Consumer Complaint Behavior and Promotional Out-‐of-‐Stock
by
Roosmarijn Luitjes
University of Groningen Faculty of Economics and Business
Msc. Business Administration Marketing Management
July 2012
Address: Madoerastraat 2b Zip-‐ Code: 9715 HG, Groningen
Phone number: 06-‐44094828 E-‐mail: roosmarijnluitjes@hotmail.com
Student number: 1688421 First supervisor: Prof. Dr. L.M. Sloot Second supervisor: Dr. J.E.M. van Nierop
Table of Contents
1. Introduction ...4
1.1 Promotions...4
1.2 Out- of- Stock...5
1.3 Research question...6
1.4 Contribution and Relevance ...7
1.5 Structure of thesis...7
2. Literature review...8
2.1 Consumer OOS responses...8
2.2 Consumer Complaint Behavior ...10
2.3 Antecedents of OOS...12
2.3.1 Product-‐ related variables... 13
2.3.2 Store-‐ related variables ... 14
2.3.3 Situation-‐ related variables... 15
2.3.4 Consumer-‐ related variables ... 15
2.4 Difference between OOS and POOS...16
3. Conceptual model and hypotheses... 17
3.1 Deal proneness ...17
3.2 Promotion value ...18
3.3 Locus of control...19
3.4 Customer dissatisfaction...20
3.5 Other variables...21
3.5.1 Product-‐ related variables... 21
3.5.2 Store-‐ related variables ... 21
3.5.3 Situation-‐ related variables... 22
3.5.4 Consumer-‐ related variables ... 23
4. Research methodology ... 24
4.1 Data collection ...24
4.2 Dependent variables ...25
4.3 Main independent variables...26
4.4 Other independent variables...26
4.5 Analysis...28
5. Empirical results... 29
5.1 Socio- demographic characteristics...29
5.2 Cronbach’s Alpha...30
5.3 Multiple regression analysis...32
5.3.1 Independent variables and customer dissatisfaction... 33
5.3.2 Independent variables and voice CCB... 34
5.3.3 Independent variables and private CCB... 35
5.3.4 Customer dissatisfaction and CCB ... 36
5.3.5 Effect of other explanatory variables ... 36
5.3.6 Customer dissatisfaction as mediator... 38
5.3.7 Interaction effects of promotion value ... 39
6. Discussion ... 41
6.1 Effect of main independent variables...42
6.2 Effect of other independent variables ...42
7. Managerial implications ... 44
7.1 Implications for retailers...44
7.2 Implications for manufacturers...45
8. Limitations and further research... 46
9. Acknowledgements... 47
10. Appendices... 48
10.1 Questionnaire...48
10.2 Correlation matrix ...57
11. References... 58
1. Introduction
In the Netherlands common unavailability of products and especially promotional out-‐ of-‐ stocks (POOS) list high on shoppers’ irritation lists (ConsumentenTrends 2010 CBL).
The pressure on promotions has increased due to a severe price war that has been started in the Netherlands in 2003, which made customers more sensitive to prices and price image (Van Heerde et al. 2008). Likewise, according to research of SymphonyIRI the promotional pressure in supermarkets increased from 13,3% in 2008 to 17,2% in 2010 (ConsumentenTrends 2010 CBL). So, promotions became more important in grocery
shopping.
It is indicated that about 50% of out-‐of-‐stock (OOS) situations occur through ordering and forecasting causes (Gruen and Corsten 2002) and sales promotions are putting additional pressure on ordering and replenishment in response to more unpredictable demand. Therefore, it is expected that POOS rates are higher than OOS rates. In some cases the differences are negligible, but in most cases the difference is significant which makes it of major importance for retailers to focus on POOS (Corsten and Gruen 2003).
1.1 Promotions
Important marketing activities for fast moving consumer goods are sales promotions. Companies keep increasing their promotional budget. For example, U.S.
packaged good manufacturers peaked in the late 1990s with more than 50% of their marketing budget spending on promotions (Alaiwadi et al. 2006). Promotions can serve different goals such as short-‐ term profit maximization and increasing sales volume. The latter is often mentioned to be a goal for retailers and which can be accomplished by increased store traffic or increasing market share (Ailawadi and Gedenk 2001).
According to Ailawadi et al. (2006), promotions vary in the following four characteristics:
1) The promotion (discount depth, presence of feature, presence of buy one get one x% off [BOGO])
2) The brand (unit share, price, advertising);
3) The category (penetration, distribution, concentration); and
4) The store (store type, market demographics, competition density).
Promotions can result in significant sales increases for a brand, which can be
explained by (1) increased consumption (Bell et al. 1999), (2) brand switching (Van Heerde et
al. 2003), and (3) stockpiling (Neslin 2002). Stockpiling occurs because the promotion induces customers to buy sooner or to buy more than they would have otherwise (Blattberg et al. 1981). However, short-‐ term sales increases can have long-‐ term negative effects, a post-‐ promotion dip caused by large stockpiling inventories of customers (Van Heerde et al.
2000).
Moreover, sales increases on promoted products can also be explained by consumer store switching (Kumar and Leone 1988). Bucklin and Lattin (1992) showed that direct store switching is small; this means that promotions do not alter customers’ choice of which store to visit. However, indirect store switching may be substantial. Customers typically shop in more than one store and indirect store switching implies that customers visit store A and buy the promoted product in this store, whereas otherwise he or she might have bought the product in another store. The deeper the discount the higher the probability that customers consider store switching because it is more likely that economic savings outweigh possible costs. Another positive effect of discount depth is that new customers are more likely to try products from the category or existing customers buy more of it (Assuncao and Meyer 1993). According to Narasimhan et al. (1996), featured promotions induce more store switching than unfeatured promotions. So, previous research indicates that promotions are helpful to retailers in attracting more customers and stimulate customers to buy more in the store. Still, retailers have to pay a price for promotions, especially deep discounts and featured promotions are associated with greater funding of promotions by the retailer (Ailawadi et al. 2006)
1.2 Out-‐ of-‐ Stock
Out-‐ of-‐ stock (OOS) is a regular phenomenon for grocery shoppers (Sloot et al.
2005). According to Gruen and Corsten (2002), the average OOS rate happens to be 8.3% in
the United States (U.S.). In the Netherlands the average OOS rate is lower with 5% (Sloot et
al. 2005). However, customers think promotional out-‐ of-‐ stocks are annoying and they
become dissatisfied when they experience OOS in the store (Fitzsimons 2000) and in
response OOS can result in major revenue losses for both retailers and manufacturers (Sloot
et al. 2005). However, the magnitude of losses due to OOS, and whether the retailer or the
manufacturer is primarily affected depends strongly on how customers react. If customers
switch between brands as response to the OOS situation this can be harmful to the
manufacturer. Conversely, if customers decide to switch between stores for the missing
item, this is detrimental to the retailer. OOS can result in substantial revenue losses for
manufacturers and retailers (Campo et al. 2000). For instance, retailers can face sales losses up to 14% caused by an OOS (Emmelhainz et al. 1991).
As might be evident there are major costs associated for retailers and manufacturers. In addition, consumers face costs when an OOS situation occurs like substitution, transaction and opportunity costs (Campo et al. 2004).
Therefore, it is important that manufacturers and retailers anticipate on OOS (Campo et al. 2000) and store managers need to balance the costs related to replenishing inventory and the costs of out-‐ of-‐ stocks (Musalem et al. 2010). So, in a reaction to the negative influence of OOS, some efficient consumer response (ECR) projects have focused on creating ways to improve the supply chain (Sloot et al. 2005; EFMI 2000). For instance, retailers can decrease OOS by 55% if they make use of continuous replenishment planning (Vergin and Barr 1999). However, no significant decreases in OOS levels are observed yet (EFMI 2000). Because of assortment expansions and due to fixed shelf space in the short and mid-‐ term, OOS still remains a common phenomenon for manufactures, retailers and shoppers (Sloot et al. 2005).
However, there is a difference between OOS and POOS. The average percentage of POOS is 10% (Diels and Wiebach 2011) or even 15% (Grocery Manufacturers of America 2002). This is significantly higher than the mentioned percentage of regular OOS. Besides, customers respond different to POOS since they are more likely to adapt their buying behaviour to promotional products (DelVecchio et al. 2006). This makes customers especially dissatisfied when encountering POOS in the store (Diels and Wiebach 2011).
Furthermore, promotions are only temporally available and in one retail chain (Kumar and Leone 1988), which make POOS significantly different from OOS.
Another construct dealing with consumer behavior is complaining in times of dissatisfaction, which is called consumer complaint behavior (CCB) (Singh 1988). In a POOS situation it is more likely that consumers complaint than in an OOS situation, because they will not find the same promotion in other stores of another retail chain. As a result of these differences in behavioral responses more research in the field of POOS is of high relevance (Diels and Wiebach 2011)
1.3 Research question
What types of consumer complaint behavior are common among customers when confronted with POOS and which antecedents can explain these different types of complaint behaviour in a grocery context?
In order to provide an answer to the research question, the following sub questions are formulated:
1. What types of consumer complaint behavior explain consumer responses towards POOS in a grocery context?
2. What are important antecedents of consumer complaint behaviour in a grocery context?
1.4 Contribution and Relevance
In marketing literature, the topic of OOS has been researched thoroughly since the 1960s (Peckham, 1963). However, there is still limited research available concerning POOS (Diels and Wiebach 2011). Furthermore, previous research mainly focused on the behavioral responses towards OOS. However, limited research is conducted to the effect of OOS on customer dissatisfaction. Finally, to my knowledge this study is the first combining POOS with consumer complaint behavior.
This study will be especially valuable for retailers because customers being confronted with a POOS are more likely to postpone purchases or are reluctant to buy substitutes, which results in severe losses for the retailer (Diels and Wiebach 2011). This study can provide retailers with practical advise in the field of POOS and help them to control the negative impact of a POOS.
1.5 Structure of thesis
The study is organized as follows. The next section will review the literature on consumer OOS responses, consumer complaint behavior and antecedents. Then, the conceptual model and hypotheses are described. Furthermore, the research methodology used to test the conceptual model and hypotheses are discussed. The empirical results are reported and discussed in the subsequent sections. This study will conclude with a discussion, managerial implications of major findings, and indicate study limitations and directions for future research.
2. Literature review
As mentioned in the introduction this study will describe the antecedents of POOS in a grocery context. However, the amount of literature on POOS is still very limited. Therefore, the literature review will focus on a quite similar phenomenon, which is a regular OOS situation. At the end of the literature review the difference between POOS and OOS will be explained as well as the importance of more research in the field of POOS.
2.1 Consumer OOS responses
A lot of research has been done on consumer responses towards out-‐ of-‐ stock situations (Peckham 1963; Walter and Grabner 1975; Schary and Christopher 1979;
Emmelhainz et al 1991; Verbeke et al. 1998; Campo et al. 2000; Fitzsimons 2000; Zinn and Liu 2001; Sloot et al. 2005). Based on these studies, the following six behavioral consumer responses towards OOS can be distinguished:
1) Store switch: going to another store on the same day;
2) Item switch: switch to another format or buy another variety of the same brand;
3) Postponement: postponement of the intended product to the next supermarket trip;
4) Cancel: the intended product is dropped at all or postponed for a longer period of time;
5) Category switch: a substitute from another category is chosen; and
6) Brand switch: switch to another brand within the same product category (Sloot et al.
2005).
Previous studies agree on the low frequencies of cancel and category switch reactions (Sloot et al. 2005). However, other results vary significantly from study to study, which makes it difficult to draw some general patterns of consumer responses towards OOS.
For example, Schary and Christopher (1979) observed that 48% of consumers’ reactions towards OOS is store switching, and 11% decides to postpone the purchase. On the other hand, Emmelhainz et al. (1991) report that 32% of the consumers switches brands, 17,5%
switches to another item within the same brand, 14% switches stores to purchase the desired product, and 12,3% decides to delay or postpone the purchase. Finally, Campo et al.
(2000) found that 2% of consumers confronted with OOS switches stores, 49% postpones
the purchase, and 44% switches brand. These strong variations in findings between the
studies are partly caused by differences in research design used (Diels and Wiebach 2011).
Most studies apply a survey (Walter and Grabner 1975; Campo et al. 2002; Sloot et al. 2005), a true field experiment (Emmelhainz et al. 1991; Verbeke et al. 1998), a quasi-‐ experiment (Peckham 1963; Schary and Christopher 1979; Zinn and Liu 2001) or a laboratory experiment (Fitzsimons 2000).
Moreover, the studies don’t include all the six behavioral consumer responses simultaneously (Sloot et al. 2005). Peckham (1963), explained OOS reactions in an explorative way, and measured substitution buying. The study of Walter and Grabner (1975) focused on the financial consequences of OOS and the main OOS reactions measured are store switch, brand switch, item switch, and defer. A couple of years later Schary and Christopher (1979) were the first in trying to prove OOS reactions, like item switch, brand switch, product switch, store switch, no buy, and postpone. Emmelhainz et al. (1991) continued on explaining OOS reactions and focused on item switch, brand switch, product switch, delay purchase, different store, and special trip. Verbeke et al. (1998), focused on postponement of buying, brand switching, and store switching only. The latter mentioned, Campo et al. (2000), and Zinn and Liu (2001) developed and tested theory-‐ based models to explain OOS reactions (Sloot et al. 2005). Campo et al. (2000) took size switch, item switch, store switch, defer, and cancel into consideration as main reactions towards OOS. Besides, Zinn and Liu (2001) measured the following OOS reactions: substitute item, delay purchase, and leave the store. The study of Sloot et al. (2005) focused on brand switch, store switch, item switch, and postponement. According to Corsten and Gruen (2003), consumers are more likely to switch in some categories rather than others, especially if brands are not personally attached to them, for instance more switching behavior occurs for paper towels than for feminine hygiene (Corsten and Gruen 2003).
Furthermore, there is a great lack of understanding on consumers’ attitudes towards OOS. Next to an understanding of consumer behavior it is even more important to understand consumers’ attitudes for two reasons. First, attitudes influence behavior;
second, understanding store attitude can help a retailer to measure the effectiveness of their strategy (Rani and Velayudhan 2008). In an OOS situation only a few researchers considered evaluative responses like consumers’ attitude towards the store (Schary and Christopher 1979; Fitzsimons 2000). However, they did not make an empirical estimation.
Attitudes have a directive and dynamic role affecting behavior. Even though behavior is also influenced by non-‐attitudinal factors, attitudes are far more stable over time. Whilst non-‐
attitudinal variables differ between behaviors and social settings and besides, they are
difficult to track and are large in number (Eagly and Chaiken 1993). Furthermore, attitudes
that are based on direct experience are fairly stable over time and more enduring than attitudes based on indirect experiences (Fazio and Zanna 1981; Eagly and Chaiken 1993; Rani and Velayudan 2008). It is important to understand attitudes’ tendency to influence behavior as well as the before mentioned implications (Rani and Velayudhan 2008).
2.2 Consumer Complaint Behavior
Consumers respond to OOS by changing their evaluations of satisfaction with the decision process and by changing their store-‐ switching behavior (Fitzsimons 2000).
According to the Consumer Report (1987) that is describing a study of mail-‐ order companies, customers reported OOS as their most frequent complaint. Furthermore, numerous researchers indicate that there are links between satisfaction and various behavior responses, like complaining behavior (Yi 1991). As discussed earlier, dissatisfaction is a common result of POOS and dissatisfied consumers engage more often in complaint behavior, either to the store or in negative word-‐ of-‐ mouth towards friends (Zinn and Liu 2001).
Previous research on CCB and its consequences has shown a critical relationship with the explanation and prediction of consumer repurchases intention and brand loyalty (Day 1984; Engel and Blackwell 1982; Richins 1983). The conceptual meaning of CCB consists of different agreements. First, the CCB construct is activated by feelings and emotions of perceived dissatisfaction, which is a condition in appointing consumers’ responses as CCB.
Furthermore, CCB responses can be divided into two broad categories, behavioral and non-‐
behavioral. Behavioral responses refer to all consumer actions, which transfer an expression of dissatisfaction (Landon 1980). These actions consist of responses directed towards the seller (manufacturer or retailer), towards third parties (legal actions), or friends and relatives (negative word of mouth) (Day 1984; Richins 1983). Furthermore, a non-‐ behavioral CCB response, for instance doing nothing after a dissatisfying situation like a POOS, is still recognized as a legitimate CCB response. To understand why people complain or do not complain after a dissatisfying experience it is necessary to include non-‐ behavioral responses in order to grasp the underlying processes of CCB (Singh, 1988).
However, there are also some differences in the definitions used over the last few
decades. According to Jacoby and Jaccard (1981), CCB is an action started by an individual to
express his/her negativity about a product towards a company or a third party. Others call it
a consequence after a confrontation with a highly dissatisfying experience so that they
neither liked it psychologically nor quickly forget (Day 1980). Meanwhile, Fornell and
Wernerfelt (1987) argue that CCB is an attempt to change a dissatisfying experience.
Day and Landon (1977) proposed a hierarchical classification of CCB after dissatisfaction occurs. The first level consists of “take some action” (behavioral) and “take no action” (non-‐ behavioral). The second level distinguishes between “public action” (seeking redress from the seller, legal action and complaining to agencies) and “private action”
(boycott retailer or manufacturer and negative word of mouth to friends and family). As a reaction to some doubts about the basis of the classification scheme of Day and Landon (1970), Day (1980) proposed a different basis of classification for the second level. Day stated that the purpose of complaining is more relevant in understanding CCB than the product involved in the dissatisfaction. Day classifies behavioral CCB in the following broad categories: (1) Redress seeking (seek for a remedy), (2) complaining (complain for other reasons than for a remedy), and (3) personal boycott (discontinue purchase of the seller).
Singh (1988) proposed another taxonomy of CCB responses based on the object towards the responses are directed. First, voice CCB includes objects outside consumers’
social sphere or informal relationships and also involves the dissatisfaction transaction (retailer or manufacturer). As no-‐ actions are about feelings directed towards the seller they are also included in this category. People undertake no actions in the occasion of a dissatisfying experience because they are loyal to the retailer/ manufacturer or they think it will not be productive (Hirschman 1970). Second, third party CCB is also directed towards an external object. However, they are not involved in the exchange, which caused the dissatisfying experience (legal agencies or newspapers). Third, private CCB involves objects, which are internal to consumers’ social circle and are not involved in the transaction, which caused the dissatisfaction (self, friends, family).
Hirschman (1970) divided the CCB responses into exit, voice, and loyalty. Exit is an active and destructive reaction to dissatisfaction, in which the consumer ends the relationship with the object (retailer, supplier, brand or product). On the other hand, voice is a verbal and constructive reaction addressed towards friends, customers and organisations.
Consumers expect that the organisations’ practice and policies will be changed after they exhibit their complaints. Finally loyalty has two dimensions; constructive and passive and the consumers look out for positive things to happen.
Several researchers (Day and Landon 1977; Singh 1988) proposed taxonomies for
responses and a classification based on variables. In contrast, taxonomy of response styles is
based on a partitioning of people. According to Singh (1990), a response style is a unique set
of responses from one or more consumers to cope with dissatisfaction. In fact, consumers
engage in multiple responses and therefore a typology based on people is necessary. Singh
(1990) distinguishes four consumer clusters with distinct response styles: (1) Passives: for this group it is least likely to take any action. Therefore happens to be consistent with the no-‐ action group from previous research. (2) Voicers: these consumers have little need to engage in negative word of mouth. This segment scores highest on complaining to the seller to seek redress, which is similar to the segment voice from past research. (3) Irates: this segment of consumers engages above average in negative word of mouth to friends and family and stop patronage the retailer, moreover, they complain directly to the retailer, switch patronage and are less likely to take third-‐ party actions. (4) Activists: this group scores high on all three dimensions of complaint responses, especially third – party actions.
Attitudes towards complaining refer to the personal tendency from dissatisfied consumers to seek compensation from the firm (Richins 1980, 1982, 1983a, 1987; Bearden and Mason 1984). According to Fishbein and Ajzen (1975), attitudes positively correlated with intention. Therefore, it is expected that consumer’s attitude towards complaining positively correlates with their intention to complain. Consumers with positive attitudes towards complaining are less likely to engage in negative intention and behavior, such as negative word-‐of-‐mouth and exit (Day and Landon 1976). However, recent research revealed that dissatisfied consumers are tended to engage in indirect behaviors, rather than to complain directly to the firm (Best and Andreasen 1977; TARP 1986; Tschol 1994), which doesn’t provide the firm the opportunity to improve the customer service. So, a firm needs to stimulate dissatisfied consumers to complain and manage the complaints sufficient (Kim et al. 2003).
In a grocery context it is not likely that consumers confronted with POOS are taking third-‐ party actions, like legal actions or writing to the newspaper. Therefore, this study excludes the dimension third party CCB. This is completely in line with Singh (1988) who believes that the CCB construct consists of three different dimensions, which benefit researchers through investigating the dimensions of CCB individually and in doing so provide a better explanation of CCB.
2.3 Antecedents of OOS
Other studies try to identify fundamental determinants of OOS responses (Schary and Christopher 1979; Emmelhainz et al. 1991; Verbeke et al. 1998; Campo et al. 2000; Zinn and Liu 2001; Sloot et al. 2005), so how come people response in the way they do. Some studies relate consumer responses to buyer and product characteristics (Schary and Christopher 1979); to product-‐ related attributes and situational factors (Emmelhainz et al.
1991); retail competition, store loyalty and shopping patterns (Verbeke et al. 1998);
consumer, situational and perceived store characteristics (Zinn and Liu 2001); product, consumer and situational characteristics (Campo et al. 2000). Attitudinal and behavioral responses occur, as a reaction to the same situation and therefore, determinants of behavioral responses are appropriate for attitudinal responses as well (Rani and Velayudhan 2008).
Campo et al. (2000) identified three drivers, which influence consumer responses towards OOS. These drivers are opportunity costs, which means that consumers are not able to consume directly, the substitution costs of using a less preferred product or brand, and the transaction costs of the time to require the product. This is not exclusively monetary in value, but also the time and effort costs are part of the transaction costs. These costs can be divided in three different types, namely: (1) search costs, which are the time and effort to find a replacement product, (2) handling costs, which also include storage costs, and (3) transportation costs in case of store choice (Park et al. 1989; Bell et al. 1998).
In line with prior research on OOS (Campo et al. 2000; Zinn and Liu 2001; Sloot et al.
2005), the following clusters of antecedents are distinguished: (1) product-‐ related variables, (2) store-‐ related variables, (3) situation-‐ related variables, and (4) consumer-‐ related variables.
2.3.1 Product-‐ related variables
This group consists of variables relates to the product category, including the brands, for which the OOS appears (Sloot et al. 2005). According to past research brand loyalty is an important determinant of consumer responses and attitudes towards OOS.
Perceived differences among brands lead to consumer preferences for one brand over the other (Rosen 1984; Bass et al. 1972). Therefore, brand loyal consumers are less likely to switch brand in case of an OOS and instead prefer to switch stores to buy the preferred brand (Campo et al. 2000; Emmelhainz et al. 1991; Peckham 1963; Verbeke et al. 1998; Sloot et al. 2005).
A second important antecedent is the availability of acceptable alternative items.
Campo et al. (2000) show that the availability of acceptable alternatives is positively related to brand switching and negatively related to store switching. Furthermore, Emmelhainz et al.
(1991) state that consumers’ perceived risk concerning the alternatives and brand switching are negatively related. However, Diels and Wiebach (2011) show that this behavior is more likely to occur in a regular OOS situation than in a POOS situation. Consumers who face a POOS situation tend to postpone their purchase or visit another store of the retail chain to
buy the promotion.
Third, promotions can extrinsically motivate consumers to variety seeking (McAlsiter and Pessemier 1982; Gupta 1988). If consumers in a particular product category only buy the products on promotion this is called deal proneness (Hackleman and Duker 1980). This type of consumers is more likely to switch stores/ items and is not so much bothered through OOS. Even though deal proneness does not significantly influence OOS responses (Campo et al. 2000). It is not yet investigated what the influence of deal proneness is in a POOS context Finally, Sloot et al. (2005) investigated the influence of brand equity and the hedonic level of products. Brand equity can be divided in high-‐ and low-‐equity brands (Chandon et al.
2000). According to Keller (2002), identified brands have a higher customer-‐based brand equity, which means that consumers react more favourably compared to non-‐ identified brands. Besides, the hedonic level of products is based on the benefits that a product provides to consumers (Sloot et al. 2005). These benefits can be hedonic or utilitarian.
Hedonic benefits, like ice cream provide more experiential consumption, fun, pleasure and excitement. On the other hand, utilitarian benefits are functional and instrumental, like toilet paper (Dhar and Wertenbroch 2000; Sloot et al. 2005). The findings of Sloot et al.
(2005) indicate that consumers are more willing to switch stores to acquire the products and brands with high brand equity and high hedonic level.
2.3.2 Store-‐ related variables
The second group of variables relates to the store or retail chain where OOC occurs (Sloot et al. 2005). Several researchers (Campo et al. 2000; Emmelhainz et al. 1991) argue that store loyalty is an important antecedent in predicting OOS reactions. Store loyalty is an indicator of consumers who are less likely to switch stores if a product is OOS. In contrast, Sloot et al. (2005) found weak evidence for this relationship in OOS situations; however, the question remains what will be the effect of store loyalty on CCB in a POOS situation.
According to the study ConsumentenTrends (2010), 87% of the respondents visit different grocery stores per month, with an average of 2.8 times. Only 13% is completely loyal to one grocery chain. In fact consumers are more eager to hunt for promotions and 40% of the consumers visit more stores to cherry pick.
A second variable is the availability of alternative stores close to the store in which the OOS appears. It is not only about the number of stores around, but also about the type of stores (Sloot et al. 2005). The following types of supermarket formulas are distinguished in the Netherlands:
1) Full service: a large assortment, high service level and high prices like Albert Heijn
and Plus.
2) Value-‐ for-‐ money: medium service, medium prices. Many local oriented retailers like C1000, Boni and Dekamarkt.
3) Quality discount: high service, large assortment, and low prices like Jumbo
4) Hard discount: rock bottom price, strong focus on fancy label like Aldi and Lidl (ConsumentenTrends 2010).
In theory it is rational to formulate that consumers with comparable stores in the vicinity of the store in which the OOS occurs are more willing to switch stores (Sloot et al.
2005). However, none of the studies supported this expectation yet (Verbeke et al. 1998). In case of a POOS situation it is more likely that consumers switch to other stores from the same retail chain as the promoted products are probably not available for the same price in other retail chains (Diels and Wiebach 2011).
2.3.3 Situation-‐ related variables
The third group of variables pertains to specific conditions of the consumers’
shopping trip in which the OOS situations occurs (Sloot et al. 2005). According to Campo et al. (2000), the type of shopping trip is a determinant of consumer responses towards OOS.
Customers who are currently undertaking a major shopping trip, which is very time consuming and they are therefore reluctant to spend additional time in another store to get the products which were OOS.
Furthermore, buying urgency is researched a lot as an antecedent of OOS response (Campo et al. 2000; Emmelhainz et al. 1991; Zinn and Liu 2001). When consumers need the promoted product immediately they cannot postpone the purchase (Sloot et al. 2005).
2.3.4 Consumer-‐ related variables
The fourth group of variables is related to the consumer who is confronted with OOS (Sloot et al. 2005). Among others, an important variable is the shopping attitude, which refers to the perception of shopping as a duty or something to be fun. Consumers with a positive attitude towards shopping are more likely to switch stores in case of an OOS because they enjoy shopping (Campo et al. 2000).
Furthermore, shopping frequency can be seen as another relevant variable in an OOS situation. Consumers who are shopping more frequently are more likely to postpone their purchases in an OOS situation (Sloot et al. 2005). However, this relationship is not yet
empirically confirmed.
Consumers’ time constraint or time pressure implies that consumers have limited
time to spend in the store due to employment, hobbies etc. Therefore, this group of people is less likely to switch stores in case of an OOS (Campo et al. 2000).
Finally, demographics influence consumers’ responses in OOS situations. According to Day and Landon (1977), higher educated people are more likely to complain because they know how, where and when to do so. Furthermore, it is showed that men and younger people complain more often (Reiboldt 2002).
2.4 Difference between OOS and POOS
So, in previous research a lot of research is done towards OOS. However, POOS situations are different from OOS situations and therefore, consumer reactions might also differ. Therefore, several authors highlighted the importance of additional research into the field of POOS (van Trijp et al 1996; Sloot et al. 2005).
Although, both a POOS and a regular OOS limit consumers’ choices in a specific category, a promotion draws the attention towards that particular product and it becomes temporarily more attractive. Often promotions are featured in a folder and consumers might plan to visit the store in advance to purchase the product in promotion. This induces store-‐
switching behavior and customers are more willing to make additional transaction costs.
Moreover, in a regular OOS consumers can more easily switch stores to acquire the desired product but in a POOS consumers cannot switch to just another store and benefit from the same promotion. They need to visit a store from the same retail chain to make buy the product in promotion. In practice, people probably will not switch stores for one product and they just purchase an alternative in the store, which causes additional substitution costs
for the customer.
Furthermore, consumers are inclined to perceive OOS to be higher in promoted rather than non-‐ promotional items, which results in higher consumer dissatisfaction (Grant and Fernie 2008). Besides, as many customers are adapting their behavior to promotions, they especially are dissatisfied if an attractive promotion is OOS (DelVecchio et al. 2006;
Gupta 1988). Dissatisfied consumers are also more motivated to engage in negative word-‐
of-‐mouth, which makes the negative effect of POOS even bigger (Zinn and Liu 2001).
Therefore, this research will examine the possible antecedents in explaining customer responses towards POOS in a grocery context.
3. Conceptual model and hypotheses
In figure 1, the conceptual model is depicted. The hypothesized model is focused on
feature advertising, deal proneness, promotion value, and locus of control. Their relationship with consumer dissatisfaction and consumer complaint behavior, both voice and private CCB are measured. Besides, the relationship between consumer dissatisfaction and CCB is hypothesized. In the full model more variables are included, which could be important antecedents of consumer POOS reactions according to literature. Again, these independent variables are divided into four categories: product-‐, store-‐, situation-‐, and consumer-‐ related.
Figure 1: antecedents of consumer responses towards a promotional out-‐ of-‐ stock.
3.1 Deal proneness
The construct of deal proneness is first used by Webster (1965) and is defined as “a general proneness to respond to promotions because they are in deal form” (Lichtenstein et al. 1990, 1995). Scheider and Currim (1991) distinguished two dimensions, active and passive. High deal-‐ prone consumers are more attracted by in-‐ store price discounts because they are in the form of a deal rather than simply offering a lower price. In general, they show a positive attitude towards promotional information, and are using this information in their decision making process. For example, active deal-‐ prone consumers are more sensitive to promotions, process more information outside the store environment and conduct an
!"#$%
&'()")"**%
+'(,(-()%
.#$/"%
0(12"%334%
3/*5(,"'%
61**#-*7#2-()%
+'1.#5"%334%
!"#$%&'()*+,-%#,.*
• %+#'5%(7%58"%9"":%
• %;8(&&1)<%5'1&%
• %4/=1)<%/'<")2=%
/'(0$1,+)*+,-%#,.*
• %>)2(,"%
• %?6/2#-()%
• %@<"%
• %A")6"'%
• %+'(6/25%1).($.",")5%
• %+'12"%2()*21(/*)"**%
!#'+,)*+,-%#,.*
• %;5('"%$(=#$5=%
• %;5('"%5=&"%
• %+'(B1,15=%*5('"%(7%
*#,"%'"5#1$"'%
• %+'(B1,15=%*5('"%(7%
(58"'%'"5#1$"'%
2+'.$3#)*+,-%#,.*
• %4'#)6%$(=#$5=%
• %4'#)6%"C/15=%
• %D"6()12%$"."$%
• %>,&/$*"%E/=1)<%
F(2/*%(7%
2()5'($%
G/$$%,(6"$%
D=&(58"*1H"6%,(6"$%
DI#J%DIEJ%DI2%
DK#J%DKEJ%DK2%
DL#J%DLEJ%DL2%
DME%
DM#%
