The Impact of Online Application Competition, Discount Prices and

Kontigensi: Jurnal Ilmiah Manajemen

Vol. 9, No.1, June 2021, pp. 215 - 223

ISSN 2088-4877

215

Cinemax Service Innovation on Online Cinema Ticket Purchase Decisions

in Medan City

Safira Nurul Sakinah Kacaribu

, Hendra Jonathan Sibarani

Universitas Prima Indonesia

E-mail: [email protected]

, [email protected]

ABSTRACT

This study was conducted to explain the impact of online application competition, discounted prices,

and Cinemax service innovation on online cinema ticket purchasing decisions in Medan. The quantitative

descriptive method is used in the research. The online cinema ticket ordering application (TIX ID) in the East

Medan area is 112,482 people, and the sampling technique using the slovin formula is obtained by 100

respondents. The results of this study are the application competition variable (X1) obtained a value of thtung>

t-table (3.935> 1.290), the discounted price variable (X2 ) obtained a value of t-count> t-table (1.715> 1.290),

the service innovation variable (X3) obtained a value of thtung> t-table ( 5.795 > 1.290 ) has a significant positive

effect with a value of < 0.1 on online cinema ticket purchasing decisions in the city of Medan,

Keywords: Online Application Competition, Discount Price, Service Innovation, Purchase Decision

INTRODUCTION

Increasingly advanced and developing times,

there are many various developments, for

example, developments in technology. Almost all

work is done using technology due to the

emergence of very sophisticated technology that

can increase the effectiveness and efficiency of a

person in doing work. Indonesia is a country that

has experienced developments in technology,

and the Indonesian people have also been very

able to accept developments in technology such

as technology on smartphones; at this time,

almost everyone from young to old age already

has a smartphone and understands how to use it

the technology. One example of the development

of existing smartphone technology is the

emergence of a cinema ticket booking

application. In the past, when someone wanted to

watch a movie, they were required to go to the

place to buy tickets directly, but now in this fast

and sophisticated era, there is a development in

cinema ticket booking services, namely the online

cinema ticket booking service. The service aims

to make customers who want to get show tickets

films gain convenience and efficiency. In the city

of Medan, this online cinema ticket booking

application has entered the world of Cinemax;

several cinemas in the city of Medan have already

made reservations for cinema tickets online and

to purchase cinema tickets. It is also convenient,

only by ordering and transactions via smartphone

and then prints the ticket on the Cinemax of

choice. It is what makes some people in the city

of Medan decide to buy cinema tickets online.

However, some still like to buy cinema tickets face

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to face because they still consider it more

practical to make direct purchases. The decision

is meant by choosing between two or more

actions from the available options. In other words,

when making a decision, the person must choose

one of all options. To fulfill their daily needs,

consumers must ensure the products and

services they will use. Most options and

circumstances encountered in considering

something make the difference in the decisions

obtained from one individual to another. For

example, e-ticketing sales are currently the

people's choice to make cinema ticket purchasing

decisions because making purchases online can

increase time efficiency and is practically used

where buyers can buy tickets anywhere and

anytime. Competition is intended as something

that businesses will do, namely competing for

profits, total sales, and market share as in this

online cinema ticket booking application which is

increasingly in demand from year to year; as a

result, various online cinema ticket booking

applications have emerged, some of which are

TIX. ID, CGV Cinemas. Cinepolis Indonesia,

MTIX, Book My Show, Go Tix and Traveloka.

Because of that came the competition between

these applications; they competed with each

other to attract consumers and become superior

among others.

Discount prices play a vital role in attracting

people to make decisions about their purchases.

For example, discount prices are often presented

from one of the TIX ID online cinema ticket

booking applications, namely buy one get one

with the intention that they will get one free ticket

just by buying one ticket, but in giving the

discount, it is not given for free, but based on the

terms and conditions. Likewise, a 50% discount

price is given if we buy the first ticket in the latest

film show and of course there are many other

discount prices provided by the TIX ID

application. Another reason that can influence

someone to make a purchase decision is service

innovation. With the nature of people who want to

be practical and do not want to miss technological

advances, this application service must vary and

develop services to make it easier and attract

consumers. As an example of service innovation

presented by the TIX ID application, namely

developing in terms of payments, for example,

collaborating with applications in payments such

as through the DANA application, and more. So

that consumers can more easily pay for online

cinema tickets. For this research, the type of

online application service for cinema ticket

booking chosen is TIX ID because this application

is very prominent and has many devotees

compared to other online cinema tickets booking

applications. PT publishes this application.

Nusantara Raya Sejahtera, this company was

founded in 1985 and was only issued on March

21, 2018. According to data from the 2021

Appstore, the TIX ID application is the most

popular online ticket ordering application with a

total rate of 4.8/5 out of 49.1 thousand ratings,

while the data shows Playstore 2021 TIX ID gets

more than 5 million users with a total rate of 4.7/5

from 408 thousand reviews.

Literature review

1. Competition

Fauzi (2015:17) states that marketing

competition is a situation in which a company

advertises certain items or services with or

without specific laws to achieve the advantages of

its clients. Basrowi (2011) explained the meaning

of competitors as people who pursue target

markets accurately. Compare products, prices,

distributors, promotions with competitors must

continue. The indicators of competition in this

application are (1) speed of access, (2) quality of

service (3) number of competitor sizes.

2. Discount Price

Aryani and Rosinta (2010) stated that price

is the amount of value that customers trade by

owning or utilizing a product or service for various

advantages. Discounts can contribute to the

perception of poor product quality and therefore

discourage buyers from buying. Therefore,

optimal discount price to optimize product sales is

significant. (Puligadda 2012). The price discount

indicators put forward by Wahyudi (2017): (1)

discount frequency (2) the amount of discount

and according to Belch & Belch (2009:342)

discount price indicators (1) Trigger mass

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purchases by customers. (2) predict the

promotion of competitive aid (3) to increase the

volume of aid.

3. Service Innovation

Hartini (2012) suggests that innovation is the

openness of new ideas to thoughts. Delafrooz et

al. (2013) stated that this innovation plans to

implement creative steps that lead to developing

new products or services. Innovation is a

combination of activities that impact each other by

producing new goods, causing more customer

interest in procurement decisions proposed by

Myers and Marquis in Kotler (2014:36). In

Siyamtinah's research (2011), there are factors to

analyze the process of building innovation

capabilities, including (1) new product

development, (2) interaction and communication,

(3) technology strategy, (4) marketing capability,

(5) production and operation capabilities

4. Buying decision

Sangadji et al. (2013) suggested the decision to

choose an action from two or more options. In

Sinambow (2015), consumer choice is the reason

or impetus for something customers buy for their

requirements and desires. Kotler and Keller

(2012:154) suggest that there are three indicators

of purchasing decisions, namely: (1) the stability

of a product, (2) providing recommendations to

others (3) repurchasing.

Conceptual Framework

Based on the description of the theory, a

conceptual framework in the research was drawn

up the picture below:

Research Hypothesis:

H1: Application competition has an impact on

purchasing decisions for online cinema tickets in

the city of Medan

H2: Discounted prices have an impact on

purchasing decisions for online cinema tickets in

the city of Medan

H3: Service innovation has an impact on

purchasing decisions for online cinema tickets in

the city of Medan

H4: Application competition, discounted prices,

service innovations have an impact on online

cinema ticket purchasing decisions in the city of

Medan

METHOD

The descriptive method through quantitative

approach applied in research. This study's

population is the community from the district of

Medan Timur, amounting to 112,482 people

sourced from https://medankota.bps.go.id/. The

type of sampling in this study is accidental

sampling through the type of qualified volunteer

sample. It is because respondents who meet the

criteria are willing to become respondents. The

slovin formula is the technique used in this study.

Data were obtained from questionnaires,

meaning that they gave some written questions to

the people of the East Medan sub-district, which

were then sampled in this study. Thus, primary

data is direct research data. In comparison,

secondary data comes from data that already

exists, such as books and journals.

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RESULT and DISCUSSION

1. Validity and Reliability Test

Table 1 Validity Test.

Question

Application Competition

Discount Price

Service Innovation

Buying decision

Pearson

correlation

significant

Pearson

correlation

significant

Pearson

correlation

significant

Pearson

correlation

significant

0.558

0.001

0.712

0.000

0.809

0.000

0.585

0.001

0.663

0.000

0.765

0.000

0.653

0.000

0.527

0.003

0.625

0.000

0.462

0.010

0.536

0.002

0.607

0.000

0.749

0.000

0.625

0.000

0.573

0.001

0.672

0.000

0.621

0.000

0.623

0.000

0.605

0.000

0.756

0.000

0.548

0.002

0.511

0.000

0.749

0.000

0.663

0.000

0.697

0.000

0.640

0.000

0.683

0.000

0.616

0.000

0.757

0.000

0.632

0.000

0.693

0.000

0.680

0.000

The test data for the validity of the application

competition variables, discount prices, service

innovation, and purchasing decisions obtained

the correlation value more significant than the r

table value of 0.30 and the significant value less

than 0.1, meaning that all questions were

declared valid.

Table 2. Reliability Test

Variable Name

Cronbach Alpha

N Of Items

Information

Application Competition

0.694

Reliable

Discount Price

0.850

Reliable

Service Innovation

0.848

Reliable

Buying decision

0.701

Reliable

The table data above shows that the variables

of application competition, discount prices,

service innovation, and purchasing decisions are

reliable and can be continued in research

because the Cronbach alpha value found is more

significant than 0.60.

2. Descriptive Statistics

Table 3. Descriptive Statistics

Minimum

Maximum

mean

Std. Deviation

Statistics

Std. Error

Statistics

App competition

100

24.09

,235

2,349

Discount price

100

41.28

,356

3,565

Service innovation

100

40.33

,360

3,596

Buying decision

100

23.89

,294

2,940

Valid N (listwise)

100

Based on table 3, the application competition

variable has a sample of 100 respondents with an

average value of 0.235, the lowest value of 20,

the highest value of 29, and a standard deviation

of 2.349. The discount price has an average value

of 0.356, the lowest value of 35, the highest value

of 47, and a standard deviation of 3.565. Service

innovation has an average value of 0.360, the

lowest value is 34, the highest value is 46, and the

standard deviation is 3,596. Finally, the purchase

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decision has an average value of 0.294, the

lowest value is 19, the highest value is 29, and the

standard deviation is 2,940.

3. Classic assumption test

This test confirms certainty if the equation

from the regression obtained has accuracy in the

estimation and is consistent.

a. Multicollinearity Test

Ghozali's (2018:105) multicollinearity test is

used to test whether there is a relationship

between the independent variables identified in

the regression model. Tolerance and VIF values

can be used to see multicollinearity testing. For

example, if the tolerance value is > from 0.10,

there is no multicollinearity, but there is

multicollinearity if the tolerance value is < from

0.10. Meanwhile, in VIF, if VIF < 10.00 means no

multicollinearity, but if VIF > 10.00 means

multicollinearity occurs.

From the results of the variable test, it is

known that the tolerance value for the application

competition independent variable is 0.325 > 0.1.

On the other hand, the discount price

independent variable is 0.690 > 0.1, the service

innovation independent variable is 0.308 > 0.1,

while the VIF value for the application competition

independent variable is 3.076 < 10.00. The

independent variable discounted price is 1.449 <

10.00, and the independent variable is service

innovation 3.243 < 10.00 that there is no

correlation between the independent variables

between application competition, discounted

prices, and service innovation.

b. Normality test

In testing the regression model, it is found that

the independent and dependent variables are

standard or not, which is the purpose of the

normality test (Ghozali 2018:111).

Figure 1. Histogram Normality Test

In the histogram graph, this test shows

that the data forms a bell-shaped curve graph,

and the data also does not bend to the right or the

left so that it can be defined that the data is

typically distributed.

Figure 2. PP Plot Normality Test

Based on the pp plot normality test image in

Figure 2, it can be seen that the line is followed by

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parallel points and is located around the diagonal

line. With this, it is known that the data meet the

normality assumption test.

It is known that if the probability is > 0.1, then

the data distribution is considered normal.

However, from table III.5 of the significance as

much as 0.200. Therefore, the significance value

is more significant than 0.1, meaning that the data

is typically distributed.

c. Heteroscedasticity Test

The purpose of this heteroscedasticity test

stated by Ghozali (2018:135) aims to find out

whether the variance inequality between residual

observations exists in the regression model. After

the data is processed, there are results that all

variables contain a significant value greater than

0.1. The application competition variable is 0.316

> 0.1, the discount price is 0.726 > 0.1 and the

service innovation is 0.517 > 0.1. So it can be

summarized that there are no heteroscedasticity

symptoms that occur and can meet the criteria for

the classical assumption test.

Figure 3. Scatterplot. Heteroscedasticity Test

From Figure 3, it is summarized that there is

no heteroscedasticity symptom because the data

points for the spreader are above and below the

number 0, and the spread is not patterned.

4. Multiple Linear Regression Analysis Test

Several linear regression tests were

conducted to see how the independent variable

(independent) affects the dependent variable

(dependent). Based on the table above, the

multiple linear regression equation obtained is:

Y = 6.754 + 0.425 X1 + 0.084 X2 + 0.420 X3 + e

With this, it can be interpreted that the

constant of -6.754 means that if the value of the

independent variable, namely application

competition, discount prices, and service

innovation, increases by one percent, the value of

the purchasing decision variable will also find a

decrease of -6.754 and when the value of the

independent variable decreases by one percent,

the value of the purchasing decision variable will

decrease increase by – 6,754.

Application competition (X1) has a regression

coefficient of 0.425 and is positive; this means

that when it increases by 1 percent the application

competition variable will increase purchasing

decisions by 0.425 or 42.5% if the other variables

are assumed to be constant. With this, it can be

concluded that application competition is a

change in increasing purchasing decisions.

The discount price (X2) has a regression

coefficient of 0.084 and is positive; this means

that when it increases by 1 percent, the discount

price variable will increase the purchasing

decision by 0.084 or 8.4% if the variable is

assumed be constant. With this, it can be

concluded that the discount price is a change in

increasing purchasing decisions.

Service innovation (X3) has a coefficient of

0.420 and is positive; this means that when the

service innovation variable increases by one

percent, it will increase purchasing decisions by

0.420 or 42.0% if the variable is assumed to be

constant. With this, it can be concluded that

service innovation is a change in increasing

purchasing decisions.

5. Coefficient of Determination ( R 2 )

Ghozali (2011: 97) states how far the model

can represent fluctuations in the dependent

variable, especially measuring coefficients. For

example, if the value of R Square is minus or

negative in the research, it can be interpreted that

there is no impact between the X variable and the

Y variable. Likewise, if the R Square value

increasingly leads to the number 1, the impact will

be more substantial.

From the summary model, it can be seen that

the coefficient of determination or R Square is

0.767 or 76.7%. This figure means that the

competition variable (X1), discounted prices (X2),

and service innovation (X3) together have an

impact on the purchasing decision variable (Y) by

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76.7%. In comparison, the remaining 23.3% is

influenced by other variables outside this

regression equation or on variables that are not

examined, such as service quality, promotion,

customer satisfaction, and more.

6. Hypothesis testing

a. Simultaneous Hypothesis Testing (F Test)

It is known that this F test aims to see how the

impact of application competition, discount prices,

and service innovation on online cinema ticket

purchasing decisions in the city of Medan.

Table 4. F . test

ANOVA

Model

Sum of

Squares

Mean Square

Sig.

Regression

656,703

218,901

105.554

,000b

Residual

199,087

2,074

Total

855,790

a. Dependent Variable: decision

b. Predictors: (Constant), innovation, price, competition

From Table 4, it can be obtained that the

Fhtung value is 105.554, and the significant value

is 0.000. The value in F-count is compared with

the Ftable value, which is 2.70 (obtained by

looking at Ftable with criteria df1 = 3 and df2 =

greater than 97). so that the F-count value > F-

table (105.554 > 2.70) and with a significance

value of 0.000 < 0, 1 with this it can be concluded

that application competition, discount prices, and

service innovation together have a positive and

significant impact on purchasing decisions for

online cinema tickets in the city of Medan.

b. Partial Hypothesis Testing (T-Test)

The t-test was carried out to test the

independent variables partially, namely

application competition (X1) discounted prices

(X2) service innovation (X3) which had a positive

and significant impact on purchasing decisions for

online cinema tickets in the city of Medan.

Table 5. T . test

Coefficients

Model

Unstandardized Coefficients

Standardized

Coefficients

Sig.

Std. Error

Beta

(Constant)

-6.754

1,889

-3,576

.001

App competition

,425

,108

,340

3,935

,000

Discount price

,084

0.049

,102

1,715

0.090

Service innovation

,420

,072

,514

5,795

,000

a. Dependent Variable: Purchase decision

Based on Table 5, it can be seen that:

1. Partially test the hypothesis that the

application competition variable from the

table above is known to have t count > t table

(3.935 > 1.290) and the significance is less

than 0.1, meaning that the application

competition variable (X1) partially has a

significant positive impact on purchasing

decisions (Y) for online cinema tickets. In the

city of Medan. It is in line with previous

research conducted by Hermanto (2018). In

this study, it was stated that there was an

impact of competition on purchasing

decisions.

2. Partially test the hypothesis of the

discounted price variable from the table

above, it is known that t-count> t-table

(1.715> 1.290) and the significance is

smaller than 0.1 means the discounted price

variable (X2) partially has a significant

positive impact on purchasing decisions (Y)

online cinema tickets in Medan city.

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Furthermore, reported from the journal

processed by Hendra Jonathan Sibarani et

al. (2018), there is a significant impact of

discount prices on purchasing decisions.

3. Partially test the hypothesis of the service

innovation variable from the table above t-

count> t-table (5.795 1.290) and the

significance is smaller than 0.1, means that

the service innovation variable (X3) partially

has a significant positive impact on

purchasing decisions (Y) online cinema

tickets in the city of Medan. Thus, innovation

has a significant influence on buyer

decisions. This statement is confirmed by

research from Supriyatim Darham and

Herawati (2017).

CONCLUSION

Based on the questionnaire results in this

study, it is summarized that the residents of the

East Medan area, users of the online cinema

ticket booking application, prioritize the quality of

the application's service in deciding its use

because if the service quality is the best. It can be

ascertained that the application works well, too;

therefore, the online cinema ticket booking

application. Therefore, this company must

prioritize the quality of its services in order to face

the competition. From the results of hypothesis

testing, it was found that t-count > t-table (3.935 >

1.290) and the significant value (0.000 < 0.1)

means that the competition variable partially has

a significant positive impact on purchasing

decisions.

The public will be presented with attractive

promos and fantastic discounts when using an

online cinema ticket booking application. It makes

people interested in purchasing cinema tickets

through an online cinema ticket booking

application. Timur strongly agrees that this

application can apply the public's first choice to

choose an online cinema ticket booking

application by providing attractive discount prices.

It is also an advantage for this application

because it can anticipate promotions from

competitors. The hypothesis test summarized

from this study is t count > t table (1.715 > 1.290)

and the significant value (0.090 < 0.

Factors that impact purchasing decisions are

service innovations due to some people want to

be fast and practical; it is very suitable to decide

to buy cinema tickets through this online cinema

ticket booking application. Based on the results of

this research questionnaire, residents of the East

Medan area stated that interaction and

communication became an innovation that

impacted purchasing decisions. A good

relationship between the service and the user

would have a positive impact on increasing

purchasing decisions, and this is in line with the

results of the research hypothesis test in this

journal stated by t t > t table (5.795 > 1.290) and

a significant value (0.000 < 0.1) namely the

service innovation variable has a significant

positive effect on purchasing decisions. In this

study, the value of F-count >

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