How do the retailers’ communication strategies influence the consumer satisfaction of POOS? 2012

Master Thesis

2012

How do the retailers’

communication strategies

influence the consumer

satisfaction of POOS?

Faculty of Economics and Business

Msc. Business Administration --- Marketing Management

March 2012

Jiang Li Kun 姜力坤 (Victor Jiang)

1

_{Supervisor: Prof. Dr. L.M.Sloot}

2

_{Supervisor: Dr. J.E.M. van Nierop}

Address: Surinamestraat 113

Zip- Code: 9715 PS, Groningen

Phone number: 0031614831000

E-mail: victor010816@hotmail.com

1

Table of Contents

PREFACE --- THE INCUBATION OF THE TOPIC: ... 4

EXECUTIVE SUMMARY: ... 5

1 INTRODUCTION ... 6

1.1 POOS and Customer reactions ... 6

1.2 Contribution and Relevance ... 8

1.3 Research Questions ... 8

2 LITERATURE REVIEW ... 9

2.1 Out-of-Stock (OOS) ... 9

2.2 Promotion and Consumer behavior ... 14

2.3 Phantom Alternative ... 16

2.4 Customer Satisfaction ... 16

2.5 Service Failure and Service Recovery ... 18

2.6 Hedonic and Utilitarian products ... 20

2.7 Recommendation ... 20 2.7.1 Price-Related Scenario ... 21 2.7.2 Brand-related Scenario ... 22 2.8 Compensation ... 23

3 RESEARCH METHODOLOGIES ... 24

3.2 Decision satisfaction measurement ... 26

2

4 EMPIRICAL RESULTS ... 28

4.1 Descriptive data (Socio-demographic Characteristic) ... 28

4.2 Cronbach’s Alpha ... 29

4.3 Wilconxon Signed-Rank Test ... 30

4.3.1 Apology... 30 4.3.2 Availability Announcement ... 31 4.3.3 Recommendation ... 32 4.3.4 Compensation ... 35 4.4 MRA ... 35 4.5 Conclusion... 37

5 DISCUSSIONS ... 38

6 MANAGERIAL IMPLICATIONS ... 41

6.1 Implications for retailers ... 41

6.2 Implications for manufacturers ... 41

7 LIMITATION AND FURTHER RESEARCH ... 42

8 ACKNOWLEDGEMENTS ... 43

APPENDIX 1: ONLINE SIMULATION SURVEY EXAMPLES ... 44

1 Scenario of “Hedonic product with no POOS communications” ... 47

2 Scenario of “Utilitarian product with no POOS communications” ... 48

3 Scenario of “Hedonic product with apology” ... 49

3

5 Scenario of “Hedonic product with availability annoucement” ... 51

6 Scenario of “Utilitarian product with availablity annoucement” ... 52

7 Scenario of “Hedonic product with higher priced recommendation” ... 53

8 Scenario of “Hedonic Product with lower priced recommendation” ... 54

9 Scenario of “Hedonic product with national brand recommendation” ... 55

10 Scenario of “Hedonic product with private brand recommendation”... 56

11 Scenario of “Utilitarian product with higher priced recommendation” ... 57

12 Scenario of “Utilitarian product with lower priced recommendation” ... 58

13 Scenario of “Utilitarian product with national brand recommendation” ... 59

14 Scenario of “Utilitarian product with private brand recommendation” ... 60

15 Scenario of “Hedonic product with compensation” ... 61

16 Scenario of “Utilitarian product with compensation” ... 62

17 Preferences ... 63

4

Preface --- The incubation of the topic:

5

Executive Summary:

In retail marketing, Out-of-Stock (OOS) and Promotional-Out-of-Stock (POOS) are inevitable

phenomenon in everyday practice. Previous studies have investigated the factors influencing consumers’ buying behavior when they encounter OOS. The academe and the retail managers have gained insightful knowledge about the antecedents and the consequences of OOS. This study, based on the framework of Fitzsimons (2000) and Sloot et.al (2005), investigates the effectiveness of POOS service recovery

strategies under two product groups (Hedonic and Utilitarian). To do that, the various available communication strategies were proposed. The effectiveness of the strategies was statistically tested under 16 simulated online surveys. The results indicated that the consumers are likely to be reluctant to the communication strategies of POOS in hedonic product group. However, the uses of apology,

availability announcement and recommendation have shown the power of leveling up post-POOS satisfaction in Utilitarian groups.

6

1 Introduction

1.1 POOS and Customer reactions

A rather common phenomenon of physical lack of salable product on retailers’ shelf is defined as Out-of-Stock (OOS). From customers’ perspective, 21% of a shopper’s time is probably wasted on looking for an OOS item which leads to a less satisfied shopping experience (Gruen, 2007, Sloot et al, 2002). Consumer Reports study of mail-order companies found that mail-order customers reported “out-of-stock items” as their most frequent complaint. Unsurprisingly, potential behavioral responses of those unsatisfied customers include item switching, brand switching, store switching, as well as purchase postponement and cancellation (Diels and Wiebach, 2011). On the other hand, from the eyes of the retailers, Out-of-Stock (OOS) is not only a prevalent problem in today’s retailing practice but also of high relevance in other online and service sectors such as airlines or hotels. The resulting gross margin losses for the retailers are estimated to lie between 7 and 12 billion dollars per year in the US (Andersen Consulting, 1996).

OOS particularly occurs for promoted items. Due to the tendency of extending assortments, combined with the fact that shelf space is often fixed in the short and mid-term, leads to the conclusion that OOS is unlikely to disappear and inevitable for almost every retailers (Sloot et al, 2002). Hence, some recent publications have underlined that the domain of POOS requires further research (Sloot et al., 2005). Many scholars have (empirically and intuitively) noticed that there will be certain specific impacts of Out-of-Stock for retailer’s “promotional campaigns”. Because if the research on phantoms (Pratkanis & Farquhar, 1992, Highhouse, 1996) held, due to the promotion, the relative positions in the attribute space are changed and the OOS item can be construed as an asymmetrically dominating or a relatively superior phantom, intuitively causing a bias on reactions.

The reactions of POOS are initially triggered through a series of psychological changes. Fitzsioms (2000) shared the same theoretical base with Brehm (1996) that when an individual’s freedom is restricted through the elimination of (or threat of elimination of) a behavior, that individual will experience a state of psychological reactance (defined as a motivational state directed toward re-attaining the restricted freedom). The encounter of OOS or POOS in essence is the response to the removal of an option to choose. As long as a consumer attaches some value or utility to the option to choose an alternative, it is intuitively appealing that he or she will respond when that option is taken away. It is important for retailers to notice that OOS of an alternative that has a high option value, and that the decision maker is therefore more personally committed to, becomes much more likely to elicit a response from the consumer.

7

communication of promotions or POOS, a more negative response to the OOS is expected; vice versa good communication of promotions or POOS decreases the decision difficulty, leads to relatively positive reactions.

Futhermore, Westerbrook et al. (1979) identified a set of aspects of the decision experience that they believed to be related to consumer satisfaction, the availability of information, worry about outcome are included. In practice, some retailers do explicitly notify customers about the duration and size of promotion before the campaign, which is considered as an effort to enhance the transparency of information and to ease consumers’ anxiety and doubts. Wicklund (1974) provided evidence that if people are told outright that they will have limited or no control over their choices in a social setting their reactions is much less severe than if they are led to believe they will have control, only to have it taken away.

Intuitively, POOS is supposed to have more negative impact on certain groups of consumers. For example, those consumers who are indentified as “promotion hunters” .They pay the visits to the retailers with a straightforward goal of shopping: shopping for value. As they were informed the opportunity of purchasing the promotional items, they immediately include an item in his or her consideration set, they have engaged in some effortful processing to make that judgment and assumed that the option to choose the item would be open to them. Therefore, if POOS unfortunately exits, they will probably experience severer feeling of “service failure” than others. Inferentially, the similar reaction is likely to be recognized among brand loyal or store loyal consumers, since as the perceived personalization of POOS announcement increases, the POOS will increase in attractiveness, and a decision maker’s commitment to the alternative will increase (Fitzsimons, 2000).

Thanks to the study of Praktanis and Farquhar (1992) on Phantom alternatives which offers a surplus knowledge to explain preference shifts in case of reduced choice sets. It is relatively easier for us to understand the differences among the reactions of consumers. Be more specific, from the customer’s point of view, known phantoms are distinguished from unknown phantoms: if decision-maker is aware of the unavailability of the alternative at the beginning of the choice process, the option is classified as known/recognized phantom. On the other hand, if consumers are surprised by the unavailability subsequent to the decision-making, alternatives are referred to as unknown/unrecognized phantoms. Therefore, the adoption of communication about POOS is equivalent to the presence of

known/recognized phantom scenario. Instead, no communication about POOS is associated with unknown/unrecognized phantoms. A theory-based analysis of phantom alternatives thus represents a valuable avenue for further research which is worthwhile of investigating the consumer reactions in these two scenarios. To be honest, there were only a few OOS studies prior to this study, even fewer researches have been focused on POOS. Future research should take a broader perspective and examine the conditions under which the potential effects of unavailable alternatives manifest themselves. What we know so far about consumers’ reactions toward POOS can be categorized into two

8

tries to identify fundamental determinants of OOS responses. The results show that product-related characteristics, store-related characteristics, consumer-related characteristics and situation-related characteristics are the major antecedents of OOS reactions (Sloot et al, 2005, J.L.Diels and N.Wiebach, 2011). However, little research has been dedicated to illustrate how the OOS or POOS responses can be intervened by marketing tools, for example: customer communication.

1.2 Contribution and Relevance

Therefore, this study aims to fill in the research gap by following the theoretical framework of decision-making difficulty, phantom theory and the antecedents of POOS reactions which are already discovered. To investigate what is the interactive rationale between the communication strategies of the retailers and the adjustments of the consumer reactions. In other words, we have known what the possible consequences of POOS are; it is time to move one step further to the question: “what retailers can actually do to minimize the negative impacts.”

Besides the theoretical contribution, this study provides valuable insights for retailers in terms of managerial decision making. Similarly, in which retailers would like to focus, is the way that consumers respond to POOS situations. More importantly, they would like to know how they can manage the reactions in an appealing way to fulfill the original intention of promotion, rather than encountering any negative consequences if POOS is inevitable to be avoided. If consumers would be willing to invest behavioral efforts in the SKUs which are POOS, retailers would like to know if their strategic interferences would have any effect.

Moreover, the results of this study are not only restricted within retailing industry. It is also applicable to other service sectors which are looking for solutions of similar problems, for example: hotel and airplane ticket booking businesses.

1.3 Research Questions

In conclusion, the problem statement of this research lies on the question “Does the communication of POOS help customers to achieve better shopping experience.”

In order to provide an answer to the research question, the following sub questions are proposed: 1. Which kinds of communication strategies are available and effective for retailers to achieve

higher customer satisfaction level when the POOS exists?

9

2 Literature Review

In this section, a detailed review and comparison of prior studies on OOS (POOS) will be provided. Earlier marketing research on OOS has focused on identifying and measuring the magnitude of different

possible OOS reactions (Peckham 1963, Walter and Grabner 1975). Later the focus has been switched to creating a comprehensive conceptual model that identifies multiple predictor variables that impact those reactions (Campo et al. 2000, Emmelhainz et al. 1991). In spite of the importance of consumer responses to OOS, there is much we do not know about how these responses are formed (Campo et al, 2000; Verbeke et al, 1998). Hence, further researches have been dedicated to discover the antecedents of OOS responds (Campo et al. 2000, Zinn and Liu 2001, Sloot et al. 2005). However, there is still little knowledge about how retailers can mitigate the reactions in a manner that minimize the revenue losses caused by OOS? Even though some researches of OOS policy has been observed very recently

(Verhoef and Sloot 2005, Breugelmans et al. 2006).

In an attempt to answer the research question, the literature review here will start with the discussion of prior studies on OOS (phenomenon, consequence, consumer behavioral reactions, and antecedents). Since the special attention has been given to the OOS situation with promotional products, the second part of literature review will mainly discuss the research findings of promotion. Furthermore, the research gap will be identified and some inquiries of psychological antecedents toward consumer behaviors will be provided, for example, phantom alternative theory and consumer satisfaction etc. Particularly, consumer satisfaction with the decision process or decision satisfaction – is proposed to be influenced by the decision environment that is created after the unavailability of the preferred

alternative. It has been demonstrated that consumers feel significantly lower levels of decision

satisfaction than those that do not encounter OOS (Fitzsimons 2000). Last but not least, the conceptual model and hypotheses will be proposed based on the previous research findings in order to consolidate the effectiveness of retailer’s communication strategies on POOS.

2.1 Out-of-Stock (OOS)

10

Christopher 1979, Walter and Grabner 1975). Interestingly, there are also a few researches analyze how retailers can use OOS deliberately to increase profits by offering rain checks, diverting consumers to higher margin items or decreasing price competition between firms (Balachander and Farquhar 1994, Gerstner and Hess, 1990). However, the greatest interest on retail OOS focuses on consumer reactions to OOS situations, quite a few researchers have dedicated their investigations on identifying what are the main reactions toward OOS and how to explain the differences among consumer responses

(Emmelhainz et al.1991, Gruen et al. 2002, Campo et al. 2000, Zinn and Liu 2001, Fitzsimons 2000, Sloot et al. 2005). In this section, some highly relevant researches will be discussed chronologically, which gives us a clear idea about how the topic has been involved. Some findings will be served as the theoretical basis to develop conceptual model in the following part of this paper. At the end of this section, a comparison and contrast of research methodology will also be summarized to provide insights about the operationalization of such an empirical research.

Schary and Christopher (1979) studied “product availability “through surveys. They initiated the awareness that “the store encountering frequent OOS may begin to lose much of its own power to attract patronage.” Besides, they gave warning to the retailers that behavior caused by OOS leads to diversion or loss of patronage and future goodwill. In general, the OOS can be expected to result in a frustration of a purchase plan, caused by the inability to complete it. They asked consumers to rate the image of the store. The findings suggest that the OOS perception is not universal and that reactions to OOS influence the total image of the store. Be more specific, store image ratings were lower for consumers who had reported an OOS than for consumers who had not. They also raised the suspicion that the perception and response predominantly come from a single occupational class. It appears to reflect on the degree of preplanning of product selections, which is determined by other dimensions of store image. There also appears to be a saliency stemming from the OOS situation which influences other perceptions. Last but not least, they proposed a model of OOS behavior modules which contains four components modules, namely “Store and product decisions”, “consumer-behavior”, “Response to OOS situations” and “Retail merchandising strategy”. However, till now, most researches so far haven’t paid enough attention to the topic of “how to mitigate the store and product decision, consumer behavior by using appropriate retail merchandising strategy.”

Emmelhainz and Stock (1991) investigated consumer responses to retail OOS, they found at least fifteen different OOS behaviors are taken by consumers when provided with brand, size and variety

11

intervention of product-related attributes and situational factors, retailers can take specific actions to influence the OOS reactions of the consumers.

Verbeke et al (1998) undertook a Dutch domestic research regarding the consumer responses to the preferred brand OOS situation. The study approved that almost half of the consumers were not willing to switch brands when their preferred brand was OOS; they either switched stores or postponed the purchase. It seems that consumers were willing to undertake behavioral efforts in order to obtain their preferred brand. They also discovered how consumer shopping characteristics have an effect on the OOS response, store loyalty, the size of purchase moderate consumer responses toward OOS

simultaneously. However, they found that the competitive retail environment did not have an effect on the willingness of consumers to switch stores. This phenomenon is particularly true for those “cherry pickers”, seeking some of the preferred brands priced a bit cheaper. Therefore, visiting the different stores in order to get the preferred brand cheaper was a salient characteristic of the shopping behavior from the consumers of the OOS store. Even though according to Ailawadi (2009), neither the size of the cherry picking segment nor its negative impact on retailer profits is as high as it is generally believed, Verbeke’s research is highly valuable in the perspective that the retailers might be able to know if their own competitive environment would have any effect on dealing with OOS, if consumers would be willing to invest behavioral efforts in the brand.

Fitzsimons (2000) was the pioneer who explicitly discussed consumer responses to OOS, both in terms of consumer satisfaction with the decision process and in terms of subsequent store choice behavior. On one hand, his study was largely built on the findings of Brehm (1966), which states that when

individual’s freedom is restricted through the elimination of (or threat of elimination of) a behavior, that individual will experience a state of psychological reactance. On the other hand, he primarily introduced Farquhar and Pratkanis (1987)’s “Phantom Alternative” to the field of OOS research. Hence, he

proposed that as long as a consumer attaches some value or utility to the option to choose an alternative, it is predictable that he or she will respond when that option is taken away in the form of OOS. Furthermore, he showed that the consumer’s commitment to a particular choice alternative is affected by three factors:

(1) overall preference for the good,

(2) whether the alternative is actively considered,

(3) the degree to which the announcement of an OOS of the alternative is personally directed toward the decision maker.

In addition, he elaborated consumer response to OOS in both evaluative and behavioral manners by linking several previous studies of “consumer satisfaction with the decision process” (Westbrook et al. 1997; Fitzsimons, 1997, Thaler, 1985). Those papers collectively suggest that decision satisfaction is an appropriate way to examine consumer response to changes in the decision environment, in particular, the investigation of consumer response to OOS. After conducting four studies in the research,

12

the intervention of those two factors may lead to different (probably less devastated) consumer responses.

Campo et al. (2000) were the first to explicitly build a theoretically based conceptual framework to explain consumer reactions to OOS. They showed concerns regarding the fact that most studies about OOS only empirically analyze associations between product, consumer, and situation characteristics on the one hand, and stock-out on the other hand, but they provide neither complete picture nor rationale for the observed effects. Therefore, they performed an empirical test on the “cost” of shoppers. The results were in line with Schary and Christopher (1979) about the loyalty implies a substantial decrease in the likelihood of item and store witching. Furthermore, they reported that when acceptable

alternatives are available, consumers are much more inclined to choose another SKU. Hence the managerial implication to retailers is obvious that using “recommendation” strategy may moderate the negative reactions of customers. Their research included several explanatory factors that are previously believed to affect OOS reactions. Interestingly, the insignificance of “Deal proneness (attitude towards and tendency to use promotions in category) suggests that promotion reactions are not a clear-cut indicator of OOS reactions. This emphasized the need to treat stock-out reactions as a separate research issue.

Similarly, the paper of Zinn and Liu (2001) also reports results of a research study of consumer short-term response to OOS. They managed to find out that consumers are generally able to separate a single and recent OOS experience from other dimensions of their overall attitude toward a store. This result therefore casts doubt on the general perception that OOS significantly damage a store’s overall image. The managerial implication seems to be that a single OOS has lesser impact on a store’s overall image than previously thought. However, the question remains: can we expect the same result from a POOS situation and is there any moderating effects diminished the damage of consumers’ perceptions toward the store?

13

lower level of satisfaction (CBL 2000, Fitzsimons 2000). They investigated the effect of brand equity and the hedonic level of a product on OOS responses, as well as the moderating effect of the hedonic level of the product on the effect of brand equity. In their opinions, OOS responses can be explained in a reasonable way only through the use of comprehensive models which sufficient number of antecedents should be considered. They discovered that in hedonic product groups, consumers are more likely to switch to another store. Yet two significant moderating effects are valuable to retailers to repair the losses. Consumers perceive products as hedonic items with high brand equity are less inclined to postpone but switch to another item of the same brand. Therefore, intuitively if there is an OOS situation, retailers may use “recommendation” to help customers to accomplish the item switch behavior. On the other hand, they also conclude that product and brand related antecedents appear particularly more important for explaining OOS reactions than the others.

Meanwhile, Verhoef and Sloot (2006) initialed a study which focuses on the discussion of the reactions, antecedents and management solutions on OOS. They unprecedentedly take managerial policies into account in the OOS investigation. Specifically, they tested the impact of using shelf announcement which considers as an additional service to consumers. The announcement can at least provide five different message to customers to mitigate their negative behavioral reactions, including

recommendation, apologize, availability announcement, service failure explanation and compensation. It suggests that half of the consumers found the announcement useful, though the effectiveness of these five methods differs between 30% and 50%. They also investigated whether OOS announcements led higher satisfaction level. The results showed that the use of communication strategies do not necessarily guarantee higher satisfaction perceived from customers.

In the research of Anderson et al. (2006), they conducted a field test in a mail-order catalog to measure both the short and long-run costs of an OOS to a catalog company. To evaluate the short-run cost, they compare how OOS affect the final disposition of both the OOS item and other items in the order. The findings provide clear evidence that firms can mitigate the cost OOS by changing the explanations that their customer service representatives offer to customers when items are OOS. There is considerable variation in the effectiveness of the five responses at both preserving the current order and encouraging future purchases. Stating that the item is “extremely popular” was the most effective response for encouraging customers to backorder rather than cancel the item. This response was also the most effective at encouraging customers who experienced an OOS to reorder.

14

Diels and Wiebach (2011) are one of the few researchers who paid special attention to the promotional-out-of-stock phenomenon. The theoretical basis of their research is based on context theory and phantom alternative theory. If customers face an OOS situation, they are confronted with an entirely new decision situation represented by an altered choice set. Therefore, we claim that the rank ordering of preferences may change and the relative attractiveness will be built on different reference criteria to compare the alternatives (Sheng et al. 2005). However, when the promotion is involved, the relative positions in the attribute space are changed and the OOS item can be construed as an asymmetrically dominating or relatively superior phantom causing the negative similarity effect to diminish. In other words, Empirical studies on consumer behavior have significantly proven promotional activities to affect customers’ purchase decisions. In an attempt to save money, customers adapt their purchase patterns to promotional activities. Consequently, if the respective purchase plans are hindered by OOS situations, behavioral responses can be expected to differ. Therefore, they detect difference in OOS responses and substitution patterns for promoted and non-promoted items. Customers who encounter POOS tend to postpone their purchases or visit another outlet of the same retail chain to buy the promoted items. One very interesting finding which differs from the classical OOS-induced behavior is that the negative similarity effect of POOS items diminishes since customers significantly less often choose a similar substitute but consider the choice of an unalike product. It is because the promoted but unavailable item dominates the similar and available alternatives, for which reason it is perceived as less attractive in a direct comparison. Last but not least, a rather new and important reaction of “branch switching” (the behavior of voluntarily visit another branch of the same retailer to nevertheless profit from the promotional offer) has been observed in their research. It suggests that if retailers use communication strategy to inform other purchasing options in a different store, they can protect themselves from the loss of “store switching”.

2.2 Promotion and Consumer behavior

Communication and promotion decisions are a critical element of retailer customer experience

management strategy (Ailawadi et al. 2009). A retailer hopes that promotions not only increase sales of the promoted items but also attract more consumers to the store because, once consumers are in the store, they are likely to also buy products other than those on promotion. For example, Ailawadi et al. (2006) provide some insights into the ability of promotions in one category to influence sales in other categories in the store. In addition, retailers also use promotion strategy to drive store traffic (Dreze 1995, Walters and MacKenzie 1998).

Does promotion work? Ailawadi et al. (2006) find that 45% of the short-term promotion bump is incremental for the retailers in their study. Srinivasan et al. (2004) also find that promotions have a positive impact on manufacturer revenues, their impacts on retailer revenue and margin is mixed. As far as long-term effects of promotions are concerned, from the retailer’s perspective, studies on this issues show that they are not significant (Nijs et al. 2001, Srinivasan et al. 2004).

Historically, a few theoretical researches have been made to explain the relationship between

15

promotion and consumer choices, for example, Schneider and Currim (1991) identified promotion type and Leone and Srinivasan (1996) identified promotion value.

In general, it is believed that promotion shapes consumers’ behavior toward buying promoted products (Rothschild 1987). However, what is worth mentioning is that consumers sometimes even may make negative attributions about the product as they look for explanations to why the product or brand needs a promotion (Blattberg and Neslin 1989). Combining the phantom alternative and the consumer

decision satisfaction, it is rather interesting to investigate “Do consumers react to POOS differently because of their perception of the promotion?”

Furthermore, a large body of behavioral research demonstrates that the manner in which a deal is framed influences consumer perception of the deal value, purchase intent, and search intent (Ailawadi et al. 2009). The results from this research stream for retailers is that deals should be carefully framed because small modifications in wording and the information provided can have a significant impact on the effectiveness and efficiency of the deal. There has been some interesting work on the effectiveness of different types of promotions, such as promotions with quantity limits, multiple unit promotions, and bonus packs. Peter and Raghubir (1997) find that by communicating with consumers with the presence of a restriction (e.g., purchase limit, purchase precondition, or time limit) serves to accentuate deal value and acts as a “promoter “of promotions. This suggests that consumers will probably invest more emotional attachment to the restrictive promotions. On the other hand, it is also quite common for marketers to use coupon promotions as a strategy of price discrimination because only price-sensitive buyers are willing to expend extra efforts to collect and redeem coupons (Narasimhan, 1984).

Apparently, there are important behavioral mechanisms at play between promotion and consumer reactions. Yet, how do those buyers who have exercised extra emotional or behavioral efforts during consumption react to promotional Out-of-stock is insufficiently studied. What we only know is just as OOS; promotion also affects behavioral changes to consumers. A promotion may have one of the following changes in consumer behaviour: brand switching, store switching, and category expansion and purchase acceleration (Narasimhan et al.1996). Unfortunately, the research of OOS (POOS) is an

emerging scheme in academic. There is not much literature which discusses the concurrent consumer reactions of “promotion” and “Out-of-Stock”.

The literature review so far confirmed the fact that there is a research gap lies in the discussion of retailer’s interventions toward OOS, particularly when it comes to the promotional scenario. Hence, the following part of this paper will primarily demonstrate a framework describing:

16

2.3 Phantom Alternative

As it mentioned before, Fitzsimons (2003) was the first one who explicitly proposed “phantom

alternative” is endogenously associated with customers’ reactions toward OOS. According to Pratkanis and Farquhar (1992), Phantom alternative is “a choice option that looks real but is unavailable at the time a decision is made”. From a decision-maker’s perspective, phantoms can differ on at three important dimensions: knowledge of availability at the time the decision is made, perceptions of the nature of the constraint or reason for unavailability and the sense of entitlement to possess the

phantom. The phantom can be either known at the beginning of a decision problem (the decision maker is fully aware that the option is not real or is unavailable) or the phantom can be unrecognized (the decision maker does not identify an alternative as potentially unavailable and may later be surprised to find out that it is unobtainable). In the world of retailing, an example of known phantom refers to the retailers inform the customers that the promotion is “op=op” or by setting up a clear quantity limit. An example of unrecognized phantom refers to the most common OOS (POOS) situation: consumer finding out that the product which retailers placed on promotion is no longer in stock, yet there is no

communication from the retailers.

Furthermore, based on the theory developed by J.W.Brehm (1966), Pratkanis and Farquhar (1992) made a link between the psychological reactance and the “frustration-deprivation effect”. Basically, when there is perceived freedom of choice, the unavailable object may be viewed as more attractive, with the individual increasingly motivated to obtain it. Therefore, when the phantom alternative is out of reach, frustration aroused. Therefore, the encounter of OOS (POOS) is able to be considered as an

approximation to the “reactance” and “frustration-deprivation effect” for future research.

Lastly, Pratkanis and Farquhar (1992) compared their work with the findings of Freeman et al. (1990), the comparison points out that a phantom can induce different psychological processes and results based on subtle manipulations, which implies a concept of “environmental structure”: the meaning of a phantom and how to resolve self-motives aroused by a phantom is determined by the structure of the immediate environment. It gives theoretical clues that the negative effects of phantom alternative can be moderated by interventions. In other words, the consumer reactions caused by the experience of “phantom alternative” are able to be mitigated by retailers with the right settings of the immediate environment.

2.4 Customer Satisfaction

17

(1) Shopping system satisfaction concerns evaluations of the availability of products and retail outlets in a market area.

(2) Buying system satisfaction focuses on the selection, purchase and receipt of products from retail stores.

(3) Consuming system satisfaction concerns the usage and consumption of goods and services (Renox 1973, Westbrook 1981)

It is rather clear that when there is a POOS, the attitudinal evaluation of satisfaction should be focused on consumers’ decision (dis)satisfaction and their shopping system (dis)satisfaction. Therefore, the effectiveness of retailer’s interventions toward POOS should also be measured in those two dimensions. What is the relationship between decision making difficulty and customer (dis)satisfaction? Shugan (1980) introduced a widely applicable model to explain the difficulty in decision making. He argued that the difficulty of making a choice from a product set is highly predictable, based on a number of factors, such as “the size of their choice set” and “the attribute rating covariance”. And he noticed that if a consumer encounters any choice confusion, they are possible to be manipulated by changing the choice setting. Fitzsimons (2000) ably imported this finding to his own research on OOS; he further confirmed that the role of difficulty of choosing an alternative is also related to consumer response toward OOS. This is in line with the assumption that consumers will prefer less difficult decision environment which leads to a positive customer (decision) satisfaction.

It is also has been confirmed that when making retail purchase decisions, consumers tend to adopt task-simplifying decisions rules (Hoyer 1984). Particularly, consumers tend to apply it in a disrupted choice environment, for example: POOS. POOS items can be seen as ‘phantom’ products, customers are only aware of the unavailability when they stand in front of it and try to purchase it. POOS reduce the appeal of the product category and may make consumers uncertain as to which item to select. This is especially true when highly preferred items (promotional items) are missing and when few appropriate substitutes are available or at expected price level (Boatwright and Nunes 2001; Campo et al. 2000; Sloot et al. 2005). Similarly, Wicklund (1974) also discovered the same emotional process that tied frustration to restricted freedom and reactance. Aggression is often expected to follow from frustration (Dollard et al. 1939) and is more likely to cases when the frustration is unexpected. As a result, consumers who encounter a POOS may experience the feeling of dissatisfaction, by making decision to defer or cancel planned purchases.

Figure 1 The psychological antecedent of Dissatisfaction on POOS

Promotion _Alternative Phantom POOS Frustration Dissatisfaction

18

2.5 Service Failure and Service Recovery

Nevertheless, most retail customers have encountered mistakes and defects during the course of their retail experiences, for example, the situation of OOS or POOS. As it demonstrated before, OOS or POOS does incur direct and indirect damage to the retailers (Campo et al. 2003; Fitzsimons 2000; Sloot et al. 2005). When a defect, mistake, or failure occurs, it is imperative for retailers to rectify mistakes through the utilization of effective recovery strategies. Simply because when a retail failure does occur, a likely initial response from consumers is dissatisfaction (Day and Landon 1977; Singh 1988).

Even though service failure is one “pushing determinate” that drives customer switching behavior (Roos 1999), yet successful recovery can mean the difference between customer retention and defection. Service recover refers to the actions a service provider takes in response to service failure (Gronroos 1988). Until now, little is known about how customers evaluate recovery efforts, what constitutes successful recovery, and the potential of recovery to convert customer dissatisfaction into satisfaction. De facto, the prior researches demonstrate a series of contradicting results on this topic. It is known as “recovery paradox” (McCollough and Bharadwaj 1992) or the question of whether customers who experience a failure followed by superior recovery might rate their satisfaction as high as or even higher than they would have had no failure occurred. For instance, Hart, Heskett and Sasser (1990) state, “A good recovery can turn angry, frustrated customers into loyal ones. It can, in fact, create more goodwill than if things had gone smoothly in the first place.” Even though “recovery paradox” has been

challenged by some researches (Berry et al. 1990, Zeithaml etal. 1996), no theoretical explanation of why a recovery paradox effect is possible has been offered.

Based on the theory of “service recovery” and its paradoxical impacts on service failure, it is reasonable to make the assumption that when the consumers confronted with a service failure like POOS, if the retailer adopts appropriate service recovery strategies, the consumers will probably feel more satisfied with their shopping visit.

Hocutt et al. (1997) found that under conditions of high redress, responsiveness, empathy and courtesy, post-recovery satisfaction can be higher than the no-service failure. MaCollough et al.

(2000) verified that the higher the recovery performance, the higher the post-recovery satisfaction supports the importance of superior service recovery.

It seems that service failures represent positive opportunities for retailers to establish long-term customer relationship. Now the question is: how does retailer change the choice setting on POOS to help consumers reduce the decision making difficulty. Are those strategies (for example, communication with consumers) effective in terms of leveraging their customer satisfaction? Some significant headway has been made recently. According to Verhoef and Sloot (2005), there are several actions retailers are able to implement to mitigate the negative OOS effects. They specifically point out the use of

communication strategy to make consumers feel like additional service is added, expecting them to perceive the efforts of service recovery. Based on the findings of Verhoef and Sloot (2005), OOS communication can provide the following different messages on the OOS:

19

(2) They can inform the customer of the approximate or exact time when the product or brand will be available again;

(3) They can suggest other products, to increase item or brand switching;

(4) They can provide information on additional services or promotion to compensate consumers for the inconvenience of the OOS.

According to Verhoef and Sloot (2005), approximately half of the consumers believed that the announcement provided them useful information, which reduced their confusions on OOS. However, they also noticed that the categories without OOS announcement are rated significantly higher than categories in which OOS announcement are used. Thus, the effectiveness of OOS announcements stays uncertain, especially in the more complicated scenario of POOS. Just as OOS announcements, the communication of POOS might increase the awareness of the POOS among consumers, making it more obvious and probably creating more dissatisfaction.

In this study, the findings of Verhoef and Sloot(2006) will be served as the theoretical ground to develop the research framework and hypotheses:

Figure 2 The availability of communication strategies on POOS

The hypotheses about the relationship between “communication strategies” and “consumer

satisfaction” are derived on how POOS policy elicits different purchase incidence and choice decisions. According to Breugelmans et al. (2006), the consumers will probably judge a retailer’s POOS policy on:

(1) the benefits it generates for them (Perceived fairness of the outcome)

(2) whether it is thought to be guided by customer-serving versus self-serving motives (perceived fairness of the procedure)(Palmer et al. 2000)

Furthermore, if we draw lessons from the factors which moderate consumer reactions to OOS, it is shown that consumers are mediated by three main types of benefits/costs (Campo et al. 2000, Corstjens and Corstjens 1995). Alternative OOS reactions (item switch, store switch, defer or cancel the purchase) have been shown to differentially affect each of these costs (Campo et. al. 2000):

20

(1) Consumption benefits (substitution costs) from alternative choices in the category (Iyengar and Lepper 2000);

(2) Transaction costs (search, holding or shopping costs) (Sloot et al.2005); (3) Opportunity costs (cost of foregoing some decision options).

Consumer, product and situational factors found to moderate consumer reactions toward OOS, moderator effects are linked to the flexibility of the purchase decision (frequent shoppers), the importance of the purchase decision (high or low involvement will attach different level of importance to making the purchase decision), and the consumer’s ability to develop adequate reaction patterns (e.g. knowledgeable consumers will experience less problems in finding a suitable alternative when their favorite one is OOS).

2.6 Hedonic and Utilitarian products

Several previous studies have noticed that the type of product is an important variable in explaining OOS behavior (Campo et al.2000, Emmelhainz et al.1991 Schary and Christopher 1979, Sloot et al 2005). One of the most frequently discussed product characteristics is the nature of being utilitarian or hedonic products. The reason why so many researchers have given attention to the different nature of utilitarian and hedonic products lies on the fact that it may affect our buying process. Consumers are believed to be driven mainly by rational buying motives in the buying process of utilitarian products. In contrast, they are likely to be mainly driven by emotional motives in the buying process of hedonic products. Hedonic products provide more emotional values to the consumer. Intuitively, it leads more emotional attachment with the hedonic products in their buying process. Dhar and Wertenbroch (2000) has confirmed that consumers are less satisfied if they experience a problem in the hedonic dimensions of a service and that consumers bond more to hedonic benefits. But the question now is: are consumers more satisfied if they experience a service recovery in the hedonic dimension?

H1: When confronted with a POOS, if the retailers uses apology to communicate with the consumers, the consumers will feel more satisfied with their shopping experience than there is no communication strategy is used.

H2: When confronted with a POOS, if the retailers communicate with the consumers about the next availability of POOS items, the consumers will feel more satisfied with their shopping experience than there is no communications.

2.7 Recommendation

21

In the retail environment, as grocery choice decisions, consumers tend to follow a sequential process. They first use simple tactics or cues to form a reduced set of choice alternatives (Roberts and Lattin 1991), which are then evaluated more thoroughly to make a final choice. Depending on the choice heuristic used, POOS may disproportionately increase attention for alternatives that

(1) share important attributes with the OOS item (Bell and Fitzsimons 1999; Campo et al. 2003); (2) have an acceptable price level (Jedidi and Zhang 2002);

(3) are highlighted by in-store elements ( Dreze et al. 1994);

(4) have been purchased very recently (Bronnenberg and Vanhonacker 1996).

Suggesting another item from the assortment as a replacement may limit the decrease in category attractiveness and, consequently, the consumers’ tendency to drop a category purchase. For one, suggesting a substitute may divert the customer’s attraction away from the OOS item (the service failure). In addition, it may reduce preference uncertainty, the recommendation providing a simplifying choice heuristic that helps consumers to select a substitute (Fitzsimons and Lehmann 2004).

H3: When confronted with a POOS, if the retailer uses recommendation strategy to suggest an item as a substitute for the POOS item, consumers will feel more satisfied with their shopping experience than there is no communication strategy is used.

2.7.1 Price-Related Scenario

However, it is wise not to make assumptions that using recommendation strategy necessarily lead to a higher level of customer satisfaction. Since the researches has found that under certain circumstances recommendations can be perceived negatively by the decision maker and can result in unexpected results in terms of ultimate choice, as well as a backlash toward the source of the recommendation. Specifically, if the decision maker has a prior attitude toward a particular option in a set that is either positive or negative, and the recommendation runs counter to that prior attitude, the recommendation will be unwelcome.

In the service recovery of POOS, the recommendations are received after preferences have been formed in consumer’s mind. To the extent that expert advice is consistent with the choice tendency of

individuals: Choice moves in the recommended direction, decision difficulty decreases, and confidence and satisfaction increase. When expert advice goes contrary to individual choice tendencies, however, some unusual patterns emerge even when there is a close substitute available. Previous research (Brehm 1966, Fitzsimons 2000) has shown that reactance responses to recommendation involve a variety of affective, cognitive, and behavioral dimensions. In other words, making unpopular

recommendations, while normatively desirable, may be counterproductive: it simultaneously makes life more difficult for the decision maker and produces behavior opposite to the recommended course of action.

22

price serves as a significant moderating factor in consumer’s mind when it comes to recommendation.

Consumers have an acceptable price in mind before POOS (Jedidi and Zhang 2002; Xia et al. 2004 hence the recommendation of higher-priced item will be valued less. In addition, the consumers become

suspicious about the retailer’s fairness. Hence, the assumption was made that probably consumers can not be lured into purchasing more expensive items by retailer’s recommendation strategy:

H3 (1) : When confronted with a POOS, if the retailer uses recommendation strategy to suggest a higher priced substitution will negatively moderate the (positive) effect of recommendation strategy.

H3 (2): When confronted with a POOS, if the retailer uses recommendation strategy to suggest a lower priced substitution will lead to a higher decision satisfaction.

2.7.2 Brand-related Scenario

In addition to the recommendation of products with different price level, there is another common scenario in the retail context that a recommendation will be made to an either national brand SKU or private label SKU.

Sales of private labels have grown significantly over the last decade in Europe, in the Netherlands, it accounts for approximately 20% market share (Verhoef et al. 2002). The emergence of private lable appears to be driven by retailers’ strategic considerations of “market concentration”, “profit uplifting” and “format modification” (Dahr and Hoch, 1997; Messinger and Narasimhan, 1995). Therefore, using “private label” has become an important strategy for retailers, which directly compete with national brands.

For those consumers who have the intentions to take advantage of a store’s advertised specials, search for shelf or display promotions to save money; they can achieve that by opting for a private label brand that is typically priced below non-price promoted nationally branded goods (Garretson et al. 2002). Numerous prior researches have only addressed the characteristics of “the deal prone shopper” and “the label prone consumer”, yet why consumers with a common goal to save money have different attitudes toward and purchasing habits for national brand deals and private label brand remain unstudied.

“Attribution theory” suggests the general attitudes toward private labels is that a low price for private label brands may be attribute to some problematic aspect of the product, which is then perceived as inferior in the overall level of quality (Garretson et al. 2002, Sawyer and Dickson, 1984). On the other hand, intuitively, it is likely for consumers to question the retailer’s true intention behind making a private label recommendation. Since many retailers have realized that the private label products are exclusive to the store, if effectively marketed, may build great store traffic and loyalty (Dick et al. 1996).

H3 (3): When confronted with a POOS, if the retailer uses recommendation strategy to suggest a national brand substitution will lead to a higher decision satisfaction.

23

2.8 Compensation

Wirtz and Mattila(2004) conducted a study on consumer responses to compensation base on the justice theory. On one hand, they confirmed that service recovery outcomes, policies and interactional style jointly influence customer perceptions following a service failure (POOS). It demonstrated that the complexity of satisfaction evaluations in a service recovery setting. Just as using recommendation, the effectiveness of implementing compensation as a POOS policy is questioned. Even though, it is widely held that compensation might not only reduce the conflict between the customers and the retailers, it also increases controllability attributions (Bitner 1990), yet it might not add value (e.g. not increase satisfaction) under certain circumstances:

H4: When confronted with a POOS, if the retailer communicates the consumers by using compensations, the consumers will feel more satisfied with their shopping experience than there is no communications.

24

3 Research Methodologies

The impact of academic studies depends upon the appropriateness and rigor of the research methods chosen (Scandura and Williams, 2000). Design choices about instrumentation, data analysis, and

construct validation, and more may affect the quality of conclusions that are driven (Sackett and Larson, 1990). Historically, researchers have conducted experiment designs (either true experiment or Quasi-experiment) to investigate OOS reactions (Peckham 1963, Schary and Christopher 1979, Emmelhainz et al. 1991, Verbeke 1998, Zinn and Liu 2001). The field study investigates behavior in its natural setting. The idea of obtrusive primary data collection involves data that are collected by researchers. It

maximizes realism of context since it is conducted in a field setting, however it can be low on precision of measurement and control of behavioral variables. Similarly, the field experiment involves collecting data in a field setting but manipulating behavioral variables. It is moderately high on precision of measurement and realism of context but low on generalizablity. However, it is important to notice that true field OOS experiments are rare, because they are expensive and potentially very risky for the retailer (Verbeke et al. 1998). Hence, there is a high probability that using experiment with retailers will be constrained due to the considerations of expense and permanent loss of customers.

Secondly, an alternative methodology which is prevailing in OOS research is that setting up a

hypothetical OOS situation and using survey or interview to approach consumers (Walter and Grabner 1975, Campo et al. 2000, Fitzsimons 2000, and Sloot et al. 2005). The sample surveys maximize population generalization but are low on realism of context and precision of measurement. It is

important to mention that survey design contains the risk of generating biased or unrealistic feedbacks from customers. For example, Sloot et al. (2002) have observed that the reported store switch

percentages are generally higher in survey designs than the parameters in experimental designs. Nevertheless, the survey is expected to reach its maximal validity by conducting interviews of shoppers shortly after their shopping trips. It helps consumers to recall a vivid shopping experience which the POOS situation is more salient for them (Sloot et al, 2002). It therefore provides a relatively enhanced validity of the answers.

Last but not least, due to the development of information technology, more researchers have showed great interests in using laboratory experiment or computer simulation to conduct marketing-related studies. The laboratory experiment brings participants into an artificial setting for research purposes (Meltzoff, 1998). This strategy maximizes precision in measurement of behavior; the tradeoffs are low generalizability and low realism of context. Computer simulation involves artificial data creation or simulation of a process in a PC or online environment. This research strategy is moderately high on population generalizability and realism of context but lower on precision of measurement.

25

evidence that computer simulated shopping experiments provide highly realistic buying behavior data (Burke et al. 2002). Therefore, each different source of data provides a unique perspective on what had been happening after the POOS and how it affected consumers evaluates of satisfaction variance.

3.1 Data collection

With the qualitative literature reviews as a foundation, the quantitative phase of research reported next focused on how to empirically test the conceptual model. Customer satisfaction appears to most

typically be measured through surveys (McNeal and Lamb 1979). The popularity derives from its directness, ease of administration and interpretation, clarity of purpose and face validity. Data on POOS satisfaction were collected primarily by means of questionnaire, a data collection procedure sued by several other researchers (Peckham, 1963; Walter and Grabner, 1975; Schary and Christopher, 1979; Zinszer and Lesser, 1981; Emmelhainz et al.,1991). However, the questionnaire will be sent through the online channel to achieve two goals: (1) reach a reasonable amount of sample size; (2) slightly rely on the idea of computer stimulation to create a more realistic shopping environment for participant to recall their shopping experiences.

On one hand, a structured questionnaire offers good opportunities to collect data about consumer satisfaction towards POOS. In this research setting, the hypothetical POOS situations instead of real ones will be adopted, which have been used in previous explanatory studies (e.g., Campo et al. 2000). The advantage is that it enables us to study POOS behavior without negatively damage the brand equity or image of the retail stores (Sloot et al. 2005, Verbeke et al. 1998). Furthermore, the POOS behavior for different products groups and brands with varying brand equity levels can be investigated. However, people do not always act in the same way they claim that they would or sometimes have difficulties imagining what action they would actually take.

On the other hand, the application of new (electronic) technology for data collection was encouraged. Web-based data collection methods are attractive to researchers in marketing because of low costs and fast response rates (Ilieva et al. 2002). Another advantage of web-based survey is the better display of the questionnaire which is particularly suitable in this study context. It is important to set up a close-to – realistic retail environment for participant to recall their shopping experiences, thus they are able to accurately rate their satisfaction level. Online surveys allow messages to be delivered instantly to their recipients, irrespective of their geographical location. The same applies for the speed of the response. The response rate for the electronic survey was estimated to be around 50%. More than 60% of the online surveys took only one month (Ray et al. 2000). The hypothesis that web-based survey provides more complete information is supported by research conducted independently by different authors (Mehta & Sivada 1995; Bachman et al. 1996; Stanton 1998). In addition, it has been reported that people view online surveys as more important, interesting and enjoyable than traditional

self-administrated surveys (Edmonson 1997). Simultaneously, the limitations of an online survey should be kept in mind when interpreting findings; for example: a sample of respondents with Internet access may not be representative of certain populations. A lot of academic studies have been conducted by only inviting college students to participate, yet in this study, the questionnaire will be delivered to

26

yield statistically significant results. As a result, constructs and concepts must be captured parsimoniously in an interactive way.

3.2 Decision satisfaction measurement

Decision satisfaction refers to the level of feelings of satisfaction or regret regarding the chosen alternative or rejected alternative. Satisfaction therefore is associated only with the decision about the outcome choice. In this study, the decision satisfaction is treated as a dependent measure to examine how promotional out of stock (unavailability) and the retailer's communication strategies affect the attitudinal evaluation of consumer's satisfaction. The first scenario represents the response provided to customers who encountered a POOS without any communication. This condition was used as a

benchmark against which to compare the other alternative communication strategies.

Statistically speaking, one analytical technique to use in this problem would be to ask a sample of customers what they would evaluate their satisfaction level if the communication strategy for POOS was adopted. The method of “random assignment” will be used to appropriately compare the results against the sampling distributions frequently used in tests of statistical significance (Berk and Brewer. 1978). The advantage of using random assignment is that it ensures differences between the groups on all variable, assessed or not, are nonsystematic (Shaver, 1993)

Participants will be asked to complete a series of questions designed to measure their decision satisfaction. A six-item composite measure developed by Fitzsimons et al. (1997) will be adopted with minor modifications to better achieve the research goal. The items are as follows (all items have endpoints 1= strongly disagree, 7= strongly agree, unless otherwise noted):

(1) I found the process of deciding which product to buy frustrating;

(2) I am satisfied with my experience of deciding which product option to choose; (3) I found the process of deciding my shopping options easy and interesting;

(4) I believed the retailer has made fairly enough effort to facilitate my shopping visit; (5) I believe I am informed several good options to choose from;

(6) I was not disappointed or regretted by the unavailability of promotional products;

3.3 Execution of online simulation

The online simulation environment will be set up by using a prevalent online research tool: Qualtrics. The survey consists of five steps:

(1) A short questionnaire to collect general demographic information;

(2) A display of purchase stimulate that recalls participants the “close-to-reality” shopping experience, and helps respondents to understand the research setting (a display of shopping list with the

information of promotions);

27

shown to the participants by using the research tool “Qualtrics”. “Qualtrics” will also be set to

randomize the product group (hedonic or utilitarian) and the sequence of the scenarios. Therefore, it enables all the strategies to be rotated between two product groups. By doing this, the research setting is controlled for its validity on one hand. On the other hand, this helps the participants to experience the “close-to-reality” shopping environment. All the scenarios in this study are presented in Appendix 1. (4) All the participants will be invited to evaluate their decision satisfaction with a six-item

measurement. The participants will be asked what their decision satisfaction would have been had this product not been available but the communication from retailer is received.

(5) All the participants will be asked to give their personal preference over the products which appear in this research.

28

4 Empirical Results

4.1 Descriptive data (Socio-demographic Characteristic)

The online survey receives more than 117 respondents; however, it takes back 104 valid responds that have been appropriately filled. The ratio of callback of valid questionnaire is 88.9%. The first section of the survey records the demographic information of the participants. Various important demographic variables are summarized as shown in the following figure 4.1. The sample here is compared with a similar study which took place in Dutch supermarket. Interestingly, as can be seen from the comparison, a few demographic variables demonstrate divergent pictures, for example, the gender and age of the participants. One possible explanation of the differences here is the research methodology. This study was conducted with a computer simulated approach through online survey. The comparable study was taken place in the Dutch supermarkets by a team of three to four experienced interviewers of a research agency (Sloot et al. 2005). Due to the fact that online population is not reflective of the offline

population distribution and it is changing continually. Therefore, to infer for a general population based on a sample drawn from an online population is not as yet possible and will not be possible until the online and offline populations reflect each other (Kehoe and Pitkow, 1996). This sample contains the typical characteristics of online demographic statistics (Andrew et al. 2003). For instance, there are more male respondents than female ones. It also shows a rejuvenation of population in the sample, the majority of the respondents are younger than 35. Additionally, the average education level is relatively higher.

Besides the study of Sloot et al. (2005), Verbeke et al. (1998) once revealed some valuable insights into the Dutch supermarket (retail) context. According to their study, the supermarkets in The Netherlands share the following three important characteristics and trends:

(1)The emergence of “Large stores”, yet the ability of carrying private brands differs; (2)The “first-mover advantage” is significant, especially in rural areas;

(3)The shopping trips are carefully planned for Dutch shoppers.

As can be seen from the sample, only 21% of participants carry unplanned weekly grocery shopping, which is also in line with the number generated from CBL (2001), with a comparable percentage of 23% unplanned shopping.

Table 4.1

Demographic Variable Research Sample (2012) Sloot et al. (2005)

29 Household Income (%) Blow 500 17% 500 --- 1000 15% 1001 --- 2000 25% 2001 --- 3000 19% Above 3000 24%

Education (Dutch System)

Lower (%) 9% 27%

Middle (%) 35% 42%

Higher (%) 55% 30%

Weekly Shopping Frequency (%)

Daily planned Shopping 21% 2 or 3 Times and planned 58%

Spontaneous shopping 21% Leaflet Awareness (%) High Awareness 13% Moderate Awareness 52% Low Awareness 35%

4.2 Cronbach’s Alpha

In this study, the concept of satisfaction is directed at the construct of “decision satisfaction” which is measured with six-item scales (Fitzsimons et al. 1997). In their previous research, the measure of

internal consistency has been statistically confirmed. In this study, the construct of “decision satisfaction” is rated accordingly to 16 different scenarios; the participants used “six-item” measure to reflect their satisfaction level. Hence, the Cronbach Coefficients were examined separately. Table 4.2 gives a summary of all the values of Cronbach Coefficients involved in the study.

Table 4.2

Six-Items Satisfaction Measure Hedonic Product Group Utilitarian Product Group

POOS with NO Communications 0.663 0.694

POOS with Apology 0.703 0.753

POOS with Availability

Announcement 0.726 0.707

POOS with Higher Priced

Recommendation 0.610 0.711

POOS with Lower Priced

Recommendation 0.749 0.637

POOS with National Brand

Recommendation 0.662 0.784

POOS with Private Brand

Recommendation 0.741 0.683

30

It is rather obvious to notice that all the Crobach Coefficient Alpha is higher than the threshold of 0.6, which implies a sufficient level of scale reliability (Malhotra 2007). It also suggests that using the construct of “decision satisfaction” derived from Fitzsimons et al. (1997) is an appropriate measure of satisfaction in the POOS context.

4.3 Wilconxon Signed-Rank Test

The effectiveness of the POOS communication strategies will be statistically tested by using the

Wilcoxon Signed-Rank Test. In the following chapters, the statistical result of individual communication strategy will be reported. It is important to point it out that all the communication strategies are not compared with each other but with the “baseline” case. This is because in reality, not all of the

communication strategies are all suitable for retailers to implement on their POOS products at the same time.

The Wilconxon Signed-Rank test is equivalent to the dependent T-test. It gives clear comparison and contrast between the data that are paired and come from the same population. Since one important feature of Wilcoxon Signed-Rank Test is the absence of normality, the normality test of data will not be discussed here. In this study, the sample size is 104, therefore the test statistic W will be computed as:

And the Z-Score will be further computed as:

4.3.1 Apology

Table 4.3.1 gives a summary of the comparisons of using apology as communication tactics to consumers. As it shows that in the hedonic product group, the implementation of apology led to an adverse effect by having a lower mean (4.3558) than there is no communication strategy (5.1987). However, it is interesting to notice that consumers actually have a relatively high level of satisfaction deviation when they confronted with the apology advertisement. The Z-test score (-4.475) and p-value (one-tailed: 0.000, <0.05) elicit the fact that the difference between two paired samples are significant. The apology strategy seems to play a counter-effective for hedonic POOS products.

On the other hand, the result from Utilitarian product shows an opposite picture. The apology strategies scored higher satisfaction than no communication strategy was imposed. The mean score of consumer decision satisfaction were 4.6699 and 4.4071 respectively. The Z-test and p-value (0.011) indicate the difference is significant. Apology strategy increases consumer’s decision satisfaction when the POOS happens.