Pain of paying and brand loyalty : what is the role of functional and symbolic brandassociations?

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Master’s thesis

MSc Business

Administration

Pain of Paying and Brand Loyalty: What is the

role of functional and symbolic brand

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Student Name: Eline Oudolf Student Number: 11411309 Supervisor: Marco Mossinkoff Date: 21-06-2018

Version: Final thesis

Qualification: MSc. In Business Administration – Marketing Track

University: University of Amsterdam Word Count: 14173 words

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Statement of Originality

This document is written by Eline Oudolf, who declares to take full responsibility for the contents of this document.

‘I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it’.

The Faculty of Economics and Business is responsibly solely for the supervision of completion the work, not for the contents.

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Acknowledgement

I would like to thank my supervisor Marco Mossinkoff, who has guided me throughout this thesis process. He has ensured that I have been able to get the best out of this learning experience. In addition, I would like to give a thanks to my classmate and friend Lieke Janssen, for the useful brainstorm sessions. Also, I would like to thank all members of my family and friends who encouraged me during my thesis process. In addition, thanks to my native English friend Alice IJzerman for the translation of my questionnaire. Finally, thanks to all the respondents that filled in my questionnaire. Without them I could not have done my research.

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Abstract

Successful branding requires strong, favorable, and unique brand associations in the mind of customers (Eren-Erdogmus and Budeyri-Turan, 2012). This thesis aims to identify the moderating role of functional (perceived quality) and symbolic (brand prestige and

personality congruence) brand associations on the relationship between pain of paying and brand loyalty (Esmaeilpour, 2015). Pain of paying is the immediate and direct displeasure caused by the action of payment (Zellermayer, 1996). Paying cash leads to higher pain levels than paying by Credit or Debit Card (Shah et al., 2016). Based on the study of Shah et al., (2016), this thesis expects that higher levels of pain of paying lead to more brand loyal

customers. Thus, paying cash leads to more loyal customers. Which is interesting, considering the fact that former studies have revealed more positive outcomes of customer loyalty for lower levels of pain of payments. For instance, the study of Mazar et al., (2016) found that paying by Credit Card leads to higher spending during the transaction.

Within this thesis it is expected that the positive significant relationship between pain of paying and brand loyalty is stronger for higher values of symbolic and functional brand associations (Esmaeilpour, 2015). In response to this expectation, four hypotheses have been formulated. Each of the four hypotheses were rejected. The researcher expected to find a positive significant relationship between pain of paying and brand loyalty, as per the findings of Shah et al., (2016). However, a negative, not significant relationship has been found. Furthermore, the researcher expected a positive significant moderating effect of brand quality and personality congruence on the relationship between pain of paying and brand loyalty, in

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line with the study of Esmaeilpour (2015). In contrast, a negative, not significant effect has been found. Lastly, a positive but not significant moderating effect of brand prestige on pain of paying and brand loyalty has been found. The researcher expected this moderating effect to be positive and significant based on the Esmaeilpour (2015) study.

Key words: Brand loyalty, functional and symbolic brand associations, pain of

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1. Introduction

‘‘A new Walmart just opened up a couple miles from my home. I’m not a fan of

Walmart. I fought against allowing this Walmart to be built at all. Now that it’s open, though, I did go in just so I could try out the new high-tech future of shopping – Walmart Scan & Go’’. Said by Tony Bradley1_.

Digitalisation of the purchase process of products and services is a fast-growing

phenomenon. Retail transactions have been rapidly transformed during the last two decades (Quix & Van der Kind, 2014). For instance, customers have the possibility to buy their products in physical stores, as well as online. Furthermore, digital developments go far beyond that: mobile phone applications can be used during the purchase process, as well as artificial intelligence and augmented reality. These developments lead to a seamless shopping experience due to an integration of the digital and physical logic of retailing. This is

referenced as an ‘omni-channel’ experience and bridges the gap between online and offline stores (Hagberg et al., 2017). Omni-channeling is supposed to lead to a higher business

performance, efficiency, better sales, and more convenience for the customers. It could lead to sustainable competitive advantage for companies (Hagberg et al., 2017).

The emerging market of digital payments is one of the most fast growing and competitive digital developments in retail transactions (Neyer, 2017). Digitization of

payments is part of the concept of self-service technology (SST), which is a way of enabling customers to serve themselves (Hilton et al., 2013; Collier et al., 2014). The growth of self-service technologies changed the way of interacting between customers and companies, and

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requires fast adapting marketing strategies (Neyer, 2017). For instance, Scan & Go is a

mobile phone application that makes it possible to scan your needed products in offline stores,

and to charge these products through the app. This application requires less interaction between customers and employees and contributes to the efficiency and easiness of the purchase experience. Walmart is currently expanding this Scan & Go program, in order to compete with other retailers. Implementing this program will lead to more convenience by decreasing the in-store checkout lanes (Keyes, 2018).

Digital innovations should lead to a better customer experience and thus, to more brand loyal customers (Hagberg et al., 2017). Brand loyalty is important in order to make a company a long-term player in a competitive environment. Brand loyalty leads to brand credibility, high customer lifetime value, and a high spread of word-of-mouth (Eren-Erdogmus and Budeyri-Turan, 2012; Liu et al., 2012). According to Shah et al., (2016) however, innovations such as digital payments would not lead to greater brand loyalty due to the underlying reason of ‘pain of paying’. Customers’ experience high levels of pain if they pay cash, and they feel less pain if they pay by Debit Card or Credit Card. Cash payments lead to a greater awareness of the expenditure, which makes customers more loyal, as a certain degree of pain is experienced (Shat et al., 2016).

The aim of this research is to expand on the literature on the effect of pain of paying and brand loyalty. The research by Shah et al., (2016) is the only research that revealed that high levels of pain lead to high brand loyalty. This thesis aims to find out whether this is still the case when brand associations are taken into account. According to Esmaeilpour (2015), it is necessary to create strong, unique and favourable brand associations in the mind of

customers, in order to gain loyal customers. It is useful for marketeers to know with which type of brand associations the relationship between pain of paying and brand loyalty becomes stronger. As a result, managers can adapt their marketing strategies to these brand

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associations. It is all about building the right type of associations, with the ultimate goal of increasing brand loyalty (Shah et al., 2016).

The relevance of focusing on the relationship between ‘pain of paying’ and ‘brand loyalty’ lies in the decreasing use of cash payments. The Netherlands has one of the most cashless environments in Europe. This makes it relevant to do further research on the impact of cashless payments on brand loyalty of Dutch respondents. Furthermore, customers

nowadays have a lot of options to choose from in any given category, which results in decreasing brand loyalty and increasing turnover rates (Van Belleghem, 2013). There are significant advantages to brand loyalty, such as decreased operating and marketing costs, less switching to competitors, customer retention, and customer relationships on the longer term (Reisenwitz & Gupta, 2011). Therefore, it is highly important for managers to find ways to increase brand loyalty.

The research question of this thesis is as follows: ‘What is the impact of pain of paying on brand loyalty in the fashion brand industry, and what is the moderating role of functional and symbolic brand associations?’. This research strives to provide a clear answer to this question. The structure of this thesis is as follows: Firstly, a review of the relevant literature is provided. The concept of pain of paying and its influence on brand loyalty is further explained, as well as the moderating role of functional (perceived quality) and symbolic (personality congruence and brand prestige) brand associations (Eren-Erdogmus and Budeyri-Turan, 2012). The focus is on the fashion brand industry. Secondly, the research method, sample, measures and results are discussed. Lastly, the implications, limitations and suggestions for future research is provided in the discussion and the conclusion section(s).

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2. Literature review

The literature review gives background information that is relevant in order to provide an accurate answer to the main question. Paragraph 2.1. goes deeper into the importance of creating a good customer experience. An optimal customer journey leads to a competitive advantage for a company (Lemon & Verhoef, 2016).

2.1. Creating an optimal customer journey

According to Lemon & Verhoef (2016), it is critical for firms to create a good understanding of the customer experience and customer journey over time. Customers interact with firms through multiple touchpoints in different media and channels (Lemon & Verhoef, 2016). The customer journey is formed by all these moments of interaction between the customers and the brand. The customer journey can be divided in three different stages: the pre-purchase, purchase, and post-purchase stage (Rosenbaum et al., 2017). According to Dunn and Davis (2003), the ‘brand touchpoint wheel’ gives an overview of the points where customers interact with the brand during the entire purchase process. Figure 1 shows the brand touchpoint wheel in detail (Dunn & Davis, 2003). The pre-purchase phase contains the PR and the marketing campaigns of a firm. The purchase experience can be defined as the moment of transaction of products and services, that takes place in physical stores and / or in online stores. The post-purchase experience is the phase where customers decide whether the product or service meets their expectations. This phase contains customer feedback, response time, and service request as well (Lemon & Verhoef, 2016).

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Figure 1. Source adapted from Dunn and Davis, 2003.

Creating an excellent customer experience has become critical for companies

(McColl-Kennedy et al., 2015). Bolton et al., (2014) define customer experience as ‘holistic in nature’. It contains any direct and indirect contact between the customer and the brand, based on physical, social, cognitive, affective, and emotional responses that customers have towards the brand (Bolton et al., 2014). Optimising this experience leads to more satisfied customers and a competitive advantage. It is about the contextual and unique interpretation of the experience by the customer (McColl-Kennedy et al., 2015).

Customers expect seamless shopping experiences, due to the rise of fast growing technological developments (Hagberg et al., 2017). There has been a shift from a multi-channel to an omni-multi-channel experience provided by companies. Digital multi-channels changed the way people behave during their searching and shopping process. Omni-channel retailing

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applications, and offline stores. It creates easiness during the shopping experience that leads to high quality customer experience (Verhoef et al., 2015).  

Customer experience is especially important for hedonic consumption, due to the individual’s ‘peak’ or ‘extraordinary’ experience of it (McColl-Kennedy et al., 2015). Luxury consumption is an example of hedonic consumption. Hedonism refers to emotive and fantasy aspects of one’s experience with a product. It emphasizes the emotional value of products and brands. On the other hand, there are utilitarian goods. These goods (e.g. microwaves) are chosen primarily for their functional and instrumental elements (Dhar and Wertenbroch, 2000). According to Amatulli and Guido (2012), retail managers should improve the in-store experience of luxury brands, as luxury goods are bought because they convey pleasure and positive emotions to customers (Amatulli and Guido, 2012).

The next chapter takes a further look into the digitalisation of the retail process. Digitalisation could optimize the customer experience, which is important for companies to keep up with the competition. Previous literature showed that this is not always the case. Shah et al., (2016) revealed that digitization of payments leads to negative results, namely lower levels of brand loyalty. Therefore, it is important to implement the right digital developments in order to attain optimal results.

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2.2. Digitization of retail

There is a fast-growing trend of digitization within the pre-purchase, purchase, and post-purchase phases. Digitalisation of the customer journey challenges companies to compete with others and creates optimal convenience for customers (Hagberg et al., 2017).

Digitization has made it possible for brands to introduce new technologies and functionalities, such as mobile applications, augmented reality, artificial intelligence and other developments like smart-shelf technologies. These technologies all have a corresponding goal of collecting customers' data and thus responding to their needs (Quix & Van der Kind, 2014). These developments benefit both the retailers’ selling experience and the customers’ purchase experience (Inman & Nikolova, 2017).

Shelves are getting smarter by automatically keeping track of inventory in a retail establishment. Real-time data of the products can be collected, as shelves can indicate whether products have been removed (Syrjala, 2012). Another technological trend, artificial intelligence (AI), also contributes to the customer experience. AI is based on the concept that computer systems are capable of performing human tasks such as decision-making, visual perception, and speech recognition. AI provides easy access to powerful data and insights through machine learning technology and advanced analytics. It contributes to customers’ expectations by being able to make faster and smarter marketing decisions (Burgess, 2018). Another example is the development of augmented reality (AR). This technology provides a direct as well as indirect live view of physical real-world environments. The elements are ‘augmented’ with computer generated images. AI brings the digital and real world together and contributes to the integration of different channels (Brom et al., 2017). According to Brom et al., (2017) the trend of AR will develop much further in the future, with a great impact on retail. If stores could be augmented at home, it would become unnecessary for

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people to visit physical stores. AR also comes with some risk concerns, especially in relation to privacy (Brom et al., 2017).

The acceptance of digitization in retail by customers is an important phenomenon. Not all customers respond to digital developments in the same way. There are several factors that have an influence on the level of digital acceptance. There has been a significant amount of research carried out on the acceptance process, which will be further elaborated in the next paragraph.

2.3. The acceptance of digitization in retail

The technology acceptance model (TAM) is a model that shows how customers use a

technology, and how they come to accept a technology. It was first developed by Davis et al. (1989) and represents the fact that customers who are exposed to a new technology, are influenced by two factors: ‘perceived usefulness’ and ‘perceived ease-of-use’. These factors influence whether a customer will use a new digital technology and if so, then how. Perceived usefulness emphasizes the level of enhancement of job performance and perceived ease-of-use is mentioned as the degree to which using a particular system would be free of effort (Davis et al., 1989). This technology acceptance model by Davis et al., (1989) has been re-investigated several times and eventually it has been upgraded to both the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003), and TAM 2 (Venkatesh, 2000).

In 2003, Van der Heijden combined the antecedents of the technology acceptance model with antecedents based on a trust perspective. This study focused on antecedents that have an influence on customers’ attitude towards online purchasing at electronic e-commerce websites. A correlation has been found between ‘attitude towards online purchasing’ and the concept of ‘online purchase intention’, driven by four antecedents. Figure 2 gives a clear overview of these antecedents. ‘The perceived usefulness’ and ‘the perceived ease-of-use’

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belong to the technological perspective, and ‘trust in online store’ and ‘perceived risk’ belong to the trust perspective. Results revealed that ‘perceived risk’ and ‘perceived ease of use’ have a direct influence on both the attitude towards online purchasing, and purchase intention. There was a strong negative effect from ‘perceived risk’ on attitudes (found in two cases), and a positive effect of ‘perceived ease of use’ on attitudes (found in one case) (Van der Heijden, 2003).

Figure 2. Conceptual model adapted from Ajzen & Fishbein, 1980; Davis, 1989; Jarvenpaa et al., 2000).

There are some differences in results between various studies. The results from the study of Kim et al., (2008) revealed that customer’s trust has an indirect and direct effect on purchase intention of e-commerce. It has a strong positive effect on the purchase intention and a strong negative effect on perceived risk. ‘Perceived risk’ has also a downstream effect on purchase intention, but trust is the most significant indicator of purchase intention (Kim et al., 2008). According to Wu et al., (2017), customers adopt digital payments because of certain

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into the concept of digitization of payments. Trust also plays an important role in the acceptance of digital payment methods (Ozturk et al., 2017).

2.4. Pain of paying

This chapter analyses the concept of pain of paying. Pain of paying will be further elaborated because it is strongly related to the concepts of digitization (of payments) and the customer experience phenomenon.

2.4.1. Digitization of payments

The amount of cash transactions has considerably declined in the past two decades. In 1999, 60% of the in-store payments consisted of cash or checks. Whereas in 2010, this percentage decreased to 40% as plastic cards became the most popular form of payment. This trend seems to be advancing, with online and mobile transactions as an upcoming tendency (Shah et al., 2016).

The continuous development of digital payments contributes in most cases to the enrichment of the customer journey and customer experience (Hagberg et al., 2017). Uber is an example of how ‘seamless paying’ leads to a positive customer experience, partly due to its ease, speed and safety. Different types of e-commerce payments have been developed, such as payment by Credit Card or Debit Card. A Digital Wallet is a type of e-commerce payment, which is stored within your PC. The Digital Wallet provides access to your personal and banking information and makes payment possible at the check-out page of an e-commerce website. Furthermore, E-cash and Mobile Payment have become popular as well. With

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Mobile Payment, customers use their mobile phone to complete a purchase. Due to these new developments, paying with cash is becoming less important and interaction with employees has declined2_.

The acceptance and (dis)advantages of digital payments

Various research has been conducted on digital payments and customers’ acceptance of these developments. Different factors affect customers’ intention to use digital payments. Classic variables from the previous mentioned Technology Acceptance Model, combined with various payment forms, have been studied. These classical variables are often combined with variables from recent studies, such as individual mobility, compatibility, personal innovation, and security. For instance, Ozturk et al., (2017) studied the impact of negative valence

(privacy concern and perceived risk) and positive valence (convenience and utilitarian value) perceptions toward the use of near field communication (NFC)- mobile payment (MP)

technology. Convenience, privacy concern, and utilitarian value had a significant influence on the technology acceptance. Interestingly, in this study risk does not have a significant

influence on customers’ technology acceptance. Furthermore, smartphone affinity has a positive influence on the positive valence perceptions. Compatibility significantly affects both the positive and negative valence perceptions (Ozturk et al., 2017). According to Shin (2009), there’s a significant moderating effect of ‘demographics’ on the relations among classical variables such as perceived usefulness and ease of use, security, and trust (Shin, 2009). Digitization of payments come with both advantages and disadvantages. The greatest barrier of mobile payment is customer apathy (Viehland & Leong, 2007). Furthermore, people are uncomfortable with the idea of paying digitally (‘fear of the unknown’) and are

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unwilling to use these technologies. People are often concerned about risk factors. For example, mobile wallets provide access to personal and sensitive information. This poses a high risk in cases of stolen or broken phones, due to the possibility of identity theft (Jupiter Research, 2008). A great advantage of digital payments is the contribution to a seamless customer experience. It speeds up the payment process and reduces the queues (Shah et al., 2016). However, self-service technologies are time-consuming and expensive to implement (Kokkinou & Cranage, 2015).

In the next chapter the definition of pain of paying will be explained. This concept strongly relates to the digitization of payments and is therefore an important concept to review. Trust, perceived risk, perceived usefulness, perceived ease of use and satisfaction, also play an important role in digital payments. However, the main focus of this study is on the underlying concept of pain of paying, as less research has been conducted on this relationship between pain of paying and brand loyalty.

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2.4.2. The concept of pain of paying

People don’t always make rational decisions, and customers’ payment behavior is often irrational. According to Dan Ariely (2009), customers’ irrational behavior is systematic and predictable, and therefore useful in marketing. For instance, customers perceive a product to be of greater value if it is placed next to a product that is much more expensive. The reason for this is when the customer doesn’t know how to value a product, they assess the value by comparing it to the products next to it (Ariely, 2009).

The concept of ‘pain of paying’, introduced by Zellermayer in 1996, is the immediate and direct displeasure received from the action of payment. Pain of paying is a psychological concept, not a physical one. Customers see and feel the money they’re spending and this causes pain (Shah et al., 2016). It can also be seen as hedonistic discomfort that leads to decrease in pleasure (and lower money expense) or no purchase at all (Van der Horst & Matthijsen, 2013). According to Shah et al., (2016), these negative effects of pain are in relation to the purchase phase, not for the post-purchase phase (see paragraph 2.4.3.). Several factors have an influence on pain of paying. For instance, the mode of

payment (using cash or a card) or the time between consumption and payment could have an influence on the level of pain. Paying cash is more painful than swiping a card, because a card payment is less tangible than a cash payment (Mazar et al., 2016). Customers tend to spend more if they experience less discomfort (Mazar et al., 2016) and if the form of payment is more transparent (Shah et al., 2016). Furthermore, if payment is deferred (by using a Credit Card), the depletion of money is less visible and could lead to higher spending (Mazar et al., 2016). The same situation exists if payment is made prior to transaction, in case of a (gift) voucher or an electronic wallet (Van der Horst & Matthijsen, 2013). Another factor could be the level of income. People with higher incomes tend to experience less pain during the

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Different methods of payments have an influence on customer’s purchase experience. Research revealed that if customers are inclined to using Credit Cards, product features become more important (Van der Horst & Matthijsen, 2013). However, if people pay with cash, the cost of products become more important. Furthermore, payment methods have an influence on purchase types as well. Less painful purchase modes (such as Credit or Debit Cards) lead to more impulsive purchases. For example, those who pay by card are more likely to buy unhealthy food than those who pay with cash (Van der Horst & Matthijsen, 2013). In conclusion, cash is seen as a payment method that leads to a higher pain level, less impulse purchases, and lower purchase values (Van der Horst & Matthijsen, 2013).

The results in these studies mentioned, show positive outcomes of less painful forms of payment in the purchase phase. Various research has been conducted on the effects of the purchase phase. For example, greater point-of-purchase satisfaction and higher amounts of spending lead to better spending results (Shah et al., (2016). On the contrary, less research has been carried out on the influence of pain of payment on the post-purchase phase. In the next chapter the relationship between the level of pain of paying and post transaction connection, and thus brand loyalty, will be examined.

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2.4.3. Pain of paying and post-purchase outcomes

Above results on pain of paying showed that less painful forms of payment lead to positive outcomes during the purchase phase and customer deliberation. Customers are willing to pay more, and customers are more satisfied due to a seamless customer experience. Priming the use of cash leads customers to focus more on the negative attributes and cost features,

whereas priming the use of cards leads to a focus on positive attributes and product’s benefits (Van der Horst & Matthijsen, 2013).

Little research has been carried out on the link between the level of pain of paying and post-transaction connection (Shah et al., 2016). The article of Shah et al., (2016) focuses on the link between pain of paying and post-transaction connection and emphasizes the role of attachment that customers feel (or do not feel) after purchase. Shah et al., (2016) carried out research on whether the mode of paying (Credit Card, Debit Card, or cash) changes how much customers feel committed to a brand and whether they show brand loyalty. Shah et al., (2016) revealed a positive relationship between pain of paying and post transaction

connection. Higher pain of payment lead to a higher post transaction connection. This means that the mode of payment influences the amount of connection that people feel with the product and organization (after purchasing the product). Painful payments lead to repeating behavior, higher emotional connection, a decreasing commitment with non-chosen

alternatives, and the willingness to show their behavioral commitment. Overall, customers become more brand loyal after having paid with a painful method (Shah et al., 2016).

Surprisingly, these results revealed that painful forms of payments lead to positive outcomes for the post-purchase phase, in contradiction to the negative results for the purchase-phase.

According to Shah et al., (2016), brand loyalty is especially important for companies that are commitment focused. Such as specialty, luxury and high-end brands. These

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companies should increase the pain of payment during the transaction. The next chapter explains the relevance of brand loyalty.

2.5. Brand loyalty

Brand loyalty is the tendency of a customer to purchase products from the same brand rather than competing brands. Companies strive for loyal customers that make repeating purchases over time (Sasmita, 2015). According to Esmaeilpour (2015), brand loyalty can be

distinguished in attitudinal and behavioral loyalty. Behavioral loyalty indicates repeating customer behavior, such as frequency and amount of purchases. Attitudinal loyalty

emphasizes strong cognitive elements that lead to strong affective loyalty. Feeling internally positive about a brand and telling others about a brand, refer to attitudinal loyalty

(Esmaeilpour, 2015). Furthermore, brand attitude is mentioned as the overall evaluation of the customer on the brand. The positive influence of brand attitude on brand loyalty is empirically proven (Esmaeilpour, 2015). Therefore, managing attitude strategically, leads to repeating customer behavior (purchases) and internally positive feelings about a brand (Eren-Erdogmus and Budeyri-Turan, 2012; Liu et al., 2012).

Marketing strategies that contribute to customer loyalty could be brand ambassadors, rewards programs, trials, and incentives such as free samples. Since the rise of the digital age, the internet has had an influence on creating brand loyal customers. Customers are less committed to specific brands, because of the possibility of comparing competitors’ offerings online and through independent research. For this reason, companies should continue meeting the needs of the customer to keep up the competitive environment (Atwal, et al., 2017). Creating strong brand loyalty with existing customers has several advantages. Loyal customers are more likely to stick with a brand, purchase more frequently, and spend more per transaction. Advertising could be risky and expensive, and with existing customers this

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isn’t necessary, as loyal customers are already aware of the brand (Esmaeilpour, 2015). In addition, the chance of selling to an engaged customer is much higher than to customers that have not previously bought a product. Such customers are trustworthy, and still ‘believe’ in a company even if the company would make a mistake. Furthermore, loyal customers

contribute to word-of-mouth sales by recommending products to others. Products

recommended by customers are more trustworthy than advertisements. To conclude, brand loyalty leads to a higher customer lifetime value, an enhanced brand credibility, and positive word-of-mouth referrals. This makes a company a long-term player in a competitive

landscape (Eren-Erdogmus and Budeyri-Turan, 2012; Liu et al., 2012).

To gain loyal customers, an important factor is creating an optimal customer

experience with every aspect in the customer journey. Providing high valued products, good service, and a brand that aligns with customers’ values can contribute to this. Furthermore, if customers have a good association with a brand, they become more loyal (Eren-Erdogmus and Budeyri-Turan, 2012; Liu et al., 2012). Esmaeilpour (2015) made a distinction between functional and symbolic brand associations and found a significant effect of functional (perceived quality) and symbolic (personality congruence and brand prestige) brand

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2.6. The role of functional and symbolic brand associations

Brand associations are the attributes of a brand that are thought of by the customers when speaking about the brand. The meaning of a brand is determined by what the customer thinks the brand stands for and what it is imagined to be like by the customer (Homer, 2008). Companies strive for creating strong, unique, and eligible brand associations perceived by customers (Eren-Erdogmus and Budeyri-Turan, 2012). The importance of brand associations is one of the four elements that belong to brand equity. Brand equity refers to having a brand name that is perceived by customers as well-known and recognizable. Organizations strive for building a strong brand with significant equity that leads to loyal customers (Keller, 2001). The four elements of brand equity are brand loyalty, brand associations, perceived quality and brand awareness (Esmaeilpour, 2015).

Esmaeilpour (2015) examined the effects of functional (perceived quality) and symbolic (brand prestige, personality congruence, brand tribalism, and user imagery

congruence) brand associations on attitude and brand loyalty towards luxury fashion brands. Figure 3a and 3b present the proposed and revised model with the hypotheses of Esmaeilpour (2015).

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Figure 3a. Proposed model of Esmaeilpour, 2015.

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As shown in figure 3b, the positive influence of brand attitude on brand loyalty is empirically proved. Companies should manage brand attitude successfully in order to create loyal

customers. Perceived quality is the strongest predictor of brand attitude and loyalty. Perceived quality is defined as one of the key elements of brand equity and refers to the level of

superiority or excellence of a product as perceived by the customer. Product’s intrinsic and extrinsic attributes, appearance, and performance play an important role in this dimension. Intrinsic attributes consist out of physical characteristics of the product. Characteristics such as label, price, advertisement, and brand name belong to the extrinsic quality cues

(Esmaeilpour, 2015). Aaker (1996) stated that brands that offer high quality create loyal customers. For that reason, Esmaeilpour expected (and proved) that perceived brand quality has a significant and positive effect on brand loyalty in the luxury fashion brand industry (Esmaeilpour, 2015).

Personality congruence indicates customers’ preference to compare themselves with a brand to see whether a brand matches their self-concept. There are some human-like

characteristics that can be associated with a brand, such as excitement, competence,

ruggedness, sophistication, and sincerity. Personality congruence is measured as the absolute difference between the personality of the respondent and the personality of their (preferred) brand. Customers are able to express themselves through a brand and foster their

identification. Personality congruence (mediated by perceived brand quality) has an indirect positive effect on brand loyalty (Esmaeilpour, 2015).

Brand prestige is related to achieving and keeping a positive social identity. It refers to analogic self and social rank. Furthermore, brand prestige can enhance customers’ purchase intention by increasing their confidence during brand selection, and social status after purchase. Prestigious brands have a positive effect on attitude and loyalty by creating customer value through conspicuous consumption and status (Eren-Erdogmus and

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Budeyri-Turan, 2012). Brand prestige, mediated by brand attitude and perceived brand quality has an indirect positive effect on brand loyalty (Esmaeilpour, 2015).

Only the functional brand associations ‘perceived quality’ and the symbolic brand associations ‘personality congruence’ and ‘brand prestige’ of Esmaeilpour (2015) are taken into account in this thesis. The symbolic brand associations ‘brand tribalism’ and ‘user imagery congruence’ are excluded. ‘User imagery congruence’ showed no significant effect on both brand attitude and brand loyalty in the study of Esmaeilpour (2015). Based on the study of Eren-Erdogmus and Budeyri-Turan (2012) the researcher decided not to take ‘brand tribalism’ into account. This study showed a significant effect for perceived quality,

personality congruence and brand prestige, but not for brand tribalism. Furthermore, ‘user imagery congruence’ and ‘brand tribalism’ are excluded due to the length requirement of the questionnaire, to maintain reliability.

The functional and symbolic brand associations are used as mediators in the study of Esmaeilpour (2015) and are turned into moderators in this thesis. The next paragraph provides a summary of the literary review and a description of its theoretical relevance.

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Theoretical relevance and conclusion

In summary, the goal of this study is to expand on the literature on the relationship between pain of paying and brand loyalty. A considerable amount of research has been carried out on the concept of pain of paying, and the outcomes during the purchase phase. Generally, low levels of pain (e.g. cash payments) during the transaction lead to positive outcomes for the brand. Customers would spend more if they would pay with a card (Mazar et al., 2016). On the contrary, less research has been carried out on the relationship between pain of paying and the post-purchase phase, such as creating brand loyal customers. Shah et al., (2016) revealed that more painful forms of payment (e.g. cash payments) causes an increasing post transaction connection. Customers are more brand loyal after paying cash instead of paying by Credit or Debit Card.

In accordance with the results of the study by Shah et al., (2016), this thesis expects to find a positive significant relationship between pain of paying and as well as brand loyalty. This thesis investigates whether this is the case in the Netherlands. The moderating role of functional and symbolic brand associations are taken into account. It is expected that the positive significant relationship between pain of paying and brand loyalty is stronger for higher values of symbolic and functional brand associations. This is the first time that

symbolic and functional brand associations are taken into account as moderators, according to the relationship between pain of paying and brand loyalty. Therefore, this thesis has filled a research gap.

The expectations of this thesis are based on two main studies (Shah et al., 2016 and Esmaeilpour, 2015). These main studies are focused on the fashion brand industry. For this reason, the focus of this thesis is also on the fashion brand industry. The main question that will be answered is as follows:

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‘What is the impact of pain of paying on brand loyalty in the fashion brand industry, and what is the moderating role of functional and symbolic brand associations?’.

The next chapter takes a deeper look into the methodology of this thesis, starting with the conceptual model and the hypotheses. Following, that the survey design, the sample, the data collection, and the measures will be discussed.

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3. Methodology

The research objective of this thesis is to explore the effect of pain of paying on brand loyalty, and the moderating role of functional and symbolic brand associations. The following

conceptual model has been created in response to this research question:

Figure 4 Conceptual Model

Based on previous literature, it is expected that there is a positive significant

relationship between pain of paying and brand loyalty. The higher the pain during transaction (which is in the case of cash payments), the more loyal customers become after purchasing (Shah et al., 2016). Based on these assumptions, hypothesis 1 states:

Pain of Paying Brand loyalty

H1 + Symbolic brand association: personality congruence Symbolic brand association: brand prestige Functional brand association: perceived quality H4 + H3 + H2 +

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H1: There is a positive significant relationship between pain of paying and brand loyalty.

Functional and symbolic brand associations are taken into account as moderators on the relationship between pain of paying and brand loyalty. It is expected that these moderators have a significant positive influence on this relationship, meaning the relationship is stronger for higher values of functional and symbolic brand associations. ’Perceived quality’ relates to functional brand associations, and ‘brand prestige’ and ‘personality congruence’ relate to symbolic brand associations (Esmaeilpour, 2015). Based on these presumptions, hypothesis 2, 3, and 4 state:

H2: The positive significant relationship between pain of paying and brand loyalty is

moderated by the functional brand association called ‘perceived quality’, so that this relationship is stronger for higher values of ‘perceived quality’.

moderated by the symbolic brand association called ‘personality congruence’, so that this relationship is stronger for higher values of ‘personality congruence’.

moderated by the symbolic brand association called ‘brand prestige’, so that this relationship is stronger for higher values of ‘brand prestige’.

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3.1. Survey design

The deductive, quantitative research in this thesis is based on a survey design. It is of explanatory nature. In order to answer the research question, a questionnaire was created (through Qualtrics.com) and distributed amongst respondents living in The Netherlands. The research time horizon is cross-sectional, and the purpose of this research design is

correlational. The reason for this is as it predicts a relationship between the independent variable (‘pain of paying’) and the dependent variable (‘brand loyalty’), and the functional and symbolic brand associations as moderators. The researcher ensured that the questionnaire was not too long in length, yet still valid, in order to achieve the best result from the

respondents and maintain reliability.

The questionnaire was distributed digitally and physically. During physical handouts of the questionnaire, it was asked whether the respondent had enough time to fill it in accurately. If this was not the case, the survey was not handed out. This was not necessary with the digital surveys, as respondents were able to fill in the survey at their own preferred place and time, so there was no time pressure or influence from a watching researcher while the respondent filled in the questionnaire. In both cases, the aim was to ensure reliability. The survey is directly translated into Dutch for a better understanding for Dutch citizens. This translation process is based on back-translating, where the English survey is translated into Dutch and then back-translated into English. This was done in order to ensure linguistic validity. The English version was handed out to English speakers.

A pre-test was carried out on a group of thirty respondents, in order to gain feedback. The final questionnaire can be found in appendix 1 (English version) and appendix 2 (Dutch version).

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3.2. Sample and data collection

A non-probability convenience sampling method has been used in this research. This research looks at respondents that recently bought one or more fashion products. If the respondent did not buy a fashion product recently, the survey questions ended after the first question (‘Did you recently buy one or more fashion products?’). Furthermore, it looks at customers that have bought a fashion product in an offline store, and not through an online store. The reason for this is that the primary focus is on the relationship between pain of paying and brand loyalty. Pain of paying can be divided into two options, namely Debit Card/Credit Card and cash payments. All three options are only provided in stores, and not online.

There were no further requirements or restrictions attached to the survey. Respondents could be any age (as long as they were able to make a purchase), and it did not matter where they precisely lived in The Netherlands (as long as they were a resident of The Netherlands). Neither did it matter where the customer bought the fashion product or what kind of product it was, as long as it was a fashion product. Through control variables, the result-chapter will show whether this additional information had an influence on the results.

The researcher attempted to collect as many respondents as possible during the data collection period. However, for accurate measurement, the minimum number of respondents should be 384, with a confidence level of 95%. The more respondents, the higher the external validity will be of the thesis (Saunders & Lewis, 2009). It was expected that the response rate would be higher for offline channels than for online channels, due to the personal approach. The approach was to distribute digital surveys through online channels such as Facebook and Linkedin, and to hand out physical surveys personally. It appeared that Facebook response in general was low, therefore Facebook-groups were approached where users are highly

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post a message in such a Facebook-group, so that the posts are taken very seriously by everyone. People appear to be highly involved in such groups. Furthermore, this study also used online databases where the researcher was able to gain respondents in return for completing another researcher’s survey.

The physical questionnaires were personally distributed. One way in which these questionnaires were distributed was through door-to-door canvassing. A date was then agreed on which the researcher was able to collect the forms, to give the respondent sufficient time to complete the questionnaire. This avoided the risk of time limitations whilst completing the questionnaires. Another way in which it was distributed, was to parents that were sat in a playground minding their children. These people were approached as they were not in a hurry, and thus could complete the questionnaire without rushing.

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3.3. Measures

The questionnaire asked respondents for their demographics, such as gender and education. These demographics are mentioned as control variables. Previous literature revealed the relevance of these control variables. Das (2014) found a significant moderating role of gender on the relation between symbolic brand associations (i.e. brand personality and

self-congruity) and brand loyalty. Bell and Eisingerich (2007) found a significant effect of customer education on brand loyalty. Furthermore, the variables ‘product type’ (question in the survey: ‘What kind of fashion product was this?’) and ‘product category’ (question in the survey: ‘How can this fashion product be categorized?’) where labeled as control variables as well. This insight could be of relevant if it appears that product type or category will influence the results. For example, if it turns out that the effect of pain of paying on brand loyalty is more significant when it comes to sunglasses instead of clothing, or whether it is about a luxury product instead of a general fashion product. In such a case, managers can adapt their managerial strategies to these results.

Two questions were added to the questionnaire to draw the attention of the respondent to the fact that this survey is about one specific purchase and not about the purchase of

fashion products in general. These questions were as follows: ‘Where did you buy the fashion product (city)?’ and ‘Wat is the name of the brand?’. These questions were included to make respondents aware that they have to think about one specific purchase.

The other constructs will be measured with existing and validated Likert scales. The variables are adopted from two studies, from Shah et al., (2016) and Esmaeilpour (2015). These studies adopted their measurements from several other studies. This thesis uses the same measurements used in these studies. The validated 42-item 5-point Likert scale by Aaker (1997), is used by this thesis to measure personality congruence. Respondents

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(‘personality of the brand’) where they have bought the fashion product (the absolute difference indicates the personality congruence). Five constructs (sincerity, excitement, competence, sophistication, and ruggedness) are measured on the basis of 42 items in total. Thus, respondents had to give answer to 42 items for both the ‘personality of the respondent’ as for the ‘personality of the brand’. Sincerity is measured by the items down-to-earth, honest, wholesome, cheerful, family-oriented, small-town, sincere, real, original, sentimental and friendly. Excitement is measured by daring, trendy, exciting, spirited, cool, young,

imaginative, unique, up-to-date, independent and contemporary. Competence is measured by reliable, hardworking, secure, intelligent, technical, corporate, successful, leader and

confident. Sophistication is measured by upper class, glamorous, good looking, charming, feminine and smooth. Ruggedness is measured by outdoorsy, masculine, Western, tough and rugged.

The validated eight item 5-point Likert scale by Eren-Erdogmus and Budeyri-Turan (2012) was used to measure perceived quality. The four item 5-point Likert scale used for brand prestige is derived from the same author. Brand loyalty is measured by three items from Algesheimer et al. (2005), which is a 5-point Likert scale as well. ‘Pain of paying’ is

measured by a binary ordinal variable (cash = high pain of paying, Debit Card and Credit Card = low pain of paying). According to Esmaeilpour (2105), it is expected that cash leads to higher brand loyalty and a card leads to lower brand loyalty. All the 5-point Likert scale items are measured on a range from ‘strongly disagree’ to ‘strongly agree’, with ‘neither agree nor disagree’ in between. Only the variable ‘personality congruence’ is measured on a range from ‘not at all descriptive’ to ‘extremely descriptive’. The questions posed in the questionnaire could be used directly for this research and no further adaptions where necessary. This is due to the fact that this research is about the fashion industry, as well as the previous studies from Shah et al,. (2016) and Esmaeilpour (2015) that are used to adopt the measures from.

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4. Data Analyses and Results 4.1. Data analyses

Firstly, the pre-test has led to helpful feedback regarding the survey. Slight adoptions have been made after this test. For instance, some main questions or introduction texts were not clearly formulated. Furthermore, in some cases Qualtrics did not work out perfectly. Thus, some adaptions in the Qualtrics settings where necessary. Other people made some slight notes about the lay-out, that the researcher adapted as well.

Furthermore, the collection of respondents went better and faster than expected. Approaching people on the street worked out well. Thanks to the personal approach, people felt involved in the research and so, a large number of the surveys were filled in. These forms were picked-up by the researcher at one’s house, where everyone has thrown his or her questionnaire through the letterbox. The Facebook-groups, such as ‘Je bent Haarlemmer als …’, led to a really high response as well.

In total, 546 respondents filled in the questionnaire. 508 questionnaires were completed online and 38 respondents completed a physical questionnaire. The data from the physical questionnaires were manually entered into SPSS by the researcher. 63 respondents filled in the questionnaire via the Facebook / LinkedIn page of the researcher herself. 10 questionnaires were filled in through an online database where you get a respondent if you fill in another student’s survey. Around 30 respondents are collected by approaching colleagues, family and friends. The biggest response (405 questionnaires) was obtained by the Facebook-communities, such as ‘Je bent Haarlemmer als …’.

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Counter-indicatives, Cronbach’s Alpha’s and Factor Analyses

There are no counter-indicative items found in the survey, so it was not necessary to recode any variable. This is also shown by the fact that no negative Cronbach’s Alpha has been found after calculating the reliability of the scales.

Reliability is the degree that analysis procedures provide consistent findings (Saunders & Lewis, 2009). This internal consistency is referred to as Cronbach’s Alpha and must be higher than 0.7. Not all questionnaires have been fully completed for various reasons. For instance, respondents who answered ‘no’ to the first question did not have the possibility to complete the survey completely. Because the survey closed after a ‘no’ answer. Another reason was that Qualtrics prematurely ended the questionnaire for unknown reasons. As a result, the respondent did not have the opportunity to fully complete the questionnaire. For this reason, there were some missing values in the dataset. 237 fully completed questionnaires remained. The margin of error is 6,36% with a confidence level of 95% and 237 respondents. The Cronbach’s Alpha has only been calculated on the complete questionnaires. By ‘complete questionnaires’ is meant the questions that cover the most important variables, such as the dependent variable ‘brand loyalty’, and the moderators ‘perceived quality’, ‘personality congruence’, and ‘brand prestige’. The Cronbach’s Alpha could not be calculated on the independent variable (‘Pain of Paying’), because this is a binary ordinal variable (cash = high pain of paying, Debit Card and Credit Card = low pain of paying). Table 1 indicates that all four variables have a Cronbach’s Alpha > .7, which means that there is internal consistency of all these variables. Table 1 provides an overview of the Cronbach’s Alpha of each variable.

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Table 1

Cronbach’s Alphas

Variable Cronbach’s Alpha # Items Brand loyalty .796 3 items Perceived quality .907 8 items Personality congruence

> Personality of the brand .945 42 items > Personality of the respondent .925 42 items Brand prestige .810 4 items *N = 237, 5-point Likert Scale.

To sum up, the brand loyalty scale has acceptable reliability (.796), the perceived quality scale has excellent reliability (.907), the personality of the brand scale and the personality of the respondent scale both have excellent reliability (.945 and .925), and brand prestige has good reliability (.810). The corrected item-total correlations of all the items are above .30, which means a good correlation with the total score of the scale. Also, if one of the items would be deleted, none of these would substantially influence reliability.

The shared variance (similarities) between variables has been calculated through a factor analysis (Saunders & Lewis, 2009). It looks at how much a factor explains other than its own variance and measures an underlying variable. The Confirmatory Factor Analysis (CFA) is used because the priori structure is known, and this thesis has the desire to look whether our data fits this structure (Saunders & Lewis, 2009). A principle axis factoring analysis (PAF) was conducted with a Oblimin with Kaiser normalization rotation on the scales.

Firstly, the variable ‘brand loyalty’ was analysed. A principle axis factoring analysis (PAF) was run. The results showed that one component had eigenvalues over Kaiser’s

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revealed a levelling off after the first factor. This in agreement with Kaiser’s criterion. To sum up, the initial factor analyses based on the eigenvalues showed clearly one dimension.

Therefore, forcing the factor analysis to one factor was unnecessary. With as a result, the extracted dimension had an explanatory power of 57.375% of the underlying concept of brand loyalty. Table 2 in the appendix shows the factor loadings, presented in a ‘factor matrix’ table. Item 1 (‘I intend to buy this brand in the near future’) has the highest factor loading (,822). This means that this item has the strongest association to the underlying variable brand loyalty.

Secondly, the variable ‘personality of the brand’ was analysed. The results showed that eight dimensions had eigenvalues over Kaiser’s criterion of 1 and these eight components together explained 53.950% of the variance. In agreement with Kaiser’s criterion, the scree plot showed a decreasing line after the eighth factor. After inspecting the factor loadings on all dimensions, it appeared that there is only one dimension with considerably higher factor loadings than the other dimensions. Due to the fact that ‘brand personality’ is not defined in literature as consisting of eight dimensions, this study decided to force the factor analyses to one dimension. This thesis therefore accepts the decreasing explanatory value. After forcing the eight dimensions to one dimension, the explanatory value decreases from 53.950% to 30.580%. The normal procedure would be the removal of items and then repeating the analysis (to get the highest possible explanatory factor). This study decided not to do this, because the items are based on the theory of Aaker (1997), which is seen as a high-quality and a reliable theory. At the moment the items would be removed, the link with the theory would be lost. Table 3 (in the appendix) shows the Factor Matrix of the variable ‘personality of the brand’. Because one factor was retained, the Factor Matrix is presented and not the Rotated Factor Matrix. There are two factor loadings that are considerably higher than the other factor loadings, namely ‘unique’ (,732) and ‘intelligent’. (,701). ‘Feminine’ (,310) and ‘masculine’

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(,388) are considerably lower than the other factor loadings. The researcher suspects that reason for these two lower factor loadings may be that female respondents would rather choose the option ‘feminine’ than ‘masculine’ and men would rather go for the ‘masculine’ option. The underlying reason for this could be the automatic transfer from the ‘personality of the brand’ to the ‘personality of the respondent’. If customers see the brand as feminine, they judge themselves as being feminine as well and the other way around (Yuan et al., 2016). It may also be that a brand has both a masculine and feminine appearance, so that respondents would not choose for masculine neither for feminine and filled in the option ‘neither agree nor disagree’.

Thirdly, the variable ‘personality of the respondent’ was analysed. Ten components had eigenvalues over Kaiser’s criterion of 1 and these ten components together explained 54.903% of the variance. For this variable counts the same as for ‘personality of the brand’. The researcher does not want to lose the link with the theory of Aaker (1997) and decides to force ten dimensions to one dimension. As a consequence, the explanatory value decreases from 54.903% to 24.288%. Table four in the appendix shows the factor matrix of the personality of the respondent variable. ‘Leader’ and ‘cheerful’ have the highest factor loadings. ‘Unique’ scores high as well, with a factor loading of ,609. This means that respondents associate the personality question the most with being cheerful and being a leader.

Fourthly, the variable ‘perceived quality’ was analysed. An initial analysis was run to obtain eigenvalues for each component in the data. One component had eigenvalues over Kaiser’s criterion of 1 and explained 57.551% of the variance. It is not necessary to force various factors to one factor because there is already one underlying factor. The item ‘products having this brand’s name are of good quality’ shows the highest factor loading

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perceived quality.

Lastly, the variable ‘brand prestige’ was analysed. One component had eigenvalues over Kaiser’s criterion of 1 and explained 52.242% of the variance. Due to the fact there has been found only factor, it was not necessary to force to one dimension. The item ‘this brand is very prestigious’ has the highest factor loading (,789), which is therefore the best explanatory item of the variance (table 6, appendix).

Furthermore, the Kaiser-Meyer-Olkin measure demonstrates the sampling adequacy for the analyses. In all cases the KMO showed that the sampling adequacy was sufficiently large for PAF. Brand loyalty showed a KMO of .704 and Bartlett’s test of sphericity χ² (3) = 220.901, p < .001. Perceived quality showed a KMO of .904 and Bartlett’s test of sphericity χ² (28) = 1249.824, p < .001. Personality of the brand showed a KMO of .912 and Bartlett’s test of sphericity χ² (861) = 5326.549, p < .001. Personality of the respondent showed a KMO of .879 and Bartlett’s test of sphericity χ² (861) = 4786.386, p < .001. Brand prestige showed a KMO of .778 and Bartlett’s test of sphericity χ² (6) = 308.069, p < .001.

After having conducted these factor analyses, new variables have been created with scores that result from these analyses. The scores are normally distributed standardized values with a mean of 0 and standard deviation of 1. These new variables are used into the regression section in the next chapter. ‘Personality congruence’ consists of the absolute difference

between ‘personality of the brand’ and ‘personality of the respondent’. For this reason, a new absolute variable has been created for ‘personality congruence’.

In conclusion, after inspecting the Cronbach’s Alphas, all the variables show an internal consistency (Cronbach’s Alpha > ,7). The factor analyses show that brand loyalty, perceived quality and brand prestige have only one underlying dimension that explains the variance of these variables. This corresponds to the literature. Personality of the brand and personality of the respondent show more than one dimension, which cannot be explained by

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literature. The components are forced to one component, while accepting the decrease of the explanatory value.

Correlation analyses

Table 7 provides a full correlation matrix. Before this one could be created by the researcher, the variable pain of paying should be recoded from a nominal variable into a categorical variable, where the option of cash is recoded into ‘1’ and the option of Debit Card and Credit Card into ‘0’. The option of ‘other’ was already labelled as a missing value. Furthermore, ‘personality congruence’ consists of the absolute difference between ‘personality of the brand’ and ‘personality of the respondent’. For this reason, a new variable has been created for ‘personality congruence’.

Table 7 Correlation Matrix Pearson Correlation # Variable name M SD 1 2 3 4 5 1 Brand Loyalty 0,000 0,900 ,796 2 Pain of Paying 0,131 0,338 -0,013 x 3 Brand Prestige 0,000 0,906 0,200** 0,064 .810 4 Personality Congruence 0,639 0,481 -0,051 0,019 -,130* x 5 Brand Quality 0,000 0,967 0,334** 0,116 ,200** -,109 .907

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Table 7 shows the correlation matrix of this study. It gives an overview to what extent each variable in the table is associated with each of the other variables. The Cronbach’s Alphas are placed on the diagonal. There are two missing Cronbach’s Alphas, namely from pain of paying and personality congruence. Pain of paying is a binary categorical variable and it is not possible to calculate a Cronbach’s Alpha from a binary categorical variable, only from an interval/ratio variable. Because personality congruence is the absolute difference between personality of the brand and personality of the respondent, so a Cronbach’s Alpha cannot be calculated from this variable as well.

This study has decided to make use of a Pearson Correlation Matrix instead of a Spearman Correlation Matrix. It could be argued that a Spearman Correlation Matrix could be used better, because this matrix does not make the assumption that variables are normally distributed or that the variables are interval / ratio variables. Pain of paying is a binary categorical variable what initially leads to the demand for a Spearman Model. In contrast to this statement, the researcher has decided to choose for a Pearson Correlation Matrix, because the underlying variable of pain of paying could be considered as normally distributed and continuous. When customers do a purchase and pay cash, the level of pain does not increase immediately from 0 to the maximum level of pain (which is in case of a binary variable). The pain gradually increases, as the payment proceeds. The gradually increase of the pain level justifies more or less the use of a Pearson Correlation Matrix instead of a Spearman

Correlation Matrix.

Table 7 shows that pain of paying has a negative but not significant correlation with brand loyalty (r = -0,013; p = 0,843). This suggests that if this result holds in the regression analysis that there is no association (= correlation) between pain of paying and brand loyalty. If so, the main relation (which is covered by hypothesis 1) will be rejected. Brand prestige has a positive significant correlation with brand loyalty (r = 0,200; p = 0,002). This result is in

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line with the results from Esmaeilpour (2015), who also found a positive effect between brand prestige and brand loyalty, but via the mediator brand attitude. Brand quality has a positive significant correlation on brand loyalty (r = 0,334; p < 0,001). This result is in line with the results from Esmaeilpour (2015) as well, who also found a positive significant direct effect between brand quality and brand loyalty. Based on these founding the researcher assumes that the validity and reliability of this research is acceptable. In contradiction to the findings of Esmaeilpour (2015), this study finds a negative significant correlation between brand prestige and personality congruence (-,130), while Esmaeilpour (2015) found a positive effect from personality congruence to brand prestige. If personality congruence decreases the brand prestige increases, and the other way around. Brand quality has a positive significant

correlation on brand prestige (r = 0,200; p = 0,002). This result is again in line with the results from Esmaeilpour (2015), who also found a positive significant direct effect from brand prestige to brand quality. This means that this thesis has sufficient reliability and validity based on the research from Esmaeilpour (2015). None of the moderators (perceived quality, personality congruence, and brand prestige) have a significant correlation with pain of paying, which suggest that there is no association between the moderators and this independent

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4.2. Regressions

A multiple regression model with four blocks / models has been created. Before this multiple regression model has been set up, several pre-regressions have been conducted. Firstly, the main relation between pain of paying and brand loyalty has been analyzed, without taking any other variable into account. Results revealed that there was no significant effect of pain of paying on brand loyalty. The Adjusted R Square was almost close to 0 (-,006) and the ANOVA did not show a significant value (,806). Thereafter, the effects of the control variables were measured separately. Before this could be done, ‘purchase type’ (= control variable) was recoded into dummy variables. Thereafter, the linear regression with all the dummies (footwear, sunglasses, watches, jewelry, sportswear), except one (clothes), has been conducted. This control variable showed that the type of product leads to a difference in brand loyalty. The biggest differences can be find between clothes and watches (p = ,016) and clothes and sunglasses (p = ,072). This means that the average brand loyalty will be higher when customers buy clothes compared to watches and sunglasses. ‘Purchase category’ was recoded into a categorical variable before the linear regression analysis has been conducted. After checking the effect of the four control variables separately, the researcher decided to take ‘gender’, ‘education’, ‘purchase type’ and ‘purchase category’ into account. Results showed that these control variables were not able to make the relationship between pain of paying and brand loyalty significant but pushed the results more towards significance. Which is a good sign. Table 8 shows a multiple regression model of brand loyalty.