Integrative Model of the Implementation of e-WOM, Destination Image and Intention to Behave
Article in Journal of Environmental Management and Tourism · October 2018
DOI: 10.14505/jemt.v9.4(28).18
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Integrative Model of the Implementation of e-WOM, Destination Image and Intention to Behave
Naili FARIDA Diponegoro University, Indonesia
Suggested Citation:
Farida, N. (2018). Integrative Model of the Implementation of e-WOM, Destination Image and Intention to Behave. Journal of Environmental Management and Tourism, (Volume IX, Summer), 4(28): 841-850. DOI:10.14505/jemt.v9.4(28).18
Article’s History:
Received April 2018; Revised May 2018; Accepted June 2018.
2018. ASERS Publishing. All rights reserved.
Abstract
This study aims to examine: the influence of e-WOM and perceptions of quality on customer satisfaction and the effect of customer satisfaction and the image of the destinations on the intention to behave. The sample was 150 tourists visiting Sidomukti water spring in Bandungan Semarang Central Java Indonesia. Analysis of this study uses Structural Equation Modeling and PLS software to process the data. The results showed that: (1) e-WOM and perceptions of quality have a positive and significant influence on customer satisfaction, (2) Customer satisfaction has a positive and significant impact on the intention to behave, and (3) image of the destination has a positive and significant impact on the intention to behave.
Keywords: E-WOM; perception of quality; customer satisfaction; image of the destination; and intention to behave JelClassification: C52; C55; L86; Z32.
Introduction
Data from the Department of Culture and Tourism in Central Java showed that there is a decline in the number of tourist visitors in 2013, as many as 27 million domestic tourists from 29 million in the previous year. One of the factors that caused the decline in the number of domestic tourists is the decline of tourism activity from 294 to 200.
On the other hand, there is an increase in the number of foreign tourists visit, from 372,000 visitors in 2012 to 372,000 visitors in 2013.This trend continued to 2014 when the number of foreign tourist visit reached 420,000.
The focus of tourism development is directed at the development of Karimun Jawa Island; tourist villages and MICE (Meeting, Incentive, Conference and Exhibition) potential, which include the cities of Magelang, Solo, and Purwokerto to enhance the creative economy in Central Java. Central Java is one of the 222 National Tourism Development Zones in the 33 provinces in Indonesia with a total of 50 Indonesian National Tourism Destination.
There are 3 National Tourism Destinations which become a leading national tourism development zones, namely, national tourism destination of Pangadaran-Nusa kambangan and the surroundings; Semarang Karimun jawa and the surrounding and Solo-Sangiran and the surrounding (Attachment of The Republic of Indonesia Minister Regulation Numbered 50 Year 2011 - About the National Tourism Development Master Plan Year 2015).
Indonesia has a tremendous natural wealth. One of them is the natural beauty of its natural wealth as the potential of tourism in Indonesia. Tourist destination is one of the potential to create foreign exchange. SWA Magazine in 2015 explained how the tourism sector accounted for foreign exchange amounted to more than 10 million US Dollars. Therefore, the management of tourist destinations needs to be considered carefully so as to produce a bigger profit (Farida, Naryoso and Ardyan 2017).
Customer satisfaction is very important for managers of tourist destinations (Meleddu, Paci and Pulina 2015). The relationship between customer satisfaction and the company's success has historically been a problem of trust, and satisfaction studies have also supported the case. Customer Satisfaction has always been considered
DOI: http://dx.doi.org/10.14505/jemt.9.4(28).18 DOI: https://doi.org/10.14505/jemt.v8.3(19).01
an important business goal because it is assumed that satisfied customers will buy more. However, many companies have started to see a high customer defection despite high satisfaction rating (Oliver 1999; Taylor 1998). This phenomenon has prompted a number of academics (e.g., Jones and Sasser 1995; Oliver 1999;
Reichheld 1996) to criticize that the ultimate goal is not how to create satisfaction, but creating customer loyalty (Chi and Qu 2008)
Various studies describe the importance of tourism destination loyalty (Alegre and Phou 2013; Chi and Qu 2008; Meleddu et al. 2015; Yuksel, Yuksel and Bilim 2010).
The goals of this research are to:
▪ Test the influence of e-WOM on customer’s satisfaction
▪ Test the influence of quality perception on customer’s satisfaction
▪ Test the influence of customer’s satisfaction on intention to behave Test the influence of destination image on intention to behave.
1. Literature Review 1.1. e-WOM
Today, consumers are using blog media, search engines, Internet community, social media, etc. to find information about the desired product (Yoo, Sanders and Moon 2013). Consumers are not just looking for information on the technical usability of a product, but also seeking testimony from people who are already using the product he would buy. The testimony concept developed in marketing science is often referred to as electronic word of mouth (e- WOM). Hennig-Thurau and Walsh (2003) defined e-WOM as a positive or negative statement made by the potential, actual, or former customers about a product or company for many people and institutions via the Internet.
The findings of Jalilv and et al. 2012 showed that e-WOM has a positive and significant impact on customer satisfaction. Shankar, Smith and Rangkaswamy (2003) finding showed that hotel customers in the context of online as well as offline environment showed that the level of satisfaction with online services is equal to that of offline ones, but the loyalty of online service providers is higher. Based on these descriptions, it is constructed the following hypotheses:
H1=There is influence of e WOM on satisfaction 1.2. Perceived Value
There are some definitions of value in the view of some experts. Value is the ratio between the total benefits to total costs (Kotler and Keller 2012). Zeithaml (1988) defines value as an overall assessment of the usefulness of consumer products based on the perception of what is acceptable and what is given. The finding of Ching and Sian (2010) explains that perceived value has a positive and significant impact on satisfaction, meaning that the higher the perceived value, the more satisfied the customers. Based on the description, it is constructed the following hypotheses:
H2: There is influence of perceived value on satisfaction 1.3. Customer’s Satisfaction
Customer satisfaction is one of the key factors to measure how successful an organization is (Reichheld 1996). To achieve true customer satisfaction, Fecikova (2004) argues that organizations must do several things, among others; consumer-oriented culture, consumer-concentrated organization, employee empowerment, process ownership, team building, and partnering with customers and suppliers. Satisfaction occurs when what is expected happens in reality (Kotler and Keller 2012). Satisfaction is also identical to the result of something that is not wrong, a sense of fun (Fecikova 2004), excitement, and satisfying the needs and desires of consumers.
To manage customer satisfaction, the company invested high cost to find an effective method to guarantee customer satisfaction. This is because customer satisfaction is helpful to improve the behavior of consumers (Fecikova 2004), for example repurchase, recommend or buy at a high price. Satisfaction affects the loyalty of tourists in visiting tourist destinations (Chi and Qu 2008).
Giese and Cote (2000) defines three main components of satisfaction1) customer satisfaction in the form of response (emotional or cognitive), 2) Response regarding particular focus of expectation or consumption, 3) Response occurs at a particular time (after consumption of a product or service), if the consumer is satisfied, there will be intention to behave to perform repeated consumption of the products or services. Intention to behave of religious tourism services is a desire to buy back or make a return visit.
Customer satisfaction will increase profits and market share of the company (Angelova 2011).
In the tourism industry, there is a variety of empirical evidence to explain that customer satisfaction is one of the drivers for the tourist intention to visit again and recommend the destination to others (Beeho and Prentice 1997; Juaneda 1996; Kozak 2001; Kozak and Rummington 2000).
1.4. Destination Image
The concept of the image is the subject of research across fields such as social studies, psychology, marketing or consumer behavior (Pestek and Cinjarevic 2014). It then develops, in the hospitality industry where the concept of destination image is used in various studies (Chen and Phou 2013; Cherifi, Smith, Maitland and Stevenson 2014;
Chi and Qu 2008; Chon 1992; Sirgy and Su 2000). Study on the destination image begins when Hunt (1975) developed the concept of the role of image in the development of tourist destinations.
Image of the destination is defined as the overall perception of the individual or the total of a collection of impressions on a site (Phelps 1986). Aaker (1996) or Kapferer (2008) believes that the brand image is the most powerful part of a brand that will be developed. Especially in the study of tourist destinations, the image of the destination is important in the decision-making process and Ankomah 1993). Tourist perception on the image of tourist destinations is a key driver to have the intention of selecting a tourist destination (Lee 2009; Phau, Shanka and Dhayan 2010). The more positive of the image of a tourist destination, the more tourists will choose them.
Image of the destination will also affect the intention to behave (Jalilvand and Samiei 2012; Ryu, Han and Kim 2005) or the intention to visit the tourist spot in the future and recommend the destination to others (Alcaniz, Garcia and Blas 2005; Bigne, Sanchez and Sanchez 2001; Court and Lupton 1997).
Based on the description, it can be concluded the following hypotheses:
- H4= there is influence of destination image on the intention to behave This is the empirical research model in this research:
Figure 1. Empirical Research Model
2. Measurement
No Variable Names Indicators Sources
1.
Electronic word of month (e- WOM) is a system for communicating product, information or services Online to consumers through the company website or organization.
▪ Frequently reading reviews about a trip on line to find out the destination frequently visited by the tourist.
▪ Make sure you select a correct destination
▪ You should always communicate or consult with other tourists to help chosen this memorable tourist destination.
Jalilvand et al. 2012
No Variable Names Indicators Sources
▪ Always make it a habit to seek information from a website or other tourists before we traveled to the destination
▪ If you did not read the travel guide, you will worry whether it is suitable and memorable place for you or not
▪ When we traveled to tourist attractions, a travel website will make a review of our destinations and we are confident to make that choice.
2.
Perceived Value is the value which is perceived based on the benefits of a product or service, and it is perceived by consumers
▪ The quality of eco-tourism which is perceived
▪ The experience of eco-tourism makes me bett
▪ The participation of tourism activities brings good impressions to others.
Chiu et al. 2014.
3.
Destination Image
The perception of tourist about the spot, tourism destination. Besides, active communication can influence the creation of image and management. Different messages are conveyed to tourists so that they know and understand.
▪ Secured
▪ Offering interesting places to visit
▪ Beautiful scenery and natural atmosphere
▪ Having a friendly climate
▪ Offering low cost for tourism
Jalilvand et al. 2012;
Oter and Ozdogan 2005
4.
Satisfaction
There are 3 main components of satisfaction
1) customer satisfaction in the form of response (emotional or cognitive),
2) Response regarding a particular focus of expectation or consumption
3) Response occurs at a particular time (after consumption of a product or service)
▪ The interpretation of the tour guide
▪ Happiness in the experience of eco-tourism
▪ The maintenance of ecological environment
▪ Flora and fauna diversity
Chiu et al. 2014
5.
Intention to Behave
Something which refers to the impact of the services rendered by the buyers of services to customer behavior, after they enjoy those services
▪ The intention of revisiting despite the higher amount to pay
▪ The intention of revisiting for the next time.
▪ The willingness to recommend to others.
Zeithamal et al. 1996
3. Research Method
This study used a survey method approach where respondents will be asked to answer questions via a questionnaire regarding the variables of the research by indicating the satisfaction model of e-WOM, Perceived Value, Destination Image on Intention to Behave of Tourism in Semarang regency, namely Sidomukti spring water.
Tourist site: Sidomukti water spring. The population in this study is all the tourists who are visiting Central Java.
Sampling technique used is purposive sampling, as many as 150 tourists. The analytical tool used in this study is the Partial Least Square (PLS) using software Smart PLS2.0M3 for Windows. The amount of data samples taken in this study was 150 data. PLS is a powerful analytical method, because it is not based on many assumptions.
PLS using boot strapping approach of each indicator with at-table.
4. Results and Discussion
The first step taken was to test whether the model meets the convergent validity, namely whether the loading factor indicator for each construct has met convergent validity. To meet the convergent validity, the value of loading factor must be >0.50. If the loading factor value is <0.50 then it must be removed from the analysis. Having been removed, It has to be run back to meet the convergent validity. The next step is to assess the outer model (Measurement Model) to see croos loading factor discriminant validity and composite reliability of the construct. This is the diagram
of the route of PLS output results before the deletion of some indicators regarded do not meet validity or before the fit PLS model:
Figure 2. Diagram of The Route of PLS Output Results before The Fit Model
Outer Model or Measurement Model is an assessment of there liability and validity of the study variables.
There are three ways to judge the outer models, they are, Convergent validity, discriminant validity, and composite reliability. Results from outer models show the reliability and validity of test results for each variable.
Table 2. Outer Loadings
Based on the table 2, loading factor values obtained at E3indicator<0.50, so that the indicators are remove first in order that the PLS model fit to perform the next test. After the previous analysis, there is one indicator that is not significant. Indicator that is not significant (E6) is then eliminated, so that the results are as follows:
Figure 3. Diagram of the Route of PLS Output Results after the Fit Model
The estimation results in the image above shows that the model has been fit. It appears that the loading factor value of all the indicators have been more than 0.5, thus it can be concluded that all indicators in the model are fit.
Outer Model or Measurement Model is an assessment of liability and validity of the study variables. There are three ways to judge the outer models, namely the convergent validity, discriminant validity, and composite reliability. Results from outer models show the test results of the reliability and validity for each variable.
Convergent validity of the measurement model with are flexible indicators is assessed based on the correlation between item score/ component score which is estimated by using Smart PLS software. According to Chin, 1998, for early stage research on the development of measurement, the loading value scale measurement of 0.5to 0.6 is considered adequate. The following is the table of outer loading factor of all indicators that have already been fit with the PLS model.
Based on the table, the loading factor values obtained for each indicator is >0.05. So that the correlation are between the constructs and the variable meets the convergent validity. Based on the results of loading factor, there is no indicators eliminated from the model
Other test is to assess the validity by noticing the value of Average Variance Extracted (AVE). It is required that a good model is a model which has the AVE for each construct with the value of greater than0.50. Here are the Table 4 about the value of the square root of AVE.
Table 4. The Value of the Square Root of AVE (Average Varianve Extracted) AVE
√
DESTINATION IMAGE 0.4042 0.6357
E-WOM 0.5290 0.7273
INTENTION TO BEHAVE 0.7237 0.8528
PRECEIVED VALUE 0.6165 0.7852
SATISFACTION 0.5970 0.7727
In the table, the values of all AVE square roots are >0.5, indicating that all of the variables in the estimated model meet the criteria of discriminant validity.
Composite reliability value is used to test the reliability of the variable. Variables that have good reliability are shown by the value of the composite reliability and Cronbach alpha which are >0.60. Here is presented table 5 of the composite reliability values.
Table 5. Composite Reliability
Based on the above table, it can be seen that the entire value of reliability and Cronbachs alpha of each indicator has exceeded 0.60, thus it can be concluded that research variables have met the requirement for good reliability.
Tests on the structural model are done by looking at the value of R-square which is a test of goodness-fit model. The Testing of inner model or structural models made to look at the relationship between variables, significance value and R-square of the research model.
In assessing the models with PLS, it begins with looking at R-Square for every dependent latent variables.
Here is presented in Table 6, the R-square value of each research variable which is influenced by other variables.
Table 6. R Square
Based on table 6, it can be interpreted that:
1. Construct Variability of Intention Behavior which can be explained by the construct variability of e WOM, Perceived Value, Destination Image and Satisfaction totaled 20.6%, while the rest 79.3% is explained by other variables outside research focus.
2. Satisfaction construct variability can be explained by e WOM construct variability, Perceived Value totaled 30.9%, while the rest 69.1% is explained by other variables outside the study.
The basis used in testing hypotheses is the values in output result for inner weight. In PLS, statistically every hypothesized relationship is done using simulation. In this case, hypotheses testing are done using re sampling boot strap method. The testing using boot strap is also meant to minimize the abnormality of research data. The application of resampling method enables the data to be freely distributed, no need of normal distribution
assumption, no need of big sample. Testing statistic used in this research is t-testing. The significance of estimated parameter gives very useful information about the relationship among research variables. The following is table 7 about path coefficients for hypotheses testing which has been proposed before.
Table 7. Path Coefficients (Mean, STDEV, T-Values)
Based on the table of Path Coefficients, it can be known that the original value of the highest sample lies on the influence of Perceived Value on Satisfaction, that is 0.4135. Meanwhile, the original value of the lowest sample lies on the influence of Destination Image on Intention to Behave, that is 0,1351. The biggest sample mean value lies on the influence of perceived value on Satisfaction, that is 0,4131. Meanwhile, the lowest sample mean value lies on the influence of Destination Image on Intention Behavior, that is 0,1841.
The highest value of standard error (STERR) lies on the influence of satisfaction on Intention behavior, that is 0,0931. While the lowest value lies on the influence of E-WOM on Satisfaction, that is 0,0795. The biggest t- statistic value lies on the influence of perceived value on Satisfaction, that is 4,5571. Meanwhile, the lowest t- statistic value lies on the influence of Destination Image on Intention Behavior, that is 1,5729. The following is the table 8 about hypotheses testing results in this research.
Table 8. Hypotheses Testing Results
Hypotheses Coefficient Significance t-tabel t-statistic Results Level
H1 0,2319 5% 1,657 2,9162 H1 accepted
H2 0,4135 5% 1,657 4,5571 H2 accepted
H3 0,3812 5% 1,657 4,0959 H3 accepted
H4 0,1351 5% 1,657 1,5729 H4 rejected
Based on the table 3.40 about boot strapping hypothesis testing results of the PLS analysis, it can be seen that the hypothesis1, 2 and 3 are accepted, while the hypothesis 4 is rejected. H1 is a hypothesis about the influence of e-WOM on satisfaction with significance level of 5%, the t-table of 1,657 and t-statistic of 2.9162. It can be seen that the t-statistic is greater than t-table, thus it can be concluded that H1 is accepted. Based on the table, it can also be seen that the coefficient on the positive H1is 0,2319 so it can be concluded that e-WOM has a positive effect on satisfaction.
H2 is a hypothesis about the influence of Perceived Value on satisfaction, with a significance level of 5%, the t-table of 1.657 and t-statistic of 4.5571. It can be seen that the t-statistic is greater than t-table, thus it can be concluded that H2 is accepted. Based on the above table, it can also be seen that the coefficient on the positive H2 is 0.4135, then it can be concluded that p received value has positive effect on satisfaction.
H3 is a hypothesis about the influence of satisfaction on behavior intention, with the significance level of 5%, t-table of 1,657 and t-statistic of 4,0959. Thus, it can be seen that t-statistic is greater than t-tabel, so it can be concluded that H3 is accepted. Based on the above table, it can also be seen that coefficient on the positive H3 is 0,3812. Thus, it can be concluded that satisfaction has a positive influence on Behavior Intention.
H4 is a hypothesis about the influence of destination image on behavior intention, with the significance level of 5%, t-table of 1,657 and t-statistic of 1,5729. It can be seen that t-statistic is smaller than t-table. Thus, it can be concluded that H4 is rejected. Based on the above table, it can also be seen that coefficient on the positive H4 is 0,1351. Thus, it can be concluded that Destination image does not have significant influence on Behavior Intention.
Conclusion and Implication
In this study, it was found that the E-WOM and perceived Value has a positive and significant impact on customer satisfaction. The study also found that customer satisfaction affects intention to behave positively and significantly, but destination image is notable to improve tourists intention to behave positively and significantly.
Managerial implications of this research are:
1. The manager of tourism destinations should manage website well. In addition to the website should be clear and accessible, another thing which should be done is to find as many positive testimonials as possible to be written on the website. The manager of tourist destinations should also seek testimony from various other websites and connect the tourism destination website to other websites giving positive testimony.
2. The manager of tourism destinations should create added value or value added to the customer so that customer satisfaction is increasing. For example, providing a more diverse gaming arena, training activities in door and out door and giving facilities of wider road access.
Limitations of this study are:
1. The object of the research is still a local scale so that not many people in Indonesia know about the object of the research.
2. The respondent in this study is limited to domestic tourists and did not accommodate foreign tourists yet.
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