Revisit Intention and Satisfaction: The Role of Electronic Word of Mouth and Perceived Risk

Revisit Intention and Satisfaction: The Role of Electronic Word of Mouth and Perceived Risk

Nadya Rahmadianti¹, Abdul Wahib Muhaimin², Silvana Maulidah², Fitria Dina Riana², Rosihan Asmara²

¹Postgraduate of Agribusiness, Universitas Brawijaya, Malang, Indonesia

²Department Socio Economics, Universitas Brawijaya, Malang, Indonesia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.801103

Received: 29 December 2023; Accepted: 03 January 2024; Published: 08 February 2024

ABSTRACT

What potential tourists do these days before going on a tourist destiation is to consider several factors such as electronic word of mouth about the place they are going to visit. Electronic word of mouth (eWOM) is the exchange of consumer information about a tourist destination via the internet. This research also examines the perceived risk felt by potential tourists before visiting a tourist destination. The aim of this research is to analyze the influence of electronic word of mouth and perceived risk on satisfaction, as well as the influence of satisfaction on the intention to revisit tourist destinations. This research uses a non-probability sampling method with judgmental sampling technique with a sample size of 100 respondents. The data analysis technique in this research uses SEM PLS (Partial Least Square) with Warp PLS 8.0 software. The research results show that: (1) Electronic word of mouth has a positive and significant effect on satisfaction of 0.63; (2) Perceived risk has a negative and significant effect on tourist satisfaction of -0.22; (3) Electronic word of mouth has a positive and significant effect on revisit intention of 0.32; (4) Perceived risk has no significant effect on revisit intention of 0.11; (5) Tourist satisfaction has a positive and significant effect on tourists’ intention to revisit by 0.50.

Keywords: eWOM, perceived risk, satisfaction, revisit intention, SEM-PLS

INTRODUCTION

The tourism sector is one of the main drivers of the economy because it is able to provide several benefits such as generating foreign exchange for the country, expanding employment opportunities, and introducing the country’s culture (Sabon, et al., 2018). Indonesia has cultural diversity and abundant tourism potential. Good management of tourism potential will benefit many parties. Tourism has the main elements of the services provided to tourists. This includes attractions, amenities and accessibility, which are the elements that stand out the most and pay attention when visiting a tourist destination. This aspect needs to exist to attract tourists to visit Indonesia.

Bromo Tengger Semeru National Park is one of the tourist attractions in East Java which is quite popular and often visited because it has many attractions that tourists can enjoy, such as Mount Bromo, Bromo Sand Sea, Savana, sunrise on

Mount Penanjakan, Mount Semeru, Madakaripura Water Falls, Lake Ranu Kumbolo, Lake Ranu Regulo, and Lake Ranu Pane. Mount Bromo tourism is one of the most important and most iconic parts of the Bromo Tengger Semeru National Park. Mount Bromo is a volcano that is still active and is a popular tourist attraction worldwide. Mount Bromo is famous for its beautiful sunrise or sunrise views. Apart from natural charm, there are also cultural attractions such as the Kasada Ceremony or “Eksotika Bromo”. Mount Bromo itself is located in four districts, namely Malang Regency, Pasuruan Regency, Probolinggo Regency and Lumajang Regency. The potential of Bromo Tengger Semeru National Park can have a big positive impact on all parties involved if it can be managed well.

In today’s digital era, everyone uses the internet to access various things. This is no exception in the tourism sector where the public will be influenced by the results of reviews given by tourists who have visited the place via Electronic Word of Mouth (eWOM). By utilizing eWOM, people can find out reviews, photos, locations and prices of travel packages for tourist attractions. Providing reviews via eWOM has a wider impact because it can be seen easily by anyone from various different places. Zarrad & Debabi (2015) stated that potential tourists tend to look for external information such as eWOM and blogs for references to tourist destinations they want to visit.

Tourists use travel information during the planning phase of their trip to form expectations and perceptions about travel in general which then influence their intention/decision to travel (Beerli and Martin 2004). This is especially the case with information shared by fellow travelers via Internet-based electronic media (eWOM) platforms. This has been identified in recent years as an important influence on satisfaction and intention to visit a particular destination (Doosti et al., 2016; Jalilvand et al., 2012).

Tourists’ perceptions of risk and safety are one of the factors in the decision-making process to travel to a destination (Gut & Jarrell, 2007). Previous studies reveal that tourists’ risk perceptions have a significant impact on their behavioral intentions (An, Lee, & Noh, 2010; Artuger, 2015; Cetinsoz & Ege, 2013). Generally tourists try to avoid traveling to a destination if they consider it risky (Cetinsoz & Ege, 2013; Chew & Jahari, 2014), while many of them take risks as part of the excitement of their trip (Imboden, 2012; Lepp & Gibson, 2003). The aim of this research is to analyze the influence of electronic word of mouth and perceived risk on satisfaction, the influence of electronic word of mouth and perceived risk on revisit intention and the influence of satisfaction on revisit intention.

METHODOLOGY

The approach used in this research is quantitative. This research was conducted in Bromo Tengger Semeru National Park in October 2023. The method used to determine tourist respondents was judgmental sampling. The number of respondents was calculated using the Lemeshow formula and the number of respondents was 100 people. The data collection technique in this research uses a questionnaire distributed online. Validity and reliability tests were carried out before the questionnaire was distributed to respondents. The data analysis method used is the SEM-PLS method with Warp PLS 8.0 software. SEM-PLS method steps: (1) designing the inner model); (2) designing the outer model; (3) create a path diagram; (4) conversion of path diagrams to systems of equations; (5) Evaluation of Goodness of Fit; (6) hypothesis analysis.

FINDINGS AND DISCUSSION

1. Respondent Characteristics

The results of the characteristics of 100 respondents show that tourists at Bromo Tengger Semeru National Park are dominated by female tourists with a total of 66 people, the age range is 19-24 years with a total of 60 people and the majority come from East Java. Based on this age range, the majority of tourists have an educational background of at least high school. Tourists are dominated by students and private employees and have an average income of IDR. 2,500,000 – Rp. 5,000,000,-

2. Outer Model Evaluation

a. Covergent Validity Test

The convergent validity test is the first stage to determine whether or not the variables used in the research are valid. Convergent validity can be seen through the correlation coefficient value between the reflective indicator scores and the latent variable scores. This can be seen in factor analysis in the form of factor loading values. This evaluation can be seen by checking the loading factor value above 0.7 and if the value is below 0.4 then the indicator must be removed and the AVE criteria is ≥ 0.50 (Hair et al, 2017). It is known that all factor loading values are ≥ 0.70. These results show that all of them meet the standard requirements for factor loading values, so that the indicators are declared valid. Based on the AVE value, all reflective constructs are able to explain more than half of the variance indicators because the AVE value in the research is greater than the determination (≥ 0.50), so it meets the requirements for convergent validity.

b. Discriminant Validity Test

Discriminant validity is used as the extent to which the value of a construct is different compared to the value of another construct as an empirical standard (Hair et al., 2017). The discriminant validity approach is divided into 2, namely discriminant validity for each indicator and discriminant validity for the questionnaire or all indicators. The discriminant validity of each indicator can be seen from the loading and cross loading values. If the loading value of each indicator on that variable is greater than the cross loading on other latent variables, it is said to meet discriminant validity. Discriminant validity in the study was met in the first order condition and second order condition. The loading value > cross loading and the AVE root value are greater than the correlation coefficient in the same line between the construct and other constructs, so the questionnaire is valid

c. Reliability Test

Reliability is a measure of a questionnaire’s ability to measure variables consistently. Questionnaire reliability analysis can use two methods, namely measured by composite reliability and Cronbach’s Alpha results. For the results of the first order and second order conditions, it is known that the composite reliability value is ≥ 0.7 and the Cronbach’s Alpha α value is ≥ 0.60. These results indicate that the questionnaire in this study meets composite reliability and is reliable as a research instrument.

Table 1. Composite Reliability Value and Cronbach’s Alpha

3. Inner Model Evaluation

a. Path Coefficient Evaluation

Model testing using path coefficients shows the direction of the relationship between electronic word of mouth, perceived risk, satisfaction, and revisit intention of Bromo Tengger Semeru National Park tourists.

Figure 1. Path Coefficient Value in Structural Model

b. R-Squared Evaluation

R-Squared or coefficient of determination is used to measure the percentage of endogenous constructs that can be explained by exogenous constructs (predictors). According to Hair (20017), the higher the R-Squared value, the better the model displayed.

Table 2. R-squared

Based on table 2, the R-squares value for satisfaction is 0.62. This shows that the contribution of electronic word of mouth and perceived risk to satisfaction is 62% and the remaining 38% is influenced by other variables outside the model. Meanwhile, the R-squares value for revisit intention is 0.49, which means that the contribution of satisfaction has an influence of 49% and the remaining 51% is influenced by other variables outside the model.

c. Goodness of Fit (GoF) Evaluation

Goodness of Fit (GoF) is useful for validating structural models. GoF evaluation is also used to measure the quality of a model with 10 model fit measures. The ten standard values of Goodness of Fit (GoF) are presented in table 3 below.

Table 3. Goodness of Fit (GoF) Value

Based on the fulfillment of the 10 GoF index measures described previously, it can be concluded that the research model as a whole is feasible and has good model fit.

4. The Result

The results of data analysis show that the R-squares calculation value for satisfaction is 0.62. This shows that the contribution of electronic word of mouth and perceived risk to satisfaction is 62% and the remaining 38% is influenced by other variables outside the model. Meanwhile, the R-squares value for revisit intention is 0.49, which means that the contribution of satisfaction has an influence of 49% and the remaining 51% is influenced by other variables outside the model.

Hypothesis testing is carried out to determine the relationship between one variable and other variables that have previously been proposed in the existing hypothesis. Hypothesis testing in analysis using WarpPLS is carried out using the t test rule, which in this study uses the condition that if the p-value ≤ 0.05 with an alpha of 5% then the hypothesis can be accepted. Following are the results of hypothesis testing.

Table 4. Hypothesis Testing Results

The results of testing hypothesis 1 state that the electronic word of mouth variable has a positive and significant influence on the satisfaction variable. This can be seen from the path coefficient value of 0.63 and is positive with a p-value of <0.001, which means it is smaller than 0.05 and shows significance. Thus, electronic word of mouth has a positive and significant influence on tourist satisfaction. The electronic word of mouth variable has 5 indicators, namely discussing through reviews of other tourists (EWM.1), reading reviews to ensure the right choice of tourist destination (EWM.2), collecting information from reviews of other tourists (EWM.3), feeling worried if not reading reviews before traveling (EWM.4), and feeling confident when traveling if you have read reviews (EWM.5). According to Castaneda et al., (2007), the more satisfied tourists are with accessing information via the internet, the higher their satisfaction with the tourist destinations they visit. This is because electronic word of mouth provides more information that tourists need, allowing them to plan their trips well to meet their needs and expectations.

The results of testing hypothesis 2 state that perceived risk has a negative and significant effect on satisfaction. This is proven based on the path coefficient value of -0.22 and has a negative sign with a p-value of 0.01, which means it is less than 0.05 and shows significance. Thus, perceived risk has a negative and significant influence on tourist satisfaction. This perceived risk variable is reviewed based on: (1) physical risk (PR.1), health risk (PR.2), financial risk (PR.3), and facility risk (PR.4). Previous research has proven a significant negative relationship between perceived risk and satisfaction (Sohn et al., 2016). Likewise, high perceived risk reduces tourist satisfaction and also has a negative impact on customers’ repurchase intentions (Wirtz & Mattila, 2001).

The results of testing hypothesis 3 state that the electronic word of mouth variable has a positive and significant influence on the revisit intention variable. This can be seen from the path coefficient value of 0.32 and is positive with a p-value of <0.001, which means it is smaller than 0.05 and shows significance. Thus, electronic word of mouth has a positive and significant influence on revisit intention. This is in line with previous research which considers eWOM as an important source of information that influences tourists’ travel intentions and destination choices (Ying & Chung, 2007; Jalilvand & Samiei, 2012b).

The results of testing hypothesis 4 state that the perceived risk variable does not have a significant influence on the revisit intention variable. This can be seen from the path coefficient value of 0.11 and is positive with a p-value of <0.14, which means it is greater than 0.05 and indicates not significant. The findings in this research show that not all tourists consider perceived risk in determining tourist destinations. Some of them actually want to revisit tourist destinations that have recently experienced fire disasters just to find out the latest conditions and show them on social media. However, according to Kozak et al. (2007) tourists who consider tourist destinations too risky tend to avoid these destinations in their future travel plans. Once tourists encounter problems during their tourist trip, they will immediately develop a perception of risk related to the tourist destination. In the end, dissatisfaction will emerge (Rindrasih, 2018) and influence the level of intention to return to visit to decrease.

The results of testing hypothesis 5 state that the satisfaction variable has a positive and significant influence on the revisit intention variable. This can be seen from the path coefficient value of 0.50 and is positive with a p-value of <0.001, which means it is greater than 0.05 and shows significance. Thus, the satisfaction variable has a positive and significant influence on revisit intention. Satisfaction has 4 indicators, including enjoying the travel experience (SAT.1), worth of time and money (SAT.2), fulfillment of expectations (SAT.3), and overall satisfaction (SAT.4). Meanwhile, the revisit intention variable is viewed from 3 indicators, including the chosen destination being better than other places, willingness to visit again in the near future, and this destination being the most preferred choice of destination. (Lin, 2014) believes that tourist satisfaction will influence tourists’ intentions to make return visits to tourist destinations. Building a high level of tourist satisfaction with a tourist destination is important for creating positive behavioral intentions in the future, thereby increasing and maintaining the competitiveness of the destination (Ramseook et al., 2015). Therefore, tourist destination managers must plan marketing strategies and service delivery that are more efficient and effective to meet the expectations and needs of tourists with the aim of improving the tourist travel experience.

CONCLUSIONS

Based on the research that has been conducted, it was found that (1) electronic word of mouth has a positive and significant effect on tourist satisfaction. This is because electronic word of mouth provides more information that tourists need, thus enabling them to plan their trips well to meet their needs and expectations. (2) perceived risk has a negative and significant effect on satisfaction. Tourists who consider tourist destinations too risky tend to avoid these places in their future travel plans. Once tourists encounter problems during their tourist trip, they will immediately

create a perception of risk related to the tourist destination which will ultimately result in dissatisfaction. (3) electronic word of mouth has a positive and significant effect on revisit intention. Prospective tourists consider eWOM as an important source of information that influences tourists’ travel intentions and destination choices. (4) Perceived risk has no significant effect on revisit intention. The findings in this research show that not all tourists consider perceived risk in determining tourist destinations. Some of them actually want to revisit tourist destinations that have recently experienced fire disasters just to find out the latest conditions and show them on social media. (5) Satisfaction has a positive and significant effect on revisit intention. Building a high level of tourist satisfaction with tourist destinations is important for creating positive behavioral intentions in the future, so as to increase and maintain the competitiveness of tourist destinations.

LIMITATIONS

This research collects data from local tourists only, excluding international tourists. Therefore, it is necessary to carry out a comparative analysis of the revisit intention of local and international tourists.

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