TAO LIBIN EGE130011

PUSHPAVINDRAR A/L YELLAMARAN EGE150012

WAN MOHAMAD FARHAN BIN AB RAHMAN EGE150020

MUHAMMAD RIDZUAN HAKIM B. MOHD MUSLEH EGE150021

Literature Review Factor analysis (FA) is one of the most important statistical techniques in

multivariate study. FA is used mainly for two purposes which are exploratory

factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is the

exploration and combine large number of variables into few factors (Stalec et al,

2012)

Conjoint analysis was firstly be conducted by Luce and Tukey in 1964. Later the

technique was introduced into marketing literature by Green and Rao in 1971, as

well as by Johnson in 1974, conjoint analysis has developed into a method

preference studies that receives much attention from both theoreticians and those

who carry out filed studies (Gustafsson et al, 2001)

Conjoint analysis is used by many researchers and marketing firms as a market

data gathering analytical tool. Conducting a conjoint analysis is an effective

method to see consumers’ needs. It also gives insights that how consumers feel

about product attributes. As products are brought into the market, firms gather

market data to support the strategic product development direction (Gross, 2014)

Samah (2012) studied the performance of facility in health

organization in Malaysia outpatient department which rated by users

and concluded that no facility was assessed as good quality and

advised to improve facility.

Hassali et al (2014) conducted a research on patient satisfaction and

concluded that waiting time was the primary factor to affect patients’

satisfaction level. The authors suggested to decrease waiting time.

According to the study of Direkvand-Moghadam, Hashemian,

Delpisheh, Sohili and Sayehmiri (2014), the authors tried to find

salient variable affect patients’ satisfaction level and concluded that

doctors and nurses’ decision making are the main element on patients’

satisfaction. It was recommend to enhance the quality of doctors and

nurses in order to enhance the service quality for patients.

Problem Definition

1. Marketing Research Problem: To determine on the

various needs of the customers and to the extent of

which current services satisfy those needs to influence

the level of satisfaction on public healthcare in Malaysia

2. Management Decision Problem: To focus on possible

factors that would eventually influence overall level of

satisfaction of the customers using public healthcare in

Malaysia

Research Questions

1. Do increasing number of trained nurses influence

customer satisfaction on public healthcare

services in Malaysia?

2. Do improving advanced facility influence

customer satisfaction on public healthcare

services in Malaysia?

Research Hypotheses

Ho : Customer satisfaction are not influenced with the

additional number of trained nurses

H1 : Increasing number of trained nurses help to

influence customer satisfaction in public healthcare

services in Malaysia

Ho : Advanced facility do not influence on customer

satisfaction on public healthcare services in Malaysia

H1 : Customer satisfaction are influenced with advanced

facility in public healthcare services in Malaysia

Research Design

Duration of study

November 2015 - December 2015

(within three weeks time)

Data Collection

Online and manual survey collection

Online survey was done by using Google docs application where

we host the questionnaire on the app

We distributed the questionnaires to the target respondents and

were collected after certain time period to allow ample time for

answering the questionnaires.

Sampling

Sampling

5 groups were divided and each group has to gain at least 80

respondents

Target sample = 400

Convenience sampling is used in the study.

Discussion

Preliminary step

In data exploration, out of 400 respondents, we have removed

66 respondents due to missing values and wrong entry of

categories in particular variables

Discussion

6.2 Socio-demographic variables

The total respondent for this study is 334, where 38% of them were male and 62% were female. Majority of the respondents age are between 18 to 24 years old

The percentage of ethnicity are 57.2% Malay, 33.5% Chinese, 7.5% Indian, and 1.8% others.

About 145 respondents monthly personal income was less than RM1500 (Highest Frequency)

Respondent in this category are students, unemployed respondent, retiree/pensioner, and others that make up 40.4% of the total sample.

291 of the respondents stated that the hospital they went were in the urban area

Most of the public hospital in Malaysia are located in urban area rather than in rural area.

Variable

Less than 5

minutes

From 6 to

10 minutes

More than 10

minutes

Chi-Square Test Question B3:

How long do you usually have to wait

before being called to the treatment room

Area of the public hospital (2) =1.163

p-value=0.559 Urban 2.1% 16.2% 81.8%

Rural 4.7% 14.0% 81.4%

Variable

1-2 2-5 More than 5

times

Chi-Square Test Question B2:

How many times have you received treatment

at the hospital within this year

Gender (2) = 0.75

p-value=0.685

Male 75.6% 15.0% 9.4%

Female 72.9% 14.5% 12.6%

Table 5: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .966

Bartlett's Test of Sphericity

Approx. Chi-Square 10601.787

df 435

Sig. .000

Component

Initial Eigenvalues

Total % of Variance Cumulative %

1 17.954 59.847 59.847 2 2.300 7.667 67.515 3 1.386 4.618 72.133 4 1.020 3.399 75.532 5 .714 2.379 77.911 6 .603 2.010 79.920 7 .570 1.899 81.819 8 .501 1.669 83.488 Extraction Method: Principal Component Analysis.

Non-redundant residuals with absolute values greater than 0.05 is 11% which indicate that the model fit is good

.

• Cronbach's alpha = 0.967 Staffs

services

• Cronbach's alpha = 0.941 Quality of

physical facilities

• Cronbach's alpha = 0.939

Time

management

• Cronbach's alpha = 0.939 Doctors services

In addition,

there would

be no

improvement

on deleting

any of the

item in each

factor

Factor Item Factor

Loading

Corrected item total

correlation(CITC)

Cronbach's alpha

if item deleted

Cronbach's alpha

Staffs services

C18 0.840 0.842 0.963

0.967

C19 0.787 0.854 0.963

C110 0.761 0.851 0.963

C17 0.758 0.861 0.963

C15 0.756 0.806 0.964

C16 0.736 0.766 0.965

C14 0.712 0.838 0.963

C112 0.696 0.844 0.963

C11 0.676 0.795 0.964

C111 0.659 0.773 0.965

C113 0.658 0.800 0.964

C2a9 0.640 0.816 0.964

C12 0.636 0.764 0.965

Quality of physical facilities

C38 .831 0.819 0.931

0.941

C37 .801 0.835 0.930

C34 .761 0.764 0.935

C39 .734 0.831 0.930

C35 .729 0.749 0.936

C310 .719 0.834 0.930

C36 .649 0.740 0.937

C33 .645 0.750 0.935

Time management

C2a4 .767 0.883 0.919

0.939

C2a2 .757 0.800 0.930

C2a5 .733 0.843 0.924

C2b2 .727 0.761 0.935

C2b4 .684 0.826 0.926

C2a7 .611 0.798 0.930

Doctors services

C2b7 .760 0.891 0.898

0.939 C2b8 .739 0.865 0.919

C2b6 .732 0.866 0.917

The dependent variable is the respondents’ perception on different aspects of services provided by the public hospital. The model estimated is shown as below: U = b0+ b1X1+ b2X2+ b3X3+ b4X4+ b5X5+ b6X6+ ξ where:

dummy variables

representing general

staff/nurses;

dummy variables

representing doctors; X

5, X

6 :

dummy variables

representing general

facilities

The R square indicates that the model is fairly good fit

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 .695a .483 -1.066 .28678

Service profile and ratings

Data coded for dummy variable regression

The method of least squares has been used

for estimation of model coefficients :

𝑏0 6.981

𝑏1 0.250

𝑏2 0.160

𝑏3 -0.068

𝑏4 0.096

𝑏5 -0.068

𝑏6 -0.054

Level 3 for each attribute has been treated as the base

level. The coefficients that may related to the utility or part-

worths(αij) is shown in the table below

-0.1500

-0.1000

-0.0500

0.0000

0.0500

0.1000

0.1500

Prompt service (baselevel)

Capable of bearingwith annoyance

Empathy

-0.1000

-0.0800

-0.0600

-0.0400

-0.0200

0.0000

0.0200

0.0400

0.0600

0.0800

0.1000

Accurateprescription (base

level)

Professionlism Open to discussion

It is clearly seen that prompt

service, professionalism

and enough seats have the

highest utility as shown in

the figures

-0.0400

-0.0300

-0.0200

-0.0100

0.0000

0.0100

0.0200

0.0300

0.0400

0.0500

Technologicallyadvanced

equipment (baselevel)

Comfortable waitingarea

Enough seats

Part-worth functions of general staff/ nurses

Part-worth functions of doctors

Part-worth functions of general facilites