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Article

Untangling the Potential of Sustainable Online Information Sources in Shaping Visitors’ Intentions

by
Salamatu Bellah Conteh
1,2,
Moiz Malik
3,
Mohsin Shahzad
4,* and
Sana Shahid
5
1
Department of Foreign Languages & Societal Culture, Faculty of Business & Finance, Transit College Sierra Leone, Freetown 00232, Sierra Leone
2
Department of Economics and Commerce, Fourah Bay College, Freetown 00232, Sierra Leone
3
School of Economics and Management, Dalian University of Technology, Dalian 116024, China
4
LNU-MSU College of International Business, Liaoning Normal University, Dalian 116024, China
5
School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(19), 14192; https://doi.org/10.3390/su151914192
Submission received: 16 August 2023 / Revised: 19 September 2023 / Accepted: 22 September 2023 / Published: 26 September 2023
(This article belongs to the Special Issue Social Sustainability and Circular Economy Agenda through the Lens of Stakeholders and Technological Innovation)
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Abstract

:
Tourism has gained enormous attention, and every country is determined to attract more visitors. Concerned stakeholders are trying to promote their country’s image and that of their tourist destinations. Travelers attain information from different sustainable sources, and these different sustainable sources might be critical in shaping the perceived image of a country. Not enough research has been conducted with respect to investigating the association between these perceived images and sustainable information sources regarding any country. Hence, this study tries to fill this research gap by integrating country and destination image, outbound travel motivation, and information sources to obtain relevant information. Considering the above-mentioned context, data were gathered from a survey completed by respondents who had visited a developing country, i.e., Pakistan. Partial least squares structural equation modeling was utilized to examine the validity of the data of 205 potential visitors. Our findings show that a country’s image has a significant favorable influence on destination image. Destination and country image favorably impact travelers’ intentions to visit a certain destination, though this is also partially mediated by outbound travel motivation. Furthermore, the critical role of sustainable information sources in shaping country and destination image is highlighted in this paper. The proposed model offers novel insights into the literature and can be used to assist in the design of appropriate marketing strategies for the tourism sector by incorporating perceived image, outbound travel motivation, and information sources. This research offers pertinent recommendations to enhance tourism. Though we feel our research makes a pertinent contribution to the literature, in the future, other researchers may test the proposed model by integrating the data from other Chinese cities to gain more insights.

1. Introduction

Tourism is one of the world’s most significant business sectors, as it is vital to social and economic growth [1,2]. The tourism industry has enormous potential to generate employment opportunities and tax revenue and facilitate consumption, boosting economic growth [3]. Tourism is among the largest financial sectors, creating one in ten jobs (330 million) globally and contributing 10.3% to the world’s GDP. In 2019, the travel and tourism industry increased by 3.5%, faster than the world economy by 2.5% for the ninth year in a row [4]. Through the lens of global tourism, countries’ names, flags, and other associated signs reflect tourist destination country brands [5].
Research in the existing literature has revealed a significant positive association between country image (CI) and destination image (DI), and the critical role of DI and its influence on visitor’s decision-making processes has been broadly covered in the currently available studies on DI [6,7,8]. The perceptions a visitor has about a specific destination possess unique characteristics, and prior publications in the literature have also highlighted that the link between visitors’ sustainable intention to visit (ITV) and DI empirically support one another [9]. DI and CI have received considerable attention in Western travel studies; however, this topic has received relatively little attention in developing countries [10]. Contextualized insights from developing countries are necessary to understand the contribution of DI to a particular country. Despite dire economic, social, and political perceptions about a country, developing countries are perceived positively from a tourism viewpoint [11]. Even though CI and DI have been studied in several contexts, a small portion of the research has explored their influence on ITV, particularly in developing countries. Despite extensive analyses of the portrayal and perceptions of a location, most scholars have examined DI from a tourist’s point of view, forgoing the consideration of a more comprehensive context. Hence, DI is derived from economic, political, social, and cultural standpoints [12]. This research article introduces a different point of view by examining how intercountry tourists perceive Pakistan as a travel destination, incorporating contextual elements to more fully appreciate the phenomena under scrutiny. Although, DI and CI significantly shape visitors’ ITV [13,14], sustainable Information Sources (IS) could substantially influence visitors’ perceptions of a specific destination. IS emphasize sustainability issues regarding different media platforms such as the Internet, printed media, mass media, and interpersonal communication sources [15,16,17]. However, this study is mainly focused on sustainable digital avenues such as social media platforms due to their momentous importance and features in the current era.
Developing countries such as Pakistan have encountered numerous social, political, economic, and sustainability-related challenges. As a result, travelers are hesitant to visit these countries. However, Forbes Magazine recently highlighted Pakistan’s appeal as a desirable destination. This study contends that the significance of IS can be pretty remarkable within developing countries, as they lack the capacity for substantial investments to improve their tourism sector. Previous research on IS has also acknowledged that information collected from various origins plays a crucial part in shaping the perception of tourist spots [18] and advancing sustainable progress. Furthermore, individuals possess varying degrees of motivation when traveling to a foreign country. This study proposes a psychological mechanism to answer the following research question: how does perceived image influence travelers’ sustainable intentions by integrating their outbound travel motivation and sustainable information sources? This study provides numerous contributions. For example, this study’s initial sections describe the essential connection between CI and DI and their influence on ITV. Later on, the critical function of IS in shaping the perceived image of a certain destination is also examined. Additionally, this study also describes how outbound travel motivation (OTM) mediates between perceived image and ITV.

2. Review of Literature and Theoretical Background

2.1. Intention to Visit

If a destination has poor infrastructure, a bad image, and political issues, this will affect ITV. The key question is, what defines the intent of visiting a destination country? DI was initially assumed to be the only determinant of ITV, but the results from recent research studies suggest that that there is no particular model that addresses all the variables of ITV [19,20,21,22]. ITV is a significant response variable that is strongly associated with tourism behavior. In the context of travel, the greater a visitor’s intention to visit a certain destination is, the more likely it is that they will visit that destination [17]. Previous research has shown that the DI impacts visitors’ choice of destination; however, this can also depend on their assessment of the trip and their intention to visit in the future [23]. It has been determined that the DI significantly affects ITV [23]. Theoretical viewpoints generally support the idea that a positive image increases ITV [24]. A pleasant travel experience naturally leads to a favorable DI and an optimistic perception of the destination. In turn, it also influences ITV [25]. Pakistan has not achieved its expected percentage of intercountry visitors, which is much lower compared to neighboring countries like Malaysia, Thailand, Indonesia, and Vietnam. This situation can be attributed to various factors, such as poverty, terrorism, educational issues, underemployment, and injustice [26]. However, there is dearth of research studies that integrate OTM and IS, and the present research study aims to fill this gap. This study’s proposed model is presented in Figure 1. The figure illustrates how CI and DI act as predictors of ITV. Additionally, CI also has an impact on DI. OTM plays a mediating role between CI, DI, and ITV. Lastly, the parameter of IS is included in the model to emphasize its contextual significance. This research paper aims to provide novel insights for both professionals and researchers.

2.2. Influence of Country Image and Destination Image

The subject of DI has been extensively described in other tourism-related studies in the literature [20]. CI falls within the scope of intercountry marketing, which is seen as a significant factor that influences consumer purchasing influence. Many studies have focused on this multi-faceted arrangement and proposed that image represents the entirety of an individual’s cognitions and understanding about a given location [27,28,29]. Therefore, the image of a country is formed due to a blend of a tourist’s emotive and logical understanding of two intimately interlinked concepts: the cognitive assessment of their own knowledge and the evaluation of their opinions, thoughts, and feeling regarding the destination [21]. The definition of a destination can vary from urban to rural settings, cities, provinces, and even entire countries. When a country is regarded as a destination, CI becomes a vigorous element in framing the image as a tourist spot [29]. CI can be formed by the goods promoted as being ‘made in’ a certain country, contributing to CI as a standard label. On the contrary, DI is influenced by various kinds of significant details, including surveys and images of the destination that might relate to its recreation sector or the efforts of tourism marketers to convey a particular perception of the location [30,31]. Nonetheless, DI fundamentally contributes to tourism in conjunction with tourism practices and necessities such as tourist attractions, transportation, food, etc. [32]. Despite unfavorable perceptions regarding developing countries due to economic, political, and social opinions, these countries can be viewed as pure, creating paradises through the lens of tourism [28,33]. CI and DI impact assessments of a country’s goods, including its tourism offerings [29]. They have both been researched separately, and only a few researchers have discussed them together [11,29,34]. However, the focus of researchers regarding these two parameters remains confined [35]. Furthermore, researchers in the tourism field have recently begun exploring how CI and DI affect tourists’ ITV. This study aims to fill the gap in the CI and DI literature by outlining these fundamental concepts and proposing and investigating the following hypothesis:
H1. 
Country image has a positive impact on destination image.
CI is a word taken from the worldwide marketing literature that tells us how CI affects the analysis of a product manufactured in a specific country [2,11]. However, in contrast to marketing research, this idea was adopted in tourism-related research to clarify the complete perceptions of a country that influence tourists’ perception of the country as a tourist destination [11]. As a result, the following hypothesis is proposed:
H2. 
Country image has a positive impact on intention to visit.
The phenomenon of the country-of-origin effect has been a widely discussed subject in global marketing literature [20]. Significant research efforts focusing on judging the impact of the image associated with a country (usually referred to as country-of-origin or product-country images) have been made. This impact can be categorized into two distinct types: the halo and summary effects. These effects influence consumers’ beliefs and attitudes toward a brand [2,36]. Some scholars argue that the CI can help companies maintain a sustained competitive advantage in global marketing [20]. Papadopoulos and Heslop (2000) [29] proposed that CI meaningfully influences consumers’ decisions, exceeding the importance of the brand name. Furthermore, they accept that all target markets, including consumers, tourists, organizational purchasers, retail merchants, and foreign investors, are subject to the effect of the image associated with a country or region. Lastly, they introduced country equity and branding concepts [2,20,21,22,36]. When the location under study is a country, the CI can be swapped with DI. For such instances, researchers have established the unique idea of “destination country image [37]”. This relates to a visitor’s perception of a country as a primary tourist spot. A substantial connection between CI and DI will occur if a DI aligns with the CI, which precisely impacts the viewer’s ITV [11]. Thus, the following hypothesis is proposed:
H3. 
Destination image has a significant positive effect on intention to visit.

2.3. Influence of Sustainable Information Sources

IS plays a vital role in shaping traveler’s decision-making process [15,17,38]. This research study examines IS as a crucial element that influences visitors’ image formation before visitation. Before deciding to visit, tourists always seek relevant and accurate information about the destination. The tourism industry, being intangible and reliant on information, is highly susceptible to both internal and external incidents. Even minor incidents can substantially impact defining the DI, as they compel people to revise their perceptions of a destination. Many studies have shown that events caused by human violence are mostly viewed with less sympathy than those derived from natural disasters and that they receive more attention from global media organizations. Consequently, tragic circumstances or conflicts can affect travel decisions until the memory of these events fades from people’s minds [39]. The influence of IS on the formation of DI is not insignificant [40]. A mechanism for image formation that includes many sources of information acting individually to create a unified image in the minds of all visitors has been proposed in the literature [21,22]. Furthermore, scholars have emphasized that the factors contributing to DI formation include stimuli factors such as physical objects or external stimuli such as past experiences and IS [41]. Whether the DI is positive or negative primarily depends on the information visitors have gathered about the destination, which shapes their expectations for their travel experience and can influence their choice of destination [42]. The intangible nature of tourism products and services makes it an information-form phenomenon, as tourists cannot physically experience or judge them beforehand (unlike tangible products) [43]. Secondary channels like social media, travel agencies, travel guides, newspapers, and media reports play a vital and significant role in decision-making [40]. At the same time, other information sources like recommendations from friends and family, personal visit experience, and word-of-mouth are classified as the most trustworthy sources of information [41]. Moreover, the Internet has given rise to electronic word of mouth (eWOM), which is highly effective and helpful due to its affordability and reliability [44]. Most studies identify the Internet as a primary source of tourist information. Research has also indicated that 6 out of 10 people aged 15 and older use the Internet to search for travel information [45,46]. Research on IS and the distribution of information via social networking platforms like YouTube, Facebook, Twitter, etc., has revealed that visitors consider User-Generated Content (UGC) to be a more reliable source of information than offline sources. Unlike traditional tourism websites, UGC comprises real-time content and observational insights and opinions that many travelers find more reliable than information provided by tour operators [47,48]. The intangibility and unpredictability of tourism outcomes increase the importance of risk reduction and acquiring more knowledge about certain destinations before making travel decisions [17]. Unconventional promotional IS, such as Internet services, travel guides, or visitor information centers, are more credible than conventional promotional sources like commercial newspapers or tour operators that market tourism products [16]. The concept of “image” has usually been viewed as an attitudinal approach that is linked to the amount of information a person possesses about a destination. Thus, the following hypotheses are proposed.
H4a. 
Sustainable information sources significantly influence country image.
H4b. 
Sustainable information sources have a significant influence on destination image.
Contemporary research mainly focuses on different factors that influence the perception of a particular destination. These factors encompass information from various sources and visitors’ characteristics, such as travel motivation [49]. Research on individual and employee change behavior is vital and essential for facilitating innovation, including sustainability initiatives [15,28]. Research shows that the most adequate and practical approach to facilitating organizational change requires empowering employees through a bottom-up, employee-centric approach that is supported by solid leadership and effective and efficient communication methods [50]. Barriers to the implementation of change can be overcome by keeping individuals informed, minimizing surprises, increasing the reliability of the change with positive messages, and promoting two-way dialogic communication [50]. A similar perspective on visitors’ decision-making can be implemented in tourism. Visitors’ behavior and buying intentions are informed by motivation entrenched in information, an essential element [36,49]. A person’s desire to visit somewhere directly corresponds to their use of trustworthy sources to collect information on that particular place. Hence, the following hypothesis is proposed:
H4c. 
Sustainable information sources have a significant influence on outbound travel motivation.

2.4. Role of Outbound Travel Motivation

Outbound travel is a type of travel that enhances opportunities for the local population by offering them a more comprehensive range of options for consumption [36,49]. Outbound tourism encompasses the trend of people traveling from one country to another. For instance, from a Pakistani perspective, visitors from China are inbound tourists; however, from a Chinese viewpoint, they are outbound tourists. Motivation is the catalyst behind travel that drives travelers to adopt a certain attitude to fulfill their desires [51]. It is described in travel-related studies as “motivation, the need that urges a person to behave in a particular manner to obtain the desired satisfaction” [40]. Motivation is a psychological term described as the intrinsic motivating force that induces the conduct or action of individuals [2,52]. Researchers have acknowledged that specific inherent forces push and pull tourists to act. These forces describe how internal forces restrict people and how they are dragged by external forces associated with traveling motivation. Push motives are internal factors that derive from inner motives such as the need for comfort and relief, the need for a break, social interaction, and recognition. The external factors (pull motivations) are related to the qualities of a destination, such as its environment, amenities, accessibility, and lodging facilities [22,28,53]. Motivating factors differ from tourist to tourist; for example, young travelers prefer the thrill and sensation of the tourism experience, whereas normal visitors love traveling to a comparatively familiar setting [54]. Presenting a country through advertising by labeling its distinct features is quite prominent in the tourism industry, and a country can be identified as a tourist destination according to its touristic features [2,51]. A positive and attractive CI and DI can play a vital role in boosting motivation. For instance, Turkey has a negative CI but has been identified positively as a tourist destination [11]. Accordingly, we propose that a positive CI and DI can increase tourists’ motivation to visit and vice versa:
H5a. 
Country image has a positive influence on outbound travel motivation.
H5b. 
Destination image has a positive influence on outbound travel motivation.
The actions of tourists can usually be predicted according to their reason for traveling, as intention is often seen as more important than behavior in studies of the human mind [55]. ITV is the outcome of a mental process that contributes to one’s conduct, i.e., turning motivation into action. OTM is the main phase that triggers the visit decision before the tourist’s actual visitation; therefore, a decision to visit somewhere can be made based on an internal motivation to fulfill this need [56]. A person is more likely to visit a destination if they feel more motivated to go there; thus, the following hypotheses are proposed:
H5c. 
Outbound travel motivation positively influences sustainable intention to visit.
H5d. 
Outbound travel motivation mediates the association between CI and sustainable intention to visit.
H5e. 
Outbound travel motivation mediates the association between DI and sustainable intention to visit.

3. Methodology

3.1. Measures

The DI and CI measurements included in this study were derived from relevant studies in the literature and modified to match our research context. All constructs regarding the research were adapted using multiple-item scales. CI scales were modified from [14,29]. The scale for measuring IS, including various information channels such as the Internet, travel agents, friends, family, TV, etc., was adapted from [18]. The statements regarding the investigation OTM were adopted from [57,58]. ITV was adjusted according to [37], and respondents were asked to comment on whether they intend to visit Pakistan in the foreseeable future. The last section comprised queries regarding demographic data, such as age, gender, educational levels, and country. The survey research tool was established by revising the items from previous research that utilized well-established scales [40,59,60]. Each scale was measured via a seven-point Likert scale (“1 = strongly disagree to 7 = strongly agree”). Prior research has found that utilizing a 7-point Likert-type scale increases precision and decreases interpolation, making it perfectly suitable for use in online surveys [61].

3.2. Data Collection

Our research involved the use of a quantitative data collection technique, and an online survey was conducted to assess the aforementioned theoretical framework and hypothesis. This is advantageous because it enables the testing of the model and the exploration of structural linkages [62]. The reason for why we followed this procedure is that a standardized survey can garner more respondents, irrespective of their place and time. Respondents were asked about Pakistan. As a holiday destination, Pakistan offers a lot, such as destinations linked to the country’s ancient civilization, heritage sites, religious customs, and expedition sites, the latter of which mostly attract young visitors. Pakistan is a favorable destinations among mountaineers, hikers, and travelers. Five out of the fourteen highest mountains in the world are in Northern Pakistan, including the mighty K2, which has the second-highest peak after Mount Everest. In addition, the northern part of Pakistan, also called the world’s roof, is the junction point of the three highest mountain ranges, namely, the Karakorum, the Himalayas, and the Hindu Kush [63]. Pakistan is also one of the most popular destinations in terms of religious tourism. Pakistan hosts practitioners of three major religions, i.e., Buddhism, Islam, and Hinduism. It is the birthplace of the Sikh community as well. South Asia, in general, and Pakistan, in particular, draw a lot of attention from the intercountry media. Clashes with its rival (India), terror attacks, and conflicts comprise the central part of the country’s global media coverage, which hinders tourism in the country. The tragic incident of 9/11 and the military actions that followed in Pakistan had a detrimental effect on its reputation in the intercountry community [64].
The intended population of our study was Chinese citizens and foreigners living in China who were older than 18 years. A self-administered online questionnaire consisting of screening questions was established for this study, and the questionnaire was shared via an online link that was posted in different relevant social media groups in major Chinese cities. We received a total of 260 responses. After the elimination of unfinished questionnaires, 205 truly valid online questionnaires comprised the survey data; according to the proposed model, this is an acceptable sample size to test our hypotheses [65,66]. Data were collected from June to August 2021. Demographic details are provided in Table 1. Furthermore, the questionnaire was originally written in English before being translated into Chinese. To authenticate the accuracy of the translated version, we requested native Chinese speakers who were fluent in English to back-translate the questionnaire. We then split the initial questionnaire into two variants and consulted another bilingual scholar to ensure accuracy.

4. Analytical Approach and Results

Data were analyzed by using a software package called SmartPLS (version 3.3.9). The partial least square structural equation modeling (PLS-SEM) method was utilized for various reasons, including its exploratory and predictive nature, which makes it particularly suitable for formative constructs. It is also more appropriate for small sample sizes without considering normal distribution [66]. Overall, the PLS-SEM approach was used because it can examine all causal interactions concurrently [67]. Thus, two-step techniques were adopted in concert with a distinct assessment of the metric and structural models in compliance with the guidelines [67].

4.1. Common Method Bias and Multicollinearity

A single questionnaire was used to obtain data. Common method bias (CMB) remains the dominating concern in questionnaire surveys [68]. We used many procedural and statistical approaches to monitor and assess CMB. A full collinearity test was used. This is an advanced method of checking CMB [69]. The results revealed that all variance inflation factor (VIF) values were below the limit value of 3.3, guaranteeing that CMB is not a major problem in this study [69].

4.2. Measurement Model Analysis

Measurement model analysis was conducted through assessing reliability, convergence, and discriminant validity (DV) [67]. “Composite reliability (CR), Cronbach alpha (CA), average variance extracted (AVE)”, and factor loading were evaluated to determine the reliability and convergent validity of all calculated constructs. The results presented in Table 2 show that the CR and CA values are greater than 0.70 and that the AVE values are greater than 0.50. In comparison, the calculated items of the intended constructs have uniform factor loadings within the limit of 0.625–0.904 (significance of p < 0.01). These results indicated strong precision and convergence of validity [70]. Moreover, DV was measured using Fornell–Larcker’s criterion and the heterotrait–monotrait ratio (HTMT). Fornell–Larcker’s criterion is considered a conventional technique, and the results derived from its use indicate that the square root of AVE was greater than the correlated values [71]. The HTMT ratio is the newest method for evaluating DV [72]. The obtained values for the HTMT ratio were lower than the criterion of 0.85, which implies that the measurement of every variable did not reflect the other variables given in Table 3 and Table 4. Therefore, this study contains no precision and validity concerns. The model used in the current study includes a second-order formative construct, i.e., CI and DI. The conventional approaches are not adequate for evaluating reliability and validity [73]. In accordance with [74], the values of the first-order predictive constructs were calculated, and the findings showed their significance. Additionally, these constructs have lesser VIF values, confirming their validity. Ultimately, all latent constructs’ inner VIF values and all items’ outer VIF values were less than the required threshold of 5.0, as given above as a multi-collinearity warning [67]. Thus, after obtaining the above-mentioned results, we continued with our structural analysis.

4.3. Structural Model Analysis

The structural model was verified using “explanatory power (R2), predictive relevance (Q2), and path coefficient (β) values”. Table 5 shows that all predictors demonstrate a reasonable variance in independent variables (i.e., OTM and ITV), with R2 = 0.364 and 0.635, respectively. The statistical validity of the study model was evaluated using Q2, which includes values over 0.00. The Q2 values of our model are 0.448 for ITV, which reflects strong predictivity [67]. This study used a bootstrapping method with 5000 subsamples to justify the hypotheses. Our findings confirm that CI substantially positively impacts DI (H1 β = 0.562, t = 7.083). CI and DI have a significant association with ITV and OTM (H2 β = 0.296, t = 3.855, H3 β = 0.14, t = 1.925, H5a β = 0.247, t = 2.18, H5b β = 0.208, t = 2.273). Further, IS has a significant relationship with CI, DI, and OTM (H4a β = 0.488, t = 7.239, H4b β = 0.224, t = 2.407, H4c β = 0.265, t = 3.149). In addition, OTM has a significant strong relationship with ITV (H5c β = 0.498, t = 6.77). Further details are provided in Table 5.

4.4. Mediation Analysis of Outbound Travel Motivation

The mediating role of OTM was investigated by using a series of steps [75]. Firstly, this study examined the indirect effect of the CI and DI. We found the substantial indirect effect of CI β = 0.123 (H5d) and DI β = 0.104 (H5e). In the second stage, the CI and DI’s direct effect was measured without removing a mediator. A substantial positive association was found. The results shown in Table 5 indicate partial mediation. Furthermore, our analysis resulted in the discovery of a sign of both effects and the same positive indication for all paths; thus, it may be inferred that OTM had complementary partial mediation. Therefore, H5 is fully reinforced.

5. Discussions and Implications

5.1. Discussion of Key Findings

This study aimed to describe the association between CI and DI and the role of IS in shaping the perceived image of a destination and their impact on ITV. The mediating role of OTM on ITV has also been explored in the context of Pakistan as a developing country. The results regarding H1 suggest that CI strongly impacts DI, and this is consistent with previous research [22]. H2 and H3 demonstrate that CI and DI significantly affect ITV. Past research studies have reported evidential data showing that CI and DI strongly correlate with ITV [11,76]. Hence, this result means that CI and the DI should be regarded as important ITV factors in developing 34countries. H5a and H5b are linked to the relationship between CI and DI, and tourist OTM revealed a positive mediation with ITV, which is consistent with the general gratitude of the influence of tourist motivation regarding these two variables. Prior research has also highlighted that visitors who feel that visiting a place is valuable and advantageous are more inclined to demonstrate future visit intention [19].
Regarding H5c, we noted that OTM fully mediates ITV. In contrast, investigating H5d and H5e revealed that an outbound motivation was observed to partially mediate the relationship between CI and ITV and the relationship between DI and ITV. These results provide new insight into the mediating role of tourist OTM in the relationship with ITV. Certain results are inconsistent with those reported by the authors of [17], who revealed that motivation fully mediates the effects of DI, such as revisiting intention and recommending a location to others. Researchers have also argued that technological advances with respect to transportation (e.g., autonomous vehicles and high-speed trains) may have an impact on visitors’ intention to visit [74,77]. For instance, once countries develop better systems and improved infrastructure, these improvements will be shared on different social media platforms and eventually motivate people to visit specific countries and destinations.
Finally, IS affects CI (H4a), DI (H4b), and OTM (H4c). IS is one of the critical variables that participates in the image formation process, which remains unexplored in the context of developing countries. This study is the first attempt to explore the crucial role of IS in Pakistan. In the context of this study, IS directly affects CI, DI, and OTM because individuals decide to travel or not and where to travel to based on the perceptions they form as a result of gathering information from different sources. Surprisingly, the availability and accessibility of information in developing countries are limited compared to developed countries; therefore, the unavailability of data gives birth to negative CI or DI. The unavailability of data is not a strong reason to label a country as a bad destination [78].

5.2. Theoretical Implications

This study significantly contributes to the existing literature on ITV in many ways. Firstly, this research has established a conceptual framework for use within the context of developing countries such as Pakistan. In this framework, CI and DI are presented as ITV predictors. This study highlights the necessity to comprehend the difference between CI and DI. Unlike developed countries, in developing countries, DI measurements encompass fundamental aspects like economic growth or political stability, and poor perceptions of these aspects can result in negative DI. This investigation shows the dual aspects of CI and DI from similar perspectives, offering visitors an alternative understanding of DI that is independent of CI. Secondly, DI and CI have been comprehensively explored in tourism-related studies. This study underscores their particular significance in Pakistan (a developing country). The relationship between CI and DI reveals that CI significantly shapes the DI perception of visitors before their actual visit. Thirdly, the present study has inspected the intermediary role of IS and described its importance in forming pre-visit impressions. Potential tourists who have not yet visited a destination rely heavily on information sources to reduce doubt. Collecting relevant and trustworthy information about a destination is critical in making travel decisions. Thus, this study enables tourists to assess the image formed through IS before their visit. Lastly, we have concluded that OTM plays a dynamic role in ITV. However, it only partially mediates the relationship between CI and ITV and the relationship between DI and ITV.

5.3. Practical Implications

Our results, when taken into consideration from a business and marketing perspective, have significant consequences for tourist locations. Pakistan suffers from a negative country image due to its representation in intercountry media; we recommend that authorities focus on improving the destination image of Pakistan by using social media platforms and engaging bloggers to enhance the destination image because it stimulates tourist motivation and prompts travelers to visit in a sustainable manner. As we mentioned earlier, visitors regard UGC as the most reliable source of information, especially positive word-of-mouth referrals. Tourism administrators should use social media to develop and disseminate up-to-date information and photographs to boost DI. The use of social media platforms such as Facebook, blogs, Twitter, Instagram, and YouTube has grown in recent years due to visitors using their personal accounts on these platforms to look for information [79]. Since tourism is an “information-intensive business” [80], visitors prefer to take tips from other visitors via social networking sites [81], which enhances their sustainable development. Tourism operators should use social media as an advertising tool. Considering the importance of sustainable IS, authorities related to the tourism industry should disseminate positive information about Pakistan via different channels. The tourism industry should make efforts to invite social media celebrities to promote positive CI. These efforts should include easing visa conditions for intercountry visitors and actively promoting local attractions, among other measures. Motivation is a psychological term described as the intrinsic and extrinsic motivating force that induces the conduct or action of individuals. Attractive packages must be offered to intercountry visitors to promote positive information on different channels, as this will help to improve CI. Thirdly, destination managers should target the Chinese outbound tourist market, the foremost tourism outbound market in the world [82]. Incentives must be given to attract Chinese bloggers to motivate Chinese outbound tourists. Destination managers should organize for well-educated and trained employees to stay at popular hotels and book places for them at restaurants and famous tourist destinations, as this would help generate a positive image of the local destinations.
In addition, countries should focus on implementing autonomous vehicles and high-speed trains into their transportation infrastructure and promote improvements in facilities on every available platform since these facilities will improve tourism opportunities.

5.4. Limitations and Future Research

Further work is required to verify the findings of this study. Firstly, due to Pakistan’s CI and DI being the research subject of this study, the results of our analysis might not generally apply to other countries. Additionally, data were collected online from different relevant social media groups centered around metropolitan cities. However, a lot of people may not be part of such groups. Future studies may feature respondents from wider populations for better generalization. Secondly, the theoretical model used in this study includes a small number of constructs. To deepen our understanding of tourism psychology and behavior, we suggest that additional constructs (e.g., tourist infrastructure and social environment) should be adopted in future research studies. The third limitation, OTM, was considered to be a mediating variable between DI and CI in this study. Nonetheless, other mediating variables (e.g., place attachment or tourist satisfaction) should probably be explored in subsequent research to develop a comprehensive background. In addition to the above limitations, this study only investigated certain facets of CI (safety and security and economic and political environment) that deviate from the cognitive CI dimensions. Using a variety of CI scales and examining specific aspects of CI could lead to different results. More researchers should also investigate the degree to which DI may affect CI, as this may yield significant results for countries suffering from a relatively negative CI, such as Pakistan.

6. Conclusions

The tourism industry has attracted immense attention, and every country is committed to drawing more visitors; stakeholders are also working to promote a favorable image of their country. Travelers obtain information from various sustainable information sources, and these sources can significantly affect a destination’s perceived reputation. Thus, this investigation aimed to bridge this knowledge by combining CI and DI with travelers’ motivation and IS. CI and DI play a vital role in shaping travelers’ ITV. Visitors’ decision-making also depends on their OTM since it mediates the proposed association of ITV. Furthermore, the significant role of IS in shaping CI and DI is essential when potential tourists come to make travel decisions. The suggested framework provides valuable insights into the existing body of knowledge. It supports the creation of effective and helpful marketing tactics for the tourism sector to promote tourism sustainably. This research study presents suitable and appropriate suggestions for boosting tourism and the importance of IS.

Author Contributions

Conceptualization, M.M. and M.S.; Methodology, M.S.; Software, S.B.C. and S.S.; Investigation, S.B.C. and S.S.; Resources, S.B.C.; Writing—Original Draft Preparation, M.M. and M.S.; Writing—Review and Editing, M.S. and S.B.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

During data collection stage, we have ensured that data will be used collectively. We will consider data privacy concerns so no personal data will be shared to anyone.

Conflicts of Interest

The authors declare no conflict of interest.

Correction Statement

This article has been republished with a minor correction to the existing affiliation information. This change does not affect the scientific content of the article.

References

  1. Kontogianni, A.; Alepis, E. Smart tourism: State of the art and literature review for the last six years. Array 2020, 6, 100020. [Google Scholar] [CrossRef]
  2. Chu, Q.; Bao, G.; Sun, J. Progress and Prospects of Destination Image Research in the Last Decade. Sustainability 2022, 14, 10716. [Google Scholar] [CrossRef]
  3. Tu, J.; Zhang, D. Does tourism promote economic growth in Chinese ethnic minority areas? A nonlinear perspective. J. Destin. Mark. Manag. 2020, 18, 100473. [Google Scholar] [CrossRef]
  4. The World Travel & Tourism Council (WTTC). The World Travel & Tourism Council (WTTC)Represents the Travel & Tourism Sector Globally. 2019. Available online: https://wttc.org/ (accessed on 1 March 2021).
  5. Tasci, A.D.A.; Gartner, W.C.; Cavusgil, S.T. Measurement of destination brand bias using a quasi-experimental design. Tour. Manag. 2007, 28, 1529–1540. [Google Scholar] [CrossRef]
  6. Li, F.; Wen, J.; Ying, T. The influence of crisis on tourists’ perceived destination image and revisit intention: An exploratory study of Chinese tourists to North Korea. J. Destin. Mark. Manag. 2018, 9, 104–111. [Google Scholar] [CrossRef]
  7. Iordanova, E.; Stainton, H. Cognition, emotion and trust: A comparative analysis of Cambodia’s perceived and projected online image. Tour. Stud. 2019, 19, 496–519. [Google Scholar] [CrossRef]
  8. Atadil, H.A.; Sirakaya-Turk, E.; Altintas, V. An analysis of destination image for emerging markets of Turkey. J. Vacat. Mark. 2015, 23, 37–54. [Google Scholar] [CrossRef]
  9. Choi, S.H.; Cai, L.A. Dimensionality and associations of country and destination images and visitor intention. Place Brand. Public Dipl. 2016, 12, 268–284. [Google Scholar] [CrossRef]
  10. Gibson, H.J.; Qi, C.X.; Zhang, J.J. Destination image and intent to visit China and the 2008 Beijing Olympic Games. J. Sport Manag. 2008, 22, 427–450. [Google Scholar] [CrossRef]
  11. Martínez, S.C.; Alvarez, M.D. Country versus destination image in a developing country. J. Travel Tour. Mark. 2010, 27, 748–764. [Google Scholar] [CrossRef]
  12. Stepchenkova, S.; Shichkova, E. Country and Destination Image Domains of a Place: Framework for Quantitative Comparison. J. Travel Res. 2017, 56, 776–792. [Google Scholar] [CrossRef]
  13. Tan, W.K.; Wu, C.E. An investigation of the relationships among destination familiarity, destination image and future visit intention. J. Destin. Mark. Manag. 2016, 5, 214–226. [Google Scholar] [CrossRef]
  14. Zhang, H.; Fu, X.; Cai, L.A.; Lu, L. Destination image and tourist loyalty: A meta-analysis. Tour. Manag. 2014, 40, 213–223. [Google Scholar] [CrossRef]
  15. Christopher, A.C.; Myria, W.A. Sustainability information sources: Employee knowledge, perceptions, and learning. J. Commun. Manag. 2013, 17, 292–307. [Google Scholar] [CrossRef]
  16. Cho, M.-H.; Kerstetter, D. Travel-Related Information Sources. Int. J. Tour. Sci. 2002, 2, 11–22. [Google Scholar] [CrossRef]
  17. Jacobsen, J.K.S.; Munar, A.M. Tourist information search and destination choice in a digital age. Tour. Manag. Perspect. 2012, 1, 39–47. [Google Scholar] [CrossRef]
  18. Almeida-García, F.; Domígunez-Azcue, J.; Mercadé-Melé, P.; Pérez-Tapia, G. Can a destination really change its image? The roles of information sources, motivations, and visits. Tour. Manag. Perspect. 2020, 34, 100662. [Google Scholar] [CrossRef]
  19. Rashid, B.; Zainol, N.A.; Madden, K. Determinants of Intention to Visit Phuket, Thailand. Ottoman J. Tour. Manag. Res. 2017, 2, 31–43. [Google Scholar] [CrossRef]
  20. Sio, K.P.; Fraser, B.; Fredline, L. A Contemporary systematic literature review of gastronomy tourism and destination image. Tour. Recreat. Res. 2021, 25, 100733. [Google Scholar] [CrossRef]
  21. Li, J.; Weng, G. Hawkeye sees dragon: A longitudinal study of China’s destination-country image projected by the Discovery Channel. J. Destin. Mark. Manag. 2022, 25, 100733. [Google Scholar] [CrossRef]
  22. Chaulagain, S.; Wiitala, J.; Fu, X. The impact of country image and destination image on US tourists’ travel intention. J. Destin. Mark. Manag. 2019, 12, 1–11. [Google Scholar] [CrossRef]
  23. Chen, C.F.; Tsai, D.C. How destination image and evaluative factors affect behavioral intentions? Tour. Manag. 2007, 28, 1115–1122. [Google Scholar] [CrossRef]
  24. Assaker, G.; Hallak, R. Moderating Effects of Tourists’ Novelty-Seeking Tendencies on Destination Image, Visitor Satisfaction, and Short- and Long-Term Revisit Intentions. J. Travel Res. 2013, 52, 600–613. [Google Scholar] [CrossRef]
  25. Carlos, B.; Da Silva, R.; Salgueiro, F. The direct and indirect impact of country personality on behavioral intentions for traveling: The full mediation effect of the affective country image. Int. J. Bus. Econ. Dev. 2014, 2, 1–12. [Google Scholar]
  26. Raza, S.A.; Jawaid, S.T. Terrorism and tourism: A conjunction and ramification in Pakistan. Econ. Model. 2013, 33, 65–70. [Google Scholar] [CrossRef]
  27. Al-Azri, H.I.; Morrison, A.M. Measurement of oman’s destination image in the US. Tour. Recreat. Res. 2006, 31, 85–89. [Google Scholar] [CrossRef]
  28. Kozak, M.; Gnoth, J.; Andreu, L.; Alvarez, M.D.; Korzay, M. Influence of politics and media in the perceptions of Turkey as a tourism destination. Tour. Rev. 2008, 63, 38–46. [Google Scholar] [CrossRef]
  29. Nadeau, J.; Heslop, L.; O’Reilly, N.; Luk, P. Destination in a country image context. Ann. Tour. Res. 2008, 35, 84–106. [Google Scholar] [CrossRef]
  30. Roth, K.P.; Diamantopoulos, A. Advancing the country image construct. J. Bus. Res. 2009, 62, 726–740. [Google Scholar] [CrossRef]
  31. Gallarza, H.C.G.M.G.; Saura, I.G. Destination image: Towards a conceptual framework. Ann. Tour. Res. 2002, 29, 56–78. [Google Scholar] [CrossRef]
  32. Zhang, H.; Wu, Y.; Buhalis, D. A model of perceived image, memorable tourism experiences and revisit intention. J. Destin. Mark. Manag. 2018, 8, 326–336. [Google Scholar] [CrossRef]
  33. Öztürkmen, A. Turkish tourism at the door of Europe: Perceptions of image in historical and contemporary perspectives. Middle East Stud. 2005, 41, 605–621. [Google Scholar] [CrossRef]
  34. Mossberg, L.; Kleppe, I.A. Country and Destination Image—Different or similar image concepts? Serv. Ind. J. 2005, 25, 493–503. [Google Scholar] [CrossRef]
  35. Zhang, Y.C.J.; Wu, B.; Morrison, A.M.; Tseng, C. How Country Image Affects Tourists’ Destination Evaluations: A Moderated Mediation Approach. J. Hosp. Tour. Res. 2016, 6, 904–930. [Google Scholar] [CrossRef]
  36. Xin, L.; Seo, S. The role of consumer ethnocentrism, country image, and subjective knowledge in predicting intention to purchase imported functional foods. Br. Food J. 2020, 122, 448–464. [Google Scholar] [CrossRef]
  37. Zhang, H.; Xu, F.; Leung, H.H.; Cai, L.A. The Influence of Destination-Country Image on Prospective Tourists’ Visit Intention: Testing Three Competing Models. Asia Pac. J. Tour. Res. 2016, 21, 811–835. [Google Scholar] [CrossRef]
  38. Frías, D.M.; Rodríguez, M.A.; Castañeda, J.A. Internet vs. travel agencies on pre-visit destination image formation: An information processing view. Tour. Manag. 2008, 29, 163–179. [Google Scholar] [CrossRef]
  39. Sönmez, S.F.; Apostolopoulos, Y.; Tarlow, P. Tourism in crisis: Managing the effects of terrorism. J. Travel Res. 1999, 38, 13–18. [Google Scholar] [CrossRef]
  40. Beerli, A.; Martín, J.D. Factors influencing destination image. Ann. Tour. Res. 2004, 31, 657–681. [Google Scholar] [CrossRef]
  41. Baloglu, S.; McCleary, K.W. A model of destination image formation. Ann. Tour. Res. 1999, 26, 868–897. [Google Scholar] [CrossRef]
  42. Fodness, D.; Murray, B. A model of tourist information search behavior. J. Travel Res. 1999, 37, 220–230. [Google Scholar] [CrossRef]
  43. Ye, B.H.; Zhang, H.Q.; Shen, J.H.; Goh, C. Does social identity affect residents’ attitude toward tourism development?: An evidence from the relaxation of the individual visit scheme. Int. J. Contemp. Hosp. Manag. 2014, 26, 907–929. [Google Scholar] [CrossRef]
  44. Jeong, E.H.; Jang, S.C.S. Restaurant experiences triggering positive electronic word-of-mouth (eWOM) motivations. Int. J. Hosp. Manag. 2011, 30, 356–366. [Google Scholar] [CrossRef]
  45. Xiang, Z.; Gretzel, U. Role of social media in online travel information search. Tour. Manag. 2010, 31, 179–188. [Google Scholar] [CrossRef]
  46. Xiang, Z.; Wang, D.; O’Leary, J.T.; Fesenmaier, D.R. Adapting to the Internet: Trends in Travelers’ Use of the Web for Trip Planning. J. Travel Res. 2015, 54, 511–527. [Google Scholar] [CrossRef]
  47. Buhalis, D.; Law, R. Progress in information technology and tourism management: 20 years on and 10 years after the Internet-The state of eTourism research. Tour. Manag. 2008, 29, 609–623. [Google Scholar] [CrossRef]
  48. Munar, A.M. Tourist-created content: Rethinking destination branding. Int. J. Cult. Tour. Hosp. Res. 2011, 5, 291–305. [Google Scholar] [CrossRef]
  49. Seetaram, N. Estimating demand elasticities for Australia’s international outbound tourism. Tour. Econ. 2012, 18, 999–1017. [Google Scholar] [CrossRef]
  50. Shahzad, M.; Qu, Y.; Zafar, A.U.; Appolloni, A. Does the interaction between the knowledge management process and sustainable development practices boost corporate green innovation? Bus. Strategy Environ. 2021, 30, 4206–4222. [Google Scholar] [CrossRef]
  51. Ladeiro, M. A Eficácia dos Sites Turísticos. Estudo de Caso: Lisboa e os Principais Concorrentes. 2012. Available online: http://hdl.handle.net/10400.26/4459 (accessed on 22 April 2012).
  52. Pizam, A.; Neumann, Y.; Reichel, A. Tourist satisfaction. Uses and misuses. Ann. Tour. Res. 1979, 6, 195–197. [Google Scholar] [CrossRef]
  53. Park, D.B.; Yoon, Y.S. Segmentation by motivation in rural tourism: A Korean case study. Tour. Manag. 2009, 30, 99–108. [Google Scholar] [CrossRef]
  54. Ewert, A.; Jamieson, L. Current Status and Future Directions in the Adventure Tourism Industry; Elsevier Science Ltd.: Amsterdam, The Netherlands, 2003. [Google Scholar] [CrossRef]
  55. Gnoth, J. Tourism Motivation and Expectation Formation. Ann. Tour. Res. 1997, 24, 283–304. Available online: https://www.sciencedirect.com/science/article/abs/pii/S0160738397800023 (accessed on 1 March 2021). [CrossRef]
  56. Jang, S.; Bai, B.; Hu, C.; Wu, C.M.E. Affect, travel motivation, and travel intention: A senior market. J. Hosp. Tour. Res. 2009, 33, 51–73. [Google Scholar] [CrossRef]
  57. Law, R.; Rong, J.; Vu, H.Q.; Li, G.; Lee, H.A. Identifying changes and trends in Hong Kong outbound tourism. Tour. Manag. 2011, 32, 1106–1114. [Google Scholar] [CrossRef]
  58. Pappas, N. UK outbound travel and Brexit complexity. Tour. Manag. 2019, 72, 12–22. [Google Scholar] [CrossRef]
  59. Baloglu, S. Image variations of Turkey by familiarity index: Informational and experiential dimensions. Tour. Manag. 2001, 22, 127–133. [Google Scholar] [CrossRef]
  60. Fuchs, G.; Reichel, A. An exploratory inquiry into destination risk perceptions and risk reduction strategies of first time vs. repeat visitors to a highly volatile destination. Tour. Manag. 2011, 32, 266–276. [Google Scholar] [CrossRef]
  61. Finstad, K. Response interpolation and scale sensitivity: Evidence against 5-point scales. J. Usability Stud. 2010, 5, 104–110. [Google Scholar]
  62. Cobanoglu, C.; Cobanoglu, N. The Effect of Incentives in Web Surveys: Application and Ethical Considerations. Int. J. Mark. Res. 2003, 45, 1–13. [Google Scholar] [CrossRef]
  63. Baloch, Q.B. Managing tourism in Pakistan: A case study of Chitral valley. J. Manag. Sci. 2007, 2, 169–190. [Google Scholar]
  64. Arshad, M.I.; Iqbal, M.A.; Shahbaz, M. Pakistan tourism industry and challenges: A review. Asia Pac. J. Tour. Res. 2018, 23, 121–132. [Google Scholar] [CrossRef]
  65. Alcañiz, E.B.; García, I.S.; Blas, S.S. The functional-psychological continuum in the cognitive image of a destination: A confirmatory analysis. Tour. Manag. 2009, 30, 715–723. [Google Scholar] [CrossRef]
  66. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  67. Sarstedt, M.; Ringle, C.M.; Hair, J.F. Partial Least Squares Structural Equation Modeling; Springer International Publishing AG: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
  68. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879. [Google Scholar] [CrossRef] [PubMed]
  69. Kock, N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int. J. e-Collab. 2015, 11, 1–10. [Google Scholar] [CrossRef]
  70. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1988. [Google Scholar] [CrossRef]
  71. Fornell, C.; Larcker, D.F. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics; SAGE Publications Sage CA: Los Angeles, CA, USA, 1981. [Google Scholar]
  72. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. Adv. Int. Mark. 2009, 20, 277–319. [Google Scholar]
  73. Diamantopoulos, A.; Winklhofer, H.M. Index Construction with Formative Indicators: An Alternative to Scale Development. J. Mark. Res. 2001, 38, 269–277. [Google Scholar] [CrossRef]
  74. Yang, X.; Zou, Y.; Chen, L. Operation analysis of freeway mixed traffic flow based on catch-up coordination platoon. Accid. Anal. Prev. 2022, 175, 106780. [Google Scholar] [CrossRef]
  75. Nitzl, C.; Roldan, J.L.; Cepeda, G. Mediation analysis in partial least squares path modelling, Helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 2016, 116, 1849–1864. [Google Scholar] [CrossRef]
  76. Jeong, Y.; Kim, S. Exploring a suitable model of destination image: The case of a small-scale recurring sporting event. Asia Pac. J. Mark. Logist. 2019, 31, 1287–1307. [Google Scholar] [CrossRef]
  77. Ren, X.; Zeng, G.; Dong, K.; Wang, K. How does high-speed rail affect tourism development? The case of the Sichuan-Chongqing Economic Circle. Transp. Res. Part A Policy Pract. 2023, 169, 103588. [Google Scholar] [CrossRef]
  78. Croes, R.; Kubickova, M. From potential to ability to compete: Towards a performance-based tourism competitiveness index. J. Destin. Mark. Manag. 2013, 2, 146–154. [Google Scholar] [CrossRef]
  79. Hays, S.; Page, S.J.; Buhalis, D. Social media as a destination marketing tool: Its use by national tourism organisations. Curr. Issues Tour. 2013, 16, 211–239. [Google Scholar] [CrossRef]
  80. Gretzel, U. Digital Marketing for Destination Marketing Organisations. In Proceedings of the Bali Tourism Forum International, 1st World Destination Management Outlook, Bali, Indonesia, 25–27 November 2015. [Google Scholar]
  81. Casaló, L.V.; Flavián, C.; Guinalíu, M. Determinants of the intention to participate in firm-hosted online travel communities and effects on consumer behavioral intentions. Tour. Manag. 2010, 31, 898–911. [Google Scholar] [CrossRef]
  82. World Tourism Organization. Guidelines for Success in the Chinese Outbound Tourism Market; World Tourism Organization: Madrid, Spain, 2019. [Google Scholar] [CrossRef]
Sustainability 15 14192 g001
Figure 1. Conceptual Model.
Figure 1. Conceptual Model.
Sustainability 15 14192 g001
Table 1. Sample Profile (N = 205).
Table 1. Sample Profile (N = 205).
Attributes DistributionFrequency%
GenderMale14771.71
Female5828.9
Age18 to 30 years10852.68
31 to 40 years4722.93
41 to 50 years3316.10
51 years and older178.29
EducationHigh School94.39
College136.34
Bachelors10551.22
Masters5124.88
Doctorate2713.17
Income (Chinese Yuan; CNY)Less than or equal to 5000178.29
5001–75006531.71
7501–10,0004320.98
Above 10,0008039.02
NationalityChinese11757.07
Foreigners8842.93
Table 2. Reliability and validity.
Table 2. Reliability and validity.
Latent ConstructsFactor LoadingCronbach’s AlphaComposite Reliability AVE
Local Attractions (LA) 0.9160.9350.705
Item-10.851
Item-20.812
Item-30.807
Item-40.863
Item-50.856
Item-60.848
Perceived Value (PV) 0.8980.9290.767
Item-10.897
Item-20.904
Item-30.875
Item-40.824
Hospitality and Entertainment
Services (HES)		 0.9200.9380.716
Item-10.862
Item-20.876
Item-30.817
Item-40.859
Item-50.795
Item-60.864
Economic Environment (EE) 0.8670.9040.654
Item-10.715
Item-20.842
Item-30.804
Item-40.866
Item-50.811
Perceived Safety and Security (PSS) 0.8830.9200.742
Item-10.876
Item-20.890
Item-30.875
Item-40.801
Political Environment (PE) 0.8660.9090.715
Item-10.740
Item-20.897
Item-30.872
Item-40.865
Information Sources (IS) 0.9240.9370.627
Item-10.625
Item-20.786
Item-30.772
Item-40.845
Item-50.672
Item-60.852
Item-70.874
Item-80.831
Item-90.832
Outbound Travel Motivation (OTM) 0.8940.9190.654
Item-10.823
Item-20.780
Item-30.828
Item-40.812
Item-50.750
Item-60.855
Intention to Visit (ITV) 0.8750.9140.727
Item-10.865
Item-20.819
Item-30.848
Item-40.878
Table 3. Fornell–Larcker criterion.
Table 3. Fornell–Larcker criterion.
EEHESISITVLAOTMPEPSSPV
EE0.809
HES0.6040.846
IS0.5000.4470.792
ITV0.6490.4460.4880.853
LA0.5890.6960.4310.5720.840
OTM0.5050.3840.4910.7220.4850.809
PE0.7390.5240.3360.5360.4860.4370.846
PSS0.6920.4450.4370.5220.4560.4230.6460.861
PV0.6280.6390.4580.5650.7400.4890.4980.4900.876
Abbreviations: Local Attractions (LA), Perceived Value (PV), Hospitality and Entertainment Services (HES), Economic Environment (EE), Perceived Safety and Security (PSS), Political Environment (PE), Information Sources (IS), Outbound Travel Motivation (OTM), Intention to Visit (ITV).
Table 4. Heterotrait–monotrait ratio (HTMT).
Table 4. Heterotrait–monotrait ratio (HTMT).
EEHESISITVLAOTMPEPSSPV
EE
HES0.678
IS0.5440.475
ITV0.7390.4930.525
LA0.6670.7520.4600.634
OTM0.5680.4140.5220.8100.527
PE0.8370.5860.3530.6040.5370.488
PSS0.7830.4960.4660.5890.5080.4700.733
PV0.7170.6980.4910.6300.8140.5370.5580.549
Abbreviations: Local Attractions (LA), Perceived Value (PV), Hospitality and Entertainment Services (HES), Economic Environment (EE), Perceived Safety and Security (PSS), Political Environment (PE), Information Sources (IS), Outbound Travel Motivation (OTM), Intention to Visit (ITV).
Table 5. Structural model analysis.
Table 5. Structural model analysis.
Hypothesized PathsPath Coefficientst-ValuesHypothesis CodeDecision
CONTI → DESI0.5627.083H1Supported
CONTI → ITV0.2963.855H2Supported
CONTI → OTM0.2472.18H5aSupported
DESI → ITV0.141.925H3Supported
DESI → OTM0.2082.273H5bSupported
IS → CONTI0.4887.239H4aSupported
IS → DESI0.2242.407H4bSupported
IS → OTM0.2653.159H4cSupported
OTM → ITV0.4986.77H5cSupported
Mediation Analysis
CONTI → OTM → ITV0.1232.098H5dPartial Mediation
DESI → OTM → ITV0.1042.103H5ePartial Mediation
R2 (ITV)0.635
R2 (OTM)0.364
Note(s): CONTI—country image; DESI—destination image; ITV—intention to visit; OTM—outbound travel motivation; IS—information sources.
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Conteh, S.B.; Malik, M.; Shahzad, M.; Shahid, S. Untangling the Potential of Sustainable Online Information Sources in Shaping Visitors’ Intentions. Sustainability 2023, 15, 14192. https://doi.org/10.3390/su151914192

AMA Style

Conteh SB, Malik M, Shahzad M, Shahid S. Untangling the Potential of Sustainable Online Information Sources in Shaping Visitors’ Intentions. Sustainability. 2023; 15(19):14192. https://doi.org/10.3390/su151914192

Chicago/Turabian Style

Conteh, Salamatu Bellah, Moiz Malik, Mohsin Shahzad, and Sana Shahid. 2023. "Untangling the Potential of Sustainable Online Information Sources in Shaping Visitors’ Intentions" Sustainability 15, no. 19: 14192. https://doi.org/10.3390/su151914192

APA Style

Conteh, S. B., Malik, M., Shahzad, M., & Shahid, S. (2023). Untangling the Potential of Sustainable Online Information Sources in Shaping Visitors’ Intentions. Sustainability, 15(19), 14192. https://doi.org/10.3390/su151914192

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