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https://doi.org/10.20867/thm.30.1.10

The influence of social impact and community attribute toward tourist trust formation on social commerce in the aftermath of the COVID-19 pandemic: A case study of a tourist village in Indonesia

Yonathan Dri Handarkho orcid id orcid.org/0000-0002-2789-5524 ; Ph.D., Assistant Professor Universitas Atma Jaya Yogyakarta Informatic
Ike Devi Sulistyaningtyas orcid id orcid.org/0000-0001-9418-3802 ; M. Si, Lecturer Universitas Atma Jaya Yogyakarta Communication
Rebekka Rismayanti ; M.A., Lecturer Universitas Atma Jaya Yogyakarta Communication


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preuzimanja: 163

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Sažetak

Purpose – This research proposed a theoretical framework based on social impact, and
community attributes theories to identify factors that influence the formation of trust among
tourists towards Social Commerce (SC) utilized by a tourist village in Indonesia, particularly
in the aftermath of COVID-19. In detail, this study focused on tourist village marketing
through SC which emphasizes trust formation on destination readiness to welcome visitors,
leading to the intention to revisit the village in the aftermath of the COVID-19 pandemic.
Methodology/Design/Approach – 258 responses were obtained from Indonesian SC users
interested in visiting tourist villages in Indonesia to evaluate the proposed model. The
collected data were processed using confirmatory factor analysis followed by the Structural
Equation Modeling (SEM) technique to analyze the direct, indirect and moderating effects of
trust predictors.
Findings – The results showed that endorsement by other members had the most significant
positive effect on trust, followed by Quality-assured shared information and Online Bridging.
In contrast to the proposed hypothesis, responsiveness had a negative impact. Although
parasocial interaction and followers’ comments were rejected, the indirect effect suggests that
both factors influence trust through quality-assured shared information as a mediator. Overall,
the result suggests that the quality of interaction and information in SC can reduce users’
concern about the consequences of COVID-19 in a destination and thus increase users’ trust
in the willingness of places to host tourists.
Originality of the research – This study provides a comprehensive approach that incorporates
social and community aspects that influence tourists’ trust in Social Commerce marketing of
tourist destinations after the COVID-19 pandemic. As this approach has not been studied in
depth before, this study contributes to filling this gap, followed by a comprehensive analysis
that includes direct, indirect and moderating effects.

Ključne riječi

Trust; Tourist village; social commerce; aftermath of the COVID-19

Hrčak ID:

313979

URI

https://hrcak.srce.hr/313979

Datum izdavanja:

6.2.2024.

Posjeta: 839 *




INTRODUCTION

Globally, the COVID-19 pandemic has been found to affect the tourism industry, and a social distancing policy has been applied to prevent the spread of diseases, leading to various restrictions caused the tourism industry to suffer enormous losses (Leung et al., 2022). However, after the vaccination began, many countries loosened their restrictions and reopened borders for tourism purposes (Qiu et al., 2021), leading people to adjust to the new normal life, including Indonesia. However, one of the challenges in tourism recovery is fear and worry about being at risk of the coronavirus being transmitted in a physical place. On the other hand, the COVID-19 situation also significantly increases the use of Social Network Sites (SNS) due to this disease hindering physical interactions during the pandemic (Handarkho et al., 2023). People utilize SNS for communication and as a platform to share and gather information, including for commercial purposes (Statista, 2023). Consequently, this also led many businesses to adopt SNS as a marketing platform to respond to user online behavior above, known as Social Commerce (SC). Specifically, SC refers to the use of SNS by the vendor and customer as a platform to share information and experience related to particular products and services (Handarkho, 2020a). This platform has been considered able to increase user trust toward a service because the information regarding services shared on the sites is derived not only from the vendor but also from the customer side (Herrando et al., 2019).

In tourism, many stakeholders have taken various approaches to restore tourist trust toward the safety and readiness to welcome visits, including using the SC strategy (Pachucki et al., 2022). In Indonesia, tourist village is one domestic tourism that already uses SC as a promotional and marketing platform. Herawati et al. (2018) defined a tourist village as “a rural area which presents the entire pristine view in terms of space structure, architecture, and the pattern of its residents’ social and cultural life. It provides the components of the main needs of tourists such as accommodation, food, beverages, souvenirs, and tourist attractions.” Tourist villages have begun utilizing SC as a medium for marketing, persuasion, and communication regarding the destination’s readiness to welcome tourists through health protocols. However, they also face the challenge of restoring trust toward the safety and readiness of the destination to welcome visitors due to the characteristic of COVID-19, which quickly spreads through human

contact and makes people feel worried about being infected through physical meetings (Handarkho et al., 2023). During the COVID-19 pandemic, the tourist village was severely impacted due to the Indonesian government’s strict restrictions in mid- 2021 to control the spread of the virus. As a result, these villages had to shut down their operations since people were required to maintain physical distance, leading to a significant decrease in consumer demand. However, after the lifting of COVID-19 restrictions by the Indonesian government at the end of 2022, tourist villages must take immediate action to rebuild the people’s trust in the readiness of the sites to provide a safe service for tourists, including by implementing the SC strategy.

In the context of tourism recovery, several prior studies already support the role of SC as a platform that facilitates many destinations to restore tourist trust toward the safety and readiness of sites to re-welcome visitors (Castaldo et al., 2021; Rather, 2021). Furthermore, Higgins-Desbiolles et al. (2021) and Hao et al. (2021) mentioned the role of social community and bonding as a factor that can encourage tourists to keep their intention to revisit tourist objects. Several studies have already mentioned the impact of social aspects on trust formation that leads to particular individual intentions (Qiu et al., 2021; Castaldo et al., 2021; Rather, 2021). Handarkho (2021a) stated that people consider other individuals’ experiences as a guarantee to justify their actions. Meanwhile, the online community attribute is also an aspect that Handarkho mentioned (2021b) as a significant construct attached in SC that facilitates user bonding and engagement toward the platform. Pachucki et al. (2022) and Rather (2021) stated that this bonding is essential for tourists because they tend to rely on the information provided when uncertain situations are faced. Therefore, this study proposes a theoretical model based on social impact and community attributes that affect trust formation in SC. Several aspects as a consequence of the COVID-19 pandemic, such as perceived inconvenience and fear of being at risk of the coronavirus, are also involved because it has an essential effect on individual attitudes and behavior (Rather, 2021; Hao et al., 2021).

This study focuses on trust formation toward tourist village information in SC and its implication for individual intention to revisit the destination. Even though previous literature involved trust as a part of their exploration, none of them investigated trust formation toward tourism destinations related explicitly to SC usage to recover the perceived readiness of tourist villages. Therefore, this study fills the gap by focusing explicitly on trust formation toward content in SC to promote people’s intention to revisit tourist villages in the aftermath of the COVID-19 pandemic. Finally, the two postulated study questions are:

RQ1. Which factors significantly impact the tourists’ trust in information about tourist villages in SC after the

COVID-19 pandemic?

RQ2. What theoretical and practical contributions does this study offer to tourism recovery after the COVID-19 pandemic?

LITERATURE STUDY AND HYPOTHESES DEVELOPMENT

Table 1 shows an overview of prior studies on the tourism industry’s recovery from COVID-19. The previous study included trust as a factor that needs to be considered to reduce the perceived risk of revisiting tourist destinations (Castaldo et al., 2021; Rather, 2021). In detail, Castaldo et al. (2021) identify Trust as a factor for predicting tourists’ intention to go on a cruise amidst COVID-19. On the other hand, Rather (2021) suggests that using SNS as a marketing strategy during the pandemic may minimize user-perceived risk and influence users’ intention to revisit tourism sites in the future. Other literature also emphasizes using social media as an essential platform to minimize the consequences caused by COVID-19 (Pachucki et al., 2022; Qiu et al., 2021; Rather, 2021). Some also underlined the role of social interactions and community bonds that encourage tourists to keep their intention to revisit tourist objects one day (Pachucki et al., 2022; Higgins-Desbiolles et al., 2021; Hao et al., 2021; Rather, 2021). Pachucki et al. (2022) and Rather (2021) emphasize using SNS by tourism sites as an effective strategy to minimize tourists’ perceived risk during the crisis. By communicating with tourists through SNS, tourism sites can address tourist concerns about perceived risks that affect visitors’ intention to visit the sites (Hao et al., 2021). This communication is crucial during a crisis, as it relates to the protocol and approach taken by tourism sites during and after the crisis (Chang & Wu, 2021). Comprehensively, previous studies have demonstrated that an SNS strategy can significantly enhance tourist trust in a destination’s capacity to manage crises. This, in turn, leads to a reduction in the perceived risk when planning future visits.

Therefore, this study focused on tourist village marketing through SC, emphasizing trust formation on destination readiness to welcome visitors. Specifically, it proposed a model to predict factors affecting tourist trust toward information provided in SC, leading to the intention to revisit the village in the aftermath of the COVID-19 pandemic. Based on Table 1, topics that explicitly investigate tourist trust formation toward the readiness of tourist sites in the aftermath of the COVID-19 pandemic through SC strategy have not been explored profoundly by prior related studies. Hence, our investigation offers an alternative approach that bridges the gap and adds to the existing body of knowledge.

Table 1: Overview of Prior Literature

The focus of the study

Research Methods

Results

Reference

The impact of social media

live streaming in the context of tourism to reduce industry losses due to the COVID-19 pandemic

Content analysis

Live streaming in SC helps to minimize

the impact of COVID-19 restrictions, leading to virtual tourism.

Qiu et al. (2021)

The opportunity and barriers

in socializing tourism after the COVID-19 pandemic

Conceptual analysis

Socializing tourism post-COVID-19

pandemic requires transformation from stakeholders involving social solidarity and community bonds.

Higgins-Desbiolles et

al. (2021)

The effect of risk, reputation,

trust toward the company, and social and personal factors on tourist intentions to cruise in the pandemic situation

Multiple regression models

Trust toward the cruise company and

its reputation affects individual intent to cruise and minimize the perceived risk related to the COVID-19 threat.

Castaldo et al. (2021)

The impact of the COVID-19

pandemic in altering people’s willingness to travel during the pandemic era

SEM

The negative impact of COVID-19 was

found to affect costs, transportation, health risks, pleasure, and social community, influencing people’s willingness to travel.

Hao et al. (2021)

The effect of risk, fear, and infor-

mation circulated in social media on individual intention to visit tourist destinations.

SEM

Social media engagement helps the

tourist to keep their intention to revisit tourist objects in the aftermath of COVID-19 pandemic.

Rather (2021)

The impact of the COVID-19

pandemic on tourism destination social media communication and tourist engagement toward the platform.

Linguistic analysis

The COVID-19 crisis significantly

affected the character of linguistic use in destination social media posts which influenced tourist engagement.

Pachucki et al. (2022)

Factors influence individuals’

acceptance of virtual tourism in China as a part of the tourism industry’s recovery.

Quantitative survey and qualitative inter- views

Virtual tourism helps tourists stay

connected with tourist destinations and encourages them to visit the place.

Junyu & Zixuan (2021)

Exploring the criteria factors

and practices can be used in stakeholders’ decision-making in the tourism industry during the COVID-19 pandemic.

TRIZ and DEMATEL

method

“Quality management” is the factor

that needs to be prioritized followed by conversion policies and tourism regulations as a secondary factor.

Chang & Wu (2021)

Social Impact Theory

Many studies showed the association between trust and social impact, including in online platforms. Handarkho et al. (2022) mentioned that the decision of people to accept information is affected by what they see in others’ decisions and behavior toward the same issue. Lisana & Handarkho (2023) stated that social interaction facilitates individual belief and acceptance in an uncertain situation. People tend to rely on others’ decisions when faced with uncertain conditions, which leads to trust formation (Vedadi & Warkentin, 2020). In the context of SC, this platform allows users to access others’ experiences and decisions related to particular acts due to the ability of SC to facilitate interactions between actors (Lu et al., 2016; Handarkho, 2020a). Therefore, information related to the readiness to welcome visitors is derived from the tourist village and other people who have already experienced the sites, which affects trust and Intention to revisit. Based on the discussion, social impact and capital theories are employed to provide a comprehensive and profound understanding of trust formation in the context of SC managed by a tourist village.

According to the Social Impact theory (Latané, 1981), the quality of social interaction can be measured using numbers, tie strength, and emotion. The numbers refer to the tendency of people to be confident toward certain acts or behavior because it has been performed or adopted by many people (Lisana & Handarkho, 2023). In the context of a community formed in SC, people tend to rely on other members’ approval toward uncertain decisions related to trust formation (Cheng et al., 2019). This approval can be in the form of the number of likes, favorable comments, or followers that help minimize their skepticism toward SC, leading to trust formation. This study, however, used the factor of endorsement by others to represent the number aspect. The construct refers to the number of other user endorsements in the form of likes, followers, or positive comments, implicating individual trust toward content and information in SC (Cheng et al., 2019). An individual uses the number of attitudes and behavior of other people as an assurance toward uncertain conditions (Lisana & Handarkho, 2023) in the context of information shared by tourist villages related to COVID-19. Therefore, the following hypothesis is proposed:

H1. Endorsement by other members in SC positively affects an individual’s trust in tourist village content and information

shared.

Social Capital Theory

The second aspect of social impact theory is Tie strength, which refers to bonds formed with other people considered to have an equal understanding of a specific issue (Handarkho, 2020a). In the context of the online environment, Horng and Wu (2020) used social capital theory to explain the impact of connection formed between individual interactions. In detail, social capital refers to the valuable resource that grows from the relationship between individuals in the social unit. This theory details the connection above into Online Bridging and Bonding. This Online Bridging refers to the connections that occur because of a mutual benefit instead of emotion. Profoundly, these ties are formed due to users’ interest in the information that other people provide and infrequently occur (Granovetter, 1983). In trust formation, individuals seek other consumer reviews, ratings, or feedback to validate their transactional decisions rather than emotional ones. This kind of bonding occurs between people involved in a community with the same purpose and interest, including in SC (Handarkho, 2020a). Meanwhile, Online bonding relies more on emotional than informational support (Horng & Wu, 2020). Therefore, this study proposed Online Bridging as a factor representing Tie’s strength rather than emotional connection. The ability of the community in SC to provide information to the user who has concerns and worries about tourist villages in the context of COVID-19 will raise individuals’ trust in the platform and the information it shares. Therefore, the following hypothesis is developed:

H2. Online Bridging positively affects an individual’s trust in tourist village content and information shared in SC.

The third aspect of social impact theory is emotion, which refers to the closeness and intimacy feeling formed based on emotional construction (Handarkho, 2020a). One strategy commonly used in the SC context is the use of influencers. According to Sokolova & Kefi (2020), this strategy manages the ties between individuals and people they idolize or admire. Vendors also use this strategy in the tourism industry to promote the travel destination experience and reach potential consumers who follow influencers on social media (Pop et al., 2022). It is related to an individual’s tendency to rely on the behavior of people respected or admire, leading to a tendency to accept anything the influencer offers (Jin & Ryu, 2020). Therefore, this study believed that the information shared by an influencer in SC could boost user trust toward the readiness of the tourist village to welcome the visitors, leading to the following hypotheses:

H3. Parasocial interaction positively affects an individual’s trust in tourist village content and information shared in SC.

Community Attributes

Community attribute refers to the ability of SC as an online social platform promoting users to develop bonding and feel as a part of the community (Handarkho, 2021b). Specifically, this aspect covers the element attached to the platform to facilitate interaction among members in SC to encourage user engagement (Goraya et al., 2019). In the context of SC, which puts social interaction as a primary aspect, user-generated information in the form of comments is a part of the attributes that affect user trust formation (Nikbin et al., 2022; Lu et al., 2016). Users will adopt a more optimistic and proactive perspective toward the problems they encounter when they believe the knowledge presented can alleviate some of their concerns. (Handarkho, 2021b). Positive opinions of other customers can convince users of the readiness of the tourist village to handle their concerns related to the threat of COVID-19. Therefore, the following hypothesis was proposed:

H4. Followers’ Comments positively affect trust in tourist village content and information shared in SC.

Cheng et al. (2019) believed that the quality of the information in SC, measured by its accuracy, correctness, and usefulness to the user, will significantly affect the development of trust. These qualities will help the individual to achieve their objectives in answering questions about specific distressing issues (Handarkho, 2020b). Nikbin et al. (2022) stated that the quality of the information could influence user trust toward specific issues or information. In the current study scenario, users’ trust in the preparedness to receive tourists would increase when SC can provide detailed information about COVID-19 worries. Therefore, the following hypothesis was proposed:

H5. Quality-assured shared information positively affects an individual’s trust in tourist village content and information

shared in SC.

People tend to measure the quality of information from specific criteria such as accuracy, reliability, and correctness (Cheng et al., 2019). However, an individual may evaluate information based on an emotional aspect, such as parasocial interaction. As mentioned by Handarkho (2021a), people who idolize an influencer or artist tend to use the measurement of the admired person to evaluate the information received. Therefore, people tend to imitate the behavior or action of their role models (Handarkho et al., 2022; Singh & Banerjee, 2018). In this context study, tourists will positively evaluate the information received in SC when promoted by their role model. Therefore, the following hypothesis was proposed:

H6. Parasocial interaction has a positive effect on Quality-assured shared information in SC.

The positive opinions of other customers will affect user perception toward the quality of information shared and circulated in SC. As Nikbin et al. (2022) mentioned, the opinions derived from other customers will affect individual perceptions, leading to a positive attitude toward the brand. This is also supported by Handarkho (2020a) and Chen & Chang (2018), which believed the review from other members would facilitate user perception and decisions toward specific issues and reduce their doubt about the quality of the information. Therefore, the following hypothesis was proposed:

H7. Followers’ Comments have a positive direct effect on Quality-assured shared information in SC.

Another aspect related to community attributes is the responsiveness of tourist villages in answering and responding to user inquiries in SC. This is related to the vendor’s ability to respond quickly to customer queries to solve their concerns about specific issues (Potgieter & Naidoo, 2017). In this study, responsiveness is the willingness of tourist village representation to help and respond to questions about issues. According to Nikbin et al. (2022), a brand or vendor is considered trustworthy when they can provide quality service, including their responsiveness toward customer questions on specific online sites or platforms. This statement is also supported by Kabanov & Vidiasova (2019), which stated that the attentiveness of vendors toward customer queries would affect their feelings and lead to trust formation. Singh et al. (2021) declared that vendor responsiveness affects the perception of the service, specifically when facing a particular problem, increasing user trust in the services. Therefore, the following hypothesis was proposed:

H8. Responsiveness positively affects an individual’s trust in tourist village content and information shared in SC.

COVID-19 Aspects

This study formulated the COVID-19 aspect based on the characteristic of the virus that is known quick to spread through human contact, thereby limiting and frightening people from socializing in physical sites freely due to the possibility of being infected (Handarkho et al., 2023), even in the post-pandemic situation. Concerning the tourism industry, Obembe et al. (2021) denoted how health-related crises, such as COVID-19, negatively impact tourist visits rather than other concerns. In the context of the post-COVID-19 pandemic, tourist intention to visit is also affected by individual perceptions and worries about the extent to which the disease is still threatening them. According to Handarkho et al. (2023), when individuals feel inconvenienced by a particular situation, they tend to avoid and retract from the case (Leong et al., 2020). Concerned individuals are more likely to misinterpret or mishandle facts in the trust establishment process (Handarkho et al., 2022). The likelihood of doubting information supplied by tourist villages is high when tourists continue to feel inconvenienced toward meeting people in physical places due to COVID-19 concerns. Therefore, the following hypothesis was proposed:

H9. Inconvenient has a negative effect on trust in tourist village content and information shared in SC.

Another aspect affecting user trust toward information in SC is fear of possibly being infected by COVID-19. This is formulated from the worries of individuals about the consequences of being infected by the virus (Chakraborty et al., 2021). Specifically, the risk of the infection also affects individuals psychologically, including their perception and decision toward any information received (Carvalho, 2022; Handarkho et al., 2022). In detail, when COVID-19 is still perceived as a threat, they question the related information received (Erhardt et al., 2021). Therefore, the following hypothesis was proposed:

H10. COVID-19 Fear negatively affects an individual’s trust in tourist village content and information shared in SC.

Trust and Intention to Visit

This study formulated the context and definition of trust as the extent to which tourist believes the content and information in SC will meet their expectations (Handarkho, 2021a). It means this construct explains individual psychological acceptance toward the vulnerability of SC based on the platform’s ability to satisfy their expectations. Specifically, it is related to the readiness of tourist villages to welcome visitors in the COVID-19 situation. Therefore, when an individual believes in information shared in SC, the uncertainty can be reduced (Hakim et al., 2021). In the context of tourist visits, when they have trust in related information, the intention to travel to the destination will be promoted (Setiawan et al., 2021). Therefore, the ability of the destination to help the visitor accept the site’s vulnerability and fulfill their expectations through the provided information will increase tourist intention to visit (Su et al., 2020). Therefore, the following hypothesis was proposed:

H11. Individuals’ trust in tourist village content and information shared in SC positively affects the intention to visit.

STUDY DESIGN AND METHODOLOGY

The proposed theoretical model was grounded by three comprehensive approaches involving social impact, community attributes, and COVID-19 consequences. This study uses the purposive sampling method to select participants due to the non- existence of an adequate sample frame (Neuman, 2014). Specifically, the model was validated using data that collected online from respondents who had already received information about tourist villages through SC and were interested in visiting. The instrument used was adopted from a prior study presented in Table 2, which was validated by involving three professionals with experience in related research. This stage was conducted to ensure that the adoption process and translation represent the original content and are suitable for the research context. This study also conducted a pilot study to ensure the instrument is suitable and can be understood by the respondents, which involved five people interested in tourist villages and SC. Furthermore, the questionnaires used a five-point Likert scale distributed using the cross-sectional approach according to Neuman’s (2014) guideline. Kline (2016) stated that the minimum valid sample required is not less than 200 respondents for structural equation modeling (SEM) analysis. Furthermore, five experts reviewed each question’s translation to verify the accuracy and consistency with the original form of the instrument before its final release. Exploratory Factor (EFA) and Confirmatory Factor Analyses (CFA) were used to prepare the data, followed by the use of SEM to examine the direct and moderating effects in the theoretical model based on Kline’s (2016) guidance.

THEORETICAL MODEL AND MEASUREMENT

The proposed theoretical model consists of nine predictors, and two moderating factors are presented in Figure 1. Meanwhile, the instrument used was adopted from the previous study, as shown in Table 2.

Figure 1: The theoretical model

Covid-19 Aspect

Number

Social Impact

Theory

Tie Stength

Emotional

Community atributes

Table 2: Indicators and Measuring Instrument

Variable (Symbol)

Indicator

Measuring Instrument

Adopted from

Perceived Inconvenient

I feel inconvenient..

Handarkho et al. (2023)

PI1

to visit the tourist villages since the threat of the COVID-19 virus is still

high.

PI2

to look for tourist villages for vacation during the COVID-19 season

PI3

maintaining physical interaction with other travelers in tourist villages

during the COVID-19 season.

Fear of COVID 19

Even at this time..

Chakraborty et al. (2021)

FO1

I am often worried about being at risk of the coronavirus

FO2

I feel extremely anxious when I think of an individual and his family

problems after a possible risk of the incidence of coronavirus

FO3

I get anxious and disappointed when I think of being affected by a

coronavirus

FOC4

I am living with the fear of infection

Intention to visit

IV1

I intend to visit the tourist villages promoted in SC in the future

Handarkho (2020a)

IV2

I have a firm intention to visit the tourist villages promoted in SC if others

recommend it

IV3

I choose to consider and accept the tourist villages recommendations from

others in SC when I decide to visit it.

Trust

T1

Information shared in SC provided accurate content about tourist villages

Handarkho et al. (2022)

T2

SC provided sufficient information about tourist villages

T3

I believe the content in SC related to tourist villages would be reliable

Variable (Symbol)

Indicator

Measuring Instrument

Adopted from

Endorsement by other

ED1

Favorable comments from other members in SC make me feel that the

tourist villages the member recommended is good.

Cheng et al. (2019)

ED2

The number of likes from other members in SC makes me feel that the

tourist villages the member recommended is good.

ED3

The number of contents promoted by other members in SC makes me feel

that the tourist villages the member recommended is good.

ED4

The number of fans of other members in SC makes me feel that the tourist

villages the member recommended is good

Online Bridging

BRI1

Interacting with SC friends makes me interested in things about the tourist

villages

Horng & Wu

(2020)

BRI2

Interacting with SC friends makes me want to try new things, including

visiting the tourist villages

BRI3

Talking with SC friends makes me curious about the tourist villages

BRI4

I am willing to spend time to support general SC activities related to the

tourist villages

Parasocial Interaction

PAR1

I aspire to visit the tourist village advertised by my favourite SMI (Social

media influencer)

Pop et al. (2022)

PAR2

I have willing to buy products or services from the tourist village

recommended by my favourite SMI

PAR3

I have a desire to visit the tourist village recommended by my favourite

SMI.

Followers’ Com- ments

I have more confidence about information in SC when..

Nikbin et al. (2022)

FC1

the average rating of the tourist village in SC is high.

FC2

the comments on the tourist village facility and scenery are positive.

FC3

the comments on the service of the tourist village are positive.

FC4

overall comments on the tourist village are positive

Quality- assured shared information

I think members on the SC can provide..

Cheng et al. (2019)

QI1

useful information related to the tourist village.

QI2

accurate information related to the tourist village that I want to visit.

QI3

reliable information related to the tourist village.

QI4

sufficient information when I decide to visit the tourist village.

Responsiveness

I like when the owners of SC..

RE1

gives prompt responses to its SC followers’ requests.

Nikbin et al. (2022)

RE2

always willing to help its SC followers’ requests.

RE3

is never too busy to respond to its SC followers’ requests.

DATA PREPARATION AND DESCRIPTIVE ANALYSES

In total, 258 valid responses were collected and used to evaluate the theoretical model. This study employed CFA to check the convergent validity of data based on the value of variance Extracted (AVE) and Composite Reliability (CR). The result showed that the variables satisfy the requirement proposed by Fornell & Larcker (1981). Meanwhile, the equivalence reliability of data through the value of Cronbach’s Alpha (CA) met the criterion provided by George and Mallery (2003). Furthermore, the discriminant validity was accessed through the value of AVE square roots higher than the correlation coefficients between the latent variables (Handarkho, 2020b). The calculations of CFA are presented in Tables 3 and 4. Meanwhile, the respondents consist of males, 51.2% of the population. Meanwhile, 48.4 % are below 20 years, followed by 20-25 years at 36.1% and above 25 years at 15.6%. The experiences in using SC were dominated by 1-5 years with 52%. Furthermore, Instagram was the SNS that the most respondents used at 67.2%.

Table 3: Factor Analysis and Cronbach’s Alpha Coefficient

Indicator Loadings AVE CR CA Indicator Loadings AVE CR CA

T1

0.714

0.477

0.732

0.732

QI10.7970.649

0.881

0.879

T2

0.656

QI20.835

T3

0.701

QI30.771
IV10.7890.520.76

0.735

QI40.817
IV20.798RE10.870.8290.936

0.934

IV30.549RE20.941
ED10.7370.5030.801

0.797

RE30.919
ED20.662FC10.8990.7720.931

0.931

ED30.775FC20.852
ED40.657FC30.901
BR10.8240.6790.864

0.865

FC40.861
BR20.85PI10.8990.7780.913

0.911

BR30.798PI20.909
PAR10.8440.6790.864

0.861

PI30.837
PAR20.752FO10.8770.6730.891

0.894

PAR30.872FO20.853
FO4 0.799FO30.747

Table 4: Discriminant Validity

IV T

ED

PAR

QI

RE

FC

PI

FO

BR

IV

0.721

T

.456**

0.690

ED

.553**

.674**

0.709

PAR

.470**

.477**

.577**

0.824

QI

.536**

.683**

.657**

.622**

0.649

RE

.456**.440**

.543**

.404**

.633**

0.910

FC

.534**.592**

.636**

.537**

.759**

.696**

0.878

PI

.102.013.043

.130*

.049

.018

.100

0.882

FO

.125.106.097

.159*

.043

.062

.170**

.696**

0.820

BR

.605**.559**

.613**

.535**

.660**

.538**

.615**

.057

.098

0.824

RESULT OF DIRECT, INDIRECT, AND MODERATING EFFECT ANALYSIS

The result of the SEM analysis is presented in Figure 2 and Table 5. The direct and indirect effects are presented in the order of the unstandardized effect and its statistical significance. Other values in parentheses consist of the standardized effect and the magnitude. Symbols showed the statistical significance value *, **, ***, and NS, which refer to significance at 0.05, 0.01, 0.001, and no significance, respectively. The magnitude is presented in format S, M, and L, which refers to small, medium, and large values. The result showed that endorsement by other members (H1) has the most significant direct effect on trust formation, followed by Quality-assured shared information (H5), Responsiveness (H8), and Online Bridging (H2). Meanwhile, the effect of Parasocial interaction (H3), Followers’ Comments (H4), Inconvenient (H9), and COVID-19 fear (H10) were rejected. However, the indirect effect shows that parasocial interaction and followers’ comments affect trust through Quality- assured shared information as a mediator. Even though the impact of the responsiveness of tourist villages on trust was found to be significant (H8), the analysis showed the value is contrary to the proposed hypothesis, with a negative effect. Therefore, higher responsiveness of a tourist village in SC will decrease visitor trust toward information.

Figure 2. The Result of Direct Effect in the Theoretical Model

Covid-19 Aspect

Number

Social Impact

Theory

Tie Stength

Emotional

Community atributes

Table 5: Hypothesis Testing Results

Direct effect

Total Effect

Status

H1. Endorsement by other members → Trust

0.512***(0.529L)

Accepted

H2. Online Bridging → Trust

0.182*(0.220M)

Accepted

H3. Parasocial interaction → Trust

-0.053NS(-0.082S)

Rejected

H4. Followers’ Comments → Trust

-0.033NS(-0.043S)

Rejected

H5. Quality information → Trust

0.494***(0.538L)

Accepted

H6. Parasocial interaction → Quality information

0.246***(0.353M)

Accepted

H7. Followers’ Comments → Quality information

0.509***(0.610L)

Accepted

H8. Responsiveness → Trust

-0.190**(-0.221M)

Accepted

H9. Inconvenient → Trust

-0.024NS(-0.044S)

Rejected

H10. COVID-19 Fear → Trust

0.062NS(0.119M)

Rejected

H11. Trust → intention to visit

0.808***(0.668L)

Accepted

Indirect effect

Total Effect

Status

Parasocial Interaction → Quality Infor → Trust

0.121***(0.189M)

Accepted

Followers’ Comments → Quality Infor → Trust

0,251***(0.328M)

Accepted

Table 6 provides the value of fit statistics as a result of SEM analysis. The result showed that the value of the proposed theoretical

model meets the requirement based on Kline’s (2016) criterion. Meanwhile, the moderating factor results are presented in Table

7. The output showed only Age and Gender that significantly impact the predictor of trust. The result showed that respondents 20 years above put endorsement by others as a significant factor affecting their trust formation. The older respondents also consider the responsiveness of tourist villages in SC as a negative factor that affects trust. From a Gender perspective, male respondents consider the ability of the community to provide the information needed as a factor that significantly affects their trust. Meanwhile, female respondents who assume the highly frequent response of the tourist villages toward communication will decrease trust in the information.

Table 6: Fit Statistic for the Proposed Model

Sample Size

Normed chi-square

(NC) = χ2/df

RMR

GFI

AGFI

NFI

IFI

CFI

RMSEA

2581.7410.0470.842

0.810

0.873

0.856

0.941

0.054

R2: IV=0.446; T= 0.891; QI=0.758.

Note(s): R2 is the proportion of the variance explained by the variables affecting it

Table 7: Moderating Factor Results

AGE <20

>= 20

Statistical Significance

Endorsement by other →Trust

0,205NS0,988***

**

Responsiveness → Trust

-0,057NS-0,411***

**

GENDER

Male Female

Online Bridging è Trust

0,252**-0,062NS

*

Responsiveness è Trust

-0,06 NS-0,334**

*

  • 5. DISCUSSION

    1. Direct, Indirect, and Moderating Effects

This study depended on social impact theory, community attributes, and COVID-19 aspects to predict tourist trust toward content and information shared by tourist villages in SC. From Social impact, endorsement by other members (H1) and online Bridging (H2) directly impacted tourist trust. People tend to rely on others’ approval toward uncertain situations, including trust in information shared by tourist villages (Handarkho, 2020a). The number of other users who endorse and provide information can minimize the concerns and worries about the readiness of tourist villages to welcome visitors (Horng & Wu, 2020). The effect of Parasocial interaction has an indirect impact on trust through Quality-assured shared information. Therefore, when information is shared by admired or idolized people, it will impact tourist evaluation of the information received (Singh & Banerjee, 2018). The moderating factors provided another insight related to the social aspect. The older respondents tend to increase the number of endorsements by other members in the form of likes, reviews, or followers, which will affect their trust. Meanwhile, males tend to rely more on informational than emotional support.

Further, the development of trust is significantly affected by the quality of information transmitted in SC, including the accuracy, correctness, and usefulness in answering questions (Nikbin et al., 2022). Meanwhile, responsiveness had a negative impact on trust, which is contrary to the hypothesis proposed to have a positive effect (Kabanov & Vidiasova, 2019; Singh et al., 2021). This opposite result occurs due to users’ tendency to rely more on information from other users or customers than the service vendor. People will believe the reliability of information when derived from other customers who have experienced and dealt with certain concerns. From moderating factor analysis, older and female respondents tend to set responsiveness as a factor that negatively affects their trust toward information. The positive opinions of other customers can affect trust through the mediator of Quality-assured information. Therefore, positive reviews from other members in SC will facilitate user perception and decisions toward specific issues and reduce their doubt about the quality of the information received, leading to trust formation (Handarkho, 2020b; Chen & Chang, 2018).

From the COVID-19 aspect, Inconvenient and Fear were not significant toward trust formation. This result might be caused by people’s perception toward COVID-19 spread, specifically after the vaccination process that made many countries decrease their rules and restrictions. It is also supported by the moderating analysis, where fear of COVID-19 does not moderate the effect of the trust predictor.

Theoretical implication

This study focused on how a tourist village can maximize the use of SC for users to trust the destination’s readiness to welcome visitors in the COVID-19 situation. Specifically, it proposed a theoretical model using social impact, community attributes, and the COVID-19 aspect to offer a comprehensive understanding of trust formation. The involvement of social impact and community attributes in theory development is aligned with aspects that construct SC (Handarkho, 2021b). Furthermore, the involvement of the COVID-19 aspect is intended to investigate people’s perceptions and information related to the readiness of tourist villages to welcome visitors. This study offered a comprehensive investigation that covers all possible constructs affecting trust in SC marketing used by tourist villages, including the involvement of direct, indirect, and moderating effects analysis to enrich the finding, which is not explored deeply by previous related studies.

Practical implication

This section proposed several practical actions based on the finding. First, the favorable comment, the number of likes, followers, and content promoted by members increase user trust. Therefore, the owner of tourist villages can use some approach to promote other users toward the SC platform. Several actions, such as posting favorable comments or feedback through “story” or “Newsfeed”, will increase user awareness toward the number of endorsements. Tourist villages can also collaborate

with other SC sites or influencers to increase attention and attract potential tourists to see, visit, and follow their content and information. The ability of the community to provide information can also raise individuals’ trust in the platform. Therefore, tourist villages need to provide a convenient environment that encourages users to interact. This goal can be achieved by providing a topic and content that attract other members to discuss it. A recent trending subject might serve as a conversation starter to draw people in and get them talking about important topics. This approach is also related to other findings that indicated the excessive involvement of tourist village owners in discussions. It means people tend to believe in information they receive from other users or customers rather than from the owner of a tourist village. Therefore, the tourist village needs to promote other members of SC to involve and share their opinion on the platform.

The second factor found to be most influential toward trust formation is the quality of the information shared in SC. Based on indirect factor analysis, this construct can be increased by parasocial interaction and followers’ comments. People tend to positively evaluate the information received in SC when promoted by those they idolize, such as artists or respected people. However, selecting influencers who can represent and communicate the readiness of tourist villages is essential to justify the credibility of SC and the information shared. Inappropriate selection can harm user perception of the quality of SC, influencing people’s evaluation of the information shared in SC (Handarkho, 2020a).

User confidence in the quality of information shared is also affected by the positive comment provided by another user. Therefore, other users’ opinions will reduce doubts about the quality of the information (Nikbin et al., 2022). Tourist villages need to encourage their visitors to have the willingness to share positive experiences in SC. This can be achieved through deals in the form of discounts or other rewards when the tourist shares their review in SC. The willingness of users to share their positive experiences can be induced when they have an emotional relationship with the destination. Therefore, customers are motivated to give positive reviews when given exceptional services.

CONCLUSION

This study found that the quality of interaction and information in SC can diminish user concern toward the consequence of COVID-19 in a tourist destination. Specifically, endorsement by other members is the most influential predictor of trust, followed by Quality-assured shared information and Online Bridging. Surprisingly, responsiveness has been found to have a negative direct effect on trust, which indicates that tourists tend to rely on information shared by other users. The indirect effect of Parasocial interaction and Followers’ Comments showed the effect of constructs on trust through Quality-assured shared information, which enriches the finding. Therefore, the constructs from the COVID-19 aspect were rejected, followed by the moderating effect analysis. The result reported that only Age and Gender moderate the direct effect of endorsement, Responsiveness, and Online Bridging on trust.

In summary, the involvement of the social impact, community attribute, and COVID-19 aspect offered a comprehensive perspective toward understanding trust formation in the context of SC and the readiness of tourist villages to welcome visitors. Concerning the limitation, this study ignored the emotional connection between tourists with destinations. Therefore, the involvement of constructs that explain individual links, such as Place (Handarkho et al., 2023) or Destination attachment (Pandey & Sahu, 2020), can offer more understanding and insight toward tourist trust formation related to the intention to visit tourist destinations.

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Acknowledgements

The authors gratefully acknowledge the support from the “Ministry of Research, Technology and Higher Education of the

Republic of Indonesia” and from Universitas Atma Jaya Yogyakarta (UAJY), Indonesia.


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