, draw their own image close to the image of the destination, and invest emotional commitment to the destination so as to achieve “balance” in the way of emotional decision-making.

It must be pointed out that in the Web 2.0 era, social media was not only an information platform but also an influence platform ( Hanna et al., 2011 ), enabling consumers to have more power than ever before. When interests are damaged, consumers can express their dissatisfaction through negative word of mouth, which can have great negative impacts on the destination image as well as the sustainable development of the local tourism industry ( Liu et al., 2020 ). Thus, all stakeholders should work toward transforming the tourism market from unregulated to regulated ( Liu et al., 2021 ).

5.2 Implications

5.2.1 theoretical implications.

First, this study innovatively integrates rational and emotional decision-making paths to explore the impact of social media tourism information on consumers’ tourism intention. The generation of tourism intention comes not only from the rational evaluation based on functional information clues but also from the emotional evaluation based on self-congruity and trust. It is verified that the tourist destination has both functional value and symbolic value for tourists. Second, from the perspective of self-congruity, this study explores the behavior tendency before traveling. Previous studies mainly focused on the impact of self-congruity on post-tour behavior, such as tourist satisfaction ( Murphy et al., 2007 ) and revisit intention ( Matzler et al., 2016 ). This study enriched the research on the impact of self-congruity on consumers’ pre-tour behavior and confirmed that self-congruity also impacts consumers’ destination behavior intention before traveling. Third, it verifies the boundary conditions of the tourism information quality on tourism decision-making. Information asymmetry is the premise of the value of social media. Because of the asymmetry of tourism information, consumers pay more attention to the non-trading attribute of social media and tend to offset uncertainty perception with the help of user-shared information. When the asymmetry of tourism information decreases, the impact of social media tourism information will also decline, and tourists’ prior knowledge can offset tourists’ dependence on tourism information. Expert tourists are more insensitive to the risk of tourism information asymmetry than novice tourists. As the empirical analysis results of this study show, the impact of tourism information quality on tourism intention is more obvious among novice tourists.

5.2.2 Practical implications

Social media has endowed tourism consumers with more ways of self-expression and value demands. In the context of “attention economy,” this study provides empirical support for guiding destination operators to carry out content marketing, destination image building, and tourism enterprise service innovation. First, this study found that the quality of TGC directly affects consumers’ travel intentions and indirectly affects consumers’ travel intentions through self-congruity and trust. Therefore, tourism destination management should pay attention to the incentive and management of TGC, and encourage tourists to create value together. On the one hand, tourism managers should encourage publishers to continue to create and share high-quality tourism information that is complete, rich, eye-catching, authentic, and credible so that visitors, especially consumers who have not visited the destination, can form a clearer and rational understanding of the destination through the functional attributes of information transmission. On the other hand, the social attributes of media platforms should be utilized to create opinion leaders and destination spokespeople through hidden attributes such as identity, relationship, and popularity of publishers, to stimulate consumers’ emotional experience and value demands for maintaining their own image. For consumers are not only “motivated by reason” but also “motivated by emotion.” Especially, the destination administrations should establish a good image and conduct transparent supervision on the unethical incident timely and efficiently. Second, this study found that the more consistent the consumer’s self-image with the destination image, the easier it is to generate tourism intention. Therefore, the tourism destination management party should not only pay attention to the dissemination of functional information but also use the symbolic meaning of the destination to design publicity information to shape their personality and differentiation advantages. It will stimulate consumers to regard the destination image as an extension of their self-image, so that consumers can express their personality and maintain their image by traveling to the destination. Third, the direct impact of the quality of TGC on tourism intention mainly exists in the novice tourist group, while the role of the expert tourist group can be almost ignored. Therefore, for novice tourists, tourism destination operators or managers should pay attention to guiding and encouraging publishers to introduce the security factors such as tourism destination services and infrastructure that novice tourists are concerned about so as to weaken the negative impact of cognitive bias on tourism intention. Especially during the pandemic, fear of COVID-19 and perceived risk significantly negatively impact attitude ( Bratić et al., 2021 ; Rather, 2021 ). Destination operators should use social platforms to pass on authentic information about safety measures to visitors to minimize tourist’s negative feelings and diminish the perceived fear of COVID-19.

This study also found that novice tourists are more likely to rely on symbolic cues or emotional cues of tourism information than expert tourists. Therefore, the choice of publishers is very important. When the image of publishers is consistent with the brand image, it is easier for visitors to remember the brand ( Knoll et al., 2015 ). Publishers with a higher matching degree with the brand/destination image are more likely to arouse positive interaction of visitors and lead to a positive attitude toward the destination.

In conclusion, this study provides a new research perspective on how social media travel information affects the decision-making of potential travel consumers. Furthermore, it verifies the symbolic value of a tourism destination and provides a theoretical reference for optimizing its brand image value. At the same time, it provides support for destination management to carry out content marketing.

5.3 Limitation and future work

The world tourism industry has been inevitably influenced a lot due to unprecedented mobility restrictions caused by the novel coronavirus (COVID-19) ( Gössling et al., 2020 ). Nevertheless, we did not consider the epidemic’s influence as a leading factor because of the following considerations. First, it is often difficult to reasonably operate the independent variable of COVID-19 when collecting data from the questionnaire. Future research should encourage the application of big data analysis techniques (including natural language processing, machine learning, etc.) and experimental methods to gain more and deeper insights into changes in tourist behavior and new tourism consumption phenomena brought about by the COVID-19 pandemic ( Chen and Li, 2022 ). Besides, China may be different from other countries and regions in terms of epidemic prevention policies. How to localize epidemy-related tourist behavior studies in China is also a topic that need attention and future directions.

In addition to the above, this study still has several limitations that lead to directions for future work. First, this study took the tourism information quality on social media as an overall dimension to test its impact mechanism on consumers’ travel intention and did not subdivide it into functional and emotional information cues, which can be further tested in future work. Second, we use prior knowledge as the moderating variable in this study. Finally, since consumer involvement ( Geng and Chen, 2021 ; Yadav et al., 2021 ) may influence consumer decision-making, involvement can be tested as a moderating variable in the future. Third, the empirical data of this study only covers vacation tourism destinations in China, and whether the findings are applied to other destination types should be further verified.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

HW: formal analysis, writing—original draft preparation, and supervision. JY: visualization and project administration. Both authors contributed to the conceptualization, methodology, validation, data curation, writing—review and editing, and read and agreed to the published version of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Abd Aziz, N., Musa, G., and Sulaiman, A. (2010). The influence of travel motivations and social factors on travel web site usage among malaysian travellers. Asian J. Bus. Account. 3, 89–116.

Google Scholar

Ahn, T., Ekinci, Y., and Li, G. (2013). Self-congruence, functional congruence, and destination choice. J. Bus. Res. 66, 719–723. doi: 10.1016/j.jbusres.2011.09.009

CrossRef Full Text | Google Scholar

Alba, J. W., and Hutchinson, J. W. (2000). Knowledge calibration: What consumers know and what they think they know. J. Consum. Res. 27, 123–156. doi: 10.1086/314317

Ayeh, J. K., Au, N., and Law, R. (2013). Predicting the intention to use consumer-generated media for travel planning. Tour. Manag. 35, 132–143. doi: 10.1016/j.tourman.2012.06.010

Beerli, A., Meneses, G. D., and Gil, S. M. (2007). Self-congruity and destination choice. Ann. Tour. Res. 34, 571–587. doi: 10.1016/j.annals.2007.01.005

Belk, R. W. (1988). Possessions and the extended self. J. Consum. Res. 15, 139–168. doi: 10.1086/209154

Bertot, J. C., Jaeger, P. T., and Grimes, J. M. (2010). Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies. Gov. Inf. Q. 27, 264–271. doi: 10.1016/j.giq.2010.03.001

Bhattacharya, C. B., and Sen, S. (2003). Consumer-company identification: A framework for understanding consumers’ relationships with companies. J. Mark. 67, 76–88. doi: 10.1509/jmkg.67.2.76.18609

PubMed Abstract | CrossRef Full Text | Google Scholar

Browne, M. W., and Cudeck, R. (1992). Alternative ways of assessing model fit. Sociol. Methods Res. 21, 230–258.

Bovee, M. W. (2004). Information quality: A conceptual framework and empirical validation . Ph.D. dissertation. Lawrence, KS: University of Kansas.

Bratić, M., Radivojević, A., Stojiljković, N., Simović, O., Juvan, E., Lesjak, M., et al. (2021). Should I stay or should I go? Tourists’ COVID-19 risk perception and vacation behavior shift. Sustainability 13:3573. doi: 10.3390/su13063573

Chen, G., and Li, M. (2022). Tourist behavior research related to the COVID-19 pandemic. J. Chin. Ecotourism 12, 114–124.

Cheunkamon, E., Jomnonkwao, S., and Ratanavaraha, V. (2020). Determinant factors influencing thai tourists’ intentions to use social media for travel planning. Sustainability 12:7252. doi: 10.3390/su12187252

Chon, K. S. (1992). Self-image/destination image congruity. Ann. Tour. Res. 19, 360–363. doi: 10.1016/0160-7383(92)90090-C

Chung, N., and Han, H. (2017). The relationship among tourists’ persuasion, attachment and behavioral changes in social media. Technol. Forecast. Soc. Change 123, 370–380. doi: 10.1016/j.techfore.2016.09.005

Chung, N., Han, H., and Koo, C. (2015). Adoption of travel information in user-generated content on social media: The moderating effect of social presence. Behav. Inf. Technol. 34, 902–919. doi: 10.1080/0144929X.2015.1039060

Cordell, V. V. (1997). Consumer knowledge measures as predictors in product evaluation. Psychol. Mark. 14, 241–260. doi: 10.1002/(SICI)1520-6793(199705)14:3<241::AID-MAR3>3.0.CO;2-B

Coudounaris, D. N., and Sthapit, E. (2017). Antecedents of memorable tourism experience related to behavioral intentions. Psychol. Mark. 34, 1084–1093.

Egota, T., Chen, N. C., and Golja, T. (2022). The impact of self-congruity and evaluation of the place on wom: Perspectives of tourism destination residents. J. Travel Res. 61, 800–817. doi: 10.1177/00472875211008237

Elliott, R., and Wattanasuwan, K. (1998). Brands as symbolic resources for the construction of identity. Int. J. Advert. 17, 131–144. doi: 10.1080/02650487.1998.11104712

Escalas, J. E., and Bettman, J. R. (2005). Self-construal, reference groups, and brand meaning. J. Consum. Res. 32, 378–389. doi: 10.1086/497549

Fan, A., Shen, H., Wu, L., Mattila, A. S., and Bilgihan, A. (2018). Whom do we trust? Cultural differences in consumer responses to online recommendations. Int. J. Contemp. Hosp. Manag. 30, 1508–1525. doi: 10.1108/IJCHM-01-2017-0050

Fast, N. J., and Tiedens, L. Z. (2010). Blame contagion: The automatic transmission of self-serving attributions. J. Exp. Soc. Psychol. 46, 97–106. doi: 10.1016/j.jesp.2009.10.007

Filieri, R., Alguezaui, S., and McLeay, F. (2015). Why do travelers trust Tripadvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tour. Manag. 51, 174–185. doi: 10.1016/j.tourman.2015.05.007

Filieri, R., and McLeay, F. (2014). E-WOM and accommodation: An analysis of the factors that influence travelers’ adoption of information from online reviews. J. Travel Res. 53, 44–57. doi: 10.1177/0047287513481274

Flores-Ruiz, D., Elizondo-Salto, A., and Barroso-González, M. O. (2021). Using social media in tourists sentiment analysis: A case study of andalusia during the COVID-19 Pandemic. Sustainability 13:3836. doi: 10.3390/su13073836

Fornell, C., and Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 18, 382–388.

Fotis, J., Buhalis, D., and Rossides, N. (2012). “Social media use and impact during the holiday travel planning process,” in Information and Communication Technologies in Tourism , eds M. Fuchs, F. Ricci, and L. Cantoni (Vienna: Springer), 13–24. doi: 10.1007/978-3-7091-1142-0_2

Füller, J., Matzler, K., and Hoppe, M. (2008). Brand community members as a source of innovation. J. Prod. Innov. Manag. 25, 608–619. doi: 10.1111/j.1540-5885.2008.00325.x

Geng, R., and Chen, J. (2021). The influencing mechanism of interaction quality of UGC on consumers’ purchase intention–an empirical analysis. Front. Psychol. 12:697382. doi: 10.3389/fpsyg.2021.697382

George, D., and Mallery, P. (2003). SPSS for windows step by step: A simple guide and reference 11.0 update , 4th Edn. Boston, MA: Allyn & Bacon.

Gössling, S., Scott, D., and Hall, M. C. (2020). Pandemics, tourism and global change: A rapid assessmet of COVID-19. J. Sustain. Tour. 29, 1–20. doi: 10.1080/09669582.2020.1758708

Hair, J. F., Ringle, C. M., and Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Plann. 46, 1–12.

Hajli, M. N. (2014). A study of the impact of social media on consumers. Int. J. Mark. Res. 56, 387–404. doi: 10.2501/IJMR-2014-025

Hanafiah, M. H., Md Zain, A., Asyraff, M. A., and Makhasi, G. Y. M. (2022). The roles of technology acceptance, user credibility and Covid-19 perceived health risk in tourist’s social media user-generated-content usage intention. Enlightening Tour. Pathmaking J. 12, 337–365. doi: 10.33776/et.v12i1.5427

Hanna, R., Rohm, A., and Crittenden, V. L. (2011). We’re all connected: The power of the social media ecosystem. Bus. Horiz. 54, 265–273. doi: 10.1016/j.bushor.2011.01.007

Hayes, A. F., and Rockwood, N. J. (2017). Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation. Behav. Res. Ther. 98, 39–57.

Helgeson, J. G., and Supphellen, M. (2004). A conceptual and measurement comparison of self-congruity and brand personality: The impact of socially desirable responding. Int. J. Mark. Res. 46, 205–233. doi: 10.1177/147078530404600201

Hudson, S., and Thal, K. (2013). The impact of social media on the consumer decision process: Implications for tourism marketing. J. Travel Tour. Mark. 30, 156–160. doi: 10.1080/10548408.2013.751276

Iordanova, E., and Stainton, H. (2019). Cognition, emotion and trust: A comparative analysis of Cambodia’s perceived and projected online image. Tour. Stud. 19, 496–519. doi: 10.1177/1468797619837970

Jacoby, J., Troutman, T., Kuss, A., and Mazursky, D. (1986). Experience and expertise in complex decision making. ACR North Am. Adv. 13, 469–472.

Kapoor, P. S., Balaji, M. S., Jiang, Y., and Jebarajakirthy, C. (2022). Effectiveness of travel social media influencers: A case of eco-friendly hotels. J. Travel Res. 61, 1138–1155. doi: 10.1177/00472875211019469

Kerstetter, D., and Cho, M. H. (2004). Prior knowledge, credibility and information search. Ann. Tour. Res. 31, 961–985. doi: 10.1016/j.annals.2004.04.002

Kim, A. J., and Johnson, K. K. (2016). Power of consumers using social media: Examining the influences of brand-related user-generated content on facebook. Comput. Hum. Behav. 58, 98–108. doi: 10.1016/j.chb.2015.12.047

Kim, J. H., Ritchie, J. R. B., and McCormick, B. (2012). Development of a scale to measure memorable tourism experiences. J. Travel Res. 51, 12–25. doi: 10.1177/0047287510385467

Kim, S. E., Lee, K. Y., Shin, S. I., and Yang, S. B. (2017). Effects of tourism information quality in social media on destination image formation: The case of Sina Weibo. Inf. Manag. 54, 687–702. doi: 10.1016/j.im.2017.02.009

Knoll, J., Schramm, H., Schallhorn, C., and Wynistorf, S. (2015). Good guy vs. bad guy: The influence of parasocial interactions with media characters on brand placement effects. Int. J. Advert. 34, 720–743. doi: 10.1080/02650487.2015.1009350

Kressmann, F., Sirgy, M. J., Herrmann, A., Huber, F., Huber, S., and Lee, D. J. (2006). Direct and indirect effects of self-image congruence on brand loyalty. J. Bus. Res. 59, 955–964. doi: 10.1016/j.jbusres.2006.06.001

Kumi, R., and Limayem, M. (2012). “Impact of online content on attitudes and buying intentions,” in Proceedings of the eighteenth Americas conference on information systems , Seattle, WA.

Lam, T., and Hsu, C. H. (2006). Predicting behavioral intention of choosing a travel destination. Tour. Manag. 27, 589–599. doi: 10.1016/j.tourman.2005.02.003

Lata, S., and Rana, K. (2021). What are the determinants of consumers’ online reviews adoption for hotel bookings: A structural equation modelling approach. Enlightening Tour. Pathmaking J. 11, 171–193. doi: 10.33776/et.v11i1.5354

Levy, S. J. (1959). Symbols for sale. Harv. Bus. Rev. 37, 117–119.

Li, M. W., Teng, H. Y., and Chen, C. Y. (2020). Unlocking the customer engagement-brand loyalty relationship in tourism social media: The roles of brand attachment and customer trust. J. Hosp. Tour. Manag. 44, 184–192. doi: 10.1016/j.jhtm.2020.06.015

Li, R., Laroche, M., Richard, M. O., and Cui, X. (2022). More than a mere cup of coffee: When perceived luxuriousness triggers chinese customers perceptions of quality and self-congruity. J. Retail. Consum. Serv. 64:102759. doi: 10.1016/j.jretconser.2021.102759

Liu, Y., Yao, Y., and Fan, D. X. F. (2020). Evaluating tourism market regulation from tourists’ perspective: Scale development and validation. J. Travel Res. 59, 975–992. doi: 10.1177/0047287519871107

Liu, Y., Zhang, R., and Yao, Y. (2021). How tourist power in social media affects tourism market regulation after unethical incidents: Evidence from China. Ann. Tour. Res. 91:103296. doi: 10.1016/j.annals.2021.103296

Lu, X., Ba, S., Huang, L., and Feng, Y. (2013). Promotional marketing or word-of-mouth? Evidence from online restaurant reviews. Inf. Syst. Res. 24, 596–612. doi: 10.1287/isre.1120.0454

Luo, Q., and Zhong, D. (2015). Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites. Tour. Manag. 46, 274–282. doi: 10.1016/j.tourman.2014.07.007

Mahat, Z. D. N., and Hanafiah, M. H. (2020). Help me TripAdvisor! examining the relationship between Tripadvisor E-WOM attributes, trusts towards online reviews and travellers behavioural intentions. J. Inf. Organ. Sci. 44, 83–112. doi: 10.31341/jios.44.1.4

Malik, Z., Hashmi, K., Najmi, E., and Rezgui, A. (2018). Wisdom extraction in knowledge-based information systems. J. Knowl. Manag. 23, 23–45. doi: 10.1108/JKM-05-2018-0288

Matzler, K., Strobl, A., Stokburger-Sauer, N., Bobovnicky, A., and Bauer, F. (2016). Brand personality and culture: The role of cultural differences on the impact of brand personality perceptions on tourists’ visit intentions. Tour. Manag. 52, 507–520. doi: 10.1016/j.tourman.2015.07.017

Mayer, R. C., Davis, J. H., and Schoorman, F. D. (1995). An integrative model of organizational trust. Acad. Manag. Rev. 20, 709–734. doi: 10.5465/amr.1995.9508080335

McAllister, D. J. (1995). Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations. Acad. Manag. J. 38, 24–59. doi: 10.5465/256727

McCracken, A. (1987). Emotional impact of possession loss. J. Gerontol. Nurs. 13, 14–19. doi: 10.3928/0098-9134-19870201-06

Mitchell, A. A., and Dacin, P. F. (1996). The asessment of alternative measures of consumer expertise. J. Consum. Res. 23, 219–240. doi: 10.1086/209479

Moorman, C., Zaltman, G., and Deshpande, R. (1992). Relationships between providers and users of market research: The dynamics of trust within and between organizations. J. Mark. Res. 29, 314–328. doi: 10.1177/002224379202900303

Morand, J. C., Cardoso, L., Pereira, A. M., Vila, N. A., and de Almeida, G. G. F. (2021). Tourism ambassadors as special destination image inducers. Enlightening Tour. Pathmaking J. 11, 194–230. doi: 10.33776/et.v11i1.5137

Morwitz, V. G., and Schmittlein, D. (1992). Using segmentation to improve sales forecasts based on purchase intent: Which “intenders” actually buy? J. Mark. Res. 29, 391–405. doi: 10.1177/002224379202900401

Murphy, L., Benckendorff, P., and Moscardo, G. (2007). Linking travel motivation, tourist self-image and destination brand personality. J. Travel Tour. Mark. 22, 45–59. doi: 10.1300/J073v22n02_04

Nezakati, H., Amidi, A., Jusoh, Y. Y., Moghadas, S., Aziz, Y. A., and Sohrabinezhadtalemi, R. (2015). Review of social media potential on knowledge sharing and collaboration in tourism industry. Procedia Soc. Behav. Sci. 172, 120–125. doi: 10.1016/j.sbspro.2015.01.344

Onkvisit, S., and Shaw, J. (1987). Self-concept and image congruence: Some research and managerial implications. J. Consum. Mark. 4, 13–22. doi: 10.1108/eb008185

Park, D. H., Lee, J., and Han, I. (2007). The effect of online consumer reviews on consumer purchasing intention: The moderating role of involvement. Int. J. Electron. Commer. 11, 125–148. doi: 10.2753/JEC1086-4415110405

Petty, R. E., and Cacioppo, J. T. (1986). Communication and persuasion: central and peripheral routes to persuasion. New York, NY: Springer.

Petty, R. E., Cacioppo, J. T., and Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. J. Consum. Res. 10, 135–146. doi: 10.1086/208954

Petty, R. E., Wegener, D. T., and Fabrigar, L. R. (1997). Attitudes and attitude change. Annu. Rev. Psychol. 48, 609–647. doi: 10.1146/annurev.psych.48.1.609

Ratchford, B. T. (2001). The economics of consumer knowledge. J. Consum. Res. 27, 397–411. doi: 10.1086/319617

Rather, R. A. (2021). Monitoring the impacts of tourism-based social media, risk perception and fear on tourist’s attitude and revisiting behaviour in the wake of COVID-19 pandemic. Curr. Issues Tour. 24, 3275–3283. doi: 10.1080/13683500.2021.1884666

Ridings, C. M., Gefen, D., and Arinze, B. (2002). Some antecedents and effects of trust in virtual communities. J. Strateg. Inf. Syst. 11, 271–295. doi: 10.1016/S0963-8687(02)00021-5

Roehm, M. L., and Sternthal, B. (2001). The moderating effect of knowledge and resources on the persuasive impact of analogies. J. Consum. Res. 28, 257–272. doi: 10.1086/322901

Rowley, J. E. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. J. Inf. Sci. 33, 163–180. doi: 10.1177/0165551506070706

Servidio, R., and Ruffolo, I. (2016). Exploring the relationship between emotions and memorable tourism experiences through narratives. Tour. Manag. Perspect. 20, 151–160. doi: 10.1016/j.tmp.2016.07.010

Sheeran, P. (2002). Intention—behavior relations: A conceptual and empirical review. Eur. Rev. Soc. Psychol. 12, 1–36. doi: 10.1080/14792772143000003

Sin, K. Y., Mohamad, A. A., and Lo, M.-C. (2020). A critical review of literature in the rising tide of social media towards promoting tourism. Enlightening Tour. Pathmaking J. 10, 270–305. doi: 10.33776/et.v10i2.4887

Sirgy, M. J. (1982). Self-concept in consumer behavior: A critical review. J. Consum. Res. 9, 287–300. doi: 10.1086/208924

Sirgy, M. J., and Su, C. (2000). Destination image, self-congruity, and travel behavior: Toward an integrative model. J. Travel Res. 38, 340–352. doi: 10.1177/004728750003800402

Sirgy, M. J., Grewal, D., Mangleburg, T. F., Park, J. O., Chon, K. S., Claiborne, C. B., et al. (1997). Assessing the predictive validity of two methods of measuring self-image congruence. J. Acad. Mark. Sci. 25, 229–241. doi: 10.1177/0092070397253004

Sirgy, M. J., Johar, J. S., Samli, C. A., and Claiborne, C. B. (1991). Self-congruence vs functional congruence: Predictors of consumer behavior. J. Acad. Mark. Sci. 19, 363–375. doi: 10.1007/BF02726512

Smith, K. H. (2004). Implementing the “marketing you” project in large sections of principles of marketing. J. Mark. Educ. 26, 123–136. doi: 10.1177/0273475304265542

Sparks, B. A., Perkins, H. E., and Buckley, R. (2013). Online travel reviews as persuasive communication: The effects of content type, source, and certification logos on consumer behavior. Tour. Manag. 39, 1–9. doi: 10.1016/j.tourman.2013.03.007

Teichmann, K. (2011). Expertise, experience and self-confidence in consumers’ travel information search. Int. J. Cult. Tour. Hosp. Res. 2, 184–194. doi: 10.1108/17506181111139591

Torres, E. N., Singh, D., and Robertson-Ring, A. (2015). Consumer reviews and the creation of booking transaction value: Lessons from the hotel industry. Int. J. Hosp. Manag. 50, 77–83. doi: 10.1016/j.ijhm.2015.07.012

Tsiakali, K. (2015). Tourists’ personality and consumer behaviour in social media. Mytilene: University of The Aegean.

Wang, R. Y. (1998). A product perspective on total data quality management. Commun. ACM 41, 58–65. doi: 10.1145/269012.269022

Wearing, S. L., and Foley, C. (2017). Understanding the tourist experience of cities. Ann. Tour. Res. 65, 97–107. doi: 10.1016/j.annals.2017.05.007

Woodside, A. G., and Lysonski, S. (1989). A general model of traveler destination choice. J. Travel Res. 27, 8–14. doi: 10.1177/004728758902700402

Xiang, Z., and Gretzel, U. (2010). Role of social media in online travel information search. Tour. Manag. 31, 179–188. doi: 10.1016/j.tourman.2009.02.016

Yadav, N., Verma, S., and Chikhalkar, R. D. (2021). eWOM, destination preference and consumer involvement–a stimulus-organism-response (SOR) lens. Tour. Rev. 77, 1135–1152. doi: 10.1108/TR-10-2020-0506

Yeap, J. A., Ignatius, J., and Ramayah, T. (2014). Determining consumers’ most preferred E-WOM platform for movie reviews: A fuzzy analytic hierarchy process approach. Comput. Hum. Behav. 31, 250–258. doi: 10.1016/j.chb.2013.10.034

Yu, J., and Ko, E. (2021). UGC attributes and effects: Implication for luxury brand advertising. Int. J. Advert. 40, 945–967. doi: 10.1080/02650487.2021.1898773

Yu, Q. Q., Pickering, S., Geng, R., Dorothy, A., and Yen, D. A. (2021). Thanks for the memories: Exploring city tourism experiences via social media reviews. Tour. Manag. Perspect. 40:100851. doi: 10.1016/j.tmp.2021.100851

Zehrer, A., and Grabmüller, A. (2012). Social media marketing in tourism education: Insights into the development and value of a social network site for a higher education institution in tourism. J. Vacat. Mark. 18, 221–228. doi: 10.1177/1356766712449368

Zhang, K. Z., Zhao, S. J., Cheung, C. M., and Lee, M. K. (2014). Examining the influence of online reviews on consumers’ decision-making: A heuristic–systematic model. Decis. Support Syst. 67, 78–89. doi: 10.1016/j.dss.2014.08.005

Zhao, X. R., Wang, L., Guo, X., and Law, R. (2015). The influence of online reviews to online hotel booking intentions. Int. J. Contemp. Hosp. Manag. 27, 1343–1364. doi: 10.1108/IJCHM-12-2013-0542

Keywords : information quality, travel intention, self-congruity, trust, prior knowledge

Citation: Wang H and Yan J (2022) Effects of social media tourism information quality on destination travel intention: Mediation effect of self-congruity and trust. Front. Psychol. 13:1049149. doi: 10.3389/fpsyg.2022.1049149

Received: 20 September 2022; Accepted: 28 November 2022; Published: 22 December 2022.

Reviewed by:

Copyright © 2022 Wang and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jinzhe Yan, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Please note you do not have access to teaching notes, social media analytics in tourism: a review and agenda for future research.

Benchmarking: An International Journal

ISSN : 1463-5771

Article publication date: 14 November 2022

Issue publication date: 1 December 2023

The significance of social media in our lives is manifold. The tourism sector closely interacts with existing and potential tourists through social media, and therefore, social media analytics (SMA) play a critical role in the uplift of the sector. Hence, this review focus on the role of SMA in tourism as discussed in different studies over a period of time. The purpose of this paper to present the state of the art on social media analytics in tourism.

Design/methodology/approach

The review focuses on identifying different SMA techniques to explore the trends and approaches adopted in the tourism sector. The review is based on 83 papers and discuss the studies related to different social media platforms, the travelers' reactions to a particular place and how the tourism experience is enriched by the way of SMA.

Findings indicate different sentiments associated with tourism and provides a review of tourists’ use of social media for choosing a travel destination. The various analytical approaches, areas such as social network analysis, content analysis, sentiment analysis and trend analysis were found most prevalent. The theoretical and practical implications of SMA are discussed. The paper made an effort to bridge the gap between different studies in the field of tourism and SMA.

Originality/value

SMA facilitate both tourists and tourism companies to understand the trends, sentiments and desires of tourists. The use of SMA offers value to companies for designing quick and adequate services to tourists.

• Social media analytics
• Social network
• Destination image
• Tourist satisfaction

Mukhopadhyay, S. , Jain, T. , Modgil, S. and Singh, R.K. (2023), "Social media analytics in tourism: a review and agenda for future research", Benchmarking: An International Journal , Vol. 30 No. 9, pp. 3725-3750. https://doi.org/10.1108/BIJ-05-2022-0309

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What's the problem with overtourism?

With visitor numbers around the world increasing towards pre-pandemic levels, the issue of overtourism is once again rearing its head.

When locals in the charming Austrian lakeside village of Hallstatt staged a blockade of the main access tunnel, brandishing placards asking visitors to ‘think of the children’, it highlighted what can happen when places start to feel overrun by tourists. Hallstatt has just 800 residents but has opened its doors to around 10,000 visitors a day — a population increase of over 1,000%. And it’s just one of a growing number of places where residents are up in arms at the influx of travellers.

The term ‘overtourism’ is relatively new, having been coined over a decade ago to highlight the spiralling numbers of visitors taking a toll on cities, landmarks and landscapes. As tourist numbers worldwide return towards pre-pandemic levels, the debate around what constitutes ‘too many’ visitors continues. While many destinations, reliant on the income that tourism brings, are still keen for arrivals, a handful of major cities and sites are now imposing bans, fines, taxes and time-slot systems, and, in some cases, even launching campaigns of discouragement in a bid to curb tourist numbers.

What is overtourism?

In essence, overtourism is too many people in one place at any given time. While there isn’t a definitive figure stipulating the number of visitors allowed, an accumulation of economic, social and environmental factors determine if and how numbers are creeping up.

There are the wide-reaching effects, such as climate change. Coral reefs, like the Great Barrier Reef and Maya Bay, Thailand, made famous by the Leonardo DiCaprio film, The Beach , are being degraded from visitors snorkelling, diving and touching the corals, as well as tour boats anchoring in the waters. And 2030 transport-related carbon emissions from tourism are expected to grow 25% from 2016 levels, representing an increase from 5% to 5.3% of all man-made emissions, according to the United Nations World Tourism Organisation (UNWTO). More localised issues are affecting locals, too. Renters are being evicted by landlords in favour of turning properties into holiday lets, and house prices are escalating as a result. As visitors and rental properties outnumber local residents, communities are being lost. And, skyrocketing prices, excessive queues, crowded beaches, exorbitant noise levels, damage at historical sites and the ramifications to nature as people overwhelm or stray from official paths are also reasons the positives of tourism can have a negative impact.

Conversely, ‘undertourism’ is a term applied to less-frequented destinations, particularly in the aftermath of the pandemic. The economic, social and environmental benefits of tourism aren't always passed on to those with plenty of capacity and, while tourist boards are always keen for visitors to visit their lesser-known attractions, it’s a more sustainable and rewarding experience for both residents and visitors.

What’s the main problem with it?

Overcrowding is an issue for both locals and tourists. It can ruin the experience of sightseeing for those trapped in long queues, unable to visit museums, galleries and sites without advance booking, incurring escalating costs for basics like food, drink and hotels, and faced with the inability to experience the wonder of a place in relative solitude. The absence of any real regulations has seen places take it upon themselves to try and establish some form of crowd control, meaning no cohesion and no real solution.

Justin Francis, co-founder and CEO of Responsible Travel, a tour operator that focuses on more sustainable travel, says “Social media has concentrated tourism in hotspots and exacerbated the problem, and tourist numbers globally are increasing while destinations have a finite capacity. Until local people are properly consulted about what they want and don’t want from tourism, we’ll see more protests.”

A French start up, Murmuration, which monitors the environmental impact of tourism by using satellite data, states that 80% of travellers visit just 10% of the world's tourism destinations, meaning bigger crowds in fewer spots. And, the UNWTO predicts that by 2030, the number of worldwide tourists, which peaked at 1.5 billion in 2019, will reach 1.8 billion,   likely leading to greater pressure on already popular spots and more objection from locals.

Who has been protesting?

Of the 800 residents in the UNESCO-listed village of Hallstatt, around 100 turned out in August to show their displeasure and to push for a cap on daily visitors and a curfew on tour coach arrivals.

Elsewhere, residents in Venice fought long and hard for a ban on cruise ships, with protest flags often draped from windows. In 2021, large cruise ships over 25,000 tonnes were banned from using the main Giudecca Canal, leaving only smaller passenger ferries and freight vessels able to dock.

In France, the Marseille Provence Cruise Club introduced a flow management system for cruise line passengers in 2020, easing congestion around the popular Notre-Dame-de-la-Garde Basilica. A Cruise Lines International Association (CLIA) spokesperson said, “Coaches are limited to four per ship during the morning or afternoon at the Basilica to ensure a good visitor experience and safety for residents and local businesses. This is a voluntary arrangement respected by cruise lines.”

While in Orkney, Scotland, residents have been up in arms at the number of cruise ships docking on its shores. At the beginning of 2023, the local council confirmed that 214 cruise ship calls were scheduled for the year, bringing around £15 million in revenue to the islands. Following backlash from locals, the council has since proposed a plan to restrict the number of ships on any day.

What steps are being taken?  

City taxes have become increasingly popular, with Barcelona increasing its nightly levy in April 2023 — which was originally introduced in 2012 and varies depending on the type of accommodation — and Venice expects to charge day-trippers a €5 fee from 2024.

In Amsterdam this summer, the city council voted to ban cruise ships, while the mayor, Femke Halsema, commissioned a campaign of discouragement, asking young British men who planned to have a 'vacation from morals’ to stay away. In Rome, sitting at popular sites, such as the Trevi Fountain and the Spanish Steps, has been restricted by the authorities.

And in Kenya’s Maasai Mara, meanwhile, the Narok County governor has introduced on-the-spot fines for off-roading. He also plans to double nightly park fees in peak season.

What are the forecasts for global tourism?  

During the Covid pandemic, tourism was one of the hardest-hit industries — according to UNWTO, international tourist arrivals dropped 72% in 2020. However, traveller numbers have since been rapidly increasing, with double the number of people venturing abroad in the first three months of 2023 than in the same period in 2022. And, according to the World Travel Tourism Council, the tourism sector is expected to reach £7.5 trillion this year, 95% of its pre-pandemic levels.

While the tourism industry is forecast to represent 11.6% of the global economy by 2033, it’s also predicted that an increasing number of people will show more interest in travelling more sustainably. In a 2022 survey by Booking.com, 64% of the people asked said they would be prepared to stay away from busy tourist sites to avoid adding to congestion.

Are there any solutions?  

There are ways to better manage tourism by promoting more off-season travel, limiting numbers where possible and having greater regulation within the industry. Encouraging more sustainable travel and finding solutions to reduce friction between residents and tourists could also have positive impacts. Promoting alternative, less-visited spots to redirect travellers may also offer some benefits.

Harold Goodwin, emeritus professor at Manchester Metropolitan University, says, “Overtourism is a function of visitor volumes, but also of conflicting behaviours, crowding in inappropriate places and privacy. Social anthropologists talk about frontstage and backstage spaces. Tourists are rarely welcome in backstage spaces. To manage crowds, it’s first necessary to analyse and determine the causes of them.

Francis adds: “However, we must be careful not to just recreate the same problems elsewhere. The most important thing is to form a clear strategy, in consultation with local people about what a place wants or needs from tourism.”

As it stands, overtourism is a seasonal issue for a small number of destinations. While there is no one-size-fits-all solution, a range of measures are clearly an option depending on the scale of the problem. For the majority of the world, tourism remains a force for good with many benefits beyond simple economic growth.

Related Topics

• OVERTOURISM
• SUSTAINABLE TOURISM

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• Published: 17 October 2013

Using social media to quantify nature-based tourism and recreation

• Spencer A. Wood 1 , 2 ,
• Anne D. Guerry 1 , 2 ,
• Jessica M. Silver 1 , 2 &
• Martin Lacayo 2  

Scientific Reports volume  3 , Article number:  2976 ( 2013 ) Cite this article

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• Conservation biology
• Ecosystem services
• Environmental economics
• Socioeconomic scenarios

Scientists have traditionally studied recreation in nature by conducting surveys at entrances to major attractions such as national parks. This method is expensive and provides limited spatial and temporal coverage. A new source of information is available from online social media websites such as flickr. Here, we test whether this source of “big data” can be used to approximate visitation rates. We use the locations of photographs in flickr to estimate visitation rates at 836 recreational sites around the world and use information from the profiles of the photographers to derive travelers' origins. We compare these estimates to empirical data at each site and conclude that the crowd-sourced information can indeed serve as a reliable proxy for empirical visitation rates. This new approach offers opportunities to understand which elements of nature attract people to locations around the globe and whether changes in ecosystems will alter visitation rates.

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Introduction.

Recreation and tourism are important components of many national and local economies and they contribute in innumerable ways to quality of life, sense of place, social connection, physical wellbeing, learning and other intangibles. Information on patterns of recreation and tourism and the factors that influence behavior in these realms is typically collected using site-specific surveys or interviews. The recent emergence of social media creates exciting alternative possibilities to assess how people use and respond to nature and other cues for recreation and tourism. One problem, however, is that while they generate “big data”, it is often unclear how to tease meaning and useful information from social media. Here we assess the relationship between the locations of photographs from the image-sharing website flickr and empirically derived visitation rates at sites around the world. This is the first study to ground-truth the use of data from social media to predict visitation rates, freeing researchers from time- and labor-intensive surveys and revolutionizing the use of social media to understand where people recreate.

A key reason for studying patterns of recreation or tourism is the economic significance of this industry. The total contribution of travel and tourism to the world's gross domestic product (GDP) in 2011 was approximately $6 B USD (9% of GDP), with expected growth to $10 B USD by 2022 1 . On more regional scales, recreation and tourism represent a significant fraction of many local economies. The economy of the Caribbean region, for example, is dominated by tourism, with 15% of GDP from tourism and 17% of the available workforce employed in the tourism sector 2 . In the US, English et al. 3 classify 156–338 nonmetropolitan counties in the US as “tourism dependent”, meaning 10% of income and 15% of jobs in these counties result from tourism. Of course, economic impacts are only one way of measuring the importance of recreation and tourism. These activities are critical contributors to diverse aspects of human wellbeing 4 . For example, outdoor recreation is a spiritual experience for many people 5 , 6 , 7 , 8 and social interactions in nature contribute to building a sense of place 9 , 10 , 11 , 12 .

A major and growing portion of the global penchant for travel and recreation is “nature-based”, involving interactions with or appreciation of the natural environment 13 . For these types of activities, characteristics of the environment influence people's decisions about where, when and how to recreate. SCUBA divers, for example, select destinations based on the water clarity, water temperature and diversity of marine life 14 , 15 . Bird-watchers are drawn to the best places to see target species 16 , which inevitably are places where natural systems support populations of desirable birds 17 .

Several previous studies have succeeded in quantifying the degree to which recreation depends on environmental attributes such as species richness 18 , the diversity of habitats 19 , 18 , precipitation 20 and temperature 21 , as well as to other attributes such as infrastructure and cultural attractions 22 , 23 . However, because people's motivations for how and where to recreate vary from place to place and depend on many factors including their origin and destination, each study must assemble new empirical data. Existing empirical data are often too coarse to illuminate the relationship between visitation and salient features of a particular location and represent point samples as opposed to landscape data. Empirical surveys are also expensive to conduct and are limited in coverage.

To avoid having to conduct costly and time-consuming surveys, researchers have long sought automated methods for collecting empirical data on visitation rates to multiple locations. Shoval and Isaacson 24 recorded the movement patterns of several tourists in Israel who volunteered to carry global positioning system (GPS) locators, an approach since repeated in other locations 25 . Pettersson and Zillinger 26 supplemented survey and GPS data with aerial images to estimate the number of people attending a sporting event in Sweden. While these techniques provide rich datasets, there remains a need for methods of estimating visitation rates across multiple spatio-temporal scales, in natural and built environments and in both developed and developing countries. We explore a new approach, using the density of existing geolocated photographs posted to the online photo-sharing website flickr as local data that reveal where people recreate.

Since its launch in 2004, over 71 M flickr users have uploaded over six B photographs to the image-sharing website. Approximately 197 M of these photographs are geotagged, meaning the image has been assigned specific coordinates of latitude and longitude ( Fig. 1 ). Since 2010, 40–50 M geotagged photographs have been uploaded annually. Most geotagged photographs on land are from locations in Europe (40%), North America (39%) and Asia (13%). Sixteen percent of all geotagged photographs are from marine and coastal environments. Most images are taken in the United States, United Kingdom and France. On a per-area basis, Vatican City, Macau and Gibraltar have the greatest number of geolocated pictures, while Antarctica and Chad have the lowest density of images.

Locations of the approximately 197 M geotagged photographs uploaded to flickr from 2005–2012.

Figure created using the maps package for R.

Visitation rates

Average user-days per year based on photographs (x-axis) vs. empirical data (y-axis) at 836 sites worldwide.

Panels display points from the individual datasets listed in Supplementary Table S1 ( ○ ) atop all data (+). Grey line is 1:1.

Average photograph-based and empirical estimates of user-days per year.

Sites are colored according to whether they are primarily a cultural (green circles [n = 498]) or natural (orange diamonds [n = 338]) attraction. Black line depicts the overall trend across all sites. Grey line is 1:1.

Sites are colored according to whether they are located in a country with a low (green circles [n = 35]) or high (orange diamonds [n = 801]) Gross National Income. Black line depicts the overall trend across all sites. Grey line is 1:1.

Visitor origins

Data from photographs uploaded to flickr also serve as a good indicator of the country of origin for travelers ( Fig. 5 ). Originating countries of incoming visitors surveyed at border crossings ( EOC ) are related to the home countries reported in the profiles of flickr users who took photos within the same focal country ( POC ), measured as proportions of incoming people (β = 0.735, F 1,102 = 169.12, p < 0.001, R 2 = 0.620). In our analysis, the flickr users traveling to five destination countries ( D ) originate from 55 nations from around the world. These nations represent a wide range of population sizes and income levels. A nonsignificant POC · D interaction term in the ANCOVA shows that the scaling relationship between EOC and POC does not differ across the five destination countries considered here. Furthermore, the intercept of the relationship between EOC and POC is also constant across all levels of D . Thus, we removed the D and POC · D terms from the model and present the results of a simplified ANCOVA in Table 2 . In summary, the home location reported by flickr users who take and upload pictures in a particular country can predict the originating countries of travelers to that nation. Furthermore, the relationship between photo- and empirically-derived visitation rates is equal across the five destination countries examined.

The average proportion and originating country of travelers who arrived to five destination countries each year, according to stated home locations of flickr users who took at least one photograph within the country (x-axis) and immigration data (y-axis).

Names of outlying originating countries are abbreviated. Datasets are distinguished by colors and symbols and described in Supplementary Table S2 . Black line depicts the overall trend across all sites. Grey line is 1:1.

Visitation over time

Visitation rates estimated from flickr images match expectations over time at sites selected as examples of cultural (Zuccotti Park and Black Rock Desert) and natural (Vermont) attractions. There is a single spike in PUD at Zuccotti Park that corresponds to the duration of the encampment there ( Fig. 6a ). Increasing numbers of photos begin to appear on flickr when the protests start on September 17, 2011 and drop abruptly when the park is closed on November 15, 2011. In the Black Rock Desert, there is an annual spike in the numbers of photos taken that corresponds to the three-week period surrounding the Burning Man festival each year ( Fig. 6b ). Similarly, there is a period of higher PUD in southern Vermont each October ( Fig. 6c ) during the prime month for viewing the fall foliage.

Total photo-user-days each week from 2006–2012 in (a) Zuccotti Park, the site of the occupy protest, (b) Black Rock Desert, the site of the Burning Man festival and (c) southern Vermont, an area popular for viewing autumn foliage. Grey shading indicates time periods from the start of the protest until people were barred from the park (a), three weeks spanning the annual week-long festival (b), or the month of October each year (c).

A lack of useful information about where people go during their leisure time has hindered progress toward understanding what draws and repels people to and from various recreation sites around the world. Here, we show that crowd-sourced information can offer new perspectives on this old problem, revolutionizing the way we study people and understand their choices. We hypothesized that pictures could indicate visitors and furthermore, that photographs uploaded to an image-sharing website could record people's choices and provide useful data worldwide. Our comparison of visitation data collected from 836 sites in 31 countries with data generated from geotagged photographs uploaded to flickr shows that the crowd-sourced data are indeed a suitable proxy for the more traditional time- and labor-intensive empirical estimates. This represents a significant advancement, as this new proxy measure of visitation can be applied almost anywhere: in developed and developing countries, data-poor and data-rich locations, urban areas and wilderness. Wherever people are taking and uploading pictures we can use that information to indicate their visit and learn from it.

Home countries given by users on flickr correspond with the home countries of travelers recorded at immigration entry points, making crowd-sourced data not only useful for estimating visitation rates, but also for understanding where visitors originate. Because the time and money that people spend traveling indicates how much they value the destination, these data on the origin and destination of recreators are enormously beneficial for economic methods for valuing recreation sites. One preferred approach for quantifying value is to use a “travel cost model” which uses the cost of travel to estimate peoples' willingness to pay to recreate at particular sites 27 . Travel cost studies are often criticized for not accounting for people who visit multiple sites on a single trip away from home. Crowd-sourced visitation data can potentially address this issue since users often upload images throughout their journey.

The ability to estimate visitation rates without survey data allows for models that can anticipate changes in visitation in response to changes in ecosystems, relative to other types of change (in built features, social capital, etc.). Random utility models are one example of an economic technique for quantifying the marginal benefits of natural environments and other attributes. Typically, telephone surveys are conducted asking respondents where they live, which recreational sites they visit and why. These individuals' choices about which sites to visit reflect their preferences for certain characteristics of sites and the tradeoff between the costs (e.g., travel) and benefits (e.g., presence of wildlife) of the trip. Here, we show that the same data can be gathered using the locations of photographs and spatial data on the presence of features such as swimming beaches, cultural events, or other attractions. Enticing evidence that this approach is suitable for understanding people's choices is demonstrated by the match of flickr photos to known temporal aggregations of people in Zuccotti Park, Black Rock Desert and southern Vermont ( Fig. 6 ). We offer these as initial examples and hope to spark further use of this approach to understand what draws and repels people to and from particular places.

Of course, this method is imperfect. There may be biases in who is taking digital photographs and uploading them to social media sites. Different recreational activities may be more or less suited to taking photographs. Surfers, for example, while likely possessing cameras and internet access, may prefer not to take photographs while surfing. Also, the perceived value of a trip may influence whether an individual takes or shares photographs, resulting in a bias against images from visitors who travel shorter distances from home. We observe, for example, that tourists visiting Nepal from neighboring Sri Lanka and India upload fewer photographs to flickr than predicted based on the overall trend ( Fig. 5 ). Similarly, local visitors may be less inspired to take or share photographs of commonly-visited sites. While we find strong correlations between the crowd-sourced information and empirical data at attractions, such as national parks, we do not look at correlations between crowd-sourced information and visitation to more mundane locations, like shopping centers, that might be popular sites for recreation by local people. Further work is needed to explore the utility of this approach at locations that are not major attractions or landmarks. Other social media such as geotagged tweets might serve as more effective proxies for some types of recreation, particularly in urban areas.

New technologies and digital social media have begun making vast amounts of geolocated data available for a wide range of creative purposes, including art, targeted advertising, crime prevention and scientific research. Some authors are rightfully raising concerns about the appropriate and ethical use of these data and the potential for apophenia: to see patterns in “big data” where none actually exist 28 . In response to their calls for more critical assessments of digital data, this study vets a novel method for using geotagged photographs from flickr to provide sources of information for understanding where people go. We conclude that crowd-sourced information can not only break the log-jam of expensive empirical data requirements for predicting and valuing how changes in the landscape alter recreation and tourism, but also can provide revolutionary information for understanding questions about where people recreate, in ways unimaginable before the existence of the internet and social media.

While data from social media are fascinating, a critical question that will determine their utility for understanding where people go is: how well do they reflect on-the-ground visitor surveys and records? If we can establish reliable statistical relationships between image- and field-based records then we will have a powerful new tool for tracking how people interact with nature during recreation. To address this question, we first compare photograph- and field-based estimates of visitation rates at recreational sites around the world. Then, we assess how demographics of flickr users, specifically their home country, compare to survey responses by tourists entering through immigration checkpoints into five nations.

We assembled data from nine independent empirical datasets that quantified visitation to 836 sites in 31 countries around the world ( Supplementary Table S1 ). These empirical datasets represent a wide range of attractions, from amusement parks to national parks and from historic battlefields to art galleries. In each study, total empirical annual visitor user-days ( EUD ) were counted at a defined recreation site such as a park or museum. User-days are defined as one person spending a portion of one day within a site. The smallest and largest sites in the dataset are both in the USA, ranging from the 80 m 2 Thaddeus Kosciuszko US National Memorial in Pennsylvania to the Gates of the Arctic National Park and Preserve in Alaska (30,448 km 2 ). All nine datasets were available publicly on the world wide web. For a study to be included in our analyses, we required measurements of total annual user-days to at least nine sites for two years between 2005–2011.

To create an alternative measure of visitation rate at the same sites, we used metadata associated with photos uploaded to the online photo-sharing website flickr. We used flickr's public API to download metadata for photographs that were taken within the bounds of each site. Along with the location of pictures, we also stored the photographer's identification number and the date that the image was taken. We used these two additional variables to convert the metadata, often including numerous photographs from the same person on the same day, into total annual user-days over the same time period as the EUD data were collected at each site. Thus, for photographs, user-days are defined as the total number of days, across all users, that each person took at least one photograph within each site ( PUD ).

To account for potential variation in the scaling relationship between EUD and PUD , we recorded two additional attributes for each of the 836 study sites. First, each site was characterized as either a cultural or natural attraction (dummy variable A ). Cultural sites are those visited primarily for recreation in the built environment and socializing with other people (e.g., amusement parks) or learning about human culture (e.g., art galleries, historic battlefields) irrespective of the natural setting. Natural attractions, on the other hand, are sites where people go primarily to appreciate the flora, fauna, or natural setting (e.g., state beaches, recreation areas, botanical gardens). In reality, the two categories are not exclusive, so we selected the category capturing the primary reason that most people visit each site. Second, we characterized each site as either high- or low-income (dummy variable I ) based on the nation's per-capita gross national income (GNI) published by the World Bank. We divided the World Bank's four income levels into two categories, low and high, with low-income countries having a per-capita GNI below $4,035 USD in 2008.

We collected a second empirical dataset of origin countries of tourists arriving to five destination countries (see Supplementary Table S2 ). Empirical estimates of the proportion of visitors from each country ( EOC ) derive from passenger surveys conducted at entry points such as airports and overland border-crossings. We gathered values from public sources, found online, that reported the EOC per origin country for at least one calendar year between 2005–2010. For comparison, we assembled a dataset of the origination locations of users who uploaded photographs to flickr taken within the destination country during the same time that the empirical data were collected. flickr users have the option to list a “current location” in their profile. We assumed each user's current location was the origination of each trip and used this information to calculate the proportion of photographers originating from each country (hereafter, POC ).

Finally, we present examples of how geotagged images can track the aggregate movement of people responding to cultural and environmental cues. As examples of cultural attractions, we examine visitation rates to Zuccotti Park, in New York City, the epicenter of the Occupy Movement of 2011 and to the Black Rock Desert, Nevada, home of the Burning Man festival. In the first, we expect higher PUD values from the start of the protest on September 17, 2011 until people were barred from the park on November 15, 2011. In the second, we expect annual peaks of visitation, measured as PUD , surrounding the annual week-long festival. To display the influence of a natural attribute on visitation, we plot seasonal patterns of PUD in southern Vermont where “leaf-peepers” are drawn by the colors of the foliage annually in October.

World Travel and Tourism Council. Travel and Tourism: Economic Impact (2012).URL http://www.wttc.org/site_media/uploads/downloads/world2012.pdf Accessed March 2013.

Carr, L. M. & Heyman, W. D. Jamaica bound? Marine resources and management at a crossroads in Antigua and Barbuda. Geogr J 175, 17–38 (2009).

Article   Google Scholar  

English, D. B., Marcouiller, D. W. & Cordell, H. K. Tourism dependence in rural America: estimates and effects. Soc Nat Resour 13, 185–202 (2000).

Russell, R. et al. Humans and nature: how knowing and experiencing nature affect well-being. Annu Rev Env Resour 38, in press (2013).

Trainor, S. F. & Norgaard, R. B. Recreation fees in the context of wilderness values. J Park Recreat Adm 17, 100–115 (1999).

Google Scholar  

Sweatman, M. M. & Heintzman, P. The perceived impact of outdoor residential camp experience on the spirituality of youth. World Leis J 46, 23–31 (2004).

Heintzman, P. Nature-based recreation and spirituality: a complex relationship. Leis Sci 32, 72–89 (2009).

Heintzman, P. The spiritual dimension of campers' park experience: management implications. Manag Leis 17, 291–310 (2012).

Eisenhauer, B. W., Krannich, R. S. & Blahna, D. J. Attachments to special places on public lands: an analysis of activities, reason for attachments and community connections. Soc Nat Resour 13, 421–441 (2000).

Stedman, R. C. Is it really just a social construction?: the contribution of the physical environment to sense of place. Soc Nat Resour 16, 671–685 (2003).

Ryan, R. L. Exploring the effects of environmental experience on attachment to urban natural areas. Environ Behav 37, 3–42 (2005).

Willox, A. C. et al. From this place and of this place: climate change, sense of place and health in Nunatsiavut, Canada. Soc Sci Med 75, 538–547 (2012).

Balmford, A. et al. A global perspective on trends in nature-based tourism. PLoS Biol 7, e1000144 (2009).

Williams, I. D. & Polunin, N. V. Differences between protected and unprotected reefs of the western Caribbean in attributes preferred by dive tourists. Environ Conserv 27, 382–391 (2000).

Uyarra, M. C., Watkinson, A. R. & Ct, I. M. Managing dive tourism for the sustainable use of coral reefs: validating diver perceptions of attractive site features. Environ Manage 43, 1–16 (2009).

Article   ADS   Google Scholar  

Naidoo, R. & Adamowicz, W. L. Biodiversity and nature-based tourism at forest reserves in Uganda. Environ Dev Econ 10, 159–178 (2005).

Puhakka, L., Salo, M. & Sksjrvi, I. E. Bird diversity, birdwatching tourism and conservation in Peru: a geographic analysis. PLoS One 6, e26786 (2011).

Article   CAS   ADS   Google Scholar  

Loureiro, M. L., Macagno, G., Nunes, P. A. & Tol, R. Assessing the impact of biodiversity on tourism flows: an econometric model for tourist behaviour with implications for conservation policy. J Environ Econ Policy 1, 174–194 (2012).

Neuvonen, M., Pouta, E., Puustinen, J. & Sievnen, T. Visits to national parks: effects of park characteristics and spatial demand. J Nat Conserv 18, 224–229 (2010).

Loomis, J. B. & Richardson, R. B. An external validity test of intended behavior: comparing revealed preference and intended visitation in response to climate change. J Environ Plann Man 49, 621–630 (2006).

Richardson, R. & Loomis, J. B. Climate change and recreation benefits in an alpine national park. J Leis Res 37, 307–320 (2005).

Mills, A. S. & Westover, T. N. Structural differentiation: a determinant of park popularity. Ann Tourism Res 14, 486–498 (1987).

Hill, G. W. & Courtney, P. R. Demand analysis projections for recreational visits to countryside woodlands in Great Britain. Forestry 79, 185–200 (2006).

Shoval, N. & Isaacson, M. Application of tracking technologies to the study of pedestrian spatial behavior. Prof Geogr 58, 172–183 (2006).

McKercher, B., Shoval, N., Ng, E. & Birenboim, A. First and repeat visitor behaviour: GPS tracking and GIS analysis in Hong Kong. Tourism Geogr 14, 147–161 (2012).

Pettersson, R. & Zillinger, M. Time and space in event behaviour: tracking visitors by GPS. Tourism Geogr 13, 1–20 (2011).

Ward, F. A. & Loomis, J. B. The travel cost demand model as an environmental policy assessment tool: a review of literature. Western J Agr Econ 11, 164–178 (1986).

boyd, d. & Crawford, K. Six provocations for big data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society 10.2139/ssrn.1926431 (2011).

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Acknowledgements

The authors are grateful to Peter Kareiva, Katie Arkema, Eric Fischer, Greg Guanell, Steve Polasky, Mary Ruckelshaus, Rich Sharp, CK Kim, Kent Kovacs, Mike Papenfus, Derric Pennington, Jodie Toft, Gregg Verutes and the Santa Fe Institute. Funding was provided by the Gordon and Betty Moore Foundation, Google, the Tides Foundation, World Wildlife Fund and the Summit Foundation.

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Spencer A. Wood, Anne D. Guerry & Jessica M. Silver

Natural Capital Project, Woods Institute for the Environment, Stanford University, Stanford, CA, USA

Spencer A. Wood, Anne D. Guerry, Jessica M. Silver & Martin Lacayo

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S.A.W., A.D.G., J.M.S. and M.L. designed the study. S.A.W., J.M.S. and M.L. performed the analyses. S.A.W. and A.D.G. wrote the manuscript.

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Wood, S., Guerry, A., Silver, J. et al. Using social media to quantify nature-based tourism and recreation. Sci Rep 3 , 2976 (2013). https://doi.org/10.1038/srep02976

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Digital Travel Summit APAC 2024

August 14 - 15, 2024

Equarius Hotel, Sentosa, Singapore

The Role of Social Media in Tourism Marketing

Social media has changed every single aspect of our lives, including the way we consume. These developments have significantly affected businesses mainly through enabling new marketing strategies. Tourism, being one of the most vibrant sectors of the global economy, is undoubtedly a part of all these.

Merging social media and tourism marketing will lead to excellent results for your business. Here we have gathered information about the essentials of social media in tourism marketing: what is the role of it and how it can be effectively used.

Importance of the Social Media

Social media impact on tourism is seen in the ways people research before going on a trip. Now people are encouraged to share their travel experiences. Thus, social media has transformed the way people make decisions. People build their trust in a tourism agency based on the reviews of the others.

Social sharing for better customer relations

Customer service is another essential aspect of the tourism industry that has changed with social media. Now brands and businesses can reach their customers directly through social media. When people are unsatisfied with a tourism service, they can call the companies to account for that. Thus, solving the problems of the customers in the kindest ways will lead to a better reputation for a company.

Social sharing might be the most significant factor that affected the tourism industry. Social media enables especially young people to share the most significant memories from their travels with a vast audience. Tourism companies should know that this is a more powerful way of attracting new travellers than simple advertisements and encourage people to share their real experiences online.

As seen above, the rise of social media led to the development of two-way communication between agencies and customers and customers to customers. To benefit from this impact of social media on the tourism industry, turning to social media is vital for a tourism agency.

How to Create Strategies

The tourism industry is highly competitive. Once tourism companies are aware of the possible benefits of social media for their business, they use ways through which they can increase their brand awareness.

Engaging content is the king

Creating engaging content is the most crucial step to go if you aim to attract more customers. Since the tourism industry is significantly connected with visual experience, visual material is the most engaging way to catch attention. You should use catchy photos and impressive videos that are simple and fun.

User-generated content is one of the best ways to get people to engage with your business. Influencer marketing will help a lot to make your business visible among the others. After specifying your target location and audience, you can get in touch with tourism influencers and experts. You will not believe how fast your brand is reaching followers, thanks to influencers.

Increase visibility

After you are familiar with the role of social media in tourism marketing, you notice that whatever you do, being social is the key. To increase your visibility, you should be actively contacting your customers by listening to them or answering their questions. You can research your keywords and join in the conversations around your service. As we stated above, providing customer service online is an excellent way of making your voice heard.

The tourism industry is extensive and seasonal, so you should be relevant when it comes to timing. Whether popular or undiscovered, every location has its own season. ​ Digital marketing agencies ​ can help you provide up-to-date campaigns to advertise your newest services.

Best Social Media Channels for Tourism Marketing

As in any other industry, Big Three of the social media -Facebook, Twitter, and Instagram have been the leader in the tourism industry as well. Even though these channels have their own audience, travel is among the most shared topics on all of them.

Facebook is an excellent platform to catch users among various social groups. Facebook’s Recommendations feature enables people to share their experiences. It can be used effectively for travelling purposes, to reach information about what users are telling about your travel business.

Thanks to its emphasis on visual material, Instagram is one of the most effective social media channels. It is a great platform for tourism businesses to engage with their current and future customers. Using Instagram will help you attract people, especially the millennials, as they form a group that is highly active on Instagram. You should follow the travel hashtags and create your own to maximize the popularity of your posts.

While Instagram takes over your visual material, Twitter is your voice. This platform allows you to talk about short travel tips and promos. Even though Twitter can be used for photos and videos of your brand, its actual strength is being especially useful for providing customer service. If you want to join in conversations, you need to have an active Twitter account.

Other than the Big Three, there are various platforms that you can use to get in contact with different groups of travellers. For example, LinkedIn is significant for B2B. Business travellers share information on Linkedin groups about many topics, including business trips. If you want to reach business travellers, you use LinkedIn to reach them depending on the locations in which you provide service.

Widely used by generation Z, Snapchat is another platform that is important for your tourism marketing strategy​ if you are targeting young customers. It is a rapidly growing social media channel on which you can share what is happening at the moment. You can share gripping snaps about your tourism business to catch attention.

Social media has transformed the ways to build a reliable brand. For the tourism industry, the age of brochures and billboards are over. The key to business success is to collect social shares, positive user reviews and customer satisfaction on social media.

In this text, we tried to show that social media is a big opportunity for tourism companies. To make use of its advantages, the ​ Digital Agency Network​ can help you find the most suitable agency for your tourism marketing.

Author:Gizem Tas

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(HQ) UBF North America & Europe Social Media Contest!!!-Prizes: 1,800!!!

• by UBF HQ MEDIA
• Aug 24, 2024

UNLOCK YOUR CREATIVITY!

University Bible Fellowship North America & Europe Social Media Contest

Dear UBF North America (USA/Canada) and Europe UBF Members,

UBF HQ is excited to host the first social media contest of 2024 for UBF North America & Europe UBF members. Please submit your YouTube Shorts URL, YouTube long-form videos URL, or TikTok video URL to us by the deadline, December 16, 2024, at 10 a.m. CST. Winners will be chosen based on the requirements provided below with prizes. Please see more detailed information below. We encourage many of UBF members in North America and Europe to participate and have a chance to be rewarded.

•     First Place (1 person): $500
•     Second Place (2 people): $250 each
•     Third Place (4 people): $100 each
•     Fourth Place (20 people): $20 Amazon Gift Card or Cash each

Requirements: Your YouTube Shorts (1 minute), Long Form (no time limitation), or TikTok videos (no time limitation) will be judged based on the total number of views, comments, and likes. The videos with the highest combined totals will win. Minimum Requirements: Each submitted video must have at least:

•         1,000 views
•         100 likes
•         10 comments

Scoring Formula: Videos that meet the minimum requirements will be scored using the following formula: [# of Views] + [# of Likes × 10] + [# of Comments × 20] = Total Score Example: If your video has 1,200 views, 150 likes, and 30 comments, your total score would be: 1,200 + (150×10) + (30×20) = 1,200 + 1,500 + 600 = 3,300 points Eligibility: Only UBF members who live in North America (USA/Canada) and Europe can participate. Language: Videos must be in English (no other languages will be accepted). Hashtags: Each video must include the hashtag #ubf #universitybiblefellowship #fyp #viral #foryou #christian #bible in the description. Video Contents & Theme: The content should be Christian / Bible-related. But, no specific subjects are provided, so feel free to be creative! For example, the video can be of dancing, vlogs, skits, portraying your talents in the visual and musical arts, etc. Videos that are politically motivated or show imagery of violence, sex, and drugs will not be permitted. Privacy Settings: Videos must be set to public (not private or unlisted) on YouTube and TikTok. Channel Requirement: Only videos from personal channels will be accepted. Submission Limit: Each person may submit only one video url. We strongly encourage you to post multiple videos, but please submit only one video url that has garnered the most views, likes, and comments. Submission Format:     - Send the video link URL (not the video file)     - Your full name     - Country name (USA or Canada or Europe)     - UBF chapter name     - Phone number (Optional)     - Describe your contents Upload Period: Videos must be uploaded between September 16 and December 16 (3 months). Deadline: Qualified videos must be submitted by December 16, 2024, at 10 a.m. CST. The view counts, clicks, and comments will be counted as of the deadline. Send the video link to [email protected] with your full name / UBF chapter name Winners Announcement: Winners will be announced on ubf.org with your videos and notified via your email.

Questions? Contact: [email protected]

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Kaiya Shute boasts on social media while fighting to keep name secret in Connor Boyd manslaughter case

A young Auckland model jailed over the death of a teenager boasted on social media during the case as she fought to keep her name secret.

Kaiya Shute and her former boyfriend William Allister Grace lost suppression this week after a years-long battle that continued even after they were both sent to jail.

The pair were found criminally responsible for the dragging death of 18-year-old Connor Boyd outside an Auckland Central nightclub in 2022

Shute, now in her 20s, didn’t let her persistent bid for permanent name suppression stand in the way of maintaining a TikTok account where she dished out life advice in the style of an aspiring influencer.

And in one video posted just days before a jury found her guilty of manslaughter, Shute sings that she is “bad as f***” as she lies in bed smiling at the camera.

Singing along to Everything Nice by American rapper Dreamdoll, Shute said:

“All of these bitches be mad as f***, why?

“Cause I’m bad as f***.”

Shute and Grace were accused of having grabbed 18-year-old Boyd as they drove away from a CBD club late one night in April 2022.

Boyd was seen on CCTV clinging to the side of Grace’s SUV before falling onto Customs St East outside Saturdays nightclub in Britomart, where the three had earlier crossed paths.

He was taken off life support after suffering unsurvivable head injuries.

Grace, who was driving during the incident, claimed he feared for his and his passengers’ safety after Boyd voiced a threat through the open window and allegedly started throwing punches. Shute, meanwhile, testified that she never grabbed Boyd’s arm and was in shock when her co-defendant did so.

But jurors, who repeatedly watched the horrific CCTV footage of the tumble, didn’t buy the defence and neither did the sentencing judge.

The incident occurred after a night of drinking and “aggressive bullying”, Justice Ian Gault noted during the duo’s sentencing hearing in February.

“He was outnumbered by you and your friends and did not demonstrate any physical aggression towards you,” Gault told Shute.

“Were it not for your animus to Mr Boyd through that night, coupled with your assistance to Mr Grace in the vehicle, Mr Boyd’s death would not have resulted.”

Boyd’s father John faced the pair in court at that hearing, during which Shute was sentenced to two years and two months’ jail and Grace to two-and-half years.

“There are no words to describe the infinite pain I feel,” John Boyd said during his emotional victim impact statement.

“I will forever be haunted by this nightmare.”

‘Lose everything and everyone to find yourself’

During her ongoing fight to keep her name secret, Shute’s lawyer – Julie-Anne Kincade KC - said when her client applied for jobs in the future, prospective employers would Google her name and see stories about the case.

Despite that, after her trial had kicked off, Shute posted a video featuring a montage of selfies accompanied by captions of “things I’ve learnt in my 20 years of life”.

They included advice to “switch your mentality from ‘I’m broken and helpless’ to ‘I’m growing and healing’.”

“Sometimes you lose everything and everyone to find yourself,” she wrote.

Comments were turned off on her videos, the most popular of which garnered nearly 3000 views. She did not mention the case or appear to break suppression in her videos.

In other social media profiles, Shute described herself as a hairdresser, makeup artist and model.

Kincade, in arguing in favour of suppression, also argued her client’s youth and “ongoing safety concerns” created extreme hardship. She said social media commentary on the case had frequently referred to her client as a “murderer” even though she was never charged with that offence.

But Shute failed to get permanent suppression at the High Court and then had her bid rejected on appeal.

“...We do not consider any likely social media commentary to be of such a level that it will compromise Ms Shute’s clear potential for rehabilitation,” three judges wrote when declining her appeal last month.

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