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Keywords: stockout, logistics, retail and consumer behavior.
potential sales lost remains unknown (Zinn and Liu, 2001).
component, leaving long-run ones as a suggestion for future studies.
.follows this path: attempting to explain consumer reactions to stockouts based on independent variables related to consumer characteristics. the level of stockouts has remained high for long.. Sloot et al. The purpose of this study was to determine the independent variables that influence consumer reaction to stockouts. 2001. et al. Zinn and Liu. 2005. Fitzsimons. It is as yet unknown whether independent variables are valid for different products and store types. the article will attempt to answer the following questions: Can consumer reaction to stockouts be predicted? What independent variables explain consumers leaving stores when faced with a stockout? Prior studies typically focused on surveying consumers with questionnaires as they exited stores. 2007). Understanding the consumer reaction to retail stockouts may lead retail practitioners to develop more appropriate merchandising. The issue of retail stockouts is far from trivial. 2000. To summarize. purchase situation and store type. item families and store formats. In spite of every effort made so far. This information is also important for logistics practitioners to determine the optimum level of service for their products portfolio. The results show that consumer reactions can be predicted and their short-term costs can be estimated. Replicas of this kind of study may lead to a better understanding of consumer reaction to stockouts and to a generalization or not of the results for different items. the topic has so far undergone little examination. establish more consistent customer- recovery policies and implement more efficient loyalty programs. Rani and Velayudhan.
. The first of them reviews the literature. The remainder of the paper has been divided into four sections. these independent results show that the problem is of a chronic nature. the appropriate levels of stockouts will increasingly remain a source of competitive advantage because the level of service offered by competing retailers is not equivalent. 2006). 2001).3 percent. which warned retailers and manufacturers of the potential losses arising from the absence of products on shelves. describes the research method: experiment planning. limitations and managerial implications are discussed in the final section. Zinn and Liu.2 percent is close to Roland Berger’s (2003) 8. Chopra and Meindl. is shown in the review next. 1994.reported at 8. It seems evident that the level of stockout will never drop to zero. Gijsbrechts and Nisol. which is the focus of this study. the Progressive Grocer produced a two-essay series documenting the observed frequency of stockouts for items sold in supermarkets. Others have attempted to understand the consumer reaction to stockouts (Campo. divided in three parts. and data collection and analysis. The historic evolution of this second approach. Findings. The first study to investigate stockouts was Peckham (1963). variables selection. The third section reports our results. In 1968. 2000. The second. As a result. Although the two may not be directly comparable because of changes to the products mix in the course of the forty years that separate the two studies. LITERATURE REVIEW The literature does not reveal a model intended to estimate the cost of stockout.
. they conducted an experiment. proposing a more comprehensive model capable of mapping out all possible consumer reactions to stockouts.based only on the quantification of unsold items. level of brand loyalty attained by certain product categories and. Examining the reactions of customers of liquor stores in Ohio. Instead of relying on questionnaire-based surveys. Shycon and Sprague (1975) also proposed a model to estimate the cost of stockouts. which influenced the majority of subsequent studies. After recording the frequency of stockouts and the expected reactions. A unique approach can be found in Charlton and Ehrenberg (1976). Therefore. Walter and Grabner (1975) supplemented the 1968 Progressive Grocer study. 158 consumers were visited at home and given the opportunity to purchase selected detergent and tea out of a mix of three brands of each product. On recording stockouts.
. market shares had not returned to their pre-strike levels. After the end of the four-month strike. Schary and Becker (1978) also investigated the long-term effect of stockouts. Motes and Castleberry (1985) partially replicated the Chardon and Ehrenberg (1976) study using actual potato-chip and cereal brands. But the share was lower than during the strike. two regional and two national.brands were created specifically for the study. These studies minimize the effect of stockouts on the long-term behavior of consumers.167 consumers at two suburban stores of a London supermarket chain. As expected. This study indicates a substantial effect of stockouts on the long- term behavior of consumers. Another large-scale research of consumer reactions was done in England by Schary and Christopher (1979).4 percent) and experienced at least one stockout. indicating a return to the favorite brand as soon as the stockout was resolved. the four brands kept a bigger share than initially. but return to it as supply was reestablished. They obtained similar results. When the situation was most recently analyzed. 30 months after the strike. 343 consumers (29. Only four brands. Out of this sample. These two studies did not consider the possibility of switching stores as a response to stockouts. brand loyalty and demographic variables. Consumer reaction was correlate to store image. Some differences in behavior by age bracket and profession were observed. the four gained market share during the stockout period. Stockouts were introduced in the course of the study and the reaction of consumers was measured. The opportunity emerged out of a teamsters strike in Seattle in 1971 that limited the supply of beer. who interviewed a sample of 1.
. orange juice. The observed market-share decline was 10 percent on average. The simulation did not include store brands. Zinszer and Lesser (1980) pioneered identification of a positive correlation between product features and purchase situations. Consumers were interviewed at the checkout line as to their reaction to the stockout. The items were the main brands’ best-sellers in the categories: coffee grinds. peanut butter and tomato sauce. Store image was also affected by stockouts. Straughn (1991) was the first to use scanner information in a stockout study. Their model investigated how stockouts for promotional items affect consumers of different social demographic variables and how they affect the store image and the dissatisfied consumers’ intent to return to the store and make future purchases. This occurs because retailers prefer to stock up their shelves with the best-selling brand. The short- term effect was irrelevant. The results varied widely for the five tested items compared to other out of stock items. Farris. and stockouts. Brands with bigger market share benefit more than those with smaller shares when the other brand was out of stock. Emmelhainz. defined as more than five straight weeks of stockout. was substantial. and Stock (1991) removed five items from the shelves of a discount supermarket. Olver and Kluyver (1988) developed a simulation model which showed a positive and curved relationship between distribution and market share. tooth paste. Emmelhainz. The long-term effect.whose main wage earner was a manager or a professional tended to leave the store and visit a competitor. This spiraling process explains the curved relationship between distribution and market-share. For a period of four days. She investigated the effects of stockouts on the market share of chocolate.
. bread. On the other hand. sodas. Almost half the tracked items were out of stock at least one per month. and diapers. surprise. combining store audits. as a consequence. Competitor B expects an offsetting move from its counterpart A at a future point in time or in a different region. The figure rises to 11 percent on Sundays and 15 percent of promotional products. The study tracked items in the categories: yogurt. The Andersen Consulting (1996) study was a comprehensive survey of retail stockouts. promotion. bottled water. These results oppose the natural trend of the literature on stockouts. the previous Progressive Grocer (1968) study showed that stockouts peaked on Mondays (20 percent). scanner data and personal interviews with the industry and consumers.
.purchase) and unexpectedness of the stockout. on the other hand. Gruen. Their findings carried the subject of stockouts to a higher level of analysis and generalization.
. as it is a typical consumption item for the selected target public: university students. The chosen method was experiment planning in order to enable manipulating various purchase situations and assess their impact on consumer reactions. The experiment’s store chain marketed appliances and electronics. the chosen item was Personal Digital Assistants (PDA). METHODOLOGY The purpose of this study was to identify independent variables to explain consumer reaction to stockouts. This method allows manipulating scenarios and testing combinations of situational variables that a questionnaire-based study is unable to address. more specifically.
. whether or not the stockout was unexpected.” The second paragraph indicates that the respondent was surprised by the stockout. . In addition to a picture of each item available. Finally. The store should have this item in stock.
. (2005) Kucuk (2003) Quality-oriented Sloot et. al. Read consumer personal urgency unexpectedness reaction to information scenario scenario stockout questionnaire Variables Selection Table 1 lists the relevant variables found in previous studies that influence consumer reaction to stockouts. (2005) Store loyalty Emmelhainz et. al. al.al.al. al.(2000) Zinn and Liu (2001) Kucuk (2003) Sloot et.al (2005) Shelf space Kucuk (2003) Accessibility Kucuk (2003) Advertising Kucuk (2003) Stockpile effect Sloot et.(1998) Campo et. al.(1998) Campo et.(2000) Assortment Campo et.(2000) Zinn and Liu (2001) Level of planning Zinn and Liu (2001) Sloot et. (2005) Perceived risk Emmelhainz et. (1991) Verbeke et.al.(2000) Sloot et.(2000) Sloot et.al.al. (1991) Verbeke et.(1998) Campo et. al. al. (1991) Urgency Emmelhainz et.Read planning. al.al (2005) Kucuk (2003) Perception as discount store Zinn and Liu (2001) Kucuk (2003) Sloot et. Table 1: Relevant independent variables found in previous studies Independent variables Author Schary and Christopher Brand loyalty (1979) Emmelhainz et. al.
. Scenario recollection and respondent tiredness were also investigated. Ninety-five percent of respondents answered all questions correctly. To assess the understanding and recollection of the experiment’s scenarios. it was determined that no fatigue issues had emerged. Finally. The questions determined whether respondents understood the level of purchase urgency (yes or no). three questions were asked about each scenario. The average score obtained was 5. After completing the pre-trial. Fitzsimons 2000). students are frequent participants in experiments. 1996. and no abnormalities were reported. impulse purchase). (2007) around 10 percent of American university students owned a PDA in 2007 while another 10 percent owned a combined PDA-mobile phone. Before collecting the data. and whether the stockout had been unexpected (yes or no). were not statistically significant in explaining consumer reaction to stockouts (Zinn and Liu. Besides. university students are actual PDA consumers. According to Salaway. 2001). including those for stockout studies (Petty and Cacioppo. This score indicates that the experiment’s purchase situation closely reflects reality.particular age and schooling. respondents were asked to rate its perceived realism on a 7-point scale where 7 stood for agree in full” and 1 meant “disagree in full” with the statement that the purchase situation was realistic. The purpose of the pre-trial was to check for scenario realism and clarity. Finally.
. Faced with a stockout. and perceived store type.. consumer characteristics. One possible approach was to assess the effect of each individual variable on consumer reactions through a chi-squared test and/or a group average differences test. These methods. the reactions of brand-loyal consumers in an urgent purchase situation might differ from those of brand-loyal consumers in a planned purchase situation. we had to choose a multivariate analytical method to assess the relative influence of several simultaneous independent variables on the dependent variable – consumer reactions. however. This method.respondents did not complain of tiredness during the informal interviews and completed the experiment in under 10 minutes. as seen in Table 2. whose purpose is to predict changes in the dependent variable in response to changes in the independent variables. consumers may “substitute the missing item” or “leave the store”. For example. As a result. RESULTS The main goal of the study was to investigate the relationship between consumer reaction to stockouts and the several independent variables listed under the categories: purchase situations.al.
. The resulting logit model was considered useful in estimating consumer reactions to stockouts based on several independent variables relative to the purchase process for a product at a specific store. Based on this information. It was decided that a sample of 221 would suffice for the purposes of validation. This left 331 observations to estimate the log regression. Logit regression also allows accommodating non-metric independent variables such as urgency of the purchase (yes or no) through dichotomic-variable encoding. The experiment’s database includes 552 observations. The two groups. in particular the normality of variables and equal variance and co- variance matrices across groups. analysis and trial. contain 331 and 221 observations respectively. Logit regression was therefore considered the most appropriate analytical method for the study at hand. logistics practitioners can develop tailor-made strategies to compute and reduce the cost of stockouts. however. (2007) is 20 observations per independent variable. are not met (Amemiya and Powell. 1980).assumptions of both are met. which makes them sufficiently comparable in size so as to not affect the estimation nor the ranking processes.
.05 11. X4 and X7 show the highest differences across groups.48 5.26 0.00 0.53 0.46 0.359 1. Table 3: Descriptive statistics of the groups and equality test for the log regression.716) correlation between store loyalty and the interaction of store loyalty and brand loyalty.89 1.39 1.50 0. such as the absence of perfect multicollinearity across the model’s predictive independent variables.50 0.618 0. which shows the group averages for each independent variable based on the 331 observations that make u the analysis sample.77 0. X7= purchase urgency.47 1: Leave store 3.8 4.50 0.32 164 4 Total 12. Even so. The logit regression method is considered robust enough to overcome a potential violation of group variance/co-variance homogeneity and of the normality of error distribution.83 0.50 331 4 Standard deviation for independent variables 0: Substitute item 3.57 11.001 0. X3= store loyalty.49 0.93 1.52 0.3 4.81 0. We begin our assessment of the log regression by examining Table 3. To prevent multicollinearity problems. some requirements had to be met.324 0.8 4.47 Total 3.251 0.63 0.50 0.62 4.7 2 8 3 43 Significance level 0.66 3. Variables X1.73 1. X3.91 0.00 9 0 0 a Y = consumer reaction to stockouts b X1= brand loyalty.021 0. X6= unexpectedness of stockout.50 0.68 167 4 1: Leave store 12.91 4. we had to remove the variable of interacted store loyalty and brand loyalty before running the regression.05 0. Group averages for independent variables Group Dependent X1 X2 X3 X4 X5 X6 X7 size variable 0: Substitute item 11.52 0.50 Equality test for group averages Univariate F-ratio 6.249 50. X5= purchase planning.50 0.50 0. X4= perceived as discount store.48 1.
.254 4. Wald DF Sig.664 1.149 0.129 1. which shows multicollinearity problems.001 1.
.031 -0.1 Percentage This leads to the conclusion that the log regression model finds strong empirical support in validation with separate samples.000 Store loyalty -0.138 -0.1 Leave the Yes 38 126 76.037 -0.8 25 85 77.000 Store loyalty and -0. taken together. showing an accuracy rate of 69. as seen in Table 6.8 70. Given this additional indicator. they appear to satisfactorily explain consumer reaction to stockouts in the relevant period.227 1.3 Store Total 69.108 1.152 1.9 70 41 63. Store loyalty Planned Brand loyalty Store loyalty and urgent purchase purchase Brand loyalty 1.8 percent for the analysis sample and 70. Table 6 – Validity test for the log regression model Analysis Sample Validation Sample Predicted Result Predicted Result Leave the Store Leave the Store Accuracy Accuracy Actual Result No Yes No Yes rate (%) rate (%) No 105 62 62. with results at the same approximate level.1 percent for the validation sample. using these independent variables with the model is certainly statistically viable.000 urgent purchase Validation of the log regression model can be assessed based on the predicted and actual results.000 Planned purchase 0. In sum.
.129 (or approximately 13 percent) increase in the probability of the consumer leaving the store. A one-unit gain in average brand-loyalty perception also affects the probabilities that the consumer will substitute the missing item because the opposite reaction to leaving the store is product substitution. store loyalty and urgent purchase (at least at the .9 percent. Table 4 shows results for the probability of consumers leaving the store in response to a stockout event as a function of the set of independent variables. A one-unit gain in perceived brand loyalty increases the probability of leaving the store by more than 12. It is worth noting that the coefficient for this variable is high. This is an additional indication that the variable can be used to estimate the probability of leaving the store.129 is contained in the interval with lower limit of 1. The stronger the consumer’s perception of brand loyalty.109 and upper limit of 1. the coefficient Exp (B) reported in the table represents the change in the probability of the consumer leaving the store given a marginal change in the independent variable. The direction of the relationship between each of these variables and consumer reactions is intuitive. The greater the consumer’s loyalty to the store. the coefficient of the first variable (brand loyalty) means that a one-unit gain in the average perception of consumer brand loyalty – say. The Exp(B) result of 1. from 3 to 4 on a 5-point scale – will cause a 0. Note that the abridged version of variables’ names can be found in brackets in Table 2. Results for store loyalty are similarly intuitive. For each independent variable. The results show that four variables can be used to build the model.216. planned purchase.
. It is a challenge for retailers to attempt to convince customers of this kind to not leave the store and thereby preserve the revenue. and tend to substitute the missing item. This supports retailers’ investment in building store-loyalty in consumers. The results for planned purchase reached the highest level of significance. Consumers tend to replace the missing item within the store itself when urgency is a factor. To this end. store-loyal customers are less prone to leave the store in the event of a stockout.6 percent. we find that the model is statistically fit to estimate the probability of a certain customer leaving the store under certain circumstances. The next finding relative to the probability of leaving the store requires more detained analysis. the probability of leaving the store is even lower and the probability of substitution rises by 13. we must draw the log equation based on the estimated coefficients.gain in store loyalty reduces the probability of leaving the store by more than 7. The store-loyalty variable is conversely related with the probability of leaving the store in response to a stockout.
. substitute indicates that the more brand-loyal a customer is. Therefore. For example.Findings Our results show that consumer reactions can be estimated based on a limited set of consumer characteristics. + Store loyalty and Urgent purchase + - These results are managerially relevant for stores whose business strategy is based on customer-loyalty building. Table 7 provides a summary showing all of the significant relationships the study found between the independent variables and consumer reactions. A positive sign indicates a direct relationship. consumers who have urgent need of an item are more likely to substitute that those that do not have such urgency. This results underlines the need for retailers to invest in customer-loyalty building actions and visual merchandising to increase customer loyalty. Consumers at such stores are more likely to substitute an item in the event of stockouts than the consumers at stores that not do invest in store-loyalty. + Store loyalty + - Planned purchase . purchase situation and perceived store type. The signs pertain to the direction of the relationship. or last-minute Holiday shoppers. Table 7: Summary of significant relationships – Logit Regression Model Variable Substitute Leave the Store Brand loyalty . Purchase urgency is correlated with a larger probability of item substitution. who tend to substitute the missing item.
. New issues also arose in the course of the study: in a planned-purchase situation. Limitations It is important to point out some of the study’s limitations. The one-variable (planned purchase) coefficient is high. The results show that consumer reaction to stockouts can be determined under certain circumstances and that the probability of an individual leaving the store in the presence of certain characteristics can be estimated. as this encourages consumers to leave the store in the event of stockouts. and experiments done with students. how to encourage customers to substitute an item in the event of a stockout. At this point. This would be a future research goal. a single store chain. The variable brand loyalty has an average coefficient of 12.Planned for purchase is correlated with a greater probability of the customer leaving the store.9 percent. The present stage of research into retail stockouts does not enable arguing that it is possible to estimate consumer reactions to stockouts based on independent variables that can be applied to different items and store types. This emphasizes manufacturers’ need to invest in brand promotion. instead of leaving the store? Are there cost-of-stockout differences between impulse-purchase items and comparative-shopping items? In sum. Planned purchase was determined to be the main reason why consumers leave a store upon encountering a stockout. at 66. the study reports the results of a study of consumers’ short-term reaction to stockouts. Three limitations are recognized: a single stockout experience.4 percent. Development of the issue of stockouts and its direction towards studies involving consumers appear to be fertile grounds for investigation.
. we admit that it remains a limitation.
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