The Effect of Service Quality Dimensions on Student Satisfaction

in Higher Education X

Friska Sipayung

, Endang Sulistya Rini

and Liasta Ginting

Department of Management Universitas Sumatera Utara, Jl. Prof.TM Hanafiah, Medan, Indonesia

Keywords: Service Quality, Satisfaction, Academic Services

Abstract: Higher Education X as part of the higher education system is faced with various challenges both in the

development of science and technology, globalization and competition with other educational institutions.

Therefore, management improvements, customer orientation, and the implementation of quality

management are demands to be able to survive and be able to compete in the future. One important step that

must be done to start making changes is the need to know how satisfied students are. This study aims to

determine and analyze the effect of service quality dimensions on student satisfaction. This research was

conducted in X college. Data collection techniques were questionnaires. Data is processed using the SEM-

Partial Least Square-Smart PLS application. The results showed that, 65.85% of student satisfaction could

be explained by lecturer competence, leadership commitment, lectures, physical facilities, supporting

facilities, administrative and student services. Lecturer competence, lectures, physical facilities, supporting

facilities, administrative services, and student affairs have a positive influence on student satisfaction.

1 INTRODUCTION

Produce qualified graduates who are able to develop

science, technology, humanities, and arts, based on

religious morality. Being able to compete at the

national and international levels, is one of the

missions of X universities. In an effort to realize the

mission and goals, without exception all faculties,

and study programs must be able to carry out their

functions and objectives. This is intended to be able

to produce qualified students who are competent in

their field. Thus, they can make good name and

image of the university, and be calculated by

prospective students, users and recruiter. Various

efforts have been carried out to improve the quality

of education services.

In accordance with the new paradigm of higher

education management as a service industry, it is

necessary to improve the quality of services.

Service quality consists of curricular services,

research services, community service, administrative

services and extra-curricular services. One form of

curricular services is the implementation of lectures,

among others: curriculum, lecture design, syllabus,

lecture material, lecture process and evaluation.

Curriculum services will be quality if supported by

adequate facilities and infrastructure. As a service

industry, customer satisfaction is an indicator of the

success of educational institutions in carrying out

their functions.

In addition, customer satisfaction is an essential

factor in the application of Total Quality

Management (TQM). Therefore, education and

training institutions in this case universities must

identify customers and their needs carefully and try

to satisfy them. The main step that must be done in

implementing Total Quality Management is to view

students as the main customers who must be served

(Ivancevich, 2014). Through sustainable service

development programs will be able to be presented

and provided educational services in accordance

with customer needs so that customer satisfaction

will be created. Higher Education X as part of the

higher education system is faced with various

challenges both in the development of science and

technology, globalization and competition with other

educational institutions.

Therefore, management improvements, customer

orientation, and the implementation of quality

management such as quality assurance, are demands

to be able to survive and be able to compete in the

future. One of the important steps that must be done

to start making changes is the need to know how

Sipayung, F., Rini, E. and Ginting, L.

The Effect of Service Quality Dimensions on Student Satisfaction in Higher Education X.

DOI: 10.5220/0010103118291836

In Proceedings of the International Conference of Science, Technology, Engineering, Environmental and Ramiﬁcation Researches (ICOSTEERR 2018) - Research in Industry 4.0, pages

1829-1836

ISBN: 978-989-758-449-7

1829

satisfied students are in academic and non-academic

services so far. Without these initial steps, it is

difficult to make further improvements. Therefore,

the purpose of this study was to find out and analyze

the factors that influence the satisfaction of college

students X. The problems are formulated as follows:

How is the influence of service quality dimensions

on student satisfaction?

2 CUSTOMER SATISFACTION

THEORY AND SERVICE

QUALITY IMPROVEMENT

Customer satisfaction according to Gerson (2014), is

"customer perception that expectations have been

met or exceeded". Based on this theory, customer

satisfaction lies in customer expectations of a

product. Customers will feel satisfied if the product

they consume is the same as the customer wants the

product. Mowen and Kotler stressed that customer

satisfaction lies in the attitude shown by customers

after they use a product that attitude can indicate

they are happy or they are disappointed. This

customer pleasure is indicated that the customer is

satisfied, on the contrary if the customer is

disappointed it can be said that they are not satisfied.

Kotler (2011) argues that: Customer form

expectations about the value and satisfaction that

market offers will deliver and buy accordingly.

Satisfied customers buy again and tell others about

their good experience. The theory states that

customers who are satisfied with a product, it is

certain that the customer will make a repeat

purchase. And the other thing that is done by a

satisfied customer is word of mouth marketing about

an experience that satisfies him. Customer

satisfaction is a situation where the wishes,

expectations, and needs of customers are met. While

a service is considered satisfactory if the service can

meet customer needs and expectations. So the

relationship between satisfaction and service quality

is: if the quality of service is high or high, customer /

customer satisfaction will increase or be high. In

other words, the customer / customer will be

satisfied or very satisfied if the quality or quality of

service can be trusted, relied on and tested.

Satisfaction and quality of service delivery are two

inseparable things. Some experts have succeeded in

identifying the 10 main factors that determine

service quality, including: reliability,

responsiveness, competence, access, courtesy,

communications, credibility, security, understanding

/ knowing the customer and tangibles.

Referring to these definitions, student

satisfaction means a feeling of pleasure, satisfaction

and the relief of learners in higher education for

what they need during the study. Students are said to

be customers because he pays education services to

study. This is certainly accompanied by the desired

expectations in the education process such as

service, facilities, quality of lecturers, and

leadership. Referring to these expectations, of

course, every student has different perceptions from

one another. There are those who perceive with high

standards so that they cannot be fulfilled by the

institution, some are moderate and some are low.

3 MEASUREMENT OF

CUSTOMER SATISFACTION

In measuring customer satisfaction Kurtz and Louis

(2009) argue that: “Satisfaction can be measured in

terms of the gaps between what customers expect

and what they perceived they have received. This

theory can be concluded that real satisfaction can be

measured, by looking at customer expectations of a

product and how the company meets these

expectations. If positive results and customers feel

fulfilled, then it can be said that customers are

satisfied. Zeithaml, Mary and Gremler (2013) argue

that:“Customer satisfaction is influenced by spesific

product or service features, perception of product

and service quality, and price”. According to this

theory that customer satisfaction can be influenced

of product specifications , there is a perception of a

product and service quality and how much the

company gives to the product. Dann argue that:

Customer loyalty is seen by Whitwell, Lukas and

Doyle (2003) as being influenced by satisfaction

with the quality of the value offering, which in turn

is affected by five factors: (1) Realiability(2)

Responsiveness (3) assurance(4) empathy (5)

tangibles.

4 RESEARCH METHODS

4.1 Research Location, Samples and

Data Collection Techniques

This research was conducted in X college, with a

total sample of 640 students with proportional

randomized composition based on the Faculty. Data

ICOSTEERR 2018 - International Conference of Science, Technology, Engineering, Environmental and Ramiﬁcation Researches

1830

collection techniques in this study are questionnaires

or questionnaires

4.1 Analysis Technique

The research data was processed using the SEM -

Partial Least Square (PLS) application of the

SmartPLS application. Measurement of satisfaction

using the Likert-Scale based on the instructions of

Riduwan and Sunarto, (2013).

4.3 Conceptual Research Model and

Research Instrument

The conceptual model in this study is a modification

of the total quality management model, while the

instrument used to measure the quality of higher

education service refers to in-depth literature review

of previous research, interviews with several

students and deans, as well as a combination of

Team Student Satisfaction FIU (2002), Ardi R

(2011) and Singgih M (2008), Wibisono, (2012).

5 RESULTS AND DISCUSSION

5.1 Identification of Service Quality

Attributes

The FGDs were conducted for students in 2016, the

materials presented were services thought out by

students, positive aspects experienced by students

during the service, negative aspects experienced by

students , ideal service in the eyes of students. Based

on the results of the FGD, there were general themes

related to the quality of higher education services.

Further review of the themes that emerge yields 62

attributes of higher education service quality.

5.2 Formulation of Service Quality

Measurement Instruments

Each statement is measured using a Likert scale with

a range of 1-5, where 1 shows strongly disagree and

5 shows strongly agree to the features in the

statement. Model Structural.Analysis

5.3 Outer Model Instrument/Analysis

Testing

Good instruments are valid and reliable instruments.

To test the validity and reliability of an instrument,

the dimensional of the instrument must be fulfilled.

The dimensional can be seen from the loading factor

of each variable. Outer model analysis is carried out

to ensure that the measurement used is feasible to be

used as a measurement (valid and reliable). Outer

analysis of this model specifies the relationship

between latent variables and their indicators. Tests

carried out on the outer model are:

5.3.1 Convergent Validity

The converging values of validity is the value of the

loading factor on the latent variable with the

indicators. Expected values > 0.6. Outer Loadings

(measurement model) or convergent validity are

used to test the uni dimensional of each construct.

According to Chin (1998), the indicator loading

factors which are greater or equal to 0.5 can be said

to be valid. SmartPLS output for loading factor gives

results as in Figure 1 and Table 1.

Figure 1 shows that the item A-8,C-6,C-9,D-1,E-

5,E-6,E-7,F-7,F-8,F-9,G-1,G-4,H-6 dan H-7 has a

factor loading below 0.6. Therefore, it must be

removed from the model. Thus, the model used is as

shown in Figure 2.

Validity testing for reflective indicators uses

correlations between item scores and their construct

scores. Measurements with reflective indicators

indicate changes in an indicator in a construct if

other indicators in the same construct change (or are

removed from the model). Reflective indicators are

suitable for measuring perception so that this study

uses reflective indicators. Table 2 shows that the

loading factor gives a value above the recommended

value of 0.5. The smallest value is 0.640 for the G3

indicator, namely soft skill development. This means

that the indicators used in this study are valid or

have met convergent validity. The following is a

diagram of the loading factor of each indicator in the

research model:

5.3.2 Discriminant Validity

This value is a cross loading value factor that is

useful to find out whether the construct has adequate

discriminant that is by comparing the loading values

in the intended construct must be greater than the

loading value with another construct.

The Effect of Service Quality Dimensions on Student Satisfaction in Higher Education X

1831

Source: Output SmartPLS 2017

Figure 1. Loading Diagram Factors Affecting Service

Quality Dimensions Against Student Satisfaction at

Higher Education

Table 1: Outer Model (Weights or Loadings)

Source : Output SmartPLS 2017

Figure 2. Loading Diagram Factors Influencing Service

Quality Dimensions Against Student Satisfaction At

Higher Education (revised)

Table 2: Discriminant Validity

A B C D E F G

0.881

0.437 0.886

0.346 0.419 0.856

0.483 0.492 0.377 0.737

0.384 0.237 0.275 0.594 0.782

0.441 0.470 0.357 0.440 0.278 0.921

0.315 0.393 0.326 0.547 0.461 0.303 0.761

0.439 0.413 0.509 0.612 0.695 0.461 0.552

Source : Output SmartPLS 2017

From Table 3 it can be seen that the loading value of

each item on the construct is greater than the cross

loading value. From this analysis it can be stated that

there are no problems with discriminant validity.

5.3.3 Unidimensionality Test

Unidimensionality test is done by using the

Composite Reliability and Alpha Cronbach

indicators. Data that has a composite reliability > 0.7

has high reliability.

Original

Sample

Original

Sample

<- A 0.945 C

<- C 0.922

<- A 0.842 D

<- D 0.706

<- A 0.902 D

<- D 0.826

<- A 0.854 D

<- D 0.726

<- A 0.977 D

<- D 0.680

<- A 0.850 E

<- E 0.713

<- A 0.787 E

<- E 0.799

<- B 0.919 E

<- E 0.780

<- B 0.950 E

<- E 0.833

<- B 0.906 F

<- F 0.895

<- B 0.877 F

<- F 0.953

<- B 0.974 F

<- F 0.988

<- B 0.772 F

<- F 0.863

<- B 0.764 F

<- F 0.936

<- B 0.903 F

<- F 0.918

<- C 0.836 F

<- F 0.984

<- C 0.936 G

<- G 0.866

<- C 0.846 G

<- G 0.640

<- C 0.755 H

<- H 0.684

<- C 0.854 H

<- H 0.830

<- C 0.818 H

<- H 0.663

<- C 0.884 H

<- H 0.715

<- C 0.845 H

<- H 0.755

ICOSTEERR 2018 - International Conference of Science, Technology, Engineering, Environmental and Ramiﬁcation Researches

1832

Table 3: Composite Reliability

Composite Reliability

Lecturer competence 0.9605

Leadership commitment 0.9667

Lecture 0.9611

Physical facilities 0.8257

Supporting facilities 0.8630

Administrative Services 0.9753

Student Affairs 0.7291

Student Satisfaction 0.8514

Source : Output SmartPLS 2017

Table 4. shows that all constructs have

composite reliability values above 0.7. It can be

stated that there is no reliability / uni dimensional

problem in the model formed.Cronbach Alpha.

Reliability tests are reinforced with Cronbach Alpha.

Expected values > 0.6 for all constructs.

Table 4: Cronbach’s Alpha

Cronbach’s Alpha

Lecturer competence 0.9516

Leadership commitment 0.9599

Lecture 0.9543

Physical facilities 0.7216

Supporting facilities 0.7919

Administrative Services 0.9702

Student Affairs 0.8886

Student Satisfaction 0.7807

Source : Output SmartPLS 2017

Table 4. shows that the Cronbach Alpha value

for all constructs is > 0.6, meaning that there is no

reliability / unidimensionality problem in the model

formed. Next is to look at the value of Average

Variance Extracted (AVE). Expected AVE value >

0.5.

Table 5: Average Variance Extracted (AVE)

Average

Variance

Extracted (AVE)

Lecturer competence 0.7773

Leadership commitment 0.7853

Lecture 0.7338

Physical facilities 0.5436

Supporting facilities 0.6124

Administrative Services 0.8495

Student Affairs 0.5791

Student Satisfaction 0.5356

Source : Output SmartPLS 2017

Table 5. shows that the value of Average

Variance Extracted (AVE). for all constructs is >

0.5, it means that there are no

reliability/unidimensional problems found in the

model.

5.4 Inner Model Analysis

Inner model analysis / structural analysis model is

done to ensure that structural models are built

robustly and accurately. Inner model evaluation can

be seen from several indicators which include:

The coefficient of determination (R2)

The first time is to look at the R-Square Value.

Assessment criteria for the R-Square Value are as

follows:

- R-Square value of 0.67 is categorized as

substantial

- R-Square value of 0.33 is categorized as

moderate

- R-Square value of 0.19 is categorized as weak

- R-Square value of> 0.7 is categorized as

strong (Riduan, 2013)

Here at Table 6 are the R-Square values in the

construct:

Table 6: R-Square (R

)

Nilai R-Square

Lecture 0.3761

Physical facilities 0.2425

Supporting facilities 0.2564

Administrative

Services

0.2817

Student Affairs 0.1551

Student Satisfaction 0.6585

Source : Output SmartPLS 2017

R-Square value of student satisfaction is 0.6585.

It can be explained that the influence of lecturer

competence variables, leadership commitment,

lectures, physical facilities, supporting facilities,

administrative services and student affairs on student

satisfaction gives a value of 0.6585 which can be

interpreted that construct variables student

satisfaction can be explained by constructing

variable lecturer competence, leadership

commitment, lectures, physical facilities, supporting

facilities, administrative and student services

65.85%. While the remaining 34.15% is explained

by other variables outside the one studied. Student

R-Square value of 0.1551 can be explained that the

influence of the leadership commitment variable on

The Effect of Service Quality Dimensions on Student Satisfaction in Higher Education X

1833

student affairs is 0.1551. While the remaining

34.15% is explained by other variables outside the

one studied.

Student R-Square value of 0.1551 can be

explained that the influence of the leadership

commitment variable on student affairs is 0.1551.

The value of R-Square Administrative Services is

0.2817 can be explained that the influence of the

leadership commitment variable on administrative

services is 0.2817. The value of R-Square supporting

facilities is 0.2564 can be explained that the

influence of the leadership commitment variable on

administrative services is 0.2564. The R-Square

value of physical facilities is 0.2425 can be

explained that the influence of the leadership

commitment variable on administrative services is

0.2425. The value of the R-Square lecture is 0.3761

can be explained that the influence of the leadership

commitment variable on lectures is 0.3761.

5.5 Hipothesis Testing

The Path Coefficient output, as shown in Table 7,

looks at the significance of the influence of each

variable. These variables are lecturer competence

variables, leadership commitment, lectures, physical

facilities, supporting facilities, administrative and

student services by looking at the parameter

coefficient (original sample).

Table 7: Path Coefficients

Original

Sample

Mean

Standard

Deviation

-Statistics P

Values

A->H 0.3610 0.4667 3.6673 3.6673 0.5049

B->C 0.4197 0.4086 3.3437 3.4374 0.0006

B->D 0.4924 0.4878 4.5791 4.5791 0.0000

B->E 0.2376 0.2260 1.6326 2.6326 0.1032

B->F 0.4709 0.4726 4.6506 4.6506 0.0000

B->G 0.3939 0.3916 2.7085 2.7085 0.0070

C->H 0.2426 0.2352 0.0807 3.0049 0.0028

D->H 0.0851 0.1081 0.1034 0.8230 0.4169

E->H 0.4493 0.4431 0.0889 5.0570 0.0000

F->H 0.1310 0.1385 0.0910 3.4407 0.1503

G->H 0.1495 0.1386 0.0953 2.5681 0.1175

Source : Output SmartPLS 2017

The magnitude of the parameter coefficient for

lecturer competence variables on student satisfaction

is (original sample) 0.2610 which means there is a

positive influence between the lecturers' competence

on student satisfaction. Or it can be interpreted that

the better the competency of the lecturer, the student

satisfaction will increase. The t-statistic value is

0.6673 not significant (t 5% significance table =

1.96). Therefore, the t-value of statistics is smaller

than the t-table of 1.96 (0.6673 <1.96). The

parameter coefficient for the leadership commitment

variable towards the original sample is 0.4197 which

means there is a positive influence between the

leadership commitment to the lecture. Or it can be

interpreted that the better the commitment of the

leader, the better the lecture will be. The t-statistic

value of 3.4374 is significant (t table of 5%

significance = 1.96). Therefore, the t-value of

statistics is greater than the t-table of 1.96 (3.4374>

1.96). The parameter coefficient for the leadership

commitment variable to the physical facility is

(original sample) 0.4924 which means there is a

positive influence between the leadership's

commitment to physical facilities. Or it can be

interpreted that the better the commitment of the

leader, the better physical facilities will be. The

value of t-Statistics of 2.6326 is significant (t table

of significance 5% = 1.96). Therefore, the t-value of

statistics is greater than the t-table of 1.96 (4.5791>

1.96). The parameter coefficient for the leadership

commitment variable to the physical facility is

(original sample) 0.4924 which means there is a

positive influence between the leadership's

commitment to physical facilities. Or it can be

interpreted that the better the commitment of the

leader, the better physical facilities will be. The

value of t-Statistics of 2.6326 is significant (t table

of significance 5% = 1.96). Therefore, the t-value of

statistics is greater than the t-table of 1.96 (4.5791>

1.96). The parameter coefficient for the leadership

commitment variable for supporting facilities is

(original sample) 0.2376 which means there is a

positive influence between the leadership

commitment to the supporting facilities. Or it can be

interpreted that the better the commitment of the

leader, the better physical facilities will be. The

value of t-Statistics of 2.6326 is significant (t table

of significance 5% = 1.96). Therefore, the t-value of

statistics is greater than the t-table of 1.96 (2.6326>

1.96). The parameter coefficient for the leadership

commitment variable to the original administrative

service is 0.4709 which means that there is a positive

influence between the leadership commitment to

administrative services. Or it can be interpreted that

the better the leadership commitment, the better the

administrative services. The value of t-Statistics of

4.6506 is significant (t table of significance 5% =

1.96). Therefore, the t-value of statistics is greater

than the t-table of 1.96 (4.6506> 1.96). The

parameter coefficient for the leadership commitment

variable towards the original sample is 0.3939 which

ICOSTEERR 2018 - International Conference of Science, Technology, Engineering, Environmental and Ramiﬁcation Researches

1834

means there is a positive influence between the

leadership commitment to administrative services.

Or it can be interpreted that the better the

leadership's commitment, the better student affairs.

The value of t-Statistics of 2.7085 is significant (t

table of significance 5% = 1.96). Therefore, the t-

value of statistics is greater than the t-table of 1.96

(2.7085> 1.96).

The parameter coefficient for lecture variables

on student satisfaction is (original sample) 0.2426

which means there is a positive influence between

the leadership commitment to administrative

services. Or it can be interpreted that the better the

leadership's commitment, the better student affairs.

The value of t-Statistics of 3.0049 is significant (t

table of significance 5% = 1.96). Therefore, the

value of t-statistic is greater than t-table 1.96

(3.0049> 1.96). The parameter coefficients for

physical facility variables on student satisfaction are

(original sample) 0.0851 which means there is a

positive influence between the leadership

commitment to administrative services. Or it can be

interpreted that the better the leadership's

commitment, the better student affairs. The value of

t-Statistics of 0.8230 is not significant (t table of

significance 5% = 1.96).Therefore, the t-value of

statistics is greater than the t-table of 1.96 (0.8230

<1.96). The parameter coefficient for supporting

facility variables for student satisfaction is original

sample 0.4493 which means there is a positive

influence between the leadership commitment to

administrative services. Or it can be interpreted that

the better the leadership's commitment, the better

student affairs. The value of t-Statistics of 5.0570 is

significant (t-table of significance 5% = 1.96).

Therefore, t-statistic value is greater than t-table 1.96

(5.0570> 1.96. The parameter coefficient for

administrative service variables on student

satisfaction is (original sample) 0.1310 which means

there is a positive influence between the leadership

commitment to administrative service. Interpreted

that the better the commitment of the leader, the

better the student affairs. T-value-Statistics of

3.4407 is significant (t table of 5% significance =

1.96). Therefore, t-statistic value is greater than t-

table 1.96 (3.4407> 1.96. The parameter coefficient

for student variables on student satisfaction is

(original sample) 0.1495 which means there is a

positive influence between leadership commitment

to administrative services. Or it can be interpreted

that the better the commitment of the leadership, the

better the student affairs. T-Statistics value of 2.5681

is significant (t-table of 5% significance = 1.96).

There is a statistic value greater than t-table 1.96

(2.5681> 1, 96.

6 CONCLUSION

1) That the leadership commitment variable has a

significant positive effect on lectures, physical

facilities, supporting facilities, administrative

services, while the positive student affairs are not

significant.

2) Whereas the variables of lecturer competence,

leadership commitment, lectures, physical facilities,

supporting facilities, administrative and student

services have a significant positive effect on student

satisfaction.

3) The most dominant variable affecting student

satisfaction is the variable supporting facilities and

then the competence of lecturers

4) The influence of lecturer competence variables,

leadership commitment, lectures, physical facilities,

supporting facilities, administrative and student

services on student satisfaction gives a value of

0.6585 which can be interpreted that the construct

variable student satisfaction can be explained by

constructing variable lecturer competence,

leadership commitment, lecturer, physical facilities,

supporting facilities, administrative and student

services 65.85%. While the remaining 34.15% is

explained by other variables outside the one studied.

Based on the results of the research, it is advisable to

X universities, namely:

1) Supporting facilities owned should be more

considered, so that student satisfaction can increase

2) Lecturer competence should be further

enhanced, for example by following training training

in accordance with the field of science

3) The number of respondents in this study is still

minimal so that the results obtained are less

representative. To further improve the quality of the

results of subsequent studies, the number of

respondents is even more

REFERENCES

Al-Rafai Adnan etall. (2016) Measuring Student

Satisfaction with Performance Enhancement

Activities: Evidence from Business Education,

International Journal of Information and Education

Technology, Vol. 6, No. 10, October.

Arikunto, Suharsimi (2013) Prosedur Penelitian, Jakarta,

PT. Rineka Cipta.

David Schüller, Martina Rašticová..(2013) Measuring

student satisfaction with the quality of services offered

The Effect of Service Quality Dimensions on Student Satisfaction in Higher Education X

1835

by universities – Central European View. Acta

Universitatis Agriculturae et Silviculturae

Mendelianae Brunensis, LXI, No. 4, pp. 1105–1112

Fakultas Kedokteran Universias Islam Indonesia Dalam

PBL (Problem Based Learning) USU, Rencana

Strategis USU 2015–2019

Garvin David.( 2003) Managing Quality: The Strategic

and Competitive Edge, Simon and Schuster.

Gerson,Richard. (2014) Mengukur Kepuasan Pelanggan:

Panduan Menciptakan Pelayanan Bermutu, Publisher,

PPM, Jakarta.

Hasnain, Manzoor (2013). Measuring Student Satisfaction

in Public and Private Universities in Pakistan, Global

Journal of Management and Business Research

Interdisciplinary, Volume 13, Global Journals Inc.

(USA).

Ivancevich John M, Konopaske, Robert ; Matteson,

Michael.(2014), Organization Behavioral

Management, New York, Mc Graw Hill, 10 th ed.

Kolb David, A.(2014) Experimental Learning: Experience

as the Source of Learning and Development, Pearson.

Second Edition.

Kotler, Philip. (2011) Marketing Management, Prentice

Hall, 14 th edition.

Kotler, Philip & Amstrong, Gary. (2014) Principles of

Marketing, Pearson, 15th ed.

Kurtz David and Boone Louis(2009) Contemporary

Marketing. Cengage Learning.

Kuswanti, Eko.(2010) “Pengaruh Antara Kebiasaan

Belajar dan Penilaian terhadap Sistem Evaluasi dengan

Prestasi Belajar Mahasiswa”. JPP Vol.8 No.1 March.

LP USU, Rencana Strategis Lembaga Penelitian

Universitas Sumatera Utara Tahun 2011 – 2015

Rahman, A. (2001) A comparative study of TQM Practice

and Organizational performance of SMEs with and

without ISO 9000 certification. International Journal

of Quality and Reliability Management.

Ramayah MR.(2009) Learning Style: Gender Influence On

Preferred Learning Style Among Business Students.

Journal of US - China Public Administration.

Riduwan dan Sunarto.(2013) Pengantar Statistika

Pendidikan, Sosial, Ekonomi, Komunikasi dan Bisnis,

Bandung, Alfabeta.

Simamora. (2014) Membuat Karyawan Lebih Produktif

DalamJangkaPanjang(Manajemen SDM). STIE

YKPN,Yogakarta.

Sopiatin, Popi (2010) Manajemen Belajar Berbasis

Kepuasan Siswa, Gramedia Indonesia, Jakarta

Supranto, J. (2011) Pengukuran Tingkat Kepuasan

Pelanggan Untuk Menaikkan Pangsa Pasar (Cetakan

Keempat). Jakarta : PT. Rineka Cipta.

Tatiana, Endang etall. (2005) Model Prestasi Akademik

Mahasiswa: Potensi Akademik Dan Gaya Belajar,

Usahawan, No 4 TH XXXIV April.

Thomas Foster. (2016) Managing Quality: Integrating the

Supply Chain, Global Edition, Pearson.

Tjiptono, Fandy and Gregorius Chandra. (2016) Service

Quality & Satisfaction. Andi Offset., Yogyakarta.

Tjiptono, Fandy. (2015) Strategi Pemasaran, 4 th ed, Andi

Offset, Yogyakarta.

Tri Bowo, E. (2008) Korelasi Antara Gaya Belajar Dengan

Prestasi Akademik Mahasiswa.

Umi Marfuah, Renty Anugerah Mahaji Puteri. (2016)

Pengukuran Tingkat Kepuasan Mahasiswa Terhadap

Pelayanan Akademik Fakultas X Universitas

Xyz,Website : jurnal, umj.ac.id/index.php/semnastek,

Vanany, etall. (2005) Perancangan dan Pengukuran

Kepuasan Mahasiswa di Perguruan Tinggi: Studi

Kasus ITS, Usahawan, No 4 TH XXXIV April.

Wibisono Yogi Y dan Nainggolan Marihot. (2012)

Pengembangan Instrumen Pengukuran Mutu Jasa

Pendidikan Tinggi, Laporan Penelitian, Lembaga

Penelitian Dan Pengabdian Masyarakat Universitas

Katolik Parahyangan, Bandung.

Whitwell Gregory, Lukas Bryan and Doyle Peter. (2003)

Marketing Management: A Value-Based Approach 1st

Edition, Wiley.

Zeithaml Valarie, Bitner Mary and Gremle. (2013),

Services Marketing, McGraw-Hill Learning Solutions.

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