International Journal of Health and Pharmaceutical The Influence of Service.
Price, and Promotion on Online Transportation User Decisions in Medan Rahmat Alamsyah Harahap1*.
Eszra Nicolas Sibarani2.
Melisa Romaito Siahaan3.
A Riris Victoria M Pangaribuan4.
Frida T Telaumbanua5
1,2,3,4,5
Universitas Prima Indonesia.
Medan.
Indonesia *Corresponding Author:
Email : rahmatalamsyahharahap@unprimdn.
Abstract.
Online transportation first became popular in Indonesia with the arrival of Gojek in There are several online transportation services available in Medan, such as Grab.
Gojek.
Maxim, and InDrive.
Service issues include inconsistency in service delivery, drivers who are unfriendly and unprofessional.
Pricing issues include price differences between application platforms and complex fare structures.
Promotional issues include excessive reliance on promotions to attract new users and a lack of user education on promotions in payment methods.
Quantitative descriptive research is the research method, and explanatory research is the nature of the research.
Interviews, questionnaires, and documentation studies are the data collection methods.
Multiple linear regression with classical assumption testing, namely normality, multicollinearity, and heteroscedasticity, is the method of The research population consists of 300 users, 171 of whom were selected through simple random sampling divided into 21 sub-districts in Medan, and the validity and reliability were tested on 30 of them.
The simultaneous effect of service, price, and promotion (F-tes.
has a positive effect on the success rate.
Fcount 524 > Ftable 2.
66 with sig.
000 < 0.
Partially .
-tes.
, service tcount 4.
> ttable 1.
65392 and sig.
000 < 0.
05, price tcount 1.
494 < ttable 1.
65392 and sig.
137 > 0.
05, promotion tcount 1.
769 > ttable 1.
65392 and sig.
079 > 0.
The adjusted Rsquare coefficient test result is 0.
372, meaning that 37.
2% of the decision to use online transportation services in Medan is influenced by service, price, and promotion, while the remaining 62.
8% is explained by other factors.
Keyword: Service, price and promotion.
INTRODUCTION
The development of digital technology, particularly in the internet and mobile applications, has had a significant impact on various sectors of life, including the transportation sector.
One innovation that emerged from this development is online transportation services, namely application-based transportation systems that allow users to order vehicles simply through smartphones.
Online transportation first became popular in Indonesia with the arrival of Gojek in 2010, which initially only served motorcycle taxis through a call center system.
Over time and with increasing public demand.
Gojek then launched a mobile application in 2015.
Not long after.
Grab, originating from Malaysia, entered the Indonesian market, followed by other services such as Maxim and InDrive, further expanding the choice for the public.
The emergence of online transportation is an answer to the problems of conventional transportation, which has long been considered inefficient.
Obstacles such as difficulty in finding public transportation, uncertain fares, long waiting times, and uncertain security are the main reasons people have started to switch to application-based services.
Online transportation is considered more practical, transparent, and fast.
Good service in online transportation, such as ease of ordering, speed of delivery, friendly drivers, and affordable prices, significantly influence users' decisions to continue using the service.
Users of online transportation services simply open the application, order a vehicle, find out the estimated cost, and can even track the driver's location in real time.
Online transportation has several advantages, such as easy access only through smartphones, transparent fares, a choice of cash or digital payment methods, and rating and review features to maintain service quality.
When consumers feel well served, they are more likely to use the service again and even recommend it to others.
Conversely, poor service can reduce consumer satisfaction and trust, causing them https://ijhp.
International Journal of Health and Pharmaceutical to switch to other platforms that are perceived as more responsive and professional.
Price is a crucial factor influencing users' decisions when choosing online transportation services.
Price changes, whether increases or decreases, can significantly impact user interest and choice of an app or type of transportation service.
Price is also a crucial factor influencing consumers' decisions when using online transportation services.
Prices offered must be competitive, affordable, and commensurate with the quality of service provided to ensure consumers feel they are receiving value for money.
In practice, users of services like Gojek.
Grab.
Maxim, and InDrive often compare fares before booking, especially for frequent or long-distance trips.
Excessively high prices without a corresponding improvement in service quality can lead consumers to switch to other platforms offering lower prices for similar services.
Promotions significantly influence online transportation users' decisions.
An effective promotional strategy can increase interest in and use of online transportation services.
Promotions can take the form of discounts, promo codes, or other attractive offers that encourage users to try or continue using the Promotions also significantly influence consumers' decisions to use online transportation services.
Promotions are an effective marketing strategy for attracting new users and retaining existing ones.
Promotions frequently used by service providers like Gojek.
Grab.
Maxim, and InDrive include discounts, travel vouchers, cashback, and loyalty programs.
Amidst increasingly fierce competition, promotions can provide a unique appeal that leads consumers to choose one platform over another.
In fact, in many cases, consumers are willing to switch platforms simply because of a more profitable promotion.
In this study, researchers only examined the influence of service, price, and promotion on the decisions of online transportation users, specifically for people in Medan City.
The online transportation platforms selected were Gojek.
Grab.
Maxim, and InDrive.
Food and goods delivery apps were not examined in this study.
In the results of the initial survey conducted by the researcher, there were several obstacles in carrying out services, prices and promotions regarding the decisions of users of the online transportation application There were several problems such as problems that arose in the service, namely the consistency of service according to the promises given, drivers who were less friendly and unprofessional, punctuality of pick-up and drop-off, slow customer service response, unclear identity and less than adequate vehicle Problems that arise in online transportation prices include price differences between online transformation application platforms, the complexity of the tariff structure .
istance, time, traffic conditions, vehicle typ.
, the mismatch between tariffs and distance, and hidden additional costs.
Problems with promotions include excessive reliance on promotions to attract new users, lack of user segmentation in promotions, low promo claim failure rates, and lack of user education on promotions and payment From the phenomena that have been obtained, the researcher will conduct research on this company and choose the title "The Influence of Service.
Price, and Promotion on the Decisions of Online Transportation Users in Medan".
II.
METHODS
Research methods This research uses a quantitative approach that aims to measure phenomena based on numerical data and analyze them statistically.
The sample was determined using specific sampling techniques and analyzed to test the research hypotheses.
Types of research This type of research is quantitative descriptive with an explanatory approach.
This approach aims to describe the characteristics of variables based on numerical data and explain the relationships between these variables.
Nature of Research This research is explanatory in nature, aiming to understand the relationships between the variables Analysis is conducted to determine how one variable influences another, thus providing a deeper understanding of the interrelationships between variables.
https://ijhp.
International Journal of Health and Pharmaceutical Data collection technique Data collection techniques used include:
Questionnaire Interview Documentation Analysis and Research Methods The research model used in this study is multiple linear regression analysis.
This model is used to measure the influence of the independent variables, namely service (XCA), price (XCC), and promotion (XCE), on the dependent variable, namely the decision of online transportation users (Y) in Medan.
The multiple linear regression model used in this study is as follows:
Y = a bCAXCA bCCXCC bCEXCE e Information:
Y = Decision of online transportation users a = Constant bCA Ae bCE = Regression coefficient of each independent variable XCA = Service XCC = Price XCE = Promotion e = Error .
Hypothesis Determination Coefficient (RA) R Square (RA), which displays the coefficient of determination.
This test aims to measure, in percentage (%), the extent to which the model influences the dependent variable .
ser decisio.
and independent variables .
ervice, price, and promotio.
on the use of online transportation in Medan (Grab.
Gojek.
Maxim, and Indriv.
Simultaneous Hypothesis Testing (F Tes.
This test is used to determine whether the dependent variable .
he decision to use online transportatio.
is significantly influenced by the independent variables, namely service, price, and In this study, the calculated F value will be compared with the F table at a significance level () = 5%.
The criteria for testing the hypothesis in this F test are:
H0 is accepted if Fcount O Ftable for a significance level of = 5% H1 is accepted if F count > F table for significance level = 5% Partial Hypothesis Testing (T-Tes.
This test is used to see whether the independent variables in the regression model, namely service, price, and promotion, have a significant partial influence on the dependent variable, namely the user's decision to use online transportation tools such as Grab.
Gojek.
Maxim, and InDrive in Medan City.
The decision making criteria in the t test are as follows:
H0 is accepted if Ae t table O t count O t table .
ith a significant level of = 5%) H1 is accepted if Ae t count < -t table or t count > t table .
ith a significance level of = 5%) i.
RESULT AND DISCUSSION
Research Results Descriptive Statistical Analysis Table 1 Service Price Promotion User decision Valid N .
Minimum Maximum Mean Standard Deviation 3,807 3,326 2,794 3,424 Source: Results of SPSS data processing https://ijhp.
International Journal of Health and Pharmaceutical From Table 1, the service variable (X.
with a sample size of 171 respondents has a minimum value of 24 units for respondent numbers 34, 79, 83, 89, 101, 104, 111, 169, 171 and a maximum value of 40 units for respondent numbers 22, 38, 51, 164.
The mean value is 29.
73 and has a standard deviation of 3,807.
the price variable (X.
with a sample size of 171 respondents, the minimum value is 24 units for respondent numbers 137, 143, 146, 150, 151, 168 and the maximum value is 38 units for respondent numbers 38, 113.
The mean value is 29.
44 and has a standard deviation of 3.
In the promotion variable (X.
with a sample size of 171 respondents, the minimum value is 23 units for respondent numbers 116, 125, 132, 137, 144, 150, 151, 152, 158, 168 and the maximum value is 34 units for respondent number 109.
The mean value is 97 and has a standard deviation of 2,794.
In the user decision variable (Y) with a sample size of 171 respondents, the minimum value is 23 units for respondent number 52 and the maximum value is 40 units for respondent numbers 7, 13, 22, 37, 50, 113, 122.
The mean value is 28.
82 and has a standard deviation of Results of the classical assumption test a.
Normality test Test graph Histogram graph Fig 1.
Histogram graph In Figure 1, the histogram graph shows a number of bell-shaped lines that do not deviate to the left or right, thus it can be concluded that the data from the researcher's calculations are normally distributed.
Probability plot graph Fig 2.
Probability plot graph In Figure 2, it can be seen that the data points are spread around the diagonal line, the distribution of the data is seen to be mostly close to the diagonal line, thus it can be concluded that the data from the research test results are said to be normally distributed.
Statistical test (Kolmogorov-Smirnov metho.
Table 2.
Statistical test (Kolmogorov-Smirnov metho.
Normal Parametersa,b Most Extreme Differences Mean Standard Deviation Absolute Unstandardized Residual ,0000000 https://ijhp.
International Journal of Health and Pharmaceutical Positive Negative 1,228 Kolmogorov-Smirnov Z Asymp.
Sig.
-taile.
Test distribution is Normal Calculated from data.
From Table 3.
2, the data processing results show an Asymp.
Sig .
-taile.
value of 0.
accordance with the provisions, if the Asymp.
Sig .
-taile.
value is greater than 5% .
, then the residual data is normally distributed.
Multicollinearity Test Table 3.
Multicollinearity Test
Collinearity Statistics Model
Tolerance Service Price
Promotion VIF
1,967
2,087
1,872
From Table 3, the results of the research test show that the tolerance value for the service variable is 508, the price is 0.
479, and the promotion is 0.
534, which are above 0.
For the VIF value for the service variable, 1.
967, the price is 2.
087, and the promotion is 1.
872, which are below 10.
So it can be concluded that in this study there is no multicollinearity between the independent variables .
ervice, price, and promotio.
in the regression model.
Heteroscedasticity Test Scatterplot Graph Test Fig 3.
Scatterplot Graph In Figure 3, the Scatterplot graph shows an irregular distribution of points, some of which are above or below the number zero .
on the Y axis.
The point pattern image is not gathered in one place, so the results of the test on the Scatterplot graph can be concluded that there is no heteroscedasticity.
Glejser Test Table 4.
Glejser Test Model (Constan.
Unstandardized Coefficients Std.
Error
-2,552
1,333
Standardized Coefficients Sig.
Beta
Service 1,914 2,307 Price
1,794
Promotion Dependent Variable: ABS_RES https://ijhp.
International Journal of Health and Pharmaceutical In Table 4, the significant value of the service variable is 0.
022 < 0.
05, the price variable is 0.
05, and the promotion variable is 0.
343 > 0.
Therefore, the results of the Glejser test can be concluded that heteroscedasticity occurs in the service and price variables, while heteroscedasticity does not occur in the promotion variable.
This means that in the service and price variables, the residual variance is not constant at all levels of the independent variable .
ervice and pric.
Meanwhile, in the promotion variable, the residual variance is constant at all levels of the independent variable .
Results of research data analysis Research methods Table 5.
Research methods Unstandardized Coefficients Std.
Error
8,846
2,160
Model (Constan.
Service Price Standardized Coefficients Beta Promotion Dependent Variable: User decision (Y) User decision = 8,846 0,374 service 0,135 price 0,180 promotion 5% Information :
The constant 8.
846 states that if service, price and promotion are absent then the user's decision is 8.
The service regression coefficient is 0.
374 and has a positive value, this states that every 1 unit increase in service will increase user decisions by 0.
374 units, assuming other variables remain constant.
The price regression coefficient is 0.
135 and has a positive value, this states that every 1 unit increase in price will increase the user's decision by 0.
135 units, assuming that other variables remain constant.
The promotion regression coefficient is 0.
180 and has a positive value, this states that every 1 unit increase in promotion will increase user decisions by 0.
180 units, assuming other variables remain R2 Test (Coefficient of Determinatio.
Table 6.
Test of Determination Coefficient Model R Square Adjusted R Square Standard Error of the Estimate 2,714 a Predictors: (Constan.
Service (X.
Price (X.
Promotion (X.
b Dependent Variable: User decision (Y) In Table 6, the results of the coefficient of determination test obtained an Adjusted R Square of 372, meaning that 37.
2% of the variation in the dependent variable .
ser decisio.
can be explained by variations in the independent variables .
ervice, price, and promotio.
Meanwhile, the remaining 62.
% - 37.
2% = 62.
8%) can be explained as having an influence on user decisions caused by other Test (Simultaneous Tes.
Table 7.
Simultaneous Test Model Regression Residual Total Sum of Squares 3,000 167,000 Mean Square 254,347 7,367 34,524 Sig.
Dependent Variable: User decision (Y) Predictors: (Constan.
Service (X.
Price (X.
Promotion (X.
In Table 7, the results of the simultaneous test show a calculated F value of 34.
524 > F table 2.
with a significance probability level of 0.
000 < 0.
Therefore.
H1 is accepted and H0 is rejected.
This means that the independent variables .
ervice, price, and promotio.
simultaneously have a positive and significant effect on the dependent variable .
ser decisio.
https://ijhp.
International Journal of Health and Pharmaceutical T-Test (Partial Tes.
Table 8.
Partial Test Model (Constan.
Service Price Promotion Unstandardized Coefficients Std.
Error
8,846
Standardized Coefficients Beta
4,096
4,883
1,494
1,769
Sig.
Dependent Variable: User decision (Y) Based on Table 3.
8, the partial test obtained the service variable t count 4.
883 > t table 1.
65392 and a significant value of 0.
00 < 0.
Therefore.
H1 is accepted and H0 is rejected.
This means that the partial test results indicate that the service variable .
has a significant influence on the user decision variable .
of online transportation in Medan.
Thus, hypothesis H1 is accepted and H0 is Based on Table 3.
8, the partial test obtained a price variable t count of 1.
494 < t table 1.
65392 and a significant value of 0.
137 > 0.
Therefore.
H0 is accepted and H2 is rejected.
This means that the partial test results indicate that the price variable .
does not have a significant influence on the user decision variable .
of online transportation in Medan.
Thus, the hypothesis H0 is accepted and H2 is rejected.
Based on Table 3.
8, the partial test obtained a promotional variable t count of 1.
769 > t table 1.
65392 and a significant value of 0.
079 > 0.
Therefore.
H0 is accepted and H3 is rejected.
This means that the partial test results indicate that the promotional variable .
does not have a significant influence on the user decision variable .
of online transportation in Medan.
Thus, the hypothesis H0 is accepted and H3 is rejected.
Discussion of research results: The influence of service on user decisions Based on Table 8, the partial test shows that the service variable has a calculated t of 4.
883 > t table 65392 and a significant value of 0.
00 < 0.
Therefore.
H1 is accepted and H0 is rejected.
This means that the partial test results indicate that the service variable .
has a significant influence on the user decision variable .
of online transportation in Medan.
Thus, hypothesis H1 is accepted and H0 is rejected.
The results of this study align with those of a previous study by Ardiansyah and Rangkuti .
entitled "The Influence of Driver Attributes and Service Quality on Consumer Decisions Using PT.
Grab Indonesia's Online Transportation Service in Medan.
" The results showed that the service quality variable has a positive and significant influence on consumer decisions.
In general, the quality of driver service is still relatively low and unsatisfactory.
Various consumer complaints are caused by several factors, including poor vehicle condition, drivers smoking while driving, and drivers unilaterally canceling orders.
According to Zeithaml.
Bitner, and Gremler .
, good service creates a positive experience, which can increase loyalty and repeat purchase decisions by consumers.
The influence of price on user decisions Based on Table 3.
8, the partial test obtained a price variable t count of 1.
494 < t table 1.
65392 and a significant value of 0.
137 > 0.
Therefore.
H0 is accepted and H2 is rejected.
This means that the partial test results indicate that the price variable .
does not have a significant influence on the user decision variable .
of online transportation in Medan.
Thus, the hypothesis H0 is accepted and H2 is rejected.
The results of this research are in accordance with the results of previous research by researchers from Agung,Suneni and Ika .
with the research title "The Influence of Service Quality.
Price and Brand Image on Consumer Satisfaction of Grab Online Motorcycle Taxi Users".
The results of the study showed that the price variable had no effect on consumer satisfaction where the results of the study obtained a calculated t value of 1.
780 < t table 1.
There are several reasons why price does not influence consumer decisions, such as: consumers prefer good service quality, brand image of online transportation, consumers are used to it nowadays and consumers feel that online transportation has become a daily https://ijhp.
International Journal of Health and Pharmaceutical The influence of promotions on user decisions Based on Table 8, the partial test shows that the promotional variable has a calculated t-value of 769 > t-table of 1.
65392 and a significant value of 0.
079 > 0.
Therefore.
H0 is accepted and H3 is This means that the partial test results indicate that the promotional variable .
does not have a significant influence on the user decision variable .
of online transportation in Medan.
From the results of this study, promotions do not have a strong enough influence to influence the decision to use online transportation.
Thus, the hypothesis H0 is accepted and H3 is rejected.
The results of this study are in accordance with the results of previous research by Sukmawati.
, and Nasir.
M .
with the title "Analysis of the Influence of Service Quality.
Price, and Promotion on Online Transportation User Decisions in Semarang City.
" The results of the study indicate that the Promotion variable has no significant effect on online transportation user decisions in Semarang City with a calculated t = 1.
123 with a sign of According to Tjiptono .
, promotion is a form of marketing communication that functions to convey information, influence, and remind the target market about the existence of a product or service, and encourage purchasing decisions.
There are several reasons why promotions do not have a significant influence on online transportation decisions: more important service quality, competitive prices, user habits, lack of trust, availability of information, security, comfort, and punctuality.
IV.
CONCLUSION AND SUGGESTION
Conclusion Based on Table i.
8, the partial test shows that the service variable has a calculated t of 4.
> t table of 1.
65392 and a significant value of 0.
00 < 0.
Therefore.
H1 is accepted and H0 is This means that the partial test results indicate that the service variable .
has a significant influence on the user decision variable .
of online transportation in Medan.
Thus, the hypothesis H1 is accepted and H0 is rejected.
Based on Table i.
8, the partial test obtained for the price variable t count 1.
494 < t table 65392 and a significant value of 0.
137 > 0.
Therefore.
H0 is accepted and H2 is rejected.
This means that the partial test results indicate that the price variable .
does not have a significant influence on the user decision variable .
of online transportation in Medan.
Thus, the hypothesis H0 is accepted and H2 is rejected.
Based on Table i.
8, the partial test shows that the promotional variable has a calculated tvalue of 1.
769 > t-table of 1.
65392 and a significant value of 0.
079 > 0.
Therefore.
H0 is accepted and H3 is rejected.
This means that the partial test results indicate that the promotional variable .
does not have a significant influence on the user decision variable .
of online transportation in Medan.
Thus, the hypothesis H0 is accepted and H3 is rejected.
Based on the results of the simultaneous test (F Tes.
, the results of the simultaneous test obtained a calculated F value of 34.
524 > F table 2.
66 with a significance probability level of 0.
Therefore.
H1 is accepted and H0 is rejected.
This means that the independent variables .
ervice, price and promotio.
simultaneously have a positive and significant effect on the dependent variable .
ser decisio.
Based on the results of the determination coefficient test (R2 Tes.
, the Adjusted R Square was 372, meaning that 37.
2% of the variation in the dependent variable .
ser decisio.
can be explained by the variation in the independent variables .
ervice, price, and promotio.
While the remaining 8% .
% - 37.
2% = 62.
8%) can be explained as having an influence on user decisions caused by other variables.
From the results of this study, it was found that the variable that had the most influence on the decision of online transportation users in Medan (Y) was the service variable (X.
with a calculated t of 4,883, followed by the promotion variable (X.
with a calculated t of 1,769 and the price variable (X.
with a calculated t of 1,494.
https://ijhp.
International Journal of Health and Pharmaceutical Suggestion In running an online transportation business, especially in the city of Medan, the company must formulate a sales strategy by implementing: good service, clear promotions and competitive prices with existing online transportation companies in Medan.
For the Faculty of Economics.
Prima Indonesia University, it is recommended that the results of this research be published so that they can be used as reference material for further research.
For future researchers, it is recommended to add other variables beyond those already studied.
The coefficient of determination (RA) test yielded an Adjusted RA value of 37.
The higher the RA value, the more significant the independent variable's influence in explaining variation in the dependent variable.
REFERENCES