brand loyalty and brand defection - au...
TRANSCRIPT
BRAND LOYALTY AND
BRAND DEFECTION in the Danish Telecom Market
Gry Hjerrild Mikkelsen Department Business Administation
Louisa Thuy Tien Vu Supervisor Polymeros Chrysochou
May, 2014 Characters 78,939
Abstract Purpose
This thesis examines which factors have a significant influence on brand loyalty and
brand defection in the Danish telecommunications market. Based on a number of
identified factors, hypotheses were developed and set up. The purpose was to confirm
(or reject) what previous research has already found out and possibly generalise the
results to the Danish telecommunications market. Additionally, the paper aimed at
identifying other factors affecting brand loyalty and brand defection that have not
already been identified in previous research.
Design/methodology/approach
A quantitative survey was conducted in which participants were asked to rank the
importance of a number of factors on their decision to stay. They were also asked to
rank the likelihood of them defecting from a brand based on a list of factors. The
relevant factors were identified based on previous research in the field. Additionally,
some factors were identified on the basis of what was listed on the websites of Danish
mobile service providers and the patterns observed in the Danish market.
The questionnaire was developed in Danish and distributed through Facebook. The final
data set yielded a sample size of 212 valid answers with the majority of the respondents
being postpaid consumers in their twenties. The data was first reduced by factor
analysis which yielded four factors for brand loyalty (product characteristics, switching
costs, services, and price) and three factors for brand defection (service issues, costs
issues, and customer retention issues). Subsequent analyses were performed by simple
regression analysis.
Findings/implications
The findings support five of the seven proposed hypotheses. Analysis results show that
switching costs and price have a significant negative effect on brand loyalty, and
services have a positive significant effect on brand loyalty. It was thus inferred that the
higher in importance customers rank switching costs and price on their decision to stay,
the less loyal they are. Conversely, the higher in importance customers rank services,
the more loyal they are. This implies that if switching costs and prices are low, they are
more likely to be disloyal, and if services are high in quality, they are more likely to be
loyal.
Additionally, issues related to costs and retaining current customers proved to be
significant on brand defection in a positive way. Implying that in the presence of such
issues, the more likely the customers will defect.
The research thereby provides useful knowledge for managers that can help them
better understand what affects the choices of their customers. Hence in practice, this
can direct the attention to critical product attributes and services which need to be
fulfilled in order to retain customers or prevent defection. In theory, the results are
consistent with previous research. However, this study also manages to identify new
factors, most importantly price of subscription package, which the study argues can be
very relevant for future studies. This is especially relevant for other markets where
subscription packages are popular.
Originality/value
This thesis adds to the existing knowledge about consumers’ switching behaviour in the
telecommunicaitons market. More specifically, it contributes to the understanding of
which controllable factors that management can influence in order to gain loyalty or
prevent defection. This study, however, is limited mostly due to its sample size and
sampling technique. In order to generalise the findings of this paper to a larger
population, a more representative sample should thus be collected in future studies
about the Danish market.
Table of contents
1. Introduction ................................................................................................................................................ 1
1.1 Delimitation ......................................................................................................................................... 3
1.2 Literature review ............................................................................................................................... 5
2. Theoretical concepts ................................................................................................................................ 7
2.1 Switching intention/brand defection and switching behaviour ..................................... 7
2.2 Consumer loyalty/brand loyalty .................................................................................................. 8
2.3 Switching costs ................................................................................................................................... 9
2.4 Customer retention and acquisition ......................................................................................... 11
2.5 Loyalty programs ............................................................................................................................. 12
2.6 Brand image ....................................................................................................................................... 13
3. Overview of the Danish telecommunications market ............................................................... 14
3.1 History and regulation ................................................................................................................... 15
3.2 Danish telecommunications providers and market shares ............................................. 15
3.3 Danish consumers’ mobile habits .............................................................................................. 16
4. Conceptual frameworks ........................................................................................................................ 18
4.1 Brand loyalty factors ...................................................................................................................... 18
4.2 Brand defection factors ................................................................................................................. 20
5. Methodology .............................................................................................................................................. 23
5.1 Questionnaire design ...................................................................................................................... 23
5.2 Procedure ............................................................................................................................................ 26
5.3 Participants ........................................................................................................................................ 26
5.4 Data analysis ...................................................................................................................................... 27
6. Results ......................................................................................................................................................... 28
6.1 Factor analysis .................................................................................................................................. 28
6.2 Hypotheses ......................................................................................................................................... 29
6.3 Regression analysis ......................................................................................................................... 32
7. Discussion .................................................................................................................................................. 35
7.1 Findings ............................................................................................................................................... 35
7.2 Theoretical implications ............................................................................................................... 36
7.3 Managerial implications ................................................................................................................ 37
7.4 Limitations and future studies .................................................................................................... 38
8. References .................................................................................................................................................. 41
Appendix 1 ...................................................................................................................................................... 45
Appendix 2 ...................................................................................................................................................... 49
List of figures
Figure 1: Danish telecommunications providers’ market shares 2013 .................................. 16
Figure 2: Mobile number portings in Denmark 2005-2012, 1000 MNP ................................ 17
Figure 3: Data traffic - standard and add-on datasubscriptions 2010-2013 (semi-annual
statistics), bn MB .......................................................................................................................................... 18
Figure 4: Conceptual framework for influential factors on brand loyalty ............................. 21
Figure 5: Conceptual framework for influential factors on brand defection ........................ 22
List of tables
Table 1: Standard and add-on datasubscriptions 2010-2013 .................................................... 18
Table 2: Underlying dimensions of brand loyalty ........................................................................... 30
Table 3: Underlying dimensions of brand defection ...................................................................... 31
Table 4: Model summary ........................................................................................................................... 32
Table 5: ANOVA ............................................................................................................................................ 32
Table 6: Coefficients .................................................................................................................................... 33
Table 7: Model summary ........................................................................................................................... 34
Table 8: ANOVA ............................................................................................................................................ 34
Table 9: Coefficients .................................................................................................................................... 34
1 of 49
1. Introduction
People differ radically in their preferences. This is important for companies to be aware
of when targeting their respective customers. Companies must adapt their businesses to
the customers so that high priority is given to the attributes that the customers value
highly. On the other hand, companies may reduce the time and effort put into the
attributes not valued as highly by the customers.
More than anything, this applies in the telecommunication industry in which customers
are known to switch between mobile service providers (Sathish, Santhosh,
Jeevanantham & Naveen, 2011).
Many researchers have already documented their studies on consumer behaviour in the
telecommunications industry. A number of articles have more specifically been devoted
to the switching behaviour of consumers. One reason for this consumer switching
behaviour, which has been suggested by several authors, includes the availability of
many subscriber options in the telecommunications industry (Sathish et al., 2011). It
has been suggested that many countries have reached market saturation due to the
large number of mobile communication service implementations (Srinuan, Annafari &
Bohlin, 2011; Martins, Hor-Meyll & Ferreira, 2013). This implies that potential
customers have already been captured by mobile operators. Telecom service providers
have thus come to encourage this switching behaviour. This means that the competition
in the telecom market is not only extremely intense, but providers have had to shift
from acquiring new subscribers to retaining existing customers and trying to attract
customers away from rival providers (Aydin, Özer & Arasil, 2005; Srinuan et al., 2011).
Also the existing literature does not appropriately take the fast technological change
within the communication technology into consideration when they investigate reasons
for staying or defecting. The fast developing technology within the mobile industry
implies that the telecommunications providers must act in a dynamic environment with
ever changing customer needs. The customers’ growing interest in using new
technologies pushes telecommunications providers to constantly add new schemes,
offers and technology advancements in their services which also encourage customers
to switch between different providers (Sathish et al., 2011; Lianjv & Xin, 2014).
2 of 49
Especially the possibility to use the internet on one’s mobile phone has radically
changed what the customers demand from their telecommunications providers. Several
mobile applications have been created that make it possible to for instance call and send
messages for free as long as access to a network is available. Such applications have an
impact on what the consumers need, thus implying that they have an impact on which
kind of subscription packages telecommunications providers should offer to stay
attractive.
Moreover, the implementation of MNP in more countries has proved to encourage
switching behaviour. MNP stands for mobile number portability and refers to the
possibility for consumers to switch to another provider without having to change
mobile number. Therefore, MNP ensures that consumers do not experience a lock-in
from their providers if they want switch to a different provider but still want to keep
their original phone number. MNP increases consumer welfare and intensifies the
competition between telecommunications providers (Lianjv & Xin, 2014). A study
conducted in Korea has proved that the implementation of MNP in the Korean
telecommunications market has substantially reduced the switching costs for Korean
consumers (Lee, Kim, Lee & Park, 2006).
This suggests that the availability of MNP has significant importance for the switching
behaviour of telecommunications subscribers.
Several authors have also pointed out that retaining existing customers appear to be a
critical competitive advantage as it has been described as a key point to winning in the
field (Lianjv & Xin, 2014; Martins et al., 2013; Aydin et al, 2005). Arguments which go in
favor of retaining customers instead of acquiring new ones are related to the costs
associated with the respective strategies. It has been pointed out that signing up new
subscribers is more expensive and difficult than preventing existing customers from
defecting. The costs of acquiring a new customer is said to be five times higher than the
costs of retaining a current subscriber. This is partly because mobile service providers
already have knowledge about the behaviours and preferences of their current
customers which allows for smoother implementation of strategies to accommodate
their needs, and partly because departing customers lower future revenue streams but
not fixed costs (Lee & Murphy, 2005; Martins et al., 2013).
3 of 49
To sum up, fierce competition in the telecommunications industry along with mobile
number portability (MNP) and customers’ changing needs have encouraged switching
behaviour. Under these conditions companies focus more on retaining customers rather
than acquiring customers. When a provider knows its customers’ preferences well, it
may be easier to obtain loyal customers and prevent defection. Such arguments
emphasise the importance of knowing what causes intentions to defect or stay
loyal. Thus, providers must know how important the customers find different factors
and how these influence the decision to stay with a brand of defect from it. Accordingly,
the research question explored in this paper is the following:
Which factors affect brand loyalty and brand defection in the Danish
telecommunications market?
This question is aimed specifically at Danish consumers for whom such research has yet
to be explored. The study addresses the research question empricially by conducting a
questionnaire survey in which respondents were asked a series of questions about
brand loyalty and brand defection. The aim is to identify the factors which seem to be of
importance for customers’ decision to either stay with a brand or leave a
brand. Subsequent interpretation of the data will be made on the basis of factor and
regression analysis.
1.1 Delimitation
The choice to focus on both brand loyalty and brand defection, and not exclusively one
or the other, is based on the findings of previous research in the field. It is reasonable to
believe that if customers do not defect, they are considered loyal, and conversely, if they
do defect, they are considered disloyal. Based on this, one may infer that a factor that
positively influences loyalty will have the opposite influence on defection.
However, depending on how loyalty is defined, customers who stay with a brand, and
do not defect, are not necessarily loyal. Some customers may decide to stay with a brand
even though they are not satisfied.
In Gremler and Brown’s (1996) definition of customer loyalty, it is argued that loyal
customers have a positive attitude towards the brand. Therefore, according to this
4 of 49
definition, dissatisfied customers who stay with a brand are not be defined as being
loyal even though they do not defect.
In Lee and Murphy’s (2005) study in the telecom market it is clear that loyalty and
switching are not complete opposites because the determinants are not the same. More
specifically, the determinants which have a positive effect on loyalty do not have the
same negative effect on defection. In their study, switching costs, loyalty programs and
price, in ranked order, were proven the three most important determinants for loyalty.
However, price, service quality and loyalty programs were proven the three most
important determinants for switching intentions.
The above mentioned argumentations suggest that loyalty and defection are not
opposites, and therefore it is more appropriate to investigate loyalty and defection
independently.
The decision to explore the research question specifically in the Danish market, and the
relevance of this, is based on a number of conditions. Looking into the literature, the
research conducted within the field of brand loyalty and brand defection in the
telecommunications industry is limited to a number of countries. More importantly,
such research appears to be lacking in Denmark. It seems, however, that providers in
the Danish telecommunications market advertise heavily in order to acquire new
customers. Therefore, it may be assumed that consumers ending their subscription
contract with their provider are not uncommon in Denmark, and it is thus useful to
explore the intentions and attitudes of these.
Furthermore, the existing literature does not agree on which product attributes have an
influence on consumers’ decision to switch or stay loyal to a brand.
For instance, a study conducted in Singapore concluded that price is the most important
factor which affects the customer to switch from one provider to another whereas a
study in the U.S. concluded that price has an insignificant effect on switching intentions
(Lee & Murphy, 2005; Shin & Kim, 2008). In another example, two articles, conducted in
Turkey and the U.S. respectively, coincide in their reports of switching costs being a
critical loyalty factor, but they reached opposite conclusions in regards to offered
service quality; one article reports that service quality needs to be raised in order to
gain loyalty while the other reports that service quality is critical for switching
5 of 49
intentions but less important for gaining loyalty (Aydin et al., 2005; Lee & Murphy,
2005).
Thus, influential factors on consumers’ intentions to stay loyal or to switch differ from
country to country. This suggests that these factors cannot be generalised across
national borders. That leaves this particular field unexplored in regards to the
preferences of Danish consumers.
The literature used in this study comprises academic articles from the databases
Emerald Insight, ScienceDirect, and EBSCO. The exact expressions ‘consumer switching
behavior’, ‘brand defection’, ‘brand loyalty’, ‘telecommunication’ and ‘cellular’ were
used in the search for relevant literature. Only those articles which mainly or partly
examine loyalty, defection or switching in relation to the telecommunications industry
were considered. Finally, the references of the identified papers were checked for
relevance.
The remainder of the paper is structured as follows: previous research conducted
within the field will briefly be presented in order to give an overview of what other
researchers have found. Next, key theoretical concepts will be defined, and a short
overview of the Danish telecommunications market will be described in order to give an
understanding of the market that the Danish telecommunications providers operate in.
Following this, the conceptual framworks developed for this study will be presented.
The methodology and results of the survey are then documented. The theoretical and
practical implications of the results will subsequently be evaluated and discussed in a
managerial perspective. Finally, the paper concludes with a list of limitations of the
study and areas for further research will be recommended.
1.2 Literature review
Sathish et al. (2011), the geographical area covered in the study was Chennai, India.
Factors which affect the consumers into switching to another mobile service provider
were grouped into the following four categories: customer service, service problems,
usage cost and others. Some of the factors include improper consumer service,
unknowledgeable employees, long wait times for consumer service, error in billing,
poor network coverage, frequent network problems, no new schemes or gradation,
6 of 49
unsuitable plans for different age groups, costly value added services, high call rates,
hidden charges, high SMS charges, better features offered by competitors, influence
from family and fancy number.
It was concluded that poor network coverage, influence from family and friends,
frequent network problems and high call rates are the most important factors in
regards to the consumers’ switching behaviour.
Aydin, Özer and Arasil (2005), the geographical area covered in the study was Turkey.
The study focused on the effect of switching costs as a moderator variable on
antecedents of customer loyalty. High service quality, how much customers trust the
company, and switching costs were mentioned as factors which affect customer loyalty
and thus customers’ decision to stay with a brand.
Results show that switching costs make a significant contribution to customer loyalty.
Martins, Hor-Meyll and Ferreira (2013), the geographical areas covered in the study
were Brazil and Germany.
This study attempted to compare factors which affect customers switching intentions
across the two countries. The result shows that there were no major differences in the
decision-making styles between Brazilians and Germans.
Factors such as customer satisfaction, service performance and perceived value were
concluded to have an importance on customers’ decision to switch. Switching barriers,
however, proved to have an insignificant effect on switching intentions.
Lee and Murphy (2005), the geographical area covered in the study was Singapore.
This study examined what keeps customers loyal and what makes the same customers
switch. The following ten factors were initially identified: price, technical service
quality, functional service quality, switching costs, loyalty programs, reference group
influence, brand trust, behavioural factors, handset upgrade and technology.
In ranked order, the determinants of loyalty were found to be: 1) switching costs, 2)
loyalty programs, 3) price, 4) functional quality, 5) technical quality, 6) brand trust and
7) reference group influence. Likewise, the determinants of switching intentions were
found to be: 1) price, 2) technical quality, 3) functional quality, 4) loyalty programs, 5)
behavioural factors, 6) brand trust and 7) reference group influence.
7 of 49
Shin and Kim (2008), the geographical area covered in the study was the U.S.
This study’s main focus was switching barriers under mobile number portability (MNP)
in the U.S mobile market.
The findings indicate that customer lock-in and switching costs influence switching
barriers which in turn has a negative influence on customer switching intentions.
Additionally, while price has an insignificant effect on customers’ decision to switch,
customer satisfaction was found to have a direct influence on switching intentions. This
implies that customers will likely not switch to another provider if they are satisfied.
Nakhleh (2012), the geographical area covered in the study was Vadodara, India.
This study aimed at investigating the effect of a number of factors on customer loyalty.
Results show that service quality, price, brand image, value offered, customer trust, and
customer satisfaction all make a significant contribution to customer loyalty.
2. Theoretical concepts
The following theoretical concepts are useful for the understanding of the overall paper
as they are mentioned throughout. More importantly, the concepts along with the
literature review make up the foundation of the conceptual frameworks that were
developed for this paper.
2.1 Switching intention/brand defection and switching behaviour
According to Keaveney (1995), switching intention is a customer’s psychological
tendencies to stop using a current brand and start using another brand. Switching
intentions are directly impacted by the consumer’s attitude after having used a product
or service (Ganesh, Arnold & Reynolds, 2000).
This paper uses the terms ‘switching intention’ and ‘brand defection’ interchangeably.
While the terms ‘switching intention’ or ‘brand switching’ consider the customers who
stop using a brand and start using another, ‘brand defection’ only considers the
customers who stop using a brand while ignoring whether or not they switch to another
brand. It means that brand defection considers the customers who cease to use a brand,
including the proportion of customers who leave the market altogether, and not only
8 of 49
those who go from one brand to another (Bogomolova & Romaniuk, 2009). However,
since it is hard to believe that one would leave the telecommunications market
completely, we use the term ‘brand defection’ on the assumption that people who defect
will switch to another mobile operator. Hence, customers’ intentions to switch or to
defect have the same meaning in this study.
Switching behaviour stems from consumer switching intention (Ganesh et al., 2000).
Switching behaviour is consumer behaviour where the behaviour of the consumers is
different based on how satisfied they are with the providers. More specifically, it is the
process of a consumer being loyal to one brand to switching to another brand due to
either being dissatisfied or having other problems with the provider. This implies that if
a consumer is loyal to a specific brand, the consumer will still switch to a rival brand if
the current brand does not satisfy the needs of the consumer (Sathish et al., 2011).
According to Bitner (1990), switching behaviour may occur because of constraints of
time or money, lack of alternatives, switching costs, and habits.
2.2 Consumer loyalty/brand loyalty
There are several renditions to the definition of consumer loyalty. It is defined by
Gremler and Brown (1996, p. 173) as: “the degree to which a consumer exhibits repeat
purchasing behaviour from a service provider, possesses a positive attitudinal disposition
toward the provider, and considers using only this provider when a need for this service
arises”. Consumer loyalty has also been defined as “the strength of the relationship
between an individual’s relative attitude and repeat patronage” (Dick and Basu, 1994, p.
99). Finally, Oliver (1997, p. 392) defines it as “a deeply held commitment to rebuy a
preferred product or service in the future, despite situational influences and marketing
efforts which can potentially cause the consumer to modify behaviour and switch to
another product or service”.
No matter which definition of consumer loyalty is adopted, mobile service operators
need to do the following in order to gain the loyalty (Aydin et al., 2005):
Increase subscriber satisfaction by raising the service quality.
Ensure subscribers’ trust in the firm.
9 of 49
Establish a cost penalty for changing to another service provider, making that a
comparatively unattractive option.
In today’s dynamic environment in which consumers have access to products and
services even across national borders, consumer loyalty is more important than ever for
a brand’s long term viability (Krishnamurthi and Raj, 1991). It can also be assumed that
a low level of switching intention works as an indicator of consumer loyalty. This
implies that if a consumer has no intention to switch and is satisfied with the current
provider, it would be reasonable to infer that the consumer is loyal (Aydin et al., 2005).
2.3 Switching costs
Switching costs are closely related to customer loyalty in that such costs can
differentiate products or services which were otherwise viewed as homogenous before
they were bought. Meaning, customers will display brand loyalty in the presence of
switching cost if they are given the choice between a number of products or brands
which are functionally identical to their current brand (Klemperer, 1995).
To the customer, switching costs work as the penalty price to be paid for leaving one
provider in favour of another (Porter, 1998). Switching costs have also been formally
defined as the costs which are incurred during the switching process from one supplier
to another. The costs include the economical/financial cost, the procedural costs, and
the psychological costs from becoming a customer of a new provider (Dick and Basu,
1994; Porter, 1998; Klemperer, 1995; Jackson, 1985).
Financial costs include any monetary costs which appear when a customer changes
mobile phone operator (Klemperer, 1987). For example, mobile service providers often
charge new customers some kind of sign-up fee.
Procedural costs include the effort and time put into the buying decision-making
process and the subsequent implementation of the decision (Aydin et al., 2005). For
example, it takes time to adapt to a new company as it might operate differently than
what customers are used to, customers need to do information search for other
operators before they can make a decision, and the customer often evaluates between
10 of 49
different alternatives based on some criteria such as price.
Psychological costs are perceived costs which stem from social bonds that the
customers have formed with the current company. For example, customers are familiar
with the staff and have formed a relationship with them (Aydin et al., 2005). These
relations will be lost if the customer decides to switch to another provider, and the
customer will also have to establish new relations. Uncertainty and risk associated with
dealing with an unfamiliar brand are also psychological costs (Patterson and Sharma,
2000; Sharma, 2003).
According to Klemperer (1987), there are three types of switching costs, namely
transaction costs, learning costs, and contractual costs.
In the context of mobile communications, transactions costs occur when customers who
have switched operator cannot keep their mobile numbers. These customers would
have to inform their friends and families about the new mobile number which is
thought of as a cost. This cost, however, has become less prominent with the
implementation of mobile number portability (MNP).
Learning costs are mainly associated with the effort and time that customers spend on
learning about the new provider’s service routine. The time spent on getting to know
potential providers are also learning costs (Jones, Mothersbaugh & Beatty, 2002).
Contractual costs occur when customers have already signed a long-term contract with
the current provider (Srinuan et al., 2011). This is often the case if the customer buys a
subscription package along with a new mobile phone. If customers want to switch to
another provider, they will then either have to pay a termination fee to their current
provider or in cases when they are not allowed to terminate the contract at all, they will
have to pay for two subscriptions with two different providers simultaneously. Thus,
contractual costs can also be viewed as withdrawal penalties. Additionally, loss of
loyalty benefits by switching to another provider are also thought of as contract costs
(Klemperer, 1995).
11 of 49
National Economic Research Associates (NERA) (2003) elaborated further and added
two additional types of switching costs: search costs and compatibility costs. Search
costs incur because consumers have to gather information about other mobile service
providers, and compatibility costs incur for instance because of SIM locking which
forces consumers to either buy a new mobile phone or unlock their current before they
can start using the services of other mobile phone operators.
According to Srinuan et al. (2011), switching costs are becoming a significant problem
for competition in the mobile communications market. Most mobile service operators
build up switching costs in order to prevent their current customers from leaving by
making it difficult and costly to switch to another rival operator. Building up of
switching costs can thus be considered as anti-competitive behaviour. This effectively
gives the mobile service operators a source of market power as they can affect the
competition in the market (Lee, Kim, Lee & Park, 2006). This also suggests that
operators use switching costs as a part of their customer retention strategies.
2.4 Customer retention and acquisition
As has already been expressed earlier in this study, knowing how to retain customers is
a focal point to exceeding in the telecommunications industry (Lianjv & Xin, 2014). Not
only will a loss of customers cause a serious setback in terms of a firm’s present and
future earnings, but the firm will also need to invest in, for example, advertising,
promotion, and initial discounts in order to attract new customers (Sathish et al., 2011).
Several authors have pointed out that mobile operators raise switching costs as part of
their strategy to retain as many existing customers as possible (Shin and Kim, 2008;
Srinuan et al., 2011). Providers either increase the monetary costs of switching in order
to financially punish customers who switch, or they attempt to increase perceptions of
switching costs. Examples of retention-based strategies include advertising which
stresses a) the risk of switching service providers, b) the forgone benefits lost by
changing providers, and c) the time and effort associated with adapting to a new service
provider (Jones, Mothersbaugh & Beatty, 2002).
12 of 49
Mobile phone operators also engage in more aggressive customer retention strategies.
These include better deals on upgrade handsets, incentive for longer contracts, better
customer service, and increased network spending (Shin and Kim, 2008).
It is thus clear that operators either focus on punishing or rewarding their customers in
their attempts to keep customers from switching to rival operators.
Though customer retention is important, customer acquisition strategies must not be
neglected. According to Bogomolova and Romaniuk (2009), customer acquisition is
important in order to maintain market shares and brand position. Though customer
acquisition may be costly compared to customer retention strategies, acquisition
strategies may be cost effective if customers that are easy to attract are identified.
2.5 Loyalty programs
According to Yi and Jeon (2003) loyalty programs, which are often referred to as reward
programs, are often implemented in order to achieve brand loyalty based on customers’
purchase history. Especially loyal customers are the target group of these programs
since the aim of the programs is to retain the most profitable customers by increasing
customer satisfaction. If these customers are identified and captured in a loyalty
program, they may contribute to building a successful business by buying more, paying
higher prices and recommending the given company to others.
The loyalty programs can also differentiate businesses from their competitors.
In their study, Yi and Jeon found that loyalty programs’ effects on brand loyalty depend
on customer involvement. Involvement can be low or high depending on how extensive
the buying process is. Loyalty programs can have an effect on brand loyalty under both
low and high involvement. However, the study suggests that the effects of loyalty
programs on brand loyalty are greater under high customer involvement.
Since it must be assumed that starting a subscription with a telecommunications
provider requires relatively high involvement, telecom providers can create a
competitive advantage by implementing loyalty programs.
13 of 49
Loyalty programs may not only act as an attraction to acquire new customers but also
as a switching cost deterring existing customers from replacing their current provider
with another provider. This is because loyalty programs can be used as a customer lock-
in because a change to another provider may cause a loss of earned benefits (Shin and
Kim, 2008).
Therefore, loyalty programs may be an attractive investment for businesses with high
customer involvement due to the dual effect. On one side loyalty programs help
acquiring new and loyal customers, and on the other hand loyalty programs also
prevent existing customers from defecting due to the switching costs incurred from loss
of earned benefits.
2.6 Brand image
In the literature brand image is also referred to as corporate image or corporate brand
image. Brand image is the overall evaluation and impression of a firm by consumers.
The consumers’ evaluation is a part of a longer process, and it is based on the
consumers’ ideas, feelings and previous purchases with a firm. The evaluation is also
based on the physical and behavioural attributes of the firm. This includes the business
name, architecture, variety of products/services, tradition, ideology and the consumers’
impression of the quality that is communicated by the firm (Nguyen and Leblanc, 2001).
According to Keller (1993, p. 3) brand image is defined as “perceptions about a brand as
reflected by the brand associations held in consumer memory”. Keller identifies three
different types of brand associations that make up the brand image, namely attributes,
benefits, and attitudes.
Attributes can be divided into product related attributes and non-product related
attributes. Product related attributes are the features of the product/service itself
which are necessary for the performance of the product/service. On the other hand,
non-related product attributes are those aspects external to the purchase or
consumption of the product/service (for instance packaging and user- and usage
imagery).
Benefits are the consumers’ beliefs about what the product/service can do for them.
Keller divides these benefits into three main categories: functional benefits, experiential
14 of 49
benefits and symbolic benefits.
The functional benefits are product-related and refer to the problems that the
product/service solves for the consumer. The experiential benefits refer to how it feels
like to use the product/service; these benefits are also product-related. The symbolic
benefits are often non-product related and are related to the social needs that the
consumer may want to cover. These social needs may be social approval or a desired
social status.
The last brand association that Keller identifies as having an impact on brand image is
attitude. Brand attitude is the overall evaluation of the brand, and it can be related to
the product- and non-product related attributes or the consumer’s beliefs about the
brand.
These are the associations that Keller recognises as important for brand image. A good
brand image is obtained when all the associations are favorable. That is, when the
consumers think that the brand can satisfy all their needs which in turn create an
overall positive attitude towards the brand.
The above mentioned definitions of brand image suggest that brand image can
differentiate a company from its competitors, and if the company has a good brand
image, it can strengthen the company’s position on the market and give a competitive
advantage
3. Overview of the Danish telecommunications market
Before analysing brand loyalty and brand defection in the Danish telecommunications
market, a short description of the market is given below. Additionally, some statistics on
how Danish consumer habits on this market have changed the last years will be
presented. These statistics contribute to the understanding of consumer behaviour
within mobile telephony and the dynamic environment that Danish telecommunications
providers are operating in.
15 of 49
3.1 History and regulation
Until 1996 the telecommunications industry was owned and controlled by the state, and
the sole provider was called Tele Danmark (known as TDC today). However, on July 1,
1996 the market was privatised effectively making it possible for new providers to
enter the market (Danish Business Authority, 2014a). Furthermore, mobile number
portability was implemented in Denmark on October 15, 1999 to help build an effective
competitive environment (Danish Business Authority, 2014b).
The Danish Business Authority regulates competition on the Danish telecom market in
order to protect the Danish consumers. This is done by imposing obligations on the
telecommunications providers with the strongest positions.
Every half year the Danish Business Authority publishes statistics describing the
development on the Danish telecommunications market. The data used for these
statistics are submitted by the respective telecommunications providers. The
publication provides statistics on for example market shares and consumer habits. The
most important and relevant results of the last publication from the first six months of
2013 are presented in section 3.2 and 3.3.
3.2 Danish telecommunications providers and market shares
According to the publication, the companies holding the strongest positions in the
Danish telecommunications market are: TDC A/S, Telenor A/S, TeliaSonera Danmark
A/S, Hi3G Denmark ApS and Telmore A/S. Some of these companies have subsidiaries
branded under different names. For example, the brand Oister is a part of Hi3G
Denmark ApS, BiBob and CBB Mobil are part of Telenor A/S, and Call me and Telia are
part of TeliaSonera Danmark A/S.
A chart of the Danish telecommunications providers’ market shares is shown in figure 1.
As depicted in figure 1, the chart shows that the Danish telecommunications market is
dominated by five companies. The shaded area labelled “Others” covers 14.4 % of the
market. The different market shares under this label are distributed between many
smaller providers.
16 of 49
Figure 1: Danish telecommunications providers’ market shares 2013
Note: Market shares are based on all mobile subscriptions. The total number of mobile subscriptions in 2013
was 8,219,753.
Source: Telestatistik første halvår 2013 (Danish Business Authority, 2013).
3.3 Danish consumers’ mobile habits
Since this study investigates brand loyalty and brand defection in the Danish
telecommunications market, it is interesting to look at the Danish consumers’ switching
behaviour in this market. It is difficult directly to measure the extent of switching
behaviour. However, the number of mobile number portings may be a good indicator of
the switching behaviour since it measures the number of customers who have defected
from one provider and transferred mobile number to another provider. Therefore, a
figure of the number of mobile number portings in the period 2005-2012 has been
constructed (see figure 2).
Figure 2 shows an increasing number of mobile number portings in the given period. In
the period 2005 to 2012 the number of mobile number portings has increased from
379,993 to 918,003 which is equivalent to a percentage increase of 141.6 %.
The number of mobile subscriptions has not increased to the same extent in the same
period. Compared to the total number of subscriptions of 8,312,373 in 2012, it can be
inferred that switching behaviour in the Danish telecommunications market is not
17 of 49
uncommon. Furthermore, it can be concluded that the switching behaviour has
increased extensively the last years.
Figure 2: Mobile number portings in Denmark 2005-2012, 1000 MNP
Note: The numbers from 2013 have not yet been published, but the number of mobile number portings in the
first half of 2013 has been recorded to be 450,393.
Source: Telestatistik første halvår 2013 (Danish Business Authority, 2013).
Another remarkable statistic from the publication is the amount of data traffic that has
been used from mobile phones. Figure 3 shows the development in the data traffic since
2010. The figure shows the radical change in the amount of data that the Danes have
used on their mobile phones the last years. The data traffic has increased from
482,299,342 MB in the first half of 2010 to 15,242,082,415 in the first half of 2013.
These statistics suggest that the consumers’ interest in accessing the internet from their
mobile phones has grown significantly. This implies a major change in what the
consumers demand from their mobile subscriptions.
The growing interest in internet access from mobile phones can also be seen in the
increase in the number of standard and add-on datasubscriptions which can be seen in
table 1. Standard and add-on datasubscriptions are package solutions where the
customer typically has a fixed amount of airtime, SMS, MMS and data included in one
package.
18 of 49
Figure 3: Data traffic - standard and add-on datasubscriptions 2010-2013 (semi-annual statistics), bn MB
Note: Standard and add-on datasubscriptions are mobile subscriptions with included data.
Soruce: Telestatistik første halvår 2013 (Danish Business Authority, 2013).
Table 1: Standard and add-on datasubscriptions 2010-2013
1. H. 2010 2. H. 2010 1. H. 2011 2. H. 2011 1. H. 2012 2. H. 2012 1. H. 2013
2.319.832 2.754.847 3.403.893 3.736.413 4.105.066 4.422.425 4.693.597
Source: Telestatistik første halvår 2013 (Danish Business Authority, 2013).
4. Conceptual frameworks
Taking into account the patterns identified in the Danish market and what was found
through the literature review, the idea and purpose behind the paper can be
conceptualised through two simple frameworks (see figure 4 and figure 5).
The frameworks were developed by linking these considerations with some of the key
theoretical concepts that were defined above.
4.1 Brand loyalty factors
Based on academic articles and previous research, the following factors were
considered to be relevant for consumers’ decision to stay loyal to a brand:
19 of 49
Network coverage (Lee & Murphy, 2005)
Loyalty programs (Lee & Murphy, 2005)
Brand image (Nakleh, 2012)
Employee service (Lee & Murphy, 2005)
Time and effort to search for a new provider (Aydin et al., 2005; Lee & Murphy
2005)
Costs related to switching (Aydin et al., 2005; Lee & Murphy, 2005)
Transfer process of mobile number (Aydin et al., 2005; Lee & Murphy, 2005)
Since the fast changing technology implies that telecommunications providers must
constantly update their services, new factors not included in previous research may also
be relevant to consider. To identify such factors, the websites of Danish
telecommunications providers were explored. Therefore, in addition to the factors
considered to be relevant in previous studies, inspiration from Danish telecom
providers’ websites led to the inclusion of the following loyalty factors:
Price of subscription package
Prices on additional airtime, GB etc.
Music streaming services
Free Facebook
Offers and discounts
Free calls to people with same provider
Access to physical store
Provider’s ability to accommodate changing needs
The literature also suggested price to be an important factor for consumers’ decision to
stay loyal (Lee & Murphy, 2005). However, several Danish telecom providers’ websites
revealed advertisements for package prices and not separate prices on call rates, price
per SMS or price per GB data traffic. Combined with the numbers from table 1, which
showed a radical increase in the number of standard and add-on subscriptions (package
solutions) since 2010, this study considers the price of subscription package as a
relevant factor to include. Since subscription packages typically include a fixed amount
of airtime, SMS, MMS and GB, prices on additional airtime and additional GB etc. have
20 of 49
also been included as a relevant factor.
Factors such as music streaming services, free Facebook and the opportunity to call
people with the same provider for free were also found to be popular features that the
customers can include in their subscription packages.
The websites also advertised for customer benefits not related to mobile telephony.
Examples of such benefits were discounts on movie tickets or gift cards for retail stores.
Moreover, the websites revealed that some providers do not have any physical stores
where the customers can get help when needed. These providers only help the
customers via their website or through a service hotline. Therefore, access to physical
store was considered relevant to include.
The last factor called provider’s ability to accommodate changing needs was considered
relevant because the standard subscription packages advertised by the providers may
not fit all customers’ needs. A customer may want to include more or less airtime or GB
in the subscription package over time, and hence this factor was included in this study
as well.
4.2 Brand defection factors
Based on academic articles and previous research, the following factors were
considered to be relevant for consumers’ decision to defect from a brand:
Poor network coverage (Sathish et al., 2011; Lee & Murphy, 2005)
Improper employee service (Sathish et al., 2011)
Long wait times for consumer service (Sathish et al., 2011)
Error in billing (Sathish et al., 2011)
Unsuitable subscription packages (Sathish et el., 2011)
High prices (Sathish et al., 2011; Lee & Murphy, 2005)
Hidden charges (Sathish et al., 2011)
Better offers from rivals (Sathish et al., 2011)
Recommendation of another provider from family/friends (Sathish et al., 2011;
Lee & Murphy, 2005)
No reward for being loyal (Lee & Murphy, 2005)
21 of 49
Figure 4: Conceptual framework for influential factors on brand loyalty
22 of 49
Figure 5: Conceptual framework for influential factors on brand defection
23 of 49
5. Methodology
With the aim of answering the following research question: which factors affect brand
loyalty and brand defection in the Danish telecommunications market? previous
research and theories were drawn upon to identify relevant loyalty and defection
factors to include in the study. It can therefore be argued that the methodological
approach in this study was based mainly on deductive reasoning.
However, some of the factors that were found to be relevant for brand loyalty were
included based on inductive reasoning. These factors were included based on
observations from the Danish telecom providers’ websites and patterns that were
identified in the overview of the Danish telecommunications market.
Hence, the methodology was a combination of both deductive and inductive reasoning.
5.1 Questionnaire design
The research question of this study was approached through an exploratory research
design that was based on a questionnaire (see appendix 1).
The purpose of the survey was to measure the importance of different factors on the
consumers’ decisions to stay or defect from their current providers. It was a cross-
sectional study designed to measure the attitudes and intentions of the respondents at
one point in time.
Quantitative methodology was appropriate for this study because the kind of data that
was required to answer the research question was known to the authors, and because
similar studies have previously made use of this method as well (see, for example, Lianjv
and Xin, 2014; Martins et al., 2013; Srinuan et al., 2011; Aydin et al, 2005).
Qualitative methodology was not used since information about factors affecting brand
loyalty and brand defection was already available from previous studies.
Therefore it was deemed more convinient and appropriate to apply quantitative
methods for this study. Quantitative methods deal with hypothesis testing and
statistical analysis (Erisson & Kovalainen, 2008). This is in accordance with the seven
developed hypotheses and factor and regression analysis used in this study.
24 of 49
The questions in the questionnaire were constructed based on inspiration from an
extensive literature review and also from the websites of the Danish
telecommunications providers.
Evidence of the reliability of the questionnaire and the constructed questions were
tested using Cronbach’s alpha. Conbach’s alpha was used because it is the most common
measure of scale reliability (Field, 2009). The lower limit of acceptability was 0.60 to
0.70 (Black, Hair, Babin, & Anderson, 2009). The reliability values will be presented in
the result section.
Questions
The questions addressing the factors relevant for brand loyalty consisted of 15 items in
accordance with figure 4. The respondents were asked to rank the importance of these
15 factors on their decision to stay on a five-point likert scale ranging from “very
unimportant” to “very important”.
The questions addressing the factors relevant for brand defection consisted of 10 items
in accordance with figure 5. The respondents were asked to rank the importance of
these 10 factors on their decision to defect from their current provider on a five-point
likert scale ranging from “very unlikely” to “very likely”.
Brand loyalty measure
To measure brand loyalty (BL), a five-item scale developed by Narayandas (1996) was
used. This brand loyalty scale incorporated consumers’ repurchase intentions,
resistance to switching despite attractive rival brands, and willingness to recommend
preferred provider to friends and associates (Aydin et al., 2005). The following items
were thus adapted and constructed:
1. I will keep on using my current provider
2. If I bought a new mobile phone, I would prefer my current provider
3. I recommend my current provider to other people
4. I encourage friends and family to use my current provider
25 of 49
5. Even if other brands were cheaper, I would keep on using my current provider
The measure used a five-point likert scale ranging from “strongly disagree” to “strongly
agree”.
Brand defection measure
To measure brand defection (BD), a three-item scale developed by Qi, Zhao and Zong
(2013) was used. This scale was developed based on the following three categories of
reasons for defection: negative qualities of the brand which consumers switch from,
positive qualities of rival brands, and reasons beyond the control of brand management
(Svetlana, 2010). The following items were thus adapted and constructed:
1. If I feel unsatisfied with my current provider, I will change to another brand
2. If I am attracted to other brands, I will give up my current provider
3. If my income changes, I will not use my current provider anymore
This measure used a five-point likert scale ranging from “strongly disagree” to “strongly
agree”.
During the data analysis, the third item was removed as there were issues with its
validity to measure brand defection to the same extent as the two other items.
This may be due to the adapted construction of the third item. If the original item
suggested by Qi et al. (2013) had been used, the third item would have been: “If I
change my neighbourhood, I will not use my current provider anymore”. The inclusion
of this item was thus assumed to have been relevant for the given market that was
analysed in the study by Qi et al. (2013). However, this item was not considered relevant
to apply to the Danish telecom market. Even though Danish consumers change
neighbourhood, changing telecom provider is not necessary since the Danish providers
operate on a national basis, and therefore the Danish providers’ networks covers
nationwide.
Hence a new construction of the third item was made in order to adapt this item to this
study and the Danish market. A change in income was assumed to be a factor beyond the
influence of management that could be relavant for the Danish market, and accordingly
26 of 49
the original third item was adjusted.
However, since this item turned out to be invalid, it may have been wrong to assume
that an income change would work as an indicator of brand defection.
5.2 Procedure
The questionnaire was developed in Danish using Qualtrics’ software and subsequently
distributed through Facebook. It was shared on the authors’ personal pages and pages
which are specifically intended for the distribution of different surveys. The
participation of the respondents was completely voluntarily.
Before proceeding with the actual survey, the respondents were presented with the
purpose of the study, the estimated time it would take for completion, and informed
consent about their anonymity. In order to ensure that all questions would be answered,
the survey was desgined such that the respondents could not proceed with the
questionnaire without filling out all the questions. This was done in order to avoid
uncompleted answers.
The respondents went through two main steps when filling out the survey. The first part
of the survey was dedicated to questions about brand loyalty, and the second part of the
survey was dedicated to questions about brand defection.
The survey ran for 14 days from mid-March until late-March.
5.3 Participants
A total of 318 participated in the questionnaire survey. 101 of these respondents did
not complete the questionnaire and five did not have a mobile phone, and therefore
these 106 responses were excluded from the final data set. The questionnaire thus
yielded 212 valid answers which were all voluntary.
A convenience sample was obtained through Facebook. As the name indicates,
convenience sampling involves a selection of partipants based on convenience, easy
access and availability (Marshall, 1996). Due to the time constraint of this study, this
sampling technique was chosen in order to quickly collect as many responses as
possible. However, the disadvantage of this technique is the selection biases that may
incur and cause poor representativeness.
Hence, the use of a convenience sample has restricted the representativeness of the
27 of 49
sample because only Facebook users had access to the questionnaire.
All participants were Danes since the questionnaire was only distributed in Danish.
The distribution of gender was 81 males and 131 females. The age of the respondents
ranged between 16 and 59, and the average age was 28 with a standard deviation of
10.56. Only 11 of the 212 respondents made use of prepaid mobile services whereas the
rest were postpaid consumers subscribed to a mobile operator. 42 of these postpaid
subscribers were bound by contract.
The respondents were also asked to specify which telecommunications operator was
their current provider, and the results from the data set are shown in figure 6.
Figure 6: Sample distribution of the respondents’ telecommunications providers
A comparison of figure 1 and figure 6 suggests that the sample is reasonably
representative of the respective providers’ market shares.
5.4 Data analysis
The primary statistical methods which were applied were factor and regression
analysis using SPSS.
The analysis process constituted three sequential steps. First, factor analysis was
applied in order to reduce the collected data. This was also done in order to avoid any
issues with collinearity. An apparent significant result of a predictor variable could be
28 of 49
due to it being highly correlated with another significant predictor variable, and not
because it in itself is significant. Thus, if we did not solve such problems with
collinearity before performing the regression analysis, then the interpretation of the
final result would be questionable. The factor analysis solved this problem by
combining the highly correlated variables into one factor.
Secondly, a list of hypotheses was set up based on the factor analysis.
A regression analysis was subsequently performed with the factors from the factor
analysis as predictor variables, and brand loyalty and brand defection as the dependent
variables. This was done in order to test the hypotheses and determine which factors
significantly contribute to brand loyalty and brand defection respectively. The standard
alpha level of 0.05 was used to indicate statistical significance.
6. Results
6.1 Factor analysis
Brand loyalty
Factor analysis was run on items DTS1-DTS15 (see appendix 2 for abbreviations).
Items DTS2 (prices on additional airtime, GB etc.) + DTS6 (offers and discounts) + DTS9
(access to physical store) were removed due to low communalities (below 0.50).
Only factors with eigenvalues greater than 1.0 were retained. This resulted in four
factors which were given appropriate names (see table 2).
Bartlett’s test was significant (0.00 < 0.05) indicating that factor analysis was
appropriate in this case. The value of the KMO statistic was 0.715 which, according to
Hutcheson and Sofroniou (1999), is a good value in relation to factor analysis yielding
distinct and reliable factors.
Lastly, Cronbach’s alpha was computed for each factor in order to assess for scale
reliability.
Table 2 shows the result of the factor analysis for brand loyalty.
Brand defection
Factor analysis was run on items LICO1-LICO10 (see appendix 2 for abbreviations).
Item LICO1 (poor network coverage) was removed due to a communality barely above
0.50 (0.505).
29 of 49
Following a rerun of the analysis, LICO5 (unsuitable subscription packages) was
removed due to cross-loadings which were nearly identical (0.499 vs. 0.509).
Three factors which had eigenvalues above 1.0 were subsequently retained and given
appropriate names (see table 3), and Cronbach’s alpha was computed for these.
Bartlett’s test was significant (0.00 < 0.05) and the value of the KMO statistics was
0.704.
Table 3 shows the result of the factor analysis for brand defection.
6.2 Hypotheses
The result of the factor analysis poses the following hypotheses.
For brand loyalty:
H1. Product characteristics have a significant effect on brand loyalty.
H2. Switching costs have a significant effect on brand loyalty.
H3. Services have a significant effect on brand loyalty.
H4. Price has a significant effect on brand loyalty.
For brand defection:
H5. Service issues have a significant effect on brand defection.
H6. Costs issues have a significant effect on brand defection.
H7. Customer retention issues have a significant effect on brand defection.
30 of 49
Table 2: Underlying dimensions of brand loyalty
Eigenvalue Variance explained (%) Cronbach's α Factor loadings Communalities
F1 Product characteristics 3.35 27.95 0.73
DTS5 - Free Facebook 0.77 0.72
DTS7 - Free calls to people with same provider 0.73 0.61
DTS4 - Music streaming services 0.71 0.65
DTS8 - Loyalty programs 0.60 0.55
DTS3 - Network coverage 0.51 0.44
F2 Switching costs 1.80 14.98 0.76
DTS14 - Costs related to switching 0.86 0.79
DTS15 - Transfer process of mobile number 0.85 0.76
DTS13 - Time and effort to search for a new provider 0.69 0.68
F3 Services 1.27 10.59 0.63
DTS11 - Employee service 0.76 0.61
DTS10 - Brand image 0.73 0.60
DTS12 - Provider's ability to accomodate changing needs 0.71 0.62
F4 Price
DTS1 - Price of subscription package 1.14 9.47 1.00 0.71 0.53
31 of 49
Table 3: Underlying dimensions of brand defection
Eigenvalue Variance explained (%) Cronbach's α Factor loadings Communalities
F1 Service issues 3.41 42.61 0.78
LICO3 - Long wait times for consumer service 0.83 0.80
LICO2 - Improper employee service 0.80 0.71
LICO4 - Error in billing 0.72 0.70
F2 Costs issues 1.25 15.68 0.84
LICO6 - High prices 0.88 0.84
LICO7 - Hidden charges 0.84 0.79
F3 Customer retention issues 1.15 14.42 0.65
LICO9 - Recommendation of another provider from family/friends 0.78 0.68
LICO10 - No reward for being loyal 0.70 0.63
LICO8 - Better offers from rivals 0.68 0.68
32 of 49
6.3 Regression analysis
Reliability analyses were performed on the dependent variables. In this case, the
dependent variables were brand loyalty and brand defection. Cronbach’s alpha was
0.872 for brand loyalty and 0.622 for brand defection.
What follows are the results of the regression analyses. The regression analyses were
performed using the following two models in accordance with the above hypotheses:
Brand loyalty i = 0 + 1 product characteristics i + 2 switching costs i + 3 services i +
4 price i + i
Brand defection i = 0 + 1 services issues i + 2 costs issues i + 3 customer retention
issues i + i
Brand loyalty
Below are the SPSS outputs for regression analysis on brand loyalty.
Table 4: Model summary
Model R R Square
Adjusted
R Square
Std. Error of the
Estimate
1 ,341a ,117 ,100 ,90847 a. Predictors: (Constant), DTS: F4 - Price, DTS: F3 - Services, DTS: F2 -
Switching costs, DTS: F1 - Product characteristics
Table 5: ANOVA
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 22,547 4 5,637 6,830 ,000a
Residual 170,841 207 ,825
Total 193,388 211
a. Predictors: (Constant), DTS: F4 - Price, DTS: F3 - Services, DTS: F2 - Switching costs, DTS:
F1 - Product characteristics
b. Dependent Variable: Brand loyalty
33 of 49
Table 6: Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4,155 ,594 6,995 ,000
DTS: F1 - Product
characteristics
,113 ,090 ,091 1,253 ,212
DTS: F2 - Switching
costs
-,218 ,074 -,201 -2,930 ,004
DTS: F3 - Services ,257 ,093 ,202 2,768 ,006
DTS: F4 - Price -,270 ,103 -,172 -2,612 ,010 a. Dependent Variable: Brand loyalty
The regression model proved to be significant (0.00 < 0.05). The model explained
approximately 12 per cent of the variation in the dependent variable (R2 = 0.117).
As can be seen from the coefficients table, all predictor factors were significant except
for DTS:F1 (product characteristics). Accordingly, H2-H4 were accepted and H1 was
rejected. In other words, switching costs, services, and price have a significant effect on
brand loyalty whereas product characteristics have an insignificant effect. Switching
costs and price affect brand loyalty negatively and services and product characteristics
affect brand loyalty positively.
The estimated model was the following:
Brand loyalty = 4.155 + 0.113(product characteristics) - 0.218(switching costs) +
0.257(services) - 0.270(price).
34 of 49
Brand defection
Below are the SPSS outputs for the regression analysis on brand defection.
Table 7: Model summary
Model R R Square
Adjusted
R Square
Std. Error of the
Estimate
1 ,452a ,204 ,193 ,73900 a. Predictors: (Constant), LICO: F3 - Customer retention issues, LICO: F2
- Costs issues, LICO: F1 - Service issues
Table 8: ANOVA
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 29,174 3 9,725 17,807 ,000a
Residual 113,593 208 ,546
Total 142,768 211
a. Predictors: (Constant), LICO: F3 - Customer retention issues, LICO: F2 - Costs issues, LICO:
F1 - Service issues
b. Dependent Variable: Brand defection
Table 9: Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 1,908 ,322 5,925 ,000
LICO: F1 - Service
issues
,040 ,068 ,043 ,588 ,557
LICO: F2 - Costs issues ,253 ,074 ,241 3,440 ,001
LICO: F3 - Customer
retention issues
,286 ,068 ,288 4,207 ,000
a. Dependent Variable: Brand defection
The regression model proved to be significant (0.00 < 0.05). The model explained
approximatedly 20 per cent of the variation in the dependent variable (R2 = 0.204).
As can be seen from the coefficients table, LICO:F2 and LICO:F3 were significant
predictor factors. However, LICO:F1 was found to be insignificant (0.557 > 0.05).
35 of 49
Accordingly, H6 and H7 were accepted and H5 was rejected. In other words, costs issues
and customer retention issues have a significant effect on brand defection whereas
service issues have an insignificant effect. All factors affect brand defection positively.
The estimated model was the following:
Brand defection = 1.908 + 0.040(service issues) + 0.253(costs issues) + 0.286(customer
retention issues).
7. Discussion
In this concluding section, the findings are summarised and intepreted followed by a
discussion of the implications of these from a theoretical and practical perspective. This
is done in order to assess the overall value of the study. Finally, the paper ends with a
discussion of its major drawbacks and suggestions are made for future research that
this paper calls for.
7.1 Findings
The aim of this paper was to identify factors which influence brand loyalty and brand
defection in the Danish telecommunications market. More specifically, a questionnaire
was developed in which participants were asked to rank a number of factors in relation
to their decision to stay with a provider or leave a provider.
The main findings are as follows:
Significant results
Switching costs (monetary costs, transfer process, customers’ time and effort)
significantly contribute to brand loyalty negatively. Hence, the more important
consumers find switching costs, the less loyal they are.
Services (employee service, brand image, provider’s ability to accommodate
changing needs) significantly contribute to brand loyalty positively. Hence, the
more important consumers find services, the more loyal they are.
36 of 49
Price (of subscription package) significantly contributes to brand loyalty
negatively. Hence, the more important consumers find price, the less loyal they
are.
Costs issues (high prices and hidden charges) significantly contribute to brand
defection positively. Hence, in the presence of costs issues the more likely the
consumers will defect.
Customer retention issues (no reward for being loyal, recommendation of
another provider, and better offers from rivals) significantly contribute to brand
defection positively. Hence, in the presence of customer retention issues the
more likely the consumers will defect.
Insignificant results
Product characteristics (free Facebook, music streaming services, free calls to
people with same provider, loyalty programs and network coverage)
insignificantly contribute to brand loyalty positively.
Service issues (long wait times, error in billing, improper employee service)
insignificantly contribute to brand defection positively.
7.2 Theoretical implications
The results provide theoretical support for previous findings by Aydin et al. (2005), Lee
and Murphy (2005), Nakleh (2012), and Sathish et al. (2011).
Since the majority of the results are consistent with other researchers’ findings, it
suggests that previous research, at least partly, can be generalised to the Danish market
according to this study. However, this would need to be reaffirmed through a more
extensive study involving a larger and more representative sample.
Newly identified factors which were based on what was listed on Danish operators’
websites proved to be both significant and insignificant. Price of subscription package
and provider’s ability to accommodate customers’ changing needs were significant.
However, factors which were not considered part of the core service product such as
37 of 49
music streaming services, free Facebook and free calls to people with the same provider
proved to be insignificant.
Mainly the significance of price of subscription package and provider’s ability to
accommodate changing needs adds to current theory. They are valid factors because
they are in accordance with the Danish consumers’ preference for subscription
packages in favor of traditional prepaid cards. This implies that future studies could
reasonably include these factors if the market in question is similar to the Danish
market.
7.3 Managerial implications
Based on the study, there are a number of managerial implications. Managers can
mainly gain knowledge about what to do in order to keep their current customers or
prevent them from defecting.
The results suggest that in order to gain loyal customers managers should focus on
switching costs, services and price. To prevent customers from defecting focus should
be on reducing costs issues and customer retention issues.
Telecom providers trying to target customers through low prices should be aware that
price sensitive customers, according to this study, are less loyal than those who ranked
price less important. Hence, low-cost providers may prioritise acquiring new customers
rather than trying to retain customers that easily may be attracted by competitors’
better offers.
It should also be noted that even though network coverage was excluded from the
analysis, this does not mean that network coverage is not important. Network coverage
was removed from the factor analysis because of reliability problems due to an uneven
distribution of answers. Out of a total of 212 answers 200 respondents rated network
coverage as either being important or very important, and therefore good network
coverage seems vital for mobile providers survival indicating that there is no poor
network coverage in practice.
38 of 49
The fact that network coverage was ranked highly in importance can help explain why
additional services (free Facebook, music streaming services, and free calls to people
with the same provider) proved to be insignificant. As long as access to a network is
available, the consumer will be able to use Facebook and listen to music online provided
that enough GB data is included in the subscription package. Even several applications
have been developed that allow consumers to make free calls to anyone as long as
network access is available. Based on this discussion, it is suggested that providers
should consider removing such services completely or at least put very little focus on
them.
According to this study, customer retention issues (no reward for being loyal,
recommendation of another provider, and better offer from rivals) increase
the probability that customers will defect. Therefore, if managers have no focus on
retention strategies, customers may be lost to competitors. In order to prevent
customers from defecting, managers should try to reduce the probability that customers
become attracted to other providers. This can be done by rewarding the customers for
being loyal. One way providers can manage this is to introduce loyalty programs that for
instance reward customers for each year they are loyal with some kind of extra service
(for example additional GB data).
Introducing loyalty programs can prevent defection since customers may decide to stay
with their provider because defecting may cause a loss of earned benefits. Hence,
loyalty programs will work as a switching barrier since the earned benefits will be lost
at the time the customer defect.
Another side-effect of the loyalty program is that it may also work as an attraction for
new customers.
7.4 Limitations and future studies
The research conducted in this paper is mainly relevant for the assessment of
consumers who are subscribed to a telecommunications provider and not prepaid
consumers. Firstly, the survey was primarily developed with postpaid consumers in
mind. Secondly, based on the respondents from each category (201 postpaid versus 11
prepaid), it is evident that the results mainly encompasses the attitudes of postpaid
consumers rather than prepaid consumers. However, since this study did not
39 of 49
differentiate prepaid from postpaid consumers as far as their attitudes go, it could be
useful to focus solely on the preferences of prepaid consumers and explore whether
these differ or align with those of postpaid consumers.
It should also be noted that the respondents in this study are private customers. Hence,
this study has only investigated the preferences on the business-to-consumer market
leaving the business-to-business market unexplored. Therefore, exploring the
preferences of corporate consumers in the telecom market could be addressed in future
research.
Additionally, future research could also address the limitation of this study’s sample
size and sampling technique. A sample of 212 is hardly enough in comparison to the
total number of mobile subscriptions of approximately 8 milion in 2013 (see figure 1).
A larger sample could possibly give this study more credibility and better statistical
results. Also, it could solve some of the issues with scale reliability which was
encountered during the study.
A sampling technique different from convenience sampling should also be applied in
order to ensure a random and more representative sample.
Since this study only measures the respondents’ attitudes and intentions and not their
actual behaviour, it could imply that some of them will end up acting in a way that is not
consistent with what they answered in the survey. Thus, we second Lee and Murphy’s
(2005) suggestion to conduct longitudinal research that addresses actual intentions and
behaviours rather than subjecting the participants for hypothetical situations in which
they, as in this case, should consider whether they would leave their current provider in
case of, for instance, higher prices.
This study considers mainly factors which providers directly can affect (product
characteristics, services etc.), and the regression models explain 11.7 per cent (factors
influencing brand loyalty) and 20.4 per cent (factors influencing brand defection)
respectively. This leaves a lot of the variation in brand loyalty and brand defection
unexplained.
40 of 49
Factors beyond the influence of management such as personality are not included in
this study. Some customers may be more inclined to switch between different providers
while other customers may be more loyal simply due to their personalities.
Previous research has proved personality traits to have significant influence on
consumers’ switching behaviour (Siddiqui, 2011). In his study, Siddiqui investigated
mobile phone users, and it was found that 14.5 per cent of the variation in switching
behaviour was explained by personality traits. Therefore, different personality traits
could reasonably be included in future research.
Since the participants of this study are primarily in their twenties, it could also be
interesting for future studies to include customer characteristics such as age. It would
be reasonable to believe that young consumers are more prone to switching between
brands compared to older consumers. So are there actually significant differences? And
if so, what is more important to older consumers when they make their decision to
either stay or leave. This kind of study could be useful from a managerial standpoint
since it would give managers the knowledge to better target different age groups of
their consumers.
41 of 49
8. References
Aydin, S., Özer, G. & Arasil, Ö. 2005, "Customer loyalty and the effect of switching costs
as a moderator variable: A case in the Turkish mobile phone market", Marketing
Intelligence & Planning, vol. 23, no. 1, pp. 89-103.
Bitner, M.J., Booms, B.H. & Tetreault, M.S. 1990, "The Service Encounter: Diagnosing
Favorable and Unfavorable Incidents", Journal of Marketing, vol. 54, no. 1, pp. 71-84.
Bogomolova, S. 2010, "Life after death? Analyzing post-defection consumer brand
equity", Journal of Business Research, vol. 63, no. 11, pp. 1135-1141.
Bogomolova, S. & Romaniuk, J. 2009, "Brand defection in a business-to-business
financial service", Journal of business research, vol. 62, no. 3, pp. 291.
Danish Business Authority. 2013, 10/30/2013-last update, Telestatistik første halvår
2013. Available: http://erhvervsstyrelsen.dk/file/414739/telestatistik-2013-1.pdf
[2014, 04/08].
Danish Business Authority. 2014a, 2G-udbud 1997. Available:
http://erhvervsstyrelsen.dk/2g-udbud-1997 [2014, 04/08].
Danish Business Authority. 2014b , Nummerportabilitet. Available:
http://erhvervsstyrelsen.dk/nummerportabilitet [2014, 04/08].
Dick, A.S. & Basu, K. 1994, "Customer Loyalty: Toward an Integrated Conceptual
Framework", Journal of the Academy of Marketing Science, vol. 22, no. 2, pp. 99-113.
Eriksson, P. & Kovalainen, A. 2008, Qualitative Methods in Business Research, SAGE,
London.
Field, A. 2009, Discovering Statistics using SPSS, 3rd edn, SAGE, London.
Ganesh, J., Arnold, M.J. & Reynolds, K.E. 2000, "Understanding the Customer Base of
Service Providers: An Examination of the Differences Between Switchers and
Stayers", Journal of Marketing, vol. 64, no. 3, pp. 65-87.
42 of 49
Gremler, D.D. and Brown, S.W. (1996), "Service loyalty: its nature, importance, and
implications", in Edvardsson, B., Brown, S.W., Johnston, R. (Eds), Advancing Service
Quality: A Global Perspective, International Service Quality Association, pp. 171-80.
Hair, J., Black, W., Babin, B. & Anderson, R. 2009, Multivariate Data Analysis, 7th edn,
Prentice Hall.
Hutcheson, G. & Sofroniou, N. 1999, The multivariate social scientist
, , London: Sage.
Jackson, B.B. 1985, Build Customer Relationships That Last, Havard Business Review - LA
English.
Jones, M.A., Mothersbaugh, D.L. & Beatty, S.E. 2002, "Why customers stay: measuring the
underlying dimensions of services switching costs and managing their differential
strategic outcomes", Journal of Business Research, vol. 55, no. 6, pp. 441.
Keaveney, S.M. 1995, "Customer Switching Behavior in Service Industries: An
Exploratory Study", The Journal of Marketing, vol. 59, no. 2, pp. 71.
Keller, K.L. 1993, "Conceptualizing, measuring, and managing customer-based brand
equity", Journal of Marketing, vol. 57, no. 1, pp. 1.
Klemperer, P. 1995, "Competition when Consumers have Switching Costs: An Overview
with Applications to Industrial Organization, Macroeconomics, and International
Trade", The Review of Economic Studies, vol. 62, no. 4, pp. 515.
Klemperer, P. 1987, "Entry Deterrence in Markets with Consumer Switching Costs", The
Economic Journal, vol. 97, no. , Supplement: Conference Papers, pp. 99-117.
Krishnamurthi, L. & Raj, S.P. 1991, "An Empirical Analysis of the Relationship Between
Brand Loyalty and Consumer Price Elasticity", Marketing Science, vol. 10, no. 2, pp.
172.
43 of 49
Lee, R. & Murphy, J. 2005, ""From Loyalty To Switching: Exploring The Determinants In
The Transition", a report prepared for Australia and New Zealand Marketing
Academy (ANZMAC) 2005 Conference: Consumer Behaviour", .
Lee, J., Lee, J., Kim, Y. & Park, Y. 2006, "Estimating the extent of potential competition in
the Korean mobile telecommunications market: switching costs and number
portability", International journal of industrial organization, vol. 24, no. 1, pp. 107.
Lianjv, N. & Xin, F. 2014, "The Switching Choices of Subscribers in China Mobile Market",
TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 12, no. 2, pp. 1416-
1423.
Marshall, M.N. 1996, "Sampling for qualitative research", Family practice - LA English,
vol. 13, no. 6, pp. 522.
Martins, R.C., Hor-Meyll, L. & Ferreira, J.B. 2013, "Factors Affecting Mobile Users'
Switching Intentions: A Comparative Study between the Brazilian and German
Markets", BAR - Brazilian Administration Review, vol. 10, no. 3, pp. 239-262.
Nakhleh, H.M.A. 2012, "The relationship between customer relationship marketing
tactics, relationship quality and customer's loyalty in mobile communication
industry", Academic Research International, vol. 3, no. 2, pp. 538.
Narayandas, N. 1996, "The link between customer satisfaction and customer loyalty: an
empirical investigation", working paper edn, Harvard Business School, Boston, MA.
National Economic Research Associates (NERA) 2003, "‘‘Switching costs’’, a report
prepared for the Office of Fair Trading and the Department of Trade and Industry",
vol. Economic Discussion Paper No. 5.
Nguyen, N. & Leblanc, G. 2001, "Corporate image and corporate reputation in
customers’ retention decisions in services", Journal of Retailing and Consumer
Services, vol. 8, no. 4, pp. 227.
Oliver, R. 1997, Satisfaction: A Behavioral Perspective on the Consumer, McGraw-Hill,
New York, NY.
44 of 49
Porter, M. 1998, Competitive Strategy: Techniques for Analyzing Industries and
Competitors, The Free Press, New York, NY.
Qi, M., Zhao, G. & Zong, S. 2013, "An Empirical Study of Brand Defection Influenced
Consumer-Based Brand Equity", Journal of Economics, Business and Management,
vol. 1, no. 4, pp. 380-384.
Sathish, M., Santhosh Kumar, K., Naveen, K.J. & Jeevanantham, V. A Study on Consumer
Switching Behaviour in Cellular Service Provider: A Study with reference to Chennai,
Far East Research Centre.
Sharma, N. 2003, "The role of pure and quasi-moderators in services: an empirical
investigation of ongoing customer–service-provider relationships", Journal of
Retailing and Consumer Services, vol. 10, no. 4, pp. 253-262.
Sharma, N. & Patterson, P.G. 2000, "Switching costs, alternative attractiveness and
experience as moderators of relationship commitment in professional, consumer
services", International Journal of Service Industry Management, vol. 11, no. 5, pp.
470-490.
Shin, D. & Kim, W. 2008, "Forecasting customer switching intention in mobile service:
An exploratory study of predictive factors in mobile number portability",
Technological Forecasting & Social Change, vol. 75, no. 6, pp. 854.
Siddiqui, K. 2011, "Personality influences Customer Switching", Interdisciplinary Journal
of Contemporary Research In Business, vol. 2, no. 10, pp. 363-372.
Srinuan, P., Annafari, M.T. & Bohlin, E. 2011, "An analysis of switching behavior in the
Thai cellular market", Info, vol. 13, no. 4, pp. 61-74.
Yi, Y. & Jeon, H. 2003, "Effects of loyalty programs on value perception, program loyalty,
and brand loyalty", Academy of Marketing Science.Journal, vol. 31, no. 3, pp. 229.
45 of 49
Appendix 1
Questionnaire
ENGLISH VERSION
Do you have a mobile phone?
Yes/No
Which kind of agreement do you have with your current telecommunications
service provider?
Contract/subscription
Prepaid card
Is your contract/subscription binding?
Yes/No
What is the name of your telecommunications service provider
List of the brands + an “other” option
BRAND LOYALTY (scale from 1-5)
I will keep on using my current provider
Strongly disagree/disagree/neither agree nor disagree/agree/strongly agree
If I bought a new mobile phone, I would prefer my current provider
Strongly disagree/disagree/neither agree nor disagree/agree/strongly agree
I recommend my current provider to other people
Strongly disagree/disagree/neither agree nor disagree/agree/strongly agree
I encourage friends and family to use my current provider
Strongly disagree/disagree/neither agree nor disagree/agree/strongly agree
Even if other brands were cheaper, I would keep on using my current provider
Strongly disagree/disagree/neither agree nor disagree/agree/strongly agree
The below questions are about your decision to stay with your current
telecommunications service provider. Indicate how important you find the
following factors on your decision to stay (scale from 1-5).
The price of my subscription package
46 of 49
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The prices on additional airtime, GB etc. (that is not included in my package):
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
Network coverage
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The possibility to include music streaming services such as Spotify in my package
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The possibility to include Free Facebook in my package
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
Offers/discounts (e.g. you can get 20% off in specific clothing stores)
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The possibility to call people with the same provider as myself at no charge
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The loyalty programs (you are rewarded for your loyalty e.g. by getting additional
free GB or airtime):
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The opportunity to visit a physical store in case I have any questions
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The brand (e.g. good image and reputation)
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The time and effort it takes to search for a new provider
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
47 of 49
The costs related to switching to another provider (e.g. a sign-up fee charged by
the new provider)
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
The transfer process of my current mobile number to another provider if I switch
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
Employee service
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
My current provider’s ability to accommodate my changing needs by offering
more suitable subscription packages
Very unimportant/unimportant/neither important nor unimportant/ important/very
important
BRAND DEFECTION (scale from 1-5)
If I feel unsatisfied about my current provider, I will change to another brand
Strongly disagree/disagree/neither agree nor disagree/agree/strongly agree
If I am attracted to other brands, I will give up my current provider
Strongly disagree/disagree/neither agree nor disagree/agree/strongly agree
If my income changes, I will not use my current provider anymore
Strongly disagree/disagree/neither agree nor disagree/agree/strongly agree
Below is a list of statements about your decision to leave your current
telecommunications service provider. Consider whether the following statements
would induce you to leave your current provider (scale from 1-5).
How likely is it that you will leave your current provider in case of…
Poor network coverage
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
Improper employee service (e.g. unknowledgeable employees)
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
Long wait times for consumer service
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
48 of 49
Error in billing
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
Unsuitable subscription packages
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
High prices
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
Hidden charges
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
Better offers from competitors
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
Recommendation of another provider from family and friends
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
No reward for being loyal
Very unlikely/Unlikely/Neither unlikely or likely/Likely/Very likely
DEMOGRAPHICS
Gender
Male/Female
Age
?
49 of 49
Appendix 2 Abbreviations used in SPSS
Brand loyalty factors (DTS – decision to stay)
DTS1 – Price of subscription package
DTS2 – Prices on additional airtime, GB etc.
DTS3 – Network coverage
DTS4 – Music streaming services
DTS5 – Free Facebook
DTS6 – Offers and discounts
DTS7 – Free calls to people with same provider
DTS8 – Loyalty programs
DTS9 – Access to physical store
DTS10 – Brand image
DTS11 – Employee service
DTS12 – Provider’s ability to accommocate changing needs
DTS13 – Time and effort to seach for a new provider
DTS14 – Costs related to switching
DTS15 – Transfer process of mobile number
BL - Brand loyalty measure
Brand defection factors (LICO – leave in case of)
LICO1 – Poor network coverage
LICO2 – Improper employee service
LICO3 – Long wait times for consumers service
LICO4 – Error in billing
LICO5 – Unsuitable subscription packages
LICO6 – High prices
LICO7 – Hidden charges
LICO8 – Better offers from rivals
LICO9 – Recommendation of another provider from family/friends
LICO10 – No reward for being loyal
BD – Brand defection measure
