The impact of hard discounter
presence on consumers’ tendency to
buy national brands
Master Thesis Marketing Intelligence June 2019
Tess Esmée Roelofs S2527782 Westerstaat 152 1015 MP Amsterdam 06-2450913 t.e.roelofs@student.rug.nl First supervisor: Prof. dr. L.M. Sloot l.m.sloot@rug.nl Second supervisor: Dr. J.T. Bouma j.t.bouma@rug.nl University of Groningen Faculty of Economics & Business
Department of Marketing PO Box 800
Abstract
This study investigates the impact of hard discounter on the tendency of consumers to buy national brands. Firstly, it is studied if consumers who did their shopping in
multiple stores affected their tendency to buy national brands. The results indicate that consumers who shop in multiple stores have a lower tendency to buy national brands. Secondly, the effect if one of those multiple stores is a hard discounter was studied. A significant strong effect negative effect was found for consumers who visit hard discounters. Thirdly, the effect of distance from the consumer to the hard discounter was tested. However, no significant effects could be found. Finally, the result of the research indicate that socio-demographic characteristics of consumers have an impact on their tendency to buy national brands.
Table of contents 1. Introduction 4 2. Literature review 7 3. Conceptual development 11 4. Methodology 17 5. Results 20
6. Conclusion, discussion and recommendations 31
7. Limitations and further research 33
1. Introduction
One of the most striking developments in the past decades in retailing is the worldwide rise of market share of Private Label (PL) (Ailawadi et al., 2008; Hoch, 1996; Kumar & Steenkamp, 2007). PLs are denoted as store brands or retail brands. This development is in first instance a threat for the relevance and sales of National Brands (NBs). Over the past years retailers had the desire to grow private label, because of: higher margins, negotiating leverage with national brand managers and improving consumers’ loyalty to the store (Pauwels & Srinivasan, 2004; Ailwadi et al., 2008). Some retail brands are considered strong brands themselves, which contributes to a positive store image. An example of such a strong brand is Albert Heijn, which is the only supermarket brand in the Top 50 most valuable Dutch brands (Brand Finance, 2017). A positive store image leads to a positive attitude towards the private brand (Luijten & Reijnders, 2009). Despite the positive effects of the increase of PL share for retailers, the research of Ailawadi (2008) showed that there could also be threats for retailers. The positive effect of an increase in PL share on store loyalty operates to a point. For retail chains that are service-oriented the effect of an increase in private label share on store loyalty has a maximum. After a certain point the effect of a further increase PL share become negative. For service-oriented retailers it seems key to find the right balance between PLs and NBs to optimize store loyalty. NBs have therefore not lost their relevance for service retailers.
more favourable price image. According to Van Heerde et al. (2008), the result of initiating the price war for Albert Heijn was that its price image improved. However, hard discounters also benefited from this price war. The increased sensitivity of
consumers to price image lead to an increase of store visits for hard discounters. In 2019 Albert Heijn came with a new initiative to create a more favourable price image. Albert Heijn rebranded their lowest price line ‘Goedkoopjes’ to ‘Prijsfavorieten’, an assortment of more than 1000 private label products that are at the same price level of Aldi and Lidl. The second largest retailer, Jumbo, applies a ‘Everday Low Pricing (EDLP)’ strategy and promises the lowest ‘local’ price to their consumers. Besides that Jumbo uses different prices for different price market areas. In areas where there is much competition on price, thus in areas where hard discounters are present, they use lower prices for their complete assortment.
Vroegrijk et al. (2013) were one of the first who studied the competition between hard discounters and service retailers. They concluded that there is a negative effect between the entry of a hard discounter in an area and the sales of a service retailer in that area. However, that effect is smaller than the effect the entry of a large discounter (e.g. Walmart) has on the sales of a service retailer (Ailawadi et al., 2010). The losses are smaller for retailers located very near to the hard discounter. Vroegrijk et al. (2013) proofed that consumers change their store choice and spending allocation when a hard discounter enters an area. However, they did not research if consumers make different choices while shopping at a service retailer, such as buying PLs or NBs. Do consumers buy more or less NBs when they also visit a discounter or when a discounter is close? The answer to this question can help retailers to optimize their assortment. One of the proposed strategies of Vroegrijk et al. (2013) to counter HDs is to change assortment. However, they do not give any implications on how to execute that.
Contributions to theory and practice
To fill the gap in literature, in this research the effect of the presence of discounters to the shopping behaviour (e.g. type of brand) of consumers is further explored. This might lead to clues how service retailers can increase shopper loyalty by changing their
shoppers at service retailers I will also test some other variables that might shed some light on why consumers tend to buy national brands instead of private labels. To give implications on if service retailers must change their assortment the preference of consumers to buy NBs is analysed. Additionally, the effect of other variables that might have an impact on the preference of those customers for NBs are analysed. One of those variables in the presence of a brand discounter. To examine these issues, a dataset is constructed that combines four different sources (chapter 4). The main research question that is aimed to be answered: what is the impact of hard discounter presence on consumers’ tendency to buy national brands?
The answer of this question is also relevant for retailers and brand manufacturers. For retailers it is important to know the preference of consumers in order to make decision on what the share of the assortment should be NBs. Besides that, the answer on this question gives indications on how to adapt assortment in stores where there is a hard discounter nearby store of the retailer. Should the retailer expand or contract the NB assortment?
The growth of PL and HD market share is not an advantage for brand manufacturers. The growth PL has a direct effect on the sales of NBs. Every product that consumers buy of a PL they do not buy a NB. The success of HDs has an impact on the market share of service retailers. Since NBs are predominately sold at service retailers, this also impacts brand manufacturers. To develop solution to turn this trend it is helpful for brand manufacturers what the impact is of HDs on the tendency of consumers to buy their brands at different retailers. If it the conclusion is that the impact of HD presence is lower for one retailer, it could be a solution to work more closely together with this retailer. It can help brand managers to make budget allocation.
2. Literature review
In this chapter the first two most important concepts, National Brands and Private Labels, are explained and the interaction between these two concepts. Relevant
literature about these concepts will be discussed. Furthermore, the Dutch retail market will be discussed. The focus in this part will be on the rise of HDs in this market and what the effect of that rise could be on the brand buying behaviour of consumers. 2.1 National Brands and Store Brands
National Brands
NBs are well-known brands that you can buy in most of the supermarkets. These brands are nationally distributed under a brand name. National brands can be divided into leading NBs and Second-tier NBs. NBs are brands that have a leading position in terms of total annual sales and are considered to be leading in that category according to
consumers. The rest of the market is considered to be second-tier NBs (Bontemps et al., 2008; Fornani et al, 2016). An example of this division in the canned soup category in the Netherlands are the brands Unox and Struik. Those brands are positioned at
approximately the same price point. However, Unox more well-known to consumers and outperforms Struik on sales. Unox is the 7th most sold brand in the Netherlands (IRI merken top 100). National brands pay slotting allowances to the retailers and pay for advertisements in the retailer’s leaflet.
Private label
Interaction between NBs and PLs
Although the annual sales of the Dutch top 100 NBs grew with 2,9%, the total market grew with 3,5% which gives an indications that PLs grew faster than NBs (IRI, 2019). This is a trend that was also observed by Fornari et al. (2016). They also confirm that this decrease of NBs market share is caused by the increased confidence of consumers in PLs and as a result an increased preference for PLs.
The main reason for retailers to sell private label over NBs are the higher margins on private label products. Many studies find a positive correlation between PL use and store loyalty (Kumar & Steenkamp, 2007). However, Ailawadi et al. (2008) argues that this relation is reversed: that consumers who are store loyal are more likely to buy private label. Because of higher margins and increased store loyalty mainstream
retailers have increased the PL share in their assortment over the past years. There are examples where retailers have pushed this too far. For example, in the United Kingdom: J. Sainsbury, which is similarly positioned as the Dutch chain Albert Heijn, had to scale back their private label, because profitability suffered from the expansion of private label (Kumar and Steenkamp, 2007).
Consumers who are price conscious are more likely to buy private label, because private label products are in general cheaper than NBs. However, Sinha & Batra (1999) stress the effect of price-quality association on private label purchase. Due to the entry of hard discounters, consumers became more receptive to private label (Steenkamp & Kumar, 2009). The research of Ailawadi et al. (2001) focussed on whether NBs promotions and store brands attract the same consumers who are value focussed. They concluded that consumers who buy store brands have different characteristics than consumers who buy promotions. Promotion users are impulsive, stockpilers and enjoy shopping. Key characteristics of store brand segment are opposite, and those consumers therefore prefer the everyday low prices of store brands.
cases. Mainstream-quality NBs benefit from the introduction of a budget private label, because these brands than become the middle option in terms of quality in the
assortment. Premium NBs might benefit from the introduction of a premium private label, because consumers who are more attracted to premium-quality will take the most superior product.
2.2 Retail landscape and rise of hard discount Retail landscape in the Netherlands
The retail landscape in the Netherlands can be divided into four main players. Albert Heijn, Jumbo, the Superunie members and the hard discounters. The largest Superunie member in terms of market share is PLUS. The Dutch retail market is characterized by high PL penetration and predominance of multiple retailers in very densely populated country (Fernie and Staines, 2001). The are multiple retailers active in an area. Albert Heijn, Jumbo and PLUS can be considered as mainstream retailers as defined by Steenkamp and Sloot (2018). The mainstream retailers hold an assortment between 20.000 and 35.000 stock keeping units (SKU’s) of both NBs and PLs. The main distinction between the positioning of the mainstream retailers is pricing and promotions. Albert Heijn follows a HiLo strategy with many promotions a week and with regular prices above market average. Jumbo follows a EDLP strategy with a few promotions. The pricing and promotion strategy of PLUS is similar to Albert Heijn. Unlike PLUS, some Superunie members are brand discounters with a more limited assortment, but with low prices for national brands. The active hard discounters in the Netherlands are Aldi and Lidl. Hard discounters sell predominantly private label
products. According to Steenkamp & Kumar (2009), 90% of the total sales of Aldi is from PL and 70% of the total sales of Lidl is PL. Besides this, the amount of assortment also differs from service retailers and large discounters. The assortment holds approximately 1.000 to 1.500 SKU’s.
Rise and impact of hard discount
Staines (2001) the Dutch retail market is characterized by its high PL penetration. The rise of HDs and increased consumer receptiveness towards PLs. The increased
receptiveness probably contributed to the rise of PL market share.
The first HD formats started after the second World War in Germany. In this post-war country people had had low incomes and attracted to this format with their low prices. First Aldi was founded and later Lidl followed. Also in the rest of Europe similar chains were founded. Aldi and Lidl also rapidly focussed on international expansion. HD started as a format for consumers with low incomes, but over the years also consumers with higher incomes got more attracted to the format. Steenkamp & Sloot (2018) give three reasons for this: stagnating incomes, impact of recessions and the smart shopping phenomenon. The last reasons refer to the believe that you can purchase a product at a HD with a comparable quality as NBs for a lower price. HDs won in various awards over the last years in the Netherlands that underline the good quality of their products. For example: Lidl were named the best retailer in fruits and vegetables for the last 7 years (GfK, 2018) and their PL for cleaning products ‘W5’ were awarded several times by the ‘Consumentenbond’. This puts pressure on service retailers to keep improving the quality of their PL assortment. Another benefit for consumers to shop at HD is that the choice is simpler with assortments between 1000 and 1500 SKU’s. Iyengar and Lepper (2000) proved that consumers were more likely to make a purchase when the choice is smaller. The choice overload that consumers might experience in service retailers with their extensive assortments of various PLs and NBs proved not to be beneficial in every case.
3. Conceptual development
In this chapter the conceptual model and the hypotheses are explained. The main goal of this research is to explore if the environment of the two largest Dutch retailers affects the tendency of their consumers to buy NBs. Other variables that are included are retailer specific variables, such as: promotion intensity, promotion depth and the NB share of the assortment. As control variables various socio-demographic variables are included in the model. According to (Ellickson and Mistra, 2008; Rani, 2014) these variables are important drivers for consumer behaviour. However, they are not the main topic of this research.
Figure 1: Conceptual model
Store choice variables
As mentioned before the different service-oriented players in the Dutch retail market follow different strategies (HiLo strategy or EDLP strategy). Besides those players there are also brand discounters and hard discounters active in the market. The preference of consumers for a certain chain influences there shopping behaviour. However, from literature contradictive conclusion can be drawn.
that Ailawadi et al. (2001) suggest that EDLP chains fit to consumers who prefer PLs and that consumers who prefer NBs and their promotions are more attracted to HiLo chains. However, Pechtl (2004) concluded that consumers who visit an EDLP chain have a stronger preference for well-known brand than consumers who visit a HiLo chain (Pechtl, 2004). Which of these two different strategies has positive influence compared to the other on the tendency of consumers to buy well-known NBs can therefore not be said.
H1: Consumers of a HiLo strategy supermarket have a higher tendency to buy NBs than consumers of an EDLP strategy supermarket.
Consumers can choose to visit only one supermarket for their groceries, but they can also choose to visit multiple stores. According to Fox and Hoch (2005) consumers who are price driven are more likely to visit multiple stores. The effect on the tendency of consumer to buy NBs depends on the type of price driven consumer. The research of Ailawadi et al. (2001) distinguished two groups of price driven consumers. One group who is more impulse and price promotion focussed. Since NBs have a higher promotion intensity, this group fits to a higher tendency to buy NBs. The other group who is looking for a low price every day and therefore is more likely to buy PLs.
H2: Multi store shopping influences the tendency of consumers to buy NBs
Since there are many different retailer formats in the Netherlands, it is likely that when consumers visit multiple stores for their groceries the effect might be different
depending on the format they visit. This research is focussed on consumers who primarily visit service retailers. The influence of two different types of multi store shopping are outlined: a HD visit and a BD visit.
Concerning the influence of HD visit of consumers who visit primarily also a service retailer the literature is not extensive. Vroegrijk et al. (2013) concluded that consumers who visit HDs are more receptive to PLs. It could therefore be argued that those
complementary to the incumbent service retailer. Most consumers did not replace the service retailer with the HD but switched to multiple store shopping. In some cases, they found that a HD can be complementary to the incumbent retailer, which gives an
indication that consumers go for different types of groceries to different chains. If consumers buy their PLs at HDs, they might visit the service retailer for their NBs assortment. Although HDs sell NBs as well, this is very limited compared to service retailers. In the case the complementary effect is the case there does not need to be an effect of a HD visit to the tendency to buy NBs in general. However, this complementary effect proven by Vroegrijk et al. (2013) is focussed on sales. The evidence that
consumers of HDs show a higher preference for PLs leads to the expectation that when consumers visit a HD their tendency to buy NBs becomes smaller.
H3: When consumers also visit a HD this negatively effects their tendency to buy NBs The influence of BD visit of consumers in the United States is analysed by Ailawadi et al. (2010). The entry al Walmart (a large discounter) had a large effect on the sales of the incumbent retailer. Consumers switched from the incumbent retailer to Walmart. However, the United States market is not comparable to the Dutch market. What is comparable is the overlap in assortment between Walmart and the incumbent retailer in the research of Ailawadi et al. (2010) and the assortment overlap between service
retailers and the BDs in the Dutch market. Although brand discounters carry a more limited assortment than service retailers, the well-known NBs are available at both. According to Steenkamp & Sloot (2018) BDs in general do not have a strong
performance in perishables and the main difference between BDs and service
supermarkets is the lower price level at BDs. Although a visit to a BD might therefore not be positive for the service retailer, it is expected to have positive effect on the tendency to buy NBs. Since price is the main driver of purchase intentions (Ailawadi et al. 2008) and BDs are strong in NBs, when consumers visit a BD it is expected to positively affect their tendency to buy NBs.
Distance variables
It is expected that the distance variables have a main and a moderator effect. If distance to various retailers influences the preference of consumers for certain brands has not been researched yet.
What has been researched is the effect of distance on store choice (Fortheringham and Trew, 1993; Briesch et al., 2009). The general believe is that consumers prefer shorter travel distances. Travel time and the importance of assortment to a consumer have been researched by Briesch et al. (2008). A correlation was found between consumer
responses to assortment and travel time. They concluded that the less important
assortment is to consumer’s store choice, the more the consumer values convenience of a store being close.
This researched is focussed on primary shoppers of service retailers and if their
tendency to buy NBs is affected by hard discounters. Ailawadi et al. (2010) and Vroegrijk et al. (2013) researched the effect of the entry of a large discounter and respectively the entry of a hard discounter to the sales of the incumbent services retailer. Both included a distance variable in their research. Ailawadi et al. (2010) concluded that the incumbent retailer suffers the closer the discounter is located. Vroegrijk et al. (2013) concluded that the effect of the entry of the hard discounter is U-shaped. When the incumbent retailer is close to the HD there are complementary positive effects. When the HD is located far from the incumbent retailer the losses are the least, become consumers are less likely to visit. The incumbent retailers that were on a medium distance of the HD suffered the most.
H5: The closer the distance is to the HD is the lower the tendency of consumers is to buy NBs
The closer a consumer lives to a BD the more likely it is that consumers will visit and the higher the tendency is to buy LNB. When consumers go to a service supermarket and the BD is close they are predominately confronted with NBs.
H6: The closer the distance to the BD is the higher the tendency of consumers to buy NBs.
The main believe in literature is that consumers prefer the assortment that is most accessible to them. In that case when consumers live far from a HD their tendency to buy NBs is higher. In general, as stated in ‘H5’, this might be true. However, literature claims that when a consumer despite the large distance does visits a certain store this is
positively related to store loyalty and satisfaction (Hsu, et al., 2010). Other research that concluded that travelled distance is positively related to favourable store specific
attributes is from Darley and Lim (1999). The larger the travelled distance the more important and attractive the assortment composition of that store is (Briesch et al., 2008). To make a decision if a consumer wants to visit a store its weighs the
inconvenience of travel time with the attractiveness of a store. The negative impact of distances can be compensated when the consumer is convinced of the attractiveness of the store (Darley and Lim, 1999).
When applying this literature on the tendency of consumers to buy NBs when they visit a HD or a BD distance can increase the effect of that visit. Therefore, it is expected to strengthen the effect on those variables. When consumers travelled a long distance to visit the store, literature claims that this is because they are loyal to that store and see the attractiveness of that store. In the case of the HDs that when customers who despite a large distance visit the HD, they have an even smaller tendency to buy NBs. For
consumers who visit BDs despite the large distance, they have an even higher tendency to buy NBs.
H7A: Distance to HD is expected to strengthen the effect that HD visit has on the tendency of consumers to buy NBs
H7B: Distance to BD is expected to strengthen the effect that BD visit has on the tendency of consumers to NBs.
Retailer specific variables
In general service retailers are characterized by their promotional activity (Ellickson & Misra, 2008). As indicated earlier in this chapter there are differences between the amount of promotions between retailers. According to earlier research with data of the ‘Superscanner database’ the promotion intensity and depth of both at service retailers have increased over slightly time. When the promotion intensity and depth of NBs at retailers are higher it is more attractive to buy those products. The price gap between NBs and PLs is smaller or not there. Hoch and Banerji (1993) argue that PLs perform better when promotions on NBs are less. It is expected that intensity and the depth of promotions have a positive effect on the tendency of consumers to buy NBs.
H8: The promotion intensity of NBs of the primary retailer of the consumer has a positive effect on the tendency of consumers to buy NBs.
H9: The promotion depth of NBs of the primary retailer of the consumer has a positive effect on the tendency of consumers to buy NBs.
Not only promotions draw attention to products, but also the amount of shelf space. Phillips and Bradshaw (1993) support that space has a positive influence on sales. When NBs have more shelf space they are more likely to be perceived by consumers in the store which leads to a higher tendency to buy bought.
4. Methodology
4.1 Data collection
This research is conducted in the Dutch grocery retailing market during a 23-month period, from June 2017 till April 2019. In the final dataset there are in total 1874 observations, which is on average 81 observations per month. Four different data sources where used: ‘Superscanner.nl’, ‘EFMI shopper monitor’, ‘Supermarket database’ and CBS data. ‘Superscanner.nl’ collects data on a daily basis of various retailers in the Netherlands. They collect real-time price and product information. The ‘EFMI shopper monitor’ dataset is based on monthly surveys filled in by primary grocery shoppers in the Netherlands. The respondents are representative sample of the Dutch population. The ‘Supermarket database’ contains information about all the supermarkets in the Netherlands. The ‘Centraal Bureau voor de Statistiek’ (CBS) gathers statistical information about the Netherlands, amongst others: count of the population, unemployment and population density.
4.2 Measurement
The data of the ‘EFMI shopper monitor’ is available on a monthly basis. Therefore, the other variables that are extracted from the ‘Superscanner.nl’ are aggregated on a monthly basis in order to match the data. To indicate which supermarket is closest to the respondent of the ‘EFMI shopper monitor’ the zip code of the respondent is matched with the zip codes of the supermarkets in the ‘Supermarket database’.
Table 1: Constructs
Construct Measure
1 Tendency to buy Leading
National Brands (NBs)
Number of times the respondent buys a product of a NB when PL is available (scale 0 to 10)
2 Primary supermarket Primary supermarket of the respondent
3 Multiple store shopper
(0/1)
Consumer visits more than one retailer
4 Hard discount visit (0/1) Dummy variable: Does the respondent visits a HD (0 is no /
5 Brand discount visit (0/1) Dummy variable: Does the respondent visits a BD (0 is no / 1 is yes)
6 Distance to hard discount Calculated distance from the six-digit zip code of the respondent to the six-digit zip code closest hard discounter (in meters)
7 Distance to brand
discount
Calculated distance from the six-digit zip code of the respondent to the six-digit zip code closest brand discounter (in meters)
8 Promotion intensity NBs The promotion intensity of NBs of the primary
supermarket of the respondent (in %)
9 Promotion depth NBs The promotion depth of NBs of the primary supermarket of
the respondent (in %) 10 NBs share of the
assortment
The NB share of the assortment of the primary supermarket of the respondent (in %)
11 Population density Population density of the four-digit zipcode of the
respondent
12 Region The region where the respondent lives (7 possible options)
13 Gender Dummy variable: the gender of the respondent (0 is male /
1 is female)
14 Age Age of the respondent (in years)
15 Size of household Size of the household of the respondent (in persons)
16 Income Level of the income of the respondent (7 possible options)
4.3 Construction of the dataset Construction
coordinates. From the CBS database the variable OAD, a measure for population density of an area, was added to the dataset. The ‘Superscanner database’ which includes
variables about the promotion intensity, promotion depth and the ratio between NBs and PLs of the service retailers Albert Heijn, Jumbo and Plus. The research and the questions in the ‘EFMI shopper monitor’ are focussed on consumers who primarily visit these three chains. Therefore, only those consumers are included in the final database of 1874 respondents.
Outliers, missing values and impossible values
The variable ‘Personal situation’ contained three missing values and the ‘Size of the household’ contained one missing value. These observations were excluded from the dataset. The ‘Income’ variable of 707 respondents was unknown, because the choice the option in the survey ‘I don’t know / I don’t want to say’. These missing values were imputed based on three other socio-demographic variables: ‘Gender’, ‘Region’ and ‘Size of the household’. In the ‘EFMI shopper monitor’ the respondents had to answer two questions based on a full shopping cart of 100 euros with only PL products at their primary supermarket:
1) What would the same shopping cart cost with only NB products? 2) What may cost the same shopping cart with only NB products?
Respondents who answered these two questions not within the range between 50 and 250 euros where excluded from the dataset. Since values out of that range are
5. Results
5.1 Data exploration
In this section the descriptive statistics of the data are discussed. Descriptive statistics
As can been observed in ‘Table 2’ most variables show substantial variation between the three supermarket formulas. Within the supermarket formulas most variables show substantial variation.
Of the respondents who primarily visit the three large service supermarkets (Albert Heijn, Jumbo or Plus) 1143 visit Albert Heijn, 538 visit Jumbo and 193 visit Plus. From the Albert Heijn consumers 28,0% visits a HD, from the Jumbo consumers 38,6% visits a HD and from the Plus consumers 40,9% visits a HD. For BD visit these percentages are 14,6% (Albert Heijn), 7,1% (Jumbo), 19,7% (Plus). The alternative for buying NBs are PLs. To perform a check if the alternative is valued approximately the same at every chain the average grade consumer give to the PL of that chain is analysed. As can been seen in the table below this quite comparable.
Table 2: Descriptive statistics
Albert Heijn Jumbo Plus
Construct Mean
(SD)
Min Max Mean (SD)
Min Max Mean (SD) Min Max Tendency to buy NBs 5,44 (2,83) 0 10 4,95 (2,95) 0 10 5,22 (2,88) 0 10 Distance to HD 1546 (1841) 66 19872 1548 (1682) 76 13668 1782 (2022) 56 12057 Distance to BD 7029 (9254) 18 51179 8541 (9264) 18 46578 5425 (8327) 24 45627 How they grade NB 8,11 (1,12) 1 10 7,94 (1,18) 1 10 7,94 (1,27) 1 10 How they grade PL 7,64 (1,13) 1 10 7,65 (1,14) 1 10 7,48 (1,29) 1 10 Ratio NB 0,77 (0,009) 0,75 0,78 0,75 (0,01) 0,73 0,76) 0,77 (0,003) 0,76 0,78 Promotion intensity 0,11 (0,02) 0,08 0,16 0,07 (0,01) 0,05 0,09 0,04 (0,003) 0,03 0,04 Promotion depth 0,34 (0,02) 0,31 0,37 0,26 (0,01) 0,24 0,29 0,34 (0,02) 0,31 0,40
Figure 2: Tendency to buy NBs
5.2 Empirical results Model construction
To test the hypotheses stated in the Chapter 4 and check whether the socio-economic variables have the expected effects 6 models are constructed.
The models were tested on possible issues and the issues were solved. Since the dummy variable ‘Multi store shopper’ both caused multicollinearity issues with the dummy variables ‘HD visit’ and ‘BD visit’. First a model was estimated with only the ‘Multi shopper variable’ and afterwards a second model is estimated with the variables ‘HD visit’ and ‘BD visit’. The third model that the moderation effect that is stated in hypotheses 7A and 7B is included. In these three models are pooled and include the respondents which primary supermarket are Albert Heijn, Jumbo and Plus. To test hypothesis 1 Jumbo is taken as reference for the variable ‘Primary supermarket’. To check if it is allowed to pool a Chow test is conducted. From the Chow test is can be concluded that pooling is not allowed (p-value = 0,021). Therefore, the second model (including ‘HD visit’ and ‘BD visit’) is repeated per chain. For interpretation purpose a model is constructed for Albert Heijn with only significant variables.
Pooled model
The results of the three pooled models can be found in Table 3. All the models show that socio-economic variables have a significant effect on the tendency of consumers to buy NBs.
Model 1 shows that multi store shopping has a highly significant negative effect on the tendency of consumers to buy NBs (β = -0,85, p<0,001). This means that in general consumers who visit multiple stores have a lower tendency to buy NBs than consumers who only shop at one of the three service retailers. This is probably due to the fact that the majority of the assortment of the three service retailers consist of NBs. Therefore, there is evidence that hypothesis 2 is true. To check if that effect is different for multi store shoppers who shop either at hard discounters or at brand discounters the effects in model 2 should be interpreted. Model 2 does not show any significant effect for the effect of a visit to a brand discounter, but the effect of a hard discount visit is highly significant. When consumers also shop at a hard discounter this negatively effects their tendency to buy NBs. Thus, hypothesis 3 is accepted and for hypothesis 4 there is no evidence.
Although the effect of a hard discount visit is significant, for the distance to a hard discounter no significant effect can be observed in any of the models. For the distance to a brand discounter the effect in model 2 and 3 is significant on a 5% significance level. However, the observed estimate is very close to zero, which makes the effect on the tendency to buy NBs very small. A moderation effect on the variables hard discounter and brand discounter visit can also not be observed.
The socio-economic variables are almost all significant. The population density is significant in all models, but the estimate is very small. For region the reference level is the region of Amsterdam. The other significant levels for this variable all have a negative estimate. This means that compared to consumers in Amsterdam, they have lower tendency to buy NBs. This might be caused by the fact that there are many Albert Heijn stores in Amsterdam (Supermarket database). The descriptive statistics showed that on average consumers who visit primarily Albert Heijn have the highest tendency to buy NBs compared to primary shoppers of Jumbo or Plus. Size of household and personal situation give opposite results. Size of household has a significant effect, which means that the larger the household is the lower the tendency of to buy NBs. The personal situation variable (with reference level single) gives the opposite conclusion. For example: the level ‘together living with children’, which is at least a household size of three, has a positive effect on the tendency of consumers to buy NBs. A reason for this contraction is not clear. Income has a significant positive effect. The higher the income the higher the tendency of consumers to buy NBs. This is logical, because NBs are more expensive than PLs. Age has a significant negative effect on the dependent variable. The older people get, the lower their tendency to buy NBs. This is in contrast with the research of Kalogianni et al. (2016). They concluded that older consumers are less receptive to hard discount formats and private label. However, when looking at
literature about store choice the conclusion is that it is hard to differentiate based on age (Fassnacht & El Husseini, 2013).
Although the models statistically significant (p<0,001), the models only explain a small proportion of the variation in the tendency to buy NBs (R2 is 0,069 and 0,076). The AIC of the models are comparable. As mentioned before a Chow test was conducted to test if pooling is allowed. The result of the Chow test (p-value = 0,021) is that pooling is not allowed.
Unit-by-unit model
model did not succeed in providing support to other hypothesis than the pooled model. What can be observed is that when the model Albert Heijn and Jumbo are compared the income effect is significant for high incomes for primary customers of Albert Hejin, but not for Jumbo. The higher the income is of primary Albert Heijn customers, the more likely they are to buy NBs. For Jumbo this cannot be proven.
Albert Heijn model
For interpretation purposes a model with only significant variables is constructed for the chain with the highest amount of observations and significant variables. It this case that is Albert Heijn. The results can be found in Table 5. The dependent variable
Table 3: pooled model
Dependent variable: Tendency to buy NBs
Model 1: with MSS Model 2 : with HD and BD visit Model 3: with interaction effect
Estimate Std. Error Sign. Estimate Std. Error Sign. Estimate Std. Error Sign.
Intercept 5,206 6,667 4,580 6,647 4,448 6,660
Primary supermarket (ref =
Jumbo) PLUS -0,018 0,490 0,003 0,489 -0,004 0,490
Albert Heijn 0,029 0,554 0,005 0,553 -0,010 0,554
Multi store shopper (0/1) -0,885 0,177 ***
Hard discount visit (0/1) -0,844 0,145 *** -0,879 0,194 ***
Brand discount visit (0/1) 0,143 0,217 0,185 0,262
Distance to hard discount 0,000 0,000 0,000 0,000 0,000 0,000
Distance to brand discount 0,000 0,000 0,000 0,000 . 0,000 0,000 .
Promotion intensity NBs 4,720 0,001 2,054 4,709 2,119 4,715
Promotion depth NBs 2,888 4,224 2,873 4,211 2,957 4,219
NBs share of the assortment 1,147 8,441 1,510 8,424 1,664 8,439
Population density 0,000 0,000 ** 0,000 0,000 ** 0,000 0,000 **
Region (ref = Amsterdam) Rotterdam -0,619 0,334 . -0,668 0,333 * -0,664 0,334 *
Den Haag -0,797 0,381 * -0,864 0,380 * -0,865 0,381 *
West -0,983 0,293 *** -1,038 0,293 *** -1,035 0,293 ***
North -2,008 1,653 -1,951 1,649 -1,951 1,650
East -1,454 0,368 *** -1,494 0,367 *** -1,496 0,367 ***
South -0,866 0,317 ** -0,874 0,316 ** -0,872 0,317 **
Personal situation (ref =
Single) Single, with children 0,692 0,331 * 0,753 0,331 * 0,754 0,331 *
Living together 0,510 0,195 ** 0,550 0,195 ** 0,554 0,195 **
Living together, with children 0,908 0,324 ** 0,958 0,324 ** 0,963 0,324 **
Otherwise 0,635 0,442 0,587 0,441 0,587 0,441
Gender (0 = male / 1 = female) 0,027 0,148 0,021 0,147 0,022 0,148
Size of household -0,230 0,104 * -0,223 0,104 * -0,224 0,104 * Income (ref = Less than 1000) 1000 - 2000 0,177 0,238 0,114 0,237 0,111 0,238
2001 - 3000 0,639 0,248 * 0,545 0,247 * 0,544 0,248 *
3001 - 4000 0,691 0,267 ** 0,622 0,267 * 0,618 0,267 *
4001 - 5000 1,145 0,302 *** 1,061 0,301 *** 1,057 0,301 ***
5001 - 6000 1,592 0,338 *** 1,471 0,337 *** 1,467 0,338 ***
More than 6000 1,618 0,366 *** 1,538 0,365 *** 1,534 0,365 ***
Hard discount visit (0/1) *
Distance to hard discount 0,000 0,000
Brand discount visit (0/1) *
Distance to brand discount 0,000 0,000
Table 4: unit-by-unit / chain specific model Dependent variable: Tendency to buy NBs
Albert Heijn Jumbo Plus
Estimate Std. Error Sign. Estimate Std. Error Sign. Estimate Std. Error Sign.
Intercept 8,720 9,763 -2,814 13,290 -7,299 57,320
Hard discount visit (0/1) -0,593 0,191 ** -1,481 0,271 *** -0,801 0,427 .
Brand discount visit (0/1) 0,156 0,247 -0,096 0,509 1,562 1,061
Distance to hard discount 0,000 0,000 0,000 0,000 0,000 0,000
Distance to brand discount 0,000 0,000 . 0,000 0,000 0,000 0,000
Promotion intensity NBs 0,346 0,346 -3,124 12,210 82,780 67,270
Promotion depth NBs 6,535 4,970 6,041 13,090 -22,330 13,090 .
NBs share of the assortment -6,400 12,010 13,720 14,710 24,200 77,180
Population density 0,000 0,000 * 0,000 0,000 * 0,000 0,000
Region (ref = Amsterdam) Rotterdam -0,421 0,397 -0,852 0,745 -1,884 1,204
Den Haag -0,486 0,445 -1,588 0,866 . -1,630 1,696
West -0,822 0,355 * -1,316 0,645 * -2,802 1,035 **
North -1,293 1,779
East -1,246 0,457 ** -2,143 0,737 ** -2,219 1,379
South -0,917 0,395 * -0,747 0,658 -2,679 1,124 *
Personal situation (ref =
Single) Single, with children 0,605 0,431 0,840 0,633 2,346 0,957 *
Living together 0,316 0,250 0,932 0,384 * 1,675 0,620 **
Living together, with
children 0,817 0,408 * 0,711 0,677 2,216 0,953 * Otherwise 1,082 0,568 . 0,012 0,870 0,132 1,381 Gender (0 = male / 1 = female) 0,100 0,187 -0,105 0,287 -0,560 0,523 Age -0,010 0,007 -0,021 0,010 * -0,010 0,021 Size of household -0,115 0,130 -0,340 0,222 -0,265 0,322
Income (ref = Less than
1000) 1000 - 2000 0,393 0,303 -1,004 0,440 * 1,454 0,830 .
Table 5: Albert Heijn model
Dependent variable: Tendency to buy NBs
Estimate Std. Error Sign. Hard discount visit (0/1) 5,504 0,479 *** Distance to brand discount -0,643 0,0183 *** Population denisty 0,000 0,000 . Region (ref = Amsterdam) Rotterdam -0,421 0,395 Den Haag -0,420 0,443 West -0,785 0,353 * East -1,234 0,455 ** South -0,912 0,393 * Personal situation
(ref = Single) Single, with children 0,447 0,392
Living together 0,191 0,219
Living together, with children 0,522 0,230 *
Otherwise 1,076 0,517 *
Income (ref = Less
6. Conclusion, discussion and recommendations
This thesis tried to explain the impact of the presence of a hard discounter on the tendency of consumers to buy national brands. In this chapter the main findings will be discussed. Additionally, recommendations to both service retailers and brand
manufactures will be given. Conclusion and discussion
The main research question that was aimed to be answered: what is the impact of hard discounter presence on consumers’ tendency to buy national brands? Additionally, other variables were analysed: brand discounter presence, retailer specific variables and socio-economic variables.
The constructed models were only able to explain small portion of the variation as the low R2 values show in Table 3, 4 and 5. The main research question is partially
answered. In all the constructed models a negative significant effect was found for consumers who visited a hard discounter. Consumers have a lower tendency to buy NBs when they visit a HD. This is in line with earlier research on the increased receptiveness for PL. However, no evidence was found that the distance from the consumer to the hard discounter had any effect on their tendency to buy NBs. As concluded by (Vroegrijk et al., 2013) distance to a store has an influence on store patronage and if a hard
discounter is close this negatively impacts the sales of the service retailer. This research did not succeed to prove that the distance to a HD also impacts the brand choice of a consumer, specifically: the tendency to buy NBs. The moderation effect of distance could also not be proven. What can be a reason for that consumers that do not make decisions on what kind of brands they buy based on the most accessible chains but based on socio-demographic characteristics of the consumer.
that compared to the reference level Amsterdam most other regions showed a significant negative effect.
Looking back at the descriptive statistics, an effect for the various chains would have made sense. There was variation between the tendency to buy NBs between the
different chains. The percentage of consumers who visit a HD or a BD differs per chain. The pooled model did not show any significance for the variable that indicated the primary supermarket of the consumers. Also a effect for a positive effect for BD visit would have been expected.
Recommendations
7. Limitations and further research
There are some limitations of this study that need to be addressed. First only the
primary consumers of the largest retailers in the Netherlands are included in this study. To further assess the impact of hard discount also consumers who primary visit hard discount should be incorporated in the dataset. Since most of the questions in the ‘EFMI Shopper Monitor’ were based on the primary supermarket and the ‘Superscanner.nl’ data were only available for the three big service retailers it was decided that these the consumers of these three chains were the focus for this research.
To further assess the issue if hard discounters also effect the brand buying behaviour of consumers while in a service supermarket the question needs to buy asked more
specifically. The tendency to buy NBs is now measured on a general basis. If the question would be asked on a store level basis, this could give more detailed insights. This
probably will also give better indications what the effect is for consumers who visit brand discounters.
Another limitation was the determination and aggregation of the promotion intensity, promotion depth and NB share. The data was measured on a weekly basis and manually aggregated on a monthly level. This caused that for some months the average of 5 weeks were taken and for some months the average of 4 weeks. This month was matched to the corresponding month of the ‘EFMI shopper monitor’. However, consumers might have a better remembrance of the promotions of one week before the filing date of the survey. In further research it could be wise to weigh those weeks more heavily
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Reports
