International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 Analysis of Factors Influencing Tourists Decisions to Visit International Destinations: A Case Study of the North Sumatra Region Aulia Maharani Putri Br Karo1*.
Dewi Yanti2.
Muhammad Khadry3 1,2,3 Department of Tourism Destination.
Medan Tourism Polytechnic.
Medan.
Indonesia Email: 1nauliamhr@gmail.
com, 2 dewiyanti@poltekparmedan.
id , 3chodry91@gmail.
Received on 19 September 2025
Revised on 28 September 2025
Accepted on 29 September 2025
ABSTRACT
This study aims to analyze the factors that influence the decision to visit domestic tourists from North Sumatra to foreign destinations.
The research method uses a quantitative explanatory approach with data collection techniques through questionnaires to 100 respondents selected purposively.
Data analysis was carried out using multiple linear regression using SPSS.
The results showed that of the seven independent variables studied, only tourism promotion (X.
and hobbies and interests (X.
had a significant effect on the decision to visit (Y), with coefficients of 0.
175 and 0.
376, respectively.
Meanwhile, personal motivation (X.
, tourism products (X.
, income (X.
, recommendations (X.
, and the environment (X.
did not show a significant effect.
The RA value of 0.
704 indicates that 70.
4% of the variation in the decision to visit can be explained by the variables in the model.
These findings focus on the importance of effective promotion strategies and adjusting domestic tourist destinations to the interests of the younger generation to increase the competitiveness of local tourism.
Research recommendations include strengthening digital promotion, developing interest-based tour packages, and improving destination facilities and accessibility.
Keywords: Analysis Factor.
Tourists.
Visiting Decisions.
North Sumatra.
INTRODUCTION
Tourism is a sector that continues to adapt to global trends.
In the digital era, overseas travel is becoming increasingly popular among Indonesian tourists.
Data from the Central Statistics Agency (BPS) shows that the number of tourist trips in 2024 95 million, an 18.
99% increase from the previous year.
The majority of Indonesian tourists choose Asian destinations, particularly Malaysia.
Singapore, and Thailand.
North Sumatra, with a population of 15.
8 million (BPS.
May 2.
and Kualanamu International Airport as a regional hub, has significant tourism potential, including Lake Toba, which has been designated a Super Priority Destination.
Despite this, many domestic tourists choose to travel internationally.
Contributing factors include the higher cost of domestic airfare compared to international routes .
or example.
MedanAesKuala Lumpur is often cheaper than MedanAeBanda Ace.
, the appeal of cultural tourism, and the influence of global trends like K-Pop and K-Drama.
Passenger data from Kualanamu shows a dynamic pattern: international departures decreased by 16.
40% in March 2024 compared to February, but increased 19% in December compared to November.
Overall, the number of departing passengers in 2024 increased by 21.
34% year-on-year, from 924,914 in 2023 to International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 1,122,283 in 2024.
These figures indicate a growing preference for international travel among North Sumatra residents.
Traveler decision-making is shaped by prior research, careful planning, and sensitivity to emerging trends.
While outbound tourism can reduce domestic foreign exchange flows, it also generates strategic benefits, including knowledge enrichment, innovation, competitiveness, and global cooperation.
This dual impact presents both challenges and opportunities for Indonesia's tourism In this case, the authors chose seven variables as the main factors influencing tourists' choice to vacation in international destinations, namely: Personal Motivation, encompassing the internal drive that drives individuals to travel, such as the desire for relaxation, escape from routine, or seek new experiences.
According to Yoon & Uysal .
, tourism products include the attractions, accessibility, and facilities offered by the destination.
The quality of tourism products has a significant influence on the decision to visit, as stated by Middleton .
Tourism Promotion is an effort to market a destination through various media, which can influence tourist interest.
Income is related to an individual's financial ability to travel.
Kotler & Keller .
state that income influences purchasing power and travel decisions.
Recommendations and Information from Travel Service Businesses encompass the influence of travel agent recommendations and information provided on tourist Litvin et al.
explain that information from travel agents serves to reduce uncertainty for tourists.
The environment encompasses the physical, social, and cultural conditions of a destination, as well as the comfort felt during the visit.
Hobbies and Interests relate to a person's interest in certain activities that influence the decision to visit.
Therefore, this study investigates the determinants influencing tourists' decisions to travel abroad, with a particular focus on North Sumatra.
The study aims to measure the relative weight of these factors and compare domestic and international destinations.
RESEARCH METHODS
The approach employed in this study is a quantitative approach with an explanatory research design.
Quantitative research is conducted by collecting data that can be measured numerically, which is then analyzed using statistical methods to obtain objective results (Moleong, 2.
Explanatory research aims to explain the relationship between one variable and another (Sugiyono, 2.
The data collected will consist of numerical results, interviews with relevant stakeholders, descriptive explanations, and assessments of the factors influencing the decisions of tourists from North Sumatra Province to travel abroad.
The research object is focused on the North Sumatra region, particularly tourists from the province who have either previously traveled or plan to travel to international destinations.
This study will examine the determinants influencing touristsAo decisions to visit overseas destinations, based on the variables formulated.
In this research, the population comprises tourists from North Sumatra who have traveled or intend to travel to international destinations.
The population size used in this study is based on demographic data from the Central Bureau of Statistics (BPS), which recorded North SumatraAos population at 15,785,839 people as of May 2025.
The method for determining the sample size applies the Slovin formula, as follows:
International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 Figure 1.
Slovin's Formula [Source: Google 2.
Where:
n = Sample size N = Population size e = Margin of error .
olerable error leve.
The tolerance level applied in this study is 0.
%).
Thus, the sample size obtained is: 15.
8 / 1 15.
2 = 100.
Accordingly, the total sample used in this study is 100 respondents.
The sampling method employed is nonprobability sampling, specifically purposive sampling, where respondents are selected based on certain criteria (Sugiyono, 2.
In this study, respondents Tourist from North Sumatra.
Tourists who have visited or plan to visit international destinations between 2020 and 2025.
This research utilizes both primary and secondary data.
Primary data are obtained directly from the research source through measurements such as observations, interviews, and surveys, while secondary data are obtained indirectly from books, reports, or literature (Hardani et al.
Primary data will be collected through a questionnaire distributed to 100 respondents, which will then be processed for analysis.
The questionnaires will be distributed proportionally among tourists from North Sumatra who have previously traveled or intend to travel abroad.
To measure responses, a Likert scale is The Likert scale is employed to evaluate individualsAo or groupsAo attitudes, opinions, and perceptions related to social phenomena.
In this research, variables are translated into measurable indicators that form the foundation for developing the research instruments (Sugiyono, 2.
Table 1.
Likert Scale Number Choices SS Ae Sangat Setuju S Ae Setuju N Ae Netral TS Ae Tidak Setuju STS Ae Sangat Tidak Setuju [Source: Sugiyono, 2.
Score International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 Validity and Reliability Testing Validity testing is an instrument used to measure the alignment between the data collected by the researcher and the actual conditions of the object studied (Sugiyono, 2.
Significance testing is carried out by comparing the calculated r value .
with the table r value .
An item is considered valid if it shows a significant correlation with the total score at a 0.
1 significance level.
Specifically, if r hitung is greater than r tabel and the value is positive, then the item is declared valid.
Conversely, if r hitung is smaller than r tabel, the item is considered invalid.
A reliable instrument is one that can be used repeatedly to measure the same object and produce consistent results (Sugiyono, 2.
Reliability testing is applied to the questionnaire, which serves as an indicator of the construct variables.
A variable can be considered reliable if the responses to its statements remain consistent or stable over time.
The reliability of the questionnaire will be tested using the CronbachAos Alpha technique.
A CronbachAos Alpha value greater than 0.
6 is considered acceptable.
The closer the value is to 1, the higher the level of internal consistency reliability (Ghozali, 2.
Classical Assumption Testing The multicollinearity test is conducted to ensure that there is no intercorrelation or collinearity among independent variables in the regression model.
This test aims to determine whether correlations exist between independent variables (Ghozali, 2.
The criteria are as follows: If the VIF value > 10 or the tolerance value < 0.
multicollinearity exists and If the VIF value < 10 or the tolerance value > 0.
multicollinearity does not exist.
A good regression model requires that there be no correlation among independent variables, meaning multicollinearity should be absent.
A value between zero and one indicates the percentage of the effect the independent variables have on the dependent variable (Yasmine, 2.
The normality test is conducted to assess whether the variables follow a normal distribution (Ghozali, 2.
This test is essential to validate other analyses that assume residuals are normally distributed.
if this assumption is violated, the statistical tests become invalid and parametric methods cannot be used.
The heteroscedasticity test checks for unequal variances of residuals across different observations in a linear regression model.
Its purpose is to identify whether residuals maintain constant variance .
or show varying variance .
The Glejser test, which regresses independent variables against the absolute residuals, is often used for this purpose (Gujarati, 2.
The criteria for this test are:
If the significance value of an independent variable is less than 0.
heteroscedasticity exists and If the significance value is greater than 0.
heteroscedasticity is absent.
Multiple Linear Regression Analysis This study employs multiple linear regression analysis, which is used to assess the extent of influence exerted by two or more independent variables on a dependent variable, as well as to predict the dependent variable using the independent variables.
International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 For data processing, the analysis is conducted using the Statistical Program for Social Science (SPSS) software.
The multiple linear regression formula is expressed as Y=0 1X1 2X2 U nXn AY Figure 2.
Equation of Multiple Linear Regression Analysis.
[Source: Sugiyono, 2.
Y = dependent variable CA = constant CAAn = regression coefficients of each independent variable XCAAXn = independent variables A = error term Thus, the multiple linear regression model in this study is expressed as follows:
Y = 0 - yu1ycU2 yu2ycU3 yu3ycU4 yu4ycU5 yu5ycU6 yu6ycU7 yu7ycU7 Description:
Y = TouristsAo Decision to Visit International Destinations CA = Constant CA Ae CO = Regression Coefficients XCC = Personal Motivation XCE = Tourism Products XCE = Tourism Promotion XCI = Income XCI = Recommendations and Information from Travel Service Providers XCN = Environment XCO = Hobbies and Interests AA = Error Term Coefficient of Determination (RA) The coefficient of determination (RA) indicates how well the model explains the variations in the dependent variable (Ghozali, 2.
RA values range between 0 and 1, where a low value means the independent variables have minimal ability to explain changes in the dependent variable, while a value nearing 1 implies that the independent variables almost fully account for the variations in the dependent variable.
t-Test (Partial Tes.
and F-Test (Simultaneous Tes.
The t-test .
artial tes.
is used to evaluate the degree and significance of the impact each independent variable has on the dependent variable (Baroroh, 2.
determines whether each independent variable significantly influences the dependent variable (Ghozali, 2.
RESULTS AND DISCUSSION
North Sumatra is one of the largest provinces on Sumatra Island, situated in the northern region.
It shares borders with Aceh to the west.
Riau and West Sumatra International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
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e-ISSN 2828-2590 p-ISSN 2828-5093 to the south, and the Malacca Strait to the east.
Covering around 72,981.
23 kmA, the province features varied landscapes ranging from eastern coastal lowlands to mountainous areas in the west.
North Sumatra is also known for Lake Toba, a major national tourism icon and one of IndonesiaAos super-priority destinations.
The province boasts a wide range of natural, cultural, and historical attractions, with popular sites like Lake Toba.
Bukit Lawang.
Tangkahan.
Maimun Palace, and Tjong A Fie Mansion drawing visitors from both within the country and abroad.
In addition, the province is renowned for its distinctive culinary heritage, including Bika Ambon.
Soto Medan, and Mie Gomak.
Overall.
North Sumatra holds vast tourism potential but faces intense competition from international destinations.
Factors such as accessibility, income levels, and the influence of social media significantly affect touristsAo decisions to choose overseas destinations.
In this study, the author analyzes the factors influencing touristsAo decisions to visit international destinations, with North Sumatra as the case study.
Data were collected through questionnaires distributed to 100 respondents starting on May 26, 2025, based on the predetermined criteria: tourists from North Sumatra who had traveled abroad or intended to travel abroad within the last five years.
The data obtained are interpreted and presented as follows:
Tourist Characteristics Based on Age The respondent data show that the dominant age group is 21Ae30 years, with a total of 77 respondents .
%).
The least represented age groups are 41Ae50 years .
%) and above 50 years .
%).
Thus, it can be stated that the majority of respondents are aged 21Ae30, categorized as youth transitioning into adulthood.
The dominance of respondents aged 21Ae30 indicates that Millennials and Generation Z are the main drivers of outbound tourism trends.
This age group tends to be more dynamic, technologically literate, and strongly motivated to explore new experiences .
ovelty Their characteristics align with preferences for destinations offering Instagrammable content and contemporary activities.
The high percentage also reflects a consumption pattern among younger generations, who are more willing to allocate funds for experiences compared to previous generations.
Tourist Characteristics Based on Gender The dominance of female respondents .
%) indicates several important points.
Women are generally more active in planning trips and making family travel decisions.
They also tend to be more responsive to tourism promotion content on social media, particularly related to lifestyle, culinary experiences, and shopping.
Moreover, women are more likely to share their travel experiences .
ord of mout.
, which significantly contributes to the spread of destination information.
Tourist Characteristics Based on Educational Background It shows that the majority of respondents have a senior high school (SMA/Equivalen.
education, with 60 respondents .
%), followed by diploma holders with 12 respondents .
%), and bachelorAos degree (S.
holders with 28 respondents .
%).
The composition indicates that the dominance of high school graduates .
%) suggests their travel motivation may be influenced by academic-related factors such as seeking educational opportunities.
Meanwhile, the 28% of respondents with a bachelorAos degree are likely to have more rational considerations in choosing destinations, such as networking opportunities and relaxation.
Tourist Characteristics Based on Occupation The data shows that the majority of respondents are students, totaling 64 people .
%), followed by private employees with 19 people .
%).
Entrepreneurs/business International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
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e-ISSN 2828-2590 p-ISSN 2828-5093 owners account for 8 people .
%), others 7 people .
%), and housewives only 2 people .
%).
The high percentage of students reflects factors such as greater free time during study periods, increased purchasing power supported by parents, and the growing trend of educational tours abroad, such as Al Azhar International Islamic Boarding School (IIBS) Karangpandan.
Central Java, which organized a study tour to Europe.
The fact that only 19% are private employees highlights the limited vacation time of formal workers, reinforcing the finding that young travelers tend to be more flexible in travel planning.
Tourist Characteristics Based on Marital Status Results indicate that 91 respondents .
%) are unmarried, while only 6 respondents .
%) are married.
The dominance of unmarried respondents aligns with the youthful profile of the sample.
This status allows for greater freedom in travel decision-making without complex family considerations.
They are generally more willing to take risks, more open to new destinations, and more flexible with travel budgets compared to married individuals.
Tourist Characteristics Based on Traveler Type Group travel with friends is the most preferred type at 47 respondents .
%), followed by family travel at 36 respondents .
%).
Solo travel also holds a noticeable share among the 100 respondents.
The preference for group travel .
% combined for friends and famil.
reflects the collectivist culture of North Sumatra society.
Traveling in groups is considered more economical due to shared costs, safer, and enhances social bonding.
Only 15% chose solo travel, suggesting that social factors remain a stronger consideration than individual freedom.
Tourist Characteristics Based on Sources of Travel Information Social media dominates as the main source of travel information .
%), confirming a paradigm shift in tourism marketing in the digital era.
Platforms such as Instagram and TikTok effectively shape perceptions through engaging visual content.
The very low use of travel agents .
%) indicates a declining reliance on third parties, reflecting the independence of younger generations in planning their trips.
Tourist Characteristics Based on Average Monthly Income The fact that 46% of respondents with lower incomes still choose international travel suggests several factors, such as prioritizing travel expenses, utilizing creative financing options .
avings or promotional ticket.
, and the perception that certain international destinations are more affordable than domestic ones.
This poses a challenge for domestic tourism to provide greater value compared to budget-friendly international destinations.
Validity Test Based on the collected data, it can be concluded that the validity test in this study utilized an r-table value of 0.
1946 at a 0.
05 significance level for 100 respondents.
Consequently, the calculated r-values .
-coun.
for each variable exceed this r-table value, as detailed below:
Table 2.
Validity Test of Personal Motivation Variable X2.
X2.
X2.
X2.
X2.
X2.
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,4690
0,6340
0,5250
0,5980
0,2160
0,5840
Notes Valid Valid Valid Valid Valid Valid International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
Variable X2.
X2.
X2.
X2.
0,1946
0,1946
0,1946
0,1946
0,5840
0,5470
0,6660
0,6760
e-ISSN 2828-2590 p-ISSN 2828-5093 Notes Valid Valid Valid Valid [Source: ResearcherAos Analysi.
Table 3.
Validity Test of Tourism Product Variable X3.
X3.
X3.
X3.
X3.
X3.
X3.
X3.
X3.
X3.
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,5980
0,7100
0,7150
0,7820
0,8430
0,8400
0,8480
0,7990
0,7580
0,7310
Notes Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid [Source: ResearcherAos Analysi.
Table 4.
Validity Test of Tourism Promotion Variable X4.
X4.
X4.
X4.
X4.
X4.
X4.
X4.
X4.
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,7160
0,6910
0,6980
0,7040
0,6930
0,5090
0,6960
0,6490
0,6490
Notes Valid Valid Valid Valid Valid Valid Valid Valid Valid [Source: ResearcherAos Analysi.
Table 5.
Validity Test of Income Variable X5.
X5.
X5.
X5.
X5.
X5.
X5.
X5.
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,6820
0,5330
0,6140
0,7050
0,7999
0,5410
0,7090
0,7580
Notes Valid Valid Valid Valid Valid Valid Valid Valid [Source: ResearcherAos Analysi.
Table 6.
Validity Test of Recommendation and Information International Journal of Travel.
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Variable X6.
X6.
X6.
X6.
X6.
X6.
X6.
X6.
X6.
X6.
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,8220
0,8020
0,7760
0,8630
0,8040
0,7020
0,8700
0,7900
0,7970
0,7020
e-ISSN 2828-2590 p-ISSN 2828-5093 Notes Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid [Source: ResearcherAos Analysi.
Table 7.
Validity Test of Environment Variable X7.
X7.
X7.
X7.
X7.
X7.
X7.
X7.
X7.
X7.
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,7600
0,7840
0,8180
0,7450
0,7340
0,7840
0,6850
0,8580
0,7960
0,8290
Notes Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid [Source: ResearcherAos Analysi.
Table 8.
Validity Test of Hobbies and Interest Variable X8.
X8.
X8.
X8.
X8.
X8.
X8.
X8.
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,1946
0,6760
0,6580
0,6660
0,5840
0,7370
0,7730
0,7530
0,7490
Notes Valid Valid Valid Valid Valid Valid Valid Valid [Source: ResearcherAos Analysi.
Table 9.
Validity Test of Visiting Decision Variable 0,1946 0,1946 0,1946 0,1946 0,1946 0,4240 0,7690 0,6840 0,480 0,7580 Notes Valid Valid Valid Valid Valid International Journal of Travel.
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0,1946
0,1946
0,7510
0,7440
0,5990
e-ISSN 2828-2590 p-ISSN 2828-5093 Valid Valid Valid [Source: ResearcherAos Analysi.
Based on the results from the eight tables above, analyzed using SPSS (Statistical Product and Service Solution.
software, it can be concluded that the variables personal motivation (X.
, tourism product (X.
, tourism promotion (X.
, income (X.
, recommendations and information from travel service providers (X.
, environment (X.
, and travel decision (Y) are valid.
This validity is supported by the calculated r-values .
-coun.
in the corrected-item-total-correlation section, which are all higher than the corresponding r-table values.
The travel decision variable (Y) also meets this criterion, with its r-count exceeding the r-table value.
Reliability Test To measure reliability, the statistical test used is CronbachAos Alpha ().
variable is considered unreliable if it has a CronbachAos Alpha value below 0.
Based on the data above using SPSS, it can be concluded that the variables of personal motivation (X.
, tourism product (X.
, tourism promotion (X.
, income (X.
, recommendations and information from travel service providers (X.
, and environment (X.
with an alpha value of 0.
957 > 0.
60, as well as the travel decision variable (Y) with an alpha value of 0.
808 > 0.
60, are reliable.
This indicates that the questionnaire is suitable for use in the research.
Normality Test In the normality test, data is regarded as normally distributed when the significance level exceeds 0.
Conversely, if the significance level falls below 0.
the data is deemed not normally distributed.
The three methods employed for conducting the normality test are the Histogram.
P-Plot, and Kolmogorov-Smirnov Test.
Figure 3.
Histogram Visual for Normality Test [Source: ResearcherAos Analysi.
International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
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e-ISSN 2828-2590 p-ISSN 2828-5093 Based on Figure 9 above, the histogram displays a bell-shaped curve that is symmetrical around the mean .
enter poin.
The analysis results indicate that the influence of the independent variables on the decision to visit (Y) is statistically valid.
Therefore, it can be concluded that the histogram confirms the regression model is appropriate for addressing the research questions.
The findings regarding dominant factors are not caused by data anomalies but reflect genuine relationships among the Figure 4.
P-Plot Visual for Normality Test [Source: ResearcherAos Analysi.
Referring to Figure 10, the P-P Plot (Probability-Probability Plo.
illustrates that the data points closely follow the diagonal line throughout.
This confirms that the residuals of the regression model are normally distributed, fulfilling an essential assumption in multiple linear regression.
The consistent alignment with the diagonal suggests there are no systematic deviations in the data, supporting the scientific reliability of the statistical model used to analyze factors influencing tourists' decisions to travel internationally.
Figure 5.
Kolmogrov-Smirnov Visual for Normality Test [Source: ResearcherAos Analysi.
International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 As shown in Figure 11, the Kolmogorov-Smirnov test results indicate a significance value of 0.
200 (> 0.
This means that the residuals from the regression model follow a normal distribution, satisfying a key assumption in linear regression Since the significance value is well above 0.
05, there is no meaningful difference between the residual data distribution and the expected normal distribution.
Therefore, these test results strongly support that the regression model used in this study is suitable and dependable for examining the factors affecting tourists' decisions to visit international destinations.
Multicolinearity Test Table 10.
Result of Multicolinearity Test
Independent Variable Collinearity Tolerance Personal Motivation 0,591 Tourism Product
0,273
Tourism Promotion 0,337 Income
0,842
Recommendation and 0,353 Information Environment 0,326 Hobbies and Interest
0,506
Dependent Variable: Decision to Visit
VIF
1,691
3,664
2,966
1,188
2,830
3,068
1,975
[Source: ResearcherAos Analysi.
Referring to the table above, the multicollinearity test results indicate that all variables have Variance Inflation Factor (VIF) values with tolerance levels greater than 0.
This suggests that the regression model is free from multicollinearity problems.
The VIF values are well below the critical limit of 10, and the tolerance values are significantly 1, demonstrating that each independent variable in this study is distinct and not highly correlated with the others.
Heteroscedasticity Test Table 11.
Result of Heteroscedasticity Test
Independent Variable Personal Motivation Tourism Product
Tourism Promotion Salary Recommendation and Information Environment Hobbies and Interest
Dependent Variable: ABS_RES Significant Score
0,528
0,359
0,760
0,841
0,278
0,419
0,890
[Source: ResearcherAos Analysi.
According to the table above, the heteroscedasticity test results shown in the ABS_RES table reveal that the significance values for all independent variables International Journal of Travel.
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e-ISSN 2828-2590 p-ISSN 2828-5093 This means that the regression model does not suffer from In other terms, the model satisfies the homoscedasticity assumption, ensuring that the analysis results are reliable for answering the research Multiple Linear Regression Figure 6.
Result of Multiple Linear Regression [Source: ResearcherAos Analysis From the multiple linear regression analysis results presented above, the following conclusions can be drawn:
Y = 0 - yu1ycU2 yu2ycU3 yu3ycU4 yu4ycU5 yu5ycU6 yu6ycU7 yu7ycU7
Y = 4,832 0,083 0,126 0,175 -0,058 0,124 -0,058 0,376
Note:
Y = Dependent variable 0 = Constant Based on the multiple linear regression formulation above, the following conclusions can be describe that Personal Motivation (X.
Has a small positive effect .
on travel decisions, meaning higher motivation slightly increases the likelihood of choosing foreign destinations.
However, the impact is limited compared to other Tourism Product (X.
Shows a positive effect .
, indicating that better quality products .
ttractions, accessibility, facilitie.
increase the probability of visiting Foreign destinations are often seen as superior in these aspects.
Tourism Promotion (X.
has a significant positive effect .
, where effective promotions increase the likelihood of international travel.
Strong campaigns, such as South KoreaAos K-Pop tourism strategy, illustrate this influence.
Income (X.
shows a small negative effect (-0.
, suggesting that higher income slightly decreases interest in traveling abroad.
Lower-income groups are more likely to choose foreign destinations due to cheaper packages and favorable currency exchange.
Recommendation (X.
giving a positive effect .
, meaning recommendations encourage international travel, but the influence is moderate.
They matter but are not the strongest driver of Environment (X.
is displays a small negative effect (-0.
, where better environmental conditions surprisingly reduce travel decisions.
This is likely because tourists prioritize visual appeal .
ocial media influenc.
over environmental quality.
International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 Hobbies & Interests (X.
Shows the strongest positive effect .
, making personal interests a major driver of foreign travel.
Passion-based motivations .
KPop, culinary, extreme sport.
significantly shape tourist choices.
Coefficient Determination Test (R.
Figure 7.
Result of Coefficient Determination [Source: ResearcherAos Analysi.
Referring to Figure 13, the RA value is 0.
704, indicating that 70.
4% of the changes in tourists' decisions to visit international destinations are accounted for by the seven independent variables included in the model .
ersonal motivation, tourism product, promotion, income, recommendation, environment, and hobb.
The other 6% of the variation is explained by factors not covered in this model.
T-Tabel Test (Partial Tes.
Figure 8.
Result of T Table [Source: ResearcherAos Analysi.
Based on the data above, the probability value was found to be 0.
05 and the ttable value was 1.
986, with the following formula: T Table = t (/2 .
n Ae k Ae .
= t .
= 1.
The findings can be summarized as follows: Personal Motivation (X.
does not have a significant impact on Visiting Decision (Y), with a significance value 173 (>0.
and a t-statistic of 1.
374 that is lower than the critical t-value of 1.
leading to the rejection of H2.
Tourism Product (X.
also shows no significant effect on Visiting Decision (Y), with a significance value of 0.
097 (>0.
and a t-statistic of 675, so H3 is rejected.
Tourism Promotion (X.
, however, significantly affects Visiting International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 Decision (Y), as indicated by a significance value of 0.
036 (<0.
and a t-statistic of 132 above the t-table, resulting in the acceptance of H4.
Income (X.
does not significantly influence Visiting Decision (Y), shown by a significance value of 0.
(>0.
and a t-statistic of Ae1.
202 below the critical value, causing H5 to be rejected.
Recommendation (X.
has no significant effect, with a significance value of 0.
(>0.
and a t-statistic of 1.
921 below the critical threshold, so H6 is rejected.
Environment (X.
also lacks a significant influence on Visiting Decision (Y), as the significance value is 0.
401 (>0.
and the t-statistic at Ae0.
843 is below the critical t, leading to the rejection of H7.
Finally.
Hobby and Interest (X.
have a significant impact on Visiting Decision (Y), with a significance value of 0.
000 (<0.
and a t-statistic of 491 exceeding the critical value, which means H8 is accepted.
F Test Table Figure 9.
Result of F Table [Source: ResearcherAos Analysi.
l The F-test results show that the calculated F .
> F table .
, which means H0 is rejected.
This indicates that the independent variables collectively have a significant influence on tourists' visiting decisions.
Therefore, the regression model is valid and reliable for prediction.
CONCLUSION
Based on the results of research on tourists from North Sumatra, the following conclusions can be drawn:
Several factors significantly influence decision to travel abroad.
Tourism promotion (X.
and hobbies or interests (X.
show a positive and significant effect, with regression coefficients of 0.
175 and 0.
376, respectively.
This indicates that effective promotion and personal interests are the main drivers in choosing international destinations.
Meanwhile, personal motivation (X.
, tourism products (X.
, income (X.
, recommendations (X.
, and environment (X.
do not show significant effects.
The F-test also confirms the overall validity of the regression model, with an RA value of 0.
704, meaning that 70.
4% of the variation in travel decisions can be explained by the seven independent variables in the model .
ersonal motivation, tourism products, promotion, income, recommendations, environment, and hobbie.
, while the others 29.
was influenced by other factors.
International Journal of Travel.
Hospitality and Events Volume 4 Number 3 Year 2025 Pages 271-287 DOI: 10.
56743/ijothe.
e-ISSN 2828-2590 p-ISSN 2828-5093 These findings indicate that tourists tend to choose international destinations primarily due to personal reasons, such as hobbies and interests, as well as attractive promotions, rather than financial considerations or recommendations from others.
Thus, this study provides valuable insights for tourism stakeholders in designing strategies to enhance the appeal of domestic destinations.
Tourism stakeholders should strengthen digital promotions highlighting the unique culture, cuisine, and authentic experiences of domestic destinations.
Local attractions need to adapt to the preferences of younger tourists, such as cultural, adventure, and instagrammable experiences.
Additionally, offering affordable and flexible travel packages can make domestic tourism more These strategies are expected to enhance North SumatraAos tourism appeal and reduce outbound travel leakage.
REFERENCES