LOYALTY PROGRAM ENROLLMENT STIMULATION AMONG
CUSTOMERS WITHIN THE AUTOMOTIVE SERVICE SECTOR
by
L.K. (LARS) VAN DER VEEN
LOYALTY PROGRAM ENROLLMENT STIMULATION AMONG
CUSTOMERS WITHIN THE AUTOMOTIVE SERVICE SECTOR
L.K. (LARS) VAN DER VEEN S2159120 E-mail: l.k.van.der.veen@student.rug.nl Nieuw Rapenburg 13 8935BE Leeuwarden +31 6 28739437 University of Groningen Faculty of Economics and Business Master Thesis Marketing Management
JANUARY 11TH 2016
MANAGEMENT SUMMARY
To formulate an answer on how to stimulate loyalty program (LP) enrollment in the automotive service sector, we conducted a study in four franchise units of Profile Car & Tyreservice. The sample consisted of 168 respondents. First, respondents were asked to fill in general information about themselves. Second, they were asked to fill in measures about product involvement and purchase-decision involvement. Finally, a conjoint analysis was conduced. Respondents had to rate their willingness to enroll when certain LPs were shown.
In the automotive service sector there are three distinct types of customers; private car owners (B2C), lease car drivers (B2B) and drivers with both a lease car and a private car (B2BC). We found that B2C customers are most willing to enroll in an LP, and B2B customers least willing. Surprisingly, the fact that B2BC customers often posses more cars in comparison to B2C and B2B customers, and therefore could have a higher purchase frequency, does not mean they are more willing to enroll in an LP than B2C customers.
Product involvement, defined as interest in the product category, is proven to be a significant predictor of LP enrollment. This is beneficial knowledge when creating an LP in the automotive service sector. Surprisingly, purchase-decision was not tested significantly in relation to LP enrollment. The decision where to buy does not enhance LP enrollment at the company of choice. Thus, when implementing an LP, creators have to focus on enhancing product involvement within this sector. Instead of focusing on the process of purchase decision making. Product involvement also showed a partially mediating effect in the relationship between the differences in the types of customers and LP enrollment, explaining a margin of the differences between B2C, B2B and B2BC in regard to LP enrollment.
PREFACE
INTRODUCTION
level of discount per customer based on economies of scale. The automotive service sector is, in contrary to well-established retailing sectors characterized by lower purchase frequencies. This might be a challenge for a successful introduction of an LP within this sector. Furthermore, LP enrollment and relevant variables are not yet comprehensively investigated yet within this sector. However, this might be very interesting because the automotive service sector is changing rapidly.
The automotive service sector is nowadays characterized by increased competition, overcapacity and growing importance of high volume sales, creating loyal customers are therefore more important than ever (Vermeer, 2015). Due to changes in the EU competition law, the monopoly of manufacturers and brand-dealers in regard to car maintenance has fallen completely. This means, in essence, all 7.7 million cars in the Netherlands could service their cars at universal service companies (van Beuningen, Molnár-in ‘t Veld & Bouhuijs, 2011). Before regulation (EU) 461/2010 was operative, manufacturers and brand-dealers threatened customers the warranty on their cars would expire when they serviced their cars at universal service companies. Although brand-dealers are still experienced by private car owners (B2C customers) and lease car drivers (B2B customers) as the real expert of a brand, they lose ground within the automotive service sector. Possibly due the fact that brand-dealers still have a very expensive image. Also, B2B customers prefer one-stop shopping (Vermeer, 2015). B2B customers are also increasingly bounded to service companies their leasing company has contracts with. The article of Vermeer (2015) states brand-emotion and brand-loyalty diminishes within this sector. Also, corporate buyers can stipulate high discounts. Examples of ‘new’ corporate buyers are: Kwikfit, Euromaster and Profile Car & Tyreservice (Profile). LPs could be a sufficient tool to persuade these ‘new’ customers to stay loyal. As mentioned earlier, these LPs create repeat purchase behavior and ultimately create customer loyalty (Reinartz & Kumar, 2002; Uncles, Downing & Hammond, 2003). Creating a win-win situation for both the customer as the company: rewards for the customers, increased revenues and profit for the company.
market. The B2C market consists of customers with their private cars. The B2B market consists of lease car drivers for the biggest part, but also other B2B customers. For example, taxi companies or other businesses related to automobiles, transportation or mobility. According to Van Beuningen et. al. (2012) 398.000 households of the 600.000 households in total, in the Netherlands, own a private car next to their lease car, since this group is rather big we include this into our research as a separate group labeled as ‘B2BC’. In this research we will focus on the B2C customers, B2B customers and B2BC customers. In general, the B2B market and the B2C market are quite different markets. In B2B markets, perceived value is primarily based on economical and functional considerations, while in B2C markets, emotions and relations with objects, are seen as important factors (Mencarelli & Rivière, 2015). B2C car owners are probably more emotionally attached to their cars, where a significant amount of B2B car users probably see their car purely as a practical object. Therefore, the design of the LP of Profile is probably a difficult challenge because B2B, B2C and B2BC customers might react differently to certain LP designs.
The objective of this study is researching this challenge. Therefore, our main research question is:
We also question whether there are differences in type of customer (B2B vs. B2C vs. B2BC) in regard to the probability to enroll in a loyalty program within the automotive service sector. Obviously, many more variables affect LP enrollment. Customer involvement with the firm is also stated as an important predictor whether or not customers will join an LP (Ashley, Noble, Donthu & Lemon, 2010). Therefore, this variable is thoroughly investigated in this paper. It is important to know whether or not there are differences in regard to customer involvement between the types of customers (B2B vs. B2C vs. B2BC). Is there a potential mediation effect of customer involvement on the relationship between the types of customers (B2B vs. B2C vs. B2BC) and LP enrollment? Maybe Profile should only create an LP for one particular type of customer only? Or maybe question to create an LP at all when all three types of customers are not willing to enroll in an LP. Furthermore, the actual design of an LP is important to test: are there differences between the types of customers (B2B vs. B2C vs. B2BC) and their preference for LP benefits and LP structures? LP benefit refers to the type of benefit a customer gains from enrolling in the LP. There are three main types of benefits a customer can receive; utilitarian (e.g., gifts, savings), hedonic (e.g., personalized treatment,
LP structure refers to immediate rewards, frequency rewards (e.g., collect points and get a reward) and customer tiers, assigning customers to different segments and rewards (Blattberg, Kim & Neslin, 2008; Keh and Lee, 2006). We expect the test results will be very useful when designing an LP. For example, if there are differences to be found, Profile could create different LPs for the different types of customers. Summarizing the sub research questions:
This paper should contribute to current loyalty program literature, particularly to automotive service sector specific literature. It will give insights on types of customers and the role of involvement in regard to LPs enrollment. The results of this research will also provide insights for LPs in other sectors characterized by lower purchase frequencies. Furthermore, it should contribute practically as well, by giving insights in specific LP design. To do this, secondary research must be studied extensively and will be analyzed in the forthcoming part of this research paper. Also hypotheses will be discussed. After the theoretical part, the research design will be discussed, followed by the results of this research. Finally, discussion, conclusion and recommendations will be given.
1. Are there differences between the types of customers (B2B vs. B2C vs. B2BC) with respect to their probability to enroll in an LP within the automotive service sector?
2. Is customer involvement positively related to LP enrollment within the automotive service sector?
3. Are there differences between the types of customers (B2B vs. B2C vs. B2BC) in regard to involvement?
4. Is there a potential mediation effect of customer involvement on the relationship between the types of customers (B2B vs. B2C vs. B2BC) and LP enrollment?
5. Are there differences between the types of customers (B2B vs. B2C vs. B2BC) and their preference for LP benefits (Utilitarian vs. Hedonic vs. Symbolic)?
6. Are there differences between the types of customers (B2B vs. B2C vs. B2BC) and their preference for LP structures (Customer tier vs. no Customer tier)?
THEORETICAL FRAMEWORK
Types of customers and loyalty program enrollmentAs mentioned in the introduction, an LPs ultimate goal is to reward, and therefore enhance, loyal behavior of customers (Sharp & Sharp, 1997). But before researching the success of an LP in regard to customer loyalty, one very important step cannot be forgotten: LP enrollment by customers. Because without the will of the customers to enroll in an LP the implementation of an LP can be terminated beforehand. Bijmolt et. al. (2010) state that the enrollment and adoption of an LP by a customer is an evaluation of the benefits of membership relative to the perceived costs and risks. In general LPs are more common in companies that understand the customers’ needs and wants, the competitive environment and market. This should be taken in regard when building a strategy. Ultimately, to create satisfied customers being customer-oriented as a company (Gable, Fiorito & Topol, 2008). Also, companies, which adopt LPs, are often characterized by high frequencies (Leenheer & Bijmolt, 2008). This is potentially contrasting to the automotive service sector, which is characterized by lower purchasing frequencies. Tires and car parts should last longer than perishables. A supermarket is a prime example of a high frequency purchase environment. However, the amount spend per visit might be significantly higher in automotive service companies than in environments of high purchasing frequencies, making it potentially attractive to be loyal in order to get relatively larger benefits.
In the B2B market, the leasing company is the legitimate owner. We also expect that B2C and B2BC customers have a higher level of attitudinal loyalty to the company because their choice which company to go to for service on their private cars is completely free. Where qualitative research showed us that B2B customers have to choose from a specific set of companies (on average three) to go to, with which their leasing companies have an agreement with. B2B customers might have attitudinal loyalty with a company outside this specific set and therefore just choose a company of this set at random. Contrary to this theory, higher income households possess more often a lease car (Van Beuningen, Molnár-in ‘t Veld & Bouhuijs, 2012). Elaborating on this fact, higher-income households are generally early adopters of LPs (Allaway, Berkowitz & D’Souza, 2003). Nonetheless, B2B customers being early adopters, does not particularly mean B2C customers will not enroll at all. We expect that B2B customers are less interested in an LP. Because, we expect that B2B customers weight more heavily on the importance of purchase frequency because the benefits of an LP are also expected to be less clear.
Taking the aforementioned paragraph in consideration we expect that private car owners (B2C and B2BC customers) are more willing to enroll in an LP within the automotive service sector because the perceived benefits are probably more clear than for a B2B customer. Therefore, hypothesis one is as following:
H1: In the automotive service sector, B2C and B2BC customers are more willing to enroll in an LP than B2B customers.
Importance of involvement in loyalty program enrollment
Customer involvement is a fiercely debated variable in modern literature. Involvement is, for instance, equated with product category interest, perceived product importance and perceived risk (Kapferer & Gilles, 1985). A general definition of involvement is: the degree of interest of a person in an object (Mittal, 1989). Mittal (1983) states that involvement requires a goal-object, this can be a product itself or the purchase decision. This is an important distinction, because there can be significant differences in outcomes between these two concepts. For instance, someone can find a product class very important but he or she may be indifferent in the choice of brand or company where the purchase is to be made (Mittal, 1989).
Within this sector this could be seen as the perception of importance of the customer of, for example, tires in general. One can be very strict, for example checking his or her tire pressure weekly to reduce tire wear, while the other does not care about the pressure as long as it is not flat. Another example, one can be very fanatic in changing tires per season because he or she wants to be identified as a safe driver.
Purchase-decision involvement is defined as the interest and concern that a consumer experiences when facing a purchase-decision task (Mittal, 1989). Mittal (1989) states that purchase-decision involvement has three distinct characteristics. First, it can variate in different situations. Second, it is a goal-object task, but this does not imply that it can be assessed only at the time of purchase. Thus, it could also be a well-considered decision. Third, purchase-decision involvement concerns a mind-set, not a response. A customer who is experiencing high purchase-decision involvement within this sector can, for example, do extensive research which automotive service company to go to. While a customer who experiences low purchase-decision involvement does not care which company to go to.
We expect that both types of involvement are positively related to LP enrollment. When a customer is highly involved with the product itself, it would probably like to enroll in an LP designed around these sorts of products. Because this LP might give the customer several product categorical benefits. Also, this LP could give the customer benefits in regard to their purchase-decision. For example, the decision where to buy these particular products.
Taking the paragraphs above in consideration, we expect that customers who are involved will most likely enroll in an LP.
H2a: Product involvement is positively related to LP enrollment.
H2b: Purchase-decision involvement is positively related to LP enrollment.
products to buy. Subsequently, becoming more involved and loyal to a company and to specific products, when their experience levels their expectations.
The potential differences in involvement could also be linked to agency theory, when one party, the agent (in this context the lease car driver) takes action on behalf of another party, the principle (in this context the leasing company) (Ross, 1973). We expect that B2B customers care less about the car’s wellbeing, since he or she is not the legitimate owner, it just has to bring the person from A to B. While the leasing company wants that their customer drives as economic as possible to save fuel, tires and car parts and residual value. In this case the B2B customer is probably not involved with the automotive service company, nor the products categories related to the automotive service company, nor with other stakeholders of the car. Graus and Worrell (2008) confirm the existence of this problem, their analysis shows that the principle-agent problem affects 800.000 cars in the Netherlands.
Considering the paragraph above, we expect that B2C and B2BC customers experience higher purchase-decision involvement and product involvement than B2B customers. We also expect a mediating effect of these both types of involvement in regard to the relationship between the types of customers and enrollment in an LP.
H3a: Product involvement is higher among B2C and B2BC customers than B2B customers. H3b: Product involvement is mediating the relationship between types of customer and LP enrollment.
H4a: Purchase-decision involvement is higher among B2C and B2BC customers than B2B customers.
H4b: Purchase-decision involvement is mediating the relationship between types of customer and LP enrollment.
Loyalty program design
benefits also have a sub dimension, namely: convenience. Customers may appreciate LPs because it can automate customers’ decision-making process, and therefore avoid complex and time consuming evaluations of alternatives (Mimouni-Chaabane & Volle, 2009). Within the automotive service sector, a utilitarian benefit could be for example: ‘10% discount on the next set of tires’. Also the cash-back draw programs, which are currently running among automotive service companies, could be categorized as a utilitarian benefit.
Hedonic benefits are non-instrumental, experiential, emotional and personally gratified benefits (Hirshman & Holbrook, 1982). Mimouni-Chaabane and Volle (2009) imply that hedonic benefits are relevant though two dimensions: exploration and entertainment. Examples of exploratory benefits are: trying new or innovative products or satisfying curiosity about events and promotional offers (Arnold and Reynolds, 2003). LPs could also enable customers to enjoy unique experiences that others cannot (Mimouni-Chaabane & Volle 2009). Within the automotive service sector this could be the chance of trying new tires on a racetrack for a day or a pre-launch notification of a promotional offer which is going to take place within the near future. Hedonic benefits attract customers because of the pleasure associated with collecting and redeeming points (Johnson, 1999).
Symbolic benefits are related to personal expression, self-esteem and social approval and result from intangible and often non-product-related attributes (Mimouni-Chaabane & Volle, 2009). Symbolic benefits offer an opportunity to discriminate among customers who may perceive customized offers as a sign of respect (Gordon, McKeage & Fox, 1998). LP members may feel like the firm is treating them better than non-LP members (Beatty, Mayer, Coleman, Reynolds & Lee, 1996). For example, by greeting the customers who joined a LP by name. Next to status and recognition, symbolic benefits can also be a social experience. Social experience is characterized by customers considering themselves part of an exclusive group of privileged customers, and share values with that group and the company or brand itself (Muniz & O’Guinn, 2001). Linking this to the automotive service sector, there might be companies within this sector that are operating very environmental friendly. Customers who value this highly might by proud and want to relate themselves with this particular company.
complex and time consuming evaluation of alternatives, is more applicable to B2B and B2BC customers than to B2C customers.
Nonetheless, B2C customers might appreciate financial benefits also, because they are the legitimate owner of the car and therefore financially responsible for it. Lease car drivers will probably care less about financial benefits for the legitimate owner of the car, the leasing company. But this statement is only correct when LPs are only applicable to the cars itself. If the B2BC customers can consume the advantages gained from the LP for their private car, while collected with their lease car, the whole situation changes. For the automotive service companies this would be a smart move, because they can create situations where they can profit twice, on the car drivers’ private car and their lease car. Although, not all lease car drivers own a private car next to their lease car. As mentioned in the introduction, according to Van Beuningen et. al. (2012) in the Netherlands 398.000 households of the 600.000 households in total own a private car next to their lease car. For the 398.000 households an LP, from which a customer can gain utilitarian benefits, would be appealing. On the other side, on average, lease car drivers have on average 14.000 euros per year more to spend than non-lease car drivers (Van Beuningen et. al., 2012). So there is a big chance that, of these 398.000 household, a significant number of household will not only value monetary benefits, they might value hedonic benefits as well simply because they might have more to spend freely. The other 212.000 households would probably be appealed to hedonic benefits, because they cannot financially gain from a LP. Nonetheless, they can experience pleasure associated with collecting and redeeming points (Johnson, 1999), but also enjoy unique experiences others cannot (Mimouni-Chaabane & Voller, 2009). We expect that B2C and B2BC customers will be attracted to hedonic benefits, next to utilitarian benefits as well, partly because of the emotional benefits which B2C enjoy in greater extent than B2B customers (Hirschman & Holbrook, 1982; Mencarelli & Rivière, 2015). We expect that symbolic benefits are of less importance for LP customers. Previous research has found less ground for symbolic benefits, Yi and Jeon (2003) found a non-significant relationship between program loyalty and social benefits. Recognition benefits were also non-significant.
Considering the above literature, we expect that the types of customers are different in regard to their preference for LP benefits. We expect that B2B and B2BC customers have preference for hedonic benefits while B2C customers will prefer utilitarian benefits.
Customer tier programs are characterized by grouping customers into segments of profitability and purchase volume (Blattberg et. al. 2008). When customers qualify for a higher tier their rewards will be more beneficial. Also, customer tier programs offer benefits without being proactive to redeem the reward, as long as the customer is part of that specific tier. Although, customers tier status expires, when this happens, the status has to be re-earned (Kopalle et. al., 2012). An example of a sector where customer tier programs are common is the airline sector. Frequent flier programs offer different rewards to different tiers. For example: booking guarantees and waiting list priority in lower tiers and exclusive lounges and limousine services in higher tiers (Bijmolt et. al., 2010).
There are also examples of companies which combine the two main delayed reward LP structures. This has not been studied extensively yet. An example of a company which uses a combination of customer tier loyalty program and frequency rewards is the American online poker website PokerStars. When customers play poker games they gain so called ‘Frequent Player Points’ (FPP), the more FPPs a customer gains the higher tier they will get in. Within these tiers the FPPs can be spend for a set of different rewards, obviously the higher the tier the better the rewards. According to Kopalle et. al. (2012), these combined models are more comprehensive than separate models. This is because they find that the phenomenon of ‘points pressure’, which is the customers’ increase in purchase rate as they get closer to a reward, will happen twice. Namely, when the customer gets closer to a reward, as well as when the customer gets closer to a higher customer tier.
company more often. Therefore, might come to the levels of frequencies needed in order to find a customer tier program attractive. We do not expect that immediate rewards will be very appealing, simply because the rewards will be of lower value in order to make an LP profitable for the customer. Relative to the amount spend on average in this sector, the value of the reward will be marginal. Customers will probably not take the effort to enroll in an LP when the rewards are not satisfactory.
Furthermore, we propose that B2BC customers will be attracted to a customer tier, because of this group’s expected higher purchase frequency. For the B2B and B2C customers there is probably no ground for a customer tier program, simply because the purchase frequency is too low. The effort a B2B or B2C customer can put in has a maximum level and we expect that therefore, a program without customer tier is most appealing.
H6: B2BC customers find a customer tier program most appealing, B2B and B2C customers find a program without customer tier most appealing.
In general, there are two important delayed reward LP types: frequency rewards and customer tier (Blattberg et. al. 2008). Frequency rewards are the original, so called, trading-stamp programs (Kopalle, Sun, Neslin, Sun & Swaminathan, 2012). Frequency reward programs are for example: at every 100 euros spend, a customer gains 10 points. Automotive companies can advertise by stating the amount of point a customer will gain when they, for example, buy a specific set of tires or routine maintenance inspection. Frequency reward programs require customers to be proactive in order to redeem their rewards (Kopalle et. al., 2012). An example is the concept of AirMiles, where customers can collect points in different retail stores. Subsequently, these points can be redeemed in an online web shop for different products.
Next to delayed benefit LP structures, there are also immediate benefit LP structures. These are immediate benefits that are experienced at the point of transaction. This can be small presents, discounts or price cuts offered to customers at the point of sale. Obviously, with lower value than delayed rewards (Keh & Lee, 2006). In a satisfied context, delayed rewards with higher value build higher loyalty than immediate rewards (Keh & Lee, 2006).
Considering the research of Keh & Lee (2006) we expect that all types of customers prefer frequency reward programs over immediate reward programs.
METHODS
To answer the research questions mentioned in the introduction we conducted an empirical study in four franchise units of Profile, in the cities of Drachten, Heerenveen, Leeuwarden and Sneek. The data was collected in November and December, 2015. All the units are active in the same sector: the automotive service sector. The units operate the province of Friesland, in the north of the Netherlands. The respondents consisted of customers of these franchise units. We did not discriminate between customers, every customer who visited Profile could fill in the survey. Although, there was one condition. The respondent needed to have a car, which is quite logic since the respondent is visiting an automotive service company. The questionnaire respondents needed to fill in consisted of three parts. The first part consisted of general questions. For instance, the respondents’ age, gender and car use. The second part consisted of questions in regard to product involvement and purchase decision involvement. The third part consisted of six conjoint cards in the form of loyalty cards.
General information
The total sample size consisted of 168 respondents. Of these respondents 128 were males and 40 were females. The average age was 46.35 years old, with a standard deviation of 13.225 and a range of 18-83 years. 16 respondents answered the question: How long have you been a customer at Profile? with ‘less than 1 year’, 60 answered with 1-4 years, 52 with 5-9 years and 37 with 10 or more years. The distribution of educational level was: 1 ‘no education’, 7 ‘VMBO’, 19 ‘HAVO’, 6 ‘VWO’, 47 ‘MBO’, 62 ‘HBO’, 23 ‘WO’ and 2 ‘PhD’.
Figure 2. Educational distribution of the sample
and 53 were B2BC customers. In this sample, 26 lease cars were less than one year old, 35 were two years old, 27 were three years old and 8 lease cars were more than four years old. On average, lease car drivers had 1.2 lease cars. The age of the private cars was distributed as following, 16 private cars were 0-2 years old, 57 were 3-5 years old, 53 were 6-9 years old and 62 were older than 10 years. Private car owners owned on average 1.33 private cars. Of these questionnaires, 40 were collected in ‘Drachten’ 39 were collected in ‘Heerenveen’, 53 were collected in ‘Leeuwarden’ and 36 were collected in ‘Sneek’. All general descriptives can be found in appendix 1.
Figure 3. Distribution types of customers in the sample
tests can be found in appendix 2. The full scales of Mittal (1989) and Traylor and Joseph (1984) can be found in appendix 5.2.
In the third part, a rating based conjoint analysis was conducted. The conjoint cards consisted of two attributes. The first attribute consisted of two levels, namely ‘Customer tier with three levels: bronze, silver, gold’ or ‘No customer tier’. The second attribute consisted of the seven benefits customers could gain from this loyalty program. The seven levels are as following: Convenience, Frequency reward monetary, Immediate reward monetary, Exploratory, Entertainment, Recognition and Social experience. An overview can be found in table 1. Table 1 shows the examples with a customer tier. In appendix 5.1, the examples without customer tier are shown. Of the six conjoint cards, three were characterized by a customer tier and three had no customer tier. Also, on all six conjoint cards two benefits were shown. Respondents had to fill in the likelihood of joining such a loyalty program on a scale of 1 to 10, 1 as ‘definitely not’ and 10 as ‘definitely’. The full-profile conjoint analysis consisted of 42 different conjoint cards, equally distributed over seven different questionnaires. All seven questionnaires were filled in an equal 24 times. A complete questionnaire can be found in appendix 5.2.
LP benefit Example
Convenience Convenient because you do not have to think about to which service
company to go to. This saves time and worries.
Frequency reward Monetary If you sign up for this loyalty program you will get points at every purchase. These points can be redeemed for a discount. Per 100 points you will get €60, - discount.
• In level ‘bronze’ you will get 1 point per €10, - spend • In level ‘silver’ you will get 1 point per €9, - spend • In level ‘gold’ you will get 1 point per €8, - spend
Immediate reward Monetary If you sign up for this loyalty program you will get: • In level ‘bronze’ always 3% discount
• In level ‘silver’ always 5% discount • In level ‘gold’ always 8% discount
Exploratory Before new products and serviced will be introduced you will get a
message. Therefore, you will be noticed about new product and services of Profile before others. If you are level ‘gold’ you will receive this message 1 week before level ‘silver’ and 2 weeks before level ‘bronze’.
Entertainment Once a year you stand a change winning to test new products. For example, to test the newest ‘Michelin tires’ on a circuit. The higher your level, the higher the change is you can win this price.
Recognition You will be threated better than other customers who did not sign up for the loyalty program. An example could be: only level ‘gold’ customers have access to a special waiting room with relaxation chairs and fresh high quality coffee.
Social experience You can show others you are part of a group which has shared values with Profile. For example, because of the sustainable tire reparation procedures Profile has.
RESULTS
General findingsTo get a first impression of the data, correlational tests and an independent samples T-test were conducted. The exact outcomes of these tests can be found in appendix 3. A point-biserial correlation was conducted to determine the relationship between gender and LP enrollment likelihood, which was positively statistically significant (r=.063, p=.047). Inferring females tend to score higher on LP enrollment. An independent samples T-test showed a mean of 4.43 for males and a mean of 4.84 for females on LP enrollment. In contrast to LP enrollment, males tend to score a higher mean than females on both product involvement and purchase-decision involvement. The mean score of males on product involvement was 3.172 where females scored 2.486. The mean score of males on purchase-decision involvement was 4.377, females scored 3.769. In this sample, the mean age of males was 46.83 and the mean age of females was 44.79.
Further, the variable age showed a positive statistically significant relation with purchase-decision involvement (r=.121, p=<.001). Also age and LP enrollment were negatively statistically significant related (r=-.136, p=<.001), which implies an interesting fact: customer with a higher age are less likely to enroll in an LP. Furthermore, measured with a Pearson correlational test, the relation between purchase-decision involvement and product involvement was positively significant (r=.481, p=<.001). Meaning these two involvement variables are highly related, this might cause problems because this infers collinearly.
Hypotheses test results
First of all, the GLM with main-effects showed a non-significant score on Levene’s Test of Equality of Error Variances, with p=.653. Thus, meeting the threshold, p=.653>.05. Meaning that the variances of all variables are equal. It showed a R squared of .120. An overview is shown in table 2. The full SPSS output of this test can be found in appendix 4.1.
Predictors B P-value Intercept 9.623 <0.001 Types of customer 0.386 <0.001 Customer tier 0.485 0.004 LP benefits Convenience 0.485 0.084
Immediate reward monetary 1.721 0.002
Frequency reward monetary 1.532 0.007
Exploratory 0.665 0.240 Entertainment 0.768 0.175 Social experience 0.906 0.110 Recognition 0.866 0.126 Involvement Product involvement 0.382 <0.001 Purchase-decision involvement 0.111 0.104 Control variables Gender 0,661 0.002 Age -0.028 <0.001
Table 2. Overview of main effects GLM estimation results, Dependent variable = LP enrollment
The GLM with interactions showed a non-significant score in regard to Levene’s Test of Equality of Error Variances as well, p=.211. Thus meeting the threshold, p=.211>.05. The R squared was .123. An overview of the interactions is shown in Table 3. The full GLM interaction results can be found in appendix 4.7.
Predictors B P-value
Interactions
Type of customer * Customer tier 0.146 0.514
Type of customer * Immediate reward monetary -0.117 0.643 Type of customer * Frequency reward monetary 0.435 0.084
H1: In the automotive service sector, B2C and B2BC customers are more willing to enroll in an LP than B2B customers.
To tests for differences between the types of customers in regard to their willingness to enroll in an LP we conducted a test of between-subjects effects within the GLM. This test showed a significant score of F=6.548, p=.001, meaning there were indeed differences between the three types of customers and their willingness to enroll in an LP. This test only showed us that there are differences, but did not show us the true origin of the difference. Therefore, we also had to consider the Estimates table and the Pairwise Comparisons table. The Estimates table showed a mean score of LP enrollment, which is the variable name for willingness to enroll in an LP. The estimated mean of B2C, B2BC and B2B were respectively 6.525, 6.337, 5.662. These numbers infer that B2C and B2BC both scored higher on LP enrollment than B2B. The Pairwise Comparisons table indeed showed this. B2C and B2B showed significant a Mean Difference of .863, p=<.001. Also, B2BC and B2B showed a significant Mean Difference of .675, p=.009. Not relevant for this hypothesis, but B2C and B2BC did not differ significantly, p=.334.
Considering the above paragraph, we can confirm hypothesis 1. B2C and B2BC customers are indeed more willing to enroll in an LP than B2B customers.
H2a: Product involvement is positively related to LP enrollment.
The test of between-subjects effects in the GLM showed a highly significant effect of product involvement (F= 28.374, p=<.001). Meaning there were differences between the levels of product involvement in regard to LP enrollment. The Parameter estimates table showed the exact scores. Product involvement was highly significantly related to LP enrollment, with p=<.001. The B for this effect was .382. Meaning, an increase of 1 on product involvement leads to an increase of .382 on the LP enrollment.
H2b: Purchase-decision involvement is positively related to LP enrollment.
The test of between-subjects effects in the GLM showed no significant difference of purchase-decision involvement in regard to LP enrollment. Nonetheless, a simple linear regression of purchase-decision involvement on LP enrollment showed significant scores. When product-involvement was put into this regression, the significant effect of purchase-decision involvement diminishes. This infers a relation between the two types of involvement. Collinearity statistics showed a low VIF score of 1.301, which is lower than the general threshold of 5. The correlational table showed significant correlation between the two types of involvement, r=.481, p=<.001. Since the effect of purchase-decision involvement loses its significance in a GLM with other variables, we expect that the significant effect of purchase-decision in a simple linear regression was being caused by the absence of these other variables. Therefore, we reject hypothesis 2b. Because we cannot state clearly that purchase-decision involvement on its own is positively related to LP enrollment.
H3a: Product involvement is higher among the B2C and B2BC customers than B2B customers.
To test for potential differences in types of customers in regard to product involvement a one-way ANOVA test was conducted. The test of Homogeneity of Variances showed a significant Levene’s statistic of p=.037. Meaning that there is no homogeneity of variances. Therefore, we conducted a robust test of equality of means instead of a normal ANOVA. The Welch and Brown-Forsythe tests showed both highly significant results of p=<.001, inferring differences between the three types of customer. Since we wanted the specific differences between these three groups and equal variance was not assumed, we conducted a Games-Howell Post Hoc test. This test showed a significant difference between B2C and B2B customers, Mean Difference=.512, p=<.001. The test also showed a significant difference between B2BC and B2B customers, Mean Difference=.282, p=.042. The exact means were, B2B: 2.655, B2BC: 2.937 and B2C: 3.167, a graphical representation can be seen in Figure 4.
Figure 4. Means plot of Product involvement H3b: Product involvement is mediating the relationship between types of customer and LP enrollment.
Figure 5: A mediation model
effect was statistically significant. To be certain this effect is true for the IV as a whole, we conducted an additional linear regression. As expected, this effect was significant as well with B=.436, p=<.001. Furthermore, there has to be a relationship between the IV and the mediator, marked by figure 5 as ‘a’. This effect was partially tested in hypothesis 3a. Namely, we found a significant difference between B2C and B2B and also between B2BC and B2B. To be certain this effect is true for the IV as a whole, we conducted an additional linear regression. The results of this test were as expected. We found a significant relationship between types of customers and product involvement, B=.251, p=<.001. The third assumption that has to be met is a relationship between the mediator and the DV, marked by figure 5 as ‘b’. We tested this at hypothesis 2a, there was indeed a significant relationship. To test for mediation, we conducted a linear regression, regressing types of customers and product involvement, on LP enrollment. Marked as ‘c’’ in figure 5. Full mediation can be stated, when the main effect (IV on DV) diminishes. This was not the case. Both the IV and the mediator showed significant results, respectively B=.342, p=.003 and B=.373, p=<.001. The effect of c’ was B=.436-.342=.084 lower when product involvement was included in the model. The B of .084 was the effect of product involvement in the relationship between types of customers and LP enrollment. An overview of the results is shown in table 4.
Because of the results shown in the paragraph above, we can state that there was partial mediation. Since the hypothesis stated ‘mediation’ and not ‘fully mediation’, we can accept hypothesis 3b.
Predictors M: Product involvement DV: LP enrollment
Main effects
Types of customers B = 0.251 p = <0.001 (a) B = 0.436 p = <0.001 (c)
Product involvement B = 0.398 p = <0.001 (b)
Mediation model
Types of customers B = 0.342 p = <0.001 (c’)
Product involvement B = 0.373 p = 0.003 (c’)
H4a: Purchase-decision involvement is higher among B2C and B2BC customers than B2B customers.
To test for potential differences between the types of customers and their purchase-decision involvement a one-way ANOVA test was conducted. Descriptives showed a mean of 4.205 for B2B, a mean of 4.099 for B2BC and a mean of 4.322 for B2C. The test of Homogeneity of Variances showed a significant Levene’s statistic of p=<.001. Meaning that there is no homogeneity of variances. Therefore, we conducted a robust test of equality of means instead of a normal ANOVA. The Welch and Brown-Forsythe tests showed both non-significant results of respectively p=.118 and p=.092, inferring no statistical significant differences between the three types of customer. Therefore, we reject hypothesis 4a, there is no clear evidence found that the types of customer differ in regard to purchase-decision involvement.
H4b: Purchase-decision involvement is mediating the relationship between types of customer (B2C vs. B2B and B2BC vs. B2B) and LP enrollment.
As mentioned earlier, when researching potential mediation effects, some assumptions have to be met. The relation marked as ‘b’ in figure 5 has been tested in hypothesis 2b. Unfortunately, this hypothesis was rejected. The relation marked as ‘a’ in figure 5, this relation has been tested as well in hypothesis 4a. Unfortunately, this hypothesis was also rejected. Therefore, there cannot be mediation of purchase-decision involvement. Since there was no significant relation between purchase-decision involvement and LP enrollment, and no significant relation between the types of customers and purchase-decision involvement, purchase-decision involvement cannot have significant explanatory power in the relation between the types of customers and LP enrollment. Therefore, hypothesis 4b is rejected. H5: B2B and B2BC customers find hedonic benefits most important, B2C customers find utilitarian benefits most important.
variables were shown on the conjoint cards. So the first part of the hypothesis is already rejected, we found no evidence hedonic benefits have influence on the LP enrollment score.
Since the two utilitarian benefits were significantly influencing LP enrollment, we added interactions between types of customers and immediate reward monetary as well as between types of customers and frequency reward monetary. To make the results more interpretable we have mean centered the variables used in the interactions. This GLM with interactions had a Levene’s Test of Equality of Error Variances significance statistic of p=.211, and thus, meeting the threshold. The test of between-subjects effects showed that the interactions with the utilitarian benefits were no longer significant. Types of customers*immediate reward monetary had showed p-value of .643 and types of customers*frequency reward monetary of p=.084. Inferring no significant differences between the types of customers.
Considering the above paragraph, hypothesis 5a is rejected. There is no significant difference between the types of customer in regard to their preference for benefits.
H6: B2BC customers find a customer tier program most appealing, B2B and B2C customers find a program without customer tier most appealing.
In the main-effects GLM, the customer tier dummy variable (0= Customer tier, 1= No customer tier) showed a significant effect of p=.004 in the between-subjects effects test. Meaning there are differences between customer tier of no customer tier in regard to LP enrollment. The parameter estimated showed a positively significant effect, with B=.485, p=.004. Therefore, we also interacted types of customers with customer tier. This effect was not significant. Inferring no differences between the types of customers in regard to customer tier preference. Thus, we can imply that all types of customers prefer no customer tier. Considering the above paragraph, hypothesis 6 is rejected.
H7: All three types of customers prefer frequency reward benefits over immediate reward benefits.
than the B of frequency reward, we expect that the types of customers prefer immediate rewards over frequency rewards. To test if immediate reward and frequency reward differ statistically we conducted a Z-test.
Figure 6. The Z-test formula
CONCLUSION
Answering sub question 1, we found significant differences between the types of customers within the automotive service sector and their willingness to enroll in an LP. B2C and B2BC customers are more open to enroll in an LP than B2B customers. This finding is in line with previous research about B2B and B2C markets. We expect that these significant effects originate from the fact that for private car owners (B2C and B2BC) the obtainable benefit is more clear. Because B2C and B2BC are the legitimate owners of the private car. For B2B customers, the lease car is an asset of the leasing company. We also expect that the effects originate from the fact that B2B customers are not completely free in their choice of automotive service company, as mentioned earlier, leasing companies often have contracts with franchise companies.
where to be a customer. The relationship between the types of customers and purchase-decision involvement also showed a non-significant effect. There are no significant differences between the types of customers and their purchase-decision involvement. Which we found odd since not all B2B customers are free in choice of automotive service company. An explanation can be that B2C and B2BC customers only consider a small set of companies to go to, just like the mentioned three options for B2B customers. To answer sub questions 2, we found a positive significant relation between involvement and LP enrollment. Although, only of product involvement, purchase-decision involvement was tested to be non-significant. To answer sub question 3, we did found positive significant differences between types of customers and involvement. But again, only product involvement showed significant effect, purchase-decision involvement did not.
We also found a significant partial mediating effect of product involvement in the significant relation between types of customers and LP enrollment. This is an interesting finding, a part of the positive relation between types of customers and LP enrollment can thus be explained by product involvement. Making the differences between the types of customers smaller when the effect product involvement is taken out of the equation. Nonetheless, leaving still a big difference between the types of customers unexplained. Obviously, there was no mediation of purchase-decision involvement in the significant relation between types of customers and LP enrollment. Because there was no direct significant effect between purchase-decision and LP enrollment. Answering sub question 4, there was found a statistical significant mediating effect of involvement on the relationship between types of customers and LP enrollment. But again, only of product involvement, for purchase-decision involvement we did not find a mediating effect.
where the operator is the economical owner of the car. Answering sub question 5, there were no statistical significant differences found between the type of customers and their preference for LP benefits.
We also found no difference between the types of customers and their preference for customer tier or no customer tier. No customer tier does show a positive significant effect in regard to LP enrollment. Meaning all types of customers prefer a simple non-level LP. This is in line with the above mentioned result and explanation of the intensity of contact moments. Considering the above paragraph, we can answer sub question 6 with a simple ‘No’, we found no statistical significant differences between types of customers and LP structures.
Also contrary to our hypothesis and previous literature, in the automotive service sector frequency rewards were not preferred over immediate rewards. Nonetheless, both immediate rewards and frequency rewards showed significant positive effect on LP enrollment. The effect of immediate rewards was higher than the effect of frequency rewards. Although, this difference between immediate rewards and frequency rewards was non significant. There was also no difference in these effects between the types of customers. An explanation can be that the intensity of contact moments for every group is too low to enjoy the fun and need of, for example, collecting points (frequency reward). To answer sub question 7, we found no statistically significant difference between types of customers and LP timing
RECOMMENDATIONS
Managerial implicationsBased on the results of this research we can give several managerial implications, to stimulate LP enrollment within the automotive service sector. First of all, as mentioned earlier, there are significant differences between the types of customers who visit these kind of companies. But because these groups do not react differently to LP benefits and LP structure we suggest to create a simple LP, without customer tier. The intensity of shopping seems to be too low for an LP with several levels. Both immediate rewards and frequency rewards showed a substantial positive effect on LP enrollment, therefore we suggest to consider these kind of benefits when creating an LP. An example of an immediate reward can be a percentage of discount. Other immediate rewards can be thought of as well, for example at every visit free nitrogen filling of the tires instead of normal air.
Frequency examples could be a point system, as suggested in the questionnaire. At a certain amount of euros spend, the loyalty card holder receives a certain amount of points. This points can be redeemed for discount, gifts or materials for the customers’ car.
Furthermore, product involvement is proven to be a significant predictor of LP enrollment. We suggest trying to increase product involvement with the products categories sold within the automotive service sector on a national level, but also on a regional or operational level. On national level a company can think of nation wide advertising, or active engagement in media. On regional or operational level one can think of making the processes more transparent. Try to explain the customer step-by-step what is precisely done when maintaining their car. Another example is explaining customers the specific differences about, for example, different tires.
Limitations and future research
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APPENDIX
1. General descriptives Statistics Age N Valid 164 Missing 4 Mean 46,35 Median 47,00 Mode 47a Std. Deviation 13,225 a. Multiple modes exist. The smallest value is shownGender
Frequency Percent Valid Percent Cumulative Percent Valid
Male 128 76,2 76,2 76,2
Female 40 23,8 23,8 100,0
Total 168 100,0 100,0
How long is participant a customer
Frequency Percent Valid Percent Cumulative Percent Valid < 1 year 16 9,5 9,7 9,7 1 - 4 years 60 35,7 36,4 46,1 5 - 9 years 52 31,0 31,5 77,6 10 > years 37 22,0 22,4 100,0 Total 165 98,2 100,0 Missing 999 3 1,8 Total 168 100,0
Highest education finished
HBO 62 36,9 37,1 85,0 WO 23 13,7 13,8 98,8 PhD 2 1,2 1,2 100,0 Total 167 99,4 100,0 Missing 999 1 ,6 Total 168 100,0 B2B / B2C / B2BC
Frequency Percent Valid Percent Cumulative Percent Valid B2B 27 2,7 16,1 16,1 B2C 88 8,7 52,4 68,5 B2BC 53 5,3 31,5 100,0 Total 168 16,7 100,0 Missing System 840 83,3 Total 1008 100,0
In which unit was the participant
Frequency Percent Valid Percent Cumulative Percent Valid Drachten 40 23,8 23,8 23,8 Heerenveen 39 23,2 23,2 47,0 Leeuwarden 53 31,5 31,5 78,6 Sneek 36 21,4 21,4 100,0 Total 168 100,0 100,0 Age of leasecar 1
Age of leasecar 2
Frequency Percent Valid Percent Cumulative Percent Valid ≤ 1 year 1 ,6 16,7 16,7 2 years 2 1,2 33,3 50,0 3 years 1 ,6 16,7 66,7 4 ≥ years 2 1,2 33,3 100,0 Total 6 3,6 100,0 Missing System 162 96,4 Total 168 100,0 Age of leasecar 3
Frequency Percent Valid Percent Cumulative Percent Valid ≤ 1 year 1 ,6 20,0 20,0 3 years 3 1,8 60,0 80,0 4 ≥ years 1 ,6 20,0 100,0 Total 5 3,0 100,0 Missing System 163 97,0 Total 168 100,0 Age of leasecar 4
Frequency Percent Valid Percent Cumulative Percent Valid 2 years 1 ,6 25,0 25,0 3 years 2 1,2 50,0 75,0 4 ≥ years 1 ,6 25,0 100,0 Total 4 2,4 100,0 Missing System 164 97,6 Total 168 100,0 Age of privatecar 1
Total 28 16,7
Total 168 100,0
Age of privatecar 2
Frequency Percent Valid Percent Cumulative Percent Valid 0 - 2 years 4 2,4 10,5 10,5 3 - 5 years 12 7,1 31,6 42,1 6 - 9 years 8 4,8 21,1 63,2 10 ≥ years 14 8,3 36,8 100,0 Total 38 22,6 100,0 Missing 999 1 ,6 System 129 76,8 Total 130 77,4 Total 168 100,0 Age of privatecar 3
Frequency Percent Valid Percent Cumulative Percent Valid 3 - 5 years 1 ,6 14,3 14,3 6 - 9 years 3 1,8 42,9 57,1 10 ≥ years 3 1,8 42,9 100,0 Total 7 4,2 100,0 Missing System 161 95,8 Total 168 100,0 Age of privatecar 4
2. Involvement reliability analysis Reliability Statistics Cronbach's Alpha N of Items ,866 6 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Product involvement: opinion of me 15,14 49,750 ,594 ,855
Product involvement: tell something about person
Reliability Statistics Cronbach's Alpha N of Items ,843 4 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Purchase involvement:
care about brand
13,02 17,898 ,708 ,792
Purchase involvement: importance making right choice 12,17 19,090 ,723 ,780 Purchase involvement: concerned about outcome 13,13 22,853 ,583 ,839 Purchase involvement: brands different/alike 12,47 21,340 ,725 ,786 Descriptive Statistics Mean Std. Deviation N Gender 1,24 ,427 168 Age 46,35 13,225 164
How long is participant a customer
2,67 ,933 165
Highest education finished 5,27 1,390 167
In which unit was the participant
2,51 1,078 168
purchaseinvolvement 4,2321 1,46378 168
productinvolvement 3,0210 1,37962 167
3. Correlational tables & T-test
Correlations
Gender Age purchasein volvement productinvo lvement LP score Gender Pearson Correlation 1 -,065* -,177** -,211** ,063* Sig. (2-tailed) ,041 ,000 ,000 ,047 N 1008 984 1008 1008 1008 Age Pearson Correlation -,065* 1 ,121** -,022 -,136** Sig. (2-tailed) ,041 ,000 ,493 ,000 N 984 984 984 984 984 purchaseinvolve ment Pearson Correlation -,177** ,121** 1 ,481** ,114** Sig. (2-tailed) ,000 ,000 ,000 ,000 N 1008 984 1008 1008 1008 productinvolveme nt Pearson Correlation -,211** -,022 ,481** 1 ,198** Sig. (2-tailed) ,000 ,493 ,000 ,000 N 1008 984 1008 1008 1008 LP score Pearson Correlation ,063* -,136** ,114** ,198** 1 Sig. (2-tailed) ,047 ,000 ,000 ,000 N 1008 984 1008 1008 1008
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
N 984 1002 1002 1002 1002 purchaseinvolv ement Correlation Coefficient ,043 -,135** 1,000 ,455** ,078* Sig. (2-tailed) ,181 ,000 . ,000 ,014 N 990 1002 1008 1008 1008 productinvolve ment Correlation Coefficient -,016 -,080* ,455** 1,000 ,205** Sig. (2-tailed) ,621 ,011 ,000 . ,000 N 990 1002 1008 1008 1008 LP score Correlation Coefficient -,041 ,011 ,078* ,205** 1,00 0 Sig. (2-tailed) ,193 ,723 ,014 ,000 . N 990 1002 1008 1008 1008
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
Group Statistics
Gender N Mean Std. Deviation Std. Error
4. Hypotheses
4.1 Hypothesis 1, 2a, 2b, 5, 6, 7 - GLM main effects model
Between-Subjects Factors Value Label N B2C / B2BC / B2B 0 B2C 504 1 B2BC 312 2 B2B 168 Customer tier 1 Customer tier 492 2 No customer tier 492 Convenience 0 No 703 1 Yes 281 Immediate reward monetary 0 No 704 1 Yes 280 Frequency reward monetary 0 No 706 1 Yes 278 Exploratory 0 No 704 1 Yes 280 Entertainment 0 No 706 1 Yes 278 Recognition 0 No 705 1 Yes 279 Social experience 0 No 704 1 Yes 280 Gender 1 Male 756 2 Female 228
Levene's Test of Equality of Error
Variancesa
Dependent Variable: LP score
F df1 df2 Sig.
,956 223 760 ,653
a. Design: Intercept + Type_consumerGLM + CT + benefit_con + benefit_IRmon +
benefit_FRmon + benefit_Expl + benefit_Ent + benefit_Rec + benefit_Socex + Gender + Age + productinvolvement + purchaseinvolvement
Tests of Between-Subjects Effects Dependent Variable: LP score
Source Type III Sum
of Squares
df Mean Square F Sig.
Corrected Model 913,372a 14 65,241 9,408 ,000 Intercept 304,635 1 304,635 43,930 ,000 Type_consumerGLM 90,818 2 45,409 6,548 ,001 CT 57,961 1 57,961 8,358 ,004 benefit_con 20,737 1 20,737 2,990 ,084 benefit_IRmon 63,970 1 63,970 9,225 ,002 benefit_FRmon 50,696 1 50,696 7,311 ,007 benefit_Expl 9,599 1 9,599 1,384 ,240 benefit_Ent 12,749 1 12,749 1,838 ,175 benefit_Rec 16,276 1 16,276 2,347 ,126 benefit_Socex 17,760 1 17,760 2,561 ,110 Gender 69,626 1 69,626 10,041 ,002 Age 130,401 1 130,401 18,805 ,000 productinvolvement 196,770 1 196,770 28,376 ,000 purchaseinvolvement 18,339 1 18,339 2,645 ,104 Error 6719,522 969 6,934 Total 27857,000 984 Corrected Total 7632,893 983
