How does loyalty towards the main bank of a consumer affect the relationship between perceived benefits of financial products and purchase intention
Track: Msc Business Studies – Strategic Marketing Management Student: Marnix Reintjes – 11141565
Supervisor: Ed Peelen Date: 26-08-2018
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Statement of originality
This document is written by student Marnix J. H. M. Reintjes 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 responsible solely for the supervision of completion of the work, not for the contents.
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Abstract
In recent years, the retail banking industry made a shift from the traditional product-orientation to a customer-product-orientation making loyalty a focus within an industry which is characterized by long-term customer relationships. Despite the extensive research done on loyalty, limited research has been done on the effect loyalty has on the relationship between perceived benefits and purchase intention in the context of retail banking. The goal of this study is to examine how affective, normative and calculative loyalty towards a consumers main bank effects purchase intention when he or she is presented with an offer of a competing bank and perceives benefits on sustainability, price or convenience. This was done using three different product offers: payment account, savings account and mortgage. Data was collected from 114 respondents. The results of the quantitative analysis show that the moderating effect of loyalty on the relationship between perceived benefits and purchase intention appears to be minimal. A significant effect of normative loyalty is found and a weak, non-significant effect of calculative loyalty. Affective loyalty seems to have no effect. The moderating effect of loyalty differs for the three product offers. Loyalty has no moderating effect in relation to the mortgage offer, and no significant moderating effect in relation to the savings account offer. Keywords: normative loyalty, calculative loyalty, affective loyalty, perceived benefits, pur-chase intention, retail banking,
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Table of contents
1. Introduction ... 4
2. Theory and hypotheses ... 6
2.1 Loyalty ... 6
2.2 Loyalty and purchase intention ... 9
2. 3 Perveived benefits and purchase intention ... 9
2. 4 Conceptual model ... 11 3. Methodology ... 12 3.1 Research design ... 12 3.2 Questionnaire ... 14 3.3 Sampling ... 15 3.4 Measures ... 15 4. Results ... 18
4.1 Descriptive data of sample ... 18
4.2 Normality and outliers ... 19
4.3 Correlation ... 21 4.4 Anova ... 22 4.5 Hypotheses testing ... 24 5. Discussion ... 31 6. References ... 36 7. Appendix ... 43
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1.
Introduction
The banking sector is a complex and dynamic environment where very slight product and service differences (Barnes and Howlett, 1998) combined with increasingly demanding customers, have led to a great transformation of the sector. The traditional product-orientation has made way for customer-orientation. Due to this shift, loyalty has become a main focus within the financial sector.
Brand loyalty can have a positive impact on business performance (Reichheld and Sasser, 1990; Reichheld, 1993; Sheth and Parvatiyar, 1995). Some studies even go as far as claiming that brand loyalty is the most important determinant for profitable and successful firms (eg. Caruana et al., 2000; Juhl et al., 2002). Loyalty can be based on inertia or on true brand loyalty (Dick and Basu, 1994). Changing the buying pattern based on inertia where a consumer passively accepts a brand is relatively easy because a consumer will show little resistance when a competitor can offer a valid reason to do so (Solomon, 1992). Compared to inertia, getting a true brand loyal consumer to switch is much harder for a competitor because of his or her active and emotional involvement with a favourite brand (Oliver, 1999). In both concepts of loyalty, it appears that loyalty can prevent customers to switch to other brands.
Research done by Esterik-Plasmeijer and van Raaij (2017) on loyalty in banking, shows that customers tend to be loyal but are subject to situational changes due to the great importance they put on the value offered and convenience. This would imply that consumers in banking show loyalty based on inertia instead of true loyalty. This means a brand is bought out of habit because it takes less effort than switching to another brand (Solomon, 1992) instead of a conscious decision accompanied by underlying positive
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attitudes and commitment towards a brand (Oliver, 1999). To further build on the findings of Esterik-Plasmeijer and van Raaij (2017) and add to it by finding a possible explanation to why customers refrain from buying behaviour after buying presented with an offer from a competitor, this study examines the effect of different aspects of loyalty on the relationship between perceived benefits and purchase intention within the context of to the retail banking industry in the Netherlands. The findings may also apply to other service industries with parity offerings and increase the general understanding of the relationships between perceived benefits, purchase intention and loyalty. Therefore the research question of this study will be:
How does loyalty towards the main bank of a consumer affect the relationship between perceived benefits of financial products and purchase intention in the Dutch retail banking market?
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2.
Theory and hypotheses
As a start, this thesis will provide an extensive literature review giving insights in the main elements.
2.1 Loyalty
Loyalty as a construct in marketing and service related research has been described by many researchers and it seems hard to reach consensus on the conceptual model (Knox and Walker, 2001; Rundle-Thiele and Mackay, 2001). This is because of the richness of the construct of loyalty in the sense that customers can express loyalty in many ways (eg. Backman and Crompton, 1991; Day, 1969; Jacoby, 1971; Jacoby and Chestnut, 1978; Narayandas, 1998; Pritchard and Howard, 1997). Loyalty has been defined as; “a deeply held commitment to rebuy or re-patronize a preferred product/service consistently in the future, causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behaviour” (Chaudhuri and Holbrook, 2001; Oliver, 1999). The definition provided by Chaudhuri & Holbrook (2001) and Oliver (1999) of brand loyalty shows that brand-loyal consumers are willing to pay more for a certain brand because of perceived value in that particular brand. Brand loyalty is particularly interesting for firms because higher levels can lead to: lower marketing costs (Rosenberg and Czepiel, 1983), greater market share (Assael 1998), favourable word of mouth (Dick and Basu, 1994) and greater resistance among loyal consumers to competitive strategies (eg. Dick and Basu, 1994). Some studies claim that brand loyalty is the most important determinant for profitable and successful firms (eg. Caruana et al., 2000; Juhl et al., 2002).
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Early studies on customer loyalty mainly focus on behavioural dimensions such as repeat purchasing of a certain brand over time (eg. Jacoby and Chestnut 1978; Sheth 1968; Tucker 1964) and the proportion of purchases of a customer at a single brand (Cunningham, 1956; Blattberg and Sen, 1974). Over the years many different ways to measure behavioural loyalty towards a brand were developed such as: repeat purchase rate (Colombo, Ehrenberg, & Sabavala, 2000), purchase frequency over a period of time (e.g. Sharp & Sharp, 1997), tenure (Reichheld & Teal, 1996), the proportion of brand buyers who are solely loyal (Raj, 1985) and repertoire size (e.g. Banelis, Riebe, & Rungie, 2013; Uncles & Ehrenberg, 1990). Definitions based on behavioural aspects have been criticized because high repurchase rates do not always indicate that there is high customer loyalty and vice versa (Dick and Basu, 1994; Peyrot and Van Doren, 1994; Rowley and Dawes, 2000) and also because of limitations in distinguishing between commitment and convenience (Day, 1996). Convenience repeat purchases can be described as spurious loyalty (Day, 1996). Day (1996) and Jacoby and Kyner (1973), argue that loyalty consists of two dimensions: behaviour and attitude. According to Jacoby and Chestnut (1978), Solomon (1992) and Dick and Basu (1994) the combination of these dimensions allow for a distinction in loyalty concepts:
1. loyalty based on inertia. This means a brand is bought out of habit because it takes less effort than switching to another brand.
2. true brand loyalty. This means a brand is bought because of a conscious decision which must be accompanied by underlying positive attitudes and commitment toward a brand.
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True brand loyalty according to Day (1996) means that consumers should show,
psychological and affective commitments and therefore repurchase consistency. This means that true brand loyalty is more than the simple repeat purchasing of a particular brand, but that there is a bond between a customer and a brand: attitudinal loyalty. According to Samuelsen and Sandvik (1997) true brand loyalty can be caused by affective reasons such as attachment or emotional feelings towards a brand. Hansen et al. (2003) described these affective or positive emotional feelings towards a brand as affective loyalty. Samuelsen and Sandvik (1997) further explain that loyalty can be caused by cognitive motives such as perceived risk or perceived variations in performance among the competitive brands. This can be described as calculative loyalty and is the cold calculation of costs and benefits including investments and available alternatives (Allen and Meyer, 1990; Geyskens et al. 1996). Finally, according to Allen & Meyer (1990), the bond between customer and a brand can also be caused by normative loyalty. Normative loyalty is based on a belief that the customer is responsible towards a company and the customer feels morally obliged to stay in the relationship. The retail banking industry is traditionally characterized by long-term customer relationships which, according to Stewart (1998), maybe due to normative loyalty. A study by Esterik-Plasmeijer and van Raaij (2017) on loyalty in Greek banking found that consumers tend to be loyal, but are subject to situational changes due to the great
importance they put on the value offered and convenience. This would imply that consumers in banking show calculative loyalty instead of affective loyalty. Because affective, normative and calculative seem to be relevant in relation to retail banking, all three are incorporated in this study.
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2.2 Loyalty and purchase intention
Purchase intention can be defined as the conscious planning of a consumer to buy a particular brand (Spears & Singh, 2004). Fishbein & Azjen (1975) already found that purchase intention is an important indicator to predict a consumers buying behaviour. Later studies reported a positive correlation between purchase intention and actual purchase behaviour of consumers (Morwitz and Schmittlein, 1992; Morwitz et al., 1996). Oliver (1999) explains that customer loyalty is a pattern of behavior that is shown by consumers when they are committed to a brand. The pattern of behavior is that consumers even buy products from their preferred brand when there is a different brand that is offering a similar product that is more convenient or available at a lower price. But it also protects companies from competitive strategies (Dick & Basu, 1994). The previously mentioned definition of loyalty by Oliver (1999) also states that loyalty has the potential to make costumers more resistant to marketing efforts that may cause brand switching behaviours. This could indicate that loyalty has a negative moderating effect on the relationship between perceived product benefits and purchase intention which prevents customers from responding to offers from competing banks. Therefore, the following
hypothesis is formulated:
H2: Loyalty has a negative moderating effect on the relationship between perceived
benefits and purchase intention.
2.3 Perceived benefits and purchase intention
According to Wood and Scheer (1996) purchase intention is related to the perceived value of a product. Perceived value can be defined as the overall assessment of the utility of a product based on ‘’what is given’’ and ‘’what is received’’ (Zeithaml, 1988). This means that
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social, relational) and the other part consists of sacrifices made by the consumer (effort, price, time, risk and convenience) (Dodds et al., 1991; Grewal et al., 1998; Cronin et al., 2000; Bigne et al., 2001). The relation between perceived product benefits and purchase intention was further substantiated by Kukar, Nancy and Kent (2012) who showed that perceived price benefits has a positive relation with purchase intention. According toCarlos Fandos Roig et al. (2006) this is also the case in the banking industry. Price is the most often described in the literature as a perceived benefit of a product (e.g. Cronin et al., 2000; Bigne et al., 2001, Nancy & Kent, 2012). This means that the price of a product is perceived lower than that of a comparable product and thus perceived to have positive price benefits. Because the properties of payment accounts are quite similar ("prive-bankrekening", n.d.) price is relevant to stay competitive in the Dutch retail banking industry. This is why price is measured as a perceived product benefit in this study. In recent years, perceived benefits on sustainability got more attention. Research by Bigne et al. (2001) describes that that when a consumer perceives a product to have a positive impact on sustainability, he or she rates the product higher on perceived product benefits. According to Ottman (2017), the focus on sustainable or green product benefits is of critical importance to winning over the mainstream consumer and driving organizational growth and is why this is the second perceived product benefit that is included in this study. The third perceived benefit that is measured is convenience because Esterik-Plasmeijer and van Raaij (2017) found that in banking, customers put great importance on convenience. Convenience is a possible sacrifice a consumer makes when choosing a particular product. It means that by choosing another product, a consumer believes could be negative consequences for convenience (Bigne et al., 2001).
About the relation between perceived benefits and purchase intention for individual banking products is little known. This is why this study examines this relation for three different
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products all four of the major Dutch banks carry: payment account, savings account and mortgage. The reason insurance and investment products are not included is because the fourth major bank in the Netherlands, de Volksbank, does only offer insurance and investment products through third party suppliers and thus does not have a direct influence on its
properties. This leads to the following hypothesis which will be tested for each of the described perceived benefits and described banking products:
H1: The level of perceived product benefits has a direct positive effect on purchase
intention.
2.4 Conceptual model
The hypotheses based on the literature study in previous paragraphs lead to the conceptual model presented below in figure 1. It shows that participants are presented with three product offers and subsequently perceived benefits on price, convenience and sustainability are measured. Next purchase intention is measured to understand how it relates to the perceived benefits and if there is a direct relationship (H1). Next it its tested if affective, normative and calculative loyalty have a moderating effect on the relationship between perceived benefits and purchase intention.
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3. Methodology
3.1 Research designThe main objective is of this study is to examine the moderating effect of loyalty on the direct relationship between perceived product benefits and purchase intention. To answer the main research question, the relationships and causes between the variables need to be explained which makes quantitative research appropriate for this thesis. The study is non-experimental because neither the situation nor the participants were manipulated.
Loyalty is measured towards the main bank of the participant. To better understand the influence of loyalty, different aspects are measured that are described in the literature study: affective, calculative and normative. Calculative loyalty is split into two measures in this study. Calculative loyalty is based on costs versus benefits (Lehtonen, 2012), which can be interpreted based on price, or on the amount of effort a consumer has to take in order to switch to another brand. Because it is interesting to understand both aspects separately, calculative loyalty is split in to ‘calculative loyalty price’ and ‘calculative loyalty effort’. Different aspects of loyalty may have a different effect on the relationship between perceived benefits and purchase intention (H1). Affective and normative loyalty are hypothesized to have a negative impact on the relationship between perceived benefits and purchase intention. Affective and normative because there is an emotional (Hansen et al., 2003) or moral bond (Allen & Meyer, 1990) between a consumer and their main bank. Calculative loyalty (Allen and Meyer, 1990; Geyskens et al. 1996) is hypothesized to have a positive effect because if an offer of another bank has higher perceived product benefits, a consumer with high calculative loyalty will more likely make a decision based on cognitive motives such as price benefits (Solomon, 1992).
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A total of three financial product offers will be presented to the participants, intended to score either high or low on perceived benefits on one of the three perceived benefits described in the literature study: price, sustainability and convenience. After each offer, the participant is asked to score the presented product on perceived benefits and the purchase intention of the participant is measured (H1). The offers used in this study are:
Offer 1: A payment account that has the same properties as a participants current payment account, but at 50% of the costs. Because it is unlikely that either one of the four major banks in the Netherlands will present a radically different payment account, price is an important competitive factor. The discount is relatively high, because of possible reluctancy of consumers to switch payment accounts to a different bank. This is based on the fact that numerous studies show that Dutch consumers do not often switch their payment account to a different bank compared to other European countries (Eg. Gallup Organisation, 2009;
Consumentenbond and Trigenum, 2011).
Offer 2: The second offer is a mortgage with a 0,5% point lower interest rate and all the standard administrative steps to switch to the new fictive company. At the moment this thesis is written, this is a discount of approximately 25% compared to current mortgage rates. A mortgage is a more complex product. The interest rate of the products is lower compared to the main bank of the participant, but due to the fact that it is a complex product, the
participant has to make a trade-off on perceived convenience sacrifices.
Offer 3: The third offer presents the respondent with a savings account with a 0,5% lower interest rate than their current account at their main bank, but with the guarantee that their
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savings are invested in projects that benefits communities and/or the environment. This offer is conceived because Dutch consumers appear to switch more easily between banks when it concerns savings accounts (bron). This indicates that savings accounts are more of a shopping product and customers will be more susceptible to perceived benefits. Because sustainability is an important product benefit to stay competitive, it is incorporated in this offer. To
compensate for the assumed higher level on perceived sustainability, the interest rate in this offer is lowered compared to the interest rate of the participants main bank. This means the participant has to make a trade-off between price and sustainability.
3.2 Questionnaire
The data is collected through online questionnaires and will represent one moment in time which means cross-sectional data will be used in the analysis. All participants were presented the exact same questionnaire. Basic knowledge of English was required, in order to fully understand the questionnaire. The link to the interview has been active for three weeks. The questionnaire consists of 3 parts. Part 1 measures general demographics such as age, income and gender. Next to general demographics, part 1 measures which bank the respondent considers to be his or her main bank and what products he or she has at that particular bank. Part 2 measures the different aspect of loyalty towards their main bank. In part 3 the respondent is shown three different financial product offers which they are asked to compare with their main bank and score the offers on perceived price benefit, perceived consequences for convenience and perceived consequences for sustainability. Next to
perceived benefits, part 3 also measures purchase intention based on the same offers. The full questionnaire can be found in the appendix.
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3.3 Sampling
The sampling technique that is used in this study is convenience or availability sampling because a link to the online questionnaire was distributed through e-mail to the business and personal network of the researcher. A drawback of convenience sampling technique is that it can be biased and influences beyond of the control of the researcher (Saunders et al., 2012). For non-probability sampling techniques there are no rules concerning sample size (Saunders et al., 2012). Instead it is important to understand what will have credibility and needs to be found out within the available recourses to decide the appropriate sample size (Patton, 2002). For this study a sample size of 100 was decided to be adequate.
Respondents needed to be over 18 years old, and at least have a payment account at a Dutch bank to be included in the study. At total of 160 respondents started the questionnaire of which 114 completed and fitted the requirements of the study resulting in 114 useable questionnaires for this study.
3.4 Measures
Affective loyalty
The scale is composed of three, seven-point Likert-type statements ranging from strongly disagree to strongly agree, measuring the degree of positive affect a consumer has toward a brand. The scale was developed by Chaudhuri and Holbrook (2001). Cronbach’s alpha reliability coefficient of this scale is .901. Participants were presented with statements such as “I feel good when I use this brand” and “This brand gives me pleasure.
Normative loyalty
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believes he/she should remain with a particular service provider because it is the "right" thing to do. Instead of filling in the name of a particular service provider, ‘’my main bank’’ was used. The scale was first developed by Bansal, Irving, and Taylor (2004). Cronbach’s alpha reliability coefficient of this scale is .875. The participants were presented with statements such as ‘’Even if it were to my advantage, I do not feel it would be right to leave my main bank’’ and ‘’I would feel guilty if I left my main bank now.’’
Calculative loyalty (price)
The scale is composed of two seven-point Likert-type statements used to measure a
customer's attitude regarding the financial consequences of continuing/ending the relationship with a certain company. In the case of this study the participants were asked to keep in mind their main bank when responding to the statements. Cronbach’s alpha reliability coefficient of this scale is .946. The scale was first developed by Gustafsson, Johnson, and Roos (2005) and was originally composed of three questions. One question was about location advantages in comparison to competitors, which was not relevant for this study, so the question was removed. Participants were presented with the statements ‘’ It pays off economically to be a customer of the company’’ and ‘’ I would suffer economically if the relationship were broken.’’
Calculative loyalty (effort)
Three, seven-point Likert-type statements are used to measure a person's desire to continue being a customer of a particular business due to the difficulties that are assumed to be incurred if a switch is made. The scale was developed by Verhoef, Franses, and Hoekstra (2002). Cronbach’s alpha reliability coefficient of this scale is .826. Participants were presented with statements such as ‘’Because it is difficult to stop my business at my main
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bank, I remain a customer’’ and ‘’I remain a customer of my main bank because it is difficult to take my business to another company.’’.
Purchase intention
It is a three-item, seven-point scale measuring the likelihood that a consumer will purchase a product based upon information he/she has read on the product's package. Although is this study no package is presented, the information about the offer is presented in text. This makes the questions used to measure purchase intention suitable for this study. After each offer, the question were presented to the participants. The scale was developed by Burton, Garretson, and Velliquette (1999). Cronbach’s alpha reliability coefficient of this scale is .919.
Participants were asked to answer questions such as ‘’Would you be more likely or less likely to purchase the product, given the information shown?’’ and ‘’Given the information shown, how probable is it that you would consider the purchase of the product?’’.
Perceived benefits
To measure the perceived benefits, the participants were presented with three statements after each offer ranging from strongly disagree to strongly agree. Each statement measures how participants score the presented offer on perceived price benefit, perceived consequences for convenience and perceived consequences for sustainability compared to their main bank. Because for each different offer, only one question about each perceived benefit is presented to the participant, calculating the cronbach’s alpha reliability coefficient has no use since this is 1. The participants were presented with statements such as ‘’My perception is that this is a subsantial price advantage’’ and ‘’ My perception is that this offer cannot be without negative consequences for convenience’’.
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4. Results
4.1 Descriptive data of sampleTo provide an overview of the participants in this study, the descriptive analysis is presented. The mean, standard deviation or percentage is presented in table 1 on the next page. The questionnaire was taken by 160 respondents of which 114 completed it resulting in 114 useable questionnaires for this study. The average age of the participants was 41,4 years (SD = 12,2). Of the participants 67 (58,8%) are male and 47 (41,2%) are female. Almost half of the participants (48,2%) have a HBO level of education followed by WO (36%) and MBO (15,8%). A little more than a third of the participants has an income before taxes of more than 60.000 Euros a year (36%) followed by an income between 40.000 and 60.000 a year
(32,5%), an income between 20.000 and 40.000 Euros a year (25,4%) and the smallest group makes less than 20.000 Euros a year (6,1%). Most of the participants see SNS as their main bank (63,2%). The overrepresentation of this particular group is a consequence of the self-selective nature of data collection, and of the distribution channel used for the survey (SNS business network mail-list of the researcher). ING (18,4%) and RABO (12,3%) are the two biggest groups after SNS followed by ABN (4,4%), ASN (0,9%) and other (0,9%). The descriptive information of the main variables are presented in table 2 on the next page. Of each variable, the mean, standard deviation and number of participants is shown.
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Table 1. Data of sample
Variable M (SD) / % n Age 41,4 (12,2) 114 Gender Male 58,8 67 Female 41,2 47 Education MBO 15,8 18 HBO 48,2 55 WO 36 41 Income 0-20.000 6,1 7 20.000-40.000 25,4 29 40.000-60.000 32,5 37 >60.000 36 41
Main bank ABN AMRO 4,4 5
RABO 12,3 14
ING 18,4 21
SNS 63,2 72
ASN 0,9 1
Other 0,9 1
Table 2. Descriptive statistics main variables
Variable M (SD) n
Affective loyalty 2,69 (1,08) 114
Normative loyalty 3,98 (1,49) 114
Calculative loyalty price 3,15 (0,86) 114
Calculative loyalty effort 4,37 (1,54) 114
Purchase intention payment offer 3,94 (1,50) 114
Purchase intention savings offer 3,99 (1,67) 114
Purchase intention mortgage offer 3,27 (1,54) 114
4.2 Normality and outliers
The values for skewness and kurtosis between -2 and +2 are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). All variables have skewness outcomes within the range of 0,465 and 0,667 and kurtosis outcomes within the range of
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1,060 and 0,094. All results are below in table 3. This means the skewness and kurtosis values for the variables affective loyalty, normative loyalty, calculative loyalty price, calculative loyalty effort, purchase intention payment, purchase intention saving and purchase intention mortgage are all within the acceptable range of -2 and +2 to prove normal univariate
distribution. The data was also checked for outliers. According to Kline (2015), outliers are scores that are different from others in a data set. They are observations with extreme value on one variable. There were 2 outliers found in the variable affective loyalty, but after inspection it was decided that they are not deleted. No other outliers were found in the data. Based on the values for skewness and kurtosis and the fact that there are no outliers impacting the data, the decision is made to use parametric test to analyse the data.
Table 3. Skewness & Kurtosis
Variable n Min. Max. Mean Std.
Devia-tion Skewness Kurtosis Affective loyalty 114 1,00 7,00 2,687 1,077 (0,226) 0,542 (0,449) 0,094 Normative loyalty 114 1,00 7,00 3,984 1,497 (0,226) 0,204 (0,449) -0,849 Calculative loyalty p. 114 1,00 7,00 3,149 0,856 (0,226) -0,465 (0,449) -0,504 Calculative loyalty eff. 114 1,00 7,00 4,371 1,542 (0,226) -0,266 (0,449) -0,881 Purchase intention pay. 114 1,00 7,00 3,939 1,503 (0,226) 0,253 (0,449) -0,783 Purchase intention sav. 114 1,00 7,00 3,991 1,672 (0,226) 0,198 (0,449) -1,060 Purchase intention mort. 114 1,00 7,00 3,267 1,538 (0,226) 0,667 (0,449) -0,441
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4.3 Correlation
To test if variables correlate with each other, Pearson’s correlation measurement has been used. The correlation matrix can be found in appendix 2. Examining the relationship between perceived benefits and purchase intention it is found that for the payment account offer there is a strong positive correlation between perceived benefits on price and purchase intention (r = .512, p < .001), a strong positive correlation between perceived benefits on convenience and purchase intention (r = .348, p < .001) and strong positive correlation between perceived benefits on sustainability and purchase intention (r = .429, p < .001). This means that there is a significant positive correlation between all perceived benefits variables for the payment account offer and purchase intention for the payment account offer.
The relationship between perceived benefits and purchase intention for the savings account offer show no significant correlation, although it appears there might be a weak positive relationship between perceived benefits on sustainability and purchase intention (r = .181, p = .058). This relationship was expected because there was a clear link to sustainability in the offer.
For the mortgage offer, there is a strong positive correlation between perceived price benefits and purchase intention (r = .258, p = .006). This was expected due to the nature of the offer and the type of product. Price benefits can have a significant impact on living costs of the consumer. There is no significant correlation between perceived benefits on sustainability and purchase intention and perceived benefits on convenience and purchase intention for the mortgage offer.
Examining the other main variables of this study, calculative loyalty price, calculative loyalty effort, normative loyalty and affective loyalty, correlation is found between: Normative loyalty correlates with affective loyalty (r = .570, p < 0.01). Calculative loyalty effort
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correlates with affective loyalty (r = -.266, < = 0.01). Calculative loyalty effort correlates with normative loyalty (r = -.217, p = 0.02). Calculative loyalty effort correlates with calculative loyalty price (r = -.188, p = 0.045). Affective loyalty price correlates with perceived benefits on price for the savings account offer (r = .277, p < 0.01). Normative loyalty correlates with perceived benefits on price for the mortgage account offer (r = .253, p < 0.01). Normative loyalty correlates with perceived benefits on price for the savings account offer (r = .232, p = 0.013). Calculative loyalty price correlates with perceived benefits on price for the mortgage offer (r = .211, p = 0.024). Calculative loyalty price correlates with perceived benefits on sustainability for the mortgage offer (r = -.259, p <0.01). Calculative loyalty price correlates with perceived benefits on convenience for the savings account offer (r = -.201, p = 0.032). Calculative loyalty effort correlates with perceived benefits on convenience for the payment account offer (r = .279, p < 0.01). Calculative loyalty effort correlates with perceived benefits on convenience for the mortgage offer (r = .228, p = 0.015). The correlations found for the control variables age, gender, level of education and income are not mentioned but can be found in the appendix. They seem to have little impact on the outcomes of the main variables and are not included in further results of this study.
4.4 Anova
To compare the means within perceived benefits for the different offers, the one-way ANOVA was used. In order to get a better graphical representation of the means plot, the scale of perceived price benefits and perceived sustainability benefits were reversed for the ANOVA analysis. The means plot is shown in figure 2 on the next page. There was a
statistically significant effect of type of offer on levels of perceived price benefits, F(2, 339) = 19.58, p < .001. Tukey post-hoc tests revealed that the level of perceived price benefits were significantly lower for the savings account offer compared to the mortgage offer (p < .001)
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and the payment account offer (p = .002). The test also revealed that perceived price benefits were significantly lower for the payment account offer compared to the mortgage offer (p = .012).
There was a statistically significant effect of type of offer on levels of perceived convenience benefits, F(2, 339) = 5.10, p = .007. Tukey post-hoc tests revealed that the level of perceived convenience benefits were significantly higher for the savings account offer compared to the mortgage offer (p < .001) and the payment account offer (p = .002). There was no statistically significant difference between the payment account offer compared to the mortgage offer (p = .873).
There was a statistically significant effect of type of offer on levels of perceived sustainability benefits, F(2, 339) = 5.02, p = .007. Tukey post-hoc tests revealed that the level of perceived sustainability benefits was significantly higher for the savings account offer compared to the payment account offer (p = .006). There was no statistically significant difference between the savings account offer compared to the mortgage offer (p = .077) and also no statistically significant difference between the payment account offer compared to the mortgage offer (p = .636).
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4.5 Hypotheses testing
To test the H1 and H2, this study uses the regression analysis. Regression analysis allows to understand which of the independent variables predict the dependent variable. Stepwise regression was used to test H1 and to test H2, PROCESS was used. All main variables were tested for multicollinearity and all VIF values were below 2 which means there is no
multicollinearity. The outcomes can be found in appendix 3.
Payment account: effect of perceived benefits on purchase intention
H1 states that perceived benefits has a direct effect on purchase intention. In this study this is tested for the perceived benefits on price, convenience and sustainability for three different offers: payment account, savings account and mortgage. The effect of perceived benefits on purchase intention was first tested for the payment account offer. Tests for multicollinearity indicated that a very low level of multicollinearity was present (VIF = 1.28 price benefits, 1.17 convenience benefits, and 1.29 sustainability benefits). Using stepwise regression, it was found that perceived convenience benefits (β = -.16, p = .059) does not affect purchase
intention significantly. The models of the stepwise regression are shown in table 4 on the next page. In model 1 perceived benefits on price is entered (β = .402, p < .001). This model explained 26,% (R2 = .263, F (1,112) = 39.874, p < .001) of the variance in purchase intention. In model 2 the variable perceived sustainability benefits is added (β = .268, p = .004). This model explained 31,6% (R2 = .316, F (1,111) = 25.606, p < .001) of the variance in purchase intention. This means that for offer 1, payment account, H1 is supported for perceived benefits on price and perceived benefits on sustainability, but rejected for perceived benefits on convenience.
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Table 4. Model summary payment offer
Model Summary Model R R Square Adjusted R Square Std. Error Change Statistics R Square
Change F Change df1 df2 Change Sig. F
1 ,512a
,263 ,256 1,29722 ,263 39,874 1 112 ,000
2 ,562b
,316 ,303 1,25521 ,053 8,623 1 111 ,004
a. Predictors: (Constant), PayCost
b. Predictors: (Constant), PayCost, PaySust.
Savings account: effect of perceived benefits on purchase intention
To test H1 in relation to the savings account offer, the variables perceived benefits on price, convenience and sustainability for this particular offer are put in to a stepwise regression with purchase intention for the savings account offer as the independent variable. Tests for
multicollinearity indicated that a very low level of multicollinearity was present (VIF = 1.12 price benefits, 1.27 convenience benefits, and 1.54 sustainability benefits). Using stepwise regression, it was found that perceived convenience benefits (β = -.147, p = .11) and perceived sustainability benefits (β = .113, p = .197) do not affect purchase intention
significantly. The models of the stepwise regression is shown in table 5 on the next page. This means there is only one model based on perceived price benefits (β = .555, p < .001), which is shown in table 5 on the next page. This model explained 26,0% (R2 = .260, F (1.112) = 39.340, p < .001) of the variance in purchase intention. This means that for offer 2, savings account, H1 is supported for perceived benefits on price but rejected for perceived benefits on sustainability and perceived benefits on convenience. It appears that price is the most
important benefit to consumers out of the three tested in this this study. In the means plot it is shown that this offer is rated lowest on this perceived benefit.
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Table 5. Model summary savings offer
Model Summary Model R R Square Adjusted R Square Std. Error Change Statistics R Square
Change F Change df1 df2 Change Sig. F
1 ,510a
,260 ,253 1,44498 ,260 39,340 1 112 ,000
a. Predictors: (Constant), SavCost
Mortgage: effect of perceived benefits on purchase intention
To test H1 in relation to the mortgage offer, the variables perceived benefits on price,
convenience and sustainability for this particular offer are put in to a stepwise regression with purchase intention for the mortgage offer as the independent variable. Tests for
multicollinearity indicated that a very low level of multicollinearity was present (VIF = 1.01 price benefits, 1.10 convenience benefits, and 1.11 sustainability benefits). Using stepwise regression, it was found that perceived convenience benefits (β = -.026, p = .743) and perceived sustainability benefits (β = .081, p = .317) do not affect purchase intention
significantly. The models of the stepwise regression is shown in table 6 on the next page. This means there is only one model based on perceived price benefits (β = .587, p < .001), which is shown in table 6. This model explained 35,3% (R2 = .353, F (1.111) = 60.517, p < .001) of the variance in purchase intention. This means that for the third offer, the mortgage offer, H1 is supported for perceived benefits on price but rejected for perceived benefits on
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Table 6. Model summary mortgage offer
Model Summary Model R R Square Adjusted R Square Std. Er-ror Change Statistics R Square
Change F Change df1 df2 Change Sig. F
1 ,594a
,353 ,347 1,24351 ,353 60,517 1 111 ,000
a. Predictors: (Constant), MortCost
Moderating effect of loyalty
H2 states that loyalty has a moderating effect on the relationship between perceived benefits and purchase intention. In this study this is examined by measuring the effect of the different aspects of loyalty towards a consumers main bank on the relationship between benefits perceived after being presented with an offer of a competing bank and purchase intention. In this study, loyalty is divided into four aspects: affective loyalty, calculative loyalty price, calculative loyalty effort and normative loyalty. To test if these variables have a moderating effect on the relationship between perceived benefits and purchase intention, PROCESS is used. The moderating effect is tested if in the stepwise regressing above, a significant relationship was found between perceived benefits and purchase intention. The results are shown in table 7 on the next page.
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Table 7. Moderating effect
Coefficient SE t p Payment account Price*affective .014 .126 .114 .909 Price*normative -.269 .125 -2.155 .033 Price*calculative price .168 .121 1.394 .166 Price*calculative eff. -.066 .121 -.548 .585 Sustainability*affective 0.605 ,139 .433 .666 Sustainability*normative -.214 .123 -.178 .083 Sustainability*calculativePrice .181 .116 1.553 .123 Sustainability*calculativeEffort -.085 .122 -.694 .489 Savings account Price*affective .126 .144 .878 .382 Price*normative -.018 .143 -.082 .935 Price*calculativePrice .235 .126 1.865 .065 Price*calculativeEffort -.168 .132 -1.275 .205 Mortgage Price*affective -.011 .123 -.086 .932 Price*normative -.158 .119 -1.332 .186 Price*calculativePrice .045 .108 .417 .678 Price*calculativeEffort .115 .112 1.026 .307
The results in table 7 show that only one regression coefficient is statistically different from zero; the regression coefficient for normative loyalty*perceived price benefits for the payment account offer. The regression coefficient is -.2693 and is statistically different from zero, t (110)= -2,1551, p = 0.033. Thus, the effect of perceived price benefits on purchase intention depends on normative loyalty for the payment account offer. This means that H2 is supported for normative loyalty as an effect on the relationship between perceived price benefits and purchase intention for the payment account offer. Moreover, this model accounts for 29,4% of variance in purchase intention. The interaction effect plotted in figure 3 shows that when
29
normative loyalty is high, perceived price benefits lead to a lower purchase intent of the presented offer. Figure also shows that low normative loyalty leads to a slightly higher purchase intent when perceived price benefits are high.
Figure 3. Interaction effect payment account
Although the regression coefficient of price*calculative of the savings account offer of .235 is not statistically different from zero t (110)= 1.865, p = 0.065, the effect of perceived price benefits on purchase intention for the savings account offer depends weak on calculative loyalty. Because it is not significant, H2 is not supported for calculative loyalty as an effect on the relationship between perceived price benefits and purchase intention for the savings account offer. But for this study it is still of interest to plot the interaction effect which is shown in figure 4. The interaction plot shows that when calculative loyalty is high, the effect
0 1 2 3 4 5 6
Low Perceived price benefits
High Perceived price benefits P u rc h ase in te n tion Low Normative loyalty High Normative loyalty
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of perceived price benefits on purchase intention is higher than when calculative loyalty price is low for the savings account offer.
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5. Discussion
The objective of this study was to contribute to existing literature by examining the moderating effect loyalty has on the relationship between perceived benefits and purchase intention. This was done in the context of the retail banking industry in the Netherlands. To do this, the following research question was studied: ‘’How does loyalty towards the main
bank of a consumer affect the relationship between perceived benefits of financial products and purchase intention in Dutch retail banking?’’. To address the research question, a
cross-sectional studyusing convenience-sampling was conducted among 114 Dutch consumers aged above 18 and with at least a payment account at a Dutch bank. Based on the literature study, it was hypothesized that there would be a direct relationship between perceived benefits and purchase intention (H1). This was examined for three financial product offers and three possible perceived benefits. The offered products were a payment account, savings account and a mortgage from a competing bank. The perceived benefits were price,
convenience and sustainability. In the cases a relationship between perceived benefits and purchase intention was found, the moderator effect of loyalty on the relationship was tested (H2). Loyalty was measured towards the consumers main bank. The following section of this thesis will elaborate on the interpretation and implication of the quantitative results and how this relates to existing literature on which the hypotheses were based.
A significant positive relationship between perceived benefits and purchase intention (H1) was found for the following combinations of perceived benefits and products: payment account and perceived benefits on price and sustainability, savings account and perceived benefits on price, mortgage and perceived benefits on price. For all other combinations no significant relation was found. This is in part contradictory to research done by
32
Plasmeijer and van Raaij (2017), which shows consumers in banking put great
importance on offered price and convenience. The importance of price was established, but convenience was not. A possible explanation to why the results of this study show no relationship between perceived benefits on convenience and purchase intention in relation to the payment account offer could be that payment accounts offered by Dutch banks are all highly similar and user friendly compared to Greek banking, were the research of by Esterik-Plasmeijer and van Raaij (2017) was done. This may lead to consumers who do not expect or demand any significant benefits on convenience when switching to another brand. For the savings account offer and the mortgage offer it is possible that perceived benefits on
convenience are not relevant to the consumer because the infrequent use of these type of products. A savings account is typically an account were the consumers parks his or her money until they have to do a largeexpenditure and after closing a mortgage, a consumer pays monthly interest but otherwise does not use the product. For all three offers, no relationship was found between perceived sustainability benefits and purchase intention. Although the interest of sustainability is growing among consumers, the part who actually take it in to account when making a purchase decision, appears to be relatively small when it concerns financial products according to the results of this study. This could explain why the results show there is no relation between perceived sustainability benefits and purchase intention.
Where the results established a relationship between perceived benefits and purchase
intention, the moderating effect of loyalty was tested (H2). Oliver (1999) defined loyalty as:
‘’A deeply held commitment to rebuy or re-patronize a preferred product/service consistently in the future, causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behaviour”. Based
33
on this definition, it was hypothesized that increased levels of loyalty towards a consumers main bank, would lead to a lower levels of purchase intention when presented with an offer of a competing bank. Based on the fact that retail banking is characterized by long-term
relationships (Stewart, 1998), it was expected to find a moderating effect of normative loyalty meaning consumers feel morally obliged to stay with their main bank. Based on research by Esterik-Plasmeijer and van Raaij (2017) it was also expected that calculative loyalty would have a negative moderating effect and affective loyalty might have not have a significant effect. This because of the importance consumers tend to put on value when it concerns financial products. The results showed that only in one case a significant
moderating effect of loyalty could be found: normative loyalty has a negative moderating effect on the relationship between perceived price benefits and purchase intention. This effect was found for the payment account offer. Thus, the effect of perceived price benefits on purchase intention depends on normative loyalty for the payment account offer. It is possible that this effect is found because of the high number of SNS employees that took part in the study. The employees might feel morally obliged to keep banking with SNS, as the results also showed a high number of consumers viewing SNS as their main bank which is not in line with the market share of SNS.
Although not significant, there also seems to be an effect of perceived price benefits on purchase intention for the savings account offer. Calculative loyalty price has a weak negative moderating effect on the relationship. This means that when calculative loyalty price is high, the effect of perceived price benefits on purchase intention is higher than when calculative loyalty price is low for the savings account offer.
The moderating effect of loyalty on the relationship between perceived benefits and purchase intention appears to be minimal according to the findings of this study. A significant effect of
34
normative loyalty is found and a weak, non-significant effect of calculative loyalty. Affective loyalty seems to have no effect. And this differs for each product. Loyalty has no moderating effect in relation to the mortgage offer, and no significant moderating effect in relation to the savings account offer. These products may be perceived as shopping products by the
consumer on which loyalty has no influence. The fact normative loyalty does have an effect on the relationship between perceived benefits and purchase intention may explain the inertia in the Dutch retail banking sector concerning payment accounts. Among Dutch consumers, 73% never voluntarily switched their payment account and 24% did so once (GFK, 2014). As payment accounts function as a gateway for other financial products (OFT, 2010), it is
important for marketers to understand the influence of normative loyalty when conceiving products or campaigns.
Limitations and future research
This study has several limitations and provides direction for further research which are discussed in this part of this thesis.
The final sample is composed of 114 valid responses, which is a number large enough to be statistically relevant, but considering the presence of different sub-groups inside the survey, a larger size would have been more representative. The sampling method used was
non-probability convenience sampling, which could impact external validity. In addition, due to the business network that was used by the researcher to collect respondents, a large number of higher educated SNS employees with a relatively high income participated. Furthermore this study focussed on retail banking in the Netherlands. Because of the previous mentioned reasons, the generalizability of the results is limited. A replication of a similar study among a
35
larger and more representative sample of consumers and different industries would lead to more accurate results.
Another limitation is the way the way the variable perceived benefits was operationalized. It was constructed for the purpose of this study and not a standardized measure. Future research would benefit from a more standardized way to measure perceived benefits. Furthermore it would be interesting to investigate more perceived benefits for different products and
industries to get a better insight in the moderating effect of loyalty on the relationship between perceived benefits and purchase intention. Measuring purchase intention also has a limitation as it does not measure real behaviour, but rather the consumer’s perception of his own purchasing behaviour. The actual behaviour could be different. Measuring the perceived benefits and the moderating effect loyalty had after an actual purchase would give a better understanding in actual behaviour of consumers.
Finally the offers may have had an influence on the results. Using existing brands instead of a white label offer may have a different effect due to the associations consumers have with particular brands. In future research existing brands could be compared to examine if this influences the purchase intention and the moderating effect of loyalty.
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Appendix
Appendix 1: Survey
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Appendix 2: Correlation matrix
Correlations Age Gen der Educ ation Inco me Mainb ank Affe ctiveL oy Norm ativeL oy Calcu lativ eLoy Calcu lativ eLoy Effo rt PurIn tPay PurIn tSav PurIn tMort PayC ost PayC onv PaySu st Mort Cost Mort Conv Mort Sust SavC ost SavC onv SavSu st Age Pearson Correlation 1 Sig. (2-tailed) -,31 N 114 Pearson Correlation-,031 1 Sig. (2-tailed) ,744 N 114 114 Pearson Correlation-,114 -,193 * 1 Sig. (2-tailed) ,225,040 N 114 114 114 Pearson Correlation,273 **-,349**,156 1 Sig. (2-tailed) ,003,000,097 N 114 114 114 114 Pearson Correlation-,165 -,067 -,184 ,009 1 Sig. (2-tailed) ,080,478,051,921 N 114 114 114 114 114 Pearson Correlation,010,084,184 -,026 -,431 ** 1 Sig. (2-tailed) ,919,376,050,783,000 N 114 114 114 114 114 114 Pearson Correlation-,097 ,107,173 -,162 -,339 **,570** 1 Sig. (2-tailed) ,304,256,065,085,000,000 N 114 114 114 114 114 114 114 Pearson Correlation,064,115 -,044 -,207 *-,297**,179,056 1 Sig. (2-tailed) ,498,224,645,027,001,057,553 N 114 114 114 114 114 114 114 114 Pearson Correlation-,199*-,075 -,189*,048 ,380** -,266** -,217* -,188* 1 Sig. (2-tailed) ,033,428,044,614,000,004,020,045 N 114 114 114 114 114 114 114 114 114 Pearson Correlation-,168 -,180 ,148 ,072,077 -,038 ,075 -,103 ,079 1 Sig. (2-tailed) ,074,055,117,447,413,685,429,277,406 N 114 114 114 114 114 114 114 114 114 114 Pearson Correlation-,006 -,113 ,166 ,165 -,046 ,208 *,072 -,075 -,147 ,281** 1 Sig. (2-tailed) ,950,230,078,080,627,026,444,429,118,002 N 114 114 114 114 114 114 114 114 114 114 114 Pearson Correlation,003,092 -,097 -,188 *,140,071,044 -,009 -,011 ,237* ,197* 1 Sig. (2-tailed) ,977,329,306,045,138,450,645,927,911,011,036 N 114 114 114 114 114 114 114 114 114 114 114 114 Pearson Correlation-,206 *-,123 ,192*,064,066,065,072,020,046 ,512**,147,030 1 Sig. (2-tailed) ,028,193,041,499,483,489,446,829,628,000,120,748 N 114 114 114 114 114 114 114 114 114 114 114 114 114 Pearson Correlation-,226 *,044,080,129,015 -,026 -,025 -,079 ,279**,348**,046 -,081 ,316** 1 Sig. (2-tailed) ,016,646,399,171,875,782,788,404,003,000,630,390,001 N 114 114 114 114 114 114 114 114 114 114 114 114 114 114 Pearson Correlation-,005 -,237 * ,224* ,283**-,010 -,010 ,007 -,161 ,075 ,429** ,308**,004 ,431** ,329** 1 Sig. (2-tailed) ,955,011,016,002,914,918,937,087,430,000,001,963,000,000 N 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 Pearson Correlation-,041 ,087 -,097 -,287 **,009,037 ,253**,211*-,039 ,189*,021 ,594**,208*-,160 -,005 1 Sig. (2-tailed) ,664,358,304,002,926,694,007,024,679,044,822,000,026,090,954 N 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 Pearson Correlation-,089 -,039 ,132 ,037,086,047,052,044 ,228 *,235*,049,028,084 ,360**,242**,050 1 Sig. (2-tailed) ,345,681,160,695,364,620,581,643,015,012,603,769,372,000,009,596 N 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 Pearson Correlation-,009 -,156 ,146 ,063,087,050,077 -,259 **-,017 ,192*,180,134,160,175 ,541**,104 ,306** 1 Sig. (2-tailed) ,925,099,124,505,357,598,418,006,856,042,056,156,090,064,000,274,001 N 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 Pearson Correlation-,076 -,081 ,272 **,010 -,127 ,277**,232*-,064 -,083 ,300**,510**,258**,358**,147 ,459**,232*,024 ,384** 1 Sig. (2-tailed) ,423,389,003,913,179,003,013,501,380,001,000,006,000,118,000,013,802,000 N 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 113 114 Pearson Correlation-,139 -,038 ,141 ,155,153 -,056 -,070 -,201 *,182,160,060,134 ,190*,182 ,353**,029 ,285**,365**,302** 1 Sig. (2-tailed) ,139,692,133,100,105,551,462,032,053,088,527,156,043,053,000,761,002,000,001 N 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 113 114 114 Pearson Correlation-,119 ,039,088 -,051 -,127 ,123,079,041,053,053,058,143,096,101,098,126 ,313 **,265**-,007 ,346** 1 Sig. (2-tailed) ,207,682,354,588,178,192,402,662,577,575,541,128,310,285,297,182,001,005,940,000 N 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 113 114 114 114 MortCost Age Gender Educatio n Income Mainban k Affloyal Normloy al Calcloyal P Calcloyal E PurIntPa y PurIntSa v PurIntMo rt PayCost PayConv PaySust
*. Correlation is s ignificant at the 0.05 level (2-tailed).
MortCon v MortSust SavCost SavConv SavSust
