Michelle Oude Groen | 10281495
Price Promotions in Secondary Stores and the Effect of Product Relationship, Product Category and Household Characteristics of Customers on Willingness to Buy other
Non-Promoted Products and How this Can Help Retail Managers
Abstract
One of the most important determinants of cross-shopping is pricing. Customers often visit other stores than their primary stores because of price promotions. Therefore, grocery retailers invest heavily in price promotional strategies. This study focuses on price promotions in secondary stores and additional unplanned purchases during the same grocery shopping trip. The aim of this study is to research whether unplanned purchases in complementary categories in these secondary stores depend on the focal category and how the household characteristics number of household members, housing and income affect this relationship. It was found that focal categories significantly influence unplanned purchases, meaning that the product category of the promoted product influences the degree to which the customer is willing to buy unplanned additional products as well. It was also found that complementary categories do not differ from the non-related categories, inferring that the product relationship between the promoted product and other non-promoted products does not influence the likeliness of the customer’s additional purchases. Next to that, the household characteristics do not influence this relationship as they do not change the difference between complementary and non-related products on a customer’s willingness to buy other non-promoted products. Nevertheless, the results of this research provide valuable information for retail managers when structuring price promotional strategies to increase store profit. Namely that it is important to take the focal category into account rather than the product relationship to induce additional purchases and to not invest on targeting specific types of households for price promotional strategies in the given situation.
Master’s Thesis Marketing (6314M0252Y) Supervisor: J.Y. Guyt Business Studies 2015-2017 23 January 2017
Statement of Originality
This document is written by Student Michelle Oude Groen who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Index
1 Introduction ... 3
2 Theoretical Framework ... 5
2.1 The Retailer: Promotions ... 6
2.2 The Customer: Grocery Shopping Behavior ... 13
3 Research Design and Methodology ... 19
3.1 Data Collection and Sources ... 20
3.2 The Sample ... 20
3.3 Measurements ... 21
3.4 Analyses and Predictions ... 23
4 Results ... 23
5 Discussion ... 25
5.1 Key Findings ... 25
5.2 Managerial Practice ... 27
5.2 Limitations and future research ... 27
Bibliography ... 30
1 Introduction
Grocery shopping behavior has changed substantially. Where it used to be the standard to visit one store every week for all groceries, customers nowadays often visit multiple stores and actively look for the best deal for their weekly purchases (Klein & Schmitz, 2006; Gijsbrechts, Campo & Nisol, 2008; Jayasankaraprasad & Kathyayani, 2013). One of the most important determinants for this cross-shopping behavior is pricing (Arnold, Oum & Tigert, 1983; Walters, 1991). This means grocery retailers have the opportunity to create incentives to attract new customers to their store on a weekly or even daily base. In creating these incentives, grocery retailers spend a considerable share of their marketing budget on price promotions as they provide the opportunity to draw new customers to the store (Blattberg, Briesch & Fox, 1995). Next to that, price promotions do not only accommodate short-term flexibility, but can also attribute to longer-term strategic advantages, making it an interesting investment (Smith & Sinha, 2000).
Because of these bilateral advantages, namely on the short and the long term, price promotional strategies of supermarkets have been discussed in a great extent of literature (i.e. Ailawadi, Harlam, César & Trounche, 2006; Fox & Hoch, 2005; Mulhern & Padgett,1995; Smith & Sinha, 2000; Kumar & Leone 1988). However, even though a lot of research has been done on price promotions and the effect they have on a supermarket’s store profit or store traffic, little is known on the actual effect on profitability that customers that were drawn to the store by price promotions have on regular priced products. Instead, most studies execute research on the aggregate level, putting all effects of price promotions (brand substitution, purchasing as add-ons by regular customers and store substitution) together (Kumar & Leone, 1988; Blattberg et al., 1995; Wilkinson, Mason, & Paksoy, 1982; Mulhern & Padgett, 1995). A deeper insight on individual shopping behavior would provide a clearer understanding on the effect price promotions have on customers that visit a store specifically because of price promotion and their whole shopping basket. This would provide useful information for retailers as it gives new information on the profitability of these price promotions since it does not only report whether customers buy price promoted products, but also whether they visited the store specifically because of the price promotion and whether they bought other products as well that they were not planning to buy beforehand. The latter is defined as ‘the Halo effect’ (Ailawadi et al., 2006). This means that a promotional offer in one category can have a positive effect on the sales of other products. However, in her research on the Halo effect, Ailawadi et al. (2006) do not differentiate between a customer’s primary store and secondary store that is visited for
promotional purposes specifically, while information focused only on the secondary store would provide useful insights for grocery retailers for the success of their price promotional strategies. Next to that, no information is available on the extent to which the Halo effect differs for different product categories in this scenario, even though it would be beneficial for grocery retailers to know which products are more likely to increase sales of other products and which are not likely at all. On top of that, existing research does not include information on the promoted product and the other products bought in this scenario, despite earlier research confirming the effect product relationship can have on grocery shopping behavior (Mulhern & Leone, 1991; Blattberg et al., 1995; Hruschka, Lukanowicz and Buchta, 2009).
This article will aim to map this specific behavior and intent to create useful insights in the response of customers when it comes to promoted products and buying other products during the same shopping trip. To create a full understanding, different product categories will be taken into account as different results may derive from it as discussed by Hruschka et al. (1999) and Leeflang and Parreño-Selva (2011). It will focus on stores that customers do not consider to be their primary store as it will provide more information on the relationship between the promoted products and the non-promoted products and give clear insights on the profitability resulting directly from the price promotion. Therewith, this article will not look for regular customers buying promotion products as substitute or an ‘add-on’ to their regular groceries, but rather aim to find the behavior of customers visiting another store than their primary store for promotional purposes.
To further explore the price promotional scenario, this research will also take household characteristics of the customer into account. Previous research has shown that these can have a significant influence on grocery shopping behavior, but no information is available on the effect these characteristics have in the previously described scenario (Bawa & Ghosh, 1999; Fox & Hoch, 2005).
To structure the research, the aim of this study will be to answer the following research question:
Do unplanned purchases in complementary categories depend on the focal category and how do household characteristics affect this relationship?
By studying this relationship, this article will contribute theoretically as well as managerially. Theoretically, this research will provide new insights and create a stepping stone for future research by combining previously researched relationships and creating a new
conceptual model. For managers, it is important to gain more insights in customer behavior in terms of price promotions and their influence on store revenue and store traffic as they affect overall profit.
This research will aim to provide new insights on the effects of price promotions and the results will add useful information to apply to retailers’ marketing strategy. Managers could, for example, use this information when deciding on products to place near the promoted product or when deciding on which products or product categories to promote or deliberately not promote together.
This research is theory-driven. Current literature on price promotions, grocery shopping behavior, product categories and household characteristics is studied and evaluated. The research will be based on this evaluation. Existing theories will provide an overview of existing information and views on the topics involved. A questionnaire will provide deeper insights and help draw a conclusion. This article will first analyze and evaluate existing literature in chapter two and three. This will result in a conceptual framework to visualize the patterns. The third chapter elaborates on the research design and methodology, to explain and justify the methodological choices made, followed by explanation of the data collection method and the results. In chapter five results will be given a meaning and compared to existing literature. Next to that implications as well as limitations will be discussed, to provide new insights and direction for future research and indicate to what extent the findings can be generalized. The article ends with a conclusion in which the aim is to answer the research question.
2 Theoretical Framework
The retail industry, in particular the grocery store industry, is very competitive. Not only do grocery stores compete with each other, but they are also in competition with other retail formats such as mass merchandisers and smaller local stores (Fox, Montgomery & Lodish, 2004). Therefore, it is important for retail managers to know what grocery shoppers are looking for, what aspects are considered in the decision process and what other factors influence grocery shopping behavior. In order for retail managers to identify their customers, or knowing how to target them, it is needed to understand this behavior as marketing strategies of retail stores can have a strongly influence on it (Pan & Zinkhan, 2006; Fox et al., 2004).
As previously explained, this research focuses on the strategies retailers use to have regular customers visit their store because of a price promotion and end up buying other non-promoted products as well. To effectively implement these strategies, it is important to
understand what the results of price promotions are, why and when customers visit stores because of a price promotion, which factors influence the purchasing of other products during this same shopping trip and how retailers can use this information to their benefit.
To gain a deeper understanding of the importance of this research topic and to elaborate on what has been studied before and which information is missing so far, this chapter will thoroughly explain the different aspects involved based on existing literature. With that, this framework will provide understanding and evaluation of the problem. To do so retailer and customer insights are combined: first, it is explained how retailers use price promotions and what the consequences of their strategies are, secondly, customer grocery shopping behavior is discussed, which involves shopping determinants, store loyalty and cross-shopping or cherry-picking.
2.1 The Retailer: Promotions
For retailers to attract customers and retain their current shoppers, they use short-term as well as long-term strategies. Price promotions are one of the short-term supermarket strategies to increase temporarily sales of specific products through the temporal reduction of prices (Wilkinson et al., 1982). They play a big role in grocery industries (Walters & MacKenzie, 1988), as a significant part of marketing efforts is often spend on price promotions (Blattberg et al., 1995). Retailers use price promotions for divergent reasons: they intent to clear inventories, increase store traffic, create a favorable image in the market and/or stimulate store sales (Mulhern & Leone, 1990). This increase in sales is often not only aimed at the individual sales of the promoted product, retailers also look for ways to stimulate customers that visit for promotions to buy regular priced products as well and thus increase these sales and overall store profit.
2.1.1 Consequences of Price Promotions
If a grocery retailer increases its promotional intensity, store traffic and spending respond significantly (Fox & Lodish, 2004; Fox et al., 2004). This is also the case for price promotions: when it comes to an increase in sales because of a temporary price reduction, it can be attributed to brand substitution or the purchasing of extra products by regular customers on the one hand (increase in in-store sales) and store substitution (increase in store traffic) on the other hand (Kumar & Leone, 1988; Blattberg et al., 1995; Wilkinson et al., 1982). Most studies have focused on the overall effect of price promotions, but little is known on the individual consequences of price promotions even though it would be beneficial for retailer to understand
which consequences occur for which reasons as they can adjust their marketing strategies, communications and in-store layout accordingly. For example, an increase in (store) sales does not necessarily imply an increase in (store) profit. Brand substitution in-store can lead to cannibalization of substitutes, leading to an increase in sales because of number of products sold, but a decrease in store profit as the price promoted product has lower margins than the full priced product the customer normally purchases. Next to that, a short-term increase in profit, can possibly have a long-term negative effect if only regular customers respond to the price promotion, as it possibly leads to “post-deal troughs”, meaning that products bought on sale are sold substantially less in the period after the price promotion (Blattberg et al., 1995).
An increase in store traffic does imply higher profit as it means customers who would normally not visit the store are attracted. Earlier research has found price promotions to do so. Even though Mulhern and Leone (1990) state that sales increase but store traffic does not, an extensive amount of research refutes this statement. Mulhern and Padgett (1995) found that 42,3% of grocery shoppers that visited the store for a price promotions stated that their visited store was not their primary store. Blattberg et al. (1995) confirm that advertisement of price promotions can lead to an increase in store traffic and Fox et al. (2004) also state that not only an increase in expenditures of regular customers occurs, but also an increase in store traffic. Walters (1991) poses that a change in store traffic occurs, but a notable addition to other research is that little evidence exists for the decrease of sales of exactly the same product in another store, but the effect of different but substitutable brands in others stores was significantly negative.
Based on the above it can be said that it is not only important to distinguish between brand substitution, change in in-store sales and change in store traffic in research, but also explains why it might be more interesting for retailers to focus on increasing store traffic with price promotional strategies rather than increasing in-store sales of regular customers or promoting brand substitution. Unfortunately, existing research in general does not look at individual purchasing but rather at overall store profit or short-term store traffic change. 2.1.2 The Loss Leaders Strategy
Customers will switch stores if they can take advantage of a price promotion (Kumar & Leone, 1988). However, the success of the sales of a promotional product does not always determine the success of the promotion as it does not necessarily have a positive influence on overall store profit as previously explained. However, if the price promotional store is also able to induce
customers visiting the store because of price promotions to purchase other, non-promoted products as well, the profitability of a price promotion strategy would increase. Therefore, retailers often aim for customers to buy non-promoted products next to the promoted product. In other words, the success of a promotion is in that case determined by the customers that visit the store because of the discount, increasing store traffic, and by the bundle of the items they buy (Mulhern & Leone, 1990). From this view, price promotions are interesting for retailers if the product influences the purchasing of products in other categories, such as complements (Hruschka et al., 1999), but also other, non-related, products. The strategy that focuses hereupon, is called the “loss leaders strategy”. Loss leaders are defined as “products temporarily priced at or below retailer cost” by Walters and MacKenzie (1988). With this strategy, retailers plan to take their losses on the loss leaders as they aim for an increase of total store profit as customers buy other products that they did not plan to buy as well, because of the loss leaders.
For a loss leaders strategy to be successful, the Halo effect has to take place. Ailawadi et al. (2006) state that the Halo effect occurs “if a promotion in one category affects the sales of other categories, leading to customers buying products from other categories that they would not buy otherwise”. The Halo effect has been found to be positive and substantial. It was found that, for every promoted product, 0.16 of another product was bought in the same store (Ailawadi et al., 2006). This means that a promotional offer in one category can have a positive effect on the sales of products in other categories because of the Halo effect. In his research, however, Ailawadi (2006) does not distinguish between the product relationship between the categories and between primary store and secondary stores, making the results hard to apply to a price promotion to increase store traffic and the loss leaders strategy. Next to that, Hultén and Vanyushyn (1988) found no relationship between price promotions and impulse purchasing, implying that there is no Halo effect when it comes to price promotions. Thus, further research is needed on the existence of this effect and whether it differs for primary stores and secondary stores or different product categories.
Furthermore, existing studies on the effectiveness of the loss leaders strategy are inconsistent and contradictory. Walters and MacKenzie (1988) found the individual sales of loss leaders to increase, but these loss leaders did overall not significantly increase store traffic (only significantly in two out of eight tests) nor influence store profit through the sales of non-promoted products. Instead of customers visiting the store for promotional reasons of the loss leader and buying other products as well, the loss leaders were found to be merely used as an ‘add-on’ for regular customers. Arnold et al. (1983) and Ailawadi et al. (2009) also state that
the existence of the loss leader effect is limited. Nevertheless, some studies do confirm the effectiveness of the loss leaders strategy. Mulhern and Padgett (1995), for example, found that most customers buy regular priced products next to the promoted product. In their research 75% of customers who stated that the promotional product was one of the reasons to visit the store, also bought other promoted products. Even more, the spending of these customers on non-promoted products was higher than on the actual non-promoted products. Thereby, not only a positive relationship between the purchasing of the promoted and non-promoted products was found, but customers who visited the store specifically for the promotion were equally or even more profitable in terms of sales than other shoppers. They found that customers who visit a supermarket because of price promotion have a chance of 76,8% to buy other, non-promoted products as well and that for every 1 dollar spent on promoted items, 1,63 dollars are spent on non-promoted items. Talukdar, Gauri and Grewal (2010) confirm this by stating that only 2% of customers merely buy the loss leaders and even when this 2% is included, the loss leaders strategy is still effective.
However, overall results of previous research on this subject remains inconsistent. Thereby, no information is available on the difference in effect of this strategy between customers who visit the store anyway (primary store) and take advantage of a loss leaders product and customers who specifically decide to visit the store because of the loss leaders (secondary store). In addition to this distinction, no insights are available on the differences between product categories of loss leaders and whether some extent of success of a promotion can be traced back to this.
2.1.2.2 The Halo effect and Product Relationships
Even though no research is available on the difference in outcome of the loss leaders effect between different product categories, some insights are available on the effect of the loss leaders strategy and the product relationship between the promoted product and additional purchases. As previously mentioned, the promotion of one product can lead to the purchasing of other products, even from other categories (Walters, 1991; Leeflang & Parreño-Selva, 2011). Withal, if customers buy these other, non-promoted products, wat kind of products do they buy? These purchases can occur in terms of substitutes, complements and non-related products (Mulhern & Padgett, 1995; Walters, 1991). Products that are in the same product line as the promoted product are substitutes, products in other product lines are either complements, substitutes or independent, non-related products (Mulhern & Leone, 1991). It is interesting for retailers to
know if, when and how this happens as it can attribute to a better other understanding on how to carry out a price promotional strategy.
If a price promotion leads to a higher positive effect on complements than the negative effect is has on the sales of (cross-category) substitutes, the promotion can be beneficial. (Leeflang & Parreño-Selva. 2011). Walters (1991) states that sales of complementary products always increase with price promotions, but it differs between product categories to what extent this occurs and even though the effect is significant, it is not substantial. Multiple others studies found similar results for the existence of complementary effects (Kumar & Leone, 1988; Berman & Evans, 1989; Moriarty, 1985). Sales of additional complementary products could be explained by the Halo effect and the fact that for some products consumers tend to stock in case of a price promotions and therefore buy more of the complement as well (Ailawadi et al., 2006; Walters, 1991). However, even though some evidence exists, actual effects remain relatively unclear as some relationships were found to be relatively weak and little research has been done on the differences across product categories and the depth and breadth of complementarity.
The sales of substitutes of the promoted products decrease. A price promotion for one product in a category implies substitution from other products within the category take place (Leeflang & Parreño-Selva, 2011). For example, a customer that normally buys brand A, but notices that brand B is on sale, is likely to substitute regular brand A with brand B as it is on sale and, for example, sees this as an opportunity to try the other product (Walters, 1991). It is not likely that the sales of a product increases because another product in the same category is temporary reduced in price. Therefore, this will not be taken further into account in this research.
On the relationship between the purchasing of a promoted product and other non-related products little research has been done. However, Leeflang & Parreño-Selva (2011) did find that the probability of a price promotion to affect the revenues of minimum one other category is 61%. This demonstrates that even though little is known and studied, this relationship is likely to exist. Knowledge on this relationship would be beneficial for managers as it can improve understanding in consequences of price promotional strategies. Therefore, it would be interesting to further study this relationship.
2.1.3 Product Categories
While it would be beneficial to enhance knowledge on the relationship between promoted products and the purchasing of non-promoted products, increased knowledge on the differences
between product categories could additionally provide new insights for managers. However, only little research has been done on the influence of the product category of the promoted product on the purchasing of other non-promoted products. For example, differences could occur between food and non-food or they could depend on the shelf life of the product.
Previous research by Mulhern and Leone (1991) has confirmed that complementary effects exist but depend on the product and category. For example, promotion of cake mix leads to an increase in sales of frosting, but promoted frosting did not lead to increase of sales in cake mix. Next to that, Blattberg et al. (1995) state that the effect price promotions of a product have on the sales of other products is likely to be dependable on the characteristics and category of the product and a study of Hruschka et al. (2009) that has focused on this subject, also found that results can vary for different categories. They state that only some product categories, such as canned vegetables and pasta, are appropriate when using the loss leaders strategy as customers tend to buy other non-promoted (complementary) products next to their promotional purchase. In other categories, such as exotic fruit and frozen fruit, this strategy is less interesting as it diminished the sales of the other category. Nevertheless, even though previous research has studied the differences in effect of product categories on price promotions to some extent, no research has been done on the specific influence of product categories on non-default customers visiting a store for price promotional purchases and their likelihood to buy non-promoted products as well.
In this research, four product categories will be studied, namely beer, coffee, chips and laundry detergent. Bell, Chiang and Padmanabhan (1999) explain these categories to be severely different in terms of purchase frequency, expensiveness, necessity and storability and because of that also in terms of customer behavior when they are temporarily reduced in price. In their research on brand promotion calendars Guyt and Gijsbrechts (2014) found notable different results for these different categories, showing the likelihood of different outcomes for these categories in case of price promotions. Further research on the influence of the product category of the promoted product and the likeliness of buying other products as well could help managers in further understanding and provide further direction in price promotional strategies such as the strategic placements of products in-store.
2.1.4 Product Categories and Product Relationship on Unplanned Purchases
As explained in the previous paragraphs, existing research has focused to some extent on the influence of product categories and product relationship in the buying process of customers as
well as the influence it has on a customer’s willingness to buy other unplanned products as well. However, most research has generalized these relations, whereas the focus in this article is specifically on the product category and product relationship of the price promoted product in the secondary store of the customer as this is interesting for retailers who plan to use a loss leaders strategy (or other similar strategies) for new customers. Primary store and regular price shopping is not considered.
First, the expected relationship between the product category in this scenario and the likeliness to buy other products during the same shopping trip is tested. As no actual store data was available, likeliness to buy is measured through willingness to buy. It is expected that a change in category of the price promotional product in the secondary store changes the willingness to buy other products during the same trip, or put differently, people will be more or less inclined to shop for more depending on the category that is on sale (e.g. a price promotion for coffee leads to more additional grocery shopping than a price promotion for laundry detergent). This leads to the first hypothesis:
H1: The product category of a price promotional product in the secondary store influences a customer’s willingness to buy other products during the same shopping trip, so that different categories lead to a different willingness to buy.
Next to that, earlier research has focused to some extent on the willingness to buy other products next to the price promoted product and the relationship they have with the promoted product, that is whether they are substitutes, complements or non-related. However, no distinction has been made between primary and secondary stores. In this research, it is believed that the product relationship between the price promoted product and other non-promoted product has an influence on the willingness to buy. It is expected that complementary products experience higher willingness to buy than non-related products (e.g. a price promotion for laundry detergent leads to a higher willingness to buy fabric softener, which is a complement of laundry detergent, than coffee, which is not related to laundry detergent). This leads to the second hypothesis:
H2: The product relationship between a price promoted product and other non-promoted product influences a customer’s willingness to buy other products during the same shopping trip, where customers are more willing to buy complements of the price promoted product than non-related products.
However, to be able to fully evaluate the results emerging from testing these hypotheses, it is necessary to gain deeper understanding of the grocery shopping behavior of customers in the case of price promotions in secondary stores, rather than the overall results for retailers. Furthermore, we expect the second hypothesis to be moderated by several other variables, which we discuss next. The following chapter will elaborate on drivers of store selection. 2.2 The Customer: Grocery Shopping Behavior
After evaluating retailer insights and introducing the first and second hypothesis in the previous paragraph, this paragraph will discuss grocery shopping behavior of the customer as it is beneficiary for retailers to also be aware what drives customer to their store and how they behave while shopping. Furthermore, based in this information, the third hypothesis will be introduced.
2.2.1 Determinants of Grocery Shopping Behavior
When deciding how and where to do their grocery shopping, customers rely on their extrinsic, functional, needs (e.g. efficiently in terms of resources) or intrinsic, non-functional, needs (e.g. looking for a rich experience) (Deci & Ryan, 2000). This depends per customer: whereas some customers are focused on completing their shopping efficiently and are only satisfied when the job is one, other customers aim for encountering positive emotions, such as joy and fun.
Existing studies have found diverging results in terms of determinant for choosing one store over the other (e.g. Pan & Zinkhan, 2006; East, Hammond. Harris & Lomax, 2000). Sirohi, Mc Laughlin and Wittink (1998), for example, found customers to be looking for merchandise quality, service quality, such as friendly, helpful employees and good facility design as an important cue to consider when it comes to store perception and loyalty intention. East et al. (2000), on the other hand, state that accessibility is for 50 percent of customers responsible for store choice and retention, implying that for half of customers store characteristics are of little or lesser importance. Even though differences in literature in terms of determinants critical for store patronage exist, a substantial number of determinants is overlapping as well. These determinants are price, assortment, fast checkout, location, helpful and friendly service, weekly specials and a pleasant shopping environment. Of these determinants, price and location are dominant in deciding where to shop for food (Arnold et al., 1983). As it is not possible to alter location on the short term, price is therefore the most important factor for retailers to attract customers. It is therefore important to understand how pricing can influence grocery shopping behavior
In terms of timing, customers differ as well; not all customers show the same time intervals when it comes to their grocery shopping behavior. On average, however, the shopping interval for grocery stores is 8.6 days, meaning that grocery shoppers on average visit the supermarket once every 8.6 days (Fox et al., 2004). This shows the importance for retailers to attract customers to their store each individual trip, as they will not make up their mind again for over a week. This is strengthened by the fact that the likelihood of impulse (unplanned) purchases keeps increasing. The concept of convenience is continuously changing as consumers are exposed to a wide range of stimuli on their way to work or school (Tauber, 1972). In addition, the use of internet and smartphones might also be creating extra stimuli throughout the day as well, for example to shop online. As 50 percent of items in supermarkets is bought unplanned, it is necessary that retailers understand when unplanned purchasing happens and how they can increase the likelihood of the occurrence, such as triggering recognition of need through in-store stimuli (Iyer, 1980). This raises the question whether products themselves can be in-store stimuli and strengthens the question whether customers are more or less willing to buy unplanned products if they are from one category or another or if they fit to the products they already put in their shopping basket or not.
In summary, it can be said that a great extent of determinants is involved in the decision process of the customer each time grocery shopping is done, planned or unplanned. Customers prefer some stores over others because of their features, but these same features, such as price, could be used by other stores. This raises the question if or when customers are likely to visit stores other than the one they are used to and which factors increases their likelihood of visiting other stores.
2.2.2 Cross-shopping
Of all grocery shoppers, 47 percent do not frequent one store only (Jayasankaraprasad & Kathyayani, 2013): customers are not loyal to one store only, but instead visit multiple grocery stores, creating a customer overlap for supermarkets (Stassen et al., 1999; Applebaum, 1951). Already in the early 1950’s, when competition in the grocery industry increased, supermarket loyalty started declining and the number of supermarkets that customers visit regularly started increasing (Schapker, 1966). As mobility increased and the occurrence of cross-shopping surged, consumers have adopted new shopping patterns. More specifically, they have a standard set of grocery stores they visit; 83 percent of customers stick to their shopping pattern (Gijsbrechts et al., 2008). However, customers generally do have a “primary store” they visit
most often and spend most of their total grocery spending at (e.g. Rhee & Bell, 2002; Bell, Ho & Tang, 1998; Jayasankaraprasad & Kathyayani, 2013). To this store, they are most loyal. 2.2.2.1 Primary store Loyalty
Primary store loyalty is explained by East et al. (2000) as “the customer’s expenditure in his/her primary store (i.e. where most money is spent) divided by the total customer expenditure in the retail category”. In this primary store, 94% of total spending on groceries is spent each week (Rhee & Bell, 2002). Reasons for customers to be loyal to their primary store has been studied in earlier research. Bell et al. (1998) state that shoppers get used to their shopping environment over time. They perceive benefits from their store-specific knowledge, such as knowing what is available and where they can find it. Other reasons for customers to be loyal to their primary store are laziness, habit, convenience, time saving and full enjoyment, but also reduction of cognitive effort (e.g. available assortment, lay-out of the store) and an increase in economic gains (McGoldrick & Andre, 1997; Rhee & Bell, 2002). However, even though grocery shoppers often do have a primary store where they spend most of their grocery shopping time and money, (temporary) switching stores for promotional reasons is a significant and growing phenomenon (Gijsbrechts et al., 2008; Ailawadi et al., 2006).
2.2.2.2 Reasons to Cross-Shop
A great number of customers have a set of secondary stores they visit next to their primary store, the one where they spend most of their money. However, how often these secondary stores are visited, varies across shoppers (e.g. Baltas, Argouslidis & Skarmeas, 2010; Ailawadi & Keller, 2004; Rhee & Bell, 2002; Stassen et al., 1999; Mägi, 2003; Urbany, Dickson, & Sawyer, 2000; Gijsbrechts et al., 2008). According to Walters (1991) people cross-shop to save money, but this is not the only reason for cross-shopping. Geographic, demographic and economic variables are all important determinants (McGoldrick & Andre, 1997). Customers choose to visit multiple stores because of cleverness (better prices or better quality), but also dedication, enjoyment, curiosity, time killing or time availability. Gijsbrechts et al. (2008) confirm this by challenging the statement that customers visit grocery stores for price promotional reasons alone. They state that cross-shopping occurs if the customer perceives the overall benefits, such as describe above, to exceed to overall cost like money, travel cost and search costs.
In summary, saving money is an important determinant of cross-shopping, but it is not the sole reason. However, it is one of the ways grocery retailers can attract customers to visit their stores as it has a positive effect on overall store profit as discussed in the previous chapter. 2.2.2.3 Cherry-Picking
For some customers, saving money is the most important reason to cross-shop. They mainly visit other grocery stores than their primary store for lower prices, promotional purposes or discounted products. These customers can switch stores within a week or even on one day and split their grocery lists over multiple stores. Fox and Hoch (2005) refer to these customers as cherry pickers. They refer to their behavior as “the behavior of buyers who are selective about which products or services they purchase at what location and prices” and Levy and Weitz (2004) similarly describe cherry pickers as “customers who visit a store and buy only merchandise sold at deep discounts”. The occurrence of cherry pickers could have implications for retailers as cherry-pickers are less likely to do impulse shopping and buy only low price, low margin products (Fox & Hoch, 2005).
Fortunately for the retailer, Ailawadi et al. (2009) state in their research on cherry-picking that it does occur, but that the general believe of its negative influence on a retailer’s profit and the size of cherry picking are higher than they actually are. Only 3,2% of grocery shoppers turned out to be cherry pickers and only 1,5% is considered to be an extreme cherry-picker and creating losses for the retailer (Mulhern & Padgett, 1995; Talukdar et al., 2010). Customers visiting other stores for price promotional reasons can thus be profitable as they buy other products as well. However, little is known what exactly determines what customers buy next to the promoted product.
2.2.3 Effect of Household Characteristics
As explained above, different customers show different shopping behaviors and have different reasons for it. Household characteristics are one of the influencers of this behavior. They have a significant influence on grocery shopping in terms of spending and pattern, such as cherry-picking behavior (Bawa & Ghosh, 1999; Fox & Hoch, 2005). Behavior of purchasing is in households determined by purchase price, usage rate, transaction costs, stock out costs and holding costs (Blattberg, Buesing & Peacock, 1978). Specifically, household characteristics such as demographics (e.g. number and age of household members, number of children), resource variables (e.g. housing and transportation) and income can have a significant influence on the extent to which grocery shoppers are prone to price promotions (Blattberg et al., 1978;
Fox & Hoch, 2005). If retailers have useful information on the effect these characteristics have on the customer’s purchasing behavior, they can use this to their advantage when deciding who to target in their price promotional strategies and how to do this. To remain focus in this research and, the studied household characteristics are limited to number of household members, housing and income. These characteristics were chosen as they provide valuable information applicable to price promotional strategies when determining what to promote and how to do this. For example, the number of household members provides information on the amount of groceries needed, housing shows stocking possibilities as home owners tend to have larger inventories (Blattberg et al., 1978; Bawa & Ghosh, 1999) and income gives insight on the possible expenses. For example, if bigger households are more deal prone, retailers can use price promotions on larger sized products, because bigger households show a higher consumption level.
Therefore, in this research, it is believed that the relationship between the product relationship and the customer’s willingness to buy other products is moderated by these three characteristics. Retailers can make use of this information in their price promotion strategies by implementing promotions that attract the most profitable kind of customers. The hypothesis and directing of moderation are explained in the following paragraphs.
2.2.3.1 Number of Household Members
The larger the household, the higher the consumption and thus the greater the variety of groceries and the bigger the spending, making these households more price sensitive (Bawa & Ghosh, 1999; Hoch, Kim & Montgomery, 1995; Blattberg et al., 1978). In terms of the effects these factors have on grocery shopping, results are divergent: McGoldrick and Andre (1997) found larger households to be more loyal to their primary store, but Fox and Hoch (2005) found that larger households are also more likely to cross-shop, stating that each extra household member leads to an increase of 4%. Baltas et al. (2010) confirm this: their study found that the bigger the household is, the bigger the ‘set’ of stores is that they visit. Controversially, even though larger households are more price sensitive and tend to cross-shop to save money, they are also more likely to impulsively buy products they did not plan on buying (Inman, Winer & Ferraro, 2009). This implies that if a customer has a bigger household and is cross-shopping to save money, this customer would also be more likely to buy other non-promoted products as well. However, this has not been confirmed in research yet. Because of this, this study will research this moderating effect of number of household members on the relation between product relationship and willingness to buy (e.g. when visiting a secondary store because of a
price promotion for coffee, a customer with four household members has a higher willingness to buy complementary products, such as coffee milk, than a customer with two household members). This leads to hypothesis 3a:
H3a: The number of members in the customer’s household moderates the relationship between the product relationship of a price promotional product and other non-promoted products in the secondary store and a customer’s willingness to buy other products.
2.2.3.2 Housing
Blattberg et al. (1978) state that home owners tend to have larger inventories, because of lower storage costs, giving them the possibility to do forward buying. This could explain why they found home owners tending to be more deal prone and thus more likely to fall for price promotions. This coincides with the findings of Fox and Hoch (2005), as their research has shown that home ownership has a significant influence on the existence of cherry-picking. If a grocery shopper lives in a bought house the likelihood of cherry-picking increases with 43%. However, the deal proneness could also be and indirect effect because home owners are often also car owners, giving them the advantages of buying more in one shopping trip. Nevertheless, earlier research has not studied the effect housing has on purchasing other, non-promoted product when cross-shopping for price promotions. Based on earlier research on grocery shopping, however, it would be expected that the likelihood of buying other non-promoted products next to the promoted product would be higher for home owners due to their larger inventories and possibility to do forward buying (e.g. when visiting a secondary store because of a price promotion for coffee, a home owner has a higher willingness to buy other complementary products, such as coffee milk, than a renter). Because of this, hypothesis 3b is developed:
H3b: House ownership positively moderates the relationship between the product relationship of a price promotional product and other non-promoted products in the secondary store and a customer’s willingness to buy other products.
2.2.3.3 Income
High income households are more likely to be loyal to their primary store (McGoldrick & Andre, 1997). In these households, less time is spent on grocery shopping and time is perceived as a relative important value (Bawa & Ghosh, 1999). Next to that, higher income households have diminished price sensitivity and higher opportunity costs (Fox & Hoch, 2005; Baltas et al., 2010). These factors explain why the higher the income is, the smaller the likelihood of
cross-shopping and cherry-picking is. However, Blattberg et al. (1978) found upper income households to be more deal prone, but this is expected to be an indirect effect, due to households with a higher income also being more likely to be home owners, giving them the possibility to stock. Based on this information, it would be expected that if higher income households visit other stores because of a price promotion, they would be more likely than lower income household to buy additional products next to the promoted product, because of their lower price sensitivity and higher perceived value of time (e.g. when visiting a secondary store because of a price promotion, a customer with a monthly income of €3000 has a higher willingness to buy coffee milk, a complementary product of coffee, than a customer with a monthly income of €2000). This leads to the final hypothesis:
H3c: Income positively moderates the relationship between the product relationship of a price promotional product and other non-promoted products in the secondary store and a customer’s willingness to buy other products.
In figure 1, the conceptual model, involving hypothesis 3a, b and c as well as the previously introduced hypothesis 1 and 2 are visualized.
Figure 1: Conceptual Framework
3 Research Design and Methodology
This chapter will further elaborate on how the hypotheses are tested and what design was used as well as the collection of the data and the measurement of the constructs.
3.1 Data Collection and Sources
To acquire data, a survey was conducted. Self-selected individuals were questioned on their shopping behavior in case of a price promotion in their secondary store. To ensure a large amount of data in a short period, namely one week, a survey strategy was used instead of an interview strategy (Saunders et al., 2009). In the survey, a questionnaire, digitalized with Qualtrics was used, making sure all participants received the same questions and possible answers. The questionnaire was thereby self-administered as well as internet-mediated as personal perceptions were measure, which did not require guidance.
The questionnaire was structured as followed: first, participants were introduced to the subject and questioned about their demographics and shopping behavior to ensure they were part of the target group, namely customers who are responsible for purchasing at least one of the four product categories (beer, chips, coffee and laundry detergent) and who visit other stores than their primary store for price promotional reasons. If the participant was part of the target group, they were introduced to a scenario based on the supermarket they stated to visit occasionally because of price promotional reasons and one of the products they stated to purchase. They were shown some price promotions and asked to choose the one most likely to visit the store for, to make the survey more realistic. After this, two complementary product categories and two non-related product categories were shown and willingness to buy products in these categories was measured using a Likert scale. A Likert scale measures the agreement of the participant with a statement. In this survey, a 7-point scale was used, from 1, meaning very unlikely, to ‘7’, meaning very likely.
3.2 The Sample
Only 11% or less of recipients of online surveys participate (Saunders et al., 2009). Because of this, the survey was spread widely. This provided to possibility to collect a large amount of data within the small timeframe, despite the low response rate. Eventually, 313 recipients participated, of which 185 were part of the target group of customers visiting other stores than their primary store because of price promotions. The demographics of the respondents can be found in table 1.
Table 1: Demographics of Respondents
Gender % Number of Household Members %
Male 21,7 1 15,8 Female 78,3 2 46,1 3 16,8 Age in years % 4 14,3 10-20 2,7 5 5,4 21-30 59,3 6 1,1 31-40 16,2 7 0,5 41-50 6,0
51-60 15,8 Income per month %
61-70 2,7 < €1000 25,7 €1000 - €2000 24,3 Home Ownership % €2001 - €3000 20,7 Bought House 51,2 €3001 - €4000 9,8 Rented House 47,1 €4001 - €5000 3,8 Not Specified 1,6 €5001 - €6000 1,1 €6001 - €7000 1,6 > €7000 2,2 Not Specified 10,9
As the survey was in Dutch and involved Dutch supermarkets only, the results apply to the Dutch grocery shopping industry. These results provide grocery retail managers with useful insights on factors they should consider when determining price promotions to attract customers who patronize other stores. International grocery retail managers should not apply the results one on one to their industry as a great extent of factors are different for different countries such as culture and shopping formats, but can use them for initial guidance.
3.3 Measurements
This paragraphs elaborates on the measurements of the main constructs in these hypotheses. The full questionnaire is in Appendix 1.
3.3.1 Product Category
As it is not possible to account for all available product categories, but the aim of the research is to make the results as generalizable as possible, four product categories, as previously used by Bell et al. (1999) and Guyt and Gijsbrechts (2014) were considered. As previously
explained, these categories were chosen because of their diverging purchase frequency, cost, necessity and storability. Table 2 shows what percentage of respondents stated to be
responsible for purchasing each category and the distribution of these respondent in the questionnaire.
Table 2: Product Category Distribution Respondents
Purchase Responsibility Distribution Participants
Category % Category
Beer 34 Beer 34,
Chips 51,1 Chips 22,6
Coffee 60,7 Coffee 26,6
Laundry Detergent 78,7 Laundry Detergent 16,8
3.3.2 Product Relationship
Product Relationship was divided in complements and non-related products. As previously explained, substitutes are not considered. Because all 185 participants received four possible other products in their scenario, after restructuring and transforming the data for analysis, overall N=736, with N=370 for complementary products and N=366 for non-related products. 3.3.3 Willingness to Buy
As explained before, no data is available on actual purchasing, so this research will measure willingness to buy instead. Before running analysis with Willingness to Buy, Cronbach’s Alpha was computed for willingness to buy, to ensure reliability by confirming they measure the same thing. As α = 0,984, which is very acceptable, it was possible to continue the analysis (Bland & Altman, 1997). In this research the measurements of Dodds, Monroe and Grewal (1991) were used as they have been proven to be reliable.
3.3.4 Household Characteristics
In this research, the household characteristics as introduced by Fox and Hoch (2005) will be used, as they determined in earlier research which characteristics influence grocery shopping behavior. Of these, three were selected, namely number of household members, housing and income. Respondents were asked to choose a category that applied to their situation. Number of household members ranged from 1 to 8 with an additional ‘more than 8 option’, but none of the respondents chose this option. Housing was split up in bought house, rented house and other. Income was divided in seven options ranging from “less than €1000, “€1001-€2000” and so on to “more than €7000”.
3.3.5 Other Variables
To make sure the respondent was part of the target group, both choice of primary store and secondary store were asked. If no secondary store was chosen, this means that the respondent either only visits at his or her primary store or the customer has a secondary store that was not part of the research. The question on the customer’s secondary store also provided the
possibility to make the research more realistic, as the follow-up questions contained products and prices from this secondary store. However, the influence the choice of store could possibly have on the relationships in this research was not considered, it was merely used to create a more realistic scenario. Next to that gender and age were measured to ensure neither have effect on the results. Even though this was not expected, it was decided that is would be best to confirm the assumptions than to consider them to be true.
3.4 Analyses and Predictions
Before distributing the final survey, a pretest was done to ensure reliability and internal consistency of the constructs. This pretest entailed 7 measures for willingness to buy, of which 3 were kept for the final survey. All 7 were valid (α = 0,977), but to ensure participants would not leave the survey because of the amount of questions, three of them were chosen (α= 0,969 in pretest; α= 0,984 in final survey). They were reduced to the three willingness to buy measurements of Dodds et al., which showed a sufficient Cronbach’s alpha.
4 Results
To analyze the previously described model, a hierarchical linear regression was done. This regression explores the feasibility of product category and product relationship to predict a customer’s willingness to buy, while taking number of household members, housing and income into account as moderators and controlling for age and gender. By consecutively entering the different constructs to the model, it is possible to measure the impact the different constructs have on explaining the variance of the model and whether this is significant. All steps of the regression can be found in table 3, but as the fourth model includes all constructs of this research and therefore shows the end results, this is the model to be discussed. The overall explained variance in willingness to buy for this model is 4,3% F(12, 643) = 2,397; p<.01 as can be seen in Table 3.
Statistical significance *p<.05; **p<.01; p**<.001
In this final model (see Table 4 for output), the variables on product category were statistically significant, in which the category of chips recorded the highest negative Beta value (β = -.152;
Table 3: Regression Model Willingness to Buy
Model Variables added to model R R² R²
Change
1 Gender, Age ,114 ,013* ,013*
2 Product Category ,185 ,034*** ,021*
3 Product Relationship ,185 ,034*** ,000
4 Direct Effect Number of Household Members, Housing, Income and Interaction
p<.01), followed by laundry detergent (β = -.130; p<.01) and coffee (β = -.101; p<.05). As the first product category, beer, was introduced as the dummy variable, these Betas can be interpreted as followed: if the category of the price promotional product changes from beer to chips, overall willingness to buy decreases with .152 standard deviation. If the category changes to laundry detergent or coffee, willingness to buy also decreases, -.13 and .101 standard deviations respectively.
Product relationship shows a small and non-significant effect on willingness to buy (β = .025, ns), meaning that complementary products do not significantly differ from non-related products in terms of willingness to buy other unplanned products.
In terms household characteristics, no significant direct effects were found for number of household members (β = .025, ns), housing (β = .060, ns) and income (β = .026, ns). Next to that, the model shows that a significant moderating effect on the relationship between product relationship and willingness to buy also does not exist for the number of household members (β = .026, ns), housing (β = -.057, ns) and income (β = .014, ns). However, even though the interaction effect of housing was not significant, it is substantial in comparison to the other household characteristics (β = -.057 ns). A full overview of the fourth model can be found in table 4 and a visualization can be found in figure 2.
As both product relationship and the household characteristics (direct and as moderators) did not show significant Beta values, a substantial part of the 4,3% explained variance of the model can be attributed to product category, resembling the 2,1% of explained variance in model 2.
Table 4: Regression Model of Willingness to Buy, Model 4 (All Variables) β SE t Sig.
Gender -,036 ,220 -,823 ,411
Age -,038 ,009 -,789 ,430
Product Category D1 (Chips) -,152 ,250 -3,148 ,002 Product Category D2 (Coffee) -,101 ,234 -2,152 ,032 Product Category D3 (Laundry Detergent) -,130 ,273 -2,753 ,006 Product Relationship ,025 ,168 ,644 ,520 Number HH Members ,025 ,076 ,601 ,548
Housing ,060 ,209 1,247 ,213
Income ,026 ,062 ,584 ,559
Interaction Number of Household Members ,026 ,149 ,626 ,531 Interaction Housing -,057 ,378 -1,309 ,191 Interaction Income ,014 ,112 ,337 ,736 Statistical significance *p<.05; **p<.01; p**<.001
Figure 2: Overview results
5 Discussion
5.1 Key Findings
First, as shown in chapter 4, a highly significant influence of the product category of the promoted product on the willingness to buy other non-promoted products during the same trip was found. Hypothesis 1 is therefore confirmed. The reason for this difference between categories was not thoroughly researched, but possible explanations are divergent. It could be explained by a difference in the extent to which buying the products in the different categories is planned (e.g. it is possible that chips is more often bought spontaneously than coffee) or by the strength of the Halo effect (Ailawadi et al., 2006) between the promoted and the additional product in relation to the underlying subconscious processes. On the other hand, it could be explained by the price of the promoted product and/or the additional products (e.g. even after a price promotion laundry detergent and softener are often relatively more expensive than chips and dip) or by the size of the products (e.g. laundry detergent and softener are both relatively heavy to carry in one trip relatively to chips and dip, which could be resulting in a lower willingness to pick up additional groceries).
The outcome of the first hypothesis an addition to Bermans and Evans (1989) and Wilkinson et al. (1982) who state that price promotions increase sales of the product irrelevant of its category, as this research has shown that there is a significant difference in sales of other
products during the same trip, because of the product category. Next to that, even though only 3.2% of grocery shoppers are considered to be cherry pickers, the fact that not all product categories show the same willingness to buy other non-promoted products, could mean that product categories with a lower willingness to buy attract more cherry-pickers as they only visit the store for the promotion and then leave (Mulhern & Padgett, 1995; Sirohi et al., 1998). This also means that loss leaders strategies are more interesting for some product categories than others, which could explain the diverging results on testing loss leaders strategies of Walters and Mackenzie (1988).
Secondly, the results on the relationship between product relationship and willingness to buy were non-significant, therefore rejecting the second hypothesis. Therefore, even though willingness to buy complements exists (Bermans and Evans; 1989; Walters, 1988; Walters; 1991), the results show that this does not significantly differ from willingness to buy non-related products. The rejection of the hypothesis could have occurred because complementary effects are higher, but only for some product categories. This would add to the research of Mulhern & Leone (1991) who found complementary effects to depend on the product category.
Finally, for hypothesis 3a, 3b and 3c no direct or interaction effect was found and the hypotheses were rejected. For number of household members, this means that larger households are more likely cherry-pick but not more or less likely to buy other products as well, irrelevant of the product relationship. The lack of relationship found in this study could be explained by the fact that large households spend more, but plan their grocery shopping. However, this contradicts the research of Inman et al. (2009) and should therefore be further researched. For housing the lack of relationship could be explained by the fact that the sample of this research was generally from an urban environment, making it less likely for home owners to have significantly bigger inventories (Blattberg et al., 1978). In terms of income, Fox and Hoch (2005) found higher income households to be less likely to cherry-pick and Bawa and Ghosh (1999) found them to spend less time shopping. However, once they do decide to cherry-pick, this research has shown that their willingness to buy other products and the preferred product relationship with the promoted product does not differ from lower income households. This can be explained by the fact that deal prone higher income households differ from higher income households that are not prone to deals and more alike lower income households, meaning that higher income households that are not deal prone were already subtracted beforehand.
5.2 Managerial Practice
These findings provide useful insights for retail managers when they are constructing their price promotional strategies. First, when determining the product to use for a loss leaders, or similar, strategy, managers can choose product categories with higher willingness to buy other products next to the promoted product (e.g. coffee over laundry detergent).
Secondly, the findings on household characteristics imply that customers who have already decided to visit a secondary store because of price promotion, do not differ significantly, irrelevant of their household characteristics. However, earlier research has shown significant differences in grocery shopping for different households in general (e.g. Fox & Hoch, 2005). This implies that to attract customers to the store, retail managers can use earlier research on the differences between customers based on their household, but that this is not the case for in-store marketing.
In summary, retail managers should look at the product category of their price promotion and the visibility of other products they want to sell next to the promoted product. Product relationship is not important in that scenario. Also, in-store customers with different kinds of households do not show different shopping behavior. Those shopping for promotions in secondary stores, think alike.
Table 5 provides an overview of the findings of all hypotheses.
Table 5 : overview hypotheses and findings
Hypothesis Findings
1 The product category of a price promotional product in the secondary store influences a customer’s willingness to
buy other products during the same shopping trip, so that different categories lead to a different willingness to buy.
Confirmed 2 The product relationship between a price promoted product and other non-promoted product influences a
customer’s willingness to buy other products during the same shopping trip, where customers are more willing to buy complements of the price promoted product than non-related products.
Rejected
3a The number of members in the customer’s household moderates the relationship between the product relationship
of a price promotional product and other non-promoted products in the secondary store and a customer’s willingness to buy other products.
Rejected
3b House ownership positively moderates the relationship between the product relationship of a price promotional product and other non-promoted products in the secondary store and a customer’s willingness to buy other products.
Rejected 3c Income positively moderates the relationship between the product relationship of a price promotional product and
other non-promoted products in the secondary store and a customer’s willingness to buy other products.
Rejected
5.2 Limitations and future research
This research has a few limitations. The first and most important limitation is the lack of actual store data. Because of this, willingness to buy was measured instead of actual behavior. This is expected to be the main reason most hypotheses were not confirmed. When taking the survey, respondents had to imagine the scenario and actively think about their possible behavior in the
given situation, giving them the time to evaluate their options more clearly than it is expected they would do in the real-life situation. Thus, as this intention could differ from actual behavior, it means that even though the respondents provided the given answers, the results could be different once they must make up their mind in store and actually spend money. Therefore, it would be useful for future research to take actual store data into account to find out to what extent discrepancies exist.
Secondly, no significant relationship was found on product relationship, but this could be related to visibility, as this has shown to be very important (customers are 37% more likely to buy products that are featured or advertised in the secondary store) (Fox & Hoch, 2005). The results imply that it is not the relationship with the promoted product, but the visibility of the non-promoted product that determines whether they are bought or not, but this has not been taken into account in this research and should be further investigated.
Third, even though this research has confirmed the differences between product categories of promoted products in terms of willingness to buy other non-promoted products, the short timeframe of this study has not been able to study a possible explanation for this occurrence. As Bell et al. (1999) stated, these categories differ in terms of price, necessity, storability and purchase frequency. It could be that one of these factors mediates the relationship found in this research. For example, a possible explanation could be that price of the promoted product category determines the willingness to buy, as price is the most important determinant in grocery shopping, but further research is needed (Arnold et al., 1983).
Fourth, this research focused on the Dutch urban market only, with 6 supermarket scenarios. The sample was therefore not evenly spread nationally or internationally and interpretations for other regions should be addressed carefully. Next to that, the difference occurring because of choice of supermarkets was not considered. Future research should therefore find out whether differences exists between supermarkets and regions.
Fifth, at the end of the survey it was asked to what extent lifestyle could have influenced the decisions they made in the survey. It turned out that a substantial number of respondents showed their dislike towards some of the products (e.g. not liking coffee, not drinking alcohol). Future research should take this into account and control for products that are disliked and never part of the shopping basket of the respondents.
Finally, as it is not possible to randomly survey the full population and since the survey was be based on a convenience sample and was internet-administered, there is a risk of having
a difference between the included and excluded participants. Thereby, self-reported measures were used, increasing the risk of common method bias. Next to that, the study is cross-sectional, which means results were only about one specific point in time in contrast to a longitudinal study which would provide the possibility to measure differences over time as well. The latter, however, was not possible because of the limited timeframe of this study.
