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A Research of Customer’s Choice of Reward Program to Online Travel Intermediates
By Xuan Qiu
School of Economics
Erasmus University
Rotterdam, Netherlands
AbstractAs the competition of the customer market and the cost of retaining customers
increasing having a loyal group of customer becomes one of the import strategies for
a business to be successful. The customer reward programs emerge under the
circumstance. As the effective tool of cultivating, developing and retention loyalty
customers, customer reward programs are increasingly implemented in marketing,
and related issues have also aroused the concern of academics. Though researchers
have studied the reward programs from different aspects, most of the academic
literatures focus on the impact on consumer buying patterns or the corporate profits
from the program, there still is a relative lack of published literature of putting the
affecting elements of reward program take into account.
The design of a reward program has to consider many factors such as the unique
characteristics of an industry, and the cost to establish the program etc. The structure
of each customer reward program is also different. In China, customer reward
programs are often copied from their competitors or directly brought back from the
other countries. Such programs can hardly reach the expectation. Therefore, we have
to dig out the special affecting factor for customers reward program based on the
circumstances of the special market.
This article reviews a lot of related costumer reward programs, collects the data
from various existing programs and interviews. It then summarizes the affecting
factors and establishes a model of the factors for the costumer reward programs, and
verifies the model by using a survey of the Ctrip’s reward programs. The result has
shown that: The reward value, the reward type, convenience, possibility and the
customer fit are the 5 fundamental factors of designing a reward program. These
factors affect the willingness of the customers ‘participation to the program. The
effectiveness of these five factors is different. Among them, the convenience of the
program plays the most important role. Based on the empirical research, the paper
gives out some conclusions and suggestions which are instructive for enterprises to
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design and implement a reward program.
Keywords：Reward Program, Online Travel Agency, Affecting Factors
II
Table of Contents
Introduction 1
Background 1
Content and Significance 3
Research Content 3
Research Significance 3
Research Framework 4
Innovative Points 5
Literature Review 6
The connotation of the reward program 6
The importance of the reward program 7
For customer retention 7
For customer loyalty 8
The study on reward program’s elements 10
The limitation of the existing research 14
Model and assumptions 15
Research hypotheses derived 15
Existing literatures summary 15
Interviews 16
Existing reward programs summary 17
The reward program influencing factors 19
The mediating role of customer perceptive value assumptions 19
Modeling 20
Hypotheses 21
Empirical research 23
The research object selection 23
III
Research variables and scale design 23
Independent variables 23
Intermediate variable 25
Dependent variable 25
Demographic variables 25
Questionnaire design 25
Questionnaire measures 25
Questionnaire structure 26
Preliminary test 26
Questionnaire revise 27
Data analysis 28
Sample description 28
Research method and research object 28
Sample size 28
Sample description 28
Reliability analysis 30
Validity analysis 33
Correlation analysis 35
Structural Equation Model 36
Structural Equation Model analysis 36
Model-fitting testing 37
Hypothesis testing 39
The revised model 40
Conclusions 40
Conclusions and limitations 44
Conclusions 44
Suggestions 44
IV
Research contributions 45
Research limitations 46
Further study 47
V
List of TablesTable 2.1 Reward types 11
Table 3.1 Literature summary 15
Table 3.2 Existing reward program summary 18
Table 4.1 Variables 23
Table 4.2 Independent variables measurement 24
Table 4.3 Intermediate variable measurement 25
Table 4.4 Dependent variable measurement 25
Table 4.5 Removed measure items 27
Table 4.6 Changed measure items 27
Table 5.1 Descriptive statistics 28
Table 5.2 Means and Std. Deviations 31
Table 5.3 Cronbach’s Alpha standards 32
Table 5.4 Cronbach’s Alpha coefficient 32
Table 5.5 KMO - Bartlett test 33
Table 5.6 Varimax rotation 34
Table 5.7 Extraction Sums of Squared Loadings 35
Table 5.8 Correlation 35
Table 5.9 Model-fitting 37
Table 5.10 Hypothesis testing 39
VI
List of FiguresFigure 2.1 Reward classifications 13
Figure 3.1 Research model 21
Figure 5.1 Structural Equation Model 37
Figure 5.2 Revised model 40
VII
§1. IntroductionThe purpose of this research is to examine the critical factors that influence
consumers’ choice towards reward programs.
This chapter is a general introduction of the research contents. This chapter first
states the research background, which is followed by the research content,
significance, plan and framework of this paper. The chapter ends with illustration of
the innovative points of this paper.
§1.1 Background
As marketing shifts from traditional marketing to relationship marketing (Sheth,
1994), Customer-centric marketing strategy is widely used by more and more
enterprises in order to cultivate loyal customers and expects to make more profits. As
a powerful tool to cultivate, develop and keep loyal customers, customer reward
program is widely used in the field of marketing.
The reward program is widely used in airlines, telecom, finance, retail and tourism.
Data shows that corporate profits will decrease by 25% if the customer loyalty
decreases by 5%, and corporate profits might increase by 85% if the customer loyalty
increases by 5% because that 60% of corporate customers come from loyal customers’
recommendations. Thus, according to Kim (2001), the implementation of customer
reward programs is an important guarantee to establish business relationships with
customers and maintain long-term consumers. At the same time they defined customer
reward programs as those that offer incentives to consumers on the basis of
cumulative purchases of a given product or service from a firm.
Studies have shown that in the seven biggest business areas in the United States,
more than 50% of the top ten companies are using reward programs, while the
proportion in UK is also high. On the other hand, the reward programs are very
popular among customers too. According to data from Catuity, a developer of smart
card-based loyalty software for retailers, about 70 % of U.S. households participated
in various forms of reward programs, while 83% of households regularly use the
rewards program (MacSmith, 2002). Additional data shows that 53% of the European
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grocery buyers participated in reward programs, and the corresponding figure in the
field of fashion is 21% (Cigliano, 2000)
In China, an increasing number of companies are using reward programs as a
promotional tool to develop customer loyalty. In retailing, Parkson also offers a
"loyalty card " to its customers, which gives reward to members who spent more than
4000yuan($640) a year; in financial industry, the "Dragon Card " (credit card, debit
card) issued by Chinese construction Bank Shanghai Branch, introduced credit
incentives in 1997, after which almost all commercial banks have introduced a
rewards program based on credit; in aviation, Air China introduced "Air Salon credit
card" while China Southern Airlines launched "Sky Pearl Club", and other airlines
also set up Member Clubs to attract customers.
In tourism, Ctrip (www.ctrip.tom, China's first tourism company who is listed on
NASDAQ) set up a "loyalty program" for its members, which allows their members
to accumulate points through a variety of ways, and redeems the attractive bonus gifts
according to reward points.
With the growth of both number and the information literacy of Internet users, as
well as the rapid development of Internet, e-commerce based online travel service
websites have also developed rapidly. According to China e-Business Research Centre
and iResearch Research Center, the number of online travel booking has been
growing steadily. IResearch statistics shows that more than CNY131.39 billion was
spent on online travel in 2011, increased 38.5% from a year earlier. In 2012, more
than CNY172.97 billion was spent on online travel, increased 31.6% from a year
earlier. Ctrip, Elong and LY were the top three online travel OTA (online travel
agency) (source: iResearch, 2012). Such data indicates that China's online travel
market develops extremely fast at present, and travel website undoubtedly plays a
very important role.
As enterprise's concerns shift from products to customers, analysis of the influence
factors for customer to participate in reward program and the structure of the
conceptual model for customer to participate in reward program has important
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theoretical significance and practical value for enterprise to drive customers to
participate in the rewards program more effectively, especially in promoting customer
to participate in the rewards and strengthen customer relationship.
§1.2 Content and Significance
§1.2.1 Research Content
Customers’ participation in reward programs is affected by many factors, and it is
impossible for enterprises to invest human, material and financial resources in all of
the factors. To ensure the rational use of the limited resources, the key point is to
determine the key factors.
This paper adopts Literature Research method on structural factors of reward
program to analyze the structural factors of reward program, and summarizes the
design method of each structural element. Then the paper selects several critical
factors that influence consumers’ choice towards reward programs and determined the
measurements of each factor. In addition, through the empirical analysis of Crip’s
reward program, the paper establishes the concept model based on the customers’
participation of reward program, and proposes improvements and extensions of
reward program.
§1.2.2 Research Significance
Building conceptual model of customers’ participation in reward programs can help
us further understand that there are many factors that affect customers’ participation in
a reward program, and through theoretically analyzing the complex factors, we can
better understand the real reasons for customers’ choosing a reward program.
On the one hand, the results of this thesis is expected to help enterprises to
recognize the affect factors of a reward program, therefore making their reward
program more effective; For enterprises, the affecting factor discussed in this paper
can offer them a reference in designing a reward program.
On the other hand, because of the differences between industries, it is necessary to
establish an appropriate reward program conceptual model based on online travel
website.3
§1.3 Research Framework
The content of this paper is divided into six chapters, the summaries are as follows:
Chapter 1: Introduction. This chapter discusses the research background, contents as
well as framework, and describes the innovate points of this research.
Chapter 2: Literature review. By reading about the research concerning the reward
program, this paper further put forward the affect factors of customers’ participation
in reward programs and discusses the drawbacks of the existing research.
Chapter 3: Model and assumptions. This chapter put forward the relevant elements of
the reward program, and builds a conceptual model as well as proposed assumptions,
followed by the questionnaire design.
Chapter 4: Empirical research. Statistical analysis software is used to analyze the data
and test the model, after which the model would be evaluated and improved.
Chapter 5: Data analysis. Use data reliability analysis, descriptive statistical analysis,
correlation analysis and regression analysis to test hypothesis and obtain a result.
Chapter 6: Conclusions and limitations. This chapter gives the conclusions of the
paper, summarizes the previous studies, and establishes the conceptual model and
assumptions, proposes limitations and future research direction.
This study collects its problems through empirical observation, and then determines
the research subject with questions. After literature study, hypothesis is derived and
the conceptual model is constructed. The next step is to prepare the questionnaire and
do the preliminary experiment. On the basis of preliminary experiment, questionnaire
is revised and issued on a large scale. After the research data is obtained, statistical
analysis is done and the research hypothesis is tested. Finally we obtain the
conclusion.
The concrete research methods are:
1) Consumer interview
The purpose of consumer interviews is to understand consumers’ views on research
topic. The interview also plays an important role in determining the research
hypothesis and constructing the model.
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2) Questionnaire method
This paper takes online travel websites as research objects, and
adopts questionnaire to collect relevant data to verify the proposed theoretical model
and put forward improvement measures on this basis.
3) Statistical analysis
After the reliability and validity analysis, the author uses descriptive analysis,
correlation analysis, regression analysis (Refer to appendix A) and SEM method to
examine the research hypothesis.
§1.4 Innovative Points
The main innovative points of the paper are:
1) This paper takes online travel websites as research objects, making the study more
representative and specific.
2) A new theoretical basis for online travel websites is provided to initiate a reward
program.
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§2. Literature Review
The goal of this chapter is to provide support and arguments for developing the
research question and build a theoretical framework. Our goal of this research is to
understand the real reasons for customers’ choosing a reward program through
theoretically analyzing the complex factors. There are a few researches done on
different factors that influence customers’ participation in reward programs.
Therefore, it is essential to examine the results of a research stream that is relevant to
this subject.
§2.1 The connotation of the reward program
With the advent of the age of product homogeneity and the development of e-
commerce, more and more enterprises realize that customer loyalty is the source of
competitive advantage. Existing research shows that, reducing the defection rate just
by 5% generates 85% more profits in one bank's branch system, 50% more in an
insurance brokerage, and 30% more in an auto-service chain（Reichheld, 1990） .
Loyal customers tend to buy more products, and have low price sensitivity
(Reichheld, 1996). Therefore, enterprises that have long-term loyal customers gain
more competitive advantage compared with those who have low unit cost, high
market share but high customer return rate.
Reward Programs, also known as Loyalty Programs, first appeared in 1981 with
American airlines launching frequent flyer program. After a period of rapid
development, the customer reward program has been widely used in all walks of life.
75% of American consumers are participated in at least one customer reward
program; and 98% of Canadian consumers are participated in a customer reward
program (The 2009 Colloquy Loyalty Marketing Census). The major means used by
enterprises including, membership card, customer clubs, and bonus point which is the
most popular one.
Kim (2001) argued that customer reward program is used in the lucrative market
segments to maintain a higher customer retention rate, by delivering more value and
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customer satisfaction to customers. Reward program gives rewards to quality
customers, in order to sustain profits on the base of long-term relationship with
customers (Kannan & Bramlett, 2000). Reward programs are initiated to achieve
some sort of financial pay-off or strengthening of their long-term competitive
position. (Sharp, 1997)
In this study, Reward program is defined as a marketing method to maintain a
higher customer retention rate, and mainly refers to Bonus Point Scheme.
§2.2 The importance of the reward program
§2.2.1 For customer retention
Customer retention is the process of keeping a client's business and preventing the
client from using a competitor's services or product. Customers are the most important
asset for an enterprise, and the higher the customer retention is, the stronger
profitability is, Compared to the cost of acquiring a new customer that of keeping an
existing customer is much lower. What is more, long-term customers generally have
such characteristics as repeat purchase ability and low price sensitivity, which will
help improve profits. So in order to obtain long-term competitive advantage,
enterprises have to use a customer-centric CRM strategy to retain customers that are
most valuable.
Reward program has a positive influence on customer retention and market share
(Verhoef, 2003). Bolton (2004) argued that marketing activities (such as reward
program, sales promotion, channel expansion and advertising) lack corresponding
understanding of the length, depth and width of relationship between customer and
the company. However, the reward program, an important means of relationship
marketing, will first influence customer value perception, satisfaction and
commitment, namely perception of relationship between companies and clients, and
then affect the customer behavior.
The customer retention can be divided into three dimensions, the length, width and
depth. Relationship length refers to the possibility for a customer to continue the
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relations with the enterprise; Width refers to the number of time that customers
purchase or use other products or services from the enterprises when keeping a
relationship with the enterprise; Depth refers to the number of time that customers
purchase or use products or services from the enterprises when keeping a relationship
with the enterprise (Bolton, 2004). The three dimensions of customer retention are not
independent. In fact, using the enterprise’s products or services is an essential element
of customer retention, while the use of additional products or services and
maintaining relations with the organization is based on the continued purchase or use
of the product or service.
Marketing plans represented by reward program has two common factors. First,
they have made efforts to maintain customer. Second, they become more and more
impotent for both strategies and industry development. In all markets and industries,
marketing efforts are concentrated on getting closer to the customer, providing
customers with customized products and services, as well as paying attention to
feedback from the market and selecting valuable information.
§2.2.2 For customer loyalty
From the perspective of consumer behavior, customer loyalty is the repeat purchase
of a particular product or service in a period of time (Sharp, 1997). From the
perspective of consumer attitudes, customer loyalty is the high level of commitment
to repeat purchase their favorite products or services in the future, which will not
switch to other enterprises because of changes in market and competition (Oliver,
1999). Dick & Basu (1994) believes that real customer loyalty only occurs when
repeat purchase behavior is accompanied by a high emotional attitude.
When customers decide to participate in a reward program, whether the plan itself
is worth attending comes to the first consideration (O'Brien & Jones, 1995). Since the
customers’ perception towards reward program is subjective to each individual,
different customers have different perception. If the value gain is perceived as having
greater value than the value loss, it is much more possible for customer to participate
in the program; therefore the possibility for enterprise to cultivate customer loyalty
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through reward program will also increase. As mentioned earlier, the original
intention of reward program is to cultivate customer loyalty, so a reasonable designed
reward program that is effectively implemented should promote the formation of
customer loyalty (Michael, 2009).
Sharp (1997) studied the loyalty program in Australia. The purpose of their study is
to investigate the ability for loyalty program to create “extra loyalty”. Based on the
investigating and analyzing data of nine weeks before and after the Christmas, the
study shows that only two(of six) brands’ loyalty program had significant influence on
brand loyalty. The empirical conclusion showed that loyalty programs can change the
consumers’ buying behavior, but the change is not obvious and hard to achieve.
Dreze & Hoch (1998) did an empirical analysis of loyalty programs for baby
products in a supermarket, which showed that: the effect is very significant during the
reward program implementations, and there is a significant increase in both passenger
volume and sales for all products (including non-infant products).
Bolton (2000) argued that customer reward programs can create positive impact on
customer evaluation, customer behavior and repeat purchase intention, due to the fact
that reward program members feel they get more cost-effective services.
Kopalle & Neslin (2003) believed that customer reward program is a powerful tool
to increase sales, strengthen consumer relationship and increase consumer brand
loyalty.
Yi & Jeon (2003) distinguished the influence between program loyalty and the
brand loyalty exerted by retailers’ reward programs in their study. Research indicated
that as long as the customer reward program is considered worthwhile, costumers are
willing to keep a long-term relationship with the enterprise.
Lacey (2003) studied the strategic value of loyalty programs for retailers. The
results showed that compared with customers who do not participate in the reward
program, customers who participate in the reward program, especially those who use
the reward program frequently, have a higher level of commitment and trust towards
the enterprise. However, enterprises need to spend more to keep these customers.
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Chunqing Li & Yanfeng Xu (2004) established a dynamic CRM model with reward
programs which use the return on reward program as the main variable. The results
showed that the appropriate reward programs can promote consumer purchasing.
Xiao Xiao (2008) pointed out that the state of the customer reward program in
Chinese enterprises is not optimistic. It mainly shows in lacking of strategic thinking
when implement a customer reward program, and the vast majority of reward
programs are limited in a low level (such as price discount or coupon). Moreover,
many enterprises were even forced to take action due to industry competition. These
reasons lead to the result that the value of customer reward program is not significant.
Customers did not get the appropriate value, they just got a few packages of tissues or
washing powder, that results in a lot of reward program members are not loyal.
The essence of the reward program is to give the customer a certain value, which
needs to accumulated by constant buying behavior (or recommendation), and after
reaching a certain line these values can be realized. For customers, the initial purchase
may just be a process to get the desired products to meet the functional needs, and
they did not think this process also has added value. Hence, this unexpected bonus
itself can please customer. However, once the customer emotionally considered all
these values belonging to him, he will find a way to protect it, or even looking for
ways to gain the added value, and this process will follow the rules of procedure of
reward program to restrict their purchase choice. It is the customer loyalty desired by
customer rewards program.
Now that shopping online for travel has become prevalent in China, online travel
shoppers have higher expectations of travel intermediates than before. Therefore the
problem of retaining customers has become increasingly challenging for online travel
agencies. In that context, reward program can be a useful tool for companies to
maintain customer and gain customer loyalty, and the research on reward program can
obtain realistic significance.
§2.3 The study on reward program’s elements
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Very few studies focus on affecting factors for customer to join a reward program,
so the author needs to find another way to determine the affecting factors. And it
makes sense to look into the study on rewards program’s elements in order to figure
out the affecting factors for customer to join a reward program.
Dowling & Uncle (1997) classified different types of reward program according to
the reward’s support of the product or service value proposition and the reward’s
timing.
Reward type can be divided into two types: direct and indirect reward. Direct
reward refers to the explicit rewards strongly associated with the product or service
offered to customer, namely directly enhanced product or service value. And an
incentive provided by indirect reward is not associated with the product or service
offered to customer.
Reward’s timing is also divided into two types: immediate and delayed reward.
Immediate reward provides rewards for every purchase, while delayed reward only
provides rewards after several times purchase.
Timing of RewardImmediate Delayed
Type of Reward
Directly Supports the
Product's Value Proposition
Retailer/Brand Manufacturer Promotions(Price Promotions)
Airline Frequent-Flyer Clubs.Coupons and Tokens(GM card)
Other Indirect Types of Reward
Competitions and Lotteries(Instant Scratches)
Multiproduct Frequent-Buyer Clubs(Fly Buys)
Table 2.1 Reward types
The immediate reward defined by Dowling & Uncle cannot distinguish between
short-term promotion and long-term reward program, which means they did not
differentiate short-term promotion from reward program. They think price promotion
is a type of reward program. However, these two should be distinguished from the
consideration of the enterprises’ original intention. Customer reward program
concerns about loyal customers, so it is suitable to measure the customer's behavior
from a long-term perspective; while the price promotion just a method to solve excess
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inventory, it is aimed at price-sensitive customers rather than loyal customer, and
cannot cultivate loyal customers in a long-term.
Kim (2001) considered reward program can weaken price competition by offering
incentives for repeat purchases, thus resulting in higher profits; while price-
promotion-oriented firms gain less from undercutting their prices. Considering that
the reward program plays a role as "competitive leverage" or "exit
barriers"(Klemperer, 1987), it makes sense to distinguish reward program from short-
term promotion.
For this reason, Yi & Jeon (2003) redefined and reclassified the type of reward
program based on Dowling & Uncle’s study by adding repeated reinforcements to
immediate rewards, and emphasizing that immediate reward is an immediately return
for loyal customers’ repeat purchase. For example, if a supermarket always offers
special lower prices to their rewards program members, this belongs to immediate and
repeated rewards. Unlike simple price promotion, immediate and repeated rewards
can help explain successive reinforcements of customer behavior and select target
customers (provide rewards for members only), so that it could control value sharing
toward loyal customers. (Yi & Jeon, 2003)
Dowling & Uncle also classified reward program from the perspective of reward
amount and pointed out that different reward amount can influence the buyer’s
motivation to make the next purchase. The reward amount (or discount amount)
offered by a typical reward program is equal, which means customer can become a
member when up to a certain amount of the accumulated consumption is achieved,
and after that for each dollar spent a participant gains the same number of points.
However, Dowling & Uncle considered it is better to use a differential reward to
enhance customer’s repeat purchase motivation, namely to offer more reward points
for each additional dollar spent, so that the next purchase is increasingly more
valuable to the customer.
From the above we can classify the reward program from three dimensions (reward
type, reward time and reward amount), as shown in figure 2.1:
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Figure 2.1 Reward classifications
Chunqing Li consider that a complete reward scheme should answer four questions:
who to reward (Who), when to reward (When), what to reward (What) and how to
reward (How). Who is the problem about who is the target customer for reward
program, which usually includes target market segmentation, target market
positioning and target market decision. When to reward refers to the problem about
reward timing, if it is an immediate reward, or a delayed reward. What to reward, in
fact, is a problem about the value provided to the customer that is created by reward
program, namely what can be redeemed from reward point when a customer meets the
requirements of reward. There are usually two ways (Dowling & Uncle, 1997): direct
reward (refers to the reward which is strongly associated with the product or service,
namely direct enhancing product or service value) and indirect reward (refers to the
incentive provided by reward program is not associated with the product or service.)
How to reward has three aspects of meaning: the problem about how to calculate
points, the issue of reward ratio, and the specific method of rewarding. Only when a
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reward scheme can answer the above four questions, can we judge that the structure
of the reward program to be completed.
From the discussion above, the reward program’s elements is defined and
approached from very diverted perspectives and on different levels. So far there is no
complete agreement reached over the classification of it. Therefore, further summary
of the reward program’s elements is needed for finding the affecting factors.
§2.4 The limitation of the existing research
The most common reasons for stopping a customer reward program includes the
rising cost and the reward program’s rejection by the target customer, and such
consequence usually results from the fact that the reward program does not provide
enough value to customers. The required financial and organizational costs for
forming, initiating and maintaining a customer reward program are often
underestimated, thus leading to the failure of a good reward program.
Current documents and analyses mainly focus on how a reward program can lead to
customer loyalty, and do not pay enough attention on how to attract customer to join a
reward program. So this paper aims at understanding the real reasons for customers’
choosing a reward program and providing a reference standard for online travel
agency to implement a reward program successfully.
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§3. Model and assumptions
§3.1 Research hypotheses derived
In this study, the main purpose of the exploratory study is to preliminary determine
the influencing factors of reward program based on the existing literature, the typical
user interviews and the collection of enterprises’ reward program. Based on content
analysis, the paper established empirical analysis model and raised assumptions.
§3.1.1 Existing literature summary
According to the literature analysis, the result is shown on table 3.1, the code
number refers to the project frequency appeared in literatures.
Project DefinitionCode
number
Related articles
Direct reward Rewards directly support the value proposition of the product or service offered to customers
2 Dowling & Uncle,
1997 ； Chunqing Li,
2007Indirect reward
Rewards that are designed to motivate loyalty by a more indirect route
2 Dowling & Uncle,
1997 ； Chunqing Li,
2007Tangible reward
Reward that can be measured by cash value(such as cash, products and coupon)
1
O'Brien & Jones, 1995
Intangible reward
Refers to the sense of belonging. Trying to give the consumer feelings of being recognized or make them feel special difference with other customers
1
O'Brien & Jones, 1995
Aspirational value
The degree of attracting customers for reward program
1O'Brien & Jones, 1995
Equal amount For each dollar spent a participant gains the same number of points
2 Dowling & Uncle,
1997 ； Kiveze &
Simonson, 2003Differential Offer more reward points 2 Dowling & Uncle,
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reward for each additional dollar spent, so that the next purchase is increasingly more valuable to the customer
1997 ； Kiveze &
Simonson, 2003
Immediate reward
Immediate reward provides rewards for every purchase
3 Dowling & Uncle, 1997; Yi & Jeon, 2003; Chunqing Li, 2007
Delayed reward
Delayed reward only provides rewards after n times purchase
3 Dowling & Uncle, 1997; Yi & Jeon, 2003; Chunqing Li, 2007
Reward rate The ratio of cash value to reward threshold
2 O'Brien & Jones, 1995; Chunqing Li, 2004
Reward threshold
The requirements in order to get reward
2 O'Brien & Jones, 1995; Chunqing Li, 2004
Time horizon The time constraints for Calculating the total purchase
2O'Brien & Jones, 1995; Chunqing Li, 2004
Limited reward
A limited loyalty program cannot be joined by just anybody.
2Butscher, 2005; Chunqing Li, 2007
Open reward Open loyalty program can be joined by anybody.
2 Butscher, 2005; Chunqing Li, 2007
Target customer groups
Select and segment customers
1Chunqing Li, 2007
Reward way The way to give customer reward
1Chunqing Li, 2007
Table 3.1 Literature summary
§3.1.2 Interviews
In general, interview is a way of collecting market information by visiting,
symposia, etc. Strictly speaking, interview method belongs to field survey.
Despite its high average cost per unit, the limited of investigation and quantity,
interview method has incomparable advantages such as like in-depth communication,
interactive communication, as well as reliable and abundant information sources.
Interview is not only a marketing method, but also a working style for consultants,
business leaders and managers that can be used in many occasions. In many cases,
interview also can be used at any time, in order to understand and master the local
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situation, and to accumulate information.
The interviewees of this study mainly include internal staff and regular customers.
And the interview goals are: fully understand Ctrip’s reward program through the
interview, find the difference between each reward characteristics, and excavate the
influencing factors of reward program through the interview.
The influencing factors of reward program from the interview include:
personalized products; differential reward; the validity of the points.
§3.1.3 Existing reward programs summary
Online Travel is an emerging industry in China, so we do not have many research
findings on it. For this reason, we start from the overall situation.
At present, the enterprises that use reward program in China concentrate on four
industries: banking, aviation industry, mobile telecommunication industry and retail.
The existing reward programs in this research mainly includes: supermarket’s
reward programs which are easy to participate in, such as Darunfa supermarket and
Hualian supermarket; mobile communications industry’s reward programs, whose
characteristics are high public participation, no access conditions, automatic
accumulate points after consumption; bank’s reward programs, whose characteristic is
high transparent information such as those by ICBC, CBC, BOC ; airline’s reward
programs whose characteristic is well designed compared to other industries because
airline’s reward program is the earliest one in China.
Bank’s reward points accumulating methods includes: credit card
consumption/withdrawal/installment, bank loans settlement, personal foreign
exchange trading, national debt, trust and fund, etc.
Airline’s reward points accumulating methods includes: take flight; take partner
airlines flight; stay in well-known hotel; pay by credit card; use car rental services;
use telecommunications services, etc.
Mobile communications industry’s reward points accumulating methods includes:
communication consumption, duration of use, etc.
Retail’s reward points accumulating methods includes: consumption amount.
Air miles reward scheme has the biggest number of reward choice in these four
17
industries, namely it provides more options to the customer's: not only can they swap
points in the partner airlines, but they can also choose other industries’ reward. On the
contrary, there is no points swap cooperation between the banks or the communication
companies, either in- or outside the industry. And the absolute value of reward is low.
This entire reward program did not integrate the opinions of the members, nor did
they find value driving factors. Banks and airlines provide the same service, such as
hotels discount and car rental discount, so customer's perception of value became
lower due to the lack of originality. The companies just provide these services, and do
not increase the value of the reward program by providing special or creative benefit.
Comparison banking, aviation, telecommunications, and retail reward program
reveals that they all adopt similar cumulative rules, that is, use portfolio or
transactions as a measurement. Customer reward marketing pattern has entered a
stage of alliance, not only within the same industries, but also across industry borders.
Alliance’s biggest benefit for customers is to expand the range of point’s usage,
therefore improve customer satisfaction. For companies, alliance overcomes the
limitations of enterprises set up reward program on their own and shares the costs.
Project Code number Examples
Direct reward 245 airlines, 3 telecoms, 5 supermarkets, 11 banks
Indirect reward 19 5 airlines, 3 telecoms, 11 banks
Reward rate 245 airlines, 3 telecoms, 5 supermarkets, 11 banks
Equal amount 7 5 airlines, 2 supermarkets
Differential reward 17 3 telecoms, 3 supermarkets, 11 banks
Immediate reward 5 3 telecoms, 2 supermarket
Delayed reward 245 airlines, 3 telecoms, 5 supermarkets, 11 banks
Reward threshold 245 airlines, 3 telecoms, 5 supermarkets, 11 banks
Time horizon 24 5 airlines, 3 telecoms, 5 supermarkets, 11
18
banks
Open reward 245 airlines, 3 telecoms, 5 supermarkets, 11 banks
Target customer groups
235 airlines, 3 telecoms, 4 supermarkets, 11 banks
Reward way 245 airlines, 3 telecoms, 5 supermarkets, 11 banks
Total 245 airlines, 3 telecoms, 5 supermarkets, 11 banks
Table 3.2 Existing reward program summary
§3.1.4 The reward program influencing factors
Chunqing Li’s study shows that a complete reward program should contain four
structure factors: who, when, what, and how. O'Brien & Jones (1995) argued that
customers can measure a rewards program from five aspects: Cash Value, Aspirational
Value, Redemption choice, Relevance, and Convenience.
Based on O'Brien & Jones’s study, we summarized four reward program
influencing factors: Reward Value, Reward Type, Convenience, and Possibility. And
according to the exploratory research above, we obtained one more influencing
factors: Customer Fit.
Therefore we put forward the following five reward program influencing factors:
Reward Value, Reward Type, Convenience, Possibility, and Customer Fit. The
following hypothesis is proposed:
H1 Reward value has a positive impact on customer choice.
H2 Reward type has a positive impact on customer choice.
H3 Convenience has a positive impact on customer choice.
H4 Possibility has a positive impact on customer choice.
H5 Customer fit has a positive impact on customer choice.
§3.1.5 The mediating role of customer perceptive value assumptions
Zeithaml (1998) believes that customer perceived value typically involves a
tradeoff between what the consumer receives and what he or she gives up to acquire
and use a product or service, which is a general evaluation of the utility of the product
or service. Monroe (1990) interpreted customer perceived value as a ratio of
19
perceived benefits to perceived cost, which is almost the same with Zeithaml's
definition. Woodruff (2002) also noted that customer perceived value should be a
comparison process related to competitors’ products or services value. Grewal (1998)
considered perceived value as a dynamic concept, which includes four types of value:
acquisition value, transaction value, in-use value, and redemption value. Acquisition
value refers the consumer’s benefits (or tradeoff) from acquiring the product or
service. Transaction value refers to the pleasure consumers experienced at the point of
purchase when getting a good financial deal. In-use value involves the utility derived
from using the product or service. Redemption value relates to the residual benefit at
the time of disposing the product or terminating the service.
Dodds & Grewal (1991) proved that customer perceived value as an intermediary
variable factor between price and customers’ purchasing intension.
O'Brien & Jones (1995) considered that customers can determine a program's value
from a customer's perspective from five elements. Therefore, the author also
concluded that the customer perceived value is an intermediary variable factor
between influencing factors and customers participating intension. The following
hypothesis is proposed:
H6 Customer perceived value has a positive impact on customer choice.
H6a Reward value has a positive impact on perceived value.
H6b Reward type has a positive impact on perceived value.
H6c Convenience has a positive impact on perceived value.
H6d Possibility has a positive impact on perceived value.
H6e Customer fit has a positive impact on perceived value.
§3.2 Modeling
Combining the above analysis, the research model is proposed and is shown on
figure 3.1.
20
Figure 3.1 Research model
§3.3 Hypotheses
Research model describes the relationship between independent variables (Reward
Value, Reward Type, Convenience, Possibility, and Customer Fit), the mediate
variable (Perceived value) and the dependent variable (Customer choice).
These relationships are the hypotheses of this study that need to be tested.
H1 Reward value has a positive impact on customer choice.
H2 Reward type has a positive impact on customer choice.
H3 Convenience has a positive impact on customer choice.
H4 Possibility has a positive impact on customer choice.
H5 Customer fit has a positive impact on customer choice.
H6 Perceived value has a positive impact on customer choice.
H6a Reward value has a positive impact on perceived value.
H6b Reward type has a positive impact on perceived value.
21
H6c Convenience has a positive impact on perceived value.
H6d Possibility has a positive impact on perceived value.
H6e Customer fit has a positive impact on perceived value.
22
§4. Empirical research
§4.1 The research object selection
Ctrip.com International is the biggest consolidator of hotel accommodations and
airline tickets in China. Ctrip is the first Chinese tourism company to be listed on
NASDAQ. Founded in early 1999, Ctrip is headquartered in Shanghai, China, with
Beijing, Guangzhou, Shenzhen, Hong Kong four branches, and has branches in more
than 20 large and medium-sized cities in China; the existing staff is more than 1,500.
Ctrip provides travel related services including hotel reservation, air-ticketing,
packaged tour services, internet advertising and other related services.
Ctrip as the top online travel OTA (online travel agency) among China has a
relatively complete reward program, so it is a representative object for the author to
understand the online OTA’s reward program in China.
§4.2 Research variables and scale design
Five independent variables, one intermediate variable and one dependent variable
are used in this study.
Variables Variable type Numbers of items
Reward value Independent variable 4
Reward type Independent variable 5
Possibility Independent variable 3
Convenience Independent variable 6
Customer fits Independent variable 4
Value perception Intermediate variable 4
Preference Dependent variable 4
Table 4.1 Variables
§4.2.1 Independent variables
On the basis of literature research combined with the actual interviews, the variable
measure items are shown in table 4.2.
Variables Measure items
23
Reward value
1) I think the rewards provided by Ctrip have a relatively high value.
2) Relative to my spending, I was satisfied with the return amount.
3) Compared with my spending amount, I think the discounted value of the
goods or services I obtained in return are too low.
4) I think the value of the rewards provided by Ctrip is higher than other
similar sites.
Reward type
1) The reward program provided by Ctrip offers a large variety of reward.
2) I am very concerned about what products can I get as a reward.
3) I would prefer to get products or services directly related to the website
like discounted hotel rates.
4) I would prefer to get products or services not directly related to the
website like Daily Necessities.
5) I would prefer to get tangible things like vouchers& commodities.
Possibility
1) It is easily for me to reach the amount of consumption required by Ctrip
(the minimum amount of consumption to participate in reward program)
2) I think there is a big possibility for me to get reward.
3) After using this website, I will soon be able to participate in reward
program.
Convenience
1) I prefer permanent reward program.
2) I think it is better for reward program to have a deadline.
3) Compared to delayed reward, I prefer to get immediate reward or
feedback.
4) Compared to immediate but lower reward, I prefer high-value rewards
that need time to cumulative.
5) I think reward program provided by Ctrip is easy to check. (Like Points
query)
6) I think reward program provided by Ctrip is easy to use. (Like
Redemption)
Customer fits
1) In contrast, I believe it is appropriate that different accumulate points can
be converted to a products have different values.
2) I think the higher the spending amount is, the greater the reward ratio
should be.
3) If my points are relatively high, I hope I can get special products that
others cannot have.
4) I think my membership can be recognized and respected.
Table 4.2 Independent variables measurement
§4.2.2 Intermediate variable
24
Variables Measure items
Value
perception
1) Compared to other traveling website I prefer reward program provided
by Ctrip.
2) The reward product form Ctrip‘s rewards program is exactly what I
want.
3) As far as the money, time, effort I spent, the rewards program is worth
it.
4) I think it is a good choice to participate in this reward program.
Table 4.3 Intermediate variable measurement
§4.2.3 Dependent variable
Variables Measure items
Value
perception
1) I really like Ctrip’s reward programs.
2) The rewards program will encourage me to spend more.
3) I would recommend the program to others.
4) I have a strong preference towards Ctrip’s reward programs.
Table 4.4 Dependent variable measurement
§4.2.4 Demographic variables
In order to have a preliminary understanding for the characteristics of Chinese OTA
website customers, this paper chose the following demographic variables:
Gender: defined as a binary variable (male or female).
Age: decided by participants’ age.
Income: referred to the customers’ total revenue per month.
Education situation: the highest level of education that customers have completed.
Time of usage: referred to the years that customers use the website since the first
time.
§4.3 Questionnaire design
§4.3.1 Questionnaire measures
This research adopted three kinds of measure methods: nominal scale, ordinal scale
and interval scale. Nominal and ordinal scale are mainly used in the survey to
measure participants’ demographic indicators, for example using a nominal scale to
investigate gender, using an ordinal scale to investigate education situation. And the
interval scale is used to measure survey participants’ viewpoints on a particular issue
25
or tendency.
The interval scale uses 7 point Likert Scale, with 1 means “Totally disagree” and 7
refers to “Totally agree”. All questions are closed questions, no open questions are
used. In general, the authors often use five or seven points Likert Scale, more points
means more detailed, therefore theoretically the measurement are more precise. So the
7 point scale is adopted.
However, from the author's research experience, 7 point scale also has a lot of
defects. Influenced by Chinese traditional culture, the participants tend to choose the
median 4, thus can cause a central tendency error, which means that many
participants’ attitudes are not showed. Therefore, the author would like to explore the
6 point Likert Scale by deleting the median 4, to forcing participants to show their
stances, but 6 point Likert Scale is rarely seen in the research, so 7 point scale is
adopted in this study.
§4.3.2 Questionnaire structure
In this study, the organizational structure of the questionnaire’s first draft is
arranged as following: first part is to introduce the purpose and significance of the
questionnaire to participants, and asks for participants’ serious answers, in order to get
higher quality research data; Then comes the main part of the questionnaire, 7 Likert
Scale are used to test various research variables, and this part is ended with an either-
or question about if they will choose the specific reward program; the last part of the
questionnaire is about participants’ background data, mainly used for the analysis of
samples’ characteristics.
§4.3.3 Preliminary test
After the first questionnaire draft was designed, a small-scale experiment has been
done in order to prevent the experiment questionnaire cannot be fully understand by
the participants. The author invited 10 participants to read and do the questionnaire
and asked them to point out the expression problem and the structure problem in the
questionnaire which need to be revised.
This preliminary test focused on the following aspects: (1) if the scale’s test items
26
are semantically correct; (2) if the test items can be understood by the participants; (3)
if there are some questions that are difficult to answer; (4) other difficulties that may
occur in the process of doing experiments.
§4.3.4 Questionnaire revise
Questionnaire expression is corrected and a few variables are removed based on
preliminary experiment.
Variables Removed measure items
Reward
value
I think the rewards provided by Ctrip have a relatively high value.
Possibility I think there is a big possibility for me to get reward.
Table 4.5 Removed measure items
Variables Changed measure items
Customer
fits
If my points are relatively high, I hope I can get special treatment (products) that
others cannot have.
Table 4.6 Changed measure items
27
§5. Data analysis
§5.1 Sample description
§5.1.1 Research method and research object
This research mainly used an internet-based questionnaire. The author put the
questionnaire on the internet and request Ctrip’ reward program members to fill out
the questionnaire.
§5.1.2 Sample size
245 questionnaires were collected, ant the author rejected 15 invalid questionnaires
(e.g., all the answers are the same, or there are questions unanswered), acquired 230
effective questionnaires. The effective return ratio is 93.8%.
§5.1.3 Sample description
The questionnaire’s sample characteristics are shown on table 5.1.
Variables Options Frequency
Percent
Valid Percent
Cumulative Percent
Gender
Male 125 54.3 54.3 54.3
Female 105 45.7 45.7 100.0
Total 230 100.0 100.0
Age
Under 18 2 0.9 0.9 0.9
18-30 105 45.7 45.7 46.5
31-40 104 45.2 45.2 91.7
41-50 18 7.8 7.8 99.6
Above 50 1 0.4 0.4 100.0
Total 230 100.0 100.0
Incom
e（Yuan/
month）
Under 2000 18 7.8 7.8 7.8
2001-5000 176 76.5 76.5 84.3
5001-8000 33 14.3 14.3 98.7
8001-10000 1 0.4 0.4 99.1
Above 10000
2 0.9 0.9 100.0
28
Total 230 100.0 100.0
Education
Under junior college
15 6.5 6.5 6.5
junior college
94 40.9 40.9 47.4
bachelor 103 44.8 44.8 92.2
master 18 7.8 7.8 100.0
Total 230 100.0 100.0
Using time(years)
Under 0.5 38 16.5 16.5 16.5
0.5-1 87 37.8 37.8 54.3
1-2 79 34.3 34.3 88.7
2-3 19 8.3 8.3 97.0
Above 3 7 3.0 3.0 100.0
Total 230 100.0 100.0 Table 5.1 Descriptive statistics
From the above-mentioned basic features we can see:
1) Gender
In all 230 effective questionnaires, the male account for 54.3% of the samples, and
female account for 45.7%, the male to female ratio is 1.19, in this study man samples
are more than woman samples.
2) Age
In all 230 effective questionnaires, age distributions are centralized on 18 to 30
years old samples (45.7%) and the 31 to 40 years old samples (45.2%); 41 to 50 years
old samples (7.8%); under 18 years old samples (0.9 %); And above 50 years old
samples (0.4%).
In this study, most participants are between 18 to 40 years old, accounting for
90.9% of the research object. The crowd is mainly composed of young adults, which
are the main users of OTA websites, and also the main target group of reward
program.
3) Education
29
In all 230 effective questionnaires, most samples are junior college students or
university graduates, 40.9% and44.8% of total samples respectively. These two kinds
of samples accounted for 85.7% of the total research object. And they are the main
users of new business, they are also the major customers in the future, due to they are
interested in internet business, also has the ability to use more internet business in the
future.
4) Income
In all 230 effective questionnaires, the samples are intensive in 2001-5000Yuan per
month (76.5%). And these samples are the main users of reward program because
they are affordable for a travel or ticket while they pay more attention on rewards
because of their income is not very high.
5) Using time
In all 230 effective questionnaires, most participants are using the Ctrip.com for at
least half a year.
It can be seen that most of the research objects are regular customers of Ctrip.com,
they are familiar with the website, and they use the reward program provided by Ctrip
more frequently. Therefore through their feedback we can see the actual application
situation of the reward program and its impact on customers.
§5.2 Reliability analysis
Before doing reliability analysis, the author first did a descriptive statistical analysis
on each measurement term, and mainly gave out the mean and standard deviation of
the test item, which can roughly reflect the attitude of the participants.
From table 5.2, it is easy to see that most of the test items’ mean are above 4, and
all test items’ standard deviation are above 1.2, in line with the Nunally’s (1978)
requirements on the Likert Scale that all standard deviations should be greater than 0.
5.
Variable Minimum Maximum Mean Std. Deviation
Reward 1 1 7 5.21 1.370
30
value2 1 7 3.06 1.507
3 1 7 4.63 1.291
Reward type
1 1 7 5.01 1.269
2 1 7 5.64 1.191
3 1 7 4.19 1.832
4 1 7 5.00 1.245
5 2 7 5.72 1.134
Possibility1 1 7 4.86 1.278
2 1 7 4.31 1.385
Convenience
1 2 7 5.54 1.009
2 1 7 3.73 1.872
3 1 7 5.45 1.108
4 1 7 4.20 2.225
5 2 7 5.03 1.051
6 1 7 4.50 1.128
Customer fit
1 3 7 5.72 0.897
2 4 7 5.82 0.949
3 3 7 5.34 0.975
4 1 7 4.94 1.235
Perceived value
1 1 7 5.10 1.223
2 1 7 4.96 1.144
3 1 7 4.89 1.307
4 1 7 4.84 1.175
Loyalty
1 1 7 4.96 1.236
2 1 7 5.22 1.459
3 1 7 4.93 1.321
4 1 7 4.69 1.416
Table 5.2 Means and Std. Deviations
Reliability is the degree to which an assessment tool produces stable and consistent
results (Camines& Zeller, 1979). Scores in the same scale measured by different terms
31
are affected by errors. However, the higher the reliability is, the smaller the influence
will be. Thus answers have a consistent change between different respondents, and it
can reflect the real situation.
Reliability has two types: external reliability and internal reliability. External
reliability usually refers to the consistency of the scale when measured in different
times; retest reliability is commonly used in testing external reliability. This research
adopts the cross section data, so there is no need to test the external reliability.
Internal reliability assesses the consistency of results across items within a test.
Cronbach’s Alpha coefficient is used the most to test internal consistency. This paper
uses the Cronbach’s Alpha coefficient to evaluate the reliability of questionnaire.
Cronbach's alpha Internal consistency
α ≥ 0.9 Excellent (High-Stakes testing)
0.7 ≤ α < 0.9 Good (Low-Stakes testing)
0.6 ≤ α < 0.7 Acceptable
0.5 ≤ α < 0.6 Poor
α < 0.5 Unacceptable
Table 5.3 Cronbach’s Alpha standards
Variables Numbers of items Cronbach's alpha
Reward value 3 0.703
Reward type 5 0.710
Possibility 2 0.847
Convenience 6 0.658
Customer fits 4 0.686
Value perception 4 0.756
Preference 4 0.792
32
Table 5.4 Cronbach’s Alpha coefficient
From table 5.4, we can see that the reliability of all variables is above 0.6, the
reliability is acceptable. Therefore the scale has good stability and consistency.
§5.3 Validity analysis
Validity is the extent to which an instrument does indeed measure what it is
supposed to measure in order to be valid; it reveals the relationship between the
structure variables and its measurement terms (Zikmund, 1995). So the inferences
made from scores need to be “appropriate, meaningful, and useful” (Gregory, 1992).
Validity generally falls into four categories: content validity, construct validity,
criterion validity and consequential validity (Messick, 1995)
In this paper, the Factor analysis (use principal component method to extract
influence factors) in SPSS20 is used to determine the validity. Since factor analysis is
based on correlation coefficient, Bartlett spherical analysis can be used to test whether
the correlation coefficient is greater than 0 and a significant result of spherical
analysis shows that correlation coefficient is enough to extract factors. KMO
coefficient refers to the ratio of all correlation coefficient related to the variable to net
correlation coefficient, so the bigger the ratio is, the stronger the correlation is. And
KMO should be greater than 0.5 for factor analysis. Therefore a KMO - Bartlett test
was done before a factor analysis.
Variables Kaiser-Meyer-OlkinBartlett's Test of Sphericity
Approx. Chi-Square Df. Sig.
Reward value 0.696 231.430 3 .000
Reward type 0.597 50.256 6 .000
Possibility 0.600 35.053 1 .000
Convenience 0.651 239.106 3 .000
Customer fits 0.638 161.103 6 .000
Value perception 0.753 210.750 6 .000
Preference 0.775 264.378 6 .000
Table 5.5 KMO - Bartlett test
33
All variables’ KMO are more than 0.5, the Bartlett test is significant, so we
consider the scale is suitable for factor analysis.
The orthogonal solution used a varimax rotation. The analysis results are shown on
table 5.6. We can see that the vast majority of the terms of different scale in the model
are loaded on the same factor, so the evaluation criteria are satisfied. Therefore the
scale can achieve good quality on the convergent validity.
ComponentPrefere
nceReward value
Reward type
Possibility
Convenience
Customer fit
Perceived value
L1 .796L2 .670L3 .584L4 .535
RV1 .622RV2 .779RV3 .714RT1 .719RT2 .817RT3 .744RT4 .614RT5 .429P1 .716P2 .666C1 .685C2 .858C3 .794C4 .735C5 .358C6 .584F1 .715F2 .725F3 .474F4 .683
PV1 .730PV2 .761PV3 .657PV4 .834
Table 5.6 Varimax rotation
34
Variables’ Extraction Sums of Squared Loadings are listed in the table below; it can
be seen that the variables can be explained very well.
Variables Extraction Sums of Squared Loadings
Reward value 43.154%
Reward type 58.455%
Possibility 62.989%
Convenience 48.843%
Customer fits 60.401%
Value perception 68.791%
Preference 63.929%
Table 5.7 Extraction Sums of Squared Loadings
Summary: the former analysis of the reliability and validity of the questionnaire
shows that the quality of the questionnaire is quite good and suitable for further
analysis.
§5.4 Correlation analysis
As can be seen from the table, there is a strong relationship between independent
variables and dependent variable, independent variables and intermediate variable in
the model.
Variable names
Reward
value
Reward
type
Possibility
Convenience
Customer fit
Perceived
value
Preference
Reward value
1
Reward type
.135* 1
Possibility .024 .042 1
Convenie .133* .085 .049 1
35
nceCustomer
fit.082 .122 .115 .199* 1
Perceived value
.379** .438** .496** .311** .222** 1
Preference
.460** .370** .495** .291** .166* .771** 1
**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed).
Table 5.8 Correlation
§5.5 Structural Equation Model
This article uses the Structural Equation Model (SEM) to analyze the data. SEM is
a new developing method in the field of statistical analysis, and has been extensively
used since the early 90s. SEM does not have a very strict requirement, while allowing
the existence of independent variable and dependent variable measuring errors. So it
performs better in the quantitative study of the interactive relationship between
multivariate comparing with multiple regressions, factor analysis and other methods.
As for analysis software, the AMOS 17 is used to study whether the structural
equation model can be supported.
§5.5.1 Structural Equation Model analysis
The calculation results of model path coefficient index, the error term and the load
are illustrated in figure 5.1.
36
Figure 5.1 Structural Equation Model
§5.5.2 Model-fitting testing
Absolute Fit Index Comparative Fit Index
Index DF x2 x2/DF GFI RMRRMSEA
NFI CFI
Standard model
128195.12
(p=0.000)1.523 0.898 0.0416 0.054 0.913
0.937
Table 5.9 Model-fitting
From the model-fitting index, the degree of model fitting is good.
1) The Chi Square Test: For models with around 75 to 200 cases, the Chi Square test
is a reasonable measure to test fit. If the Chi-Square is not significant, the model
is regarded as acceptable. Thex2=195.12 and the p=0.000 is not significant in this
case. If relative Chi-Square is less than 2 or 3 the model is regarded as acceptable.
37
(Kline, 1998; Ullman, 2001). As for x2/df=1.523＜2, so we consider the model-
fitting is good from these two indexes. However, Chi Square is easily affected by
the correlations in the model: the larger the correlations, the poorer the fit, so
alternative measures of fit have been developed.
2) Goodness of Fit Index (GFI): GFI typically summarize the difference between
observed values and the expected values under the model. If the Goodness of Fit
Index exceeds 0.90, the model is regarded as acceptable. (Byrne, 1994) In this
case, the GFI=0.898 is approximately equal to 0.9, so the model-fitting is good
from this index. However, this measure is influenced by sample size, so we need
more method to confirm it.
3) Root Mean Square Residual (RMR): The RMR is defined as the difference
between the observed correlation and the predicted correlation, and RMR is an
absolute measure of fit. Therefore, a value closer to zero indicates a more perfect
fit. If the value is less than 0.08, the model is regarded as good fit. (Hu & Bentler,
1999). In this case, the RMR=0.0416＜0.08, so the model-fitting is good from
this index.
4) Root Mean Square Error of Approximation (RMSEA): This is an absolute
measure of fit and the RMSEA is currently the most popular measure of model
fit. MacCallum, Browne & Sugawara (1996) has used 0.01, 0.05, and 0.08 to
indicate excellent, good, and mediocre fit, respectively. That is, RMSEA values
<0.01 are considered to indicate a good fit, RMSEA values <0.05 are considered
to indicate a suitable fit, and RMSEA＜0.08 are considered to indicate a suitable
fit. However, Hu & Bentler (1999) has suggested that RMSEA less than 0.06 the
model-fitting is not good. In this case we adopted the measurement form
MacCallum et al, the RMSEA=0.054＜ 0.08, so we consider the model-fitting
acceptable.
38
5) Bentler-Bonett Index or Normed Fit Index (NFI): It is an incremental measure of
fit. The index value ranges from 0 to 1,and when the value is between 0.90 and
0.95, the model is considered marginal; When the value is above 0.95, the model
is good fit; and when the value is below 0.90, the model is considered to be a
poor fitting model and needs to be reset. In this case, the NFI=0.913＞0.90, so
the model-fitting is suitable from this index.
6) Comparative Fit Index (CFI): This is an incremental measure and is less affected
than other indices by sample size and model complexity (Bollen & Long, 1993).
The index value ranges from 0 to 1. If the index is greater than one, it is set at
one; and the index is less than zero, it is set to zero. When the value is between
0.90 and 0.95, the model is considered a good fit; when the value is above 0.95,
the model is a perfect fit; and when the value is below 0.90, it is considered non-
satisfactory model fit. In this case, the CFI=0.937＞0.90, so the model-fitting is
good from this index.
Bentler & Chou (1987) pointed out: for a model that contains several variables, it is
difficult to fully achieve the theoretical goodness-of-fit.
This model includes 7 variables and 28 measuring terms, so some fitting indexes
cannot reach 0.9 is acceptable, and the results are approximately equal to 0.9. What is
more, the rest of the fitting indexes shows that the model fitting is good. Therefore we
consider the degree of model fitting is good in general.
§5.5.3 Hypothesis testing
There are 11 hypotheses in total in this paper, combined with model-fitting indexes
and significance test index P value, we can know that 6 hypotheses are confirmed and
5 hypotheses are rejected. The hypothesis testing result is shown in table 5.5(A
hypothesis is accepted when coefficient is significant at the 0.05 level):
39
Hypothesis Estimate S.E. C.R. P Test Results
H1: Y <--- X1 0.584 0.123 4.768 *** Accepted
H2: Y <--- X2 -0.47 0.032 -1.452 0.146 Rejected
H3: Y <--- X3 0.898 0.117 7.71 *** Accepted
H4: Y <--- X4 -0.428 0.314 -1.36 0.174 Rejected
H5: Y <--- X5 0.473 0.198 2.385 0.017 Accepted
H6: Y <--- M 0.98 0.101 9.693 *** Accepted
H6a: M <--- X1 0.914 0.419 2.18 0.029 Accepted
H6b: M <--- X2 0.514 0.307 1.673 0.094 Rejected
H6c: M <--- X3 -0.037 0.081 -0.461 0.899 Rejected
H6d: M <--- X4 0.367 0.069 2.426 0.015 Accepted
H6e: M <--- X5 0.569 0.022 0.43 0.668 Rejected
***. Coefficient is significant at the 0.01 level (2-tailed).
P＜0.05. Coefficient is significant at the 0.05 level (2-tailed).
Table 5.10 Hypothesis testing
§5.6 The revised model
The customer reward program’s influence factor model proposed by figure 3.1 can
be modified through analysis. We keep confirmed model assumptions and remove the
rejected hypotheses, hence finally determining the conceptual model of customer
reward program’s influence factor, which is shown in figure 5.1. Overall, the
customer reward program conceptual model includes 6 elements.
40
Figure 5.2 Revised model
§5.7 Conclusions
1) Reward value:
Hl&H6a are confirmed, there is a significant positive correlation between reward
value and customer choice, and there is a significant positive correlation between
reward value and perceived value. Reward value is often a major determinant for
customer to participate in a reward program, especially considering the high
homogeneity and the similar price in the OTA’s products, the influence is even greater.
Reward value can attract customers’ attention of the reward program, and promote the
consumption. In the current Chinese OTA purchasing environment, customers
generally believed that the reward value is the most worthy reward in customers’
perception, and reward value is a key point for customers to evaluate whether the
reward program is worth to attend or not.
2) Reward type:
H2&H6b are rejected, there is no significant correlation between reward type and
customer choice. Also, there is no significant correlation between reward type and
perceived value. At present, Ctrip did not provide what they want to their members
and make them feel satisfied. Any single factors, such as offering more reward type,
41
cannot play a decisive role, and listening to the customers’ voice and giving them the
right reward type may lead to a different result. At present, the majority of the
members did not really feel this kind of special advantage, so they feel these aspects
did not bring them extra perceived value, let alone to participate in the reward
program due to this factor. In the future, Ctrip should provide more reward types to
customers and try to understand the important reward types, thus enabling the
customers to feel real difference from reward type, and feel the reward program can
give them value, so that they would like to participate in the scheme more.
3) Convenience:
H3 is confirmed but H6c is rejected, which means there is a significant
positive correlation between convenience and customer choice but there is no
significant correlation between convenience and perceived value. A reward program is
easier to participate in if it more convenient. Although customers did not feel the true
value provided by Ctrip’s reward program, they would participate in a reward
program if it is convenient and would not take much time, because the costs are
relatively low and a gift is better than none. In short, customers get benefits from the
reward program but not real perceived value. But as a result of low cost
(convenience), customer will participate in the reward program.
4) Possibility:
H4 is rejected but H6d is confirmed, so there is a significant
positive correlation between possibility and perceived value but there is no
significant correlation between possibility and customer choice.
The higher possibility for customer to get reward, the higher the perceived value
they considered. On the contrary, the lower possibility for customer to get rewards,
the lower the perceived value. The possibility for customer to get reward can affect
the choice of customer to participate in reward program indirectly by influencing
perceived value. Which is to say, a high possibility can indirectly improve the
possibility of customers to participate in a reward program.
5) Customer fit:H5 is confirmed but H6e is rejected, which means there is a significant
42
positive correlation between customer fit and customer choice but there is no
significant correlation between customer fit and perceived value. The costumer will
be interested in the reward program if the customer fit is high, and would be willing to
participate. However, the motivation for customer to participate in the reward
program is just because the want to get the rewards the want and they will not be loyal
to the reward program after they get what they want. In short, customers get benefits
from the reward program but not real perceived value. But as a result of desirable gifts
(customer fit), customer will participate in reward program. So Ctrip should collect
and analysis the members’ data and launch the appropriate gifts based on these data.
By continually providing the humanized and customized reward, it can let customers
participate in the reward program and try to increase the customer perceived value at
the same time.
6) Perceived value:H6 shows that there is a significant positive correlation between perceived value
and customer choice. When customers choosing to participate in a reward program,
the first thing to consider is whether the scheme itself is worth to attend (O 'Brien and
Jones, 1995) .Customer perception of reward program is very subjective. Different
customers have different perception of reward programs. But if customers think the
benefits for participate in a reward program outweighs the costs significantly, the
likelihood of customers to participate in the plan is high, and therefore the
possibilities for enterprises to cultivate customer loyalty by reward program is high.
To sum up, this study can be the following main conclusions:
Reward value has a positive impact on customer choice and reward value has a
positive impact on perceived value.
Convenience has a positive impact on customer choice.
Possibility has a positive impact on perceived value.
Customer fit has a positive impact on customer choice.
Perceived value has a positive impact on customer choice.
43
§6. Conclusions and limitations
§6.1 Conclusions
Through the above analysis, two main conclusions are obtained:
1) Reward value, Convenience and Customer fit are the three decisive factors for
customers to choose whether they would participate in a reward program or not.
They all have a significant direct positive influence for customers to participate in
a reward program, as for the reward type and possibility, the influence of these
two factors are not significant.
Even these three factors all have significant influence on customers’ choice, the
weights of each factors are different. Among them, the convenience is the most
powerful factor, followed by the reward value, and customer fit.
2) Perceived value has significant direct positive influence for customers to
participate in a reward program. And reward value and possibility have indirect
positive influence for customers to participate in a reward program by influencing
perceived value.
Even these two factors all have significant influence on perceived value, the
weights of each factors are different. Among them, the reward value is the most
powerful factor, followed by possibility.
§6.2 Suggestions
The research conclusion in this paper for reward program has the following
significance:
First of all, a reasonable designed reward program can promote customers’
psychological intentions to participate in a reward program. The conclusion of this
paper points out that convenience is the most significant factor in promoting
customers to participate in a reward program. It shows that enterprises should pay
more attention to convenience of customers to participate in the reward program when
designing a reward program, for instance, providing multiple consulting methods,
providing more redeem way, and providing more convenient in terms of customer
44
service, etc. Customers can thus perceive more value from participating in the reward
program. Further they are likely to produce more identity and attachment feeling
towards the enterprise and become one of the most enthusiastic advocates and
supporters of the enterprise, which is the basis for customers to bring long-term value
for the enterprise.
Secondly, the customer fit is as important as the reward value. This shows that the
enterprise can consider more about providing the rewards that customers are
interested in, rather than only pay attention to reward value in the process of
designing a reward program, This point happens to be in line with the enterprise's
actual need, because the higher the reward value, the higher the enterprise’s costs. In
the process of implement reward programs, enterprise should launch customer survey
from time to time, in order to understand customer fit and enable customers to
maintain long-term relationship with the enterprise.
Finally, reward type is not significant when customer choose to participate in the
program. This shows that customer does not care about what kind of reward offered
by enterprises or whether he can eventually get the reward. This may be associated
with customers’ psychological benefits from participate a reward program. Such as
customers think they have a sense of belonging, or have a membership card is the
embodiment of the identity etc. So when design reward program conditions,
enterprises should fully consider the customer's perception, arouse the enthusiasm of
the participating possibility from psychological, and give customers appropriate
reward, and then can get maximal customer perceived benefits with minimal
economic cost.
§6.3 Research contributions
Based on relevant literature review, the paper recognized that most of the past
studies are focused on the influence of the reward program and reward program’s
impact on corporate profits, and there is few past studies that focus on influence
elements of reward program. On the basis of past studies, further study on the
45
influence of five elements, which are the reward value, reward type, convenience,
possibility and customer fit, has been done. Whether each factor will exert positive
effects on customers is the purpose of the rewards program design. So in this paper,
we focus on influence elements of reward program and established the concept model
of customer’s participation of reward program. Although many literatures argued
perceived value has impact on customers’ choice on reward program and many
literatures put forward the affecting factors for perceived value, there are few
literatures talking about the influence elements on customers’ participation on reward
program. These factors are combined in this paper. Although there are literatures
proposed the five elements that influence customer perceived value, they did not test
the theory by empirical investigations (O'Brien & Jones, 1995). Based on their
research, the new proposed five elements are tested. On the basis of above research,
the influence factor-customer fit has been added according to the interview and the
existing reward program analysis. The new element makes the research more in line
with China's actual situation, and fills the research deficiency on this aspect in China.
This study also makes clear the relationship between the affecting factors and the
customer’s participation of reward program; this is the basis for the further study of
relationship marketing and service marketing. Empirical study results show that only
part of the elements have positive influence on the measurement variables of
customer’s participation of reward program. This is not exactly the same with what
we usually think "once you have a good customer relationship, the customer will
participate in the reward program".
In practice, this research provides a measuring tool for online travel agency to test
customer’s participation towards reward program, and provides a reference standard
for online travel agency to implement reward program successfully. So the enterprise
can make reasonable use of limited resources, improve the quality of service, and
attract customers to participate in reward program.
§6.4 Research limitations
The paper did some research and exploration of the reward program’s influence
46
factors according to the relevant theories about customer reward program, practical
results, combined with the author’s own knowledge and cognitive ability. However,
because of the limitation of research conditions and resources, as well as that of the
author’s own knowledge and research level, although this paper has obtained some
research results, there are still a lot of limitations.
When summing up the research results, we need to pay attention to see if the study
conclusion can is applicable to other industries, aided by further research. Based on
the study of Ctrip.com, this paper mainly studies the problem of online travel agency.
The benefit is the ability to have a thorough understanding of this industry, but the
disadvantages can be overgeneralization. To better understand the problem in other
industry, further studies are needed.
In this study interview survey of reward program is insufficient. Due to the
limitation of time and conditions, this article only carried on four interview
investigations. And due to the small number of interviews, the subjectivity of the
conclusion is strong.
As for the research object, the online questionnaire is used in this paper, so samples
are mainly concentrated in young people who often surf the Internet. At the same
time, the sample size is not very big (230 valid questionnaires), so it is difficult to
guarantee the representation of the sample. Based on the above reasons, the external
validity of this research conclusion is difficult to guarantee. In addition, because
measurement error is widespread, and the existence of error can affect the relevance
of factors, there may have deviations for the results of the study.
§6.5 Further study
Results of this study provide a possibility for further study of the influence factors
of reward program and customers’ participation of reward program:
The study of customers’ participation of reward program, under B to B situation
can be a further direction. This paper is based on B to C situation and the research
objects are individual customers. And the problem can be different in B to B situation.
This paper mainly studies the problem of online travel agency, so further study can
47
choose more industry to verify the research conclusion of this article. Comparing
many industries at the same time can explore the influence of product category and
consumer's attributes on the research conclusion and provide research support for
market segmentation and knowledge integration.
Further study should increase the sample size in different levels, increase the
number of samples and provide sample representativeness, in order to make the model
more universal.
48
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d
Appendix A (Regression results):
Model Summary
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .647a .418 .405 .82374
a. Predictors: (Constant), customer fit, reward value, convenience, possibility, reward type
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 4.759E-005 .054 .001 .999
reward value .432 .058 .447 7.444 .000
reward type -.035 .111 -.017 -.320 .749
possibility -.299 .090 -.205 -3.324 .061
convenience .595 .094 .338 6.309 .000
customer fit .322 .105 .189 3.078 .002
a. Dependent Variable: choice
Model Summary
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .822a .675 .666 .61678
a. Predictors: (Constant), perceived value, customer fit, convenience, reward value, reward type, possibility
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 6.624E-006 .041 .000 1.000
reward value .328 .073 .186 4.464 .000
reward type .072 .081 .042 .895 .372
possibility .217 .046 .225 4.681 .000
convenience .314 .067 .215 4.659 .000
customer fit .013 .063 .006 3.155 .007
perceived value .738 .056 .637 13.287 .000
a. Dependent Variable: choice
e
Model Summary
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .871a .695 .693 .68150
a. Predictors: (Constant), perceived value
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
1(Constant) -2.190E-005 .045 .000 1.000
perceived value .893 .049 .771 18.287 .000
a. Dependent Variable: choice
Model Summary
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .705a .566 .552 .74230
a. Predictors: (Constant), customer fit, reward value, convenience, possibility, reward type
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 5.554E-005 .049 .001 .999
reward value .363 .085 .349 4.263 .000
reward type .339 .094 .230 3.594 .323
possibility .291 .052 .239 5.572 .000
convenience .020 .081 .016 .248 .805
customer fit -.065 .100 -.036 -.657 .512
a. Dependent Variable: perceived value
f
Appendix B：Questionnaire about Ctrip’s reward program
Purpose：For thesis writing, we stage an investigative questionnaire. If you have interest in
it, please help us to finish the questionnaire and write your ideas. The answers you
give will be kept confidential and used seriously for research purposes only. Thank
you.
Part 1: First, according to your own feelings on Ctrip rewards program, please
determine the extent you agree or oppose for each question, and fill in the
corresponding figures in parentheses.
1 2 3 4 5 6 7
Totally
disagreeDisagree
Partly
disagree
Not agree nor
disagree
Partly
agreeAgree
Totally
agree
1 2 3 4 5 6 7
1. Relative to my spending, I was satisfied with the return
amount.
2. Compared with my spending amount, I think the
discounted value of the goods or services I obtained in
return are too low.
3. I think the value of the rewards provided by Ctrip is
higher than other similar sites.
4. The reward program provided by Ctrip offers a large
variety of reward.
5. I am very concerned about what products can I get as a
reward.
6. I would prefer to get products or services directly related
g
to the website like discounted hotel rates.
7. I would prefer to get products or services directly related
to the website like Daily Necessities.
8. I would prefer to get tangible things like vouchers&
commodities.
9. It is easily for me to reach the amount of consumption
required by Ctrip (the minimum amount of consumption
to participate in reward program)
10. After using this site, I will soon be able to participate in
reward program.
11. I prefer permanent reward program.
12. I think it is better for reward program to have a deadline.
13. Compared to delayed reward, I prefer to get immediate
reward or feedback.
14. Compared to immediate but lower reward, I prefer high-
value rewards that need time to cumulative.
15. I think reward program provided by Ctrip is easy to
check. (Like Points query)
16. I think reward program provided by Ctrip is easy to use.
(Like Redemption)
17. In contrast, I believe it is appropriate that different
accumulate points can be converted to a products have
different values.
18. I think the higher the spending amount is, the greater the
reward ratio should be.
19. If my points are relatively high, I hope I can get special
treatment that others cannot have.
20. I think my membership can be recognized and respected.
21. Compared to other traveling website I prefer reward
program provided byCtrip.
h
22. The reward product form Ctrip‘s rewards program is
exactly what I want.
23. As far as the money, time, effort I spent, the rewards
program is worth it.
24. I think it is a good choice to participate in this reward
program.
25. I really like Ctrip’s reward programs.
26. The rewards program will encourage me to spend more.
27. I would recommend the program to others.
28. I have a strong preference towards Ctrip’s reward
programs.
I am willing to participate in the rewards program provided by Ctrip:
a. Yes b. No
Part 2: Background Information1. Gender: a. male b. female
2. Age:
A. under18 B. 18-30 years old C. 31-40 years old D. 41-50 years old
E. over51
3. Do you consider your income per month between:
a. ＜2000 Yuan b. 2001-5000 Yuan
c. 5001-8000 Yuan d. 8001-10000 Yuan
e．＞10000 Yuan
4．Education situations (already obtained or are studying):
A. under junior college (excluding junior college) B. junior college
C. Bachelor D. Master E. doctor and above
5．Years of using Ctrip:
i
A. under 0.5year B. 0.5- 1 year C. 1-2 years D. 2-3 years E. over 3 years
j