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Go Back       IAR Journal of Business Management | IAR J Bus Mng, 2(2), | Volume:2 Issue:2 ( April 20, 2021 ) : 79-85
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DOI : 10.47310/iarjbm.2021.v02i02.011       Download PDF       HTML       XML

Factors Influencing Students in Choosing Private Higher Education

In The City of Medan


Article History

Received: 20.03.2021 Revision: 30.03.2021 Accepted: 10.04.2021 Published: 20.04.2021


Author Details

Mesra B1, Dewi Mahrani Rangkuty2, Megasari Gusandra Saragih1 & Ramadhan Harahap1


Authors Affiliations

1Department of Management, Universitas Pembangunan Panca Budi, Indonesia


2Department of Economics, Universitas Pembangunan Panca Budi, Indonesia


Abstract: The very limited number of State Universities is an opportunity for private universities for students who do not qualify for PTN. But in reality there are still many private universities that experience a shortage of students and some have even decreased students continuously for the last five years. This phenomenon occurs not only for private universities that have not been accredited, even private universities that have been well accredited have also experienced a decrease in the number of students. This has become a constant concern for university administrators in order to get out of this problem. So that the purpose of this study is to determine what factors influence student decisions in choosing to study at private universities in the city of Medan. Data were collected by means of a questionnaire to 162 active students at 5 universities who were sampled using a proportional sampling technique based on a sample taken from only one sub population but well known. This study uses a factor analysis method which is carried out on 27 indicators that influence student decisions in choosing to study at private universities in the city of Medan. Based on the results of calculations using factor analysis, there are 27 indicators which are grouped into 6 factors, service, physical evidence, reputation, trust, trust and commitment. Of the six factors, these factors are the most dominant factors in influencing students in choosing private universities in the city of Medan City.


Keywords: Factor Analysis; Student Decisions in Choosing College in the City of Medan


INTRODUCTION

The limited capacity of State Universities creates great opportunities for private universities. The private universities was established and even grew so fast when compared to the growth of State Universities. Even now, the number of private universities in Indonesia is more than ten times. Likewise with the development of private universities in North Sumatra Province, especially in the city of Medan.


The Medan is the provincial capital as well as the metropolitan city in North Sumatra Province as well as the surrounding provinces. So it's no wonder the city of Medan is the main attraction for prospective students in studying. There are so many private universities in the city of Medan City with the various advantages they display in attracting prospective students to study at their institutions. Based on data from Region I Higher Education Service Institutions in 2019, there are 144 private universities in the city of Medan, consisting of 48 Academies, 9 Polytechnics, 59 High Schools, 4 Institutes and 24 Universities.


A university is a college that provides education in many fields of science, both exact and social, so that the number of students is relatively higher compared to other levels. With 24 universities in the city of Medan, it causes various statuses starting from differences in accreditation, the diversity of study programs offered and the facilities provided by the private universities. From that difference, students must be observant in determining which private universities they will enter. Good accreditation is not a guarantee that it will increase the number of students and even decrease the number of students. Here are some private universities in the form of universities that have experienced a decline in students in the last five years.


Table 1. Private Universities That Have Decreased Number of Active Students in the city of Medan City


No.


Private Universities

Year

2014

2015

2016

2017

2018


1.

Universitas Muhammadiyah

Sumatera Utara


20.694


22.943


17.691


15.668


14.823


2.

Universitas Muslim

Nusantara Al-Washliyah


8.157


7.615


7.385


6.825


6.014

3.

Universitas Dharmawangsa

3.317

3.380

3.308

3.068

2.684

4.

Universitas Al Washliyah

2.244

1.855

1.909

1.788

1.586

5.

Universitas Quality

2.551

2.183

1.975

1.621

1.251

Source: processed data, 2019


Formulation of the Problem

Based on the background and description developed above, the problems to be studied are formulated as follows:

  1. What are the factors that influence students to decide to study at private universities in the city of Medan?

  2. Among these factors, which factors have the dominant influence on student decisions to choose to study at private universities in the city of Medan?


Research Purposes

  1. To identify and analyze the factors that influence student decisions to choose to study at private universities in the city of Medan.

  2. To identify and analyze the factors that have the most dominant influence on student decisions to study at private universities in the city of Medan.


Literature Review and Hypotheses

Consumer Behavior Theory

Consumer behavior is a process that is closely related to the purchasing process. At that time, consumers carry out activities such as searching, researching, and evaluating products. Consumer behavior is the things that underlie consumers to make purchase decisions. What is included in consumer behavior is the quality of the product, the price of the product or service.


According to Mowen and Minor (2009) defines consumer behavior as a study of buying units and exchange processes that involve the acquisition, consumption of various products, services and experiences as well as ideas. According to Lamb, et al (2004) in Sumarwan (2011) states that consumer behavior is the process of a customer in making decisions to buy, use and consume purchased goods and services, including factors that influence purchasing decisions and product use. According to Lawson (2010), consumer behavior is an action that is directly involved in obtaining, consuming and consuming products and services including the decision process that precedes and follows these actions.


Factors That Influence Consumer Behavior

According to Kotler and Keller (2012), consumer purchasing behavior is influenced by cultural, social, personal and psychological factors.


Buying Decision Process

According to Kotler and Keller (2012), the stages that a buyer passes to reach a buying decision pass through five stages, namely: problem recognition, information search, alternative evaluation, buying decisions, and post-purchase behavior.


Conceptual Framework and Hypotheses

Picture Image is available at PDF file


Hypotheses:

H1: Service factors, physical evidence, reputation, satisfaction, trust and commitment influence student decisions in choosing private tertiary institutions in the city of Medan.


H2: The commitment factor has the most dominant influence on student decisions in choosing private universities in the city of the city of Medan.


Research Method

In this study the researchers took the research locations at five universities, namely: Muhammadiyah University of North Sumatra, Muslim Nusantara University, Dharmawangsa University, Al-Wasliyah University and Quality University. Taking the location refers to the research objective to find out and analyze the factors that influence students in choosing private universities in the city of the city of Medan. The choice of research object was because in the last five years the university had decreased students. The research used here is an exploratory research, namely research that explores and analyzes the factors that influence students in choosing private universities in the city of Medan.


Data and Analysis

Data obtained using sampling or survey methods. Surveys are limited to studies in which data are collected from a sample over a population to represent the entire population. In this study, the analyzed students at five universities in the city of Medan City. Primary data were collected using interview techniques and questionnaires. Interviews were conducted with the deans and academic departments of the five sample universities while the questionnaire was given to students who were selected as samples.


Population and Sample

The population of this research is active students at five universities in the city of Medan City at least who have been in college for four semesters, totaling 26,358 people. The sampling technique used accidental sampling, by means of 5 x indicators. The number of indicators in this study was 27, so the sampling was 5 x 27 = 135 samples.


RESULTS AND DISCUSSION

Factor Analysis

KMO and Bartlett's value


Table 2. Value of KMO and Bartlett's Test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.896

Bartlett's Test of Sphericity

Approx. Chi-Square

3566.976

df

351

Sig.

.000

Source: output SPSS, 2020


Table 2 shows that the KMO value is still in the interval 0.5 - 1.0, which is 0.896, and the significance value is 0.000 (<0.05), which means that it can be analyzed further.


Anti Image Correlation


No.

Indicator

MSA

No.

Indicator

MSA

1.

Z2.1

.885

15.

X1.9

.872

2.

Z2.2

.848

16.

X1.10

.855

3.

Z2.3

.926

17.

X1.11

.952

4.

Z1.1

.877

18.

X1.12

.926

5.

Z1.2

.850

19.

X1.13

.904

6.

Z1.3

.859

20.

X1.14

.913

7.

X1.1

.885

21.

X2.1

.839

8.

X1.2

.888

22.

X2.2

.840

9.

X1.3

.922

23.

X2.3

.893

10.

X1.4

.907

24.

X2.4

.934

11.

X1.5

.930

25.

X2.5

.932

12.

X1.6

.923

26.

Z3.1

.949

13.

X1.7

.903

27.

Z3.2

.681

14.

X1.8

.945




Source: output SPSS, 2020


Determination of the Number of Factors

Eigenvalues greater than 1.0 were retained and entered in the model. All eigenvalues are more than 1.0 which means that all factors are included in the model. The eigenvalues can be seen in Table 3.


Table 3. Total Variance Explained

Factor

Eigenvalue

% Variance

Cumulative %

Factor 1

12.105

44.832

44.832

Factor 2

2.721

10.078

54.910

Factor 3

1.610

5.962

60.872

Factor 4

1.350

5.001

65.873

Factor 5

1.163

4.306

70.179

Factor 6

1.070

3.964

74.143

Source: output SPSS, 2020


Classification of Components into Factors

Factor After Rotation


Table 4. Rotated Component Matrix

Rotated Component Matrixa


Component

1

2

3

4

5

6

X1.3

.846

.232

.102

.231

.072

.165

X1.2

.837

.130

.119

.249

.067

.060

X1.6

.691

.114

.270

.166

.189

.145

X1.1

.674

.119

.287

.158

.264

.281

X1.5

.642

.218

.313

.190

.053

.067

X1.4

.620

.295

.352

.245

.222

.108

X1.7

.528

.296

.195

-.015

.185

.028

X2.2

.157

.878

.137

.224

.135

.105

X2.4

.233

.877

.085

.163

.174

-.037

X2.3

.181

.847

.096

.095

.191

-.015

X2.1

.163

.828

.102

.278

.037

.097

X2.5

.427

.667

.065

.273

.152

.078

X2.6

.255

.531

.179

.315

.320

.252

X2.7

-.055

.514

.394

.047

-.197

.430

X3.2

.322

.088

.787

.106

.300

.032

X3.3

.395

.151

.739

.096

.201

.042

X3.5

.460

.100

.720

.071

.118

.051

X3.1

.019

.078

.689

.186

-.005

.286

X3.4

.486

.249

.621

.147

.164

-.166

X4.2

.257

.279

.117

.823

.153

.064

X4.1

.252

.298

.119

.810

.139

-.031

X4.3

.195

.228

.207

.790

.185

.015

X5.1

.207

.088

.180

.153

.825

.042

X5.2

.155

.326

.224

.204

.745

.123

X5.3

.251

.326

.073

.408

.512

.032

X6.2

.220

.049

.038

-.004

.072

.831

X6.1

.367

.159

.339

.037

.290

.524

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 8 iterations.

Source: output SPSS, 2020


Factor Analysis Results

A summary of the loading factors obtained by each indicator can be seen in Table 8. Based on Table 8, factor 1 includes seven indicators, factor 2 includes seven indicators, factor 3 includes five indicators, factor 4 includes three indicators, factor 5 includes three indicators and factor six includes two indicators. Below is a table for all the factors and indicators.


Table 5. Factor Analysis


Factor


Indicator

Loading

Factor





1

Service as promised (X1.3)

0.846

On time service (X1.2)

0.837

Study travel time on time (X1.6)

0.691

Services according to student needs (X1.1)

0.674

Fast service to students. (X1.5)

0.642

The same service to students. (X1.4)

0.620

Scholarships for students who excel (X1.7)

0.528





2

Tidy lecture hall (X1.2)

0.878

Complete learning facilities (X1.4)

0.877

Cool lecture hall (X1.3)

0.847

Clean lecture hall (X1.1)

0.828

Clean toilet facilities (X1.5)

0.667

Clean place of worship (X1.6)

0.531

Library with complete book collection (X1.7)

0.514






3

Various study programs offered (X2.2)

0.787

Private universities builds good relationships with the

environment (X2.3)


0.739

Private universities is a good place to work for lecturers

(X2.5)


0.720

Good impression during college (X2.1)

0.689

Private universities is a good place to work for

employees (X2.4)


0.621




4

This private universities service is in accordance with

the expectations of students (X3.2)


0.823

This private universities service has satisfied students

(X3.1)


0.810

Private universities services are in accordance with the

ideal services of students (X3.3)


0.790




5

Reprimands for employees who make mistakes in

service (X4.1)


0.825

Guarantee to students while studying (X4.2)

0.745

Private universities fulfills its responsibility for the

future of its graduates (X4.3)


0.512


6

Feel proud to be able to study at this private universities

(X5.2)


0.831

Feeling close to this private universities (X5.1)

0.524

Source: output SPSS, 2020


New Factor Naming

The new factors that were previously referred to as factors 1, 2, 3, 4, 5 and 6 will be named according to the largest loading factor obtained from the indicators in these factors, the following explanation can be seen in Table 6.


Table 6. New Factor Naming

Faktor

Eigen Value

% Varians

Indicator

Loading Faktor





1





12.105





44.832

X1.3

0.846

X1.2

0.837

X1.6

0.691

X1.1

0.674

X1.5

0.642

X1.4

0.620

X1.7

0.528





2





2.721





10.078

X2.2

0.878

X2.4

0.877

X2.3

0.847

X2.1

0.828

X2.5

0.667

X2.6

0.531

X2.7

0.514




3




1.610




5.962

X3.2

0.787

X3.3

0.739

X3.5

0.720

X3.1

0.689

X3.4

0.621


4


1.350


5.001

X4.2

0.823

X4.1

0.810

X4.3

0.790


5


1.163


4.306

X5.1

0.825

X5.2

0.745

X5.3

0.512


6


1.070


3.964

X6.2

0.831

X6.1

0.524

Source: output SPSS, 2020


Based on Table 6, these factors are named as follows:

Factor 1: Service, the eigenvalue obtained by factor 1 is 12.105 with a variance contribution of 44,832 has seven indicators, each loading factor ≥ 0.5. The service factor naming based on the biggest loading factor is obtained by the service indicator as promised (X1.10) of 0.846 so that according to the initial explanation X1.10 is part of the service factor.


Factor 2: physical evidence, the eigenvalue obtained by factor 2 is 2,721 with a variance contribution of 10,078 having seven indicators, each loading factor ≥ 0.5. The naming of the physical evidence factor based on the largest loading factor is obtained by the neat lecture hall indicator (X1.2) of 0.878 so that the 2 factor is called the physical evidence factor according to the initial concept.


Factor 3: Reputation, the eigenvalue obtained by factor 3 is 1.610 with a variance contribution of 5,962 has five indicators, each loading factor ≥ 0.5. The naming of the university's reputation factor based on the largest loading factor is obtained by the indicator of the various study programs offered (X2.2) of 0.787 so that this factor of 3 is called the reputation factor.


Factor 4: Satisfaction, the eigenvalue obtained by factor 4 is 1.350 with a variance contribution of 5,001 having three indicators, each loading factor ≥

0.5. The naming of student satisfaction factors based on the biggest loading factor is obtained by the private universities service indicator in accordance with student expectations (Z1.2) of 0.823 so that this factor 4 is called the satisfaction factor.


Factor 5: Trust, eigenvalue obtained by factor 5 is 1.146 with a variance contribution of 6.365 having two indicators, each loading factor ≥ 0.5. Naming the student trust factor based on the biggest loading factor is obtained by the indicator of obtaining information about the quality of the restaurant (X2.1) of

0.825 so that this factor of 5 is called the trust factor.


Factor 6: Commitment, the eigenvalue obtained by factor 5 is 1.146 with a variance contribution of 6.365 having two indicators, each loading factor ≥ 0.5. The naming of the affective commitment factor based on the largest loading factor is obtained by the indicator of feeling proud to be able to study at this private university (Z3.2) of 0.831 so that this factor of 6 is called the affective commitment factor.


CONCLUSION

Conclusions, Implication/Limitation and Suggestions

In this study using factor analysis method. Based on the results of factor analysis, conclusions can be drawn from the factors that influence student decisions in choosing to study in the city of Medan as follows:

  1. The results of the factor analysis show that from 27 indicators into six factors, namely service, physical evidence, reputation, satisfaction, trust and commitment.

  2. Through factor analysis, it can be seen that the most dominant factor affecting student decisions in choosing to study at private tertiary institutions in the city of Medan is commitment.


Suggestions

Based on the results of research using factor analysis, it can be suggested that the service factor is the main factor influencing student decisions in choosing to study at private tertiary institutions in Medan City. So an effective step for administrators of private higher education institutions in Medan City is to further improve services to students so that they will be satisfied. Satisfaction at a high level will make students make a commitment to themselves and if students are satisfied then they will have a high commitment to the campus where they study so that in the end they will also recommend the campus to others.


REFERENCES

  1. Kotler, Philips., Keller, Kevin lane., Goodman, M. (2012). Marketing Management. Pearson.

  2. Lawson, R. (2010). Consumer Behaviour. Marketing Theory: A Student Text. Dryden Press. https://doi.org/10.4135/9781446280096.n12

  3. Mowen, J.C. and Minor, M. (2009). Consumer Behavior. Consumer Behavior. Prentice- Hall, Inc., Upper Saddle River.

  4. https://doi.org/10.5005/jp/books/11120_9

  5. Sumarwan, U. (2011). Perilaku Konsumen. PT Ghalia Indonesia. https://doi.org/10.31227/osf.io/pfjhz

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