Phan Bao Giang* & Ali Ameen Abdulbaqi

Lincoln University College, Malaysia

*Corresponding Author’s Email:


Intention to revisit people can be improved by improving Destination Image, because Destination Image has a direct effect on Intention to revisit people, it is necessary to improve the Destination Image of people by improving the facilities and infrastructure of the tourism service and people's welfare. Likewise, the intention to revisit theory can be increased by solid Big events and their values, because Big events and their values people directly influence the intention to revisit people, so to increase Big events and their values can be done by the head of the tourism office doing coaching to Big events and their values. To increase tourist arrivals to Vietnam, it is necessary to increase Big Events and their Values, which must be specifically carried out by coordinating with relevant stakeholders, utilizing the expertise of each team member. Big Events and their Values must develop themselves routinely, determine team effectiveness, increase work skills, respect each other, interact with each other and complement each other in giving good examples to the tourism office in order to achieve a higher Intention to revisit.

Keywords: Big-events, Destination Image, Intention


Background Of The Study

Big-events can create a number of advantages as well as costs for the countries or cities hosting the events. Cornelissen & Swart (2006) asserted that both the advantages and costs are normally expected to be generated in the short and long period of time. Besides, they are also considered as an activator for development in the host destination. The popular Big-events hosted such as sporting events, cultural festivals, expos and political summits are ordinarily believed to have positive impacts. Previous Big-event literatures claim that the events have socio-cultural, environmental, and economic advantages. Furthermore, they also generate long-term benefits such as freshly-constructed infrastructures and facilities of events that are expected to be continuously used even after the events, increased visitors, additional job opportunities and employment, opportunity to start new business for locals, and enhanced reputation of the host destination (Kasimati, 2003; Walle, 1996; French & Disher, 1997).


Tourism is one of the country's potential sources of foreign exchange and has a big contribution in developing the country's economy. Today tourism has developed into a large industry that supports the economy in many countries of the world, especially when other countries experience economic crises. This makes tourism as one of the development priorities by various countries because it tends to be able to continue to grow and generate income, even

though the crisis is still ravaging. This can happen because tourism generates a large foreign exchange with the arrival of foreign tourists visiting a country.

According to the WTO (World Tourism Organization), the entire tourism industry worldwide is estimated to always increase by more than 4% every year in the period 2009 to 2018. This increase generates 296 million jobs worldwide. Revenues generated by the tourism industry in all countries in 2011 reached US $ 1,035 trillion. Meanwhile, the Asia Pacific region has generated foreign exchange of US $ 289 billion, ranking number two after Europe which reached US $ 463 billion.

The tourism sector in Southeast Asia is estimated to grow by 10.3% in 2030. This can be seen from the visits of foreign tourists in 2013 which reached 92.7 million people. This number has increased by 12% from the previous year. This increase shows that the tourism sector in Southeast Asia has become one of the main tourist destinations in the world. Southeast Asia is also a major contributor to tourism in the Asia Pacific region of 37.3% and 8.5% of the world in terms of foreign tourist arrivals.

Along with the times, each country is competing to improve its tourism sector so that it can attract tourists to come on trips so that it can increase state income. Based on data "Tourist arrivals in ASEAN" obtained from the site of ASEAN, Indonesia ranks fourth largest number of tourists after Malaysia, Thailand and Singapore. This shows that Indonesia has great potential in the tourism sector. Indonesian tourism if it is able to be packaged and managed properly will be an asset of the Indonesian State. Diversity of attractions from natural, cultural and artistic tourism as well as artificial tourism objects such as tourist parks can actually be one of the pillars of the country's economy and can also absorb a lot of labor so that human resources and natural resources can be utilized optimally. Until now, tourism in Indonesia has not run optimally, even though this aspect is very influential on increasing people's income, especially local original income. Indonesia as a country that has natural resources uses its wealth as an object to bring in foreign exchange through nature tourism.

Visually it can be seen that from 2011 to 2014 the number of tourist arrivals in 10 countries in Southeast Asia tended to fluctuate, but from the graph above it can be seen that the 5 countries with the largest number of tourists in Southeast Asia namely Thailand, Malaysia, Singapore, Indonesia and Vietnam. From 2011 to 2013, Thailand ranked first in the largest number of tourists in Southeast Asia, followed by Malaysia, Singapore, Indonesia and Vietnam, but in 2014, the position shifted, namely Malaysia was able to rank first in the number of tourist arrivals in Southeast Asia, which was then followed by Thailand, Singapore, Indonesia and Vietnam.

Figure 1: Tourist Arivals in ASEAN 2014


Source: Data Analysis, 2017

The diagram above (figure 1) shows the percentage of tourists in 10 countries in Southeast Asia in 2014. Malaysia ranks first in the largest number of tourists, which is 26.11% of the total tourists in the Southeast Asian region. Then followed by Thailand, Singapore and Indonesia with the number of tourists respectively of 23.58%, 14.36% and 8.98%. Furthermore, in the last place, there are Myanmar with 2.93% of tourists.

Scope and Problem of Tourist Changes in Vietnam

Vietnam is located in the eastern and southern part of Indochinese peninsular in Southeast Asia. The country is bordered with three other neighbouring countries: Laos, China to the north, and Cambodia to the west. The two major cities are Hanoi, which is also the capital city, and Ho Chi Minh. As one of the members of ASEAN (Association of Southeast Asia Nations) countries, Vietnam is situated at the centre of ASEAN and considered as a country with promising development, emerging markets, and evolving familiarity of technologies (Jamieson & Azzam, 2017). Vietnam, together with Malaysia, performed as the top five nations that hit the highest growth in consumer confidence. It reveals the positivity of the state of personal finances and local job prospects; besides it also shows the optimism of the country development (Maierbrugger, 2018). According to the report of World Bank (Worldbank, 2018) the economy of Vietnam is accomplishing very well with the increase of gross domestic product (GDP) estimated to have risen by 1.7% in the two first quarters of 2018. Furthermore, it is also mentioned in this report that the main sectors contributing in the GDP growth are agriculture, forestry, fishery, industry and construction as well as service sector. The service sector has grown healthily because of the support from private consumption and tourist arrivals. Based on the statistic data of General Statistics Office of Vietnam (2017) between 2010 and 2015, the tourism sector had 9.5% growth and contributed 6.6% to the country GDP. This good trend is in line with the report of World Travel and Tourism (2018) that the total contribution of tourism sector in 2017 was 9.4% and is predicted to increase by 6.2% in 2018. However, despite the robust trends shown in the statistic above and the various events and activities planned and initiated by the government, Vietnam is still behind compared to other neighbouring countries in total contribution of tourism to GDP. Vietnam is in number 47 in the Southeast average with the total contribution of tourism to GDP US$2.6bn (World Travel and Tourism Council, 2018).


Research Problem

The primary focus of this study is to examine the impact of Big-event on tourists’ perception toward destination image through trust transfer model. This study has given contribution by identifying the values of a Big-event which affect the process of building trust toward the image of host destination so that visitors are willing to return to Vietnam after the first visit. The findings of this study which responded to the purposes of the study will increase the awareness of the tourism practitioners, government, authorities, decision makers, and the people of Vietnam who are involved directly or indirectly in the tourism activities towards the importance of building trust of visitors especially in Big-events. Besides, the findings also can be used by other researchers who are interested in the similar areas as a foundation or reference of their research.

Based on the number of foreign tourist arrivals to the countries of Southeast Asia (ASEAN) throughout 2018 it reached 136.2 million visits. The number grew to 8.34% from the previous year. In the midst of trade wars between the United States and China, ASEAN countries are trying to gain foreign exchange from foreign tourists visiting tourist destinations in their respective countries. Tourism destinations in Thailand are still the choice of foreign tourists visiting Southeast Asia. This is reflected in the arrival of former foreign tourists to Thailand in 2018 reaching 38.3 million. meanwhile, Vietnam is in fifth place after Indonesia, where this year the Vietnamese Government is targeting foreign tourist arrivals of 15.4 million people and the target is obtained from the tourism sector. For this reason, global promotion and integrated tourism activities among tourist destinations in Vietnam are needed. In addition, infrastructure improvements are needed to reach tourist destinations in Vietnam.

Model Measurement (Outer Model)

According to Abdillah & Jogiyanto (2015) outer models or measurement models describe the relationship between indicator blocks and their latent variables. This model specifically links between latent variables with indicators or it can be said that the outer model defines how each indicator relates to other variables. Tests carried out on the outer model viz:

  1. Convergent Validity, assessed based on loading factors (correlation between item scores or component scores with construct scores). The indicator is considered valid if it has a AVE value (Average Variance Extranced) above 0.5 or shows all outer loading dimensions of the variable has a loading value> 0.5 so that it can be concluded that the measurement meets the convergent validity criteria (Ghozali 2008). AVE value is the average percentage score of the variance extracted from a set of latent variables estimated by loading the Standarized indicator in the iteration process algorithm in PLS (Jogiyanto, 2009).

  2. Discriminant Validity, assessed based on cross loading, the model has sufficient discriminant validity if the value of cross loading between constructs is greater than the value of cross loading between constructs and other constructs in the model (Jogiyanto, 2009).

  3. According to Jogiyanto (2009) the reliability test uses the value of Cronbach's Alpha and Composite Reliability. Cronbach's Alpha to measure the limits of the value of a construct's reliability while Composite Reliability measures the actual value of a construct's reliability.

But Composite Reliability is considered better in estimating the internal consistency of a construct. A construct or variable is said to be reliable if it gives a Cronbach's Alpha value>

0.7 and Composite Reliability> 0.7.

The statistical hypothesis proposed in this study refers to the Nidjo Sandjojo path analysis method, as follows:

  1. Statistical Hypothesis 1 H0 : β 31 = 0

    H1 : β 31 > 0

  2. Statistical Hypothesis 2 H0 : β 12 < 0

    H1 : β 12 > 0

    Description of Research Data

    The description of the data in this section includes the data Intention to revisit (Y) which is called the dependent variable, Big Events and Their Values (X1), Destination Image (X2) and Intention to revisit (Y) as independent variables.

    As observed, the data was observed as not collected from a population that is normally distributed, despite the use of probability sampling technique. The lack of normality as more severe tests is implemented necessitated the use of a more flexible approach to analysis where the assumption of normality is not strict. The use of IBM PLS AMOS and the chi-square test for absolute fit is strictly or strongly based on the assumption of normality (Lei & Lomax, 2005). Even though Cooperative Fit Index (CFI) and other tests restricted to CB-SEM could be for relative model fit indicators, these observations will not be strong enough to support the study conclusions in a non-normal distributed data. The Least Squares Structural Equation Modelling (PLS-SEM) was therefore deemed appropriate for non-normal data as argued by Ringle, Wende & Becker (2015). More on the justification of Smart PLS are offered in the sections that follow. The descriptive statistics of the data collected are presented in below as part of the test for normality.

    Figure 2: Statistics Calculation Results Using Statistics


    According to Chin (1998) and Ghozali (2012), a correlation can be said to meet the convergent validity if it has a loading value greater than 0.5. The output shows that the loading factor gives a value above the recommended value that is equal to 0.5. So that the indicators used in this study have met the convergent validity (refer to figure 2).

    Table 1: Results of Calculation of bootstrapping Research Data


    Source: Primary data output processed, 2019

    Before testing the hypothesis, it is known that the T-table value for the confidence level of 95% (α of 5%) and the degree of freedom (df) = n-2 = 100 - 2 = 98 amounted to 1.995 (refer to table 1). Hypothesis testing for each of the latent variable relationships is shown as follows:

    1. Big Events Variable Hypothesis Testing (X1) against Variable Intention (Y)

      H01 image Y

      H11 X1 Y

      Based on the results of the statistical table output for the Big Events Variable (X1) against the Intention variable (Y) of 18,329> T-table (1,995). The original sample estimate value shows a positive value of 0.754 which shows that the direction of the Big Events (X1) variable

      relationship to the Intention (Y) variable is positive. Thus H11 was accepted in the study. That is, in this study the Destination Image (X2) latent variable with its indicators influences the latent variable Intention (Y) with its indicators significantly.

    2. Hypothesis Testing Destination Image (X2) Variable Intention (Y)

H02 image Y

H12 X2 Y

Based on the results of the statistical table output for the Destination Image (X2) variable against the Intention variable (Y) of 5.547> T-table (1.995). The original sample estimate value shows a positive value of 0.600 which indicates that the direction of the relationship between the Destination Image (X2) variable and the Intention (Y) variable is positive. Thus H12 in the study was accepted. That is, in this study the Destination Image (X2) latent variable with its indicators influences the latent variable Intention (Y) with its indicators significantly.


Theoretical Implications of Findings

From the five main research questions under observation, key theoretical implications may be drawn in contribution to the concept and theories that underlie organizational uncertainty and change management. With the theory of complexity in an underlying position, the study paves way for a clear insight on how change management may achieve set objectives in a more predictable manner.

Practical Implications of Findings

Based on the results of research that has been carried out on foreign and domestic tourists, the following research conclusions are obtained:

  1. Big Events and Their Values have a direct positive effect on Intention to revisit. This means that if Big Events and Their Values are solid, the Intention to revisit will increase.

  2. Destination Image has a direct positive effect on Intention to revisit. This means that if the Destination Image is a good person, then Intention to revisit will increase.

  3. Big Events and Their Values have a direct positive effect on people's Destination Image. This means that if Big Events and Their Values are solid, the Destination Image will increase.

Conidering that organizations anticipte growth in every sector of its wrowth, change management policies must be employed in the areas of undertaking and not opposing. As elaborated by Beech et al., (2004), acceptance entails the capability of a person or an organization to realise change as a chance to innovative and attain competitive advantage.


Future researchers should enhance the current reserach model, that is by employing a qualitative approach to realise more insight in organizational change. Particularly, future research should consider using data source triangulation as well as a method aimed at

improving the present framework to gain more insight in the research may come with certain imitations, therefore, a combination of different methods can help suppress the weakness of the individual methods.

Limitations of the Study, How it Affects Results and Measured Installed to Reduce their Effect

Limitations are categorized into those associated with then methodology considered and those associated with the theory and its application to the area study. Based on the above research conclusions, the implications of the research results are proposed, as follows:

  1. Intention to revisit people can be improved by improving Destination Image, because Destination Image has a direct effect on Intention to revisit people, it is necessary to improve the Destination Image of people by improving the facilities and infrastructure of the tourism service and people's welfare.

  2. Intention to revisit people can be improved with solid Big Events and Their Values, because Big Events and Their Values people have a direct influence on people's Intentions to revisit, so increase in Big Events and Their Values can be done by the way the head of the tourism office conducts coaching to Big Events and Their Values.

  3. Big Events and Their Values can be improved by increasing Destination Image, because Big Events and Their Values directly influence Destination Image, so to increase Big Events and Their Values, it is necessary to build a strong Destination Image of tourists who will visit Vietnam through training and coaching from the Head of the tourism office.


For each organization to experience growth, it is important to make changes in the structure or mode of operation. For this reason, the main conclusions are established, taking into account the research questions are to be answered. The first question looks for the impact of Employee Tension which has on organizational change. The findings reveal that employee tension has a significant influence on organizational change. In connection with these findings, it was concluded that tensions between employees in the Vietnamese Government could affect organizational change.


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