The effect of Spatio-Temporal Factors on Tourism Destination Choice:

A Study in Vietnam

PHAM MINH HOAN

National Economics University, Hanoi,

VIETNAM

ORCID: https://orcid.org/0000-0002-5009-2295

DO THI THU HIEN

Thuongmai University, Hanoi,

VIETNAM

ORCID: https://orcid.org/0000-0002-9765-9754

Abstract: -Studies on the decision to visit a place are abundant in the tourism field, while the Spatio-Temporal

factor has not been investigated systematically. The paper proposes an expanding model of intention to visit

place using Spatio-Temporal as moderating factors. In the model, the destination image is taken as an

independent factor which affects the attitude and intention to visit a place, Spatio-Temporal were developed as

mediate factors. The destination image attributes are organized into three groups: Service, Natural

Environment, and Quality of Life of the destination. To illustrate for the model, the empirical study was

conducted with data from Vietnam, as a case study. With a total of 865 samples from all three regions: The

North, Middle and South. SPSS and AMOS software were utilized to run this structure equation modeling

(SEM) model. The results of the study demonstrated that destination image factors have an impact on attitude

and intention to visit a destination, and Spatio-Temporal issues affect remarkably the relationship between

destination image items, attitude and intention to visit a place. The study reveals that in addition to focusing on

the factor of destination image, Spatio-Temporal features must also be considered to fully understand tourist

decision-making, and it can bring more advantage to real applications.

Key-Words: - Intention to visit, Service, Natural Environment, Quality of life, Spatio-Temporal, Vietnam.

Received: July 23, 2021. Revised: February 25, 2022. Accepted: March 11, 2022. Published: March 24, 2022.

1 Introduction

Tourism is a segment of many economies that

contributes great fiscal and social significance [1].

Tourism not only enhances economic development,

but also creates favorable conditions for the

development of other service industries such as

aviation. In addition, tourism helps develop

infrastructure, promote peace and facilitate cultural

exchanges, create jobs, and reduce poverty [2]–[4].

In terms of societal value, tourism helps to improve

local quality of life, and to increase labor

productivity [2], [5]. Therefore, we must evaluate

the factors influencing the decision to choose certain

tourism destinations, in order to improve service

quality and attract more tourists, thus benefiting

both the service providers and local residents, as

well as offering tourists more travel destination

options.

Literature reveals that there are many studies on

factors affecting tourists' decision to choose a

destination [6], [7]. In those studies, it can be seen

that groups of factors are commonly mentioned,

such as: eWOM (Electronic Word of Mouth);

Destination Image; Destination Familiarity. Of these

factors, the most commonly studied is Destination

Image [8], the role of which has been proven to be a

powerful factor in predicting visitor intention in

different contexts of tourism [9]–[13]. Therefore,

this study focuses on directly measuring the factors

belonging to the Destination Image group.

Spatio-Temporal factors are closely related with

the field of tourism. Regarding the temporal factor,

there are distinct and unique aspects. For example,

the Spatio-Temporal of a country as Vietnam, the

North and the Central regions have four seasons:

spring, summer, autumn, and winter with very

different weather characteristics, while the South

differs, with rainy and dry seasons. This Spatio-

Temporal feature makes for a clear difference in

travel habits and destination choices of tourists

regarding different regions and seasons. Studies

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have shown differences around the relationship

between destination Image and Attitude and

Intention to Visit between gender groups and

between different age groups [7]. Therefore,

assessment influence of Spatio-Temporal factors

and Destination Image coincidentally is still a

dramatic need.

The study is based on a critical review of the

relevant literature on tourist intention vs. ultimate

behavior, and analyzes the connection between

destination image and tourist attitude. However, this

study extends with another perspective, that of

Spatio-Temporal factors, as a further moderator of

the relationship between the two. This study

examines the role of Spatio-Temporal elements as

moderating factors of the relationship connecting

destination image, attitude and visitor intention. In

this case, the proposed model could observe the

change of influence of impact factor on Intention to

Visit according to the change of Spatio-Temporal

factors. This feature of the proposed model will

bring more advantages to real applications.

The remain of this paper is structured as follows:

▪ Proposes research hypotheses and models.

These contents are introduced in Section 2.

▪ With assessment and analysis of the results,

the article presents the conclusions of research

conducted in Vietnam, as a case study. These

contents are mentioned in Section 3 and 4.

2 Literature Review and Research

Model

Theory of Planned Behavior (TPB) is often used as

a research guideline for predicting behavioral

intentions [14]. This theory includes three

independent deciding factors for intention: the

attitude toward the behavior; social factors viewed

as the subjective norm and perceived behavioral

control. The relative significance of these three

factors will naturally vary across behaviors and

situations when predicting intention. Therefore, it

may be found in some cases that all three predictors

make separate significant contributions, while in

other cases attitudes alone will have a significant

impact, or in still others that perceived behavioral

control and attitudes as a mediate factor are

sufficient to explain intentions [15]. Besides,

previous studies have confirmed the contributed of

mediate factors. For example, the motivation factor

as a mediate that predicting Technology_Based

Enterprises Development was proved [16].

Therefore, Attitudes can be a mediate to explain

intentions to visit a destination.

TPB is significant in many studies explaining

human intention behavior in different contexts [17].

In order to further improve the measurement of

intended behavior in the field of tourism, numerous

new factors have been appended to the model to

enhance the prediction. Literature showed that, in

the tourism field, one of the crucial factors which

added to the model was Destination Image [11].

Mehdi Tajpour (2021) revealed the role of trust in

relationship with the development of family

businesses in media firms [18]. Meanwhile, the

destination image is the crucial factors to build

tourist satisfaction that leads to a create of tourist'

trust in the destination [19]. Destination Image is a

decisive concept in studies of factors affecting a

person’s choice of destination [20]–[23].

Destination Image is defined as the accumulation of

perceptions, values, beliefs, influences, emotions

and presumptions of a person regarding a travel

destination [8], [24], [25].

Many elements of Destination Image may be

found in abundance in previous studies. [26] stated

that natural landscapes and local dishes need to be

highlighted, while physical well-being and

conviviality need to be a focus in order to improve

Taiwanese college students’ perceptions of

Vietnam. When [27] investigated tourists’

likelihood of revisiting Vietnam, they found that

infrastructure, price, natural and cultural

environment, and safety are the factors that most

affect this decision. A study by [28] investigated in

factors Affecting Domestic Tourists’ Revisitation

Intention for Ba Ria-Vung Tau Province of

Vietnam, was conducted by directly interviewing

over five hundred domestic tourists. The result

identified six items such as: the destination’s

identity; recreation and diversions; flora and fauna

along with cultural traditions and heritage; and

climate.

Besides investigating the separate Destination

Image items, the group of Destination Image

attributes was also utilized. For example, [6] studied

foreign tourists who travelled to Cambodia’s

Angkor Wat. This study identified the destination

attributes and grouped them as follows: (1)

Destination Brand: people are friendly, honest and

trustworthy; the destination offers safety and good

value for money; (2) Atmosphere: comfortable and

peaceful places for relaxation; (3) Cultural

Environment: cultural attractions and activities and

which demonstrate unique ways of life and customs;

(4) Natural Environment: beautiful lakes, parks and

mountains; (5) Entertainment: good night life,

varied gastronomy and good shopping. [7] studied

intentions to visit Australia by Chinese visitors. This

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research grouped 20 destination image items of into

three groups: Service and Tourism Provisions (7

items), Natural Environment (11 items), Quality of

Life (4 items). The total of twenty factors included

those mentioned in domestic and international

studies.

In this section, the study develops a research

model to predict tourists’ decision to visit a

destination which are affected by destination image

with the impact of Spatio-Temporal elements. The

proposed model adapts the items of the destination

image which have been analyzed in previous studies

in different contexts. Therefore, this study exposes

the connection between destination image factors

and the decision to visit a place. Furthermore, the

research model explores the impact of Spatio-

Temporal factors on travelers’ intentions. The

destination image items are derived from previous

studies and grouped into three areas: Service (S),

Natural Environment (NE), and Quality of Life

(QL). Attitude was found to be the mediating factor

of the connection between destination image and the

intention to visit. Figure 1 shows the proposed

research model.

Fig. 1: Proposed Research model

Service (S), Natural Environment (NE), Quality

of Life (QL)

The proposed model includes items contained

within three groups: Service, Natural Environment,

and Quality of Life. These are the critical groups of

criteria that directly affect the visiting behavior of

tourists.

Service refers to the customer-oriented

characteristics of the destination, such as the

qualifications of staff; the existence of good tourism

infrastructure (restaurants, accommodations);

availability of interesting night time activities, along

with shopping and sport opportunities; personal

safety; reasonable pricing and good perceived value;

and local travel convenience. In the model H is

hypotheses, and H is proposed as following:

H1: Service positively affects attitude towards a

visit to a place

Natural Environment (NE) refers to the nature

characteristic of the destination. Each destination

has its own characteristics in terms of natural

conditions such as climate, weather conditions,

topography, flora and fauna, historical sites. The NE

in this study are the features of place which includes

the climate, landscape, flora and fauna, and well-

known tourist sites.

H2: Natural Environment positively affects attitude

when visiting a place

Quality of Life (QL) is comprised of the standard

of living of the local residents within the

destination. It is determined by the conditions as

depicted by 5 items (table 3): general living

conditions, ease of transport to and from a larger

urban area, the social welfare, the availability of

nutritious foods, and the sociability of the local

people friendly and kindly.

H3: Quality of Life positively affects visitor attitude

toward a place

This group included items of Service, Natural

Environment and Quality of Life as shown in Table

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Table 1. The Elements Factors

1. ELEMENTS OF SERVICE FACTOR

Short word

Items

Qualified, helpful and sociable service staff

This is a value for money destination

The destination provides for travelers’ physical well-being

Offers a good nightlife opportunity

Quality tourism infrastructure, including restaurants and accommodation

Offers good places for shopping

Has convenient local transport system

2. ELEMENTS OF NATURAL ENVIRONMENT FACTOR

NE1

NE2

NE3

NE4

NE5

NE6

NE7

NE8

This place has a good climate

This place provides opportunities for relaxation and rejuvenation

It offers a variety of opportunities for physical recreation and adventure

This place has many distinguished and celebrated tourist sites

This place has a spectacular landscape

The environment in this place is very clean

This place has fascinating native animals and vegetation

This place includes a vast land area and a relatively small population

3. ELEMENTS OF NATURAL QUALITY OF LIFE FACTOR

QL1

QL2

QL3

QL4

QL5

This is a place with comfortable living conditions

Easy to get to this place from the big cities

This place provides quality, supportive social welfare

This place offers a good variety of quality foods

Local people are sociable and welcoming

Attitude and Intention to Visit

Based on the TPB theory, attitude has an effect on

intention towards an action, therefore this study

added two more elements to the model. Moreover,

much research in tourism reveals that attitude is a

powerful predictor of visitor intention. Also, some

researchers argue that destination image has a clear

impact on intention to visit a place, and the literature

reveals that attitude has been found to be a medium

factor between Destination Image and Visit

Intention. Therefore, the paper develops the

hypothesis H4 as follow:

H4: Attitude towards visiting a place positively

affects Visit Intention

In order to fully grasp the destination choice

behavior of tourists, in addition to the traditional

factors mentioned in many studies, other aspects are

also very important, affecting the link between

tourists, Destination Image, Attitude and Intention

to Visit. As such, we include the Spatio-Temporal

element as mentioned next.

Spatio-Temporal issues as moderating factors

The Spatio-Temporal features play an important part

in the decision process of choosing a destination.

For example, tourists tend to travel to the

mountainous areas in the winter, but in the summer,

they tend to choose to travel to coastal areas.

Furthermore, since the Spatio-Temporal element is

ubiquitous, these factors need to be included in the

study to achieve a more accurate assessment of

intention to visit a place.

Although the Spatio-Temporal aspects have been

mentioned in studies of the tourism field, there is

limited evidence in the literature confirming the role

of Spatio-Temporal matters in the interconnection

between Destination image, Attitude and Visit

Intention. As a consequence, it is important to assess

whether Spatio-Temporal elements could modify

the way the perception of Destination Image affects

tourist Attitude and, accordingly, Visit Intention.

Previous studies were somewhat lacking in the

analysis and reciprocal evaluation of the Spatio-

Temporal role, while its characteristics are closely

associated with the field of tourism. Its features

have appeared everywhere, thus this study extends

these space and time factors to the model by

proposing the hypotheses H4, H5.

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H5: Spatial characteristics refine the effects of

perceived Destination Image on tourist Attitude and

Visit Intention.

H6: Temporal characteristics refine the effects of

perceived Destination Image on tourist Attitude and

Visit Intention.

Figure 1 above shows the research model which

extends the new factors.

3 Research Method and Findings

In order to prove the model proposed in figure 1, in

this section, the paper will experiment with the

model in Vietnam and provide analysis to confirm

the research model.

3.1 The Spatio-Temporal features of

Vietnam

Vietnam has a long coastline, beautiful natural

scenery and a culture of rich identity. A Vietnam

tourism map is shown in figure 2. According to

Government Portal of Social Republic of Vietnam,

the territory of Vietnam is characterized by its S-

shape, the differences in topographical structure, the

distribution of regions, and the 54 ethnic groups

with their own customs, traditions, and cultural

identities living within the three regions of North,

Central and South. With regard to climate, Vietnam

is separated into two major zones: (1) The North

(from Hai Van Pass and above) experiences a

tropical monsoon climate, which offers four discrete

seasons (Spring-Summer-Autumn-Winter),

influenced by the northeast monsoon (from the

Asian continent) and the southeast monsoon, with

higher humidity levels. (2) The South (from Hai

Van Pass and below is less affected by the monsoon,

so the sultry climate is quite moderate, hot all year

round and divided into two distinct seasons, dry and

rainy. Also, due to the structure of the topography,

Vietnam possesses subordinate climate regions,

some with mild, balmy climates and some with a

continental climate.

Fig. 2: Vietnam Spatial Map

Geographical differences between the three

regions and season lead to differences in tourism

activities. It can be seen that, in Vietnam, Spatio-

Temporal factors may make a difference in tourist

behavior.

3.2 Overview of Research Method

Questionnaire design: Every question posited is

derived from the reasoning of the hypotheses

regarding the variables, and a five-point Likert-type

scale is used for evaluation ranging 5 points from

“strongly disagree” (1) to “strongly agree” (5). A

convenient sampling method was used to survey

residents in three regions of Vietnam. However,

some criteria are applied to collect reliable data, and

to reduce noise of data. Only the adult can be

chosen (over 18 years old) and respondents must be

visited destinations in Vietnam. Data were collected

by questionnaire using google forms, then coded

and processed by SPSS and AMOS software. The

analysis was performed: Cronbach's Alpha to

estimate the consistency of measured items by using

Reliability Analysis Method, CFA to assay whether

the measured items are in line with latent variables,

SEM (Structure Equation Modeling) help to confirm

the structural value and relevance of measurement

model, and running multigroup analysis utilizing

SEM to test the moderating effects of the Spatial

and Temporal characteristics in the relationship

among destination image items, attitude and

intention to visit.

In this study, the total number of valid tourist

questionnaires which were distributed in three

regions of Vietnam, then collected and processed,

was 865, which meets the requirements and is

generalizable, representative of the total study. The

descriptive statistic was run using SPSS software,

the result shows that there are 357, 225 and 283

observations choosing a place to visit in the North,

Middle and the South respectively. The results also

show the preferred season of travel, in that there are

193 respondents who opted for travel in Spring, and

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269, 286, 117 respondents preferring the Summer,

Autumn, and Winter respectively. The age of

respondents ranged from 18 to 65. As such, the

volume and the distribution of the sample is

representative of the whole.

3.3 Research Findings

3.3.1 Test the Reliability of the Scale by

Cronbach’s Alpha Coefficient

Evaluation of measurement scales is accomplished

by examining the appropriateness of the factors

which are used in the Cronbach’s Alpha

Coefficients. The aim of this action is to find out

what variables are suitable, and which are not,

before running the EFA analysis, so that

inappropriate variables can be deleted.

Table 2. Reliability of Measurement Scales in the Research Model

Factor

Short

word

Initial N of

Items

N of Items

after

measurement

Cronbach’s

Alpha

Item

Deleted

Service

0.914

Natural Environment

0.895

Quality of Life

0.841

Attitude

ATT

0.862

Intention to visit

INT

0.787

As shown in the Table 2 above, all the constructs

have Cronbach alpha greater than 0.7 indicating that

the all five factors are reliable: for Service and

Tourism Provisions (S) and Natural Environment

(NE) variables, Cronbach’s Alpha coefficient is

very high, respectively 0.914; 0.895; the variables

Attitude (ATT), Quality of Life (QL), and Intention

to Visit (INT) have Cronbach’s Alpha coefficient,

respectively, 0,862; 0.841; 0.787, all of which are

greater than 0.7. Therefore, no item was deleted.

3.3.2 EFA analysis Results

Factor analysis is a technique used to condense data

and ease the collection of observation variables into

the main groups employed in the analysis and tests

which follow. Factor loading guarantees the

practical significance of EFA: factor loading > 0.3 is

thought to be the minimum level; factor loading>

0.4 is considered to be important; factor loading>

0.5 is considered to have practical significance (Hair

et al. 2006). The stipulations for exploratory factor

analysis are: (1) (Factor loading) > 0.5; (2) KMO

(Kaiser-Meyer-Olkin) coefficient ranges in [0.5; 1];

(3) Bartlett test is significant (Sig.) < 0.05; (4)

(Percentage of variance) > 50% (Hair et al. 2006).

Table 3. Analysis Results of KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

0.942

Bartlett's Test of Sphericity

Approx. Chi-Square

12601.653

406

Sig.

0.000

Data analysis results in Table 3 reveal that KMO

is 0.942, reached a level greater than the threshold.

The range from 0.5 to 1, with Sig. = 0.000, has

significance, indicating that the variables in the

model are correlated. There are 28 observation

variables of 5 independent and dependent variables

which were inserted for EFA (Exploratory Factor

Analysis) analysis with the Extraction is Principal

Axis Factoring method and Matrix rotation via the

Promax method.

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Table 4. Results of Pattern Matrixa

Factor

Factor loading

Total Variance

Explained (%)

0.870

0.848

0.811

0.784

0.748

0.742

0.619

54.827%

NE6

NE7

NE2

NE8

NE1

NE4

NE5

NE3

0.789

0.771

0.763

0.742

0.722

0.695

0.678

0.602

QL1

QL3

QL5

QL2

QL4

0.806

0.771

0.718

0.640

0.636

ATT6

ATT1

ATT4

ATT2

ATT3

ATT5

0.815

0.793

0.682

0.644

0.554

0.548

INT2

INT1

INT3

0.833

0.691

0.683

Analysis of data results in Table 4 indicates that

all variables in the groups have factors loading >

0.5, thereby reaching reliability. Factors loading of

observation variables are 0.5; Total Variance

Explained divided into 5 groups with 28 variables

could explain 54.827% of the variations of the

model. As such, after EFA analysis, 28 observation

variables and 5 independent and dependent variables

are extracted.

3.3.3 CFA analysis Results

Table 5. Results of CFA and SEM Testing Model Fit

Measure

Estimate CFA

Estimate SEM

Threshold

Interpretation

CMIN

718.868

777.001

367.000

370.000

CMIN/DF

1.959

2100

Between 1 and 3

Excellent

CFI

0.972

0.967

>0.95

Excellent

SRMR

0.034

0.042

<0.08

Excellent

RMSEA

0.033

0.036

<0.06

Excellent

PClose

1.000

>0.05

Excellent

AVE

From 0.510 to 0.605

>0.5

Convergent

P value of (C.R)

0.00

<0.05

Discriminant

AVE minus MSV

AVE>MSV

Discriminant

According to Hu and Bentler (1999) a combination

of CFI>0.95 and SRMR<0.08 is sought. To further

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harden this evidence, we add the RMSEA<0.06.

Based on the criteria of Hu and Bentler (1999), all

the indices are satisfied, and we conclude that the

model is compatible with the data. The scale is

considered to have converged value when the sum

of extracted variance (AVE) of the concepts reaches

about 0.5 or more. Fornell and Larcker (1981), OR,

when the normalized weights of the scales are

greater than 0.5 and statistically significant

(Gerbring & Anderson, 1988; Hair et al., 1992).

According to the results of this study, the AVE

values are all greater than 0.5, so it can be

concluded that the factors have converged values.

Discriminant value is assessed according to the

following criteria: (1) Evaluate whether the

correlation coefficient between the factors is

different from 1 or not. (2) Compare the square root

of AVE with the correlation coefficients of one

factor with the other factors. The criterion is

satisfied when the square root of AVE is greater

than all absolute values of its correlation coefficient

with other factors, Or, AVE is bigger than MSV

(MSV is the greatest square of all correlation

coefficient squares). The results show that the

correlation coefficient between factors other than 1

and the AVE is bigger than MSV, means that they

are really distinct from each other.

As the results indicate, the model results meet the

requirement and are ready to run the SEM model to

verify the research model.

3.3.4 Results of SEM Analysis

A structural equation modeling (SEM) technique

was employed to ascertain the soundness of the

model and causal associations between variables

using IBM AMOS software version 20. The results

are presented in Figure 3 and Table 5.

Fig. 3: Structural Equation Modelling Full Model Results

According to Hu and Bentler (1999), threshold

values of CMIN/DF between 1 and 3 would be

excellent. CFI equal or greater than 0.8 is

acceptable; equal or greater than 0.9 is good and

equal or greater than 0.95 is excellent. SRMR at less

than 0.08 would be excellent. RMSEA equal or less

than 0.06 is good; and equal or less than 0.08 is

acceptable.

The results of the analysis of this study as shown

in Table 5 above indicate that the model put forward

provides a reasonable match for the data. The

CMIN/DF, CFI, SRMS, RMSEA, PClose for the

proposed model is 2.1, 0.967, 0.042, 0.036 and

1.000, respectively.

The analysis of the relationships between

constructs indicated that proposed paths were

significant p-value positive for the effect of S, NE,

QL on ATT, ATT on INT. The finding confirmed

the existing knowledge of intention to visit place.

Table 6. the results of SEM Analysis

Correlation between the factors

Estimate

S.E.

C.R.

Standardized Estimate

ATT

<---

0.248

0.023

10.18

***

0.353

ATT

<---

N_E

0.276

0.031

8.823

***

0.317

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ATT

<---

0.307

0.028

11.015

***

0.392

INT

<---

ATT

0.675

0.056

12.133

***

0.546

Table 6 also accounts for the indirect effect

among independent and dependent variables. The

result shows that for the Regression Weights the QL

has the most effect on INT with the Beta is 0.392,

following by S with Beta is 0.353. ATT has an

effect on INT with p value is 0.000 and regression

weights is 0.546.

3.3.5 Results of Multigroup Analysis

The study utilized SPSS and AMOS software to run

multigroup analysis to test the effect of the Spatio-

Temporal aspects in the relationship among

independent (S, NE, QL), mediate (ATT) and

dependent (INT) variables. The results are shown in

Table 7.

The results of the study on the impact of Spatial

elements show that there are differences among

tourists when choosing to travel in different

geographical regions, specifically the North, with p

values reaching less than 0.05 in all relationships

among the variables S, NE, QL, ATT and INT. On

the other hand, in the Central and Southern regions,

the p value of the relationship between S, NE and

ATT is less than 0.05, and the p values of the

relationship QL and ATT are all greater than the

threshold 0.05, indicating that QL is not impacted

on ATT. Looking at estimated weights, it is found

that, for tourism in the North, tourists are most

interested in the natural environment (NE=0.264),

then service (S=0.233), and, finally, quality of life

(QL=0.222). There is a similarity in all three areas,

in that visitors are most interested in the natural

environment: Specifically, NE are 0.264, 0.265 and

0.142 for North, Middle and South respectively, and

it is also the highest ratio compared with service in

the Middle (S=0.113) and the South (S=0.138).

Nonetheless, with the destinations in the North and

the South, the influence of service factor is

competitive to natural environment, while the

influence of service factor is much lower in the

Middle. The fact is that the service at destinations in

Middle is similar and low due to limited resources.

In addition, the quality of live seem to be an

important factor at the North but the Middle and the

South. This is might resulted from the level of

development in the North of Vietnam, where most

of beautiful destinations are in remote areas.

Table 7. Group Spatio-Temporal Comparisons

Factor

Spatial

Temporal

North

(n=357)

Middle

(n=225)

South

(n=283)

Spring

(n=193)

Summer

(n= 269)

Autumn

(n=286)

Winter

(n=117)

Estimate/

P value

Estimate/

P value

Estimate/

P value

Estimate/

P value

Estimate/

P value

Estimate/

P value

Estimate/

P value

S ATT

0.233/0.000

0.113/0.008

0.138/0.000

0.241/0.000

0.272/0.000

0.089/0.000

0.084/0.186

NEATT

0.264/0.000

0.265/0.002

0.142/0.000

0.177/0.000

0.312/0.000

0.121/0.000

0.051/0.527

QLATT

0.222/0.000

0.049/0.279

0.032/0.452

0.200/0.000

0.234/0.000

0.040/0.308

0.031/0.559

ATTINT

0.145/ 0.014

-0.210/0.164

-0.397/0.008

0.469/0.000

0.233/0.000

-0.963/0.000

-0.015/0.822

n: number of observations

The results on the impact of Temporal elements

show that, in the first half of the year, the p value of

all relationships is significant, at lower than 0.05.

During the first 3 months of the year, the service

quality factor received the most attention (S=0.241)

compared with the quality of life at the destination

(QL=0.234) and the natural environment

(NE=0.117). But moving to the subsequent three

months, tourists are most interested in the natural

environment, with the highest estimated weight

(NE=0.312), followed by service quality (S=0.272)

and quality of life at the destination, respectively.

(QL=0.234). This can be explained in that, at this

time, combined with the Spatial factors in the

North and Central region, the weather is intense,

hot and humid, so tourists will tend to prefer the

criteria of natural environment to choose their

destination. The results of the study also show an

interesting situation in that, in the last three months

of the year, all p values have values greater than

0.05 (table 7), showing that three groups of factors

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.78

Pham Minh Hoan, Do Thi Thu Hien

E-ISSN: 2224-2899

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Volume 19, 2022

S, NE, QL have no relation to ATT and INT. This

can also be explained, the reason, for example of

Vietnamese as a case study, the people here often

do not travel at the end of the year time because

there are many jobs that need to be solved before

the traditional Tet holiday, and at the same time

they have to complete the work. To serve the year-

end summaries, members who live and work far

from their home will prepare to return to celebrate

the traditional New Year with their families, so

even during this the travel activities is less than

others time, this can explain for factors of service

quality, environment or quality of life at the

destination do not affect tourists' attitudes and

intentions to travel in this time.

4 Conclusion

The study builds model that predicting visit

intention a place extending Spatio-Temporal issues.

In this model, 20 items of destination image which

were organized into three group, the Spatio-

Temporal as a new factor that was developed in the

model in order to contributed for better

understanding of visit intention. To illustrate the

model, an experiment analyze was conducted. The

results were proved the model and supported

almost hypotheses. The Spatio-Temporal have

affected the relationship among perceived

destination image on tourist attitude and visit

intention.

In the term of spatial, as the results reveal that

for each of tourist who chooses to travel in

different places, the importance of the factors

affecting the attitude and the attitude affecting the

intention to travel is also different. Specifically,

those who choose to travel to the North are

interested in all three groups of criteria: Service

quality at the destination (S), environment (NE)

and quality of life at the destination (QL). While

tourists who visit destination in the Central and the

South are interested in two factors of service

quality and environment at the destination with p

value both reaching less than 0.05, however, the

service quality factor at the destination is less than

0.05. with p value greater than 0.05 (p=0.279 and

p=0.452 for Central and South, respectively). So

that, in the North and the South the policies that

improve the service and quality of life might

improve the attractiveness of its destinations,

meanwhile with the Middle increment of advertise

on natural environment seem to be unique solution.

In terms of temporal, there were differences

with tourists choosing to travel at different times of

the year. Specifically, for tourists who choose to

travel in the season in the first half of the year, they

are interested in all three groups of factors:

services, natural environment, quality of life at the

destination; At the same time, attitude has an

impact on the intention to choose a destination.

While there is a difference in the second half of the

year, specifically in the last three months of the

year, all three groups of factors (S, NE, QL with p

value greater than 0.05) have no relationship.

attitude and intention to choose a destination of

tourists. Overall, the temporal factor shows great

impact to the level of influence of impact factor on

the Intention to Visit. Therefore, researchers and

managers should spend more attention on this

factor, and the research models have to combine it

as a necessary issue.

This study reveals that when the Spatial is

moving it is result in changing Temporal and vice

versa. The results of the study can be applied to

help develop policies that help attract tourists,

especially by geographical location and over time,

making appropriate policies. However, the study

can bring more advantage by researching the

impact of temporal factor in each separate region,

and the future work will follow this approach.

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