Next Article in Journal
Virtual Reality and Higher Education Sporting Events: Social Anxiety Perception as an Outcome of VR Simulation
Previous Article in Journal
Predicting Adolescents’ Leisure-Time Physical Activity Levels: A Three-Wave Prospective Test of the Integrated Model of Self-Determination Theory and the Theory of Planned Behavior
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Behavioral Sciences
Volume 14
Issue 8
10.3390/bs14080694
behavsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Influence of Social Media Usage and Perceived Government Market Orientation on Travel Intention to an Internet Celebrity City: Exploring the Mediating Effects of Place Attachment and Perceived Value

by
Wu Tang
1,
Cevat Tosun
2,*,
Ahmad Edwin Mohamed
1 and
Sinan Uslu
3
1
School of Tourism, Hospitality & Event Management, Universiti Utara Malaysia, Sintok 06010, Malaysia
2
School of Business, The George Washington University, Washington, DC 20052, USA
3
Faculty of Tourism, Necmettin Erbakan University, Konya 42090, Turkey
*
Author to whom correspondence should be addressed.
Behav. Sci. 2024, 14(8), 694; https://doi.org/10.3390/bs14080694
Submission received: 5 June 2024 / Revised: 31 July 2024 / Accepted: 6 August 2024 / Published: 9 August 2024
Browse Figures
Versions Notes

Abstract

:
As the usage of social media continues to grow and local governments expand their promotional efforts, more internet celebrity cities (ICCs) are emerging as popular tourist destinations. To investigate the internal mechanisms that affect tourists’ travel intentions to such destinations, place attachment and perceived value were proposed as two factors reflecting their deeper emotional connections and practical assessments to visit ICCs. This study was conducted to examine the relationship between social media usage, perceived government market orientation (PGMO), place attachment, perceived value, and travel intention in the context of Chongqing, a popular ICC in China. To evaluate the research hypotheses, 750 valid questionnaires were collected and analyzed by structural equation modeling. The results showed that social media usage had a positive impact on tourists’ travel intention to Chongqing, while PGMO did not significantly influence travel intention. However, both place attachment and perceived value had mediating effects in the relationship between social media usage, PGMO, and travel intention. This paper concludes with theoretical implications, suggestions for further research, and recommendations for practice.

1. Introduction

Social media has had a revolutionary impact on the development of the tourism industry [1]. Social media platforms such as Instagram, Facebook, and TikTok have become integral sources of information and inspiration for tourists [2]. They allow travelers to explore destinations virtually through user-generated content, influencers’ recommendations, and official tourism promotions [1,3]. Cities and destinations have adapted to this trend by actively using social media as a primary tool for marketing. They create engaging content that showcases their unique attractions, cultural experiences, and local flavors to capture the interest of potential visitors [4]. By strategically leveraging social media platforms, destinations can significantly enhance their visibility, cultivate a positive image, and ultimately attract more tourists [5].
In China, social media platforms like Sina Weibo (Chinese Twitter) and Douyin (Chinese TikTok) have significantly promoted cities such as Chongqing, Beijing, and Hangzhou. This has resulted in soaring city brand awareness and influence over a short period. As a result, these cities have quickly become popular internet celebrity destinations. Postma, Buda [6] emphasized that the spread of social media had reinvigorated the growth of city tourism. According to Shirly [7], Chongqing, known as an 8D mountain city, ranked first among the top 100 cities in China for tourism influence and won the “2022 City Tourism Online Popularity Award”. Over the past few years, it has become one of the hottest internet celebrity cities (ICCs).
According to Wang [8], the popularity of social media is a precondition for the emergence of ICCs. However, another important factor is the force behind the scenes such as local governments. Since city tourism can bring significant growth to the local economy, Chen, Li [9] implied that the local government had a strong motivation to transform cities into internet celebrity tourist destinations through market-oriented initiatives. Taking the city of Ganzi in China’s Sichuan province as an example, the director of the city’s Cultural and Tourism Bureau successfully transformed Ganzi into an internet celebrity tourist destination by posting short cosplay videos on social media in 2022, which resulted in a large number of tourists [10].
Scholars have investigated the influence of social media usage (SMU) [11,12,13] and perceived government market orientation (PGMO) on tourists’ travel intentions (TI) [14,15]. However, there has been little research on the internal mechanisms that affect tourists’ intentions to visit a tourist destination. Yang [16] implied that potential tourists’ intentions to visit internet celebrity tourist destinations stemmed to some extent from an emotional and psychological longing for these destinations, whether through curiosity, novelty, or attachment. In the prevalent era of social media, tourists’ use of social media often aids in fostering their sense of place attachment (PA) [17]; furthermore, the marketing efforts of destination governments frequently play a facilitating role in the formation of tourists’ PA [16]. Thus, place attachment (PA) was examined as a mediator in this study.
Nevertheless, beyond internet fame, factors such as the quality of city tourism management, infrastructure, service, and value for money also significantly impact tourists’ intentions to travel when choosing among many tourist destinations [18]. When they perceive all of these aspects of an ICC either through social media usage or government marketing orientation, the perceived value (PV) of the destination is established [19,20]. Accordingly, PV was examined as another mediator in this study.
Therefore, the objective of this research was to identify the relationships among SMU, PGMO, PA, PV, and Chinese tourists’ TI to the ICC of Chongqing. Particularly, this study aimed to determine the mediating effects of PA and PV in these relationships. The results of this study can enrich the theory of PA and PV. The findings can also benefit city governments in developing a market-oriented government in tourism and provide suggestions to destination tourism organizations during their social media marketing activities.

2. Literature Review

2.1. Internet Celebrity City and Social Media Usage

Among the groups that have developed since the rise of social media, “Internet celebrities” have been one of the most distinctive; they have been frequently discussed in China since 2015 [21]. Internet celebrities refer to people who have become famous on social media and online communities because of certain behaviors or events that are noticed by the general public [22,23]. With the prevalence of social media, internet celebrities have extended beyond individual people to encompass physical locations and ideas or symbols. Accordingly, internet celebrity attractions, cities, restaurants, etc., have emerged [24]. Lei [25] characterized ICCs as cities that became popular in a short period of time in the context of media convergence, resulting in an increase in traffic online and visitors offline.
According to Yang [16], the rise of ICCs can be attributed to three primary factors. Firstly, the rapid advancement of internet technology has led to the emergence of various new social media platforms, which have transformed people’s habits of acquiring information. Secondly, user-generated content, encompassing text, images, videos, and reviews posted by users on the internet, provides diverse and personalized content that is convenient for travelers. Thirdly, the inherent characteristics of ICCs, such as their unique historical culture, local music, cuisine, scenic landscapes, technological infrastructure, and regional dialects, contribute significantly to their popularity on the internet. Wang [8] further explained that the rise of social media was crucial for the emergence of ICCs, while government market-oriented efforts in tourism served as the driving force behind it. Furthermore, city tourism contributes significantly to urban economic growth by driving development in key sectors like lodging, transport, and restaurant chains [6]. City governments are entrusted with the fundamental task of cultivating distinctive tourism attributes and enhancing competitiveness [26].
Meanwhile, the usage of social media has become a prevalent online activity. In 2021, the number of global social media users exceeded 4.26 billion individuals, with a projected increase to 6 billion by 2027 [27]. People engage with social media for a multitude of purposes, ranging from entertainment and communication to information searching [28]. Swar and Hameed [29] described social media usage (SMU) as the actions taken by individuals to exchange information, express viewpoints, and share interests through interactive online platforms and tools. In the field of tourism, travelers often utilize social media to search for travel information before embarking on their journey [30].
For tourism providers, public organizations, such as destination-marketing organizations, view social media as a crucial tool for promoting destinations. Islam [4] implied that social media offered them a chance for extensive exposure and rapid exchange of diverse and integrated information. Song and Schuett [17] advocated that marketers should endeavor to formulate suitable social media strategies to foster PA between virtual visitors and their tourist destinations. Furthermore, an increasing number of studies have found that the usage of social media can effectively influence consumers’ PV, making it an important marketing tool for businesses [20,31,32], since social media users are directly impacted by both the costs and benefits in their search for travel information [2].

2.2. Perceived Government Market Orientation

The government is an essential player in tourism development, with varied degrees of involvement depending on the stage of tourism development [26]. In contrast to tourism in developed countries like the United States, China’s tourism industry has a fairly young history. However, the tourism authorities in China exercise comprehensive control and actively participate in various aspects of tourism development, including travel service management, transportation coordination, and destination promotion [33].
Governments must adopt market-oriented behaviors or measures, similar to the private sector, to assist in the development of tourism [34]. Market orientation, as a research construct, has various definitions, but there is a common perspective that it is related to information processing and is aimed at implementing marketing concepts to generate value and competitiveness [35]. Parra-Lopez, Martínez-Gonzalez [36] suggested that market orientation was the behavior of organizations in formulating market intelligence and policies to meet consumer needs and expectations, and this behavior was ultimately perceived by consumers. Kamarulzaman, Khairuddin [37] further divided market orientation into three subconstructs: consumer orientation, competitor orientation, and internal coordination within the organization. In the tourism sector, Zhang [14] suggested that government market orientation could be characterized as a series of market-oriented initiatives taken by the government to manage the overall image of a tourist destination, the overall regional tourism environment, and tourism products. These behaviors aimed to satisfy the requirements and expectations of tourists, ultimately seeking to be recognized and appreciated by them.
According to Song, Jiang [38], the marketing performance of the government, which acts as one of the most important entities of destination-marketing organizations, has gradually attracted the attention of scholars in tourism since the 1990s and has been further developed by scholars such as Faulkner [39] and Pike [40]. The city government adopts various marketing strategies to develop urban tourism, including city branding and promotion [41], event marketing [42], sustainable development and eco-tourism [43], tourism service and experience enhancement [44], and more. Additionally, Cervi, Calvo [45] investigated how the new digital marketing strategies for city branding were implemented during COVID-19 in the 26 most visited cities, which was found to play a promoting role in city tourism. Zhang [14] stated that market-oriented efforts by the government were beneficial for the destination’s image and reputation and tourists’ intentions to visit. Furthermore, Zhao and Dou [15] indicated that the proactive marketing efforts by local governments, including marketing promotion, consumer rights protection, and tourism information management, had significantly improved destination image and contributed to the increased influx of tourists.
In the current era dominated by social media, the government’s market-oriented approach is more perceptible to prospective tourists. Within tourism studies, scholarly research on government marketing predominantly concentrates on its significance [46], the evaluation of government marketing effectiveness [38], and deficiencies in government marketing endeavors [47]. Although a few scholars have investigated the influence of PGMO on travel intentions, these studies did not emphasize the subject of ICCs in social media contexts [14]. This study aims to address this gap.

2.3. Place Attachment

The concept of PA originated in the field of environmental psychology and was introduced to the tourism literature during the 1980s [48]. Despite lacking a robust theoretical foundation, PA is significant in effectively marketing destinations, as it correlates with tourist decision-making behavior [49]. Morgan [50] suggested that PA could be construed as the emotional or functional connection between an individual and a specific geographical locale, encompassing the perceived significance of this connection. In essence, tourists develop a profound emotional or utilitarian bond with a destination based on their cumulative experiences. Several studies in the tourism sector have delineated two key components of PA: place identity and place dependence [51,52,53,54]. Place identity encompasses the symbolic or emotional connections to a specific location, whereas place dependence refers to the practical connections that highlight the significance of a place in meeting one’s functional objectives or activities [55].
Researchers have studied the influence of PA on revisiting destinations where one has lived before [52]. A study on visitors to the revitalization park in Jakarta found that PA significantly influenced tourists’ intention to revisit [54]. Similarly, Stylos, Bellou [56] stated that PA plays a role in moderating the relationship between destination image and revisit intention.
It is widely believed that attachment to a place is gradually formed through frequent and long-term interactions [52]. However, emerging studies have indicated that individuals have the capacity to develop emotional bonds with unfamiliar locations, fostering a unique and personal connection with these places [57,58]. In the era of social media, tourists often acquire information about travel destinations through short videos, images, and other forms of content. They can easily immerse themselves in these materials, often developing a sense of presence and forming special emotional connections and dependencies as a result [57]. Therefore, more research is needed to examine tourists who have not visited a location but can still develop a sense of PA to tourist destinations.

2.4. Perceived Value

PV is one of the most studied variables in marketing literature, but academics have not yet reached a consensus on its definition [18]. According to Zeithaml [59], PV refers to how customers evaluate a product’s overall usefulness based on what they receive and what they relinquish. Most researchers have suggested that PV is a multidimensional construct [32,60]. In tourism, researchers have argued that the tourism experience encompasses various attributes, and unidimensional measurement overlooks the emotional, situational, and extrinsic attributes of this experience. Williams and Soutar [61] proposed that PV included functional value, social value, economic value, emotional value, and novelty value. They also found that all dimensions had a positive impact on tourist satisfaction. PV in this study comprises three components: novelty value, economic value, and functional value.
PV has long been recognized as one of the most significant determinants of tourist behavior and intentions. Caber, Albayrak [18], in a study of young tourists’ intentions to travel in Turkey, found that the functional value of PV was the strongest predictor of young tourists’ visits to destinations. Lee, Yoon [62] explained that PV had a significant impact on satisfaction, thus influencing tourists’ decision to recommend the destination to others.
Under the context of ICCs, some scholars from China have attempted to explore the internal mechanisms of tourists’ intentions to visit internet celebrity destinations from psychological perspectives, such as the empathy process, yet a unified understanding has yet to be established [63]. Therefore, this study introduced PA and PV as mediating variables for investigation, aiming to deepen the understanding of the psychological mechanisms of potential tourists, thereby contributing to the field’s research.

3. Hypothesis Development

ICCs have much higher social media exposure and coverage compared to other cities. Therefore, tourists with higher SMU are often exposed to more travel information about specific cities through social media, which in turn enhances their intentions to visit these cities [24]. Meanwhile, the market-oriented behavior of local governments serves as a significant driver behind the emergence of ICCs [8] and has been found to directly influence tourists’ TI [14]. Therefore, hypotheses H1 and H2 are presented as follows:
H1: 
SMU has a direct positive effect on TI.
H2: 
PGMO has a direct positive effect on TI.
Meanwhile, the role of SMU in influencing PA has been verified by many studies. For example, Hosany, Buzova [52] surveyed 410 potential Spanish tourists and found that tourists develop PA and visit intention towards destinations they have not traveled to before. A study on Pakistanis who watched tourism destination videos on social media discovered that advertisement value had a positive effect on intentions to travel, with PA serving as a mediator [64].
In addition, the social media environment encourages user interaction and engagement. When users browse websites, they are often influenced by the interactive and informational content about specific products on the website, thereby affecting consumers’ PV of the product [65]. For example, a recent study found that, even if they did not make purchases, consumers could still be influenced by Instagram advertisements in terms of their PV [32]. Simultaneously, a study of 502 social media users revealed that social media marketing activities can also significantly impact consumers’ PV [20]. Accordingly, hypotheses H3 and H4 are presented as follows:
H3: 
SMU has a positive impact on PA.
H4: 
SMU has a positive impact on PV.
According to Yang [16], cultural promotion is a crucial component of city tourism promotions undertaken by city governments. They aim to cultivate an emotional connection among tourists with destination cities. Zhu and Wang [19] explained that the governments’ tourism marketing efforts often allow tourists to perceive cost-effectiveness in aspects such as the destination city’s tourism infrastructure, quality assurance of urban services, and price control. Accordingly, this study posits that local governments actively engage in activities such as shaping the city’s image and conducting tourism marketing in their efforts to promote city tourism. Simultaneously, they proactively meet the needs and expectations of tourists through a series of market-oriented measures [15]. When exposed through media promotion and eventually perceived by tourists, these measures are expected to impact tourists’ PA and PV. Hence, the following hypotheses are proposed:
H5: 
PGMO has a positive impact on PA.
H6: 
PGMO has a positive impact on PV.
Behavioral intention pertains to an individual’s propensity to take action toward a particular activity or object [66]. TI, as a crucial outcome variable, refers to the likelihood or attitude inclination of tourists towards visiting a particular destination [67]. Specifically, destination selection intention in tourism research involves the attitude inclination of potential tourists towards whether they intend to visit a particular tourist destination. This inclination is influenced by factors such as the appeal of the destination and other environmental factors [68].
A considerable amount of research has indicated that PA [42,51] and PV [69,70] significantly impact TI. According to Jeong, Yu [42], PA positively influences the TI of South Korean tourists towards sporting events. Al-Adamat, Al-Gasawneh [69] stated that PV positively influences the TI of potential Saudi visitors to Jordan. Therefore, this study proposes hypotheses H7 and H8, as follows:
H7: 
PA has a positive impact on TI.
H8: 
PV has a positive impact on TI.
Moreover, PA, as a mediating variable, has been widely studied. For example, in a study of Pakistanis watching tourism destination videos on social media, it was found that PA serves as a mediating variable between the value of social media advertising and TI [64]. Nursyamsiah and Setiawan [54] pointed out that PA mediates the relationship between tourist satisfaction and the intention to revisit. Hence, this study proposes hypotheses H9 and H10 as follows:
H9: 
PA mediates the relationship between SMU and TI.
H10: 
PA mediates the relationship between PGMO and TI.
Social media marketing activities have been proven to influence PV, subsequently impacting consumer satisfaction, with PV acting as a mediating factor [20]. When Muslim tourists choose to travel to non-Islamic countries, the destination’s Muslim-friendly traits positively shape the PV, thereby influencing their TI [71]. The following hypotheses, H11 and H12, are proposed accordingly:
H11: 
PV mediates the relationship between SMU and TI.
H12: 
PV mediates the relationship between PGMO and TI.
Figure 1 shows the research framework. This study sought to investigate the interconnections between SMU, PGMO, PA, PV, and TI to one of the hottest Chinese ICCs: Chongqing.

4. Methodology

The study was crafted as an empirical study, aimed at investigating the factors that influence tourists’ intentions to visit an ICC. Given that this study focused on social media users, a large and diverse population, quantitative research was chosen as the appropriate methodology since it can extend its findings to larger populations by employing a significant number of participants to address the research questions [72].

4.1. Study Setting

As one of the earliest and most popular ICCs, Chongqing was selected as the context for this study. In 2017, a Doyin user uploaded a short video about Chongqing, named “Train goes through a 19-storey block of flats in Chongqing”, which received 469,000 likes and 17,000 shares [73]. In the same year, short videos about Chongqing were played 11.36 billion times, giving the city the highest rank among cities from China [74]. In addition, according to recent statistics [75], the number of visitors to Chongqing ranked first among all the cities in China for five consecutive years from 2017 to 2021.
Meanwhile, the government’s market-oriented initiatives in Chongqing have been widely recognized in China. In a recent annual selection of city branding cases, the Chongqing government received the national city marketing award for “The city that favors tourists the most in 2022–2023” [76]. Based on this discussion, as well as the first author’s sociocultural proximity and personal experience with this city, Chongqing as a tourist destination and ICC appeared to be an excellent unit of analysis to study the relationship between SMU, PGMO, PA, PV, and TI.

4.2. Data Collection and Sampling

Determining an adequate sample size that is representative is crucial for ensuring the reliability of research findings [77]. Krejcie and Morgan [78] suggested that the sample size should be more than 384. Comrey and Lee [79] recommended a sample size of 300 as good, 500 as highly satisfactory, and 1000 as outstanding. Therefore, an appropriate sample size for this study was determined to be greater than 500. On the other hand, Despite the lack of unanimous agreement regarding the ideal sample size for SEM, a pivotal sample size of 200 has been suggested as appropriate for conducting analysis and testing models [80,81]. Consequently, any number exceeding 200 is deemed sufficient to establish a solid statistical framework for data analysis [82].
Before data collection, a cohort of 50 participants was purposively chosen from the target population to pilot the survey via the social media site Douyin. Subsequently, a reliability examination was conducted to evaluate the utility of the scale. It was observed that all composite reliability values (SMU, 0.875; PGMO, 0.912; PA, 0.892; PV, 0.924; TI, 0.949) surpassed the threshold of 0.70 [83], indicating the reliability of the scale. Since one objective of this study was to investigate the effects of PGMO, this study distributed online questionnaires through the account of the Chongqing Culture and Tourism Commission in Sina Weibo and Douyin. First, we chose a city promotion post by the Chongqing government with the most page views. Then, 1000 questionnaire invitations were distributed to the commentators by short messages in Sina Weibo and Douyin through convenience sampling. Questionnaires were self-administered, and only those who had not previously visited Chongqing could answer the questionnaire. In the end, 762 questionnaires were collected; after removing 12 invalid ones, 750 valid questionnaires were used for analysis.

4.3. Measures

The measurement items for each construct in the hypothesized model were derived from validated measurements utilized in prior research. To assess each construct, rating scales with 7 points on a Likert scale were used (1 = Strongly Disagree, 7 = Strongly Agree). The concept of SMU was refined from Ellison, Steinfield [84], and the concept of PGMO was adapted and modified from Parra-Lopez, Martínez-Gonzalez [36] and Gray, Matear [85]. Meanwhile, the scales for the remaining constructs were determined by incorporating the existing literature—PA [42,86], PV [61,87,88], and TI [89]. Back translation between Chinese and English was employed to ensure consistency in conveying the same information in both versions of the questionnaire [90]. The questions for all measures are displayed in Table 1.

4.4. Data Analysis

This study employed structural equation modeling (SEM) to conduct data analysis using Statistical Package for the Social Sciences 20 (SPSS) and Analysis of Moment Structures 24 (AMOS). As explained by Kline [91], AMOS is frequently utilized in quantitative empirical research due to its strong capabilities in SEM, allowing researchers to examine both direct and mediating effects among variables. This tool facilitates a systematic investigation of direct and indirect interactions through intermediate pathways, thereby advancing the understanding of the underlying mechanisms impacting research outcomes. In addition, AMOS employs Maximum Likelihood estimation for SEM, making it a valuable tool for validating the researched model [83]. Accordingly, the data analysis in this study was divided into three stages: descriptive statistics analysis, measurement model validation, and structural model analysis. First, frequency and percentage distributions of basic sample information, as well as the mean and standard deviation of results, were analyzed. Then, following the views of Anderson and Gerbing [92], confirmatory factor analysis was utilized to test the measurement model to confirm the convergent and discriminant validity of constructs. Subsequently, the structural model was validated for its model fit, followed by path analysis and mediation effect examination to verify the research hypotheses of this study.

5. Findings

5.1. Demographic Findings

Among the 750 participants in the research, more respondents were female (n = 520, 69.3%). Most participants were in their 20s (n = 421, 56.1%). The main monthly income was below 5000 Yuan (n = 383, 51.1%). As for education level, most had a bachelor’s degree (n = 509, 67.9%). A previous study found that females dedicated a greater amount of time weekly to the Internet and displayed more favorable attitudes toward both online and offline sources of travel information [93]. Meanwhile, recent consumption data statistics from Douyin [94] show that females account for over 70% of cultural and tourism consumption on the platform. Moreover, females constitute more than 66% of the users in the Chinese main social media platforms such as Douyin and Xiaohongshu, with a predominance of young people [95,96]. Therefore, the above sample data aligned with the actual demographic characteristics of Chinese social media users.
On the basis of the sample statistics, the mean of all variables ranged from 4.670 to 5.940, and the standard deviation varied between 1.188 and 1.571. Meanwhile, the skewness value fell into a range of −1.1911 to −0.566, while the kurtosis value spanned from −0.287 to 4.174. According to the criteria outlined by [91], the skewness value should be within ±2, and the value of kurtosis should be within ±7. Therefore, this study’s results can be considered consistent with a normal distribution and suitable for further analysis.

5.2. Measurement Model

Kline [97] suggested that factor loadings should ideally surpass 0.6, with values above 0.7 considered optimal. For a reliable measurement, the composite reliability must exceed 0.7 [83], while the average variance extracted (AVE) should be greater than 0.5 [98]. For the variables in this study, factor loadings ranged from 0.677 to 0.942; composite reliability ranged from 0.920 and 0.960; and the AVE ranged from 0.661 to 0.865. These statistics suggested that this study had strong convergent validity [83,99] (Table 2).
This study examined discriminant validity using the rigorous AVE method. According to Fornell and Larcker [98], discriminant validity should take into account both convergent validity and the relationships between constructs. Thus, each construct’s square root of AVE should exceed its correlation coefficients [100]. As shown in Table 3, the diagonal square roots of AVE for each construct were greater than the correlation coefficients outside the diagonal. Discriminant validity was acceptable.

5.3. Structural Model and Hypothesis Testing

Following the application of a maximum likelihood estimation approach, the structural model exhibited a favorable level of fit. The structural model’s overall goodness-of-fit indices satisfied the specified thresholds, as indicated here: χ2 = 706.340, χ2/df = 1.775, goodness of fit index = 0.973, adjusted goodness of fit index = 0.968, relative fit index = 0.971, comparative fit index = 0.988, normed fit index = 0.973, incremental fit index = 0.988, root mean square error of approximation = 0.032, and nonnormed fit index (Tucker–Lewis index) = 0.987 [101,102]. The relationships are shown in Figure 2.

5.3.1. Direct Relationship Testing

Direct relationships were examined first. As shown in Table 4, SMU had a positive influence on TI, PA, and PV. However, PGMO did not have a positive impact on TI. In addition, PGMO had a significant impact on PA and PV. Both PA and PV had positive impacts on TI. Thus, all the hypotheses of direct relationships were supported except H2.
The findings revealed that SMU positively impacted TI, PA, and PV to the ICC as a tourist destination, aligning with findings from previous studies by Wu [24], Hosany, Buzova [52], Keng and Ting [65], and Kim, Chung [32]. Viewing videos or images of ICCs on social media and engaging in immersive online browsing can help arouse the intention to travel [21], can make tourists feel as if they are physically present, and can evoke emotional resonance [103]. This can ultimately lead to the development of a strong attachment to the place. Through extensive exposure on social media, tourists perceive unique urban landscapes, tourism pricing, and the level of tourism management related to ICCs. Driven by the interactive features of social media, tourists often develop a sense of PV for the destination even before their journey begins.
Interestingly, the results showed that the influence of PGMO on tourists’ PA and PV was relatively substantial compared to SMU. Social media users often encounter various information distractions, not solely focusing on short videos and images related to tourism [104]. However, despite the increasing involvement of Chinese local governments in the tourism industry, the market-oriented actions taken by local governments still provide a refreshing perspective for tourists. Furthermore, government market-oriented actions are purposeful, and the government’s related initiatives are closely related to tourists’ PA and PV [19].
However, this study found that PGMO did not have a positive impact on TI, which was inconsistent with the findings of Zhang [14]. On the contrary, PGMO positively impacted tourists’ PA and PV to the destination, consistent with recent studies by Yang [16] and Zhao and Dou [15]. It can be observed that if tourists perceive that the government’s market orientation is unrelated to PA and PV, their intentions to travel will not be affected. As a socialist country, the Chinese government inherently possesses authority, making it easily trusted. When local governments implement positive market-oriented initiatives that are perceived by tourists, these tourists are likely to develop favorable feelings and trust towards the tourist destinations. This occurs even before the tourists visit, creating a situation where PA is formed. Additionally, local governments’ market-oriented behaviors encompass destination image promotion, regulation of tourism business prices and services, and initiatives facilitating tourists’ travel [105]. However, if tourists cannot perceive these initiatives emotionally or in terms of benefits, they will not develop TI.

5.3.2. Mediating Effects Testing

According to MacKinnon, Lockwood [106], the bootstrap method has been demonstrated to have greater statistical power in examining mediating effects than causal steps and the product of coefficients methods. Moreover, the resampling methods provide more accurate results compared to single-sample tests. Therefore, the bootstrap method was utilized in this study. As shown in Table 5, the confidence interval of all four indirect relationships did not include 0, indicating that these mediating relationships were established. Therefore, H9, H10, H11, and H12 were supported.
Based on the analysis findings, both PA and PV were found to mediate the relationship between SMU and PGMO. SMU and PGMO serve as stimuli affecting tourists’ TI, but what truly influences TI are tourists’ PA and PV. In other words, the underlying factors of PA and PV carry more weight in determining whether individuals actually have the intentions to visit a destination. These factors reflect deeper emotional connections and practical assessments that directly impact tourists’ intention to travel.

6. Conclusions and Discussion

6.1. Theoretical Implications

This research adds to the limited body of literature investigating the ICC of Chongqing as a potential travel destination, uniquely incorporating analyses of both SMU and PGMO within this specific context. First, the existing literature on PGMO is scant and predominantly qualitative in approach. This study employed quantitative research methods to investigate the impact of PGMO on tourists’ TI, thereby addressing deficiencies in the current research landscape.
Second, research on tourists’ PA and PV has traditionally focused on tourists’ perceptions of destinations after their travels, influencing their intention to revisit. However, this study revealed that tourists could develop PA and PV even without physically visiting these places, which in turn influences their intention to travel. This finding is a rich supplement to the theories of PA and PV.
Finally, and most importantly, tourists flocking to ICCs are driven by the prevalence of social media and local governments as driving forces behind. However, there is limited research on the intrinsic mechanisms shaping tourists’ travel intentions. This study addresses a gap by evaluating the mediating effects of PA and PV, which is a major innovation in this research.

6.2. Practical Implications

The findings offer practical implications. First, governments should emphasize PA and PV in city promotions. When promoting destination tourism, local governments should avoid generic and unremarkable advertising. Marketing efforts can focus on content related to PA, highlighting local tourism characteristics that meet the needs of tourists, bridging the gap between tourists and the city and enhancing trust and favorable impressions of the city. Meanwhile, as crucial stakeholders in city tourism, local governments can strive to improve city management, control tourism pricing, optimize transportation conditions, enhance the quality of tourist services, and strengthen tourism training for local businesses. By emphasizing these aspects in promotional activities, local governments can enhance tourists’ PV, attracting them to travel to the city.
Second, before traveling, tourists not only use social media to gather information about tourist attractions, food maps, and culture but also rely on various evaluations of the destination from travel bloggers and past visitors [2]. Accordingly, the electronic word of mouth for city tourism should receive special attention. Negative evaluations undoubtedly affect tourists’ TI [107]. Therefore, local governments, destination tourism organizations, and tourism service providers can establish specialized departments or assign individuals to closely monitor relevant online evaluations. In the case of negative reviews, proactive measures should be taken to mitigate or dispel their negative impact on the destination. For positive reviews, active promotion can be undertaken to leverage their positive influence, attracting tourists to visit the city [70].

6.3. Study Limitations and Future Development

This study employed a questionnaire distributed through social media platforms for convenience sampling. The sample primarily consisted of women (69.3%), who may be more susceptible to the influence of PA. Additionally, a majority of the respondents in this study had relatively high educational qualifications, with 92.9% holding a diploma or degree, and 56.1% were in their 20s. Higher educational levels and younger age groups are often associated with increased frequency of SMU, potentially resulting in an incomplete representation of attitudes and intentions among those with lower educational qualifications or in different age groups.
For future research, it may be beneficial to target those with lower educational qualifications and relatively older age groups. Furthermore, there is currently limited research on the TI of tourists towards ICCs. TI is often influenced by multiple factors. Subsequent investigations could explore supplementary variables, such as social media influencers and destination image.

Author Contributions

Conceptualization, W.T.; methodology, C.T.; software, A.E.M.; Data analysis, W.T. and S.U.; writing—original draft preparation, W.T.; writing—review and editing, C.T. and S.U. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the Ghazali Shafie School of Government (GSGSG) Universiti Utara Malaysia (Protocol #UUM/COLGIS/GSGSG 904724).

Informed Consent Statement

Informed consent was obtained from all the subjects involved in this study.

Data Availability Statement

Datasets analyzed or generated during this study are under the control of the investigators and can be requested via correspondence to the relevant author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zeng, B.; Gerritsen, R. What do we know about social media in tourism? A review. Tour. Manag. Perspect. 2014, 10, 27–36. [Google Scholar] [CrossRef]
  2. Chung, N.; Koo, C. The use of social media in travel information search. Telemat. Inform. 2015, 32, 215–229. [Google Scholar] [CrossRef]
  3. Cervi, L. Travellers’ virtual communities: A success story. Univ.-XXI Rev. Cienc. Soc. Humanas 2019, 30, 97–125. [Google Scholar] [CrossRef]
  4. Islam, M.T. Applications of social media in the tourism industry: A review. SEISENSE J. Manag. 2021, 4, 59–68. [Google Scholar] [CrossRef]
  5. Putri, U.A.I. The Effect of Social Media, Word of Mouth on the Destination Image and Its Impact on the Visit Decision to Tourism Destinations in City of Padang. In Proceedings of the Fifth Padang International Conference on Economics Education, Economics, Business and Management, Accounting and Entrepreneurship (PICEEBA-5 2020), Padang, Indonesia, 18–19 July 2020. [Google Scholar]
  6. Postma, A.; Buda, D.-M.; Gugerell, K. The future of city tourism. J. Tour. Futures 2017, 3, 95–101. [Google Scholar] [CrossRef]
  7. Shirly. National Tourism City Brand Influence Report. Available online: https://travel.sohu.com/a/629978048_99917329 (accessed on 15 January 2022).
  8. Wang, W. The logic of the internet celebrity economy, ethical reflection and regulatory guidance. Truth Seek. 2016, 8, 43–49. [Google Scholar]
  9. Chen, X.; Li, Y.; Wang, Y.; Cai, S. Internet celebrity city, flow effect and tourism development. J. Manag. Sci. China 2022, 25, 1–22. [Google Scholar] [CrossRef]
  10. Ye, Y. Has the “Internet Celebrity” Director of Culture and Tourism Cut through the Traffic and Cashed in? Tide News. Available online: https://tianmunews.com/news.html?id=2423315 (accessed on 18 March 2023).
  11. Chu, C.-P. The Influence of Social Media Use and Travel Motivation on the Perceived Destination Image and Travel Intention to Taiwan of the Thai People. Bangkok University. 2018. Available online: http://dspace.bu.ac.th/jspui/handle/123456789/3163 (accessed on 18 March 2023).
  12. Joo, Y.; Seok, H.; Nam, Y. The moderating effect of social media use on sustainable rural tourism: A theory of planned behavior model. Sustainability 2020, 12, 4095. [Google Scholar] [CrossRef]
  13. Shang, Y.; Mehmood, K.; Iftikhar, Y.; Aziz, A.; Tao, X.; Shi, L. Energizing intention to visit rural destinations: How social media disposition and social media use boost tourism through information publicity. Front. Psychol. 2021, 12, 782461. [Google Scholar] [CrossRef] [PubMed]
  14. Zhang, Y. An Empirical Study on Hangzhou Tourism Marketing Efforts Based on Tourist Perception. J. Chang. Univ. Sci. Technol. 2012, 7, 52–56. [Google Scholar]
  15. Zhao, D.S.; Dou, Y. The influencing mechanism of government behavior on tourist destination image: A study based on grounded theory. J. Yunnan Univ. 2021, 20, 136–144. [Google Scholar]
  16. Yang, Y. A study on the dissemination of the image of the “internet celebrity city” on LittleRedBook. News Cult. Constr. 2021, 20, 19–20. [Google Scholar]
  17. Song, J.; Schuett, M.A. Examining the relationship between social media users’ motivation and place attachment to national parks. J. Outdoor Recreat. Tour. 2023, 44, 100628. [Google Scholar] [CrossRef]
  18. Caber, M.; Albayrak, T.; Crawford, D. Perceived value and its impact on travel outcomes in youth tourism. J. Outdoor Recreat. Tour. 2020, 31, 100327. [Google Scholar] [CrossRef]
  19. Zhu, H.; Wang, F. How does government microblog affect tourism market value? The perspective of signaling theory. Inf. Process. Manag. 2022, 59, 102991. [Google Scholar] [CrossRef]
  20. Chen, S.-C.; Lin, C.-P. Understanding the effect of social media marketing activities: The mediation of social identification, perceived value, and satisfaction. Technol. Forecast. Soc. Change 2019, 140, 22–32. [Google Scholar] [CrossRef]
  21. Ao, P. Why is Internet celebrity so popular?—An interpretation and reflection based on the phenomenon of Internet celebrity. Contemp. Commun. 2016, 4, 40–44. [Google Scholar]
  22. Shen, X.; Wang, G.; Yang, T.; Zhong, S. The development of the internet celebrity phenomenon in China, analysis of characteristics and countermeasures. Intell. Mag. 2016, 35, 93–98. [Google Scholar] [CrossRef]
  23. Xu, K. What Is ‘Internet Celebrity Economy’ in China. Target China. 2017. Available online: https://targetchina.com.au/article/internet-celebrity/ (accessed on 28 April 2017).
  24. Wu, W. Internet celebrity cities: The mediatization of cities driven by social media. J. Quanzhou Norm. Univ. 2020, 1, 97–102. [Google Scholar] [CrossRef]
  25. Lei, M. Exploring the communication of internet celebrity city image in the age of convergence media-Changsha city as an example. Radio&TV J. 2021, 164–165. [Google Scholar] [CrossRef]
  26. Kubickova, M.; Martin, D. Exploring the relationship between government and destination competitiveness: The TALC model perspective. Tour. Manag. 2020, 78, 104040. [Google Scholar] [CrossRef]
  27. Dixon, S. Number of Global Social Network Users 2017–2027. Statista. 2023. Available online: https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/ (accessed on 21 May 2023).
  28. Ostic, D.; Qalati, S.A.; Barbosa, B.; Shah, S.M.M.; Galvan Vela, E.; Herzallah, A.M.; Liu, F. Effects of social media use on psychological well-being: A mediated model. Front. Psychol. 2021, 12, 678766. [Google Scholar] [CrossRef]
  29. Swar, B.; Hameed, T. Fear of missing out, social media engagement, smartphone addiction and distraction: Moderating role of self-help mobile apps-based interventions in the youth. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), Porto, Portugal, 21–23 February 2017. [Google Scholar]
  30. Liu, X.; Mehraliyev, F.; Liu, C.; Schuckert, M. The roles of social media in tourists’ choices of travel components. Tour. Stud. 2020, 20, 27–48. [Google Scholar] [CrossRef]
  31. Sohaib, M.; Safeer, A.A.; Majeed, A. Role of social media marketing activities in China’s e-commerce industry: A stimulus organism response theory context. Front. Psychol. 2022, 13, 941058. [Google Scholar] [CrossRef]
  32. Kim, K.; Chung, T.-L.; Fiore, A.M. The role of interactivity from Instagram advertisements in shaping young female fashion consumers’ perceived value and behavioral intentions. J. Retail. Consum. Serv. 2023, 70, 103159. [Google Scholar] [CrossRef]
  33. Wang, S.; Qu, H. A comparison study of Chinese domestic tourism: China vs the USA. Int. J. Contemp. Hosp. Manag. 2004, 16, 108–115. [Google Scholar] [CrossRef]
  34. Molander, S.; Fellesson, M.; Friman, M. Market orientation in public service—A comparison between buyers and providers. J. Nonprofit Public Sect. Mark. 2017, 30, 74–94. [Google Scholar] [CrossRef]
  35. Murillo Oviedo, A.B.; Pimenta, M.L.; Piato, É.L.; Hilletofth, P. Development of market-oriented strategies through cross-functional integration in the context of the food and beverage industry. Bus. Process Manag. J. 2020, 27, 901–921. [Google Scholar] [CrossRef]
  36. Parra-Lopez, E.; Martínez-Gonzalez, J.A.; Barrientos-Báez, A. Millennials’ support for tourism development: The effect of market orientation by governments. Tour. Rev. 2021, 76, 858–870. [Google Scholar] [CrossRef]
  37. Kamarulzaman, N.H.; Khairuddin, N.H.; Hashim, H.; Hussin, S.R. Measuring market orientation, innovative marketing strategies and performance: Evidence from the Malaysian agro-food manufacturers. J. Agribus. Dev. Emerg. Econ. 2021, 13, 211–228. [Google Scholar] [CrossRef]
  38. Song, H.; Jiang, Y.; Lv, X. Public marketing performance of government tourism from process perspective: Theoretical framework and construction of evaluation index system. J. Bus. Econ. 2016, 2, 80–88. [Google Scholar]
  39. Faulkner, B. A model for the evaluation of national tourism destination marketing programs. J. Travel Res. 1997, 35, 23–32. [Google Scholar] [CrossRef]
  40. Pike, S. Consumer-based brand equity for destinations: Practical DMO performance measures. J. Travel Tour. Mark. 2007, 22, 51–61. [Google Scholar] [CrossRef]
  41. Kavaratzis, M. From city marketing to city branding: Towards a theoretical framework for developing city brands. Place Brand. 2004, 1, 58–73. [Google Scholar] [CrossRef]
  42. Jeong, Y.; Yu, A.; Kim, S.-K. The antecedents of tourists’ behavioral intentions at sporting events: The case of South Korea. Sustainability 2019, 12, 333. [Google Scholar] [CrossRef]
  43. Ruhanen, L. Local government: Facilitator or inhibitor of sustainable tourism development? J. Sustain. Tour. 2013, 21, 80–98. [Google Scholar] [CrossRef]
  44. Siregar, M.R.; Siregar, M.I.; Saputra, J.; Muzammil, A.; Muhammad, Z. The Mediating Role of Service Quality, Tourists’ Satisfaction and Destination Trust in the Relationship between Destination Image and Tourist Revisiting Intention. J. Environ. Manag. Tour. 2021, 12, 1603–1616. [Google Scholar] [CrossRef]
  45. Cervi, L.; Calvo, S.T.; Robledo-Dioses, K. Digital communication and the city. Analysis of the websites of the most visited cities in the world in the COVID-19 era. Rev. Lat. Comun. Soc. 2023, 81, 81–107. [Google Scholar]
  46. Wang, X. Government Destination Marketing:Post-earthquake Recovery Strategy for the Tourism Industry in Sichuan. J. Beijing Int. Stud. Univ. 2008, 9, 6–10. [Google Scholar]
  47. Li, J.; Chu, X.; Dai, H. A discussion about government tourism marketing. Chang Bai J. 2008, 3, 91–93. [Google Scholar]
  48. Tsai, C.T. Memorable tourist experiences and place attachment when consuming local food. Int. J. Tour. Res. 2016, 18, 536–548. [Google Scholar] [CrossRef]
  49. Woosnam, K.M.; Aleshinloye, K.D.; Ribeiro, M.A.; Stylidis, D.; Jiang, J.; Erul, E. Social determinants of place attachment at a World Heritage Site. Tour. Manag. 2018, 67, 139–146. [Google Scholar] [CrossRef]
  50. Morgan, P. Towards a developmental theory of place attachment. J. Environ. Psychol. 2010, 30, 11–22. [Google Scholar] [CrossRef]
  51. Dang, L.; Weiss, J. Evidence on the relationship between place attachment and behavioral intentions between 2010 and 2021: A systematic literature review. Sustainability 2021, 13, 13138. [Google Scholar] [CrossRef]
  52. Hosany, S.; Buzova, D.; Sanz-Blas, S. The influence of place attachment, ad-evoked positive affect, and motivation on intention to visit: Imagination proclivity as a moderator. J. Travel Res. 2020, 59, 477–495. [Google Scholar] [CrossRef]
  53. Dwyer, L.; Chen, N.; Lee, J. The role of place attachment in tourism research. J. Travel Tour. Mark. 2019, 36, 645–652. [Google Scholar] [CrossRef]
  54. Nursyamsiah, R.A.; Setiawan, R.P. Does place attachment act as a mediating variable that affects revisit intention toward a revitalized park? Alex. Eng. J. 2023, 64, 999–1013. [Google Scholar] [CrossRef]
  55. Loureiro, S.M.C. The role of the rural tourism experience economy in place attachment and behavioral intentions. Int. J. Hosp. Manag. 2014, 40, 1–9. [Google Scholar] [CrossRef]
  56. Stylos, N.; Bellou, V.; Andronikidis, A.; Vassiliadis, C.A. Linking the dots among destination images, place attachment, and revisit intentions: A study among British and Russian tourists. Tour. Manag. 2017, 60, 15–29. [Google Scholar] [CrossRef]
  57. Cheng, C.-K.; Kuo, H.-Y. Bonding to a new place never visited: Exploring the relationship between landscape elements and place bonding. Tour. Manag. 2015, 46, 546–560. [Google Scholar] [CrossRef]
  58. Farnum, J. Sense of Place in Natural Resource Recreation and Tourism: An Evaluation and Assessment of Research Findings; General Technical Report; Pacific Northwest Research Station, USDA Forest Service: Corvallis, OR, USA, 2005; p. 660. [Google Scholar]
  59. Zeithaml, V.A. Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. J. Mark. 1988, 52, 2–22. [Google Scholar] [CrossRef]
  60. Aslam, W.; de Luna, I. The relationship between brand Facebook page characteristics, perceived value, and customer engagement behavior: An application of stimulus-organism-response (S-O-R). Rev. Bus. Manag. 2021, 23, 43–62. [Google Scholar] [CrossRef]
  61. Williams, P.; Soutar, G. Customer value, satisfaction and intentions: Some insights from adventure tourism. In Proceedings of the 20th Annual Conference of the Australian and New Zealand Academy of Management, Yeppoon, Australia, 6–9 December 2006. [Google Scholar]
  62. Lee, C.-K.; Yoon, Y.-S.; Lee, S.-K. Investigating the relationships among perceived value, satisfaction, and recommendations: The case of the Korean DMZ. Tour. Manag. 2007, 28, 204–214. [Google Scholar] [CrossRef]
  63. Yi, K.; Wang, Q.; Xu, J.; Liu, B. Attribution model of travel intention to internet celebrity spots: A systematic exploration based on psychological perspective. Front. Psychol. 2021, 12, 797482. [Google Scholar] [CrossRef] [PubMed]
  64. Abbasi, A.Z.; Schultz, C.D.; Ting, D.H.; Ali, F.; Hussain, K. Advertising value of vlogs on destination visit intention: The mediating role of place attachment among Pakistani tourists. J. Hosp. Tour. Technol. 2022, 13, 816–834. [Google Scholar] [CrossRef]
  65. Keng, C.J.; Ting, H.Y. The acceptance of blogs: Using a customer experiential value perspective. Internet Res. 2009, 19, 479–495. [Google Scholar] [CrossRef]
  66. Sheeran, P. Intention—Behavior relations: A conceptual and empirical review. Eur. Rev. Soc. Psychol. 2002, 12, 1–36. [Google Scholar] [CrossRef]
  67. Al-Htibat, A.; Garanti, Z. Impact of interactive eReferral on tourists behavioral intentions. Mark. Intell. Plan. 2019, 37, 527–541. [Google Scholar] [CrossRef]
  68. Duong, L.H.; Phan, Q.D.; Nguyen, T.T.; Huynh, D.V.; Truong, T.T.; Duong, K.Q. Understanding tourists’ behavioral intention and destination support in post-pandemic recovery: The case of the Vietnamese domestic market. Sustainability 2022, 14, 9969. [Google Scholar] [CrossRef]
  69. Al-Adamat, A.M.; Al-Gasawneh, J.A.; Sourak, N.A. The mediating effect of perceived value on the relationship between online promotion and travel intention. Test Eng. Manag. 2020, 83, 14911–14920. [Google Scholar]
  70. Kuo, N.-T. Effects of travel website quality and perceived value on travel intention with eWOM in social media and website reviews as moderators. J. Qual. Assur. Hosp. Tour. 2024, 25, 596–627. [Google Scholar] [CrossRef]
  71. Sodawan, A.; Hsu, R.L.-W. Halal-friendly attributes and Muslims’ visit intention: Exploring the roles of perceived value and destination trust. Sustainability 2022, 14, 12002. [Google Scholar] [CrossRef]
  72. Sekaran, U.; Bougie, R. Research Methods for Business: A Skill Building Approach, 7th ed.; John Wiley & Sons Inc.: Hoboken, NJ, USA, 2016. [Google Scholar]
  73. Eastday. Train Goes through the Building on DouYin, Chongqing Goes Viral Overnight. China Daily. Available online: http://caijing.chinadaily.com.cn/chanye/2018-04/20/content_36064783.htm (accessed on 20 April 2018).
  74. Zhan, S.; Han, Q.; Yin, B.; Yang, R. White Paper on Short Video and City Image Research. T. I. DouYin, City Branding Studio, Tsinghua University. 2018. Available online: http://pdf.dfcfw.com/pdf/H3_AP201809161196406458_1.pdf (accessed on 21 June 2023).
  75. China’s Most Developed Cities in Tourism. Jiemian Chongqing. 2021. Available online: https://baijiahao.baidu.com/s?id=1714648292576738111&wfr=spider&for=pc (accessed on 26 October 2021).
  76. Jiang, S.; Ye, S. “The City That Spoils Tourists the Most” Is Recognised Again! Netizens: Chongqing Not Spoil but Drown Tourists! Available online: https://cq.cnr.cn/tp/20230705/t20230705_526316420.shtml (accessed on 14 July 2023).
  77. Etikan, I.; Bala, K. Sampling and sampling methods. Biom. Biostat. Int. J. 2017, 5, 00149. [Google Scholar] [CrossRef]
  78. Krejcie, R.V.; Morgan, D.W. Determining sample size for research activities. Educ. Psychol. Meas. 1970, 30, 607–610. [Google Scholar] [CrossRef]
  79. Comrey, A.; Lee, H. Interpretation and Application of Factor Analytic Results; Psychology Press: London, UK, 1992. [Google Scholar]
  80. Garver, M.S.; Mentzer, J.T. Logistics research methods: Employing structural equation modeling to test for construct validity. J. Bus. Logist. 1999, 20, 33–57. [Google Scholar]
  81. Hoelter, J.W. The analysis of covariance structures: Goodness-of-fit indices. Sociol. Methods Res. 1983, 11, 325–344. [Google Scholar] [CrossRef]
  82. Hoe, S.L. Issues and procedures in adopting structural equation modelling technique. J. Quant. Methods 2008, 3, 76–83. [Google Scholar]
  83. Hair, J.F.; Black, W.C.; Babin, B.J. Multivariate Data Analysis: A Global Perspective; Pearson Education: London, UK, 2010; Available online: https://books.google.com/books?id=SLRPLgAACAAJ (accessed on 12 January 2020).
  84. Ellison, N.B.; Steinfield, C.; Lampe, C. The benefits of Facebook “friends”: Social capital and college students’ use of online social network sites. J. Comput.-Mediat. Commun. 2007, 12, 1143–1168. [Google Scholar] [CrossRef]
  85. Gray, B.; Matear, S.; Boshoff, C.; Matheson, P. Developing a better measure of market orientation. Eur. J. Mark. 1998, 32, 884–903. [Google Scholar] [CrossRef]
  86. Nguyen, T.H.H.; Wassler, P.; Schuckert, M. Investigating Local Students’ Support for Inbound Tourism and Its Relationship with Place Attachment in Hue City/Vietnam. HLST Conference, Summer 2016. Available online: https://gala.gre.ac.uk/id/eprint/19540/7/19540%20NGUYEN_Investigating_Local_Students%E2%80%99_Support_for%20Inbound%20Tourism_2016.pdf (accessed on 17 February 2023).
  87. Kim, K.-H.; Park, D.-B. Relationships among perceived value, satisfaction, and loyalty: Community-based ecotourism in Korea. J. Travel Tour. Mark. 2016, 34, 171–191. [Google Scholar] [CrossRef]
  88. Huang, Y.H.; Huang, F.C. Tourists’ perceived value model and its measurement: An empirical Study. Tour. Trib. 2007, 22, 43–47. [Google Scholar]
  89. Wang, H.; Yan, J. Effects of social media tourism information quality on destination travel intention: Mediation effect of self-congruity and trust. Front. Psychol. 2022, 13, 1049149. [Google Scholar] [CrossRef]
  90. Brislin, R.W. Translation and Content Analysis of Oral and Written Materials [Methodology]; Allyn & Bacon: Boston, MA, USA, 1980. [Google Scholar]
  91. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford Publications: New York, NY, USA, 2023. [Google Scholar]
  92. Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
  93. Kim, D.-Y.; Lehto, X.Y.; Morrison, A.M. Gender differences in online travel information search: Implications for marketing communications on the internet. Tour. Manag. 2007, 28, 423–433. [Google Scholar] [CrossRef]
  94. Han, Y. Douyin Lifestyle Service Data: “She Economy” Heating Up. Available online: http://finance.ce.cn/home/jrzq/dc/202403/07/t20240307_38925207.shtml (accessed on 7 March 2024).
  95. Du, X.; Liechty, T.; Santos, C.A.; Park, J. ‘I want to record and share my wonderful journey’: Chinese Millennials’ production and sharing of short-form travel videos on TikTok or Douyin. Curr. Issues Tour. 2022, 25, 3412–3424. [Google Scholar] [CrossRef]
  96. Zhou, K.; Zhang, H.; Xia, Y.; Liu, C. New Urban Consumption Space Shaped by Social Media: A Case Study of the Geo-Tagging Places of Changsha on Xiaohongshu. Mod. Urban Res. 2021, 9, 20–27. [Google Scholar] [CrossRef]
  97. Kline, P. An Easy Guide to Factor Analysis; Routledge: London, UK, 2014. [Google Scholar]
  98. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  99. Reisinger, Y.; Mavondo, F. Structural equation modeling: Critical issues and new developments. J. Travel Tour. Mark. 2007, 21, 41–71. [Google Scholar] [CrossRef]
  100. Taherdoost, H. Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. Int. J. Acad. Res. Manag. 2016, 5, 28–36. [Google Scholar] [CrossRef]
  101. Jackson, D.L.; Gillaspy, J.A., Jr.; Purc-Stephenson, R. Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychol. Methods 2009, 14, 6–23. [Google Scholar] [CrossRef] [PubMed]
  102. Reisinger, Y.; Turner, L. Structural equation modeling with Lisrel: Application in tourism. Tour. Manag. 1999, 20, 71–88. [Google Scholar] [CrossRef]
  103. Pelet, J.-É.; Ettis, S.; Cowart, K. Optimal experience of flow enhanced by telepresence: Evidence from social media use. Inf. Manag. 2017, 54, 115–128. [Google Scholar] [CrossRef]
  104. Mount, M.; Martinez, M.G. Social media: A tool for open innovation. Calif. Manag. Rev. 2014, 56, 124–143. [Google Scholar] [CrossRef]
  105. Yuanbin, X. Tourism, Covernment-Led and Public Marketing; Wuhan University Press: Wuhan, China, 2008. [Google Scholar]
  106. MacKinnon, D.P.; Lockwood, C.M.; Williams, J. Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivar. Behav. Res. 2004, 39, 99–128. [Google Scholar] [CrossRef]
  107. Mohammed Abubakar, A. Does eWOM influence destination trust and travel intention: A medical tourism perspective. Econ. Res.–Ekon. Istraživanja 2016, 29, 598–611. [Google Scholar] [CrossRef]
Behavsci 14 00694 g001
Figure 1. Proposed model.
Figure 1. Proposed model.
Behavsci 14 00694 g001
Behavsci 14 00694 g002
Figure 2. Structural equation modeling results.
Figure 2. Structural equation modeling results.
Behavsci 14 00694 g002
Table 1. Measurements of variables.
Table 1. Measurements of variables.
Code No.SMU
SMUQ1Social media is part of my daily activities.
SMUQ2I proudly tell others that I am using social media.
SMUQ3Social media has become an integral aspect of my everyday routine.
SMUQ4If I don’t log in to social media for a while, I feel disconnected.
SMUQ5I feel like I am a part of the social media community.
SMUQ6If social media were to shut down, I would feel regretful.
Code No.PGMO
PGMOQ1The tourism policies of the Chongqing government consider the practical needs of tourists.
PGMOQ2The tourism agencies of the Chongqing government consider the future expectations of tourists.
PGMOQ3The tourism agencies of the Chongqing government consider the changing circumstances of tourists.
PGMOQ4In shaping the city’s tourism image, the Chongqing government does a better job than other cities.
PGMOQ5In conducting tourism marketing activities, the Chongqing government does a better job than other cities.
Code No.PA
PAQ1Chongqing is my top choice for travel.
PAQ2I prefer Chongqing over other cities.
PAQ3The feeling Chongqing gives me is incomparable to anywhere else.
PAQ4Traveling to Chongqing holds great significance for me.
PAQ5Chongqing is a very special city to me.
PAQ6I strongly endorse Chongqing as a tourist destination.
Code No.PV
PVQ1When traveling to Chongqing, the service quality is very good.
PVQ2When traveling to Chongqing, the management level is very good.
PVQ3When traveling to Chongqing, I can enjoy delicious food.
PVQ4When traveling to Chongqing, it is very convenient.
PVQ5When traveling to Chongqing, the pricing is very reasonable.
PVQ6When traveling to Chongqing, the cost of living is very low.
PVQ7When traveling to Chongqing, the cost-effectiveness is very high.
PVQ8When traveling to Chongqing, it can satisfy my curiosity.
PVQ9When traveling to Chongqing, I can admire the unique urban scenery.
PVQ10When traveling to Chongqing, I can experience some special new things.
Code No.TI
TIQ1It is very likely that I will choose to travel to Chongqing in the future.
TIQ2I plan to travel to Chongqing.
TIQ3I will recommend travel to Chongqing to others.
Table 2. Analysis of convergent validity.
Table 2. Analysis of convergent validity.
VariablesFactor LoadingsComposite ReliabilityAVE
SMU0.677–0.9290.9200.661
PGMO0.832–0.9420.9560.812
PA0.826–0.9140.9530.771
PV0.735–0.8870.9600.704
TI0.907–0.9420.9510.865
Table 3. Analysis of discriminant validity.
Table 3. Analysis of discriminant validity.
VariablesAVE12345
1. SMU0.6610.813
2. PGMO0.8120.5890.901
3. PA0.7710.4920.6950.917
4. PV0.7040.4660.6690.6810.890
5. TI0.8650.4630.5450.6890.6610.930
Note: The variables’ square roots of AVE are in bold in a diagonal formation.
Table 4. Path analysis results.
Table 4. Path analysis results.
Hypothesized PathsStandard ErrorCritical Ratiop ValueStandardized Regression CoefficientSupport
SMU → TI0.0383.5350.0000.126Yes
SMU → PA0.0413.3910.0000.122Yes
SMU → PV0.0372.8760.0040.106Yes
PGMO → TI0.055−1.5310.126−0.078No
PGMO → PA0.04416.4530.0000.632Yes
PGMO → PV0.04015.9690.0000.616Yes
PA → TI0.04010.7210.0000.449Yes
PV → TI0.0429.5180.0000.375Yes
Table 5. Analysis of mediating effects.
Table 5. Analysis of mediating effects.
Indirect RelationshipsPoint
Estimate		Bootstrap 1000 Times
Bias-Corrected 95%
Lower BoundUpper Bound
SMU → PA → TI0.0590.0180.126
PGMO → PA → TI0.3090.1850.440
SMU → PV → TI0.0430.0090.102
PGMO → PV → TI0.2520.1500.427
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tang, W.; Tosun, C.; Mohamed, A.E.; Uslu, S. The Influence of Social Media Usage and Perceived Government Market Orientation on Travel Intention to an Internet Celebrity City: Exploring the Mediating Effects of Place Attachment and Perceived Value. Behav. Sci. 2024, 14, 694. https://doi.org/10.3390/bs14080694

AMA Style

Tang W, Tosun C, Mohamed AE, Uslu S. The Influence of Social Media Usage and Perceived Government Market Orientation on Travel Intention to an Internet Celebrity City: Exploring the Mediating Effects of Place Attachment and Perceived Value. Behavioral Sciences. 2024; 14(8):694. https://doi.org/10.3390/bs14080694

Chicago/Turabian Style

Tang, Wu, Cevat Tosun, Ahmad Edwin Mohamed, and Sinan Uslu. 2024. "The Influence of Social Media Usage and Perceived Government Market Orientation on Travel Intention to an Internet Celebrity City: Exploring the Mediating Effects of Place Attachment and Perceived Value" Behavioral Sciences 14, no. 8: 694. https://doi.org/10.3390/bs14080694

APA Style

Tang, W., Tosun, C., Mohamed, A. E., & Uslu, S. (2024). The Influence of Social Media Usage and Perceived Government Market Orientation on Travel Intention to an Internet Celebrity City: Exploring the Mediating Effects of Place Attachment and Perceived Value. Behavioral Sciences, 14(8), 694. https://doi.org/10.3390/bs14080694

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop