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ABSTRACT
Consumer behavior research is moving beyond the simple application of traditional consumer behavior models towards a generation of new models, which are more relevant to the newmedia and communication technologies. As customers tend to have reservations about newtechnology, this study explores the role of trust and experience in customer satisfaction.Trust plays a vital role for the 留學(xué)生市場學(xué)essay寫作需求adoption of innovations, as consumers lack experience with thenew product and find themselves in a situation of high risk. Consumers therefore try to reduce
the risk associated with a certain behavioral decision. This study in particular focuses at themobile sector of Pakistan and level of customer satisfaction according to their experiences.Furthermore,investigates the customers trust on mobile phone service providers in Pakistan.
This research further explores the impact of remarkable development in mobile sector ofPakistan on general consumer’s life, which than, can help companies to sustain their share.Customers will have better services and improved network performance accompanied with lowtariffs.
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II
ACKNOWLEDGEMENTS
In the name of God who is most gracious and most merciful.I would like to express my sincere gratitude to my supervisor Dr. Ashok Jashapara whose
constant advice and guidance made this thesis possible. I would like to thank him for sharing hisideas and spending valuable time with me for the completion of this thesis.At this point of life I would also like to acknowledge that my studies would not be possiblewithout the help and support of my family especially my mother, who believe in me whateverthe situation and stand with me when I needed the most. Finally I must also thank my friendswho provided me with thoughtful guidance and advice during this project.
I would also like to thanks Royal Holloway University of London for providing me all the facilitiesand permission to use its resources. This is the time to say thanks to all the teachers whoseguidance and support made my academic year memorable.
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III
Table of Contents
CHAPTER 1: INTRODUCTION ____________________________________________________________ 1
Background to Study: _______________________________________________________________ 2
1.2 Problem Definition and Key Questions: ______________________________________________ 3
1.3 Research Aims and Objective: _____________________________________________________ 3
1.4 Motivation for Study: ____________________________________________________________ 4
1.5 Importance of Study: ____________________________________________________________ 5
1.6 Description of Research Tasks: _____________________________________________________ 5
1.6.1 Size of Sample: ______________________________________________________________ 5#p#分頁標(biāo)題#e#
1.6.2 Respondents: _______________________________________________________________ 6
1.6.3 Secondary Data: _____________________________________________________________ 6
1.7 Structure of Thesis: ______________________________________________________________ 7
CHAPTER2: LITERATURE REVIEW ________________________________________________________ 8
Introduction: ______________________________________________________________________ 9
2.2 Influence of Brand Reputation & Perceived Privacy Risk in Consumer Decision Making: _______ 10
2.3 Role of Reputation in Customer’s Choice: ___________________________________________ 12
2.4 Customer Experience as the next competitive battleground: ____________________________ 13
2.5 Experience Economy; an emerging paradigm: ________________________________________ 14
2.6 Diminishing world of goods and services & Evolution of Experience Economy: ______________ 16
2.7 Formation of Trust through the utilization of Experience: _______________________________ 18
2.8 Importance of Trust in reducing the associated risk with modern services: _________________ 18
2.9 Significance of Trust in Customer Satisfaction: _______________________________________ 19
2.10 Customer Satisfaction and Consumption of Experience: _______________________________ 23
2.11 Staging different Experiences on the basis of trust for Various Market Segments: ___________ 24
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IV
2.12 Segmentation Analysis: _________________________________________________________ 25
2.13 Conclusions: _________________________________________________________________ 27
CHAPTER 3: RESEARCH METHODOLOGY _________________________________________________ 28
Introduction: _____________________________________________________________________ 29
3.2 Questionnaire Development: _____________________________________________________ 29
3.2.1 Composition: ______________________________________________________________ 29
3.2.2 Pre-Testing: _______________________________________________________________ 31
3.2.3 Online Survey: _____________________________________________________________ 31
3.3 Sampling: ____________________________________________________________________ 32
3.4 Instrument Used: ______________________________________________________________ 33
3.5 Limitations to Primary Sources: ___________________________________________________ 35
3.6 Secondary Sources: _____________________________________________________________ 36
3.6.1 Limitations to Secondary Sources: ______________________________________________ 36
CHAPTER 4: DATA ANALYSIS ___________________________________________________________ 37
4.1 Introduction: __________________________________________________________________ 38
4.2 Descriptive Statistics: ___________________________________________________________ 38
4.2.2 Gender Wise Classification: _____________________________________________________ 40#p#分頁標(biāo)題#e#
4.2.3 Profession Wise Classification: __________________________________________________ 41
4.2.4 Age Wise Classification: ________________________________________________________ 43
4.2.5 Company Wise Classification: ___________________________________________________ 44
4. 3 Scale Items and Reliability: ________________________________________________________ 46
4.3.1 Mobile Network Reputation (MNREP): ____________________________________________ 47
4.3.2 Perceived Privacy Protection (MNPPP): ___________________________________________ 47
4.3.3 Mobile Network Experience (MNEXP): ____________________________________________ 47
4.3.4 Mobile Network Perceived Trustworthiness (MNPTR): ________________________________ 48
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4.3.5 Satisfaction (STFN): ___________________________________________________________ 48
4.3.6 Perceived benefit (BNFIT): ______________________________________________________ 48
4.4 Factor Analysis: __________________________________________________________________ 48
4.5 Association between Constructs: ____________________________________________________ 52
4.5.1 Correlations: ________________________________________________________________ 52
4.5.2 T-test Gender Wise: ___________________________________________________________ 54
4.5.3 ANOVAs: ____________________________________________________________________ 55
4.5.3.1 Age Wise ANOVA: _________________________________________________________ 55
4.5.3.2 ANOVAs Profession Wise: ___________________________________________________ 56
4.5.3.3 ANOVAs Company Wise: ___________________________________________________ 56
4.6 Regression Analysis: ______________________________________________________________ 58
4.6.1 Regression Analysis of Satisfaction as Dependent Variable: __________________________ 58
4.6.1.1 Regression between Satisfaction and Reputation: ________________________________ 59
4.6.1.2 Regression between Satisfaction and Privacy: ___________________________________ 59
4.6.1.3 Regression between Satisfaction and Experience: ________________________________ 60
4.6.1.4 Regression between Satisfaction and Trust: ____________________________________ 60
4.6.1.5 Regression between Satisfaction and Benefit: ___________________________________ 61
4.6.2 Regression Analysis of Trust as Dependent Variable: _________________________________ 62
4.6.2.1 Regression between Trust and Reputation: _____________________________________ 62
4.6.2.2 Regression between Trust and Privacy: ________________________________________ 63
4.6.2.3 Regression between Trust and Experience: _____________________________________ 63
4.6.2.4 Regression between Trust and Benefit: ________________________________________ 64
4.6.2.5 Regression between Trust and Satisfaction: ____________________________________ 64
4.6.3 Regression Analysis of Reputation as Dependent Variable: ____________________________ 65#p#分頁標(biāo)題#e#
4.6.3.1 Regression between Reputation and Privacy: ___________________________________ 66
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VI
4.6.3.2 Regression between Reputation and Experience: ________________________________ 66
4.6.3.3 Regression between Reputation and Trust: _____________________________________ 67
4.6.3.4 Regression between Reputation and Benefit: ___________________________________ 67
4.6.3.5 Regression between Reputation and Satisfaction: ________________________________ 68
CHAPTER 5: DISCUSSIONS AND CONCLUSIONS ____________________________________________ 69
Major Research Findings: ___________________________________________________________ 70
5.1.1 Association among Variables: _________________________________________________ 70
5.1.2 Regression among Variables: __________________________________________________ 71
5.1.3 Customer Satisfaction in Mobile Sector: _________________________________________ 71
5.1.4 Level of Customer Satisfaction across different Mobile Companies: ___________________ 72
5.1.5 Level of Customer Satisfaction across different Groups: ____________________________ 73
5.2 Implications of Research: ________________________________________________________ 73
5.3 Limitations of the Study: _________________________________________________________ 75
5.4 Further Research: ______________________________________________________________ 75
REFERENCES _____________________________________________________________________ 77
APPENDICES _______________________________________________________________________ 88
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VII
List of Figures
Pie chart 1.1 Market Share of Service Provider----------------------------------------------------------------7
Figure 2.1: SERVQUAL-----------------------------------------------------------------------------------------------21
Figure 2.2: SERVQUAL with Trust--------------------------------------------------------------------------------22
Graph: 4.2.1 Mean Scores----------------------------------------------------------------------------------------- 40
Pie Chart: 4.2.2.1 Gender Wise Classification in Percentage----------------------------------------------41
Graph: 4.2.2.2 Gender Wise Classification Mean Scores---------------------------------------------------41
Pie Chart 4.2.3.1: Profession wise Classification in Percentage------------------------------------------42
Graph 4.2.3.2: Profession wise Classification with Means-------------------------------------------------43
Pie Chart 4.2.4.1: Age Wise Classification in Percentage---------------------------------------------------44
Graph 4.2.4.2: Age Wise classification with Means----------------------------------------------------------44
Pie chart 4.2.5.1: Company wise classification in percentage--------------------------------------------45
Graph 4.2.5.2: Company wise classification with Means--------------------------------------------------46#p#分頁標(biāo)題#e#
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List of Tables
Table 3.4 Instrument Used for Survey--------------------------------------------------------------------------33
Table 4.2.1: Overall Mean Scores--------------------------------------------------------------------------------39
Table: 4.2.2 Gender Wise Scores---------------------------------------------------------------------------------40
Table 4.2.3 Profession wise Classification----------------------------------------------------------------------41
Table 4.2.4: Age Wise Classification-----------------------------------------------------------------------------43
Table 4.2.5: Company wise Classification----------------------------------------------------------------------45
Table 4.3: Instrument Reliability & Validity--------------------------------------------------------------------47
Table 4.4.1: Factors and Variables with Factor Loading----------------------------------------------------49
Table 4.4.2: Factor Matrix-----------------------------------------------------------------------------------------52
Table 4.5.1: Correlation between constructs----------------------------------------------------------------53
Table 4.5.2 T-test for Gender------------------------------------------------------------------------------------54
Table 4.5.3.1: Age wise ANOVA---------------------------------------------------------------------------------55
Table 4.5.3.2: Profession Wise ANOVA-----------------------------------------------------------------------56
Table 4.5.3.3: Company Wise ANOVA------------------------------------------------------------------------57
Table 4.6.1: R-Square for Satisfaction------------------------------------------------------------------------58
Table 4.6.2 R-Square for Trust---------------------------------------------------------------------------------62
Table 4.6.3 R-Square for Reputation-------------------------------------------------------------------------65
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CHAPTER 1: INTRODUCTION
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Background to Study:
Over the last two decades, the global economy has gone through the revolutionary changesand development in communication technology; this has triggered a complex change inconsumption pattern of consumers (saleem, 2005). As a result Mobile phones has much higheret al., 2000), Since the mid-1990s, the penetration of mobile phones in developed economieshas been explosive. Whereas in 1997 only 215 million people were using mobile
communication devices worldwide; by 2001 this had grown to a massive 961 million, furthergrowing to 1.16 billion by 2003. Today, Western Europe exhibits the highest penetration ofmobile phones (79%); furthermore, according to McKinsey (2001), the mobile phonepenetration in Europe will be more than 85% in 2008, followed by North America (48%), andAsia (12%). However, growth in the European mobile sector has recently slowed. (Bauer et al2005).Recently Asian mobile sector has grown at a good pace. Pakistan is also among one of the#p#分頁標(biāo)題#e#
fastest growing markets in the world along with India, Bangladesh, Sri Lanka and Malaysia (PTA,2006). “The number of cellular phones has increased 16 fold from May 2004 till Dec 2007 (PTA
2007, Joseph Wilson 2006-07). BY January 2008 cellular subscribers in Pakistan reached 78.7million, on average 2.3 million subscribers were added every month during 2006-07(PTA, March2008).By June 2007, 6,000 cities/towns/villages covered by mobile operators with 17,779 cellsites all over Pakistan. Mobile network coverage is reaching to almost 90% of the totalpopulation. The telecom sector received above US$ 1.8 billion FDI, 35% of the total FDI (PTA,May 2008).
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1.2 Problem Definition and Key Questions:
In the new millennium, the Pakistan telecom landscape has emerged as one of the mostdynamic business sectors in the country where cell phones have become a household
utility(Sadia and Sidrah, 2007). The growth of cell phone sector brings new meanings tocommunication for consumer in Pakistan but users have large numbers of complaints againstmobile phone operators as well.
The main problems prevail are the Experience below expectations and lack of trust on mobilephone companies. In addition, complaints related to operational issues such as weakness of
signals, slow or late response of SMS and spam SMS continue unabated. Customers also reportabout hidden charges and companies are not open about the costs. Finally mobile users are
not satisfied from the customer service and the way treated, by some of the companies. Keyquestions are how to reduce the gap between expectation and experience of a consumer, how
to improve the overall level of trust and customer satisfaction, how to improve the overall levelof customer Trust and identify the measures which can help companies to sustain or increase
their customer base. The future looks promising as a sizeable percentage of the population hasenough disposable income to cater to the mobile communication industry, thus uncovering alatent potential in the market (Sadia and Sidrah, 2007).
1.3 Research Aims and Objective:
This study will focus on the effects of this prompt development on consumer’s behaviour as‘Cole and O’Keefe (2000), Miles et al. (2000) and Vrechopoulos et al. (2000), state thatconsumer behaviour research is moving beyond the simple application of traditional consumer
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behaviour models towards a generation of new models, which are more relevant to the mobile
environment.’ and what they think is essential for good mobile service. Along the same lines,
Petrison et al. (1997) support that thorough Knowledge of consumer behaviour, coupled with
advances in technology, enable marketers to target customers on a more personalized,
customized and segmented basis (Hoffman and Novak 1997, Elliot and Fowell 2000,
Vijayasarathy and Jones 2001, Barnes 2002, Green et al. 2001).#p#分頁標(biāo)題#e#
Little is known about cell phone customer’s characteristics and the factors influencing their
purchase decision (Sieber 1999, Barnes 2002). while “in today’s market space the consumer is
gaining more power as new distribution systems are driving price down, making access to both
products and the information needed to compare alternatives easier” (Scultz and Baily 2000
p.50, Cole and O’Keefe 2000, Miles et al. 2000 and Vrechopoulos et al. 2000). Objective of this
research is to explore the impact of this remarkable development in mobile sector on general
consumer’s life, which than, can help companies to sustain their share. Customers will have
better services and improved network performance accompanied with low tariffs.
1.4 Motivation for Study:
Recent surveys of telecom market shows that consumers are not satisfied on the whole with
the companies. Main motivation for this study is to identify those spots which are causing the
unrest among the customers (PTA 2007). This will facilitate cell phone operators in improving
the areas where they need to. This study will also supply an autonomous observation for
companies exclusive of any prejudices and concealed interests from an external investigate.
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1.5 Importance of Study:
Reviewing the B2C Market literature, it is clear that the majority of Research efforts until now
have focused on the Web, rather than Mobiles. Recent growth is providing direct managerial
implications and challenging business Opportunities. To that end, it is obvious that consumer
behavior analysis must be utilized as a research tool for improving business activity, ‘within its
strategic marketing planning’ (Siomkos and Vrechopoulos, 2002). Besides, B2C has one generic
but crucial objective: to satisfy consumers, build strong relationships with them based on
loyalty, and enthuse to them (ECR 1999).
It is evident, therefore, that investigating consumer needs, wishes, preferences, attitudes,
characteristics, behaviors, etc., through corresponding consumer surveys, constitutes the only
reliable method towards achieving this business objective (Green et al., 2001). In addition,
Kotler (2000) states, that customer constitutes a basic source that provides the requirements to
the organization regarding the development of marketing strategies.
1.6 Description of Research Tasks:
Descriptive Research: The objective of descriptive research is to ‘portray an accurate profile
of persons, events or situations’ (Robson, 1993: 4).Hence, one of the most important purposes
of this paper is to draw a clear picture of the phenomenon on which customers have
experienced telecom revolution in Pakistan by conducting an experience indicative survey.
1.6.1 Size of Sample:
In this study, the technique of convenience sampling is employed According to Malhotra and#p#分頁標(biāo)題#e#
Birks (2000), this sampling technique can be used in exploratory research designs like the
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present one. It is mainly used for the ease of measure and cooperative, and accessibility
(Malhotra and Birks, 2006). In order to use a more balanced and convenient way of comparison
between the different groups, the sample selection process of convenience sampling is
continued until our required sample size has been reached (Saunders et al., 1997). In this sense,
the size of sampling is 150 samples from customers of different networks.
1.6.2 Respondents:
Responses are based on the convenience sampling technique; all of responses are taken from
those who are easily accessible in three large cities of Pakistan i.e. Karachi, Lahore and
Islamabad. They were asked individually through calls and also contacted through email to
complete the questionnaires.
1.6.3 Secondary Data:
Secondary Data will include the data of four large companies with more than 10% of market
penetration and operating under the conditions of license issued by Pakistan
Telecommunication Authority (PTA). PTA is the regulatory body of mobile operators in Pakistan
with the power of licensing and looks after mobile sector.
Panel consists of Top four mobile phone companies of Pakistan for research purposes,
according to Pakistan Telecommunication Authority (PTA) but market share of small companies
is also included to present full picture of market.
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Pie chart 1.1 Market Share of Service Providers
These are Mobilink, Ufone, Telenor, Warid telecom and others respectively in (2007-08) and their
respective market share is 39.2%, 20.9%, 19.5%, 17% and 3.4%. The reason for selecting this period is to
analyze mobile sector’s recent performance and effects on consumers. Collectively they serve 100% of
existing subscribers (INDUSTRY ANALYSIS REPORT 2007-08, PTA).
1.7 Structure of Thesis:
Chapter 1
Chapter 3
Chapter 2
Chapter 4
Chapter 5
Literature Review
Introduction
Research Methodology
Quantitative Data Analysis
Discussions & Recommendations
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CHAPTER2: LITERATURE REVIEW
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Introduction:
We will start with state of the art literature on Reputation, experience, trust and satisfaction.
Research methodology will be discussed in next chapter with detail conversation on instrument
development, Revision of instrument, launch of instrument, Sampling, sample size and
collection of responses etc. Data analysis will be done in fourth chapter with insight on
instrument reliability and authenticity. In last we will draw conclusions from results and then
make the recommendations for improvements.
This study will investigate about Pakistan’s Mobile phone Sector specifically regarding the#p#分頁標(biāo)題#e#
reputation of companies, consumer’s experience, consumer’s trust and their level of
satisfaction with these companies. This study is unique in its nature as there is only modest
prior work exists concerning experience and trust based customer satisfaction in mobile sector
of Pakistan. We further recognized that there were only few exact studies like the present one,
are available in mobile sector within literature. This study is related to the Experience based
Customer Satisfaction in which trust is included as an endogenous variable (Balasubramanian et
al. 2003). There were few more similar studies presented by the researcher about Trust based
consumer decision making in electronic commerce (D j, Kim et al. 2008); Consumer behavior in
the Italian mobile telecommunication market (Mazzoni et al, 2007); Customer Satisfaction in
virtual environment (Balasubramanian et al. 2003); Mobile Commerce Diffusion in Europe
(Vrechopoulos et al. 2002); Studies on Experience Economy (Pine & Gilmore. 1998-99,2001-02:
Gentile et al. 2007); Multi-Channel Consumer Perceptions (Teltzrow et al. 2007); Driving
Consumer Acceptance of Mobile Marketing (Bauer et al. 2005); The role of trust and
satisfaction in a relationship (Graf et al 2005) etc.
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Nowadays competing in a global market has become increasingly difficult and only the creation
of long-lasting competitive advantages seems to offer an avenue for survival. But where should
a company start looking to develop a competitive advantage? Many scholars advocate that one
of the main routes to reach it is by means of a much stronger focus on the customer (Douglas
and Craig, 2000; Farinet and Ploncher, 2002; Kotler and Keller, 2006; Peppers and Rogers,
2000). In the last years, and particularly in the process of devising a company’s strategy, this
growing attention on the customer resulted in an increased focus on CRM philosophies. More
recently, as the number of contact points between a company and its customers increased,
such attention to the customer revealed the fundamental importance of monitoring the many
experiences that originate from those contact points (Gentile et al, 2007).It implies the
necessity to build brand awareness by making trust among consumers. This trust must be
achieved through credibility, rather than just a marketing campaign (OnPoint, 2006).
2.2 Influence of Brand Reputation & Perceived Privacy Risk in Consumer Decision Making:
It is obvious that consumer behavior analysis must be utilized as a research tool from every
modern organization that deals with commerce (either conventional or electronic) within its
strategic marketing planning (Siomkos and Vrechopoulos, 2002). Along the same lines, Petrison
et al. (1997) support that thorough knowledge of consumer behavior, coupled with advances in#p#分頁標(biāo)題#e#
technology, enable marketers to target customers on a more personalized, customized and
segmented basis. Consumer psychology presents an excellent context for analyzing and
studying this conceptualization of human free will. Rational choice is an important part of being
a consumer (Bettman, Luce, & Payne, 1998, Baumeister et al 2008), such as in figuring out how
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to get the most for one’s money or how to avoid selecting an inferior product (Huber, Payne, &
Puto, 1982).
(Bem, 1972) argues that people's attitudes are formed from the perception of their own
previous behavior about companies. People are commonly inclined to use their previous
behavior as a heuristic basis for later decisions (Taylor, 1975; Cornelissen et al 2008). When
people decide about some consumer product, they respond by totaling up their beliefs in favor
versus against making a purchase, consult a list of their needs, and then blend in some notions
about what they want beyond that which they really need. If the net result of this calculation is
positive enough to justify the price of the product, they buy (Dunning D, 2007). But the
literature suggests that consumer decision making is not exclusively a conscious calculation
totaling up benefits and beliefs. Instead, it suggests that people often reach their decisions via
unthinking impulse (Strack, Werth, & Duetsch, 2006) or through the influence of processes lying
outside of conscious awareness (Dunning D, 2007).
One of them is Positive Reputation of Selling Party (REP), has been considered a key factor for
reducing risk (S.Antony et al 2006, A. Boot et al 1993, A. Moukas et al 1999 and P. Resnick et al
2000) and in creation of trust (P.M. Doney et al 1997, S. Ganesan et al 1994 and S.L. Jarvenpaa
et al 1999) because it provides information that the selling party has honored or met its
obligations toward other consumers in the past. REP refers to the degree of esteem in which
consumers hold a selling party (D.j. Kim et al, 2008). Reputation-building is a social process
dependent on past interactions (and in particular, the degree of honesty that a selling party has
demonstrated in those earlier transactions) between consumers and the selling party (G.
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Zacharia et al 2000). Based on its reputation, a consumer is likely to infer that the selling party
is likely to continue its behavior in the present transaction (K.J. Sharif et al 2005).
2.3 Role of Reputation in Customer’s Choice:
In the case of a positive reputation, one is likely to infer that the company will honor its specific
obligations to oneself, and therefore conclude that the selling party is trustworthy. In the case
of a negative reputation, an individual may conclude that the selling party will not honor its
specific obligations, and hence conclude that it is untrustworthy (D.j. Kim et al, 2008). And by#p#分頁標(biāo)題#e#
the same token, consumers are likely to conclude that it is inherently risky to transact with a
vendor who has a history of failing to honor its obligations, whereas it is relatively less risky to
transact with a vendor who has a history of honoring its obligations (D.j. Kim et al, 2008).
In traditional environments, trust or assurance is typically generated by a customer observing
employees' knowledge and responsiveness; the customer evaluates this trust separately from
other service quality dimensions (Parasuraman et al. 1988). Indeed, people's tentative
preferences influence their perception of input beliefs even when no ultimate decision is called
for (D. Simon, Pham, Le, & Holyoak, 2001). The same processes can be found in consumer
choice, when participants are induced to favor one choice of a product over another (Russo,
Medvec, & Meloy, 1996; Russo, Meloy, & Medvec, 1998; Russo, Meloy, & Wilks, 2000).
According to Henry L. Munn, the consumer's perception of a specific brand and decision to buy;
depends upon its physical qualities, container, packaging, price, advertising, promotion, and
merchandising (Munn, 1960: 29). By measuring a brand's attributes in terms of mentioned
categories, it can be identified that how a consumer prefers a brand to another. Therefore, if a
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brand is to be built up, it should be remembered that consumer's perception of a brand could
determine its potential growth. When an organization has a highly positive and distinctive
brand's image it means that consumers highly value it and trust it, so the company can possibly
take more market share than its rivals. To attain that further share it is eminent to reduce the
gap between Brand Reputation and Brand Experience (Pine & Gilmore, 1998, 2001a, 2002b).
2.4 Customer Experience as the next competitive battleground:
Once the decision is called for and Consumer's familiarity with a selling party increase through
frequent interactions, that may directly affect the consumer's willingness to purchase (D. Gefen
et al 2000). Familiarity captures a consumer's subjective experience with respect to the selling
entity, and is usually created by repeated interactions (e.g., prior purchase experiences (D.J.
Kim et al, 2008). In this perspective, the central idea is to expand the transaction-based notion
of Customer Relationship to the ‘‘continuous’’ concept of Customer Experience. Consequently,
it becomes necessary to consider aspects that refer to the emotional and irrational side of
customer behavior (Holbrook and Hirschman, 1982) and which, more than the only rational
ones, account for the whole experience coming from the set of interactions between a
company and its customers(Gentile et al, 2007). In addition, a similar position can be found in
the managerial field; in fact, 85% of senior business managers believe that differentiating solely#p#分頁標(biāo)題#e#
on the traditional elements, such as price, product and quality, is no longer a sustainable
留學(xué)生市場學(xué)essay寫作需求competitive advantage and even more senior managers hold the Customer Experience as the
next competitive battleground (Shaw and Ivens, 2005).
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The concept of Customer Experience was firstly conceived in the mid-1980s when, along with
the mainstream literature in consumer behavior that deemed customers as rational decision
makers, a new experiential approach offered an original view to consumer behavior (Holbrook
and Hirschman, 1982). The importance of various hitherto neglected variables was reconsidered:
‘‘the role of emotions in behavior; the fact that consumers are feelers as well as
thinkers and doers; the roles of consumers, beyond the act of purchase, in product usage as
well as brand choice’’ (Addis and Holbrook, 2001).
Despite these initial sparks, the concept of Customer Experience came more relevantly to the
fore in the 1990s with Pine and Gilmore’s book on the Experience Economy (1999); the authors
present the ‘‘experiences’’ as a new economic offering, which emerges as the next step after
commodities, goods and services in what they call the progression of economic value. Hence, in
the following years a flourishing of different contributions focused their attention on the
Customer Experience as a new lever to create value for both the company and the customer
(Addis and Holbrook, 2001; Caru` and Cova, 2003; Ferraresi and Schmitt, 2006; Forlizzi and
Ford, 2000; LaSalle and Britton, 2003; Milligan and Smith, 2002; Ponsonby-Mccabe and Boyle,
2006; Prahalad and Ramaswamy, 2004; Schmitt, 1999; Schmitt, 2003; Shaw and Ivens, 2005;
Smith and Wheeler, 2002).
2.5 Experience Economy; an emerging paradigm:
Pine and Gilmore (1999; Gilmore and Pine 2002a, 2002b) proposed the experience economy as
an emerging paradigm for enhancing business performance across a wide range of industries.
Pine and Gilmore (1999) argued that businesses need to shift their paradigm from the
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“delivery-focused” service economy that emphasizes high quality offerings to the “staged”
experience economy that creates a memorable consumption experience. Further they assert
that some of the fastest growing sectors of the global economy are related to the consumption
of experiences (Pine and Gilmore 1999; Richards 2001).
According to Pine and Gilmore (1999; Gilmore and Pine 2002a, 2002b), in the emerging
experience economy, consumers seek unique experiences beyond merely consuming products
and services because the consistent, high level of product and Service quality can no longer be
used to differentiate choices for consumers. Thus, this study serves two purposes: (1) to#p#分頁標(biāo)題#e#
provide scales for measuring experience economy concepts and (2) to empirically test the
predictive validity of experience economy concepts (Haemoon Oh, Ann Marie Fiore and
Miyoung Jeoung; 2007). Pine and Gilmore (1999, p. 12) defined experience from a business
perspective: “Experiences are events that engage individuals in a personal way”; but we
surmise that they would define experience from a consumer perspective as enjoyable,
engaging, memorable encounters for those consuming these events. According to Pine and
Gilmore (1999; Gilmore and Pine 2002a, 2002b), This new demand for unique and memorable
experiences requires firms to develop a distinct value-added provision for products and services
that have already achieved a consistent, high level of functional quality (Haemoon et al, 2007).
Adding to Pine and Gilmore’s perspectives on the experience economy, this study attempts to
introduce relevant theoretical variables, such as level of Trust, overall quality of Experience, and
customer satisfaction, in an effort to test the predictive validity of Consumer’s experience for
some important variables related to business success (Haemoon et al, 2007). From now on,
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leading-edge companies-whether they sell to consumers or businesses-will find that the next
competitive battleground lies in staging experiences (Pine and Gilmore, 1998). An experience is
not an amorphous construct; it is as real an offering as any service, good, or commodity. In
today’s service economy, many companies simply wrap experiences around their traditional
offerings to sell them better (Pine and Gilmore, 1998).
2.6 Diminishing world of goods and services & Evolution of Experience Economy:
Experiences are inherently personal, existing only in the mind of an individual who has been
engaged on an emotional, physical, intellectual, or even spiritual level. Thus, no two people can
have the same experience, because each experience derives from the interaction between the
staged event (like a theatrical play) and the individual's state of mind (Pine and Gilmore, 1998).
But experiences are not exclusively about entertainment; companies stage an experience
whenever they engage customers in a personal, memorable way (Pine and Gilmore, 1998).
Neither are experiences only for consumer industries. Companies consist of people, and
business- to-business settings also present stages for experiences (Pine and Gilmore,
1998).Experience is a new business innovation, which makes threats to inappropriate those
who refer themselves to the thinning world of supplies and services (Joseph Schumpeter; Pine
and Gilmore; 1998).
The starting point of these approaches is a renewed way to consider the well known concept of
consumption: it becomes a holistic experience which involves a person – as opposed to a#p#分頁標(biāo)題#e#
customer - as a whole at different levels and in every interaction between such person and a
company, or a company’s offer (LaSalle and Britton, 2003). what contributes to the creation of
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value is not so much selling memorable experiences but to enable the customer to live all the
moments of the relationship with a company in an excellent way, even beyond his/her
expectations (LaSalle and Britton, 2003) or, according to the viewpoint of Prahalad and
Ramaswamy (2004), to co-create their own unique experience with the company. In this
perspective, companies do not sell (or stage, according to Pine and Gilmore’s perspective)
experiences, but rather they provide artifacts and contexts that are conducive of experiences
and which can be properly employed by consumers to co-create their own, unique, experiences
(Caru` and Cova, 2003; Caru` and Cova, 2007). Indeed, Schmitt (1999) states that ‘‘as a marketer
you need to provide the right environment and setting for the desired customer experiences to
emerge’’ (Caru` and Cova, 2007).
As the scientific contributions are rich and diverse, so are the different interpretations and
conceptualizations of the Customer Experience offered by each author; nevertheless, despite
the differences of perspective and the various models proposed, one can identify some
common core characteristics of the Customer Experience. First, it has a temporal dimension
which originates from the entire set of contact points (or moments of truth, Carlzon, 1987)
between the customer and the company, or the company’s offer (Addis and Holbrook, 2001;
Caru` and Cova, 2003; LaSalle and Britton, 2003), then it is strictly personal and it involves and
engages a customer at different levels (rational, emotional, sensorial, physical and also
‘‘spiritual’’) so as to create a holistic Gestalt (Brakus, 2001; Schmitt, 1999).
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2.7 Formation of Trust through the utilization of Experience:
As consumer behavior is strongly influenced by perception of risk; consumers are usually
uncertain about the consequences of a decision or an action [Bauer 1976]. Furthermore, it has
been revealed that consumers try to minimize risk rather than maximize utility. A consumer’s
subjective risk perception can thus strongly determine his behavior [Mitchell 1999]. This is
especially true for the adoption of innovations, as consumers lack experience with the new
product and find themselves in a situation of high risk. Consumers therefore try to reduce the
risk associated with a certain behavioral decision. During an adoption decision this can result in
the refusal of an innovation (Bauer et al 2005).
Trust in the environment grows out of the service consumption experience over repeated
interactions with the service provider. These interactions help the customer form perceptions#p#分頁標(biāo)題#e#
about service attributes such as the reliability, availability, and efficiency (Balasubramanian,
Konana et al, 2003). Familiarity leads to an understanding of an entity's current actions while
trust deals with beliefs about an entity's future actions (D. Gefen et al 2000). A consumer's
familiarity based on previous good experience with the vendor's services should cause the
consumer to develop concrete and favorable ideas of what to expect in the future (D.J. Kim et
al, 2008).
2.8 Importance of Trust in reducing the associated risk with modern services:
New services users tend to have concerns about data manipulation, unauthorized data access,
and unwanted tracking of usage patterns. Another security issue concerns consumers’ privacy.
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By using the mobile medium it is possible for marketers to reach consumers anytime and
anywhere but it also poses the privacy issues as well (Bauer et al 2005). The study identifies
perceived privacy concerns as the strongest influence on trust, followed by perceived
reputation and perceived size of the Company. In general, trust increases over familiarity with
the retailer whereas the influence of perceived privacy has the same importance over different
levels of familiarity. (Teltzrow et al, 2007).
For many mobile consumers, loss of privacy is a main concern (D.J. Kim et al, 2008). The illegal
collection and sale of personal information could harm legitimate consumers in a variety of
ways, ranging from simple spamming to fraudulent credit card charges and identity theft (P.
Ratnasingham et al 1998).In a recent survey, 92%of survey respondents indicated that they do
not trust companies to keep their information private even when the companies promise to do
so (D.A. Light et al 2001). These increasing consumer concerns are forcing cell phone providers
to adopt privacy protection measures to increase their perceived trustworthiness and thereby
to encourage transactions (D.J. Kim et al, 2008). Similarly, consumers often perceive that one of
the obligations of a seller is that the seller should not share or distribute the buyer's private
information. Consequently, if buyers perceive that the seller is unlikely to protect their privacy,
they will perceive greater risk concerning the transaction with the seller (D.J. Kim et al, 2008).
Good reputation of selling party plays a vital role in the acceptance of new communication
technologies (D.J. Kim et al, 2008).
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2.9 Significance of Trust in Customer Satisfaction:
Mayer et al. (1995, p. 712)Trust as "the willingness of a party to be vulnerable to the actions of
another party based on the expectation that the other will perform a particular action
important to the trustor, irrespective of the ability to monitor or control that other party." We#p#分頁標(biāo)題#e#
http://www.400190.com/Thesis_Writing/Marketing/refer to trust as “individual-level internalization of norms of reciprocity, which facilitates
collective action by allowing people to take risks and to trust that fellow citizens will not take
advantage of them” [Grabner-Kräuter and Kaluscha 2003, p. 672]. Researchers have suggested
that trust may play a central role in customer satisfaction (Urban et al. 2000). We model trust
as an endogenously formed entity that ultimately impacts customer satisfaction, and we
elucidate the linkages between trust and other factors related to the performance of the
service provider and to the service environment (Balasubramanian, Konana et al, 2003). In
these uncertain situations, when consumers have to act, trust comes into play as a solution for
the specific problems of risk (N. Luhmann et al 1988). Trust becomes the crucial strategy for
dealing with an uncertain and uncontrollable future. As Gambetta argued, trust is particularly
relevant in conditions of ignorance or uncertainty with respect to the unknown or unknowable
actions of others. SERVQUAL (Zeithaml et al. 1990) presents a good prospect to study service
quality but inclusion of Trust will increase its performance.
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Figure 2.1: SERVQUAL
We slightly deviate from existing models of customer satisfaction (e.g., SERVQUAL) by including
trust as an endogenous variable within a study of experience-based customer satisfaction
(Balasubramanian, Konana et al, 2003). Plank et al. recognized that consumer trust could have
multiple referents — salesperson, product, and company — and accordingly defined trust as a
global belief on the part of the buyer that the salesperson, product, and company will fulfill
their obligations as understood by the buyer. Mayer et al. defined trust as a behavioral one
person based on his/her beliefs about the characteristics of another person. Based on this
definition, Mayer et al. also proposed a model of dyadic trust in organizational relationships
that includes characteristics of both the trustor and trustee that influence the formation of
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trust but that is out of the scope of this study. Scholars further assert that for customers to be
satisfied; it is essential that after achieving customers trust, also provide him/her with
anticipated Experience or even exceed the expectations (Balasubramanian, Konana et al, 2003).
Experience-based trust formation is also more likely because customers find it difficult to set
pre-consumption expectations of service quality in the product (Zeithaml et al. 2000). Trust will
also help the service providers by reducing the gap between Expected Services and Perceived
Services. In the presence of trust it is easy for customer and service providers both to#p#分頁標(biāo)題#e#
understand each other’s view point and create long-term relations.
Figure 2.2: SERVQUAL with Trust
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2.10 Customer Satisfaction and Consumption of Experience:
‘‘The Customer Experience originates from a set of interactions between a customer and a
product, a company, or part of its organization, which provoke a reaction (LaSalle and Britton,
2003; Shaw and Ivens, 2005). This experience is strictly personal and implies the customer’s
involvement at different levels (rational, emotional, sensorial physical and spiritual) (LaSalle and
Britton, 2003; Schmitt, 1999). Its evaluation depends on the comparison between a customer’s
expectations and the stimuli coming from the interaction with the company and its offering in
correspondence of the different moments of contact or touch-points (LaSalle and Britton, 2003;
Shaw and Ivens, 2005). While customer satisfaction refers to the summary psychological state
arising immediately from consumption experience (Oliver 1997). Together, overall quality and
satisfaction can provide summary rational and emotional assessments of the experience (Oh
1999). Furthermore, satisfaction, which can be viewed as a main precursor of purchase-related
attitudes, is known to result from positive arousal and affect after consuming both utilitarian
and hedonic experiences (see Mano and Oliver 1993; Oliver, Rust, and Varki 1997; Voss,
Spangenberg, and Grohmann 2003).
For many years scholars have studied service quality and customer satisfaction in physical
environments that involve face-to-face interactions between service personnel and customers
(e.g., Parasuraman et al. 1988; Oliver 1980, 1999). Customer satisfaction depends on derived
value (Anderson et al. 1994), where value may be defined as the "fairness of the level of
economic benefits derived from usage in relation to the level of economic costs" (Bolton and
Lemon 1999, p. 172). Even with high levels of perceived trustworthiness and competence,
customers can be dissatisfied if they perceive the prices to be high. Hence, a study of
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satisfaction is incomplete without incorporating the customer’s evaluation of prices paid for
transactions (Balasubramanian, Konana et al, 2003). Heejin et al, has developed a research
model to identify multi-dimensions of mobile service quality and perceived value, and
investigated their influences on satisfaction and loyalty. It has been figured out that two
dimensions of perceived value (i.e. economic value, emotional value) had significant influence
on customer satisfaction and loyalty.
Perceived quality is overall excellence of the experience (Zeithaml 1988). Furthermore,
satisfaction improves with the increased level of trust, which can be viewed as a main precursor
of purchase-related attitudes. Satisfaction is also known to result from positive arousal and#p#分頁標(biāo)題#e#
affect after consuming both utilitarian and hedonic experiences (see Mano and Oliver 1993;
Oliver, Rust, and Varki 1997; Voss, Spangenberg, and Grohmann 2003). Consumers also tend to
make purchasing decisions based on peer recommendations, direct experiences and degree of
trust (Gentile et al, 2007).
2.11 Staging different Experiences on the basis of trust for Various Market Segments:
We believe market segmentation which was, introduced by Smith’s seminal 1956 article and
has become the subject of an extensive literature provides excellent means for staging the new
and unique experiences for different segments of consumers and avenues to companies for
growth. As customers already disposed trust in the company it’s much easier to put up the
settings for preferred experiences to emerge (see, among others, Dickson & Ginter, 1987;
Fabris, 1972; Frank, Massy, & Wind, 1972; Green, 1977; Haley, 1968, 1971, 1984; Saporta, 1976;
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Wedel & Kamakura, 2003; Wind, 1978; Yankelovich, 1964). A market segment consists of a
group of consumers who share a similar set of needs and wants (Kotler et al. 2006).
This provides Marketers with better insight of market. Segment market offers key benefits over
Mass marketing (Kotler et al. 2006). Companies can presumably better design, price, disclose
and deliver the service to satisfy the target market segment (Kotler et al. 2006). It ‘‘consists of
viewing a heterogeneous market as a number of smaller homogeneous markets in response to
differing product preferences among important market segments. In the literature there are
several taxonomies of variables used for market segmentation such as demographics, life style,
life stage etc (Frank et al., 1972, p. 27; Saporta, 1976; Wedel & Kamakura, 2003, p. 7).
2.12 Segmentation Analysis:
Demographic segmentation divides market into groups on the basis of variables such as age,
gender, occupation etc (Kotler et al. 2006). Consumer wants and abilities change with age as
well as segments differ from each other in their behavior. Also men and women tend to have
different attitudinal and behavioral orientations based partly on genetic makeup and partly on
socialization i.e. women tend to be more communal minded and men tend to be more self
expressive and goal directed; women tend to take in more of the data in their immediate
environment; men tend to focus on the part of the environment that helps them achieve a goal
(Kotler et al. 2006). It is attributable to the desires of consumers or users for more precise
satisfaction of their varying wants’’ (Smith, 1956, p. 6). Thus the partitioning of the market into
different segments—i.e., different groups of consumers—is due to the fact that there exist
differences among consumers in the demand for products and services. This calls for the#p#分頁標(biāo)題#e#
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26
marketers to stage different experiences for different groups or segments of consumers (Pine
and Gilmore 1998, 1999, 2002).
Consumer’s lifestyle and occupation give general indications on values and psychological
characteristics of individuals, besides providing socio-demographic indicators, spending
behaviors, and mass media exposure. The other two dimensions, product/service attributes
and use motivations, assume specific significance in relation to a definite product/service.
However, unlike the traditional benefit segmentation (Haley, 1968, 1971, 1984) as well as some
more recent works (Ratneshwar, Warlop, Mick, & Seeger, 1997; Wu, 2001), it is believed that
there exists a distinction between the product/service attributes preferred by consumers and
individuals’ use motivations. Indeed, the two dimensions give different information in the
segmentation analysis: use motivations concern consumers’ needs, while attributes are the
characteristics of the product/service that influence consumers’ choices among the different
models and brands in the market (C. Mazzoni et al, 2007).
Segmentation analysis will also help in Predicting churn, i.e. if a customer is about to leave for a
competitor, is an important application of analyzing customer behavior (Sadia et al. 2007). It is
typically much more expensive to acquire new customers then to retain existing ones (Yan, L.,
Miller et al, 2001). Correctly predicting that a customer is going to churn and then successfully
convincing him to stay can substantially increase the revenue of a company (Frank Eichinger et
al). As promotion of sustainable consumption behavior has proved to be an arduous task
(Grunert, 1993; Pieters, Bijmolt, van Raaij, & de Kruijk, 1998; Cornelissen et al 2008; European
Commission, 2005; DEFRA, 2002). Yankee Group also indicated that mobile operators estimate
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the cost of acquiring a new client at seven times more than the annual cost of retaining an
existing subscriber on an average basis (Sadia et al. 2007, Yankee group 2001). This calls for the
more precise segmented analysis of Customers to keep them loyal and provide them with
desired experience or services conducive for desired experience to emerge.
2.13 Conclusions:
Inspecting the aforementioned preliminary research findings, it is concluded that the need for
consumer behavior research efforts in the strategic market planning and to improve consumers
Loyalty is crucial for further growth. Especially consumers tend to have low trust in modern
services and also on service providing companies accompanied with stiff competition in mobile
sector from other rivals. Emerging Experience paradigm can provide the businesses with the
opportunity to win the trust of customers (Shaw and Ivens, 2005, Sadia et al. 2007), as#p#分頁標(biāo)題#e#
differentiating solely on the traditional elements, like price, product and quality, is no longer a
competitive ground. Once trust is present between company and customers, it’s easy to
establish competitive advantage on the basis of experience. As Joseph Schumpeter termed itthat:
business innovation which threatens to render irrelevant those who relegate themselves
to the diminishing world of goods and services (Joseph Schumpeter, Pine & Gilmore 1998).
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CHAPTER 3: RESEARCH METHODOLOGY
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Introduction:
This chapter will cover the methodology used in this research. The selection of research
methodology is based on the specific research questions. The reasons behind all adopted
methodological approach will be describe in each section.
3.2 Questionnaire Development:
A consumer survey was conducted towards meeting the objectives of the present study. An
online questionnaire constituted the data collection instrument, while only Internet users
participated in the sample (Malhotra and Birks, 2000). The choice of the questionnaire as
investigation means has been taken both on the basis of some precedents (as the already
mentioned work by Calder and Malthouse, 2006) and on the ground of the fact that data
collected through questionnaires permit the use of specific statistical analyses, which can be
applied to explore the internal structure of the Customer Experience and Trust, as it has been
conceptualized in our study (Gentile et al, 2007).
It is evident, therefore, that investigating consumer needs, wishes, preferences, attitudes,
characteristics, behaviors, etc., through corresponding consumer surveys, constitutes the only
reliable method towards achieving this business objective (Green et al., 2001). In addition,
Kotler (2000) states, that customer constitute a basic source that provides the requirements to
the organization regarding the development of marketing strategies.
3.2.1 Composition:
In this research, a self-administered questionnaire is employed. Saunders (1997: 244) points
out that ‘questionnaires will enable us to identify and describe the variability in different
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phenomena. In this sense, seven different sections are conducted for different aims of this
research respectively. The Questionnaire was adopted from previous research for the added
reliability and authenticity. We have also used seven point ‘Likert Scale’ “in which respondents
were asked to indicate their degree of agreement or disagreement on a symmetric agreedisagree
scale for each of a series of statements”, due to having a median and facilitating
analysis (Burns and Bush, 2006: 306).
Questionnaire was divided in seven sections; First Section is based on Warm up questions to#p#分頁標(biāo)題#e#
acquaint with respondent (adopted from Flynn and Goldsmith, 1999). Second Section deals
with the general reputation of service provider (D.J. Kim et al. JULY 2007, D. Gefen 2000, S.L.
Jarvenpaa, N. Tractinsky, M. Vitale 2000). Third section addresses the Protection of Consumer’s
Private data (D.J. Kim et al. 2008, Balasubramanian et al. 2003, Jarvenpaa et al. 2000, D Gefen
2000). Fourth Section asks about how different aspects of customer experience are emphasized
or not emphasized by service providers. Moreover mobile phone service providers differ from
one another in the extent to which they emphasize or focus on various aspects of customer
Experience (Oyvind a Bjertnaesa, Andrew Garrattb and John Nessa, 2007). In Fifth Section, it
measures Consumer’s level of trust on the company (D.J. Kim et al. 2008, Balasubramanian et
al. 2003, Jarvenpaa et al. 2000, D Gefen 2000). Sixth Section investigates about the acquired
benefit from that experience (D.J. Kim et al. 2007, V. Swaminathan et al. 1999). In Seventh
Section, customers are asked to find about their overall attitudes towards Network service
providers in terms of different attributes that shape their level of Satisfaction and level of
motivation for peer recommendation (Balasubramanian et al. 2003).
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Ultimately, these sections are to identify correlation between customer’s expectation and their
experience. Last section classifies the consumers according to age group, gender and by
profession, which can be helpful in segmented marketing or to target them on more
personalized level. Demographics will also help us in studying level of satisfaction, trust and
quality of overall experience across different networks.
3.2.2 Pre-Testing:
In pre-testing, before using our questionnaire, we adopt 6 samples in total in order ‘to refine
the questionnaire so that respondents will have no difficulties in answering the questions and
there will be no problems in recording the data’ (Saunders et al., 1997: 269). The Questionnaire
was adopted from numerous Journal Articles for the precedence with previous research work.
3.2.3 Online Survey:
A consumer survey was conducted towards meeting the objectives of the present study. An
online questionnaire constituted the data collection instrument, while only Internet users
participated in the sample. The questionnaire was launched online at July 11, 2008 and
responses were collected till August 01, 2008 almost for three weeks.
The market research methodology included the following steps (Churchill, 1999):
• Development and Refinement of the questionnaire
• Installation of the questionnaire into an online format and pre-testing for identification of
possible problems in clarity, comprehensiveness, accuracy and functionality, before it was given#p#分頁標(biāo)題#e#
out to the sample.
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• Informing the target group about the survey via the Internet and other means (e.g., Phone,
Chat).
• Execution of the survey and collection of answers in a database
• Extraction of the data and statistical analysis. (Adam Vrechopoulos, Ioanna Constantiou, Nikos
Mylonopoulos, Ioannis Sideris, 2002)
3.3 Sampling:
The sampling technique used was convenience sampling. According to Malhotra and Birks
(2000), this sampling technique can be used in exploratory research designs like the present
one. Mostly this technique is used to improve motivation in respondents and for easy access.
Sample includes respondents from three major cities of Pakistan named as Lahore, Karachi and
Islamabad. Respondents are primarily the customers of four major companies named mobilink,
ufone, warid and telenor. There were four age groups i.e. 16-25, 26-35, 36-45 and 46-
above.The respondents were from the different walks of life for diverse feedback. We made a
conscious effort to make sample as balance as possible i.e. at least 25 responses for each
service provider, balance sample in terms of gender i.e. near to 50% for MALE and FEMALE
and even spread of sample over different age groups.
Our target of 150 respondents could not happen and instead 128 respondents have been
asked. 11 out of 128 responses were incomplete so only 117 responses were included for
analysis. 11 incomplete questionnaires were not used for any kind of analysis for the sake of
reliability and consistency.
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3.4 Instrument Used:
Below is the instrument used for this research and what it intends to measure in this research.
Scale and Items Intended to Measure
Awareness about Mobile Communication
I have a profound knowledge about mobile
communications.
In my circle of friends, I am an expert in mobile
communications.
In my circle of friends I am usually the first to know
about the latest mobile Phones
Warm up Questions
Mobile Network Reputation (MNREP)
(MNREP1)This Network Service Provider is well
known.
(MNREP2)This Network Service Provider has a good
reputation.
(MNREP3)This Network Service Provider has a
reputation for being honest.
Reputation of Company and
Perception of Customers
Mobile Network Perceived Privacy Protection
(MNPPP)
(PPP1)My Service Provider is collecting too much
Personal Information.
(PPP2)This Service Provider will use my personal
information for other purposes without my
authorization.
Consumer’s Perceived Privacy
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(PPP3)This Service Provider will share my personal
information with other entities without my
authorization.
Concerns
Mobile Network Experience (MNEXP)#p#分頁標(biāo)題#e#
(MNEXP1)Good competence to assess and treat
Customers at this Service Provider.
(MNEXP2)Good professional advice from the Service
Provider.
(MNEXP3)Ease of contact with the Service Provider in
critical situations.
(MNEXP4)Help from the Service Provider in critical
situations.
(MNEXP5)Good coverage of Service Provider.
(MNEXP6)The experiences with this Service Provider
met my expectations.
Consumer’s Experience of
Mobile Network
Mobile Network Perceived Trustworthiness
(MNPTR)
(MNPTR1)My Mobile Phone Operator is trustworthy.
(MNPTR2)My Mobile Operator gives the impression
that it keeps promises and commitments.
(MNPTR3)My Mobile Phone Operator is truthful and
open about the costs.
(MNPTR4)My Mobile Phone Operator has the best
Degree of Trust after
experiencing the Network
Service.
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interests of Customers in mind.
(MNPTR5)My Mobile Phone Operator has a
reputation for fair practices.
(MNPTR6)My Mobile Phone Operator always provides
the best price for my Calling Plan.
Perceived benefit (BNFIT)
(BNFIT1)I can save money by using this Mobile Phone
Service.
(BNFIT2)I can save time by using this Mobile Phone
Service.
(BNFIT3)Using this Mobile Phone Service increases my
productivity in Work (e.g., Stay in touch with people,
Access to information at all times).
Benefit from Experience and
overall Quality.
Satisfaction (STFN)
(STFN1)Overall, I am satisfied with my Mobile
Network.
(STFN2)How likely are you to recommend your Mobile
Network to friends?
Level of Satisfaction and
likelihood of Recommendation.
Table 3.4 Instrument Used for Survey
3.5 Limitations to Primary Sources:
In this research, grouping respondents into only three different groups may have certain
limitations. Also this study only focuses on the three major cities of Pakistan due to the limited
time period. We also encountered difficulties in reaching respondents since it was coincided
through net; it implied that there were fewer respondents. Therefore our target of 150
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respondents could not happen and instead 128 respondents have been asked. 11 out of 128
responses were incomplete so only 117 responses were included in analysis which is
substantially low from our target.
3.6 Secondary Sources:
Primarily, academic publications and journals are used in order to patronize our research
results and findings, and to effectively verify our research. Secondary data is also available from
reliable sources such as PTA, MobilinkGsm, Ufone, Telenor and Warid telecom with some
restrictions. Numerous other independent and reputable resources are available for this
purpose as well which includes journals, articles, magazines and news papers.#p#分頁標(biāo)題#e#
3.6.1 Limitations to Secondary Sources:
A very small minority of the Internet data, which are up-to-date but relatively less reliable due
to its anonymity, are collected from several websites. Nevertheless, it is attempted to avoid
certain bias by complementing the data with valid and reliable. In addition to that, by applying
this integrated way of approach to this research, it is intended to achieve significant and
meaningful conclusions.
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CHAPTER 4: DATA ANALYSIS
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38
Introduction:
This chapter will cover the data analysis used in this project. We will use different statistical
processes such as descriptive statistics, Association between constructs, T-Test, ANOVA, Factor
Analysis and Linear regression analysis to draw meaningful analysis from data. The reasons
behind all adopted processes will be describe in each section.
4.2 Descriptive Statistics:
Descriptive statistics are used to describe the basic features of the data in a study. They provide
simple summary about the sample and the measures. Together with simple graphics analysis,
they form the basis of virtually every quantitative analysis of data (Trochim et al. 2007).
The Survey questionnaire was divided in the seven Sections measuring different aspects of
Customer Satisfaction. Section A: Mobile Network Reputation is based on three constructs,
investigates about the respondent’s perception or reputation of Mobile Phone Service
Providers in Pakistan; this focused in particular on respondent’s perception of company before
their buying or use of a particular network. Section B: Perceived Privacy Protection also consists
of three constructs, looks into the Privacy Protection of Customers; particularly respondent’s
perception during the process of purchase and after that. Section C: Mobile Network
Experience is made of six constructs, inspects respondent’s experience about the service of
company. Section D: Mobile Network Perceived Trustworthiness is composed of six constructs,
studies Consumer’s level of trust about the service provider. Section E: Perceived Benefit is
made of three constructs, explores about the perceived benefit of customer as he/she uses the
service. Section F: Satisfaction is also formed from two constructs, takes a look into the level of
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satisfaction and likeliness to recommend a company. The results are based on 117 respondents,
58 of which were male and 59 of which were female. In this instance, we utilized a Seven Point
‘Likert Scale’ i.e. 1= Low & 7= High and for Satisfaction -3= Low and 3= High, due to having a
mean and facilitating analysis.
4.2.1 Overall Mean and Standard Deviation:
Scale
Items in a
Scale
N Mean Standard
Deviation#p#分頁標(biāo)題#e#
Alpha
Reputation 3 117 5.3162 1.0844 0.806
Privacy 3 117 4.5214 1.4877 0.852
Experience 6 117 4.9943 1.1691 0.928
Trust 6 117 4.9473 1.1740 0.925
Benefit 3 117 5.1026 1.3090 0.839
Satisfaction 2 117 1.4530 1.2865 0.870
Table 4.2.1: Overall Mean Scores
The table shows that companies scored highest in Reputation and Benefit with respective
means of 5.3162 and 5.1026. Better reputation score means generally companies have good
standing in customers and customers do realize the benefits of their services. Customers
experience services at 4.9943 and their level of trust is at 4.9473. Experience and trust scored
comparatively lower then Reputation means there are some differences in actual customer
experience, level of trust and reputation of companies. Customers feel that companies do take
little care about their privacy at mean score of 4.5214 which is low and people increasingly
show their concern about privacy. They are generally satisfied with the companies at 1.4530
which means that customers are happy with the service providers but there is a lot to do
specially with low scores in experience and trust.
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Graph: 4.2.1 Mean Scores
4.2.2 Gender Wise Classification:
The Section 4.2.2 Classify the different factors involved in Customer Satisfaction by Gender. This
section presents if there exist any difference among the customers regarding Service providers
or not due to their Gender. The results from ‘Section 4.2.2’ are based on 117 respondents, 59 of
which were Female and 58 of which were male. Next is a table which will further elaborate the
gender wise classification.
Gender N Reputation Privacy Experience Trust Benefit Satisfaction
Female 59 5.4011 4.5028 5.1893 5.1893 5.2768 1.6525
Male 58 5.2299 4.5402 4.7960 4.7011 4.9253 1.2500
Total 117 5.3162 4.5214 4.9943 4.9473 5.1026 1.4530
Table: 4.2.2 Gender Wise Scores
The table shows that companies scored highest in Reputation and Benefit with respective
means of 5.4011 and 5.2768 for females. Males feel the same way but with slightly low mean
scores of 5.2299 for Reputation and 4.9253 for Benefits. Females experience services and their
level of trust, both are at 5.1893. Males experience services at 4.7960 and their level of trust is
at 4.7011. Statistics also shows that females are more satisfied at 1.6525 in comparison to
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males i.e. 1.2500. Both females and males are concern about their privacy at almost similar
level.
Pie Chart: 4.2.2.1 Gender Wise Classification in Percentage:
Graph: 4.2.2.2 Gender Wise Classification Mean Scores:
4.2.3 Profession Wise Classification:
The Section 4.2.3 Classify the different factors involved in Customer Satisfaction by Profession.
This section presents if there exist any difference among the customers regarding Service#p#分頁標(biāo)題#e#
providers or not due to different professions. The results from ‘Section 4.2.3’ are based on 117
respondents; 23 were businessman/woman, 39 were professional, 33 were students, 20 were
housewives and 2 were from other professions.
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42
Profession N Reputation Privacy Experience Trust Benefit Satisfaction
Business 23 5.7246 4.5072 5.5870 5.1449 5.6087 1.6304
Job 39 5.1538 4.5983 4.8419 4.9188 5.0000 1.3333
Student 33 5.1818 4.4949 4.7475 4.7879 4.7677 1.3636
House wife 20 5.4167 4.4833 5.1833 5.2083 5.6087 1.6304
Others 02 5.0000 4.0000 3.3333 3.2500 4.5000 1.0000
Total 117 5.3162 4.5214 4.9943 4.9473 5.1026 1.4530
Table 4.2.3 Profession wise Classification:
The table shows that Business sector is mostly satisfied with service providers with
comparatively better mean scores except for Privacy which was 4.5072. Next, Housewives are
also satisfied with some concerns about privacy i.e. 4.4833. Professionals and Students are
least satisfied as mean scores are low but both of them shows less concern for privacy in
comparison to Business and Housewives. Rests of the customers are not satisfied at all with
their service providers as their mean scores are substantially low or in negative for most of the
factors.
Pie Chart 4.2.3.1: Profession wise Classification in Percentage:
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43
Graph 4.2.3.2: Profession wise Classification with Means:
4.2.4 Age Wise Classification:
The Section 4.2.4 Classify the different factors involved in Customer Satisfaction by Age. This
section presents if there exist any difference among the customers regarding Service providers
or not due to their Age. The results from ‘Section 4.2.4’ are based on 117 respondents; 43 were
from the age group ‘16-25’, 29 were from the age group ‘26-35’, 22 were from the age group
’36-45’ and 23 were from the age group ‘46-above’.
Age N Reputation Privacy Experience Trust Benefit Satisfaction
16-25 43 5.1163 4.5349 4.7946 4.7752 4.8062 1.3605
26-35 29 5.1954 4.5632 4.8736 4.8563 5.0230 1.2241
36-45 22 5.5303 4.4848 5.3333 5.2121 5.5000 1.6136
46-above 23 5.6377 4.4783 5.1957 5.1304 5.3768 1.7609
Total 117 5.3162 4.5214 4.9943 4.9473 5.1026 1.4530
Table 4.2.4: Age Wise Classification
The table shows that Customers from the age groups ’36-45’ and ’46-above’ are generally
satisfied with the services provided by the companies as their mean scores are higher than the
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44
other groups yet they are conscious about privacy protection. People from the age groups of
’16-25’ and ’26-35’ are less satisfied with the service of mobile phone companies as their mean
scores are low and their level of satisfaction is also low in comparison to the other two groups.#p#分頁標(biāo)題#e#
Pie Chart 4.2.4.1: Age Wise Classification in Percentage
Graph 4.2.4.2: Age Wise classification with Means
4.2.5 Company Wise Classification:
The Section 4.2.5 Classify the different factors involved in Customer Satisfaction by service
providers. This section presents if there exist any difference among the customers regarding
Service providers or not. The results from ‘Section 4.2.5’ are based on 117 respondents; 27
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45
were from the ‘Mobilink’, 32 were from the ‘Ufone’, 27 were from the ‘Warid’ and 28 were
from the ‘Telenor’.
Company N Reputation Privacy Experience Trust Benefit Satisfaction
Mobilink 27 4.8889 4.0864 4.8765 4.2593 4.5679 1.0741
Ufone 32 5.4479 4.8958 4.8854 5.1458 5.1771 1.6094
Warid 27 5.5432 4.7407 5.3765 5.3765 5.4691 1.8704
Telenor 28 5.5119 4.3690 5.0000 5.0833 5.3810 1.4286
Others 03 3.8889 3.8889 3.7222 3.8889 3.2222 -0.3333
Total 117 5.3162 4.5214 4.9943 4.9473 5.1026 1.4530
Table 4.2.5: Company wise Classification
The table shows that Warid Scores highest in Reputation with mean of 5.5432, Experience with
the mean score of 5.3765, Trust also with the mean score of 5.3765 and Satisfaction at
1.8704.Ufone and Telenor comes next with similar mean scores for Reputation, Privacy,
Experience, trust and Benefit. Ufone scores slightly better in overall satisfaction in comparison
to Telenor. Mobilink got the lowest mean scores in all dimensions just above the other small
service providers.
Pie chart 4.2.5.1: Company wise classification in percentage
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46
Graph 4.2.5.2: Company wise classification with Means
4. 3 Scale Items, Reliability and Validity:
Table 4.3 shows the descriptive statistics for the constructs, the reliability (Cronbach's alpha) of
the scales and the items included in a scale. The Cronbach reliability coefficients of all variables
were higher than the minimum cutoff score of 0.60 (J C Nunnally et al. 1978), 0.65 (J N Lee et al.
1999) or 0.70 (J C Nunnally et al. 1978, Nunnally & Bernstein, 1994). Construct validity was
examined by assessing convergent validity. Convergent validity is considered acceptable when
all item loadings are greater than 0.50 (Wixom et al. 2001), and the items for each construct
load onto only one factor with an eigenvalue greater than 1.0. As noted in Table 4.3, the items
for each construct did indeed load onto only one factor with an eigenvalue greater than 1.0 (D.J.
Kim et al. 2007).
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47
Construct Items in
a Scale
Alpha Eigen
Values
Sources
Reputation 3 0.806 2.191 D.J. Kim et al., Gefen et al., Jarvenpaa et al., Vitale et al.
Privacy 3 0.852 2.319 D.J. Kim et al., Balasubramanian et al., Jarvenpaa et al., Gefen et al.#p#分頁標(biāo)題#e#
Experience 6 0.928 4.445 Oyvind et al., Garrattb et al.
Trust 6 0.925 4.380 D.J. Kim et al., Balasubramanian et al., Jarvenpaa et al., Gefen et al.
Benefit 3 0.839 2.310 D.J. Kim et al., Swaminathan et al., Vitale et al.
Satisfaction 2 0.870 1.771 Balasubramanian et al.,
Table 4.3: Instrument Reliability & Validity
4.3.1 Mobile Network Reputation (MNREP):
Three items were used to measure reputation or perception about companies in the market.
Consumers were asked questions about honesty of companies, good reputation and how many
people were aware about the companies. The resulting Cronbach Alpha for the scale was 0.806
based on three questions asked.
4.3.2 Perceived Privacy Protection (MNPPP):
Three items were used to measure the customer’s perception about privacy protection for
mobile companies. Consumers were asked questions about collection of personal information,
unauthorized sharing of this information and unauthorized use of this information. Cronbach
Alpha for the scale was 0.852 based on three questions asked.
4.3.3 Mobile Network Experience (MNEXP):
Six items were used to measure the Customer’s Experience of companies. Customers were
asked questions about, competence to access consumer’s need, professionalism in advice, ease
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48
of contact in critical situations, the help in critical situation, the overall coverage of network and
level of expectations met. Cronbach Alpha was 0.928 based on six questions asked.
4.3.4 Mobile Network Perceived Trustworthiness (MNPTR):
Six items were used to measure consumer’s level of trust on companies about, their general
perception of service provider, the costs, the best interests of the customers in mind, the
reputation for fair practices, the best prices, the commitments. The resulting scale had a
Cronbach Alpha of 0.925 based on six questions asked.
4.3.5 Satisfaction (STFN):
The two items were used to measure overall satisfaction with the mobile companies, and the
customer's willingness to provide word-of-mouth recommendations. The Cronbach Alpha for
the scale was 0.870.
4.3.6 Perceived benefit (BNFIT):
Three items were used to measure the customer’s perceived benefits from service providers.
They were asked questions about, saving money by using a particular service provider, saving
time by using the particular service provider i.e. no or less congestion, reliable connectivity etc
and increased productivity. The Cronbach Alpha was 0.839 based on the three questions asked.
4.4 Factor Analysis:
One of the major objectives of market research is to summarize the data into meaningful
information through the theoretical formulation and by the empirical relationships among the
given set of variables or events (Gorsuch et al, 1983). The Variables or Events that can be#p#分頁標(biāo)題#e#
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49
measured are almost infinite so any general statement about research activities is difficult to
make (Gorsuch et al, 1983). Factor Analysis presents us the prospect to explicitly recognize any
limitations in relationships to a particular area of applicability (Gorsuch et al, 1983). Factor
analysis has generally been used for exploratory purposes. It also provides the opportunity to
analyze numerous variables at same time, as well as relationships among them (Gorsuch et al,
1983). Table 4.4.1 gives an overview about the Factors, Variables with Factor Loading and
sources with adopted variables.
Scale and Items Factor Loading Sources
Awareness about Mobile Communication
I have a profound knowledge about mobile
communications.
In my circle of friends, I am an expert in mobile
communications.
In my circle of friends I am usually the first to know
about the latest mobile Phones
N/A
N/A
N/A
Flynn and Goldsmith
[1999]
Mobile Network Reputation (MNREP)
(MNREP1)This Network Service Provider is well
known.
(MNREP2)This Network Service Provider has a good
reputation.
(MNREP3)This Network Service Provider has a
reputation for being honest.
0.880
0.891
0.775
D.J. Kim et al. [JULY
2007], D. Gefen [2000],
S.L. Jarvenpaa, N.
Tractinsky, M.
Vitale[2000]
Mobile Network Perceived Privacy Protection
(MNPPP)
(PPP1)My Service Provider is collecting too much
Personal Information.
0.803
D.J. Kim et al. [JULY
2007],
BALASUBRAMANIAN,
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(PPP2)This Service Provider will use my personal
information for other purposes without my
authorization.
(PPP3)This Service Provider will share my personal
information with other entities without my
authorization.
0.898
0.888
KONANA, AND MENON
[July 2003], S.L.
Jarvenpaa, N. Tractinsky,
M. Vitale [2000], D Gefen
[2000]
Mobile Network Experience (MNEXP)
(MNEXP1)Good competence to assess and treat
Customers at this Service Provider.
(MNEXP2)Good professional advice from the Service
Provider.
(MNEXP3)Ease of contact with the Service Provider in
critical situations.
(MNEXP4)Help from the Service Provider in critical
situations.
(MNEXP5)Good coverage of Service Provider.
(MNEXP6)The experiences with this Service Provider
met my expectations.
0.78
0.72
0.84
0.89
0.74
0.76
Oyvind a Bjertnaesa,
Andrew Garrattb and
John Nessa, june [2007]
Mobile Network Perceived Trustworthiness
(MNPTR)
(MNPTR1)My Mobile Phone Operator is trustworthy.
(MNPTR2)My Mobile Operator gives the impression
that it keeps promises and commitments.#p#分頁標(biāo)題#e#
(MNPTR3)My Mobile Phone Operator is truthful and
open about the costs.
(MNPTR4)My Mobile Phone Operator has the best
interests of Customers in mind.
(MNPTR5)My Mobile Phone Operator has a
reputation for fair practices.
(MNPTR6)My Mobile Phone Operator always
0.899
0.910
0.813
0.830
0.810
D.J. Kim et al. [JULY
2007],
BALASUBRAMANIAN,
KONANA, AND MENON
[July 2003], S.L.
Jarvenpaa, N. Tractinsky,
M. Vitale [2000], D Gefen
[2000]
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provides the best price for my Calling Plan.
0.803
Perceived benefit (BNFIT)
(BNFIT1)I can save money by using this Mobile Phone
Service.
(BNFIT2)I can save time by using this Mobile Phone
Service.
(BNFIT3)Using this Mobile Phone Service increases my
productivity in Work (e.g., Stay in touch with people,
Access to information at all times).
Satisfaction (STFN)
(STFN1)Overall, I am satisfied with my Mobile
Network.
(STFN2)How likely are you to recommend your
Mobile Network to friends?
0.631
0.871
0.823
0.918
0.901
D.J. Kim et al. [JULY2007],
V. Swaminathan, E.
Lepkowska-White, B.P.
Rao[1999]
BALASUBRAMANIAN,
KONANA, AND MENON
[July 2003]
Table 4.4.1: Factors and Variables with Factor Loading
Next is a ‘Factor Matrix’ which is showing the relationship of variables to factors. Factor Matrix
presents Factors, variables with factor loading and variables with related factors. The factor
matrix contains score where substantial relationship occurs and blank space where the variable
is not related. Factor matrix makes it easy to understand the relationship among different
variables with their weight in relating factors.
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FACTORS
Variables REPUTATION PRIVACY EXPERINCE TRUST BENEFIT SATISFACTION
MNREP 1
MNREP2
MNREP3
0.880
0.891
0.775
PPP1
PPP2
PPP3
0.803
0.898
0.888
MNEXP1
MNEXP2
MNEXP3
MNEXP4
MNEXP5
MNEXP6
0.78
0.72
0.84
0.89
0.74
0.76
MNPTR1
MNPTR2
MNPTR3
MNPTR4
MNPTR5
MNPTR6
0.899
0.910
0.813
0.830
0.810
0.803
BNFIT1
BNFIT2
BNFIT3
0.631
0.871
0.823
STFN1
STFN2
0.918
0.901
Table 4.4.2: Factor Matrix
4.5 Association between Constructs:
4.5.1 Correlations:
Correlation measures the degree of linear association between two variables and is
fundamental to linear regression analysis (Hair et al, 1998). It indicates the strength of the
association between dependent and independent variables. Correlation can be either positive#p#分頁標(biāo)題#e#
or negative where positive correlation means that both variables move in same direction and
negative means in opposite direction (Hair et al, 2000).
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Construct Reputation Privacy Experience Trust Benefit
Privacy 0.207
Experience 0.607 0.229
Trust 0.721 0.226 0.722
Benefit 0.548 0.054 0.677 0.631
Satisfaction 0.650 0.051 0.697 0.678 0.744
Table 4.5.1: Correlation between constructs
In this instance we are using the Pearson correlation coefficient (r) to measure the linear
association between different constructs. First we will study the correlations for Reputation
with other constructs. There is a weak, positive correlation exists between privacy and
reputation at [r= 0.207, n=117]. Correlation between reputation and experience is significantly
strong and positive at [r= 0.607, n=117] where good reputation is associated with higher level
of Experiences. There is also a significantly strong and positive correlation between reputation
and trust at [r=0.721, n=117], with higher level of trust is associated with good reputation.
Benefit and reputation is also moderately and positively correlated with each other at [r= 0.548,
n= 117]. Association between reputation and satisfaction is considerably strong and positive at
[r= 0.650, n=117], where higher level of satisfaction is related to good reputation.
Privacy is weakly correlated with experience and trust at [r= 0.229, n=117], [r= 0.226, n=117]
respectively, displaying low significant association with the two constructs. Privacy has no
associations with Benefit and Satisfaction at [r= 0.054, n= 117], [r= 0.051, n=117]
correspondingly.
Experience has a significant strong, positive association with trust at [r= 0.722, n= 117] with
higher level of experience related to higher trust. There is also a strong and positive
relationship among experience and benefit at [r= 0.677, n= 117], where higher level of benefits
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relates to good experience. Satisfaction and Experience is also strongly and positively correlated
with each other at [r=0.697, n= 117], where good experience is associated with higher level of
satisfaction.
Trust has a significantly strong and positive association with two other constructs i.e. Benefit
and Satisfaction at [r=0.631, n=117], [r=0.678, n= 117] respectively, with higher levels of
satisfaction and benefit correlated with higher level of trust. There is also a significant strong,
positive correlation between Benefit and Satisfaction at [r=0.744, n= 117], where higher level of
benefits is associated with higher level of satisfaction.
4.5.2 T-test Gender Wise:
The T-test assesses whether the means of two groups are statistically different from each
other. This analysis is appropriate whenever you want to compare the means of two groups#p#分頁標(biāo)題#e#
(Trochim et al. 2007). In this instance we are using it to measure the differences among
Females and Males in terms of different variables.
Variables Significance P=Sig.(2-tailed) Remarks
Reputation 0.109 0.395 No Statistical Difference
Privacy 0.002 0.893 No Statistical Difference
Experience 0.033 0.700 No Statistical Difference
Trust 0.208 0.024 Significant Statistical Difference
Benefit 0.083 0.148 No Statistical Difference
Satisfaction 0.005 0.092 No Statistical Difference
Table 4.5.2 T-test for Gender
From the above table 4.5.2, it is plain that both males and females are not significantly different
about most of the factors. Except for Trust where [p value= 0.024, n=117], which means both
males and females are statistically different from each other about the Trust.
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4.5.3 ANOVAs:
ANOVA Stands for Analysis of Variables and it is a test of the statistical significance of the
differences among the mean scores of two or more groups on one or more variables. ANOVA is
also used to perform comparisons and tracks the effects of a number of discrete factors
(independent variables), each of which may have a number of levels and may interact to affect
the dependent variable (Keith et al. 1990).
4.5.3.1 Age Wise ANOVA:
We did one way ANOVA test, to see if there are any significant differences among different age
groups or not concerning various factors of customer satisfaction.
Variables Df F Significance Remarks
Reputation Between Groups
Within Groups
3
113
1.591 0.196 No Statistical Difference
Privacy Between Groups
Within Groups
3
113
0.019 0.996 No Statistical Difference
Experience Between Groups
Within Groups
3
113
1.379 0.253 No Statistical Difference
Trust Between Groups
Within Groups
3
113
0.924 0.432 No Statistical Difference
Benefit Between Groups
Within Groups
3
113
1.821 0.147 No Statistical Difference
Satisfaction Between Groups
Within Groups
3
113
0.932 0.428 No Statistical Difference
Table 4.5.3.1: Age wise ANOVA
Subjects were divided into four groups according to their age (Group 1: 16-25; Group 2: 26-35;
Group 3: 36-45; Group 4: 46-above).It is clear from table 4.5.3.1 that all four age groups are
statistically indifferent from each other regarding all aspects of consumer satisfaction.
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4.5.3.2 ANOVAs Profession Wise:
A One-way ANOVA was conducted to explore the impact of profession on the different aspect
of Customer Satisfaction, and to analyze if there are any significant differences prevail in this
regard.
Variables Df F Significance Remarks
Reputation Between Groups
Within Groups
4
112#p#分頁標(biāo)題#e#
1.257 0.291 No Statistical Difference
Privacy Between Groups
Within Groups
4
112
0.091 0.985 No Statistical Difference
Experience Between Groups
Within Groups
4
112
3.413 0.011 Significant Statistical Difference
Trust Between Groups
Within Groups
4
112
1.649 0.167 No Statistical Difference
Benefit Between Groups
Within Groups
4
112
1.766 0.141 No Statistical Difference
Satisfaction Between Groups
Within Groups
4
112
0.436 0.783 No Statistical Difference
Table 4.5.3.2: Profession Wise ANOVA
Subjects were divided into four groups according to their Profession (Group 1: Business; Group
2: Job; Group 3: Students; Group 4: Housewives; Group 5: Others). From the above table
4.5.3.2, it is clear that all five groups were not significantly different about most of the factors.
Except for Experience where [F (4,112) = 3.413, p= 0.011], which means all groups are
statistically different from each other about the Experience by Profession.
4.5.3.3 ANOVAs Company Wise:
We also did one way ANOVA test, to see if there are any significant differences among
Customers of different service providers or not regarding various factors of consumer
satisfaction.
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Variables Df F Significance Remarks
Reputation Between Groups
Within Groups
3
113
3.218 0.015 Significant Statistical Difference
Privacy Between Groups
Within Groups
3
113
1.462 0.218 No Statistical Difference
Experience Between Groups
Within Groups
3
113
1.795 0.135 No Statistical Difference
Trust Between Groups
Within Groups
3
113
4.681 0.002 Significant Statistical Difference
Benefit Between Groups
Within Groups
3
113
3.900 0.005 Significant Statistical Difference
Satisfaction Between Groups
Within Groups
3
113
3.067 0.019 Significant Statistical Difference
Table 4.5.3.3: Company Wise ANOVA
Subjects were divided into four groups according to their company (Group 1: MobilinkGsm;
Group 2: Ufone; Group 3: Warid; Group 4: Telenor; Group 5: Others).It is clear from table
4.5.3.2 that all five groups are statistically different from each other regarding fou