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THE IMPACT OF SOCIAL MEDIA AND MOBILE APPS ON ECOTOURISM
BEHAVIOR IN THE NEW NORMAL ERA: THE MEDIATING ROLE OF
DESTINATION IMAGE
Nurhandini
1
, Widhy Tri Triastuti
2*
, Yuni Istanto
3
Faculty of Economics and Business, Universitas Pembangunan Nasional Veteran
Yogyakarta, Indonesia
1,2,3
widhi.triastuti@upnyk.ac.id
2
ABSTRACT
The objectives of this study were: 1) To test and influence social media on ecotourism behavior; 2) To
test and analyze the effect of mobile applications on ecotourism behavior; 3) To examine the effect of
social media on ecotourism behavior with destination image as a mediating variable; and 4) To examine
the effect of mobile applications on ecotourism behavior with destination image as a mediating variable.
The sample in this study were some tourists at Tebing Breksi Ecotourism Village, Sleman Regency,
Yogyakarta Special Region, totaling 286 respondents. The analysis method used in this research is
Partial Least Square (PLS). This study proves that Social Media has a significant positive relationship
to Ecotourism Behavior; Mobile Applications have a significant positive relationship to Ecotourism
Behavior; Social Media has a significant positive relationship to Ecotourism Behavior through
Destination Image; Mobile Applications have a significant positive relationship to Ecotourism Behavior
through Destination Image.
Keywords: Social Media; Mobile Apps; Destination Image; Ecotourism Behavior
INTRODUCTION
In the new normal phase, there is a change in tourist preferences where tourists prioritize
hygiene factors both in accommodation, tourist attractions, and amenities. The DIY government
has made various efforts to restore the economy, especially in the tourism sector. Ecotourism is
an alternative strategy to balance economic development, environmental conservation, and
community welfare (Kim et al., 2019; Rahman et al., 2022). According to Fang (2018),
ecotourism is usually seen as a kind of tourism aimed at relatively undisturbed natural areas and
regional protection. Sustainable tourism development aims to improve the shortcomings of the
tourism development model in the past, because tourism is highly tied and dependent on the
resources of a tourism destination, both mass and alternative, which will damage the environment
(Ei & Karamanis, 2017; Jeong et al., 2021). Well-managed ecotourism will encourage awareness of
the effects of tourism on nature, culture, and the human environment (Choi et al., 2017; Dahal et
al., 2020).
Some key aspects of ecotourism are that the number of visitors is limited or regulated so
that it is in accordance with the carrying capacity of the environment and socio-culture of the
community (vs mass tourism), environmentally friendly tourism patterns (conservation value),
friendly tourism patterns of local culture and customs (educational and tourism value), directly
helping the economy of local communities (economic value), the initial capital required for
infrastructure is not large (value of community participation and economy), and community-based
ecotourism (Sakata & Prideaux, 2013).
This ecotourism cannot be separated from tourists who care about the environment. A study
conducted by Miller, Merrilees, and Coghlan (2015) identified four pro-environmental tourist
behaviors, namely: conservation behavior, preservation behavior, environmental education and
advocacy behavior, sustainable consumption (Bilynets & Knezevic Cvelbar, 2022). Travelers who
care about the environment will tend to choose sustainable tourist destinations. With this pro-
environment demand, a new market opportunity for "sustainable tourism" has emerged.
Responsible, sustainable and universal tourism programs and strategies can also be aligned with
global missions that aim to support the achievement of the Sustainable Development Goals
(SDGs), which includes avoiding negative impacts on health and the environment, minimizing
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waste generation, ensuring an eco-friendly lifestyle (Ramkissoon & Sowamber, 2021), and
promoting sustainable tourism (Nekmahmud, 2020; Nekmahmud et al., 2021).
Yogyakarta Special Region Province (DIY) has a myriad of ecotourism destinations spread
across several districts. Among them: Baros Mangroves in Bantul Regency, Breksi Cliffs in
Sleman Regency, Mudal River ecotourism in Kulon Progo Regency and Nglanggeran Ancient
Volcano in Gunung Kidul. However, tourism performance in the Special Region of Yogyakarta
experienced a major decline in 2020. The number of tourists, both local and foreign, visiting DIY
in 2020, was only 1,848,548.00 which accounted for less than in 2019, which was 6,549,381.00.
In 2021, there was an increase in tourism performance in the Special Region of Yogyakarta in the
number of tourists visiting, which was 4,294,725.00 and in 2022, as many as 6,474,115.00.
However, in some elements such as the length of stay of tourists, the number of new attractions,
the number of pokdarwis and tourist spending money in 2022 decreased from the previous year.
In an effort to revive the tourism sector which had experienced the greatest loss due to the Covid-
19 pandemic, the DIY Tourism Office created the Visiting Jogja application. The DIY Tourism
Office explained that the Visiting Jogja application was initiated since the beginning of the Covid-
19 pandemic as a step of adaptation, innovation and collaboration as well as to answer the needs
of tourism services in the new normal era, especially in terms of collecting tourist data for testing
need sand tracing. In addition, in developing the Visiting Jogja application, the Yogyakarta
Special Region Tourism Office cooperates and is fully supported by the Yogyakarta
Representative Office of Bank Indonesia and other tourism stakeholders. Various service features
have also been developed, including a booking and payment system for tourist destination tickets,
events and tour packages, a non-cash payment system through QRIS Bank BPD Yogyakarta
Special Region, a capacity limitation system for tourist destinations and events, and a tourist
health screening system integrated with the Peduli Lindungi system and the Peduli Lindungi QR
code scanner system.
Until 2022, tourism businesses incorporated in the Visiting Jogja application are 255 tourist
destinations, 65 restaurants and other culinary businesses, 110 hotels, and accommodation 84
tourist villages 9 tour packages and 19 tourist events. The Visiting Jogja application has been
downloaded by 30,681 downloaders and until now the data on the number of tourist visits or
reservations through Visiting Jogja is 3,868,405 tourists. The Visiting Jogja application is the
only application that has an online ticket purchase feature and recording of tourist visits is
sufficient. Thus the existence of this application is not only beneficial for tourists but also for
stakeholders and other tourism industry players. Thus, tourism in Yogyakarta will be more vibrant
and the economy will grow, which in turn will improve the welfare of the people (Isdarmanto et
al., 2022).
The current popularity and recognition of mobile apps can be traced back to the
widespread use of mobile phones, which have long helped organize the visitor experience
(Chatzopoulos et al., 2017). As mobile apps are more difficult to use in ubiquitous usage
environments due to time constraints, lighting conditions, bandwidth, and other factors, user
adoption of mobile apps relies heavily on the adaptability of apps for specific usage contexts
(Abbasi et al., 2021; Han et al., 2018; Palos-Sanchez et al., 2021). In addition, mobile app
marketing has become a growing industry in the digital world that aims to better understand
travelers and determine what ecotourism should be promoted. Mobile applications have
developed into one of the important methods for tourists to find and filter information and an
important channel for tourism to reach potential tourists (Cardoso et al., 2022; Gulbahar & Yildirim,
2015).
Ecotourism is also seen as a commodity that must be promoted through digital marketing
strategies, so other marketing strategies are needed to increase economic elements, including
marketing using social media (VanMeter et al., 2015; Wagner, 2017). In addition, ecotourism
attracts visitors who want to experience the beauty of nature and preserve it for future generations.
Tourists are well aware of the use of technology to obtain information about ecotourism (Bilgihan
et al., 2016). Tseng (2019) and Simeone and Scarpato (2020) view social media as a factor that
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increases awareness of sustainable consumption as companies promote sustainable tourism to
connect customers using the internet and social networks (Tavitiyaman et al., 2021).
By increasing tourism advertising and promotion on social media that builds eco-based tourism,
it can create a good and new destination image among tourists, both local and foreign tourists.
Many ecotourism sites are far from technological infrastructure, so it is very important to improve
the destination image (Chi, 2021). Destination image is defined by Echtner and Ritchie (1991) as
the sum of beliefs, ideas, and impressions that tourists have about a destination. Destination image
is an important component to grow and increase tourists' awareness of ecotourism (Khan et al.,
2022).
Research on ecotourism behavior models has been studied by several previous
researchers, such as Kuo et al. (2019) and Khan et al. (2022). However, it still needs to be
reviewed considering that there are differences in the results of previous studies. The study
conducted by Kuo et al. (2019) managed to find a positive and significant relationship between
mobile applications and ecotourism behavior, while Khan et al. (2022) found no relationship
between the two. The objectives of this study are: 1) To examine and influence of social media
on ecotourism behavior; 2) To test and analyze the influence of mobile applications on ecotourism
behavior; 3) To examine the effect of social media on ecotourism behavior with destination image
as a mediating variable; and 4) To examine the effect of mobile applications on ecotourism
behavior with destination image as a mediating variable.
RESEARCH METHOD
Sample
The sample in this study were some tourists at the Breksi Cliff Ecotourism Site, Sleman
Regency, Yogyakarta Special Region totaling 286 respondents. Sampling was done using non
probability sampling with purposive sampling method. Non probability sampling is a sampling
technique that is not randomly selected. Elements of the population that are selected as samples
can be due to coincidence or due to other factors that have previously been planned86n. This
technique was chosen because the researcher wanted to require respondent criteria, namely
tourists who have visited the Tebing Breksi ecotourism destination in the Special Region of
Yogyakarta aged at least 17 years old actively using social media and having the Visiting Jogja
application.
Data Collection Procedure
The data collection technique used in this study is expected to be able to provide accurate
and more specific data, while the technique used is a questionnaire. According to Sekaran and
Bougie (2016) a questionnaire is formulating a set of written questions to respondents to get
answers. Researchers provided a questionnaire containing several questions and statements
regarding the characteristics of respondents, as well as an assessment of social media, mobile
applications, destination image and ecotourism behavior. The data in this study were obtained by
providing a list of questions or questionnaires to respondents online, using google forms.
Quantitative Analysis
The analysis method used in this research is Partial Least Square (PLS). According to
Ghozali and Latan (2014), PLS is one of the Structural Equation Modeling (SEM) techniques that
is able to analyze latent variables, indicator variables and measurement errors directly. PLS can
be used with a small sample size (30 data) and can be applied to all data scales. PLS is an
alternative that shifts from a covariance-based SEM approach to a variance-based one.
Covariance-based SEM generally tests causality or theory while PLS is more predictive model.
This means that SEM in the use of structural equation models to test the theory while PLS is more
towards theory development for prediction purposes.
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RESULT AND DISCUSSION
Outer Loading
Table 1 Descriptive Statistics Table of Manufacturing Companies from 2007 to 2016
Variable
Min
Max
Rata-rata
Standar Deviasi
Company Asset Structure
0.0100
0.8533
0.3542
0.2002
Company Size
3.9493
7.9891
6.0343
0.6993
Company Growth
-0.3611
1.8533
0.1280
0.2588
Company Profitability
-0.6618
0.7530
0.0558
0.1390
Structure Modal
0.07744
3.4707
0.5905
0.4512
Source: Secondary Data, ICMD 2007-2016 Processed
The validity test is carried out by looking at the outer loading. An indicator is declared valid
if it has a loading factor above 0.7. SmartPLS output for loading factors provides the following
results.
Table 2 SmartPLS output for loading factors provides
Social
Media
Mobile App
Destination
Image
Ekowsisata
behavior
X1.1
0,914
X1.2
0.903
X1.3
0,900
X1.4
0,863
X1.5
0,901
X1.6
0,881
X1.7
0,882
X2.1
0,909
X2.2
0,892
X2.3
0,882
X2.4
0,893
X2.5
0,894
X2.6
0,904
Y1.1
0,879
Y1.2
0,882
Y1.3
0,893
Y1.4
0,880
Y1.5
0,888
Y1.6
0,896
Y1.7
0,913
Z1.1
0,909
Z1.2
0,911
Z1.3
0,922
Z1.4
0,908
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Discriminant Validity
1. Cross Loading
Based on the results of the cross loading value between indicators in the table, it can be
concluded that each indicator in a construct has a difference with indicators in other constructs
which is indicated by a higher loading score in its own construct, so it can be said that the
instruments used in this study have met the criteria for discriminant validity.
Table 3 Cross Loading Value
Average Variance Extracted (AVE)
Another method to see the validity of a data is by looking at the square root value of
average variance extracted (AVE). The recommended value is 0.5. The following is the AVE
value in this study:
Table 4 The Ave Value
Average Variance Extracted (AVE)
Mobile App
0.803
Destination Image
0.833
Social Media
0.796
Ecotourism Behavior
0.793
The table above provides an AVE value above 0.5 for all constructs contained in the research
model, which means that it meets the requirements.
Social Media
Mobile App
Destination
Image
Ecotourism
Behavior
X1.1
0.914
0.882
0.878
0.858
X1.2
0.903
0.867
0.869
0.851
X1.3
0.900
0.875
0.854
0.859
X1.4
0.863
0.837
0.831
0.817
X1.5
0.901
0.874
0.853
0.853
X1.6
0.881
0.848
0.835
0.818
X1.7
0.882
0.841
0.843
0.814
X2.1
0.867
0.909
0.851
0.842
X2.2
0.845
0.892
0.832
0.825
X2.3
0.861
0.882
0.836
0.818
X2.4
0.870
0.893
0.859
0.819
X2.5
0.870
0.894
0.859
0.841
X2.6
0.874
0.904
0.858
0.846
Y1.1
0.814
0.812
0.794
0.879
Y1.2
0.840
0.820
0.824
0.882
Y1.3
0.844
0.829
0.828
0.893
Y1.4
0.833
0.819
0.832
0.880
Y1.5
0.824
0.822
0.826
0.888
Y1.6
0.840
0.837
0.837
0.896
Y1.7
0.862
0.848
0.854
0.913
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2. Reliability Test
a. Composite Reliability
Table 5 Composite Reliability
Composite Reliability
Social Media
0,965
Mobile App
0,961
Destination Imagery
0,952
Ecotourism Behavior
0,964
The table above provides an AVE value above 0.5 for all constructs contained in the
research model, which means that it meets the requirements.
b. Cronbach alpha
The reliability test can also be strengthened with Cronbach's alpha, where the
output in this study provides the following results:
Table 6 Cronbach's alpha
Cronbach's Alpha
Social Media
0,957
Mobile App
0,951
Destination Imagery
0,933
Ecotourism Behavior
0,956
The recommended value of Cronbach's alpha is above 0.7 so it can be seen from the data
above that the research data is in accordance with the Cronbach's Alpha value which is more than
0.7.
Inner Model
1. R-square
The R2 result of > 0.67 is in the good category, 0.33 - 0.67 is in the medium category, the
result of 0.19 - 0.33 is in the weak category.
Table 7 R-Square
R Square
R Square Adjusted
Destination Imagery
0.922
0.922
Ecotourism Behavior
0.898
0.897
From the data above, it can be seen that the R-Square value shows a value of 0.922
which means substantial, and for the ecotourism behavior variable it gets a value of 0.897
which means it is in the good category.
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2. Q-Squares
Table 8 Q-Squares
Q²predict
Destination Image
0,921
Ekowsisata behavior
0,888
Based on the test results above, it is found that all Q Square results> 0. Thus it can be
concluded that the model in this study has a relevant predictive value.
Hypothesis Test
For hypothesis testing using smartPLS by looking at the estimate table for path
coefficients. Testing in this study was carried out with a bootstrapping procedure.
Table 9 Hypothesis Test
Original
Sample
(O)
Sample
Mean (M)
Standard
Deviation
(STDEV)
T Statistics
(|O/STDEV|)
P
Values
Social Media -> Ekowsisata Behavior
0,456
0,456
0,131
3,474
0,001
Ekowsisata Mobile App -> Behavior
0,195
0,204
0,092
2,124
0,034
Social Media -> Destination Image ->
Ekowsisata Behavior
0,181
0,178
0,086
2,109
0,035
Mobile Application -> Destination
Image-> Ekowsisata Behavior
0,118
0,113
0,053
2,214
0,027
Based on the data above, the resulting P values show results below 0.05 which can be
explained as follows:
1. Social Media has an effect on Ecotourism Behavior has a P value of 0.001 where this value
is significant because it is smaller than 0.05, so it can be said that there is a significant
relationship between Social Media and Ecotourism Behavior. The value of 0.456 shows a
positive value so that it can be said that Social Media has a significant positive relationship.
Then the first hypothesis is accepted.
2. Mobile Applications have an effect on Ecotourism Behavior has a P value of 0.034 where
this value is significant because it is smaller than 0.05, so it can be said that there is a
significant relationship between Mobile Applications and Ecotourism Behavior. The value
of 0.195 shows a positive value so it can be said that the Mobile Application has a
significant positive relationship.Then the second hypothesis is accepted.Media Social
Media affects Ecotourism Behavior through Destination Image has a P value of 0.035
where this value is significant because it is smaller than 0.05, so it can be said that there is
a significant relationship between Social Media and Ecotourism Behavior through
Destination Image. The value of 0.181 shows a positive value so it can be said that Social
Media has a significant positive relationship through Destination Image. Then the third
hypothesis is accepted.
3. Mobile Applications affect Ecotourism Behavior through Destination Image has a P value
of 0.027 where this value is significant because it is smaller than 0.05, so it can be said that
there is a significant relationship between Mobile Applications affect Ecotourism Behavior
through Destination Image. The value of 0.118 shows a positive value so it can be said that
the Mobile Application has a significant positive relationship through Destination Image.
Then the fourth hypothesis is accepted.
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A. Model
B. SEM PLS
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C. Boot strap
CONCLUSION
Influence of social media and Mobile Apps: This research confirms that both social media
and mobile apps have a significant positive relationship to ecotourism behavior. This means that
the use of social media and mobile applications can positively affect the tendency of tourists to
engage in ecotourism activities; The Role of Destination Image as a Mediator: The results show
that destination image plays a mediator role in the relationship between social media, mobile
applications, and ecotourism behavior. This means that positive perceptions of destinations can
amplify the influence of social media and mobile apps on ecotourism behaviour; Confirmation
of Results Through Analysis Methods: The analysis method used, namely Partial Least Square
(PLS), is used to test the relationship between these variables. Thus, the study's findings are
supported by statistical analyses that reinforce the validity of the results; Research Location and
Sample: This research was conducted in Tebing Breksi Ecotourism Village, Sleman Regency,
Yogyakarta Special Region, involving 286 respondents as a sample. The location and number of
respondents provide specific context for the study's findings; Practical Implications: The results
provide a deeper understanding of how social media and mobile apps can influence ecotourism
behaviour, as well as the important role destination imagery plays in this process. The
implications can be used by relevant parties, such as stakeholders in the tourism industry and
policy makers, to enhance ecotourism promotion and build a positive image of the destination.
Thus, the conclusion of the study is that social media and mobile apps have a positive
influence on ecotourism behavior, and destination image serves as a significant mediating factor
in the relationship.
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