I NCREA S IN G CU S TO M ER L OYA LT Y
The integration of online and offline markets to increase brand commitment
Marnix Contant Summer 2013
I NCREA S ING C UST O ME R LOYA LTY
The integration of online and offline markets to increase brand commitment
Thesis Seminar Business Studies Author: Marnix Contant (10019413)
Supervisor: Mr Drs. ing. A.C.J. Meulemans Academic Year: 2012/2013
Abstract
The Internet has been of growing importance over the last couple of years. Throughout its increase, consumers tend to act more online, which results in breaking boundaries. For companies it is very important to adapt a strategy for online markets. Moreover, online and offline markets should be integrated into new marketing and strategic systems. Today, many companies fail to make this cru-cial step forward. Therefore, this study tries to find out to which extent online marketing can in-crease brand commitment. Before examining this, relationships between satisfaction & brand trust, brand trust & brand loyalty, and brand loyalty & brand commitment are shown. This is done to get insights in increasing customer loyalty and brand commitment by using online and offline markets. Questionnaires among Dutch consumers were distributed to evaluate their brand loyalty, trust, commitment, and experiences with mobile telephones. The results show that only loyal customers can get more brand commitment by using online markets like the Internet. Online markets cannot improve brand commitment among spurious brand-loyal customers on the mobile telephone market.
CO N T E N T S PA G E
Abstract 3
Foreword 6
1. Introduction 7
2. Literature Review 9
2.1 True Customer Loyalty 10
2.2 The New Era of Brand Loyalty: The Internet 11 2.2.1 The Five Forces Model by Porter 11 2.2.2 BCG in Changing Customer Behaviour 12
2.2.3 Brand Funnel by Elmo Lewis 12
2.3 Customer Services 15
2.4 E-Loyalty Framework 16
3. Conceptual Framework 19
3.1 Customer Satisfaction and Brand Trust 19 3.2 Brand Trust and Brand Loyalty 20 3.3 Brand Loyalty and Brand Commitment 21
4. Research Design 24
4.1 Research Design 24
4.2 Sample 25
4.3 Data Collection 26
4.4 Measures 28
4.4.1 Satisfaction & Brand Trust 28
4.4.2 Brand Loyalty 29
4.4.3 Brand Commitment 29
4.4.4 Online Marketing 30
4.4.5 Personal Questions 30
4.4.6 The Final Survey Construction 31
5. Results 32
CO N T E N T S PA G E
5.1.1 Sample Characteristics 33
5.1.2 Data Characteristics 34
5.2 Reliability 34
5.3 Correlations 35
5.4 Factor Analysis on Brand Loyalty 36 5.4.1 Kaiser-Meyer-Olkin Measure and Barthlett’s Test of Sphericy
36 5.4.2 Extraction by Kaiser’s Criterion and Scree Plot 37
5.5 Regression and ANOVA 38
5.6 Influences of Online Marketing 41
6. Discussion 42
6.1 Summary of Results 42
6.2 Satisfaction and Brand Trust 43
6.3 Brand Trust and Brand Loyalty 43 6.4 Brand Loyalty on Brand Commitment 44 6.5 Online Marketing on the Relationship Between Brand Loyalty and Brand Commitment
45
6.6 Theoretical Implications 46
6.7 Loyalty Implications 47
6.8 Limitations and Suggestions for Future Research 48
7. Conclusion 49
References 51
Appendix A 54
Foreword
You are about to read my first thesis. One to be proud of. This thesis was written for my Bachelor Degree Business Studies at the University of Amsterdam. Since this is my last assignment, I can assure you that producing such a big assignment in such little time can be stressful sometimes. This assignment would not have been possible without the help of my supervisor. So, first of all, I would like to thank Antoon Meulemans for all his knowledge, expertise, time, and criticisms. Secondly, I would like to thank all the people who helped me distribute my surveys online through their Face-book pages, and the people that took 10 minutes of their time to participate in my survey. Finally, my biggest thanks go to my family and girlfriend, who were always there for me and took their time to read my work and showed their interest. For now, it is time to say my computer farewell for some time.
I hope you enjoy my thesis.
1. Introduction
In the past several years, the use of social media has been of growing importance (Merks, 2013). The growth was explosive. More than 50% of the total world population that is connected to Inter-net uses one or more social Inter-networks to stay connected (Statisticbrain, 2012). 2013 is the first year in which growth has subsided. Moreover, the expansion of online shopping is enormous (CBS, 2012). The Netherlands is placed in the top ten countries in Europe for online shopping. Since 2006, the percentage of consumers who is buying online has increased with 20% (CBS, 2012). Another good example to show the results of how much the Internet is growing is the use of social media platforms. In 2012, social media network Facebook grew over 45%. This year, they only reached a 3% growth (Forbes, 2013). With more than 50% of the total world population that is connected to each other, a totally new communication system was invented (Statisticbrain, 2012). Young people state that they suffer from this new system. This suffering is called ‘Social Media Stress’. A cause for this type of stress can be that people cannot keep track of all the information that is distributed through the social media channels (NOS, 2012). They are then afraid that they are excluded from groups or friends. Youngsters further indicate that the use of social media sometimes leads to the detriment of concentration, school performance, sports, contact with relatives, or sleep. They often do not know how to handle it.
But what are social media? Kietzmann, Hermkens, McCarthy & Silvestre (p. 241, 2011) state that social media is: ‘a technology based platform where users get the ability to share informa-tion about their life, bought products, or write blogs and having discussions on the internet.’ The number of possibilities is immense. Through social media millions of people are uploading their status on the Internet, or giving their opinion about products and services. This is why the
market-ing sector in business life has to act quickly. Mangold and Faulds (2009) believe that the traditional integrated marketing communication system (ICM) is no longer complete. This system coordinates the role of public relations, sales promotion, and personal selling. On top of these crucial elements, it also tries to reveal the values and strategies of the company (Mangold & Faulds, 2009). Times are changing, and more people are connected online. For a complete system, marketing managers should include social media as a hybrid element in the integrated marketing communication system. This is supported by Kietzmann, Hermkens McCarthy & Silvestre (2011). They state that social media can significantly impact a firm’s reputation, but that not many managers can see this big transformation. Or perhaps they just choose to ignore it. Hennig-Thurau, Malthouse, Friege, Gen-sler, Lobschat, Rangaswamy, Bernd Skiera (2010) provided a framework to map the impact of rela-tionships with customers that a firm can face by using social media and online markets. Merks (p. 72, 2012) states that in many companies there is no integration between online and offline markets. Porter (2001) agrees with Merks. He states that it is very important that companies try to see the Internet as a new opportunity. Companies should integrate it into their traditional strategy, so that both markets will not consume each other (Porter, 2001). Only a few leaders in certain industries provide some synergy. An example of good synergy and loyal customers is Apple. This company has very loyal customers: whatever Apple produces, the customers like it. Apple integrated their shops in shopping streets with their online shop iStore and iTunes (Merks, p. 76, 2013).
The focus of this study will be on customer loyalty for mobile telephones. Many studies point to the fact that social media and online markets have become increasingly important issues. In addition, they also indicate what should be the strategy of a company. Earlier studies show no proof for whether or not online customers also enhance loyalty. Chaudhuri & Holbrook (2001) state that brand loyalty from a customer depends on brand trust and brand commitment. This study will
ex-amine whether this is possible, and if this brand loyalty can be integrated with online markets. In order to succeed, information from literature is reviewed to provide more information about tomer loyalty in the next century. This study will investigate if companies can increase their cus-tomer loyalty by using online markets and social media. The main research question is: To what ex-tent can online marketing increase brand commitment?
To provide an answer to this question, a Dutch survey has been distributed among several participants. They have different ages, gender, jobs, education, and interests. With the results of the survey, an overview is given on whether or not satisfaction influences brand trust, if brand trust in-fluences brand loyalty, and if brand loyalty inin-fluences brand commitment. These questions were asked for both online and offline markets to investigate if online markets can increase the customer loyalty for company products.
2. Literature review
Brand loyalty has been measured for a long time (Delgado-Ballester & Munuera-Aleman, 1999). It characterizes a relationship between a brand and the consumer, and can lead to a long-term relation-ship between customer and company. This long relationrelation-ship can result in higher sales and revenues, and a higher response from a company to competitive actions from rival companies (Delgado-Ballester & Munuera-Alema, 1999). Important measurements for brand loyalty are consumer satis-faction and brand trust. Traditionally, brand trust is defined as followed: ‘a group of beliefs held by
a person derived from his or her perceptions about certain attributes; in marketing this involves the brand, products or services, salespeople, and the establishment where the products or services are bought and sold. This group of beliefs has been divided into different dimensions and trust is
usu-ally considered a multidimensional construct that differentiates between honesty and benevolence perceived in the behaviour of the other party.’ (Flavian, Guinaliu & Gurrea, pp. 2, 2005).
A long time ago, studies examined that consumer satisfaction was important to gain brand trust (Delgado-Ballester & Munuera-Aleman, 1999). If a consumer is completely satisfied with a brand, he or she would have more trust in the brand and will be a loyal customer.
2.1 True Customer Loyalty
Bloemer & Kasper (1995) made a trade-off for a customer between Spurious Brand Loyalty and
True Brand Loyalty. Spurious brand loyalty is defined as: “the biased behavioural response ex-pressed over time by some decision-making unit with respect to one or more alternative brand out of a set of such brand which is a function of inertia” (Bloemer & Kasper, p. 313, 1995). This
means that the customer became loyal to a brand because they cannot buy the same products from different brands at the same place. Logistic problems can be a driving factor of spurious brand loy-alty (Bloemer & Kasper, 1995). True brand loyloy-alty is defined as: “the biased behavioural response
expressed over time by some decision-making unit with respect to one or more alternative brand out of a set of such brand which resulting in brand commitment” (Bloemer & Kasper, p. 313, 1995). In
this case, Bloemer & Kasper (1995) state that true loyalty will result in high brand commitment. Brand commitment is defined by Bloemr & Kasper (p. 314, 1995) as: “the pledging or binding of
an individual to his/her brand choice.” We can state from previous information that brand trust
in-creases brand loyalty. It is very important to measure brand trust (Bloemer & Kasper, 1995), but it also has implications (Delgado-Ballester & Munuera-Alema, 1999):
1. It has been known for a very long time that companies go further than their actual product to gain brand loyalty. They work with consulting firms to create brand trust.
2. Brand value and brand loyalty is not only gained through brand trust; more variables are in-volved.
2.2 The New Era of Brand Loyalty: The Internet
The Internet is of growing importance in our society (Merks, p. 14, 2012). 44% of all Internet con-nections can be found in three parts of the world: Europe, North America, and Australia. Only the richest people on earth can afford access to the Internet. The rest of the world has to wait. However, the Internet’s growth is not yet over at all (Merks, p. 217, 2012), as smartphones will give this growth an extra impulse. For example, in Africa only a small amount of people are able to access the Internet, but almost every person has a smartphone (Merks, p. 23, 2012). This growing phe-nomenon has a lot of impact on popular models, such as Porter’s Five Forces Model, the BGC, and the traditional funnel model.
2.2.1 The Five Forces Model By Porter
Porter believed that his Five Forces Model needed to be changed. The model describes the influ-ences from competitors, and he came up with five forces that influence profitability:
1. Threats of new entries 2. Bargain power of customers 3. Threats of substitutes 4. Power of suppliers 5. Internal rivalry
More than 30 years ago, Porter invented this model to teach marketers that they face more influ-ences than just their competitors (Merks, p. 53, 2012). In recent years, professors have come up
with the idea to include the Internet as a six force in his model. The Internet has a great influence on the price and market position of suppliers, and it can also change the boundaries of the entire indus-try and can increase the bargaining power of the customers. Porter did not add the sixth force be-cause the Internet is a dynamic force that influences all five previous forces in some way (Mangold & Faulds, 2009).
2.2.2 BCG in Changing Customer Behaviour
The Boston Consulting Group analysed the change in customer behaviour because of the rise of the Internet. They created three dimensions to measure ‘E-Intensity’. This relates to the development of online markets all over the world. The three dimensions were:
1. Accessibility of the Internet. The percentage of civilians that have access to the Internet. 2. Involvement of the Internet. The extent to which the Internet users use various applications. 3. Uses. The extent to which products and online services were purchased.
The outcome of this ‘E-Intensity’ was that Denmark and South Korea were at the top places in the world rankings (Merks, p. 41, 2012).
2.2.3 Brand Funnel by Elmo Lewis
In 1898, Lewis designed a brand funnel to see what steps consumers make before becoming loyal to a brand. The original model is shown in the next figure (see Figure 1.1).
Figure 1.1. The Traditional Brand Funnel Awareness Consideration
Purchase Loyalty
Throughout the rise of the Internet, this model has changed from a linear model to a cyclical model. McKinsey developed this model in 2009. The main difference between both models is that the product experience will change the buying process of the consumer now. The Internet causes a de-cline of boundaries for customers. In the past, they only had little choice between products (Merks, p. 193, 2012), while they now have every brand to choose from. The first step in the linear model (awareness) has been changed by that. Another difference in the second model is the ‘Loyalty loop‘ in which customers find themselves. They will only be loyal to a brand when it performs well and it meets the expectations (Merks, p. 194, 2012). The Internet has the ability to create forums and other websites where customers can discuss worldwide how they think a product works and should work. It is harder for companies to manage all these opinions and gain customer loyalty (Shankman, p. 71, 2011). (see Figure 1.2).
Figure 1.2. The Cyclical Brand Funnel by McKinsey
These three models have been changed by the influence of the Internet. Online markets are very important nowadays (Kietzmann, Hermkens, McCarthy & Silverstre, 2011). Kietzmann et all. (2011) state that the Internet has changed format. Where in the past the Internet was used to expand content, the Internet is now dominated by Social Media. As a company, it is very important to man-age your Social Media well and integrate your customers with an online and offline strategy (Merks, p. 266, 2012). Today, only a few companies have adopted this strategy. To build an online market, you need customers who are loyal to your brand (Gommans, Krishnan & Sheffold, 2001). In this study, we use the following definition of brand loyalty (Gommans, Krishnan & Sheffold, p. 44, 2001): "a deeply held commitment to rebuy or repatronize a preferred product/service
consis-tently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behaviour."
2.3 Customer Services
Gommans, Krishnan & Sheffold (2001) stated earlier that a good customer service increases cus-tomer loyalty, and this is supported by Shankman (2011). Shankman argued that every cuscus-tomer who is treated right could be a loyal customer forever. He built a model that needs to be followed when customers are complaining or when they need help: LUPR (see figure 1.3)
Figure 1.3 LUPR Model by Shankman
Shankman argued that the first step is listening and not acting directly. The second step is trying to understand your customer: what does he/she want from you? The third step is the most critical step in Shankman’s model: plan. It does not matter how difficult the action plan is.
Planning should always be written down. After working it out, the customer needs a response (Shankman, p. 75, 2011). The precise handling depends on the type of customer. Contrary to Gom-mans, Krishnan & Sheffold (2001), who only have one type of customer in their model, Shankman (p.74-75, 2011) provides four different types of customers:
1. Never Complained Before Customer 2. Multi Complainer
3. Social Media Complainer
The most dangerous customer who can break your loyalty is the Never Complained Before Cus-tomer. When he/she says something is wrong, there really is something wrong (Shankman, 2011). The Multi Complainer is always arguing and you need to get rid of him/her. This kind of customer always consumes most of your time, and you can never make this customer feel satisfied (Shank-man, 2011). The Social Media Complainer will make photos of everything that went wrong and can be dangerous to your brand. Missteps can be seen all over the world and everybody will talk about it. The best way to handle these kinds of customers is by telling them to send a personal mes-sage in order to solve the problem. Other people will then be able to see that you take this problem seriously and your brand will not be affected as much.
2.4 E-Loyalty Framework
Gommans, Krishnan & Sheffold (2001) developed an E-Loyalty framework where all factors that influence customer loyalty online are summed up (see Figure 1.4). This model brings all factors stated above together. It explains when customers will become loyal to your brand through online marketing. The main factors that will increase the loyalty of a customer are (Gommans, Krishnan & Sheffold, 2001):
1. Website & Technology: A website needs to be easy to work with, and has to give a good overview. If it is difficult for a customer to find the right products/services, they will try to find their products on other websites.
custo-mers. Quick follow-up with regard to customer service and a brief explanation of why things went wrong are critical points for a customer to remain loyal to a brand.
3. Trust & Security: Web shops need to be secured well. Customers need to be able to trust a company that their money will be handled safely and that their personal information is in good hands.
4. Brand Building: Brand building is a critical factor for a loyal customer. For example, a web-site needs to have a domain name that can be remembered well by a customer. When it is hard to find the brand on the Internet, customers will often walk away.
5. Value & Propositions: It is important for a customer to be treated well, and companies are able to customize their products in such a way that they can serve their customers in the exact way they want. Dell, for example, manufactures laptops on a personalized basis; their customers can choose which options they want to include in their laptop. This is very cus-tomer-friendly, which increases loyalty.
Figure 1.4 E-Loyalty Framework
Analysing all these models and earlier studies leads to a final answer: brand loyalty was measured differently in the past. The online market is now growing rapidly and it has become hard to keep customers. There are enough possibilities and competitive advantages to be reached. This study will find out to what extend online markets can increase brand commitment. It will examine if compa-nies can increase their customer loyalty by creating a better relationship between online and offline
marketing. The main research question in this study will be: ‘To what extent can online marketing increase brand commitment?’ This will be done through quantitative research.
3. Conceptual Framework
The literature review described and explored earlier research. In this section, the conceptual frame-work will be presented. This frameframe-work will present the findings on which this research is based. First of all, the expectations of customer satisfaction will be stated. Finally, the propositions regard-ing brand loyalty and brand commitment will be given. These propositions will be based on differ-ent variables (brand trust, brand loyalty, brand commitmdiffer-ent, and online marketing).
3.1 Customer Satisfaction and Brand Trust
As stated in the literature review, Delgado-Ballester & Munuera-Aleman (1999) state that the rela-tionship between brand satisfaction and brand trust has been measured for a long time. The draw-back of their statement is the old fashioned way of marketing. Their study is focused on offline markets that are not relevant for this thesis, although Ha & Perks (2005) confirmed their statement. In these times, when online purchases grow rapidly and outpace traditional selling channels, the re-lationship between brand satisfaction and brand trust is even more important for online markets and online customers. When customers feel safe and trust the brand, this will increase e-commerce (Ha & Perks, 2005). Flavian, Guinaliu & Gurrea (2005) give an example of this relationship in e-commerce: when a website is easy to use for customers, it will create trust. This trust is integrated within the variable of satisfaction. A higher degree of trust will automatically increase the level of satisfaction for customers (Flavian, Guinaliu & Gurrea, 2005). Ha & Perks’s (2005) study pointed out that the experience that online customers have has to be ‘excellent’ in order to increase their
product satisfaction. This satisfaction can be related to brand trust (Ha & Perks, 2005). Little evi-dence has been found that brand satisfaction and brand trust are related to each other in online mar-kets. Moreover, little research about the effects of this relationship for e-commerce has been done, which makes the exact relationship unclear (Ha & Perks, 2005).
As stated above, several earlier studies state that the relationship between brand satisfaction and brand trust is of big importance in offline markets. But only little research for the same rela-tionship in online markets has been done. For that reason the first proposition in this thesis is stated as followed:
H1: Consumers that score high on product satisfaction will score high on brand trust.
3.2 Brand Trust and Brand Loyalty
The literature review stated that a positive relationship had also been found between brand trust and brand loyalty (Chaudhuri & Holbroek, 2001). In the previous section, a trade-off between spurious brand loyalty and true customer brand loyalty has also been made. Another point of interest could be to test whether this important relationship also counts for online markets. Delgado-Ballester & Munuera-Aleman (2003) found a positive relationship between brand trust and brand loyalty in their research on offline markets. Their main conclusions were as followed: ‘(1) trust is viewed as
the cornerstone and one of the most desired qualities in a relationship; and (2) it is the most impor-tant attribute a brand can own.’ (Delgado-Ballester & Munuera-Aleman, pp. 35, 2003). Although
there is little evidence to prove that the same relationship is even slightly important for online mar-kets, Gommans, Krishnan & Sheffold (2001) provided an E-Loyalty framework in which brand trust is one of the key variables. They disagree with Delgado-Ballester & Munuera-Aleman’s (2003) statement that brand trust is the most important attribute for brand loyalty. This might be due to the difference between online and offline markets. Moreover, Lau & Lee (1999) state that brand trust plays a crucial role in gaining brand loyalty. They state that customers’ brand trust comes from brand characteristics. These characteristics are of big importance for brand trust. Therefore, they recommend companies to carefully change their product’s characteristics before customers lose trust and, therefore, loyalty (Lau & Lee, 1999). Finally, Bloemer & Kasper (1995) made a trade-off for a customer between Spurious Brand Loyalty and True Brand Loyalty. These types of customers each act differently.
Comparing all these earlier studies, we can state that brand trust is a very important variable for measuring brand loyalty. Since these studies were focused on situations in offline markets, it is important to investigate whether this relationship is also positive and significant for online market conditions. Therefore, we can state our next propositions:
H2: Brand trust is positively related to brand loyalty.
B Brand Trust
3.3 Brand loyalty and brand commitment
The last section in the literature review is about brand commitment. Several studies show that there is a relationship between brand loyalty and brand commitment (Chaudhuri & Holbrook, 2001;
Bloemer & Kasper, 1995; Jacoby & Kyner, 1973). Bloemer & Kasper (1995) state that there are different kinds of loyalty from customers. Attention needs to be paid to Bloemer & Kasper’s state-ment that there is a difference between the loyalty of spurious brand loyal customers and that of true brand loyal customers. Chaudhuri & Holbrook (2001) do not divide several groups. They state that brand loyalty increases brand commitment. Important to note here is that all studies are - just like the previous sections - focused on offline markets, which means that they cannot definitively say anything about online markets conditions. Amine (2011) refers to Bloemer & Kasper’s (1995) statements: he argues that brand loyalty can be measured upstream and downstream. Downstream measuring is done with variables like brand trust and brand satisfaction. Upstream measuring is linked to brand commitment. To be able to know more about brand loyalty, it is crucial to under-stand the role of customers’ brand commitment (Amine, 2011). In both upstream and downstream measuring Amine (2011) states that true customer loyalty is proven. Spurious brand loyalty has not been taken into account in this study.
Ha & Perks (2005) state that not much research has been done into online markets. They suggest that for online markets all these relationships should be tested with online marketing as a moderator variable. This study aims to fill this literary gap, and relates answers to the E-Loyalty framework from Gommans, Krishnan & Sheffold (2001).
In short, it can be said that the E-Loyalty framework is the only model that exists for online markets; it can see opportunities and threats. This study tries to adapt the existing literature and fill the gaps. Important to say at this moment, is that the difference between true customer brand loyalty and spurious customer brand loyalty has been taken into account in this study, and this difference has been compared with the opportunities and threats from the E-Loyalty framework. During the previously stated literature we can give the following, and final, propositions:
H3: Brand loyalty is positively related to brand commitment.
B Brand Loyalty
H4: Brand loyalty is positively moderated by online marketing to brand commitment
B Brand Loyalty
B Brand Commitment
B Brand Commitment
!
4. Research design
The above paragraphs discussed existing literature and information about the problem. The proposi-tions made in this survey were provided by the Conceptual Framework. This section describes which methods will be used to test the research problem and the propositions. The research design, including a questionnaire, will be explained below. After that, the sample and the measures that will be used will be discussed.
4.1 Research design
For this research, a questionnaire has been used. A survey is usually used with a deductive ap-proach, and it is a popular and common strategy in business and management research. In addition, the survey strategy is perceived as authoritative by people in general, and it is easy to explain and understand answers given by different people (Saunders, Lewis & Thornhill, p. 144, 2009). This makes it the best strategy for this survey. The propositions made are based on findings from differ-ent consumers and brand users. Using a survey enables more control over the process. It is not needed to test the complete population; only a sample is enough (Saunders, Lewis & Thornhill, p. 144, 2009). This is exactly what is needed: a sample that can give information about a large popula-tion. For a survey it is very important that participants get the same information and answer possi-bilities to fill out, as this gives generalizable answers (Saunders, Lewis & Thornhill, p. 144-145, 2009). Moreover, earlier studies about brand loyalty also used the survey method (Knox & Walker, 2010; Chaudhuri & Holbrook, 2001).
There are also disadvantages of using the survey strategy: many researchers complain that their progress is delayed by their dependence on participants (Saunders, Lewis & Thornhill, p. 144-145, 2009). Another example is that there is a limitation to the number of questions that can be
asked in a survey. This seems to be the biggest drawback in using a questionnaire (Saunders, Lewis & Thornhill, p. 365, 2009). Questionnaires are not the only technique that can be used with a deduc-tive approach. Other forms of approach are also available: structured interviews and structured ob-servations are also deductive approaches.
This questionnaire has only been handed out through Facebook and email. Moreover, this means that the questionnaire is self-administered. Advantages of this can be that anonymity can be guaranteed and participants are not often biased by the interviewer (observer bias) (Saunders, Lewis & Thornhill, p. 382, 2009). Saunders, Lewis & Thornhill (2009) also give a limitation to
self-administered questionnaires, as it can harm the reliability of a survey. Since this survey has been handed out only once, the timing of participants to fill out the questionnaire depends on their mood. This is called the subject of participant’s error (Saunders et all., 2009).
4.2 Sample
This study is focused on Dutch consumers. Only generalizable answers are needed from partici-pants that can speak or read fluently Dutch. Therefore the questionnaire was distributed in Dutch. In that way, the correct and reliable sample is found to draw generalizable results (Saunders, Lewis & Thornhill, p. 373, 2009). Moreover, it is very important in this study that participants only partici-pated if they had a mobile phone, as it would not have been a reliable sample if they did not have one. Fortunately, this was not hard, as, from 2005 onwards, there have been 100 mobile phones per 100 consumers. Only Finland (102 per 100 consumers) and the United Kingdom (109 per 100 con-sumers) scored higher on this list (CBS, 2005).
It is very important for a survey to have a high response rate (Saunders, Lewis & Thornhill, p. 219, 2009); only then is it statistically proven that the propositions and hypotheses that are made
are sufficiently tested (Saunders, Lewis & Thornhill, p. 217-218, 2009). Drawbacks for a large sample can be: budget constraints, time constraints, or needing results quickly (Saunders, Lewis & Thornhill, p. 212, 2009). For this survey the time constraint was an important issue. For that reason, the sample size is not as big as in various earlier studies (Chaudhuri & Holbrook, 2001). The mal sample size is usually 30 (Saunders, Lewis & Thornhill, p. 218, 2009), which is why the mini-mal sample size for this study will be 30. It is important to ensure gender is equally distributed, as you will want to make equal and generalizable results on the entire population - not just about one gender. Furthermore, the propositions stated that customers who are happy have more brand trust. Other propositions provided other relationships between brand loyalty and brand trust. For that rea-son, it is important that the complete questionnaire is filled out accurately (Saunders, Lewis & Thornhill, p. 218, 2009). The maximum sample size was not necessary. This survey strived to reach at least 100 participants, and this resulted in a total sample size of 103 participants, which all fully completed the questionnaire. The goal of reaching a larger sample than 100 participants was there-fore achieved.
4.3 Data collection
Two methods were used to collect all the results from the participants. The first method was an Internet-based method. www.thesistools.com was used to collect the results. This website is spe-cially developed for online questionnaires. It is very specific and all kinds of questions can be im-ported into that website. A big advantage of this website is that data is already stored in an Excel file and does not need manual processing. Another advantage of the Internet-based method is the total reach of your questionnaire. Distances are bridged digitally, and questionnaires can be filled out by participants from all over the country. To reach as many people as possible, this
question-naire has been distributed via Facebook and email. Participants were asked to fill out their ques-tionnaire, and to share the link of the questionnaire on their personal Facebook page to reach more participants. Unfortunately, the Internet-based approach also has some drawbacks. One of them is that the Internet-based approach is different from the traditional approach, and new skills need to be invented to use the system well (Rahm, Reed & Rydl, 1999). Another limitation is that you cannot reach everyone; older people do not use the Internet as much as young people. Also, not everyone will and is able to fill out the questionnaire (Rahm, Reed & Rydl, 1999).
The second method that is used was hard-copy questionnaires. On the workplace the ques-tionnaires were printed out, and colleagues were asked to fill out the questionnaire. Later on, they were administered online via www.thesistools.com to get all the data in one file. Almost every col-league was kind enough to fill out the questionnaire online. The total amount of questionnaires that were filled out by hand was two, and these questionnaires were imported into the online database.
As stated earlier, the questionnaire was written and handed out in Dutch. Because the an-swers are generalizable to Dutch people, they need to speak or read Dutch fluently. Before they could start filling out their questionnaire, the participants had to read the introduction in which they were asked to fill out the question about marketing and mobile phones. They were not told what the subject was, or what was being measured, as the awareness could lead to socially desirable answers. After participants completed their last question, they were thanked for their time, interest and effort. Participants also got the opportunity to receive the complete thesis at the end. The participants were able to contact me afterwards, which is very important, because they should be able to ask any questions they might have (Saunders, Lewis & Thornhill, p. 391-392, 1999).
4.4 Measures
For this survey, a questionnaire was distributed. Attribute values and opinion values were produced to measure the participants’ opinions about brand loyalty and their purchase behaviour online. It is important to explain why certain variables and questions are used because it can impact the survey for an important part. The order of explanation will be: satisfaction & brand trust, brand loyalty, brand commitment, and brand loyalty on online markets. Finally, a total construct will be given, in-cluding the explanation of the order of the questions. For the complete Dutch questionnaire, see ap-pendix A. Before the participants were asked to give answers about brand loyalty in the mobile phone market, they were asked if they owned a mobile phone, and, if so, which brand. Participants who did not have their own mobile phone were afterwards excluded from the survey.
4.4.1 Satisfaction & brand trust
In the theoretical framework, it was stated that satisfaction is related to trust. To measure satisfac-tion, opinion values are used, such as: ‘Wisselt u vaak van mobiele telefoon?’ (Do you often switch mobile phones?). Respondents were able to fill out: yes/no/only when my subscription year has passed/other, namely. The possibility to get a unique reaction from the respondent is critical because several reasons can influence buying behaviour. Another question about measuring satisfaction is: ‘Was u tevreden over uw laatste mobiele telefoon?’ (Were you satisfied with your last mobile phone?). This question was asked on a 7-point Likert scale. Moreover, a participant can give his/her opinion on whether or not he/she agrees with the given theorem (Saunders, Lewis & Thornhill, p. 378-379, 2009).
The other proposition that was made was about brand trust. Questions from earlier question-naires were asked to ensure reliable and valid answers. An example is a question from Aaker
(1996). He argued that a good question to measure brand trust is: ‘U zult het merk de volgende keer weer aanschaffen’ (Next time you will buy the same product again). This was also measured on a 7-point Likert scale. By using questions from earlier questionnaires and from high-ranked professors, the questions become more reliable and valid (Saunders, Lewis & Thornhill, p. 373, 2009).
4.4.2. Brand loyalty
To measure brand loyalty, earlier studies have come up with a long range of questions (Aaker, 1996; Merks, 2012). This questionnaire contained questions like: ‘Merken die u koopt zijn onderschei-dend’ (Brands that you buy are distinctive), and ‘Merken die u koopt passen bij u’ (Brands that you buy suit you’). Al these questions were asked on a 7-point Likert scale. In total it contained 9 items. Moreover, it is very important that questions are translated well and carefully (Saunders, Lewis & Thornhill, p. 383, 2009).
4.4.3 Brand Commitment
Chaudhuri & Holbroek (2001) summed up a short list of items that can be used to measure brand commitment. They were measured on a 7-point Likert scale. This was done because respondents can mark to which extent they agree or disagree with the theorem. Questions such as ‘Ik vertrouw op mijn merk’ (I rely on my brand) and ‘Mijn merken maken me blij’ (My brands make me happy) were asked. Some questions that were asked looked familiar or completely the same, because of the test’s re-test method (Saunders, Lewis & Thornhill, p. 373, 2009); when respondents give approxi-mately the same score to a familiar question, they are supposed to be reliable and valid (Saunders, Lewis & Thornhill, p. 373-374, 2009). For these three items, no weight-analysis has been done be-cause there were too few questions.
4.4.4 Online Marketing
For questions about the online markets, respondents were asked about their online buying experi-ence. First of all, they were asked a yes-no question about if they had ever bought anything online, and, if so, how often. This was done to collect behavioural variables (Saunders, Lewis & Thornhill, p. 368, 2009). Moreover, respondents needed to answer questions on why they wanted to buy some-thing online, or why they would not buy somesome-thing online. Afterwards, they were asked, on a 7-point Likert scale, whether they agreed or disagreed with theorems about buying experience online. These questions were asked to find out if online markets had unique selling points and could in-crease brand loyalty. On top of that, it also tried to find out what the drawbacks of online markets are.
4.4.5 Personal Questions
At the end of the questionnaire, respondents were asked some personal questions, such as: age, gender, education, and profession. This was asked at the end because respondents will then have familiarized themselves with the questions and subject, and will be more willing to give ‘personal’ information. The question about age was an open-ended question. The question about their gender was a close-ended question. Respondents were also asked to give their highest education level. Be-cause the Netherlands has many education tracks, this was formulated as an open-ended question. During the analysis, secondary school, MBO, HBO & WO will be separated from each other to see whether different education levels have different levels of brand loyalty. The same counts for pro-fessions; these will be sorted in sectors.
4.4.6 The final survey construction
In conclusion, the type of questionnaire is a self-administered and Internet-based questionnaire. The main attributes for this type of questionnaire are: easy to reach a large amount of participants, not much time needed to complete data collection, and automatic import of data (Saunders, Lewis & Thornhill, p. 268, 2009). Disadvantages of using this type of questionnaire are: a variable response rate, socially desirable answers (they can discuss answers with others), and creating a feasible length of the questionnaire (fewer screens is better) (Saunders, Lewis & Thornhill, p. 394, 2009). It is very important that some people read your survey before distributing it, as this will increase the face validity (Saunders, Lewis & Thornhill, p. 394, 2009). Three people have checked this ques-tionnaire before it was distributed.
To make the questionnaire reliable it was tested on internal consistency. This means that some questions were almost the same to check whether participants would fill out the same answer. If they did not do so, the participants’ data could be excluded for this study.
Before participants started their questionnaire, a short introduction was shown, to give them some information about the topic and the time needed to fill out the questionnaire (5-10 minutes). The order and flow of questions is as follows: first of all, the participant is asked if he/she is in pos-session of a mobile phone. After some simple but specific information about the mobile phone and their satisfaction about it, 7 questions about brand loyalty are asked. This is done through proposi-tions that participants can evaluate. After these 7 quesproposi-tions, 2 quesproposi-tions about brand commitment were asked in the same alignment as the first 7 questions, as it is easier for respondents to fill out a structured questionnaire (Saunders, Lewis & Thornhill, p. 371, 2009). In the same way, 4 questions about brand trust and 7 questions about online marketing were asked. During the questionnaire, it was possible for participants to give their own opinion; they were able to fill out a close-ended
an-swer. To gain unique results it was also possible for them to give their own view and enter: ‘Yes/No, because...’. For that reason, this study gained some very special results. The last questions in this questionnaire were personal ones, and because participants got familiarised with the questions it had become more likely that they would enter their personal information (e.g. education level, age, work sector). When they submitted their questionnaires, everyone was asked if they had any ques-tions and if they were interested in getting a copy of the final work to see why they answered all these questions. Participants were able to send their questions to marnix.contant@student.uva.nl
and received feedback within 24 hours. Only one participant used this option.
5. Results
This section will discuss the outcome of the questionnaires. Answers on the earlier made proposi-tions in the literature review will be provided. The structure will be as followed: firstly, some de-scriptive statistics about the sample and data will be given; secondly, their reliability will be tested (Cronbach’s Alpha); thirdly, the correlation will be explained; fourthly, a factor analysis of brand loyalty will be given; and finally, several regression analyses and ANOVA tests will be given to test our propositions.
5.1 Descriptive statistics
Before answering the propositions and main research question, it is necessary to specify the sample and respondents. Moreover, in this section the characteristics of the sample and a brief description of the data that is collected will be presented.
5.1.1 Sample Characteristics
In total, 103 participants filled out the questionnaire. Every participant filled out all the questions - except for one, who forgot to enter his age. Since this is no reason to exclude this participant for the entire research project, the final sample contains 103 respondents. The average age of the respon-dents in this sample is 27.37. 38.20% of the sample is between 20-25 years old (N=102). This will probably be because of my fellow students who helped me by filling out the questionnaire. In this sample, the minimum age is 15, the maximum age is 57 and the median age is 22. More information about the age distribution is found below in table 1.1.1 and table 1.1.2. For this final sample, 46 (44.7%) male and 57 (55.3%) female respondents participated (N=103). Most of these respondents finished a HBO degree (40.8%) (N=103). All information about the education level of this final sample can be found below in table 1.2.
Since all respondents answered that they own and use a mobile phone, they were asked what their current mobile phone brand is. 43 respondents (41.70%) use Apple’s iPhone. In second place is Samsung with 33 respondents (32%). Third is HTC with 11 users (10.7%). The complete list is showed below in table 1.3 (N=103).
5.1.2 Data Characteristics
This section will provide some information on the data characteristics. Respondents evaluate ques-tions about different variables: satisfaction, brand loyalty, brand trust, brand commitment, and fi-nally online marketing. Propositions were given and could be evaluated on a scale from 1-7 (1= not true, 7= true). Every variable had more than one item. To create a variable that included all items, first of all new variables were computed. This was done through creating a new mean (sum of means). Findings from these data characteristics can be found in table 1.4.
5.2 Reliability
Before analysing the data collection, it is needed to check if the variables are reliable, especially the new variables that were created. To test the reliability of the variable, Cronbach’s alpha test was
used; this is a good way of measuring scale reliability (Field, p. 675, 2009). In order to get reliable variables, Cronbach’s alpha needs to be higher than 0.7 (Field, p. 675, 2009). The results of the test are shown in table 1.5. All variable are reliable. The Cronbach’s alpha from satisfaction could not be calculated since one of the two items measured was only answerd by ‘yes’ or ‘no’ instead of a question that needed to be ranked from 1-7.
5.3 Correlations
Before testing the first propositions, some correlations are explored. All correlations can be found in Appendix B. Satisfaction correlates positively (r=0.362) with brand trust and is highly significant (p<0.001). Further analysis will try to find out if there really is a relationship between these vari-ables. Brand trust also correlates positively (r=0.591) with brand loyalty and is again tested signifi-cant (p<0.001). Important to state at this point is that brand trust correlates negatively (r=-0.208), but tested significant on age (p<0.036). Thus, there is a relationship between the age of the respon-dent and the brand trust of this person. Moreover, brand loyalty is positively correlated with and highly significant to brand commitment (r=0.720, p<0.001). So far, this is exactly what we expected from the questionnaire. At last we can state that online marketing is the only variable that is not tested significantly with one of the variables from the survey (p>0.05). Later on in these results, there will be an attempt to find out why this is the only variable that is not tested significantly. The
last correlation that needs to be remarked is the correlation between gender and online marketing. This is negatively correlated (r=-0.168) but almost significant (p=0.90>0.05). It is interesting to see whether gender can have a relationship with online marketing.
5.4 Factor Analysis on Brand Loyalty
Brand loyalty is measured with 8 items. To understand, identify, and structure these items, an analy-sis will be done. A principal component analyanaly-sis (PCA) was conducted on the 8 items with orthogo-nal rotation (varimax). This section will explain the results of the aorthogo-nalysis. First of all, the Kaiser-Meyer-Olkin measure (KNO) and Barthlett’s test of sphericy will be explained to find out if the cor-relations are large enough to run a PCA. Secondly, Kaiser’s criterion and scree plot is explained to choose the number of extractions for the analysis. Finally, the last section provides answers to the new factor loading after rotation and cluster names.
5.4.1 Kaiser-Meyer-Olkin Measure and Barthlett’s Test of Sphericy
Before running a PCA, it is necessary to run the KNO test and Barthlett’s test of sphericy. Both tests measure the sample adequacy and check whether it is possible to run a PCA. Outcomes for the KNO test are distributed as follows: values between .5 and .7 are mediocre, values between .7 and .8 are good, values between .8 and .9 are great and finally values greater than .9 are superb (Hutch-enson & Sofroniou, 1999; from Field, p. 647, 2009). In this analysis, the KNO value is .834 - which can therefore be classified as ‘great’ (Field, p. 647, 2009). On top of this score, it is also important to look at the anti-image matrices. Values on the diagonal need to be higher than .5. In this test, only 1 variable scored under this minimum, which is not too much of a problem (Field, p. 648, 2011) (See appendix B, table 2.b for anti-image matrices). The other test for adequacy is Barthlett’s test
of sphericy; Barthlett tests the null hypothesis that the original correlation matrix (see Appendix B, table 2.a) is an identity matrix. We do not want an identity matrix, and for that reason Bartlett’s test needs to be tested significantly (α=.05). It was tested that Barthlett’s test of sphericy χ² (28) = 268,238, p <.001. This indicates that correlations between the 8 items are large enough to run a PCA. An overview of both tests can be found below in table 1.6 and 1.7.
5.4.2 Extraction by Kaiser’s Criterion and Scree Plot
The initial analysis ran and obtained values for each component of the data. Two components had values higher than Kaiser’s criterion of 1 and in combination explained 58.90% of the variety (see table 1.8). Since less than 30 variables were used, the Kaiser criterion is accepted. This means that components that scored higher than 1 will be excluded from the analysis. A scree plot shows the same number of extractions at the point of inflexion (see appendix B, graph 1.a. for the scree plot). However, this is not an option in this scenario as that would mean that you would need over 200 participants to give a reliable answer. In conclusion, 2 is the number of components that were ex-cluded in the final analysis.
Table 1.9 shows the factor loading after rotation. The items that cluster on the same compo-nent suggest that compocompo-nent 1 represents the uniqueness of the mobile phone and that compocompo-nent 2 represents other products of the same brand. It can also be seen that by creating two clusters, the distribution of variety is more equal than before.
5.5 Regression and ANOVA
To test our propositions, this section will provide information about the regression and ANOVA analysis that have been conducted. Both these methods are able to describe if there is a relationship between the variables that are measured. The ANOVA analysis can show how much impact the in-dependent variable has on the in-dependent variable. All the outcomes will also be shown in tables.
The first proposition that was tested was the positive relation between satisfaction and brand trust. In the correlation section (4.3) can be seen that satisfaction was positively correlated and tested significantly. The results of the regression and ANOVA analysis prove that there is a signifi-cant positive relationship between satisfaction and brand trust (p<0.001). The R square showed that 13.1% of the variety in brand trust can be explained by the variable satisfaction. After running the ANOVA test, that was also tested significant (p<0.001), the F value is 15.225. Table 1.10 shows all the extra data from the regression analysis. We can conclude that our first proposition is accepted.
The second proposition was a positive relationship between brand trust and brand loyalty. Since the correlations showed us that there was a significant result, a regression analysis was conducted. In the previous section, a factor analysis was done for brand loyalty to extract new weighings for the items. For that reason, this new weighings were used to do the regression analysis. The outcome was that brand trust is positively and significantly related to brand loyalty. The new correlation be-tween these variables is r=.449, and is significantly tested (p<0.001). The R square showed that 20.2% of the variety of brand loyalty is explained through brand trust on a significant level of p<.001. In the ANOVA test the F value was quite high, 25.557 and was also tested significantly at a level of p<0.001. In table 1.11, we can see that the β=.449 which also shows a significant result (β>0.05 to be significant). Moreover, we can also state that the second proposition, brand trust is positively related to brand loyalty, is accepted.
The third proposition will be combined with the main research question in this study: ‘To what ex-tent can online marketing increase brand commitment?’ Before this is analysed, the relationship be-tween brand loyalty and brand commitment (proposition 3) will be shown. The new positive corlation showed that there is a significant result (r=.498, p<0.001). This was also supported by the re-gression analysis that showed an R square of 24.80%, p<0.001. ANOVA resulted in an F value of 33.368 which is the highest of all previous F values, p<0.001. In conclusion, we can state that all three propositions that were made can be supported. However, to answer the main research question of this study, the last model (relationship between brand loyalty and brand commitment) needs to be extended. There have been attempts to find out if online marketing can increase this relationship. A new variable is computed (sum of means from new weighings of brand loyalty * online marketing). This new variable is tested on brand commitment. This resulted in a correlation of r=.493, p<.001. Regression analysis supported this correlation by giving an R square of 23.40% on brand commit-ment, which is lower than the relationship between brand loyalty and brand commitment. Moreover, 23.40% of the variety of brand commitment is explained by the new variable (brand loyalty and on-line marketing). The ANOVA results showed an F value of 32.386, p<0.001. This F value is lower than the F value in the relationship between brand loyalty and brand commitment. All information can be found in table 1.12. We can state that online marketing and brand loyalty have a significant positive relation on brand commitment, but in a less strong way than brand loyalty alone. This out-come is not in line with earlier findings, because online marketing is not correlated significantly in the correlations section (p>0.05).
Because of these findings, the relationship between online marketing and brand loyalty is measured, like the relationship between online marketing and brand commitment is measured. Both relation-ships did not find significant answers. Correlation between online marketing and brand loyalty is not tested significantly, p=.387. The R square from the regression analysis is .1%, which is ex-tremely low and not significant (p=0.775). This is the same for the relationship between online marketing and brand commitment. Correlations are not tested significantly, p=0.269. The Beta from the regression analysis is β=-0.61. This is lower than 0.05, thus it is not tested significantly.
5.6 Influences of Online Marketing
The last part of this result section is presenting a summary of arguments that respondents gave to the items about online marketing. This is done manually, as the questions were open-ended. 16 respondents shop online. Their main reasons for doing so is: 1. It is cheaper and 2. It is easier. The other group is not shopping online. The main reasons are as follows:
1. They want to see possibilities in shops and not online, N=42 2. They do not trust online shopping, N=14
3. It is more expensive afterwards, N=10 4. It is annoying, N=5
5. They have bad experiences with online shopping, N= 8
6. Discussion
This section will discuss the findings from the previous section. Propositions that were supported and that were not supported are discussed and explained. Moreover, it will explain why certain re-sults were found and if these rere-sults are in line with previously stated theory. Finally, the implica-tions and quesimplica-tions for future research will be discussed.
6.1 Summary of Results
The questionnaire was held among 103 participants. All these 103 participants were used for the analysis. The reason why there were no participants that were excluded is that the online survey that was distributed was made in such a way that they had to answer every question. When they missed a question, the program remarked on this; thus, participants were only able to finish the question-naire by completing all questions. The small sample of participants that filled out the questionquestion-naire by hand was also checked on completion. Since the reliability of the results were good, no partici-pants were excluded.
In short, 4 relationships were tested. The propositions that were tested are as follows: 1. Satisfaction has a positive effect on brand trust.
2. Brand trust has a positive effect on brand loyalty.
3. Brand loyalty has a positive effect on brand commitment.
4. Online marketing has a positive effect on the relationship between brand loyalty and brand com-mitment.
First of all, significant correlations between all the relationships were found except for the one be-tween online marketing and brand commitment. Later in this section, explanations will be given for remarkable findings. With the correlations, regressions, and ANOVA analysis, a significant result was found for all four relationships, so all propositions were supported.
6.2 Satisfaction and Brand Trust
The first proposition stated that satisfaction will increase brand trust; a more satisfied person has more trust in his/her brand. Moreover, this is in line with earlier research from Delgado-Ballester & Munuera-Aleman (1999). They argued that this has been for a long time and still is an important measurement in statistical research. Total satisfaction will increase a customer’s trust (Delgado-Bellester & Munuera-Aleman, 1999). Other research that was in line with the results is from Aaker (1996), which also stated that satisfaction and trust goes hand in hand. This finding is in line with earlier studies that state higher satisfaction increases sales revenue and market share. This can be found in the outcomes from the result section. More than 50% of the participants of the question-naire own an Apple or Samsung. All these participants stated that they were satisfied with their phone. Not remarkable is that this can be traced to the reality in the mobile phone market, which Apple and Samsung dominate. Thus, these findings are also in line with earlier studies.
6.3 Brand Trust and Brand Loyalty
The second proposition is about the effect of brand trust on brand loyalty (Chaudhuri & Holbroek, 2001). Brand loyalty can be divided into two groups: true customer loyalty and spurious customer loyalty (Bloemer & Kasper, 1995). This two-way formulation from Bloemer and Kasper has not been tested. Result from the questionnaire was that a positive and significant correlation had been
found between the variables brand trust and brand loyalty. After the regression and ANOVA test which both gave a significant and positive relationship this proposition is accepted. These findings are in line with earlier studies from Chaudhuri and Holbroek (2001), but disagreed with studies from Knox and Walker. They came up with four different kind of loyals for a brand; 1. loyals, 2. habituals, 3. variety seekers, 4. switchers. They were not looking for relationships, but tried to clas-sify different levels of brand loyalty. Bloemer and Kasper (1995) state that brand trust does not in-crease brand loyalty, but brand satisfaction. This is not in line with the relationship and proposition stated in this study, but as other studies state (Delgado-Ballester & Munuera-Alema, 1999): Brand loyalty is not only gained through brand trust, but through many other variables.
6.4 Brand Loyalty and Brand Commitment
The third proposition that was made was about the relationship between brand loyalty and brand commitment. Earlier studies state that a more loyal customer would be more brand committed (Gommans, Krishnan & Sheffold, 2001). Other mainstream studies provided different answers. Knox & Walker (2010) state that they could find a limited relationship between brand loyalty and brand commitment. A reason for this limitation could be the different categories that they created to brand loyal customers. If different categories are measured, then different answers will appear in-stead of generalizing results over a whole sample. The results of the questionnaire are in line with results from the first study: a significant and positive relationship between brand loyalty and brand commitment has been found. In other words, we could say that customers who are loyal to a brand are also more committed to a brand.
6.5 Online Marketing on the Relationship Between Brand Loyalty and Brand Commitment The last proposition is related to the main research question of this study. Moreover, it tries to un-derstand the role of online marketing in the relationship between brand loyalty and brand commit-ment (it is used as an interaction variable). This relationship showed us a remarkable and significant result, which meant that online marketing positively increases the relationship between brand loy-alty and brand commitment. However, a note has to be made: the correlation between this interac-tion variable (Brand loyalty*Online marketing) is less strong than only brand loyalty on brand commitment. This means that online marketing is still positively and significantly correlated with brand commitment, but in a less intensive way than brand loyalty as a variable alone. Because this was a remarkable result (a non-significant negative correlation was found), some other regression analyses from online marketing on brand commitment (as a stand-alone variable) was conducted even as a regression analysis from online marketing on brand loyalty. Both regressions showed a non-significant relationship. A reason why the combination of brand loyalty and online marketing has a significant and positive effect on brand commitment could be the fact that loyal customers are adapted by the pinball framework. This is an impact in the relationship between customers and the product by the impact of new media (Hennig-Thurau, Malthause, Friege, Gensler, Lobschat, Ran-gaswamy & Skiera, 2010). Through new media the behaviour of the customer can be affected, which can lead to an increase of the relationship between customer and brand (Hennig-Thurau, Malthause, Friege, Gensler, Lobschat, Rangaswamy & Skiera, 2010). However, the findings are not completely in line with the results that are found. This study interprets the results as follows: for a more brand-committed brand you should focus on loyals customers. They can be used to increase the online market products. Hennig-Thurau, Malthause, Friege, Gensler, Lobschat, Rangaswamy & Skiera (2010) argued that for new media activities a new relationship with a customer has to be
found. In this study, it is remarkably shown that this is not necessary. You can rely on your own loyal customers who will increase the integration between online and offline markets.
The final results from the questionnaire are completely in line other earlier studies like Gommans, Krishnan & Sheffold (2001). They designed an E-Loyalty framework with the biggest threats for customers (shown in theoretical framework). Threats that were shown in the framework were quite in line with answers from participants in the questionnaire; no trust in delivery, no trust in good services, choice in shop, and not online.
6.6 Theoretical Implications
This study is an extension of several studies. Most of all, it is an expansion of Gommans, Krishnan & Sheffold (2001). They designed an E-Loyalty framework and were interested in if online market-ing can increase brand loyalty and brand commitment. This study tried to investigate this question by conducting a statistical analysis in the mobile telephone market. It provided support for existing theory about brand trust, brand loyalty, and brand commitment. In other words, relationships that were proven before were also tested significantly in this study.
Moreover, this study also provides new insights into marketing strategies. It focused on the extent of online marketing to increase brand commitment. So far, this has not been done before. The only study that was slightly related to it is from Gommans, Krishnan and Sheffold (2001). They created an E-Loyalty framework, but it was more a list of the results than hard facts. In addition, the mobile telephone market has never been tested on the integration between online and offline mar-kets to increase brand loyalty and brand commitment. Loyal customers can increase brand com-mitment with the use of online marketing, and companies should focus on these kinds of customers.
Furthermore, a non-significant relationship is found between online marketing and brand loyalty. Important to note here is that online marketing is taken as a stand-alone variable. Other studies do state that online marketing is the way to improve strategies and companies (Donovan & Henley, 2003; Mangold & Faulds, 2009; Hennig-Thurau et all., 2010). The results of this study in-dicates that this cannot significantly improve brand commitment, except when customers are al-ready loyal. A suggestion for future research can be to research how it can be made possible to im-prove brand commitment from non-loyal brand customers through online markets. Other sugges-tions will be given in the next section.
6.7 Loyalty Implications
This study examines the total extent of online and offline markets to increase customer loyalty and brand commitment. To decide whether customer are more loyal or more brand committed, first of all, different kinds of relationships are tested to see which variables drive brand loyalty and brand commitment. The outcome of this study can be very useful for companies and managers to see if they have to change their strategy in the long term to maximize their sales and revenues. The advan-tage of a strategy a company can obtain to their rivals could be even more important.
The results of the study indicate that loyal customers can significantly increase brand com-mitment by using online as well as offline markets. As expected from earlier research from Gom-mans, Krishnan and Sheffold (2001), online markets are of growing importance. Reasons that cus-tomers will not buy products via the Internet vary: cuscus-tomers have no trust in the delivery, no trust in the paying services online, or get annoyed because they do not understand the website. These drawbacks are given in Gommans et all’s E-Loyalty framework (2001).
The fact that the non-loyal customers cannot increase brand commitment by using online markets is remarkable. Future research can point out what companies and strategists can do to also involve non-loyal customers with a certain brand. What are the actions a company can take to in-crease brand commitment from every customer by using online and offline markets?
Some participants stated that buying online is easier and cheaper. Besides that, it is also bet-ter for the environment; fewer trees have to be cut to create paper, and less energy is needed for go-ing to the shop. A company can also reduce costs by obtaingo-ing an online strategy. Less staff is needed, which reduces costs. Future research can investigate what the ideal combination of online and offline markets will be in the near future. What weighings should be given to online markets and what weighings to offline markets. When companies know this, they can change their strategy and improve their business plan.
6.8 Limitations and Suggestions for Future Research
First of all, the time frame to write such a study is limited. Constructions and questions to ask for participants need to be conducted very fast, which is a major drawback of this study. With more time, a deeper and more intensive investigation could be done. Another drawback for this study is the fact that only Dutch consumers could participate. Therefore, the outcomes cannot be generalized for other countries, where others cultures and values are standard.
Besides these previously stated drawbacks, the sample size is also quite small (N=103). To be even more precise, you would need to have more participants and get a larger sample. An advan-tage was the (almost) equal distribution of male and female participants. Future research can inves-tigate whether men and women think differently about using the Internet or other sources of online markets. In this study, this is not done because of the limited time frame.
