The exploration of customer satisfaction model from a comprehensive perspective


The exploration of customer satisfaction model
from a comprehensive perspective

Wen-Bao Lin *

National Formosa University, Department of Business Administration ADD:64, Wen-Hua Road, Huwei, Yunlin, Taiwan, ROC

Abstract

This study provides a model of customer satisfaction from a comprehensive perspective and tries to use the nonlinear fuzzy neutral
network model to verify the assumptions of the study. Samples are taken from the information and tourism industries at a proportion of
2:1 based on the population in Taipei and Kaohsiung cities. A total of 207 questionnaires are returned. As the result of the empirical
research shows, the interpersonal-based service encounter is better than the technology-based service encounter in functional quality,
while the technology-based service encounter is better than the interpersonal-based service encounter in technical quality. The functional
quality has a positive and significant effect on customer satisfaction; the service quality has a positive and significant effect on service
value; the service value has a positive and significant effect on customer satisfaction. The service encounter has a positive and significant
effect on relationship involvement and the relationship involvement has a positive and significant effect on customer satisfaction.
� 2006 Elsevier Ltd. All rights reserved.

Keywords: Customer satisfaction; Service encounter; Service value; Relationship involvement; Service quality

1. Introduction

The industrial structure in Taiwan has transformed
from traditional agricultural and manufacturing industries
to the tertiary service industry that is booming and domi-
nating the market with its high employment rate and pro-
duction value. In the circumstances, it is very important for
the service industry to improve their service quality. The
core value provided by the service industry to consumers
includes not only the uniqueness of tangible and intangible
products, but also various factors involved in the process of
service delivery to customers, such as physical facilities,
company image, and quality of the service delivery.
Emphasizing customers’ perception of service quality is
important to enhance the extended perception and impres-
sion of consumers on products, and to increase the added
value of products. With the rise of consumer awareness,
requirements of customers changing from mass production

to customization and quality. Therefore, how to under-
stand the requirements of customers and provide them with
products and services they need, reduce customer costs and
improve customer value, and continually trace the satisfac-
tion of consumers are essential for an enterprise to march
toward success.

Much research on the cause of service quality and cus-
tomer satisfaction has been published in recent years. This
research focuses on the following dimensions: (1) discuss-
ing the antecedent variable that affects customer satisfac-
tion. Zeithaml and Bitner (2000), for example, found that
customer satisfaction was affected by service quality, prod-
uct quality, price, personal and situational factors. Oliver
and DeSarbo (1988) found that service quality was the
antecedent variable of customer satisfaction, while Ander-
son, Fornell, and Lehmann (1994) found that customer sat-
isfaction was dependent on service value; (2) presenting
theoretical models to explain the importance and necessity
of customer satisfaction. Anderson and Suillivan (1993)
pointed out that the satisfaction or dissatisfaction of cus-
tomers could be measured based on the gap between the

0957-4174/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.eswa.2006.04.021

* Tel.: +886 5 6315775; fax: +886 5 6313842.
E-mail addresses: e2667@ms23.hinet.net, wblin@sunws.nfu.edu.tw

www.elsevier.com/locate/eswa

Expert Systems with Applications 33 (2007) 110–121

Expert Systems
with Applications

mailto:e2667@ms23.hinet.net
mailto:wblin@sunws.nfu.edu.tw


expectations of customers before purchasing and the per-
formance of the purchased products or services. Woodruff,
Cadotte, and Jenkins (1983) raised the norms in models of
consumer satisfaction. They found that consumers evalu-
ated products of different brands based on ‘‘norms’’ and
their satisfaction was determined by the conformity among
these norms. Meuter, Ostrom, Roundtree, and Bitner
(2000) introduced the Technology Infusion Matrix to ana-
lyze how to enhance the provision of customized service by
helping staffs or consumers use hi-tech tools and ensure the
service failure compensation to improve customers satisfac-
tion; (3) bringing up research dimensions and approaches
for measuring customer satisfaction and service quality.
For example, Perkins (1993) pointed out that industries
were different in target market segmentation and, thus,
measurement of customer satisfaction varied depending
on the characteristics of individual industries. He raised
three dimensions to be taken into consideration for empir-
ical research on measurement of customer satisfaction:
operation dimension (availability, delivery on time, price,
credibility), service dimension (sales service, technical sup-
port, product line), product dimension (technical value,
reliability, design). Parasuraman, Zeithaml, and Berry
(1985) referred to ‘‘encounter’’ and ‘‘feasibility’’ as key
dimensions. After interviewing senior management and
consumers of four service industries and conducting com-
prehensive exploratory research, they raised the five-gap
theory and brought up an important measurement
approach. With this approach, excess of expected service
(ES) over perceived service (PS) was represented by
ES > PS, indicating that the overall service quality was
not acceptable, while satisfactory service quality was repre-
sented by ES = PS and excess of perceived service over
expected service was represented by ES < PS, indicating
an inclination toward ideal quality.

Previous research on customer satisfaction had the fol-
lowing characteristics: (1) emphasizing the discussion of
performance. Oliver (1980), for example, found that satis-
faction was a value consumers give to a deal. The higher
the service level that consumers expect to have, the more
difficult for the service provider to provide, the higher the
customer satisfaction. According to the conformity theory
of Mittal and Tsiros (1999), when the expectation of con-
sumers was not in conformity with the actual performance
of a product, they might change their perception of the
product and incline to eliminate the gap between the expec-
tation and performance, and thus improve the satisfaction;
(2) emphasizing the discussion of cause and result of satis-
faction. Cronin and Taylor (1992), for example, evaluated
service quality based on performance and argued that ser-
vice quality was the cause of customer satisfaction. Para-
suraman, Zeithaml, and Berry (1988) researched the gap
between service quality and customer satisfaction; (3)
explaining the cause of customer satisfaction from a single
perspective, such as the attribution theory of Bitner (1990)
and the equity theory of Oliver and DeSarbo (1988). Kotler
(2000) found that customer satisfaction was closely linked

to customer value and the measurement of customer satis-
faction should be carried out from the perspective of value.
As some previous literature shows, the topics such as ‘‘ser-
vice quality’’, ‘‘service value’’, and ‘‘customer satisfaction’’
were researched separately and only a limited number of
literature focused on integration research in the relation-
ship between the behavior intention of consumers and the
service quality, service value, and customer satisfaction as
a whole (Cronin, Brady, Tomas, & Hult, 2000). This study
provides integrated research of these variables and focuses
on their relationship as the first research motive; (4) most
of the satisfaction models used in previous research empha-
sized the perceptive (Kotler, 2000) and emotional perspec-
tives (Price, Arnould, & Tierney, 1995) of consumers.
However, what a provider of the service industry empha-
sizes is not only the interaction with consumers to ensure
their satisfaction, but also the method of changing the
trade exchange to the value increment exchange (the rela-
tionship continuum theory of Day, 2000). Therefore, this
study proceeds to the research from the perspective of rela-
tionship involvement as the second research motive.

Besides, the multivariate statistic analysis approach or
case study was commonly used for empirical research (Bol-
ton & Drew, 1991; Cronin, Michael, Brady, Hightower, &
Shemwell, 1997; Zeithaml, 1988) and the nonlinear
research model (such as the nonlinear fuzzy neural network
model) was not used often for theory verification. The non-
linear fuzzy neural network model is maturely developed
and has a wide range of applications. It is not only useful
for prediction and assortment, but also for uncertain
behavior systems. Therefore, this study uses the nonlinear
fuzzy neural network model to verify the initial data of
the research as the third research motive.

Based on the above, the purpose of this study is listed as
follows:

(1) To provide a customer satisfaction model from a
comprehensive perspective of relationship involve-
ment, service encounter, service quality, and service
value.

(2) To verify collected data with the nonlinear fuzzy neu-
ral network model and discuss its meaning for
management.

2. Literature review

Service encounters were classified as follows in the previ-
ous research: (1) according to the level of service encounter
with the staff or facilities, the service encounter was classi-
fied into high service encounter (contacting staff more fre-
quently or contacting customers widely) and lower service
encounter (mainly contacting physical facilities). Industries
belonging to the former include the hotel industry, insur-
ance industry and tourism industry, while industries that
belong to the latter include internet banks and couriers.
However, many industries of high or middle service encoun-
ters contact with customers and make deals with them

W.-B. Lin / Expert Systems with Applications 33 (2007) 110–121 111



https://isiarticles.com/article/20075