Published by LPPM Universitas Islam 45 ---- Vol. 20, No. 02, Year 2023, pp. 161-182 ---- https://jurnal.unismabekasi.ac.id/index.php/paradigma
e-ISSN : 2775-9105, p-ISSN : 0853-9081 DOI : https://doi.org/10.33558/paradigma.v20i2.7011
Halal Tourism and Social Media from Netizen’s Perspectives
Yosi Mardoni1 , Ares Albirru Amsal2
1Universitas Terbuka, Indonesia
2Universitas Andalas, Indonesia
*Corresponding author : [email protected]
Article history Received : (02-05-2023) Revised : (12-05-2023) Accepted : (02-06-2023)
ABSTRACT
This study discusses the netizen's perspective on halal tourism.
This study looks at the relationship between attitudes towards social media, online satisfaction, and destination image and how these variables affect the intention to visit halal tourist destinations in Indonesia. The quantitative method with PLS-Structural Equation Modeling (SEM) was used to analyze this research. The research data collection was carried out by purposive sampling, namely by deliberately taking samples of social media users who were members of the halal tourism community, as many as 223 respondents. This study found that attitudes toward social media positively affected online satisfaction and destination image. The attitude and destination image significantly influence tourists' behavior intention, which will influence the public to visit the destination, reflected in the ability to pay and disseminate information to other parties.
Keywords: attitude, online satisfaction, destination image, tourists' behavior, intention, netizen
INTRODUCTION
The eminence of the internet and social media in the tourism industry is already quite well-known since growing internet users frequently travel around the globe.
Travelers use social media as the primary information source before taking a trip or vacation (Xiang & Gretzel, 2010). The internet and social media have also reformed the fundamental way of sharing tourism-related information and how travelers arrange a trip and consume their travel experience (Buhalis & Law, 2008). Using the internet, social media platform has enabled users to know other travelers’ knowledge, emotion, and experience in particular destinations (Buhalis & Law, 2008; Kr.Jens Steen & Munar, 2012; Volo, 2010). These developments reshape how destinations communicate and promote their offers using social media to attract more visitors. Therefore, as an information-intense industry, it is demanding to understand changes that impact traveler behavior in the tourism sector.
Related to competitiveness in the travel industry, communication through digital channels is more widely used to invite more visitors (Uşaklı et al., 2017). Thus, as the main two-way communication tool on the internet, social media plays a vital role in marketing (Kaplan & Haenlein, 2010). Nowadays, more than half of destination marketing organizations (DMOs) have budgeted their money on social media marketing (Barnes, 2015), and the Indonesian government has also adopted this practice.
Hays et al., (2013) studied social media usage among destination marketing organizations and revealed that the usage of social media among the ten most-visited countries in the world was still primarily experimental, and further research was needed. Consequently, social media platforms cannot be capitalized on to optimally change or form travelers’ behavior.
This study aims to investigate how halal tourism from the point of view of netizens. This study looks at the relationship between social media behavior, online satisfaction, and destination image, and identifies how these variables influence behavioral intentions towards destinations in the context of halal tourism in Indonesia.
Research about social media platforms has significantly increased in the tourism
industry (Ainin et al., 2020; Chan & Guillet, 2011; Gretzel et al., 2006; Uşaklı et al., 2017;
Xiang & Gretzel, 2010). However, tourism research on the use of social media, especially in halal tourism, remains limited. It is hoped that this research can capture the latest directives on social media to provide valuable insights for marketing halal tourism online.
RESEARCH METHOD The Selection of Social Media.
There are limited studies that targeted multiple social media platforms whereas the majority have only targeted Facebook and/or Twitter (Hays et al., 2013; Hsu, 2012;
Jabreel et al., 2017; Kwok & Yu, 2012; Mariani et al., 2016; Philander & Zhong, 2016;
Stankov et al., 2010). It is based on the consideration that those two social media is the most popular platform used by tourism organizations (Uşaklı et al., 2017). The decision to choose above mentioned social network is based on the consideration of the characteristics. Facebook provides the flexibility to post on balance between image and text, while Twitter focuses on text, and Instagram is mostly used to share photos and short videos.
Data Collection
The proposed model will be empirically tested by collecting the data from the social media users of Facebook, Instagram, and Twitter in Indonesia who have followed halal tourism social media accounts. The data will be collected by a questionnaire instrument which consists of multiple items of measurements adapted from previous related studies. Then, the questionnaire will be validated through a pre- test in order to ensure the validity of the items. A convenience sampling will be used to collect the data from the sample of respondents who have accounts on three selected social media and have been using it for 6 months or more when the data are being taken. A total of 223 questionnaires have been sent back and then processed.
The changes in collecting and sharing information have occurred since social media was devised Buhalis & Law, (2008); Xiang & Gretzel, (2010), affecting how people choose trip destinations and the means destination organization manage their
activities (Uşaklı et al., 2017). Tourism products like vacation trips are classified as high involvement because they involve high-risk purchases (L. H. Kim et al., 2009). Because of the disability to evaluate prior experience (Schmallegger & Carson, 2008), most travelers rely on social media to obtain destination-related information (Amaro et al., 2016; Hudson & Thal, 2013; Zeng & Gerritsen, 2014). Potential destinations, transportation options, and accommodations are standard searches before the pre-trip phase (Cox et al., 2009). In this phase, travelers tend to surf the internet passively rather than post on social media. Therefore, a new kind of alternative information source, such as social media, is needed to reduce wrong decision-making risks for travelers (Leung et al., 2013).
Supported by the internet's advancement, social media users are increasing rapidly. Active social media users are nearly 2.4 billion people, which jumps 10%
yearly (Kemp, 2016). Social media is a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0 and allow the creation and exchange of User Generated Content” (Kaplan & Haenlein, 2010). The significant difference between social and traditional media is that it alters travelers’ behavior (Chung & Han, 2017).
Attitudes toward social media and online satisfaction
Chen et al., (2002) defined the attitude toward a website as an internet user’s state of behaving like or disliking the site's content in non-artificial conditions. Regarding social media, it could be defined that attitude toward social media is the mental state to react favorably or unfavorably to social media content in realistic situations. The research about attitude suggests that the desirable or undesirable result is correlated with perceived performance (Ajitha & Sivakumar, 2017). Satisfaction generally means affective conditions resulting from an overall evaluation of product or service exposure, creating consumer relationships, and reflecting positive consumer attitudes (Anderson & Sullivan, 1993). Digital design (on the website or on social media) has been found to correlate with internet users’ satisfaction degree as a consumer (Lohse
& Spiller, 1999). Shiu et al., (2015) found that utility such as security and convenience, affect satisfaction.
H1: Attitude toward social media has a direct positive relationship with online satisfaction.
Attitudes toward social media and destination image
Tourism destinations nowadays significantly use the internet to communicate with tourists and rely on their destination image on it to attract travelers (Kladou &
Mavragani, 2015; Nicoletta & Servidio, 2012). Several studies have researched the attitude in a brand digital channel such as a website. Karson & Fisher, (2005) in their research found that there is a positive correlation between the attitude toward the brand website and their perception of the brand image. When the users open the website of a particular brand, they tend to evaluate the favourableness of the website and this evaluation will result in their general perception of the quality of the product or services (Alcántara-Pilar et al., 2018). Some studies proved there is a positive relationship between attitude toward the site and the brand image of a given product or service (Hao et al., 2015). In this study, social media is used because it could create interactive two-way communication which is hardly accomplished by the website.
Hence, the following hypothesis will be tested;
H2: Attitude toward social media has a direct positive relationship with the destination image
Online satisfaction and destination image
There are many attempts to define destination image, but these are the most acceptable definition: ‘destination image is the set of associations that people have of the destination Day et al., (2012); ‘the perceptions of individual destination attributes and the holistic impression made by the destination’ Echtner, C. M., (1991); ‘an individual's mental representation of knowledge (beliefs), feelings, and global impressions about an object or destination’ (Baloglu & McCleary, 1999). Therefore, researchers mostly agree that cognitive, affective, and total image are the destination image component. Although those components are well accepted, most destination image studies focus on the cognitive aspect (Zhang et al., 2018).
Satisfaction plays a vital role in consumer loyalty and affected attitudes (Altunel
& Erkurt, 2015; Fauzi & Putra, 2020). The more satisfied customers with the brand, the
more positive their attitudes toward it (Castañeda et al., 2009). There is a vast amount of literature on the satisfaction that found users’ satisfaction while exploring the website fosters the usage continuity intention and positively affects user perception toward the product and services promoted on the website (Alcántara-Pilar et al., 2013;
Bai et al., 2008; Ramos et al., 2016). This study applies online satisfaction while exploring destination social media and propose that better satisfaction on the destination social media would bring a positive image of the destination.
H3: Online satisfaction has a direct positive relationship with the destination image
Destination image and tourists’ behavioral intentions
The study about tourism which researches the relationship between destination image or mental destination representations on tourist behavior frequently focuses on the willingness-to-visit (WTV) a destination (Tigre Moura et al., 2015) and how it encourages the creation of word-of-mouth recommendations (Simpson & Siguaw, 2008). (Kock et al., 2016) in their research in Denmark have discovered that destination image has a significant effect on willingness to visit, word of mouth, and willingness to pay. Thus, it is therefore hypothesized;
H4a: Destination image has a direct positive relationship with willingness-to-pay (WTP)
H4b: Destination image has a direct positive relationship with word-of-mouth (WOM) H4c: Destination image has a direct positive relationship with willingness-to-visit (WTV)
Attitude toward social media and tourists’ behavioral intention
The theory of planned behavior and the theory of reasoned action have been used as the main reference to study how attitudes affect behavioral intention (Kock et al., 2016). Those research has underlined that attitudes and behavior have a significant correlation or a direct effect. Previous studies support that attitude positively relates to behavior intention. Di Pietro et al., (2012) found that the continued usage of social media is affected by the attitude toward destination social media, and leads to the behavioral intention for the choice of destination travel.
Willingness-to-pay (WTP) reflects the behavioral intentions in tourism. It represents the value of a product or service compared to the price acceptable for the consumers and they tend to purchase the product or service (Krishna, 1991). In particular situations, WTP could be interpreted as the willingness of consumers to pay a premium price for products or services (Shin et al., 2017) Research revealed that attitudes are positively related to customers’ willingness to pay (Ojea & Loureiro, 2007;
Shin et al., 2017).
Sharing the information with others can be triggered because of the attractiveness of messages, since talking about promotional messages or ads could be satisfying, especially when the contents are unusual and eye-catching (Antonides , Raaij, W. Fred van., 1998). In line with that Kursan Milaković & Mihić, (2015) found that a positive attitude toward advertising increases the possibility of consumers’ word-of-mouth creation.
Willingness to visit in correlation with attitude is hardly found in tourism literature. Some research from another field has attempted to study action intention such as purchase with respect to attitudes. Behavioral intention, particularly purchase intention, has been studied by (Briliana & Mursito, 2017) and found that attitudes toward halal products have a positive effect on purchase intention. Attitudes towards brands positively relate to purchase intentions (Liu et al., 2017). Hence, this research purposes hypotheses;
H5a: Attitude toward social media has a direct positive relationship with willingness to pay
H5b: Attitude toward social media has a direct positive relationship with the word of mouth
H5c: Attitude toward social media has a direct positive relationship with willingness to visit
Figure 1. Model of research
Figure 1. Model of research
RESULTS & DISCUSSION
In this study, hypothesis testing uses the Partial Least Square (PLS) analysis technique with the smartPLS 3.0 program. The following is a schematic of the PLS program model tested:
Figure 2. Outer Model before dropping indicators Source: the processed research data
According to Roldán & Sánchez-Franco, (2012) evaluation of the outer- reflection model is carried out based on 4 (four) criteria, namely convergent validity, discriminatory validity, Average Variance Extracted (AVE), and composite reliability (Table 1).
Table 1. Criteria and Standardization in Outer Model Evaluation – Reflection
Criteria Standard Information
Coverage validity (Reliability indicator)
Loading value > 0.50 Assessing the power of indicators in reflecting latent variables
Chin (1998) states that if < 0.50 then the indicator must be dropped
Discriminant validity
Cross loading value
the correlation of the indicator to the latent variable is greater than the other latent variables
Measuring the accuracy of the reflection model
Composite reliability (ρc)
ρc > 0,6 stability and internal consistency of good indicators
Source: (Roldán & Sánchez-Franco, 2012) Convergent Validity
To test convergent validity, the outer loading or loading factor values are used.
An indicator is declared to meet convergent validity in a good category if the outer loading value is > 0.5. The following is the outer loading value of each indicator on the research variable:
Table 2. Outer Loading
Construct Indicator Outer Loading
Attitude Toward Social Media variable
AT 1 0.817
AT 2 0.852
AT 3 0.854
Construct Indicator Outer Loading
AT 4 -0.561*
Online Satisfaction variable OS 1 0.851
OS 2 0.883
OS 3 -0.427*
OS 4 0.841
Destination Image variable DI 1 0.875
DI 2 0.912
DI 3 0.936
DI 4 0.926
Willing to Pay variable WP 1 0.908
WP 2 0.925
WP 3 0.901
WP 4 0.939
Willing to Visit variable WV 1 0.872
WV 2 0.893
WV 3 0.865
WV 4 0.875
Word of Mouth variable WM 1 0.893
WM 2 0.911
WM 3 0.855
WM 4 0.868
* invalid indicator
Source: the processed research data
According to Chin as quoted by Imam Ghozali, the outer loading value between 0.5 - 0.6 is considered sufficient to meet the convergent validity requirements. The data above shows that there are several variable indicators whose outer loading values are below 0.5 so these indicators are declared inappropriate or invalid for research use.
Indicators with an Outer loading below 0.5 should be dropped before carrying out further analysis. Then the outer model after dropping several indicators, as follows:
Figure 3. Outer Model after Dropping Several Invalid Indicators
Discriminant Validity
Discriminant validity testing is carried out to prove whether the indicator in a construct will have the largest loading factor in the construct it forms than the loading factor with other constructs. The discriminant validity test uses the cross-loading value. An indicator is declared to meet discriminant validity if the value of the cross- loading indicator on the variable is the largest compared to other variables. The following is the cross-loading value of each indicator:
Table 3. Cross Loading Indi
cator
Attitude Toward Social Media (AT)
Destinati on Image (DI)
Online Satisfaction (OS)
Willingness to Visit (WT)
Willingness to Pay (WP)
Word of Mouth (WM)
AT 1 0.833 0.413 0.460 0.505 0.307 0.442
AT 2 0.872 0.530 0.622 0.552 0.419 0.569
AT 3 0.861 0.507 0.540 0.532 0.435 0.493
DI 1 0.497 0.875 0.491 0.349 0.366 0.372
DI 2 0.518 0.912 0.466 0.415 0.403 0.440
DI 3 0.540 0.936 0.487 0.427 0.447 0.497
DI 4 0.522 0.926 0.498 0.453 0.433 0.484
OS 1 0.547 0.404 0.876 0.498 0.504 0.620
OS 2 0.539 0.464 0.901 0.489 0.444 0.594
OS 4 0.586 0.519 0.850 0.478 0.468 0.552
WM 1 0.579 0.498 0.622 0.686 0.620 0.894
WM 2 0.557 0.461 0.619 0.602 0.585 0.911
WM 3 0.449 0.359 0.574 0.556 0.608 0.854
WM 4 0.481 0.405 0.544 0.621 0.685 0.867
WP 1 0.429 0.396 0.496 0.511 0.908 0.630
WP 2 0.396 0.429 0.478 0.501 0.925 0.613
WP 3 0.427 0.416 0.499 0.572 0.901 0.690
WP 4 0.427 0.423 0.506 0.473 0.939 0.657
WV 1 0.542 0.365 0.470 0.872 0.465 0.583
WV 2 0.607 0.446 0.502 0.893 0.552 0.655
WV 3 0.518 0.402 0.541 0.865 0.509 0.652
WV 4 0.496 0.366 0.439 0.876 0.425 0.563
Source: the processed research data
Based on the data presented in table 3, it can be seen that each indicator in the research variable has the largest cross-loading value on the variable it forms compared to the cross-loading value on other variables. Based on the results obtained, it can be stated that the indicators used in this study have good discriminant validity in compiling their respective variables.
Composite Reliability
Composite Reliability is an index that shows the extent to which a measuring instrument can be trusted to be relied on. Data that has composite reliability > 0.6 has high reliability. The following is the composite reliability value of each variable used in this study:
Table 4. Composite Reliability
Construct Composit
Reliability
Attitude Toward Social media 0.891
Destination Image 0.952
Online Satisfaction 0.908
Willingness to Visit 0.930
Willingness to Pay 0.956
Word of Mouth 0.933
Source: the processed research data
Based on the data presented in table 4, it can be seen that the composite reliability value of all research variables is > 0.6. These results indicate that each variable has met composite reliability so it can be concluded that all variables have a high level of reliability.
Inner Model evaluation
Assessing the inner model is evaluating the effect between latent variables and testing the hypothesis. The structural model was evaluated using R-square for endogenous variables and comparing count with t-table (table at 95% confidence level was 1.96).
Path Coefficient Test
Path coefficient evaluation is used to show how strong the effect or influence of the independent variable is on the dependent variable. While coefficient determination (R-Square) is used to measure how much the endogenous variable is influenced by other variables. Chin said the results of R2 of 0.67 and above for endogenous latent variables in the structural model indicated that the effect of exogenous variables (influenced) on endogenous variables (influenced) was in a good category. Meanwhile, if the result is 0.33 – 0.67 then it is included in the medium category, and if the result is 0.19 – 0.33 then it is included in the weak category.
Figure 4. Inner Model
Source: the processed research data
Based on the inner model above, it shows that the largest path coefficient value is indicated by the influence of Attitude Towards on Online Satisfaction of 12,011, Then the second largest influence is the influence of Attitude Towards on willingness to visit of 8,291. Furthermore, the third influence is the influence of Attitude Towards word of mouth 7,116. The smallest effect of destination image on willingness to visit is 2,386.
Based on the description of these results, shows that all variables in this model have a path coefficient with a positive number. This shows that the greater the path coefficient value on one independent variable on the dependent variable, the stronger the influence between the independent variables on the dependent variable.
Goodness of Fit
Based on data processing that has been carried out using the smartPLS 3.0 program, the R-Square values are obtained as follows:
Table 5. R-square Variabel Endogen
R Square
Adjusted R Square
Destination Image 0.372 0.366
Online Satisfaction 0.408 0.405
Willingness to Visit 0.399 0.394
Willingness to Pay 0.264 0.257
Word of Mouth 0.386 0.380
Source: the processed research data
Based on the data presented in the table above, it can be seen that the R-Square value for the Destination Image variable is 0.372. Obtaining this value explains that the large percentage of Destination Image can be explained by the 2 exogenous variables of 37.2%. The remaining 62.8% is influenced by other factors. The R-Square value for the Online Satisfaction variable is 0.408. Obtaining this value explains that the percentage of Online Satisfaction is explained by 1 exogenous variable of 40.8%. The remaining 59.92% is influenced by other factors. The R-Square value for the Willingness to Visit variable is 0.399. Obtaining this value explains that the percentage of Willingness to Visit is explained by 2 exogenous variables of 39.9%. The remaining 60.1% is influenced by other factors. The R-Square value for the Willingness to Pay variable is 0.264. Obtaining this value explains that the percentage of Willingness to Pay is explained by 2 exogenous variables of 26.4%. The remaining 73.6% is influenced by other factors. The R-Square value for the Word of Mouth variable is 0.386. Obtaining this value explains that the percentage of the size of the Word of Mouth is explained by 2 exogenous variables of 38.6%. The remaining 61.4% is influenced by other factors.
R-Square results are 0.67, 0.33, and 0.19 for endogenous latent constructs in the structural model, each indicating that the model is "good", "moderate", and "weak".
Based on the theory and the R-Square value on the latent construct, it can be stated that this research model has moderate goodness of fit.
Hypothesis Test
Based on the data processing that has been done, the results can be used to answer the hypothesis in this study. Hypothesis testing in this study was carried out
by looking at the T-Statistics value and the P-Values value. The research hypothesis can be declared accepted if the P-Values <0.05. Based on the data presented in the table below, it can be seen that each hypothesis proposed in the question it is accepted because it has a P-Values value of <0.05.
The following table shows the results of T-Statistics and P-Values values:
Table 6. T-Statistics and P-Values values Hypot
hesis Effect T
Statistik P Values
Result H1 Attitude Toward Social Media -> Online
Satisfaction
12.011 0.000 Accepted H2 Attitude Toward Social Media ->
Destination Image
5.180 0.000 Accepted H3 Online satisfaction -> Destination Image 4.257 0.000 Accepted H4a Destination Image -> Willingness to Pay 3.862 0.000 Accepted H4b Destination Image -> Word of Mouth 4.091 0.000 Accepted H4c Destination Image -> Willingness to Visit 2.386 0.017 Accepted H5a Attitude Toward Social Media ->
Willingness to Pay
4.215 0.003 Accepted H5b Attitude Toward Social Media -> Word of
Mouth
7.116 0.000 Accepted H5c Attitude Toward Social Media ->
Willingness to Visit
8.291 0.000 Accepted Source: the processed research data
The results of this study show that attitude towards social media has a positive significant effect on online satisfaction. This research is in line with Ajitha &
Sivakumar,(2017); Chen et al., (2002); Flavián et al., (2006). Social media users, who are the majority of the millennial generation, tend to seek information using social media.
Attitude towards social media also has a positive significant effect on destination image. In line with the research of Alcántara-Pilar et al., (2018); Hao et al., (2015);
Karson & Fisher, (2005). Social media users who will become visitors to halal tourist destinations, get a lot of information from reviews of previous visitors. When social users open information related to halal tourism on the internet, they tend to evaluate the advantages that exist in the information and this evaluation will result in their general perception of the quality of the product or service.
Online satisfaction has a positive significant effect on destination image. In accordance with research Alcántara-Pilar et al., (2018). This study found online satisfaction while exploring a destination on social media and propose that better satisfaction with the destination on social media would bring a more positive image of the destination. Satisfaction while exploring the website foster the usage continuity intention and positively affect user perception toward the product and services promoted in the website.
Destination image has a significant positive effect on WOM which is in line with (Gosal et al., 2020; Jalilvand & Samiei, 2012; Shafiee et al., 2016). The results of this study indicate that Destination Image has a significant positive effect on WTP (3.86), WOM (4.091 and WTV (2.386). This study is in line with (Kock et al., 2016). Similar to the research (Agapito et al., 2013; Gamon & Malee, 2022) destination image can directly affect tourist behavior. Destination image plays an important role in shaping the tourist experience in the decision-making process of choosing a destination (a priori), including in the revisiting process, spreading word of mouth, and recommending destinations to friends and family.
Attitude toward social media has a positive and significant effect on WTP, in line with (Briliana & Mursito, 2017; Ojea & Loureiro, 2007). Attitude also has a positive and significant effect on WTV (according to (Liu et al., 2017), further attitude toward social media has a positive and significant effect on WOM (S. Kim et al., 2016). A willingness to pay a higher price for travel to one destination more than another may indicate a greater commitment than a desire to visit or recommend.
CONCLUSION
Attitudes toward social media have a significant influence on both online satisfaction and destination image. Online satisfaction also has a significant effect on destination image. Destination image directly and positively influences tourists' behavioral intention which includes WTP, WoM, and WTV. Attitude toward social media also plays an important role in determining willingness to pay and willingness
to visit. The usage of social media is affected by the attitude toward social media and leads to the behavioral intention for the choice of destination travel.
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