how relationship marketing tactics affect customer
TRANSCRIPT
Bachelor Thesis
How relationship marketing tactics affect
customer satisfaction
(Evidence of supermarket industry)
Authors:
Weiyang Huang 910815
Hongyu Zhu 941120
Yuxin Pan 940112
Group: D3
Tutor: Pär Strandberg
Abstract
Within the competitive marketing environment, companies are faced with many
challenges to stay competitive. Companies are consistently trying to establish the long-
term relationship with customers by satisfying them as much as possible. Since
relationship marketing has highly-discussed concerns building the long-term
relationship and improve customer satisfaction, the study aims to describe how different
relationship marketing tactics affect customer satisfaction. According to previous
scholars, four different major relationship marketing tactics were selected to investigate
and described in the study, which are the quality of service, price perception, brand
perception and value proposition. The authors developed a theoretical framework by
reviewing previous works of literature to see how companies use relationship marketing
tactics as a business strategy to develop customer satisfaction. The method of
quantitative research was applied to this study and a online questionnaire was used to
collect data. In results chapter, the authors tested descriptive analysis, reliability,
validity, regression analysis by analyzing the empirical findings. There are three
hypotheses accepted and one rejected. In the end of this paper, the authors analyzed and
described the data in detail and revealed the effect of each relationship marketing tactics
on customer satisfaction. Limitation of this study and further research are also presented.
Keywords
Customer satisfaction, relationship marketing tactics, quality of service, price
perception, brand perception, value proposition.
Acknowledgement
Writing at this point means our three years’ bachelor study is about to finish. There are
some people we would like to acknowledge. Without their help, we would never be
possible to come this far.
Firstly, we greatly appreciate the guidance from our examiners. Åsa Devine, our thesis
tutor Pär Strandberg, as well as our method counselor Setayesh Sattari. We will always
be grateful for the continual guidance and support they gave us from topic selection,
project implementation until we eventually finish this thesis. Every single word from
their feedbacks and conversations is a precious gift in our academic career.
In addition, we would like to thank our opponent groups during the whole process. Your
exhaustive, meticulous and objective opinions helped us identify the shortcomings and
problems and encouraged us to make a better thesis.
In the end, we would like to thank all participants who joined the research. Your
contribution is the basis of our study. For us, all the data were not just numbers, but an
inspiration to this study that sparked our creativity for this project.
Weiyang Huang Hongyu Zhu Yuxin Pan
Table of Contents
1. INTRODUCTION .................................................................................................................... 1
1.1 BACKGROUND ..................................................................................................................................... 1
1.2 PROBLEM DISCUSSION ....................................................................................................................... 2
1.3 PURPOSE .............................................................................................................................................. 4
2. THEORY ..................................................................................................................................... 5
2.1SATISFACTION ...................................................................................................................................... 5
2.2 RELATIONSHIP MARKETING TACTICS ................................................................................................. 6
2.2.1 Quality of service (QoS) ....................................................................................................... 7
2.2.2 Price perception ..................................................................................................................... 8
2.2.3 Brand perception ................................................................................................................... 9
2.2.4 Value proposition................................................................................................................. 10
2.3 HYPOTHESIS AND CONCEPTUAL MODEL ........................................................................................ 11
3. METHOD ................................................................................................................................. 13
3.1 RESEARCH APPROACH ...................................................................................................................... 13
3.1.1 Inductive vs. Deductive ...................................................................................................... 13
3.1.2 Qualitative vs. Quantitative ............................................................................................... 14
3.2 DATA SOURCES ................................................................................................................................. 15
3.3 RESEARCH DESIGN ............................................................................................................................ 16
3.4 DATA COLLECTION METHOD .......................................................................................................... 17
3.5 SAMPLING .......................................................................................................................................... 18
3.5.1 Sampling Frame.................................................................................................................... 20
3.5.2 Selection and data collection procedure ..................................................................... 21
3.6 DATA COLLECTION INSTRUMENT .................................................................................................... 22
3.6.1 Measurement of Variables and Operationalization .................................................. 22
3.6.2 Questionnaire Design ......................................................................................................... 24
3.6.3 Piloting and pre-testing questions ................................................................................ 26
3.7 DATA ANALYSIS METHOD ............................................................................................................... 28
3.7.1 Descriptive Statistics ............................................................................................................ 28
3.7.2 Regression Analysis ............................................................................................................. 29
3.8 QUALITY CRITERIA ............................................................................................................................ 30
3.8.1 Reliability ................................................................................................................................. 31
3.8.2 Validity ..................................................................................................................................... 32
3.8.2.3 Construct validity .............................................................................................................. 33
3.9 ETHICS................................................................................................................................................ 34
4. RESULTS .................................................................................................................................. 36
4.1 DESCRIPTIVE STATISTICS ................................................................................................................... 36
4.2 RELIABILITY ANALYSIS ....................................................................................................................... 37
4.3 VALIDITY ............................................................................................................................................ 38
4.3.1 correlation analysis .............................................................................................................. 38
4.3.2 Exploratory factor analysis ................................................................................................ 40
4.4 REGRESSION ANALYSIS AND HYPOTHESIS TESTING ....................................................................... 44
4.4.1 Quality of service regression analysis ............................................................................ 44
4.4.2 Price perception ................................................................................................................... 45
4.4.3 Brand perception ................................................................................................................. 47
4.4.4 Value proposition................................................................................................................. 48
4.4.5 Satisfaction ............................................................................................................................. 49
4.5 REVIEWED CONCEPTUAL MODEL ..................................................................................................... 51
5. DISCUSSION ........................................................................................................................... 53
5.1 QUALITY OF SERVICE (QOS) AND CUSTOMER SATISFACTION ...................................................... 53
5.2 PRICE PERCEPTION AND CUSTOMER SATISFACTION ...................................................................... 54
5.3 BRAND PERCEPTION AND CUSTOMER SATISFACTION .................................................................... 55
5.4 VALUE PROPOSITION AND CUSTOMER SATISFACTION .................................................................. 57
6. CONCLUSION ......................................................................................................................... 59
7. RESEARCH IMPLICATIONS ................................................................................................. 61
7.1 THEORETICAL AND PRACTICAL CONTRIBUTION ............................................................................ 61
7.2 LIMITATION AND FURTHER RESEARCH ............................................................................................ 61
REFERENCE LIST ......................................................................................................................... 63
APPENDIX ........................................................................................................................................I
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1. Introduction
This chapter presents the background of relationship marketing and customer
satisfaction as well as relationship marketing tactics. The problem discussion describes
the current problem existing in the field and lead to the purpose and research questions
of this research project.
1.1 Background
In order to stay competitive in the current environment, companies should not only
provide a high quality of service and product, it is also necessary to know how to deal
with customers (Greenberg, 2010). Relationship marketing serves as a tool that helps
company sell more products and services. One of the most expensive and difficult tasks
for businesses is acquiring new customers and retaining them. It emphasizes on building
and maintaining a long-term relationship between company, customer, other related
parties, discussing the common interests and conducting multiple transactions between
parties. The goal of relationship marketing is to establish a permanent relationship with
customers, maintaining and developing them in order to increase overall market share
(Stone et al., 2000).
Customer satisfaction is one of the major components that is used to maintain good
relationship with customers, which is essential to lead a successful business (Homburg
et al., 2005). It is an extent to which the customers are satisfied with the purchase of
products or services (Kurtz, 2013). It is also the voice of customer and is diverse from
customers to customers (Rahman, 2015). Satisfied customers not only purchase more
than unsatisfied ones, but also help company to gather potential customers by their
positive word of mouth (Olsen, 2002; Brown et al., 2005). Numbers of prior researches
indicate that company can get higher financial performance if they have a significant
number of satisfied customers (Fornell et al, 2006). Unsatisfied customers are risky
because can switch the original supplier in favor of the competitors. In other words, it
helps the new supplier to gather more market share eventually (Ali Raza, 2012).
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According to Kotler et al. (2012), relationship marketing tactics refer to the process that
can help a company to use its limited resources on the opportunities to increase profits
and stay competitive. Marketers can implement relationship marketing tactics in many
ways which had impact on customer retention (Kotler et al., 2012). In service industry,
Peng and Wang (2006) suggests that relationship marketing tactics consist of service
quality, price perception, value offers and brand image.
Relationship marketing carries out many relationship marketing tactics that are widely
applied in current relationship strategy. Relationship marketing tactic is an efficient
solution that builds and maintains a good relationship with customers. (Ali Raza, 2012).
1.2 Problem Discussion
Competition as a concept nowadays, has become one of the most debated topics under
the business environment (Rezaei, B. et al., 2015). Within a strongly competitive
environment, companies should not only focus on retaining current customers, but also
should focus on exploring more potential customers (Terrence and Gorden, 1996;
Anderson et al., 1994). For many companies, customers are intellectual and financial
capitals and if a company knows how to manage their capitals properly, it will bring
more benefits to the company (Parisa, 2015). A good relationship is one of the
important ways of keeping and gathering more capitals (Shalaan 2013; Baidi, ei al.,
2017). Creating and maintaining a stable relationship is a challenging task for many
companies (Parisa, 2015).
Customer satisfaction is one of the major components of a good relationship (Anderson
et al., 1994). Many previous studies have illustrated the importance of customer
satisfaction in a relationship (Amin et al., 2010; Lenka et al., 2009; Mohsan et al., 2011;
Ziaul Hoq and Amin, 2010). Unsatisfied customers will lose their trust towards the
companies (Garbarina and Johnson, 1999). Thus, satisfaction could be one direction of
this study. Several literature reviews suggested that relationship marketing tactics might
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positively influence customer satisfaction and is a tool to achieve satisfaction. Four
most critical tactics are chosen from the literature review as our research subjects are
quality of service (QoS), price perception, brand perception and value proposition
(Grönroos, 1984, Parasuraman et al., Zeithaml, 1988, Peng and Wang, 2006, Martenson,
2007, Ranaweera and Prabhu, 2003, Ravald and Grönroos, 1996).
Quality of service is a relationship marketing tactic that measures whether the degree
of customer service expectations meet the service delivered by supplier (Grönroos,
1984). The scholars believe that better quality of service leads to higher customer
satisfaction. Price perception however is a fair price of a product that a company is
offering which meets the price expected by the customers (Zeithaml, 1988). Many of
previous studies indicate that a fair price is closely related to satisfaction (Dabholkar
and Abston, 2008; de Jager et al., 2010; Kotler and Lane, 2009; Neilson and Chadha,
2008; Oliver and Shor, 2003; Pancras and Sudhir, 2007; Zeithaml, 1988). On the other
hand, brand perception can be translated to brand image or brand reputation, which is
a perception or opinion existing on customers’ memory network towards the brand
(Peng and Wang, 2006). Once a company has delivered good reputation to customer,
the customers are more likely to make repurchases. Accordingly, number of scholars
believe that a well-reputed company usually has many satisfied customers (Martenson,
2007, Ranaweera and Prabhu, 2003). Finally, fourth tactic is the value proposition
which is closely tied to price perception. Zeithaml (1988) gives four simple definitions
of value, which are: (1) value is low price, (2) value is whatever I want in a product, (3)
value is the equity I get for the price I pay and (4) value is what I get for what I give.
Moreover, value proposition is one of the most successful tactics under competitive
marketing and is tied to customer satisfaction (Ravald and Grönroos, 1996).
All of these four tactics were tested and verified by many scholars, but each of the
tactics was individually conducted by antecedents and few researches have been done
in combination with these four tactics (Grönroos, 1984, Parasuraman et al., 1985, p.42-
43, Zeithaml, 1988, Peng and Wang, 2006, Martenson, 2007, Ranaweera and Prabhu,
2003, Ravald and Grönroos, 1996). Thus, this study will try to fill the research gap and
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demonstrate the relations between these four tactics and satisfaction. The study we hope
will help companies achieve customer satisfaction and build relationships in a most
simple and efficient way.
1.3 Purpose
The purpose of this study is to describe the influence of relationship marketing tactics
on customer satisfaction.
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2. Theory
The theoretical chapter presents the current researches regarding RM tactics (quality
of service, price perception, brand perception, and value proposition) and customer
satisfaction, as well as their interconnection. It will also conduct with some hypotheses
for different RM tactics and developed with an analytical model as the core concept of
this project.
2.1Satisfaction
Hunt (1977) defines satisfaction as “an evaluation of an emotion”. Rust and Oliver
(1994) confirmed this viewpoint that customer satisfaction reflects a degree of positive
feelings towards products or services. Wetzel et al., (1998) further developed the
concept based on customers’ expectations where they explain the satisfaction provided
by product/service meet the expectation of the customers. Satisfaction is a pleasurable
activity while customers consuming something. When a need, goal or desire of
customers has been reached means they are satisfied. Thus, the activity is enjoyable
(Oliver, 1999).
Accordingly, Anderson et al., (1994) argues that satisfaction is one of the critical keys
to improving and maintaining a long-term relationship with customers. As well, it is
referred satisfaction is an investment that keeping current customers and exploring
potential customers, since it can increase customers spending. Many of evidences also
confirmed the view, satisfaction is a power that engages customers repurchasing
(Martenson, 2007, Ravald and Grönroos, 1996, Peng and Wang, 2006). Furthermore,
when vast number of customers are satisfied, which usually means gathering more
market share (Anderson et al., 1994).
Relationship marketing has an ultimate aim that is maintaining long-term relationship
with customers rather than short-term or one time transaction. It is also called long-term
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orientation, it helps company minimizing total costs and achieves a goal of permanent
cooperation (Shalaan, 2013, Baidi, ei al., 2017). Thus, as mentioned above, satisfaction
is a key of improving and maintaining a long-term relationship.
Relationship marketing tactics are widely used on maintaining and developing a long-
term relationship. Many of tests have been confirmed, relationship marketing tactics as
a sub-concept of relationship marketing can be treated as a tool or measurement in order
to achieve customer satisfaction (Parasuraman et al., 1988, Mohanmmad, 2015, Cronin,
Taylor, 1992, Ba, S. 2002, Guo, S., 2011, Gruen, T. 2000, Martenson, 2007, Ranaweera
and Prabhu, 2003, Ravald and Grönroos, 1996).
2.2 Relationship marketing tactics
Relationship marketing tactics are tools that used to maintain and building a long-term
relationship (Anderson et al., 1994). Through previous literatures, some relationship
marketing tactics are presented: quality of service, price perception, brand perception
and value proposition. Detail description will be presented on following chapters.
Relationship marketing tactics References
1. Quality of service Grönroos, 1984, Parasuraman et al., 1985,
p.42-43, Parasuraman et al., 1988, Cronin and
Taylor, 1992
2. Price perception Zeithaml, 1988, Peng and Wang, 2006,
Zeithaml and Berry, 1987
3. Brand perception Parasuraman et al., 1988, Peng and Wang,
2006, Xing-Wen and Ming-Li, 2008
4. Value proposition Ravald and Grönroos, 1996, Zeithaml, 1988
Table 2.2 Relationship marketing tactics
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2.2.1 Quality of service (QoS)
The definition of QoS is ˝the outcome of an evaluation process, where the customers
compare their expectations with the service they have received˝ (Grönroos, 1984) or
according to Parasuraman et al., (1985, p.42-43) “service quality involves more than
outcome, it also includes the manner in which the service is delivered.”
Parasuraman et al., (1988) developed the concept where he pointed it as a form of
attitude but is not equal to satisfaction that results from a comparison of expectations
with the perception of performance. Parsuraman et al., (1996) further states that
''expectations are views as desire or wants of customer, i.e. what customers think a
service provider should offer rather than would offer''. The measurement of QoS is
determined by two main parameters, which are expected services and received services.
When received services are larger or equal to expected services, the QoS then is
believed to satisfy the customer needs. The result provided by Rajic et al., (2016)
substantiates the QoS' direct impact on customer satisfaction.
QoS is relatively special or superior service delivery which matches the customer's
expectations. (Cronin and Taylor, 1992). However, Cronin and Taylor (1992) argues
that satisfaction is an antecedent of QoS. The reason for this is that the higher
satisfaction level plays a significant role in determining a higher perceived service
quality. Secondly, QoS should be an attitude rather than a transaction-specific measure
(Cronin, Taylor, 1992). Chen et al., (2015), mention the quality of service involves
maximizing the user’s satisfaction in terms of response time and success rate by
addressing the scalability. On the other hand, QoS shouldn't focus on paying attention
to externalities of the services (such as functionality etc.) it is also supposed to focus
on the internal aspect of the services. The internal service means interrelationship
between supplier and customer (Chen et al., 2015).
There are two main arguments about QoS related to satisfaction in current researches.
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QoS directly influences satisfaction (Parasuraman et al., 1988). Cronin (1992) explains
the phenomenon by stating the influence of satisfaction on QoS and addresses its
significance. However, contrary to other product centric businesses where things are
measured by style, color, label, feel, fit etc, the difficulty to measure services should be
highlighted because of its intangible characteristics. When purchasing some services,
there are few tangible cues that provide some tangible form to service such as facilities,
personnel and equipment but not enough to substantiate (Cronin, Taylor, 1992,
Zeithaml and Berry, 1987,). There is no set conclusion. Thus QoS is labeled as an
independent variable and satisfaction as a dependent variable.
2.2.2 Price perception
According to Zeithaml (1988), perceived price is what a consumer gives up or sacrifices
in order to obtain a product. Many arguments are existing on the conceptualization of
price perception. Peng and Wang (2006) suggests that perceived price is viewed as one
of the most critical marketing cues in all purchase situations. In other words, higher
prices negatively impact purchase likelihoods. On the other hand, several studies
believe that not all of products or services are negatively impacted by price, for instance,
Zeithaml (1988) list four types of product (coffee, toothpaste, cold cereal, margarine),
which are treated as basic products that customers do not really care about cost. The
last argument is about price awareness among demographic groups, it indicates that
people who are female, older, married and do not work outside the home have greater
awareness for price (Zeithaml, 1988).
Price influences sales volume and market share, and price is the only element of the
marketing mix that generates revenues and profits for a business, other elements only
deal with costs. Meanwhile, price is one the most flexible element of marketing mix,
mainly because it involves decisions making which is relatively faster than others. Price
perception is also called price-perceived quality, which also means the elasticity of
products’ price and products’ quality. To be more specific, customers usually associate
higher price products with high quality (Völckner and Hofmann, 2007). The
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interconnections between the prices and customer-relationships can be understood in
relation to the price the customer paid for a particular product and the quality is
coincided to the costs or not. If customers paid a higher price for a product, and its
quality truly matches the price or more valuable, or after products discounted are still
maintaining the same quality as before (Völckner and Hofmann, 2007, Tsiligiannis et
al., 2009). Furthermore, price tactics can strongly tie and boost a closer relationship
with the customer (Tsiligiannis et al., 2009). Many of previous studies refer that price
perception is tying trust and satisfaction (Ba, S. 2002, Guo, S., 2011, Peng and Wang,
2006). Whether the changes of price are meeting customer expectations.
2.2.3 Brand perception
Brand image is the overall perception and opinion of customer of the brand in the
process of long-term contacts between customer and brand. It affects the purchasing
and consuming behavior of customers (Xing-Wen and Ming-Li, 2008). It also reduces
customer perceived monetary and risk of a service purchasing (Peng and Wang, 2006),
because of difficulty in measurement of services (Parasuraman et al., 1988). The
concept of customer loyalty is embedded by brand perception, since it is a power that
repurchasing preferred product continually. The customers’ feedbacks are sort of
sources that is connected to a customer-brand relationship, which in turn results in
brand loyalty and positive word of mouth. Whether its functional brand image and
nonfunctional brand image, they both positively affects brand relationship quality,
which helps improve the loyalty. Furthermore, a brand image can directly affect
customer perceived value/quality, and the perceived value/quality directly influences
brand loyalty (Xing-Wen and Ming-Li, 2008).
Brand image and reputation plays a special role in the service market because a strong
brand image and reputation increase customers’ trust and gives a better idea and
understand intangible products (Peng and Wang, 2006). The second evidence is brand
image existing in customers’ memory network, it affects decision-making, the most
influential feeling is trust. Brand image is often deemed as an evaluation of
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product/service quality from customers, and customers will use brand image of the
product to infer or maintain their perceived quality of products/service. Since customers
believe that products are highly qualified (Liao et al., 2009). On the other hand, many
previous studies also pointed out that most important for customer satisfaction is the
store as a brand. Customers are satisfied when the store is neat and pleasant and when
they feel that the store understand their needs. Satisfied customers are loyal (Martenson,
2007). Ranaweera and Prabhu (2003) tested the positive impact of building reputation
and trust and conclude that keeping a state of the art, clean and pristine store can lead
to higher satisfaction, in the most recent case, the mobile phone stores. Peng and Wang
(2006) also tested firms perceived with a better reputation in delivering trust and
offering higher satisfaction in their products and services will retain more customers.
2.2.4 Value proposition
“Value is considered to be an important constituent of relationship marketing and the
ability of a company to provide superior value to its customers is regarded as one of the
most successful competitive strategies.” The ability is a key to differentiate competitors’
products and seek a sustainable successful competitive strategy (Ravald and Grönroos,
1996). The way of how to make core product more valuable is value add-on, which
means add additional value into core product. Grönroos calls it supplementary service,
i.e. warranty, in order to improve total product quality. Delivery process is used as a
tool to accomplish the process of supplementary services delivery to core product. The
ultimate of a company is improving customer satisfaction from the overall value add-
on and reducing customer sacrifice (Ravald and Grönroos, 1996).
‘A satisfied customer is supposed not to defeat but to stay loyal to the company for a
long period of time and to buy more and more often than other, not so loyal, customers
do.’ -(Ravald and Grönroos, 1996)
Zeithaml (1988) gives four definitions of value, which are: (1) value is low price, (2)
value is whatever I want in a product, (3) value is the equity I get for the price I pay and
(4) value is what I get for what I give. These four definitions are not complicated to
understand, but the deeper view of value concept is interconnected to another concept
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of price perception. In general, value is always concerning its costs, which translate as
high value=low costs. The proposition meets Ravald and Grönroos, (1996), “if
customer satisfaction depends on value, then it must depend on the total costs or
sacrifice, too. We must keep in mind that buyers in most buying situations use reference
prices and even reference values when they evaluate the attractiveness of an offering.”
As mentioned above, the ultimate of a company is improving customer satisfaction
from value add-on and reducing customer sacrifice. To achieve customer satisfaction,
a company supposed to find solutions from these two sides. Argued by Ravald and
Grönroos, (1996), that a negative attribute of value add-on is imitation, specifically,
competitors can follow or copy your actions easily. In other words, the uniqueness does
not exist anymore and not able to build a long-term relationship with customers. Thus,
sacrifice reducing is another parameter to achieve satisfaction, since customers are
always more sensitive to a loss than to a gain. It is also an opportunity for a company
to improve customer-perceived value and thereby establish and maintain a long-term
relationship. If a company able to provide value in terms of reducing the customer
sacrifice, so the relationship costs are getting lower and customer performances are
getting higher (Ravald and Grönroos, 1996).
2.3 Hypothesis and conceptual model
Figure 2.3 Untested conceptual model
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H1: Customer satisfaction is positively influenced by marketing tactic of QoS.
H2: Customer satisfaction is positively influenced by marketing tact1ic of price
perception.
H3: Customer satisfaction is positively influenced by marketing tactic of brand
perception.
H4: Customer satisfaction is positively influenced by marketing tactic of value
proposition.
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3. Method
The methodology chapter presents the research approach of this study, followed by the
research design and data collection & analysis method. Moreover, the justification the
chosen method and evaluation criteria are also covered.
3.1 Research approach
This section discusses the two main research approaches which can be used in this study.
To be specific, there are two ways to create the knowledge which is inductive and
deductive as well as two ways of research strategies, qualitative and quantitative. This
chapter discusses the characteristics and differences between those concepts and
presents the chosen method for this project.
3.1.1 Inductive vs. Deductive
The principal difference between inductive and deductive, according to Bryman and
Bell (2011), is its nature. Whether if the theory leads the research, then it’s generally
deductive and when the theory is an achievement of research then it’s inductive
(Bryman and Bell, 2011). In general, deductive approach is used to test whether a theory
works under one definite condition while inductive approach is used to build a theory
to generalize one or more phenomenon under one specific condition. In other words,
the association between theory and research determines which method should be
utilized. Normally, deductive way is considered to be a more common research
approach (Bryman and Bell, 2011).
This study will be conducted with a deductive approach because of its nature. Firstly,
authors have chosen a research field with a lot of related existing theories, which means
the theory is the foundation of the research. The purpose of this study is to test the
hypothesis generated from theory and the sample in this study is from a general
14
theoretical perspective to case specific sample population. Thus, deductive is
considered as a more suitable approach for this study.
In order to carry out a deduction process, a six-steps procedure should be strictly
followed since deductive process is a very linear process. First required step is, theories
should be propounded. In this study, authors found and listed relative theories about
relationship marketing tactics and customer satisfaction in the theory chapter. Then,
hypotheses are drawn to exam the theory, which is presented at the end of the theory
chapter. The third step is data collection process which is used to provide sufficient data
for hypothesis. Authors discussed the data collection method and instrument chapters
below (3.4 and 3.5). The findings should come out in the fourth step, in this paper, it is
chapter 4. Regardless of the confirmation or rejection of the hypothesis, it should be
stated in the fifth step nevertheless. In this project, result from chapter four is discussed
in chapter five. Lastly, the theory proposed in the beginning should be revised based on
research result (Bryman and Bell, 2011). This is done by reviewing the conceptual
model in chapter 4.5.
3.1.2 Qualitative vs. Quantitative
Similar to inductive & deductive approach, qualitative & quantitative research
represents two most fundamental distinctions (research strategy) of business and
management research (Bryman and Bell, 2011). Qualitative or quantitative research
approach is closely connected to the ‘principal orientation to the role of theory in
relation to research (Inductive & Deductive)’ (Bryman and Bell, 2011). This means
that qualitative research strategy focusses on creating theory through an inductive
process. They tend to be of interpretivism and constructionism in nature. Whereas
quantitative research strategy concentrate on testing the theory through a deductive
process and more tend to be of positivism and objectivism in nature (Bryman and Bell,
2011). Qualitative research does not pursue the accurate conclusion, but more squint to
find a qualitative understanding of underlying reasons and motives. On the other hand,
quantitative research tends to quantify the data and generalize the result from the sample
15
to the overall study. The purpose of quantitative research is to accept/reject the null
hypothesis thorough out study (Creswell, 2014).
The authors of this study aimed to test the null hypothesis presented in chapter two
through statistical tools, the data be used in this study is objective measurable data.
Thus, we believe that quantitative approach is a more suitable way within this study.
Bryman and Bell (2011) outlines eleven crucial steps of quantitative research.
Compared with the six-steps of a deductive approach mentioned in the above chapter,
it's more detailed and focused on a hypothetical process. The process start with an
elaborate theory, drive to a hypothesis, where we discussed in chapter two. Then select
research design based on the nature of the research, which the authors discussed in
chapter 3.3. Step four of devising measures of concepts and implemented in
operationalization is done by chapter 3.5. After that, research sites and subjects can be
selected. Moving to step seven. Administer research instruments/collect data which are
a step of data collecting in a different way based on a different type of research design
also can be viewed in 3.5. Step eight explains the need to transform the information
gathered into usable data, which in this study, authors used Likert scale and discussed
in 3.5.2. Moving forward, data will be analysis through different techniques in step nine.
In this study, authors discussed the different data analysis techniques in chapter 3.7,
which the method mainly used are descriptive statistics and regression analysis. In step
ten and eleven, the result should be written down. The result is presented in chapter
four within this study. In the end, since the findings became the stock of knowledge
throughout the process, it is necessary in to feedback loop from step eleven to step one.
Thus, authors reviewed the conceptual model in the end of chapter four.
3.2 Data Sources
There are two types of data that can be collected to conduct a study: primary data and
16
secondary data. Primary data refers the data collected by authors him/her self for
particular reason. Secondary means data collected by others for other purpose, but
might be useful for the author in their own ongoing study (Pawar, 2004). The advantage
of primary data is firstly, since the data are customized collected towards the research
topic, it is more targeted, reliable to the study with low-error. Secondly, it is possible to
collect more additional data during the study in order to adapt to the changing situation.
The biggest advantage of secondary data is that it’s easy to collect with less costs and
in large amounts (Storch and Pauly, 2017).
Since, the authors aim to test hypothesis through survey in this project as mentioned in
the above chapter, the quality of the data is the priority since they are measurable data
and are used to test thorough statistical method, thus, authors believe that primary data
are more appropriate data source to be used in this study. In this paper, online survey is
used as a primary data collection method.
3.3 Research design
According to Bryman and Bell (2011), research design is a framework that providing
different approaches to collect and analyze the data; research design has the function of
evaluating the research findings. Depending on the purpose of research, there are five
different types of research designs for quantitative study: experimental design; cross-
sectional or social survey design; longitudinal design; case study design; and
comparative design.
Authors chose cross-sectional design as the framework in this study. We will firstly
discuss what is cross-sectional design, and discuss why we think it is the suitable design
for this study.
The cross-sectional design refers to collection of the data in more than one case at a
17
single point in time to collect a set of quantitative/quantifiable data for two or more
variables, which will be analyzed and examined to detect patterns of association. The
research method of questionnaires, structured interview, structured observation, and
content analysis is associated with cross-sectional research (Bryman and Bell, 2011).
In business study, cross-sectional study is usually used to analyze the causal effects
between dependent variable and one/more interested independent variables, as well as
test the influential order of the independent variables (Hsiao, 2014).
Authors of this study use cross-sectional design based on following considerations.
Firstly, the aim of this study is to investigate the how relationship marketing tactics
affect customer satisfaction, which is required to collect a large amount of data and
information, also a large sample base should be analyzed to make a reliable hypothesis
testing within a short period of time (around one week). Secondly, the purpose of this
study, as mentioned in above chapter, is the analysis of how relationship marketing
tactics affect customer satisfaction. Which in other words, analyze the causal effects
between dependent variable and one/more interested independent variables, as well as
influence order of the independent variables. Thus, the authors decided to make use of
cross-sectional research design.
3.4 Data Collection Method
Two ways to collect primary data are observation and survey (Pawar, 2004). Since the
authors decided to use the method of survey, according to Sarah (2017), survey can be
done either by using questionnaire or interview. Questionnaire is usually delivered to
the respondents through actual material like paper and pen with relatively closed-ended
questions setup. The advantage of the questionnaire is firstly, since all respondents
received the same questionnaire, the statistics error is lower than the interview and the
data are more ordered and focused on the topic. The advantage of interview is firstly
providing a better understanding of the questions to interviewees and followed-up
question can be asked during the interview. Moreover, Survey research center (2017)
listed four kinds of survey which could be applied whether it is questionnaire or
18
interview: telephone surveys, mail surveys, internet surveys and field surveys. Among
all these four ways, internet survey is the most efficient way to gather large amount of
respondents and most cost effective (Survey research center, 2017).
Since authors of this study aim to make a hypothesis testing regarding own developed
conceptual model, the accuracy, quality and amount of data are most relevant for this
project. According to Bryman and Bell (2011), the question of the survey should be
easy to understand. Hence, authors decided to use an internet based questionnaire as
the data collection method in this paper.
3.5 Sampling
Sample is indispensable when doing quantitative research (Bryman and Bell, 2015). In
almost every research, it is unlikely that researchers will be able to consider the entire
population (universe of units) as their respondent. Thus, a certain amount of people
should be selected as a representative of the entire population (Bjørnstad and Jan, 2010).
The selection of sampling can be based on probability or non-probability approach.
Probability sampling is built on probability theory and mathematical statistics. The
sample should be selected randomly. Each unit in the overall study population (universe)
has the same possibility of being selected. Non-probability sampling means investigator
select sample that is considered with subjective judgments (Bryman and Bell, 2015).
The biggest difference between probability sampling and non-probability sampling is
probability sampling follows the principles of randomness, while non-probability
sampling does not. Hence, probability sampling is more rigorous than non-probability
sampling theoretically, and the result of probability sampling is superior to non-
probability sampling (Raina, 2015). Authors have summarized some prominent
characteristics between two sampling approach below (Bryman and Bell, 2015; Raina,
2015; Bjørnstad and Jan, 2010; Anon1, 2008):
Probability sampling Non-probability
Basic Principle The larger the sample size, the Some representativeness of
19
less the sampling error. population features. But cannot
infer population in quantity.
Advantage 1. Rigorous, reliable in data.
2. Sampling error is estimated.
3. Result is generalization.
1. Easy to implement.
2. Low time/cost consumption.
3. Representativeness sampling
results also can be generated if
doing in the right way.
Weakness 1. Higher cost.
2. Higher time consumption.
3.Data collection process is also
longer.
1. Limits to generalization.
2. Sampling error can be
estimated.
3. Correlation between sample
and population is not clear.
Main Types 1. Simple random sample
2. Systematic sample
3. Stratified random sampling
4. Multi-stage cluster sampling
1. Convenience sampling
2. Snowball sampling
3. Quota sampling
Main Application Fields Various (social science) Exploratory/Descriptive
Research
Table 3.5 Probability sampling VS. Non-probability sampling
Prior studies suggest that marketing tactics could influence customer satisfaction in
diverse ways with different effects in different kind of market (233). Hence, to make
the data as objective as possible and to ensure the accuracy of the results, authors have
to choose one specific market. All questions about relationship marketing tactics and
customer satisfaction should be asked and answered based on the understanding
towards this market. Cléria and et al., (2013), claims that the retail industry is one of
the major user of relationship marketing tactics which enables them to strengthen
customer satisfaction because they are closely related to people’s daily life and frequent
interaction between customer and companies is common (Simbolon, 2016). Authors
hold a preliminary survey with a form of unstandardized interviews in the very early
stage of the study. The main purpose of the preliminary survey is to investigate where
people received daily relationship marketing message and interacted with. This was
done by open interview and free discussion with participants. Surprisingly, seven
people out of ten believed the relationship marketing message they most often
interacted with was messages from supermarket. The form of relationship marketing
tactics included weekly exclusive offers and bargains, super market brand promotion
20
etc. Hence, authors decided to use supermarket industry as object to gather relative data
in this study because of its closeness to everyone’s daily lives and also because of how
closely the respondents valued those relationship marketing tactics.
While keeping the focus of this paper in alignment with the purpose, that is to test the
hypothesis of the relationship between RM tactics and customer satisfaction within the
supermarket industry, it was decided to keep populations context specific, i.e. the
people who go to supermarkets on a regular basis. ̈
Due to the lack of resources and limitation of time, the study was confined and thus, in
order to be more effective in collecting data, non-probability sampling approach has
been chosen as a data collection method mainly questionnaires because of their
effectiveness and time efficient nature. (Bryamn and Bell, 2015).
3.5.1 Sampling Frame
After defining the target population this study, the next step is to determine a proper
sampling frame to implement data collection (Bryman and Bell, 2015). A sampling
frame listing, sorting, or number the overall unit can be selected as a sample. It is used
to identify the scope of the overall sampling and its structure (Anon2, 2008). A good
sampling frame should be completed but not repeated, if not, a sampling error may be
occurred. Sampling error is usually caused by inaccurate or incomplete sampling frame.
The result of a study cannot be reliable if the researcher extracts samples from an
inaccurate or incomplete sampling frame. Sampling error is not brought about by the
randomness of sampling, but imperfect sampling frame. Sampling error is a kind of
non-sampling error (Anon2, 2008). Hence, author should set their sampling frame very
carefully. Since sampling frame is closely related to the type of sampling, selecting the
sample befitting type of the study and following the tech specs of the method is a good
way to avoid sampling error (Bryman and Bell, 2015).
21
According to the result of pre-testing that was conducted earlier, customer attitude
towards different RM tactics is chaotic, thus no obvious tendency in distinct categories
could be captured. Hence, author chose to apply convenience sampling method because
of its accessibility and high response rate. Convenience sampling is a type of non-
probability sampling. Convenience sampling refers to investigator sampling in line with
arbitrariness. It is the simplest and most time & cost efficient method among other non-
probability approach (Anon2, 2008). Since the authors have decided to use an online
questionnaire in this case, the sampling frame would include those who were both
internet user and supermarket frequenters. To be more specific, the online questionnaire
was sent to acquaintances as well as posted on social media site such as Facebook and
other social media platforms.
3.5.2 Selection and data collection procedure
Once the sampling frame is settled, the next step would be to determine the sample size
of the study. Sample size is the amount of sample that is extracted from a population
(Bryman and Bell, 2015). According to Beyman and Bell (2015), there are several
aspects to consider in order to get a sample size such as absolute and relative sample
size, time and cost, non-response, heterogeneity of the population and the kind of
analysis. To be specific, larger sample size reduce the sampling error, but when sample
size ＞1000, the increase in precision will become less pronounced; Higher response
rates can help researcher collect data with relatively-low amount of questionnaire;
Higher heterogeneity of the population requires use of more questionnaire to ensure the
accuracy of the study, and for different kind of analysis, different method should be
used to set a desired number of participants. In probability sampling, sample size can
be calculated by formulas including parameters of population, the margin of error,
population variance and confidence level, Thompson (2012) explains the non-
probability sampling and its pitfalls where the margin of effort is often uncertain where
setting the sample size based on hypothesis and statistical test of the study. In the
instance where the overall correlation is discussed in this paper, the sample size (N)
22
should be N≥50+8M (variable). If the individual variables effect is discussed, then the
sample size should be N≥104+M (Thompson, 2012). Since this study focused on the
different RM tactics (M) and its influences on customer satisfaction, the sample size N
should be ≥ 104+4 （108）. Author calculate the sample size in another way through
statistical software G Power, when α (Sampling error) set at 1%, Power (1-β) set at 0.8
and r (effect size) set at medium (0.3), the total sample size should be 105, which is
very close to 108. Lastly, an expert in this field (Setayesh Sattari) suggested authors to
use at least 100 samples in this study. Hence, the sample size of this study should be at
least 108 samples. The confidence level of this study is 99%. The actual amount of
questionnaire received in this study within a limited time is 124. There were four
participants in a total of 124 participated the pre-testing, thus, those four questionnaires
have been excluded. Eventually, 120 valid questionnaires gathered within the study.
3.6 Data Collection Instrument
3.6.1 Measurement of Variables and Operationalization
The dependent variable of this study is the customer satisfaction. The dependent
variables are five RM tactics developed in the theoretical chapter (i.e. Quality of service,
Price perception, Brand perception and Value proposition).
Theoretical
Concept
(Variables)
Concept Definition Operational
Definition
Questions
Satisfaction Satisfaction is a
pleasurable
activity while
customers
consuming
something. When
a need, goal or
desire of
customers has
The concept will
be used as
dependent
variable, it is
measured by four
independent
variables: QoS,
price perception,
brand perception
Sat1- I am satisfied
with the overall
quality of service
offered by the
company.
Sat2 -I am satisfied
with the employees of
the company.
23
been reached that
means they are
satisfied (Oliver,
1999).
and value
proposition.
Sat3- I enjoy my
experiences in this
company.
Sat4- When I am
shopping in this
company, I believe
that they can satisfy
my needs.
Quality of service Parasuraman et al.,
(1985, p.42-43)
service quality
involves more than
outcome, it also
includes the
manner in which
the service is
delivered.”
This concept will
be used as
independent
variable, to
measure QoS
towards
satisfaction.
QoS1- I think that
employees of this
company are always
willing to help me.
QoS2- I think that the
facilities of the
company are better
than others.
QoS3- I think that the
stores of the company
are well equipped.
Price perception Price perception is
called as price-
perceived quality,
which means the
elasticity of
products’ price and
products’ quality.
(Völckner and
Hofmann, 2007)
This concept will
be used as an
independent
variable, to
measure price
perception towards
satisfaction.
Pri1- The prices of
company offering
meet the quality.
Pri2- I believe that the
company is
maintaining same
quality of
service/product after
discount.
Pri3- I will continue to
stay with the company
unless the price is
significantly higher
for the service.
Brand perception Brand image is the
overall perception
and opinion of
This concept will
be used as an
independent
Bra1- The company
has delivered a good
image to me.
24
customer with the
brand in the
process of long-
term contacts
between customer
and brand (Xing-
Wen and Ming-Li,
2008).
variable, to
measure brand
perception towards
satisfaction.
Bra2- I believe that
the reputation of the
company is high.
Bra3- I prefer this
brand to the other
available ones.
Bra4- I think that the
company has a strong
brand.
Value proposition (1) Value is low
price, (2) value is
whatever I want in
a product, (3)
value is the equity
I get for the price I
pay and (4) value
is what I get for
what I give.
(Zeithaml, 1988)
This concept will
be used as an
independent
variable, to
measure Value
proposition
towards
satisfaction.
Val1- I believe that
the company’s
products are valuable.
Val2- I think that the
value of what I got
matches what I paid.
Val3- The products of
company offering
meet my needs.
Table 3.5.1 Operationalization of the Variables
3.6.2 Questionnaire Design
The appropriate implementation of a survey questionnaire is very important to the
quality of obtaining data and information (Dillman, 2007). In order to appropriately
implement questionnaire, the authors followed Bryman and Bell’s() principles to design
the questionnaire.
Before the start of the questionnaire, participants were required to answer questions
25
based on their favorite supermarket. According to Bryman and Bell (2011), for
participants, closed questions are easier and understandable to answer; closed questions
can keep the accuracy of the data and the data can easily be processed. In order to make
the questions more understandable and help participants answer the questions easily,
the questions in the questionnaire of this study were structured and closed questions.
The basic structure of the questionnaire in this study is follows the technique of the
Likert Scale. Likert Scale is the most common scale in summating rating scale (Bryman
and Bell, 2011). Likert scale is composed of a set of statements, for each statement,
there are five kinds of answer including: very disagreeable, disagreeable, neutral,
agreeable, and very agreeable and respectively marked as 1,2,3,4,5 as measurable
number. One of the advantages of Likert scale is that participants will not be forced to
express their opinion, furthermore, for researchers, the data gathered from Likert scale
questionnaire is obvious and easier to understand (Gee, 2013).
The questionnaire should be designed with a logical flow since participants can
complete the questionnaire easily (Rattray and Jones, 2007). The questionnaire of this
study has 17 questions (before pre-testing, there were 20 questions) been divided based
on the analytical model and into four categories, each category concerns one
relationship tactic. The authors designed at least three different questions for each tactic,
for example, the picture below shows one question concerns quality of service.
Picture 3.6.2 Example of the question
26
The complete questionnaire can be seen in appendix.
3.6.3 Piloting and pre-testing questions
One desirable process before applying the questionnaire to the study is a pilot study.
Especially for the study which uses a self-completion questionnaire, pilot study helps
researchers to ensure the feasibility of the survey questions and is useful for the good
operation of the questions. Moreover, pilot study can significantly decrease the
misunderstanding and confusion to the participants (Bryman and Bell, 2015).
There are very different ways to test if it’s an eligibility questionnaire in the pre-testing
stage. For instance, if the vast majority reply pilot questionnaire in a similar/same way,
it means the data are not variable and should be adjusted. Also, it is important to observe
participants’ attitude during the data collection process. Especially in interview,
researchers should avoid those questions may make the interviewee feel uncomfortable.
Researchers should also deliver a clear description for every question to assure that
respondents fully understand. Respondents in the pilot study should not participate later
on in real sample which will be used for full study (Bryman and Bell, 2015).
In this study, because of the lack of communication with questionnaire, authors chose
to organize a group interview based on a questionnaire. Authors randomly chose 25
volunteers in order to conduct the pre-testing. Volunteer selection criteria were identical
to the actual sample. During the pre-test, author firstly handed out the questionnaire to
each individual and asked them if there is anything unclear or were any inconsistencies
with the pilot questions. It was further followed by a free discussion among respondents
to expand their views towards the questionnaire. After that, respondents were asked to
complete the questionnaire in order to gather test data (This process was running two
times in different occasions in order to observe the stability of measures). After
gathering data through pre-testing questionnaire, the samples were run through SPSS
27
(computerized statistics analytic software) to check the reliability in order to see if there
was an unqualified question.
The questionnaire was also supervised by an experienced mentor in quantitative
research field to revise and finalize questions.
Besides the abovementioned task, the target population of this study was huge and
complex. Which may cause a lot of uncertain factors can affect the result. For example,
people with diverse age, gender, income etc., may hold different behavior towards each
RM tactics. Whether this kind of phenomenon existed or not, determining the type of
sampling should be used in a supplementary chapter (Ex. convenience sampling, quota
sampling or etc.) in a large degree. Thus, author tried their best to find interviewee with
different background (age, gender and income in this case) for pre-testing and trying to
find out the answer through observation and conversation.
3.6.3.1 Result of pre-testing
The stability of the questionnaires was good. No obvious inconsistencies were visible.
There were no significant different in answer could be captured among people with
different demographic classification in pre-testing. Thus, no questions used to classify
participants into different demographic classification in the final version of
questionnaire. The result of SPSS reliability testing shows that there were some
questions with significantly lower reliability, authors adjusted those questions and
revised the questionnaire (operationalization) to make sure the reliability for each
variable is higher than 0.7 as Bryman and Bell suggested (2011). During this process,
three questions are deleted out of 20 questions. Which made the final version of
questionnaire contains 17 questions. No misunderstanding from interviewees could be
captured during two-way communication. Author also adjusted the sentence pattern of
some questions based on expert suggestion.
28
Hence, the operationalization in 3.5.1 is the optimized (final) version based on pre-
testing result. Detailed description of pre-testing can be found in appendix.
3.7 Data Analysis Method
According to Aaker (2013), data analysis is a process of transforming, extracting, and
modeling raw data with the purpose of discovering useful information. There are three
reasons the principles of data analysis are useful for researchers. First, data analysis can
lead researchers to gather deep insights on information. Second, it can help avoid the
wrong judgement and conclusion. Lastly, the knowledge of the power of data analysis
techniques can constructively affect research design and research objectives. In this
study, based on quantitative research nature, the information and data collected will be
analyzed in SPSS.
3.7.1 Descriptive Statistics
Descriptive statistics are widely used in empirical research in the social sciences which
conclude specific features of the data set in a study; it can help represent large data sets
in simplest of way (Jr., W.A.D., 2006). According to Brown, B. (2011), in quantitative
research, descriptive statistics can reduce a large amount of data and information to a
simple summary; to make meaningful deduction, descriptive statistics should be used
in a proper way.
The measure of distribution help finds the frequency of a range of values or individual
values of a variable. Common descriptive statistics in multi-method studies are the three
measures of central tendency: mean, median, and mode. Those central tendency
measures offer a set of values that describe the specific score in distribution scores
29
(Bryman and Bell, 2015). Additionally, Osborne and Overbay (2004) mention outliers
where the data stand far away from the norm of a population, outliers can influence the
statistical analysis because outliers may create the error; removing the outliers is the
most direct method to avoid such errors.
In this research, descriptive statistics is the first data analysis method to be used in order
to analyze the large amount of data and manage the clutter data orderly.
3.7.2 Regression Analysis
Regression analysis can be described as the relationship between dependent variable
and independent variables and distinguish the difference between dependent variable
and independent variables. Regression analysis aims to indicate the effect of one
variable to another variable (Bryman and Bell, 2015). According to Bryman and Bell
(2015), dependent variable is the main factor to research, independent variables are
factors that may affect the dependent variable. There is a represent the causal
relationship between dependent variable and independent variables: Y= β0+ β1x+u
(Y=dependent variable, X=independent variables, β 0=the status of dependent variable
when the independent variable is absent, β 1=the magnitude and direction of the relation
between dependent variable and independent variables, u=the amount of variation
(Campbell and Campbell, 2008). Regression analysis can help identify the value of each
independent variable, and accept or reject the hypothesized causal relations or find out
the most effective independent variable. There are two main kinds of regression
analysis which are simple-linear regression (one variable) and multiple regression
(more than one variable) (Campbell and Campbell, 2008).
Authors use regression analysis in this study because its nature. Since regression
analysis is a quantitative method focus on the interdependence between dependent
variables and independent variables (Bryman and Bell, 2015), it perfected fit the
hypotheses constructed within this study. In this study, the authors aim to analyze the
30
relationship of independent variables such as quality of service, price perception, brand
perception, and value proposition between dependent variable of customer satisfaction
by the help of regression analysis. Since there is more than one variable in this study, it
would be a multiple regression analysis.
3.8 Quality Criteria
Three main criteria in business research are reliability, replication and validity (Bryman
and Bell, 2011). Reliability is particularly within quantitative research. It is used to
measure whether the result of research is repeatable or not. Higher reliability indicates
the consistency, reliable and stability of the result (Bryman and Bell, 2011). The second
criterion is replication, which is quite similar to reliability, refers to the replicability of
the study. Replication works in some case when researchers decide to replicate the
findings of others based on serval reasons such as having questions in evidence and etc.
However, replication is not common in business research because of its low status in
academic research since most researchers are pursuing the originality rather than
replication in their study. The third criteria, which probably is the most important
criterion is validity. Validity is used to examine the integrity of the results within a
research. It reflects whether the research is identical to the point. In other words, it
stresses on whether a measurement measures what it is really supposed to measure.
(Raina and Sunil, 2015). In general, the validity can consist of three parts: content
validity, criterion validity and construct validity. Even though reliability and validity
are two different criteria, if the reliability is not eligible, the validity is meaningless no
matter how high it is, since the research is not trustworthy (Bryman and Bell, 2011).
In this study, the nature of the research is an exploratory study, thus is more focus on
originality study rather than replication of others, authors decides to apply reliability
and validity as quality criteria. Each of these criterions will be discussed further in the
subchapter below.
31
3.8.1 Reliability
Reliability is a key concept of a study which is concerned with the consistency, stability
and reliability of the measurements. The repeatability of the study can be predicted by
measuring the reliability. Reliability needs to be measured in three different aspects
including stability, internal reliability and inter-observer consistency (Bryman and Bell,
2011).
Stability focus on whether the measure is stable over time. Which means, does the same
sample provide different data in different time point? In order to test this, Bryman and
Bell (2011) suggested using a test-retest method. Which means organizing two data
collections from a same sample in two occasions. In this study, because of the limit in
time and operative difficulty, authors decided to use a test-retest method in pre-testing
stage rather than formal questionnaire. The result of pre-test was consistent and was a
base for the next step of data collection.
Internal reliability concerns with whether or not the indicators that make up to the mark
the scale or index are consistent (Bryman and Bell, 2011). Which means whether the
scores on one indicator are related to other indicators. The best and most common way
to test internal reliability is by using Cronbach's alpha. Cronbach's alpha calculates the
average of all possible split-half reliability coefficients (Bryman and Bell, 2011). The
range of Cronbach's alpha is 0 to1. Higher α value means better internal reliability.
There are a lot of opinions about what is the boundary value in α could be viewed as
acceptable for research. General consensus suggests that the internal reliability is
acceptable when α ＞0.6 and become excellent when α ＞ 0.7 (Saris, 2014). In this
study, because of the limit in time and operative difficulty, authors decided to use
reliability test thorough SPSS in pre-testing stage. Authors have made some
adjustments based on test results in order to increase the internal reliability in this study.
Inter-observer consistency focuses on subjective judgement factor which could cause
32
influence in consistency during the data collection process (Bryman and Bell, 2011).
The nature of this study (quantitative research, questionnaire with fixed close questions,
data are measurable numbers, etc.) emphasizes on minimizing on such inconsistencies
leaving no room for inter-observational inconsistencies.
3.8.2 Validity
According to AERA (2014), validity can be divided into three classes: content validity,
criterion validity and construct validity.
3.8.2.1 Content Validity
Content validity refers to the adequacy of test towards research content. In other words,
authors should ensure that the measure they use can reflect the concept they aimed to
measure. Expert judgement is the major method used in order to see the validity. Expert
judgement method is a qualitative analysis which requires the expert with rich
experience knowledge in related fields in order makes a systematic comparison with
test questions to see its representativeness. Another way to evaluate the content validity,
according to (233) is by using a pilot study. During the pilot study, author will be able
to pre-test the variables of the concept and gather opinions from participants in order to
discover if there’re some improper settings and then improve data collection procedure
before starting data collection.
In this study, authors decided to use both abovementioned methods to increase the
content validity. As we mentioned in above chapters (3.5.3), a pilot study has been done
with 25 participants, opinions have been collected, data’s been run through SPSS,
reliability tested and in the end, the questionnaire were handed all while being supervise
by an expert. All these procedures provided a huge help for authors to modify the
questionnaire and eventually used in data collection.
33
3.8.2.2 Criterion validity
Criterion validity examines whether the measures are related to the outcome within a
study. Criterion is also known as concrete validity since it required empirical evidence.
In order to examine the criterion validity within this study, authors aim to conduct a
hypothesis testing for four independent variables and one dependent variable within the
study based on empirical data gathered through questionnaire-Thorough regression
analysis will be conducted to test the hypothesis.
3.8.2.3 Construct validity
Construct validity concerns with the meaning of test scores from a psychology
perspective. Which means, whether test result is able to confirm the concept within a
research. One main tool used in this area is correlation analysis. Correlation analysis is
a statistical approach used to investigate the dependence of the relationship between
two variables. Correlation is a non-deterministic relationship. For instance, take a
person's height and weight in terms of X and Y, thus X and Y have a relationship for
sure, but not exactly one of them can precisely determine the value of another, which
is correlation. In this case, it would be the relationship between four RM tactics and
customer satisfaction. In this study, when calculate the interval variables, Pearson
correlation coefficient should be used. The correlation coefficient defined as “r”. If one
variable increase when another increase, then it would be a positive correlation (same
direction). Conversely, negative relationship is when one variable decreases while
another increase. Normally, when |r|>0.95 account for a significant correlation between
X and Y; |r|≥0.8 is high correlation; 0.5≤|r|<0.8 is moderate correlated; 0.3≤|r|<0.5 is
low correlation; |r|<0.3 is uncorrelated. To ensure that each variable is effectively
operationalized, the “r” for each set of two variables should not have high correlation
(higher than 0.8).
Another tool is particularly used in customer satisfaction survey to discover the
essential structure of the multivariate observation variables to evaluate the validity of
34
the study is exploratory factor analysis (EFA). EFA is a very useful method when
analyzing the questionnaire with a lot of questions and could be an excellent
supplemental method with correlation analysis. EFA follows three main procedures:
factor extraction, factor rotation and factor interpretation (233). When authors set the
variables and samples, they should estimate if its suitable of EFA based on the result of
KMO（Kaiser-Meyer-Olkin) and Bartlett's Test of Sphericity. According to Comrey
and Lee (1992), it is excellent to make a factor analysis when KMO ＞0.71 while
Bartlett test significant differences. After this, extract factors which eigenvalues ＞1
uses principal component method. Then, making a rotation by varimax method will
lead to the factor interpretation procedure. Two main value used to verify the validity
by EFA are: factor loading (＞0.5), variance accumulation contribution rate (＞50%).
The authors decided to use both correlation analysis and exploratory factor analysis
(EFA) through SPSS based on empirical data to ensure the validity of the study.
3.9 Ethics
For doing research in the field of social sciences, it is of grave concern on how
researchers collect data and gather information from people. What is the appropriate
way researchers treating people who provide data and information have been
questioned and those questions are often about ethics in nature, researchers must
consider the ethical issues from the beginning of the study (Oliver, 2010). There are
four perspectives of ethical principles: invasion of privacy, harm to participants, lack
of informed consent and deception (Diener and Crandall, 1978).
According to Bryman and Bell (2015), any violation against the privacy cannot be
accepted. Respondents should be allowed to have their identity hidden in a research
report and is the cornerstone of research ethics. From the perspective of participant
harming, “harm” can be stress, physical harm, harm to self-esteem and development of
participants; researchers can use anonymity to avoid this situation (Bryman and Bell,
35
2015). As Oliver states (2010), there are many advantages of using anonymity for both
respondents and researchers, but respondents do not always want to hide their identity.
The principle that participants are fully informed about the research project before they
participate in is an essential feature of social science research ethics, which is informed
consent (Oliver, 2010). Deception happens when researchers represent their research as
something other than what the research is (Bryman and Bell, 2010). Deception can
cause harm to both participants and researchers (Erwin et al., 2015).
In this study, the researchers keep ethical issue in the mind all the time and considered
all those four principles. Firstly, for avoiding harming participants, the survey was kept
entirely anonymous. There was no way to track the identity of the respondents.
Secondly, the comprehensive information about the research is added on the
introduction of survey to make sure participants were well-informed about the research.
Lastly, the questions that were too intrusive and were excluded from the survey. No
respondents were forced to answer the questionnaires, voluntary participation was the
key agenda for the questionnaires.
36
4. Results
This chapter presents the data gathered through the questionnaire and processed by
SPSS. Four main parts in this chapter are descriptive statistics, reliability, validity and
hypothesis testing of the conceptual model.
4.1 Descriptive statistics
In this study, authors have chosen Likert scale for questionnaire, this included a scale
from 1-5. Number “1” represents strongly disagree and number “5” represents strongly
agree. Median is “3”. Authors expected some extreme values that could occur in this
study, thus authors chose to use the median in this section in order to gauge those
outliers. Results are shown in below table:
Constructs and scale items Item Construct
Mean (s.d.) Mean (s.d.)
QoS 4.164 0.573
QoS1- I think that employees of this company
are always willing to help me. 4.267 0.719
QoS2- I think that the facilities of the company
are better than others. 4.075 0.712
QoS3- I think that the stores of the company are
well equipped. 4.150 0.741
Pri 4.222 0.566
Pri1- The prices of company offering meet the
quality. 4.217 0.663
Pri2- I believe that the company is maintaining
same quality of service/product after discount. 4.233 0.645
Pri3- I will continue to stay with the company
unless the price is significantly higher for the
service.
4.217 0.663
Bra 3.894 0.691
Bra1- The company has delivered a good image
to me. 3.892 0.858
Bra2- I believe that the reputation of the
company is high. 3.908 0.840
Bra3- I prefer this brand to the other available
ones. 3.892 0.828
Bra4- I think that the company has a strong
brand. 3.883 0.871
Val 3.456 0.658
Val1- I believe that the company’s products are
valuable. 3.500 0.674
37
Val2- I think that the value of what I got
matches what I paid. 3.433 0.730
Val3- The products of company offering meet
my needs. 3.433 0.730
Sat 4.227 0.714
Sat1- I am satisfied with the overall quality of
service offered by the company. 4.233 0.817
Sat2 -I am satisfied with the employees of the
company. 4.200 0.795
Sat3- I enjoy my experiences in this company. 4.200 0.805
Sat4- When I am shopping in this company, I
believe that they can satisfy my needs. 4.275 0.777
Table 4.1 Scales of constructs and descriptive statistics
According to this result, score for each dimension (tactic) is higher than median (3).
Even though the score of each dimension is higher than median, there were some
differences among them: for questions regard the quality of service, price perception
and satisfaction, the answers are mainly distributed in 4 and 5. And for the question
regarding brand perception and value proposition, the answers are mainly distributed
in 3 and 4.
4.2 Reliability Analysis
The Cronbach’s Alpha coefficient is often used as the criterion for testing reliability;
Cronbach alpha coefficient measures the similarity in participants’ evaluation profiles,
and shows those whose assessments are inconsistent with other participants (Mitchell
and Jolley, 2013). Cronbach’s Alpha coefficient is widely applied to evaluate the
consistency of the questionnaire respondents (Mitchell and Jolley, 2013). In the study,
Cronbach’s Alpha coefficient is used to measure the reliability of the questionnaire.
When Cronbach Alpha ≥ 0.70, the questionnaire has high reliability; 0.35 ≤ Cronbach
α <0.70, it is still acceptable; Cronbach α <0.35 is low reliability. In practical studies,
if Cronbach Alpha is greater than 0.7, the reliability is high and acceptable. The results
of this study are shown in table below:
Item CITC Cronbach's α if Item
Deleted Cronbach's α
38
QoS
QoS1 0.414 0.737
0.703 QoS2 0.648 0.446
QoS3 0.510 0.624
Pri
Pri1 0.669 0.771
0.825 Pri2 0.691 0.749
Pri3 0.684 0.756
Bra
Bra1 0.738 0.747
0.830
Bra2 0.598 0.811
Bra3 0.626 0.798
Bra4 0.667 0.780
Val
Val1 0.833 0.876
0.915 Val2 0.854 0.857
Val3 0.803 0.899
Sat
Sat1 0.845 0.877
0.916
Sat2 0.770 0.903
Sat3 0.767 0.905
Sat4 0.849 0.877
Table 4.2 Reliability analysis of the questionnaire
From this table, the Cronbach’s Alpha coefficient of each variable is above 0.7,
therefore, the questionnaire used in this study can be regarded as reliable.
4.3 Validity
4.3.1 correlation analysis
Correlations
QoS Pri Bra Val Sat
39
QoS
Pearson Correlation 1
Sig. (2-tailed)
Pri Pearson Correlation 0.287** 1
Sig. (2-tailed) 0.001
Bra Pearson Correlation 0.241** 0.260** 1
Sig. (2-tailed) 0.008 0.004
Val Pearson Correlation 0.375** 0.255** 0.232* 1
Sig. (2-tailed) 0.000 0.005 0.011
Sat
Pearson Correlation 0.264** 0.394** 0.497** 0.479** 1
Sig. (2-tailed) 0.004 0.000 0.000 0.000
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Table 4.3.1 Validity shown through correlation analysis
This can be considered as construct validity. The way of validity calculation is through
correlation analysis of independent variables, which Pearson’s r values are between -1
to +1. However, the values are not expected to exceed 0.80, because there is a risk that
different variables measure the same concept (Bryman and Bell, 2011).
The table above shows the validity in terms of the independent variables regarding our
case. Firstly, in terms of ‘price perception’, Pearson correlation’s r is 0.287 (QoS),
0.260 (Bra), 0.255 (Val) and 0.264 (Sat). It shows an acceptable range of Pearson’s r,
so the independent variable of ‘price perception’ is effectively operationalized and it
measures the theoretical constructs that are expected to be measured. The second one
is ‘brand perception’ that Pearson’s r is 0.241 (QoS), 0.260 (Pri), 0.232 (Val) and 0.497
(Sat). The independent variable of ‘brand perception’ is under 0.80 range for another 4
variables, thus it is effectively operationalized and it measures correctly. The third
phase, the term of ‘value proposition’ of Pearson’s r is 0.375 (QoS), 0.255 (Pri), 0.232
40
(Bra) and 0.479 (Sat). It indicates an acceptable range that under 0.8, so the ‘value
proposition’ is effectively operationalized and it measures what supposed to be
measured. The last independent variable is ‘quality of service’ that shows the r of 0.287
(Pri), 0.241 (Bra), 0.375 (Val) and 0.264 (Sat). It is also in an acceptable range, thereby
it measures correctly.
4.3.2 Exploratory factor analysis
Authors firstly use KMO and Bartlett's Test to see if the data is capable for factor
analysis, result in below:
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.812
Bartlett's Test of Sphericity
Approx. Chi-Square 1192.013
df 136
Sig. 0.000
According to above result, KMO is 0.812 ＞ 0.7 while Bartlett test significant
differences, thus the data is suitable for factor analysis.
Authors then used principal component method to extract five factors with eigenvalues
＞1 and making a rotation by varimax method. The results are in below tables:
Communalities
Initial Extraction
41
QoS1- I think that employees of this company are always
willing to help me. 1.000 0.648
QoS2- I think that the facilities of the company are better than
others. 1.000 0.764
QoS3- I think that the stores of the company are well equipped. 1.000 0.652
Pri1- The prices of company offering meet the quality. 1.000 0.746
Pri2- I believe that the company is maintaining same quality of
service/product after discount. 1.000 0.762
Pri3- I will continue to stay with the company unless the price
is significantly higher for the service. 1.000 0.775
Bra1- The company has delivered a good image to me. 1.000 0.762
Bra2- I believe that the reputation of the company is high. 1.000 0.605
Bra3- I prefer this brand to the other available ones. 1.000 0.622
Bra4- I think that the company has a strong brand. 1.000 0.675
Val1- I believe that the company’s products are valuable. 1.000 0.834
Val2- I think that the value of what I got matches what I paid. 1.000 0.866
Val3- The products of company offering meet my needs. 1.000 0.852
Sat1- I am satisfied with the overall quality of service offered
by the company. 1.000 0.864
Sat2 -I am satisfied with the employees of the company. 1.000 0.747
Sat3- I enjoy my experiences in this company. 1.000 0.763
Sat4- When I am shopping in this company, I believe that they
can satisfy my needs. 1.000 0.826
Extraction Method: Principal Component Analysis.
Total Variance Explained
42
Compo
nent
Initial Eigenvalues Extraction Sums of
Squared Loadings
Rotation Sums of Squared
Loadings
Total % of
Varian
ce
Cumula
tive
%
Total % of
Varian
ce
Cumulat
ive
%
Total % of
Varian
ce
Cumulativ
e %
1 6.127 36.041 36.041 6.127 36.041 36.041 3.110 18.291 18.291
2 2.149 12.639 48.679 2.149 12.639 48.679 2.805 16.498 34.789
3 1.765 10.380 59.059 1.765 10.380 59.059 2.701 15.885 50.675
4 1.591 9.356 68.415 1.591 9.356 68.415 2.267 13.334 64.008
5 1.132 6.662 75.077 1.132 6.662 75.077 1.882 11.068 75.077
6 0.731 4.299 79.376
7 0.600 3.529 82.905
8 0.492 2.893 85.798
9 0.468 2.751 88.549
10 0.382 2.247 90.796
11 0.367 2.159 92.956
12 0.307 1.806 94.762
13 0.248 1.460 96.222
14 0.222 1.303 97.525
15 0.190 1.119 98.644
16 0.126 0.741 99.385
17 0.105 0.615 100.000
Extraction Method: Principal Component Analysis.
43
Rotated Component Matrixa
Component
1 2 3 4 5
Sat1 0.881 0.243 0.070 0.148 0.049
Sat4 0.805 0.288 0.275 0.125 0.060
Sat3 0.795 0.222 0.186 0.190 0.109
Sat2 0.749 0.202 0.334 0.169 0.073
Bra1 0.215 0.839 0.021 0.102 0.016
Bra4 0.141 0.803 -0.028 0.053 0.075
Bra3 0.139 0.746 0.142 0.135 0.087
Bra2 0.276 0.713 0.113 0.014 0.081
Val3 0.126 0.108 0.896 0.109 0.096
Val1 0.249 0.036 0.861 0.119 0.123
Val2 0.294 0.063 0.853 0.035 0.217
Pri3 0.239 0.017 -0.030 0.840 0.108
Pri2 0.153 0.034 0.155 0.839 0.095
Pri1 0.072 0.246 0.131 0.808 0.102
QoS2 0.015 0.086 0.18 0.188 0.830
QoS1 0.273 -0.046 -0.014 0.061 0.754
QoS3 -0.085 0.263 0.304 0.060 0.693
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
According to above results, the variance accumulation contribution rate of five factors
44
is 75.077% ＞ 50%. Factor loading for all factors is higher than 0.5. Meanwhile, rotated
components distribution remains the consistency with the structural hypothesis of the
scale. In conclusion, the scale has a good construct validity.
4.4 Regression analysis and Hypothesis testing
Regression analysis is a way of testing hypothesis by how independents variable
influence dependent variable (Bryman and Bell, 2011). Thus, 4 independent variables
of ‘quality of service’, ‘price perception’, ‘brand perception’ and ‘value proposition’
lead the impact of dependent variable of ‘satisfaction’.
4.4.1 Quality of service regression analysis
Satisfaction is dependent variable measured by independent QoS and as model 1. As
the results show, r is 0.264, r square is 0.70 and after adjusted r square is 0.62. It
shows low range and according to ANOVA that F is 8.859 and sig. 0.004<0.01, thus
it is meaningful and effective for regression analysis.
Model 1 Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .264a .070 .062 .69130
a. Predictors: (Constant), QoS
45
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 4.233 1 4.233 8.859 .004b
Residual 56.391 118 .478
Total 60.624 119
a. Dependent Variable: Sat
b. Predictors: (Constant), QoS
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 2.857 .465 6.150 .000
QoS .329 .111 .264 2.976 .004
a. Dependent Variable: Sat
According to the coefficients table above, unstandardized coefficients of QoS is 0.329
and t equals 2.976, meanwhile, p (Sig.) is <0.01. Therefore, QoS as an independent
variable is positively influencing dependent variable satisfaction, and we would get the
regression analysis by that: Sat=0.329QoS+2.857.
4.4.2 Price perception
Satisfaction is dependent variable measured by second independent Pri as model 2. As
46
the results show, r is 0.394, r square is 0.155 and after adjusted r square is 0.148. It
shows low range and according to ANOVA that F is 21.725 and sig. 0.000<0.01, thus
it is meaningful and effective for regression analysis.
Model 2 Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
2 .394a .155 .148 .65870
a. Predictors: (Constant), Pri
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
2
Regression 9.426 1 9.426 21.725 .000b
Residual 51.198 118 .434
Total 60.624 119
a. Dependent Variable: Sat
b. Predictors: (Constant), Pri
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
2
(Constant) 2.126 .455 4.676 .000
Pri .498 .107 .394 4.661 .000
a. Dependent Variable: Sat
47
According to the coefficients table above, unstandardized coefficients of Pri is 0.498
and t is 4.661, meanwhile, p (Sig.) is <0.01. Therefore, Pri as an independent variable
is positively influencing dependent variable satisfaction, and we would get the
regression analysis by that: Sat=0.498Pri+2.126.
4.4.3 Brand perception
Satisfaction is dependent variable measured by third independent Bra as model 3. As
the results show, r is 0.497, r square is 0.247 and after adjusted r square is 0.240. It
shows low range and according to ANOVA that F is 38.611 and sig. 0.000<0.01, thus
it is meaningful and effective for regression analysis.
Model 3 Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
3 .497a .247 .240 .62218
a. Predictors: (Constant), Bra
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
3
Regression 14.946 1 14.946 38.611 .000b
Residual 45.678 118 .387
Total 60.624 119
a. Dependent Variable: Sat
48
b. Predictors: (Constant), Bra
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
3
(Constant) 2.230 .326 6.834 .000
Bra .513 .083 .497 6.214 .000
a. Dependent Variable: Sat
According to the coefficients table above, unstandardized coefficients of Bra is 0.513
and t is 6.214, meanwhile, p (Sig.) is <0.01. Therefore, Bra as an independent variable
is positively influencing dependent variable satisfaction, and we would get the
regression analysis by that: Sat=0.513Bra+2.230
4.4.4 Value proposition
Satisfaction is dependent variable measured by fourth independent Val as model 4. As
the results show, r is 0.479, r square is 0.229 and after adjusted r square is 0.223. It
shows low range and according to ANOVA that F is 35.059 and sig. 0.000<0.01, thus
it is meaningful and effective for regression analysis.
Model 4 Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
4 .479a .229 .223 .62935
a. Predictors: (Constant), Val
49
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
4
Regression 13.886 1 13.886 35.059 .000b
Residual 46.738 118 .396
Total 60.624 119
a. Dependent Variable: Sat
b. Predictors: (Constant), Val
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
4
(Constant) 2.433 .308 7.892 .000
Val .519 .088 .479 5.921 .000
a. Dependent Variable: Sat
According to the coefficients table above, unstandardized coefficients of Val is 0.519
and t is 5.921, meanwhile, p (Sig.) is <0.01. Therefore, Val as an independent variable
is positively influencing dependent variable satisfaction, and we would get the
regression analysis by that: Sat=0.519Val+2.433
4.4.5 Satisfaction
Satisfaction is the dependent variable measured by four independent variables QoS,
Pri, Bra, Val as model 5. As the results show, r is 0.635, r square is 0.427 and after
50
adjusted r square is 0.407. It shows low range and according to ANOVA that F is
21.420 and sig. 0.000<0.01, thus it is meaningful and effective for regression analysis.
Model 5 (dependent variable) Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
5 .653a .427 .407 .54963
a. Predictors: (Constant), Val, Bra, Pri,
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
5
Regression 25.884 4 6.471 21.420 .000b
Residual 34.741 115 .302
Total 60.624 119
a. Dependent Variable: Sat
b. Predictors: (Constant), Val, Bra, Pri, QoS
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
5
(Constant) .396 .493 .802 .424
QoS -.018 .098 -.014 -.182 .856
Pri .273 .096 .216 2.842 .005
51
Bra .376 .077 .364 4.857 .000
Val .374 .084 .344 4.424 .000
a. Dependent Variable: Sat
According to the coefficients table above, unstandardized coefficients of Pri, Bra and
Val are 0.273, 0.376, 0.374 and t are 2.284, 4.857, 4.424, meanwhile, p (Sig.) is <0.01.
Therefore, Pri, Bra and Val as independent variables are positively influencing
dependent variable satisfaction, and we would get the regression analysis by that:
Sat=0.273Pri+0.376Bra+0.374Val+0.396
On the other hand, p in QoS equals 0.856. It is larger than 0.01, so it might be rejected.
Even though, in the chapter 4.4.1, it indicates positive value p, which means QoS could
impact satisfaction. But satisfaction coefficients’ table shows p=0.856 is much larger
than 0.856, this means the independent variable QoS is one-side influencing dependent
variable satisfaction. On the other words, the relation between QoS and satisfaction is
only one-side, not two-side influenced by each other.
4.5 Reviewed conceptual model
Figure 4.5a Conceptual model before hypothesis testing
52
Above figure 4.5a is the conceptual model based on theories of relationship marketing
tactics and customer satisfaction which contain four independent variables and one
dependent variable. As we can see from the above figure, according to the hypothesis
test result, one hypothesis (quality of service) out of four has been rejected. Which
leads to the reviewed conceptual model in below:
Figure 4.5b Reviewed conceptual model
As we can see from above reviewed conceptual model, three independent variables
and one dependent variable remains based on hypothesis test result in this study.
53
5. Discussion
The below chapter discussed the effect of quality of service, price perception, brand
perception, value proposition on customer satisfaction respectively based on the data
and information collected from the questionnaire. This discussion is closely connected
to the theoretical framework.
We have tested several different aspects such as: descriptive analysis, reliability,
validity and regression analysis. Through descriptive analysis, we were using central
tendency method in order to get a median from respondents by each of questions. All
these four independents of central tendency are larger than 3, which means the average
answers that are mainly clustered in 3-4 (neutral to agreeable). Subsequently, reliability
gives a message that the values should be larger than 0.7, since it analyzes a repeat of
likelihoods in further researches. Each of the independents is analyzed and all of them
are larger than 0.7. Validity is used by correlation coefficient that measures
differentiations between independent and dependent, in case we have not measured two
similar concepts and they are under 0.8 in an acceptable range. The last step is
regression analysis that used to verify our hypotheses. We accept three (Bra, Pri, Val)
and reject one (QoS).
5.1 Quality of service (QoS) and customer satisfaction
Evidently, QoS is used as one of the independent variables in order to analyze
dependent variable of satisfaction. According to literature reviews in chapter 2, we have
presented a hypothesis that QoS is positively influencing on satisfaction, because many
researchers believe that QoS positively influences satisfaction. (Parasuraman et al.,
1988, Chen et al., 2015, Cronin, Taylor, 1992)
In model 1, regression analysis shows the value of adjusted r square is 0.062. It means
that 100% of the dependent variable of satisfaction is occupied in 6.2% that impacted
54
by QoS as independent and it is the lowest one in these four tactics. The p (sig.) value
in model 1 is 0.004, which is smaller than 0.01. It confirms that QoS as an independent
variable could positively influence satisfaction. However, as model 5 indicates that p
value in QoS equals 0.856 which is much larger than 0.01, which means satisfaction
does not really impact by QoS. Comprehensively, QoS can directly and positively
influence satisfaction, but satisfaction does not influence by QoS. It probably sounds
conflicted, but there are two angles that might help us to resolve. First of all, as chapter
2.2.1 has mentioned that there are two streams in QoS and satisfaction in current studies.
According to Parsuraman et al, (1996) and Grönroos, (1984), they believe that QoS
positively influences satisfaction, but Cronin and Taylor (1992) believes that
satisfaction is the cause of QoS. Thus, this is maybe the reason why we cannot give a
precise answer of it. Secondly, our target of the research is supermarket industry and
supermarket is a place people frequently for basic needs, so customers do not really
care about quality on service and they pay more attentions on products’ value, price and
brand reputation. In summary, hypotheses of QoS is rejected.
5.2 Price perception and customer satisfaction
The second independent variables used to analyze dependent variable of satisfaction
within the study, as we described above, is price perception. According to the literature
reviews in chapter 2, we have presented a hypothesis that price perception is positively
influencing satisfaction.
According to the regression analysis results of model 2 of price perception (chapter
4.4.2) and regression analysis results of model 5 satisfaction (chapter 4.4.5), authors
may grudgingly accept the hypothesis on model 2: price perception is positively
influencing on satisfaction. Firstly, the p (sig.) value in model 2 is 0.000 ＜ 0.01. It
confirms the statistically significant between price perception as independent variable
and satisfaction as the dependent variable at a confidence level of 99%. But according
to the results from chapter 4.4.2 and 4.4.5, the Adjusted R Square of price perception
55
(0.148) and the Standardized Coefficients (Beta) is significantly lower than two other
independent variables which are brand perception and value proposition, which means
that price perception caused less influence in customer satisfaction than brand
perception and value proposition. The Std. Error of price perception is clearly higher
than brand perception and value proposition. Moreover, the p (sig.) value of price
perception in 4.4.5 is 0.05, which higher than the significance level in this study (0.01).
Since significant level of 0.05 is acceptable in most studies, thus, the hypothesis of
model 2 price perception may grudgingly be accepted.
Moving back to the question of why price perception caused lower influence on
customer satisfaction, according to some previous studies, researchers claimed that not
all kinds of product are affected by price, such as some basic/daily used products which
customer do not pay a lot of attentions on price. Coincidentally, the sample in this study
is customers of super market industry, and supermarket industry is a market full of
basic/daily use goods. Which may affect the performance of price perception in a large
degree. Moreover, customer with different demographic groups may have different
sensitivity towards price, and other factors such sex, age, marital status and employment
have greater influence on price (Zeithaml, 1988). Most of the sample population in this
study are young, unmarried university students, which may also influence the result.
5.3 Brand perception and customer satisfaction
The third independent variables used to analyze dependent variable of satisfaction
within the study, as we described above, is brand perception. According to literature
reviews in chapter 2, we have presented a hypothesis that brand perception is positively
influencing satisfaction.
According to the regression analysis results of model 3 of price perception (chapter
4.4.3) and regression analysis results of model 5 satisfaction (chapter 4.4.5), authors
accept the hypothesis on model 3: price perception is positively influencing on
56
satisfaction. Firstly, the p (sig.) value in model 3 is 0.000 ＜ 0.01. It confirms the
statistically significance between price perception as independent variable and
satisfaction as the dependent variable at a confidence level of 99%. Furthermore,
Adjusted R Square of brand perception is 0.240, means that 24% of customer
satisfaction are explained by brand perception, which makes brand perception the
strongest motive to strengthen customer satisfaction among four RM tactics. Not only
that, according to the results of 4.4.5, brand perception seeking has the best
Standardized Coefficients (Beta) among others of 0.364 (while Sig.=0.000), which also
proved that brand perception cause largest influence on customer satisfaction within the
study. Hence, the hypothesis of brand perception is accepted.
It is not hard to explain why brand perception caused this big influence on customer
satisfaction based on some previous researches. Firstly, lots of studies pointed out brand
may could be the most important factor for customer satisfaction (Liao et al., 2009).
Brand image is a result of a long-term complex interaction process between customer
and brand, which makes brand a very strong and relatively stable factor affecting
customer satisfaction (Ban, et al., 2011). Good brand image could also reduce the
customer price sensitivity (Peng and Wang, 2006). Since we have asked participants to
answer the questionnaire based on the feeling of their favorite supermarket, it may also
somehow answer the question of why price perception caused lower influence on
customer satisfaction than other tactics in above chapter.
In conclusion, authors proved that the previous study about brand perception and
customer satisfaction also worked within this study, which customer satisfaction is
affected by brand perception in the supermarket industry.
57
5.4 Value proposition and customer satisfaction
The fourth independent variables used to analyze dependent variable of satisfaction
within the study, as we described above, is value proposition. According to literature
reviews in chapter 2, we have presented a hypothesis that value proposition is positively
influencing satisfaction.
According to the regression analysis results of model 4 of value proposition (chapter
4.4.4) and regression analysis results of model 5 satisfaction (chapter 4.4.5), authors
accept the hypothesis on model 4: value proposition is positively influencing on
satisfaction. Firstly, the p (sig.) value in model 4 is 0.000 ＜ 0.01. It confirms the
statistically significant between value proposition as an independent variable and
satisfaction as the dependent variable at a confidence level of 99%. Furthermore,
Adjusted R Square of brand perception is 0.223, means that 22.3% of customer
satisfaction is explained by brand perception, which makes the value proposition the
second-strongest motive to strengthen customer satisfaction among four RM tactics.
Not only that, according to the results of 4.4.5, value proposition also achieved the
second-strongest Standardized Coefficients (Beta) value of 0.344 (while Sig.=0.000),
which proved that the value proposition is the second-significant influence among four
RM tactics. The influence of the value proposition is almost equal to the biggest tactic
(brand perception).
In conclusion, authors proved that the previous study about value proposition and
customer satisfaction also worked within this study, which customer satisfaction is
affected by value proposition in supermarket industry.
According to previous studies, value proposition played a more important role than
price in consumer psychology. Simply put, value proposition = core product + value
add-on (Jansson, 2010). Value add-on could be additional value comes with product
58
such as warranty, delivery process, etc. (Ravald and Grönroos, 1996). It is about what
customer believe they get within a consumption, including all physical/non-physical
benefits, and it is what exactly what customer compared with among competitors’
products rather than a single price. Hence, value proposition is a more considerable
concept in business transaction (Monroe, 2012). The result in this study support the
opinion (which value is a more considerable parameter compared with price) through
testing the hypothesis of value proposition. It reflects that value proposition has higher
impact on customer satisfaction.
59
6. Conclusion
Conclusion chapter summarized the data gathered in this study to achieve the purpose
of this study: describe how relationship marketing tactics affect customer satisfaction.
The motivation for the study was based on some previous research regarding
relationship marketing tactics and customer satisfaction in the first place. Authors
concluded four major influence tactics may affect customer satisfaction within the
fields of relationship marketing and tried to figure out each of its performance in the
supermarket industry. Four tactics are quality of service, price perception, brand
perception and value proposition. Four hypotheses are conducted in order to test
throughout SPSS. Statistical result results of 120 samples within this study showed that
one hypothesis is rejected because of abnormal data, three hypotheses are accepted, but
each of them has different influence on customer satisfaction.
The rejected hypothesis is H1: Customer satisfaction is positively influenced by
marketing tactic of QoS (quality of service). According to the regression analysis results
of variable QoS, quality of service caused 6.2% of impact on customer satisfaction.
However, when all four tactics test together, based on the regression analysis of
satisfaction (chapter 4.4.5), the data of variable QoS become very strange. The reason
caused is result is unclear so far. It might be the problem of multicollinearity or many
other factors. Based on the α value (0.856), authors decided to reject the H1. Which
means, customer satisfaction may have a positive correlation with QoS (quality of
service), but this relationship cannot be confirmed when function together with three
other tactics based on the sample within this study.
On the other hand, three hypotheses accepted are H2: Customer satisfaction is
positively influenced by marketing tactic of price perception. H3: Customer satisfaction
is positively influenced by marketing tactic of brand perception. And H4: Customer
satisfaction is positively influenced by marketing tactic of value proposition. Among
60
these three relationship marketing tactics, price perception caused less influence on
customer satisfaction with Adjusted R Square at 0.148. In addition, two additional
variables caused almost equal influence on customer satisfaction at Adjusted R Square
at 0.240 and 0.233. Which means, above three variables caused 14.8%，24% and 23.3%
influence on customer satisfaction in this study. The regression analysis of customer
satisfaction also confirmed above result.
In conclusion, quality of service cannot affect customer applied together with other
tactics. Price perception, brand perception and value proposition work together to
influence customer satisfaction. Brand perception and value proposition are two major
influences among four tactics. From the perspective of customer satisfaction, 40.7% of
customer satisfaction in total is affected by above four relationship marketing tactics.
61
7. Research Implications
Limitation research implications in the present chapter, the authors presented a
practical and theoretical contribution of this study, followed by the limitations which
indicated the weakness of this study, also, the suggestions for further research are
presented in this chapter.
7.1 Theoretical and Practical Contribution
Although lots of researches discussed the different relationship marketing tactics could
influence customer satisfaction, authors in this study further developed a conceptual
model based on current knowledge and tested out within the chosen sample in order to
see how it works together in reality. This could be the major theoretical contribution of
this study. Furthermore, the result of the study reflects that quality of service may not
works well as other tactics in this case. Even the generalization of this study is weak
according the type of this study (Exploratory study) and sample chosen (non-probability
sample), the result of this study could contribute to some extent within this.
According to the result of the study, the two main influences on customer satisfaction
in supermarket industry were brand perception and value proposition, which happened
to coincide with some previous study on this point (Ban, et al., 2011) (Liao et al., 2009)
(Monroe, 2012) (Jansson, 2010). This may help companies in the supermarket industry
to adjust the proportion of each strategy when they tried to increase customer
satisfaction through relationship marketing tactics. When total resources are limited,
increase in the use of main influences tactics may maximize the outcome (Monroe,
2012).
7.2 Limitation and further research
As we abovementioned in chapter three, our data collected towards supermarket
62
industry, which seeking positive relations between four marketing tactics of QoS, price
perception, brand perception and value proposition with customer satisfaction. The
limitation is the results cannot be generalized, because supermarket industry is not
representative for any other industries. Not to mention the type of this study
(Exploratory study) and sample chosen (non-probability sample) already determined
the weak generalization of the study result. On the other hand, we have only collected
data from 120 respondents which still small number of people. There was no
demographic classification for the sample, which we see it as a missed opportunity,
unfortunately, the scope of the study was very confined. Further research could involve
more respondents and divided into different groups in order to verify the results more
precisely.
We have successfully verified the positive relation between the independent variable of
QoS to dependent satisfaction, which through the result of regression (model 1) shows
acceptable p value 0.004. However, hypothesis 1 is rejected. In the regression analysis
of satisfaction in model 5 gives a very huge p value 0.856. The explanation under these
two p values of relation is only existing on one-side not two-sides, which QoS does
positively affect satisfaction but satisfaction does not really affect by QoS. This is
probably the reason why there are two arguments existing in current studies, which one
believes that QoS is the antecedent and another one believes that satisfaction is the
antecedent. The true relation between these can be treated a future research topic and
find out which one is the real antecedent or they do not have relations at all.
The research has measured four marketing tactics, and based on the regression analysis
that the adjusted r square is 0.407 that means these four tactics are occupied 40.7%
likelihoods to impact satisfaction. However, there are still 59.3% likelihood impacted
by another marketing tactics or aspects such as membership, social selling etc. Further
research can focus on the empty likelihoods and even give a ranking among these
marketing tactics. It helps company maximize customer satisfaction through marketing
tactics.
63
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I
Appendix
Questionnaire
Are you a supermarket customer?
Hello! Thanks for your time to come to this page. We are three students enrolled in
marketing program who working on the bachelor thesis. Our topic is about relationship
marketing tactics and customer satisfaction. If you are a customer of any supermarket,
please take few minutes to answer the below questions (17 questions in total, estimated
time of occupancy 8 to 10 minutes.) We would appreciate your cooperation and with
you have a good day. Thanks again!
Hello! We believe that you must have a supermarket brand that is your
favorite and always willing to go there. Please select one brand, keep in
mind and finish following questions. Thank you!
Questions about quality of service
II
Questions about price perception
III
Questions about brand perception
IV
V
Questions about value proposition
VI
Questions about satisfaction
VII