Shopping Lists as an External Memory Aid for Grocery ...€¦ · Thus, memory aids can be internal...

JOURNAL OF CONSUMER PSYCHOLOGY, 8(4), 343-375 Copyright Q 1999, Lawrence Erlbaum Associates, Inc.

Shopping Lists as an External Memory Aid for Grocery Shopping: Influences on

List Writing and List Fulfillment

Lauren G. Block and Vicki G. Morwitz Stem School of Business

New York University

We explore the use of shopping lists as an external memory aid to consumers’ grocery shopping by examining the factors that influence the content ofconsumers’ shopping lists and the effectiveness of shopping lists as external memory aids. We analyze the shopping lists and actual purchase behavior of a panel of consumers during multiple grocery shopping trips conducted over a 2-month period. Our results indicate that consumers record on their lists approximately 40% of the items they ultimately purchase. Consistent with the external memory literature, we find that consumers write items on their shopping lists for which there are financial incentives to remember ( e g , manufacturers’ coupons), need-based incentives to remember (e.g., the product is frequently used), and schema-based advantages to remember (e.g,, items purchased on fill-in trips). More than 80% of the items written on the shopping list were actually purchased. Thus, shopping lists appear to be an effective external memory storage device for grocery purchasing. We discuss these and other findings in relation to the lit- eratures on external memory and planned versus unplanned purchasing.

Consider the following two grocery shopping scenarios. h i e is about to embark on a trip to his local supermarket to pick up several items his family needs for dinner that evening. Before he leaves for the store, Amie’s wife gives him a shopping list of the items she would like him to purchase: lettuce, tomatoes, cheese, milk, and Coca-Cola. When Arnie arrives at the store, he purchases the lettuce, tomatoes, and milk. He decides to purchase Pepsi Cola instead of Coca-Cola because the store is

Requests for reprints should be sent to Lauren G. Block, New York University, Stem School of Busi- ness, Management Education Center, 44 West Fourth Street, Suite 8-81, New York, NY 10012-1 126. E-mail: [email protected]

344 BLOCK AND MORWITZ

having a sale on Pepsi. Arnie also decides to buy some potato chips, some ice cream, and some chocolate bars. Arnie forgets to purchase the cheese.

Ruth, another consumer, is also about to go grocery shopping. Ruth needs to purchase many items this week, so she first writes out a shopping list before she goes to the store so she will not forget to buy any items. While putting her shopping list together, Ruth checks her cabinets to see what products she currently needs or will soon be out of. She also checks retail grocery advertisements to find out what is on sale at her local supermarket. Ruth also checks her coupon file for manufacturers’ coupons for products she might buy. Once in the store, Ruth makes sure she purchases every item on her list by crossing out each item as she puts it into her shopping basket. The store is out of one sale item that is on Ruth’s list. Before she leaves the store, Ruth goes to the service desk to get a rain check for the sold-out item.

Consumers like Ruth and h i e presumably create and use shopping lists as an aid to help them while grocery shopping. As summarized in a recent Wall Street Journal article, shopping for grocery items has become an increasingly complex task (Narisetti, 1997):

Today’s average consumer, more often than not a woman, takes just 21 minutes to do her shopping-from the moment she slams her car door in a supermarket parking lot to the moment she climbs back in with her purchases. In that time, she buys an average of 18 items, out of 30,000 to 40,000 choices. She has less time to browse; it is down 25% from five years ago. (p. A l )

When faced with a task with multiple decisions and numerous distractions, consumers may rely on aids to help simplify the decision making. For example, consumers may use shopping lists to help them make all of their planned purchases and to avoid making unnecessary impulse purchases. A study by the Point of Pur- chasing Advertising Institute found that 55% of supermarket shoppers use shopping lists (Rickard, 1995). Shopping lists are a specific form of a category of tools that cognitive psychologists call memory aids: any tool or device used to enhance remembering. To remember to bring a canned good to work for a Thanksgiving food drive, one might write a note, mentally rehearse the act, or leave the can by the front door for the next morning. Thus, memory aids can be internal or external to the person.

In this article, we explore the use of shopping lists as an external memory aid for consumers’ grocery shopping. We develop hypotheses about factors that influence the content of shopping lists and factors that influence list fulfillment (i.e., the extent to which consumers purchase items on the list) based on the literature on external memory aids and previous studies of planned and unplanned grocery shopping. We test these hypotheses by examining the shopping lists and actual purchase behavior of a panel of consumers during multiple grocery shopping trips

SHOPPING LISTS 345

conducted over a 2-month period. Panel members were recruited from several different religious and social organizations throughout the East coast of the United States. Members have varying demographic profiles in terms of household composition, household size, ages of household members, presence of children, total household income, and so forth. People were selected to be panel members if they characterized themselves as generally using shopping lists when grocery shopping. Thus, our focus is not on characterizing the differences between households that use and do not use shopping lists. Rather, we focus on understanding why some purchased items are written on lists whereas others are not and why some list items are purchased whereas others are not. Our results provide insights into the effectiveness of external memory aids for helping consumers to make planned purchases and avoid making unplanned purchases.

MEMORY AIDS AND THE PLANNING PROCESS

With the introduction of the theory of reasoned action more than 2 decades ago, Fishbein positioned intention as the proximal cause of one’s behavior (Fishbein & Ajzen, 1975). Turning intentions into behavior can often be a complex task medi- ated by planning processes (Eagly & Chaiken, 1993). Even a simple behavior, like buying milk on one’s way home from work, may require cognitive planning-for example, leaving work earlier, or taking a different route home. Or perhaps a person will place a note (“Buy milk”) on her briefcase as a last-minute reminder before leaving work. As different as these plans are, they serve the common function of fa- cilitating performance of the goal at hand (Gollwitzer, 1996).

Gollwitzer and Heckhausen (Gollwitzer, 1990; Heckhausen & Gollwitzer, 1987) suggested that the process of planning one’s behavior consists of motivational components (e.g., deciding wishes to pursue) and volitional components (e.g., deciding on how and when to act). This motivational-volitional theory of goal achievement, called the model of action phases, attempts to delineate distinct temporal tasks to turn intentions into behavior. The four sequential tasks are as follows: setting preferences between or among wishes, making plans for goal-directed actions, effectively executing goal-directed actions, and evaluating outcomes (Gollwitzer, 1996). The first and last tasks are motivational components, which Gollwitzer labeled the predecisional phase and the postactional phase. The intermediary tasks are volitional components called preacfional and actional phases.

Unfortunately, the model of action phases and the theory of reasoned action and its variants (e.g., the theory of planned behavior; Ajzen, 1985) are silent on the process by which the volitional tasks of making and executing plans are actually conducted (Eagly & Chaiken, 1993). To shed insight into the process of making and executing plans for grocery shopping, we draw on the literature on


external memory aids and on planned versus unplanned purchasing. We begin by decomposing consumers’ grocery shopping into two stages that correspond to Gollwitzer’s volitional phases: preactional and actional. In the first stage (preactional), intentions to purchase specific products are formed. It is in this stage that some consumers create an external memory aid and write a shopping list to take with them to the supermarket. The second stage is actual purchase behavior. During the purchase behavior stage (actional), items on the shopping list might be purchased or decided against, and substitute or unplanned items might be purchased. Although we believe this two-stage process characterizes grocery shopping behavior in many cases, we also note that this approach may not apply to all consumers in all situations. For example, consumers may not al- ways plan in a distinct first stage; they may at times construct a more “online” plan during the second stage while in the supermarket.

We first review the theory on external memory aids. Based on this literature and prior studies of planned purchasing, we next develop hypotheses for the list writing (preactional) and list fulfillment (actional) stages of shopping behavior.

Shopping Lists as an External Memory Aid

Memory aids are devices or strategies deliberately used to enhance memory. In daily life, people use memory aids for simple and ordinary tasks and for more complex situations. For example, a reminder note to buy milk after dropping the kids off at school is a deliberate strategy to aid recall of a simple, mundane task. A more comprehensive list including all the ingredients necessary for cooking Thanksgiving dinner is a deliberate memory device used to aid recall of a more complex and less frequently encountered situation. As trivial as these memory aids may seem, they play a major role in people’s ability to restructure information or or- ganize their daily lives (Coupey, 1994; Intons-Peterson & Fournier, 1986).

Memory aids are classified into two general types: internal and external (Harris, 1980, 1984). Znternal memory aids rely on devices internal to oneself, such as mental rehearsing and using rhymes or other mnemonic devices. External memory aids are physical, tangible memory prompts external to the person, such as writing lists, writing on one’s hand, and putting notes on a calendar. Although external devices in general are widely used, the particular device used at any given time is situation and context specific. Grocery shopping lists are external memory aids specific to grocery shopping. Intons-Peterson and Fournier (1986) found that reminder notes, like lists, are among the five most frequently used memory aids. Al- though traditional theories of memory have not explicitly included external memory aids, context cues like the consistency between the rooms in which learning took place and subsequent testing of the material can be thought of as external memory aids (Intons-Peterson, 1993).

SHOPPING LISTS 347

The literature on external memory aids primarily specifies conditions under which external memory aids are likely to be used (external vs. internal memory aids: Intons-Peterson & Fournier, 1986; younger vs. older people: Einstein, McDaniel, Richardson, Guynn, & Cunfer, 1995; retrospective vs. prospective memory tasks: Nallan, Kennedy, & Kennedy, 1991; see Intons-Peterson, 1993, for a review of external memory aids). We extend the literature on external memory aids in three important ways. First, we examine the factors that influence the content of the memory aid (list writing) and factors that influence the effectiveness of the list in aiding memory (list fulfillment). Prior studies have not addressed either of these issues (Intons-Peterson, 1993). Second, we extend the literature by explor- ing how external memory aids are used for helping people both remember to take planned actions and avoid unplanned actions. Previous studies have only examined the former. Third, we extend the literature by examining the use and effectiveness of memory aids in an important consumer task: the grocery shopping trip. By focusing on shopping lists and grocery shopping, we also shed insight into an underresearched domain of planned versus unplanned purchasing. In particular, whereas most previous studies of planned and unplanned purchasing have focused on only one or two product categories (e.g., Bucklin & Lattin, 1991; Cobb & Hoyer, 1986; Kahn & Schmittlein, 1992), our methodology allow us to study the planning process for the entire basket of supermarket products. Specifically, our study includes 227 different product categories. The inclusion of multiple product categories is an important contribution to the literature on planned purchasing. Consumers’ needs for a portfolio of products influence their store evaluation and store choice (Bell, Ho, & Tang, 1998; Bell & Lattin, 1998; Blattberg, Peacock, & Sen, 1976; Narasimhan, Neslin, & Sen, 1996). Thus, we should expect to gain better insight concerning consumer shopping behavior when we study records for the entire basket of items compared to studies that examine records for only one or two items. Our methodology of using shopping lists also allows us to answer questions about shopping behavior that previous studies have not been able to address. Specifically, our method allows us to examine factors that influence the formation of intentions for planned items, plan fulfillment, and deviations from planned purchases (e.g., brand switching). In contrast, previous studies have focused primarily on the effect of in-store influences on shopping behavior (Guadagni & Little, 1983; Gupta, 1988) and on factors influencing unplanned purchasing (Bucklin & Lattin, 1991; Cobb & Hoyer, 1986). Our contribution to prior studies of planned purchasing is further explicated in the Discussion section.

Grocery Shopping: The List Writing Stage

Consumers’ grocery shopping behavior can be thought of as a two-stage process. In the first stage of intention formation, many consumers create an external memory


aid, the shopping list, to help in their grocery planning. In their study of external memory aids, Intons-Peterson and Fournier (1986) suggested several conditions for their use, two of which are specifically applicable to grocery shopping. First, external memory aids are likely to be used when there is a high premium on accurate remembering. In grocery shopping, this may take the form of financial or need-based incentives to remember to purchase. Second, external aids are likely used when memory load is to be avoided-for example, instances when a consumer does not have a frequently accessed schema for the shopping trip. In the following sections, we draw on prior studies of planned and unplanned purchasing to generate hypotheses on specific task-related factors that influence the content of shopping lists under these two conditions.

Financial and need-based incentives to remember. Intons-Peterson and Fournier (1986) provided conditions that are likely to generate the use of an external memory aid. One of these specifies that external aids are likely to be used when there is a premium on accurate remembering. We identify two categories specific to grocery shopping: financial gain from accurate remembering and a need-based advantage to remembering to purchase grocery items. We discuss these in turn here.

In grocery shopping, a premium might be in the form of cost savings from purchasing specific items. Thus, we suggest that consumers write items on a shopping list to remember to take advantage of cost savings. We identified several promotion factors from the literature on planned grocery purchases that support this theorizing. Kahn and Schmittlein (1992) asserted that because coupons involve cutting and sorting prior to use, “using a coupon during a store visit implies that this product’s purchase was planned in advance” (p. 299). This suggests that consumers are more likely to have written a purchased item on their shopping list prior to shopping when a coupon for that item was available. Similarly, feature advertising in local newspapers offers an economic incentive to make a purchase (i.e., in the form of store coupons and price reductions). Therefore, it is likely that consumers write featured items on their shopping lists prior to shopping to remember to purchase them. Stated formally:

Hlist w”kgingl: The probability a grocery product is written on the list given it is purchased, p(product on list I purchase), is greater (a) for products in categories where manufacturers’ coupons are used more often rather than less often and (b) for products in categories where feature advertising is used more frequently rather than less frequently.

Need-based incentives would also create an advantage for remembering to purchase specific items. There are some items that consumers may write on a list because inventory of the item is running low and the item is frequently used and

SHOPPING LISTS 349

needed (e.g., baby formula and diapers). It is important to remember to buy these items during the shopping trip and therefore likely that the consumer will use an external memory aid for these items. On the other hand, for infrequently purchased items, consumers may not realize that they need the item until they are exposed to in-store stimuli. Consistent with this, in an early study on unplanned grocery shopping, Kollat and Willet (1967) found that frequently purchased products (e.g., milk, bread, eggs) have relatively high planned purchase rates, whereas infrequently purchased products (e.g., drugs and toiletries) have relatively high unplanned purchase rates. Accordingly, we test whether the probability that a purchased item is written on a shopping list is higher the more frequently items in that product category are purchased.

Hbsthting;?: P(product on list I purchase) increases with how frequently items in the product category are purchased.

Schema-based advantages to remembering. Intons-Peterson and Fournier (1986) suggested that external memory aids are used when memory load is to be avoided, such as when one needs to allocate resources to competing tasks or when one expects interference with the to-be-remembered information. Interfer- ence and forgetting are more likely in scenarios in which one does not have a well-defined and frequently accessed schema or script for the event. Schemas are cognitive structures, based on past experience, that are specific to a stimulus domain (Abelson, 1976, 1981; Fiske & Linville, 1980). We suggest that consumers have grocery shopping schemas, which are more or less accessible depending on the specific domain. For example, consumers may have schemas for shopping at familiar grocery stores. In familiar stores, consumers know the store layout and have a mental map of where items are located; the opposite is true for unfamiliar stores. Because the cognitive resources required for shopping in an unfamiliar store are more demanding, there is greater need for an external memory aid. We test whether the probability that a purchased item was written on the shopping list prior to shopping is greater when consumers shop in less familiar stores. Similarly, consumers may have schemas for groups or categories of items. The schema for salad might include lettuce, tomatoes, cucumbers, and carrots. Likewise, consumers have holiday schemas. A Thanksgiving dinner schema might include turkey, cranberry sauce, sweet potatoes, and apple cider. We expect, then, that when a consumer has a highly defined schema for a particular shopping trip, the need for an external memory aid is reduced. It follows that items are more likely to be written on a shopping list for shopping trips that do not have an accessible schema. We therefore test whether the probability that a purchased item was written on the shopping list is lower for holiday trips.

Similar to a schema, a script is a cognitive structure based on past experience within a specific domain. Scripts link repeated or habitual behaviors into an orga-


nized memory structure (Abelson, 1976,1981; Schank & Abelson, 1977). We suggest that, in addition to schemas, consumers also have grocery shopping scripts that they rely on and use for certain shopping trips. For example, consumers’ shopping trips are often described as being a major trip or a fill-in trip (Kahn & Schmittlein, 1989, 1992; Kollat & Willet, 1967). Fill-in trips are typically made between major shopping trips to satisfy urgent needs for a few products (Kahn & Schmittlein, 1992; Park, Iyer, & Smith, 1989). It is likely that many consumers have shopping scripts for major trips-for example: Get a cart; begin browsing in the produce aisle; walk up and down each aisle in order, scanning the shelves for items and promotions; check the coupon file for any matches; choose the shortest line; pay and leave. Fill-in trips, on the other hand, are less scripted, because they are specific to a subset of desired items. Thus, we expect that external memory aids, like shopping lists, are used more often for fill-in than for major shopping trips. This is consistent with previous studies of planned purchasing that have shown that the percentage of purchases that are unplanned increases with the total dollars spent on the grocery trip and is lower for fill-in trips than for major trips (Kollat & Willet, 1967). We therefore test whether the conditional probability that an item is written on a shopping list given it is purchased is higher during fill-in trips than during major trips.

H,isIwiIing3: P(product on list I purchase) is (a) greater when the consumer shops in less familiar versus more familiar stores, (b) lower during holiday seasons, and (c) greater for fill-in trips than major trips.

Additional factors from planned purchasing. The preceding hypotheses were derived from the literature on external memory aids. In our review of the planned purchasing literature, we uncovered three additional factors-price, gender, and age-that influence whether grocery shopping is planned. For instance, Cobb and Hoyer (1986) and Stern (1962) found that more expensive products are more likely to be planned than less expensive products. Perhaps consumers deliberate more in advance when they consider an expensive purchase, and this delibera- tion leads to the formation of a purchase intention. Although grocery products are typically all relatively low in price, there are some (e.g., shellfish) that appear expensive relative to substitute items. We therefore expect the probability that a purchased item was written on the list to be higher for more expensive rather than less expensive items.

Cobb and Hoyer (1986) also found that women were more likely to plan a purchase than were men. At least historically, grocery shopping has traditionally been the responsibility of the female head of household. Women may have generally been more familiar with the shopping experience (products and stores) and had a better idea of household inventory levels than men (Goldman & Johansson, 1978;

SHOPPING LISTS 351

Urbany, Dickson, & Kalapurakal, 1996). Finally, Bellinger, Robertson, and Hirschman (1978) found that the age of the head of household was also associated with planned and unplanned purchasing. Specifically, they found that young adults (under age 35) and older adults (over age 65) were more likely to make unplanned purchases than other adults.

The literature on external memory aids is either silent on these issues or pro- vides ambiguous findings. For example, Intons-Peterson and Fournier ( 1986) found that women use more overall memory aids (internal and external) than men, but they did not specify the relation specifically for external memory aids. Some prior studies suggest that the use of memory aids increases with age, whereas other studies found that use of memory aids among adults decreases with age. Intons-Peterson (1993) concluded that the use of external memory aids increases from childhood to adulthood and remains relatively constant over adulthood. Con- sequently, we draw on the planned purchasing literature to posit hypotheses concerning whether the conditional probability that an item is written on a shopping list given it is purchased is greater for women than for men and lower for younger and older adults.

Hlistwrifing4: P(product on list I purchase) is (a) greater for more expensive items rather than less expensive items, (b) greater for women than for men, and (c) lower for younger and older adults.

Grocery Shopping: The List Fulfillment Stage

Many studies of consumer behavior have found purchase intentions to be a positive and significant predictor of purchase behavior (Kalwani & Silk, 1982; Morrison, 1979; Morwitz & Schmittlein, 1992). The mere act of stating one’s purchase intentions can increase the probability of category- and brand-level purchasing (Fitzsimons & Morwitz, 1996; Morwitz, Johnson, & Schmittlein, 1993). Similarly, Gollwitzer ( 1993) found that specifying one’s intentions-for example, by stating or writing them down-increased the probability that the goal was achieved. We expect overall to find that when consumers form purchase intentions and record those intentions on a grocery list, they will be very likely to subsequently purchase these products.

Intons-Peterson and Fournier (1986) suggested that the process of creating the external memory aid (e.g., writing the shopping list) reinforces the likelihood of remembering to perform the action, regardless of whether the memory aid is available at the time of action. In their experiment, a group of students made notes while listening to a list of items they would be expected to buy at a grocery store or inex- pensive department store; the control group listened to the list, but took no notes. Students who were allowed to create the memory aid remembered more of the

352 BLOCK AND MORWllZ

items than did the control group, even though their notes were collected and not re- turned prior to the memory measure. By analogy, merely writing items on a shopping list will help the shopper remember what to buy, even if the shopper forgets to bring the list to the store. This suggests that if the shopper was involved in creating the shopping list, list fulfillment will be higher and brand switching (i.e., purchasing a brand other than that on the shopping list) will be lower than when the shopper did not participate in list writing.

Likewise, social cognitive research has found that when an intended behavior involves responsibilities to others, social pressure to fulfill the intention exists (Bandura, 1986). This is likely to apply in grocery shopping contexts. Imagine a typical scenario: One household member writes a shopping list of items that are needed because household inventory is low or they are part of a recipe and gives this list to the person who is doing the shopping that week. Once in the store, the shopper makes sure to buy everything on the list to avoid incurring the wrath of an angry spouse, parent, or child. Because we expect the amount of social pressure to increase with the number of people who will consume products purchased during the shopping trip, we expect the probability of list fulfillment to increase and the probability of brand switching to decrease with the number of people in the household. Consumers are likely to plan special menus for holiday meals, and these meals are likely to include extended family members or friends. Therefore, we also expect higher list fulfillment and lower brand switching for holiday versus nonholiday trips.

Hfulfdlmntka: The probability an item is purchased given the item was on the list, p(purchase I product on list), (a) is higher when the shopper participates in list writing compared to when the shopper does not participate in list writing, (b) increases with household size, and (c) is greater for holiday versus nonholiday trips.

HWfi lbnt lb : The probability that a brand not on the list was purchased given a competing brand in the same category was on the list, p(brand switch I brand on list), (a) is lower when the shopper participates in list writing compared to when the shopper does not participate in list writing, (b) decreases with household size, and (c) is lower for holiday versus nonholiday trips.

The preceding hypotheses were derived from the literature on external memory aids and social cognition. The literature on planned purchasing suggests several additional factors that might influence list fulfillment and brand switching. For example, in their study of grocery shopping behavior, Park et al. (1989) examined the effect of store familiarity and time available for shopping on the degree to which shoppers made intended purchases. They found that the more familiar consumers were with thegrocery store, the greater theconsumers’ success at fulfillingtheirpur-

JOURNAL OF CONSUMER PSYCHOLOGY, 8(4), 343-375 Copyright Q 1999, Lawrence Erlbaum Associates, Inc.

Shopping Lists as an External Memory Aid for Grocery Shopping: Influences on

List Writing and List Fulfillment

Lauren G. Block and Vicki G. Morwitz Stem School of Business

New York University

We explore the use of shopping lists as an external memory aid to consumers’ grocery shopping by examining the factors that influence the content ofconsumers’ shopping lists and the effectiveness of shopping lists as external memory aids. We analyze the shopping lists and actual purchase behavior of a panel of consumers during multiple grocery shopping trips conducted over a 2-month period. Our results indicate that consumers record on their lists approximately 40% of the items they ultimately purchase. Consistent with the external memory literature, we find that consumers write items on their shopping lists for which there are financial incentives to remember ( e g , manufacturers’ coupons), need-based incentives to remember (e.g., the product is frequently used), and schema-based advantages to remember (e.g,, items purchased on fill-in trips). More than 80% of the items written on the shopping list were actually purchased. Thus, shopping lists appear to be an effective external memory storage device for grocery purchasing. We discuss these and other findings in relation to the lit- eratures on external memory and planned versus unplanned purchasing.

Consider the following two grocery shopping scenarios. h i e is about to embark on a trip to his local supermarket to pick up several items his family needs for dinner that evening. Before he leaves for the store, Amie’s wife gives him a shopping list of the items she would like him to purchase: lettuce, tomatoes, cheese, milk, and Coca-Cola. When Arnie arrives at the store, he purchases the lettuce, tomatoes, and milk. He decides to purchase Pepsi Cola instead of Coca-Cola because the store is

Requests for reprints should be sent to Lauren G. Block, New York University, Stem School of Busi- ness, Management Education Center, 44 West Fourth Street, Suite 8-81, New York, NY 10012-1 126. E-mail: [email protected]


rather than less expensive items, (b) greater when coupons are available, and (c) greater for products in categories in which feature advertising is used more frequently rather than less frequently.

Hhlfillmeneb: The probability that a brand not on the list was purchased given a competing brand in the same category was on the list, p(brand switch I brand on list), is (a) higher for more expensive items rather than less expensive items, (b) lower when coupons are available, and (c) lower for products in categories in which feature advertising is used more frequently rather than less frequently.

THE STUDY

Panel Description and Method

Panel description. Households who were members in several different religious and social organizations throughout the East Coast were recruited to become members of our panel in exchange for a small gift received at the end of the study period. Snowball sampling methods were used to increase the size of our panel. On joining the panel, members were encouraged to recruit additional households to become members. In total, 28 households located across five different East Coast states agreed to be panel members. Individual panel members ranged in age from 25 to 75 years old and had an average of 2.4 people living in their household. Table 1 contains a more detailed description of the panel composition. Over the 2-month period of the study, the panel households made 369 trips to grocery stores, purchasing 5,173 items. On average over the 2 months, panel members shopped 13.2 times and wrote 7.3 shopping lists. On an average shopping trip, panel members spent $61, expected to spend $55, bought 43 items, wrote 20 items on the shopping list, and used 6 coupons to save $4.16.'

Method. Each panel household was instructed to monitor the household's grocery shopping over a 2-month period from November 1 through December 3 1. This time period was chosen because it contained two important grocery shopping holidays (Thanksgiving and Christmas) as well as nonholiday regular shopping weeks.

Prior to the starting date of the study, we provided participants with stamped, self-addressed envelopes, preshopping surveys, and postshopping surveys. For ev-

'The average number of items written on a list (20.1) is consistent with a prior study on shopping lists. Spiggle (1986) analyzed 129 shopping lists from shoppers at four different grocery stores (locations not specified) and found that shoppers wrote an average of 22 items on the list (range = 5-1 12 items).

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TABLE 1 Panel Household Characteristics’

Number of people in the household 1 2 3 4

0 1

Number of children in the household

Age of head of household 30-39 40-49 50-59 60+

7.0% 60.7% 17.9% 14.3%

60.7% 25.0% 14.3%

21.4% 10.7% 39.3% 28.6%

an = 28.

ery shopping trip, regardless of size, panel households submitted their store receipts, preshopping survey, postshopping survey, and written shopping lists (if any) in the envelopes provided.

Preshopping surveys were completed before the panel member went grocery shopping; this information included the date of the trip and expected grocery total. If there was a shopping list for this particular shopping trip, the panel member also indicated the person(s) who wrote the shopping list (self, spouse, child, or other) and the factors that influenced the shopping list (inventory low, item used up, advertisements, anticipation of future event, etc.). Panelists could check off as many reasons as applied for each shopping trip for why they wrote items on their shopping lists. Postshopping surveys contained information on the person(s) who did the shopping (self, spouse, child, or other), reasons why items not on the list were purchased (e.g., item was on sale, item was on a special display, impulse purchase, etc.), and reasons why items on the list were not purchased (e.g., item was not available, item was too expensive, forgot to purchase). Again, panelists could check off as many reasons as applied for each shopping trip. Note that the pre- and postshopping surveys only provide an indication of overall influences on each shopping trip, but do not specify influences at the individual item level.

As with any diary panel research, there is some concern that actual shopping behavior might be altered by the participant’s awareness of being monitored or filling out the surveys (Sudman & Ferber, 1979, pp. 31-38). Specifically, in this context there is potential reason to be concerned that respondents may avoid making impulse purchases and may make unusual efforts to fulfill their purchase intentions to appear to be a “good shopper” to the researcher. However, we would expect that any initial deviation from normal behavior would dissipate


over time and could be detected by observing individual panel household trends over the course of the study. We found that the number of list items, the number of items purchased, p(product on list I purchase), p(purchase I product on list), and p(brand switch I brand on list) among panel households did not change significantly over the 2-month period. We are therefore confident that the panel households did not significantly alter their shopping behavior because they were participating in this study.

Analysis Procedures

Data coding. Two coders independently reviewed all the shopping lists, store receipts, and surveys for each grocery trip. For each shopping trip, coders recorded all the information on the pre- and postshopping surveys, as well as the name of each grocery store visited, the number of store receipts and shopping lists per shopping trip, the number of items on the receipt(s) and list(s), the number and dollar value of any coupons used, and the dollar value of other discounts (e.g., store member cards) as indicated on store receipts. For each item purchased, coders recorded the product category (227 total categories based on Fader & Lodish, 1990), the brand name, the price, whether a coupon was used, the value of the coupon, whether the item’s product category appeared on the shopping list (e.g., cookies), and whether a brand name for the item appeared on the shopping list (e.g., Snackwells). Finally, for each item on the list not purchased, coders recorded the product category and the brand name (if one was specified) and indicated that it was not purchased.

Data analysis. We begin our analysis by briefly characterizing the list writing and list fulfillment stages of the panelists’ grocery shopping activities. For the analyses, an item was considered to be on the shopping list if the item was written on the list at either the category or the brand level. A list item was considered purchased if the category was written on the list and any brand in that category was purchased or if a brand was written on the list and that brand was purchased. A brand switch was considered to have occurred if a brand was written on the list and a different brand in the same product category was purchased.

We next provide some preliminary evidence concerning influences on each stage by examining the data provided during the preshopping and postshopping surveys. To more systematically examine the effect of different influences and to test our hypotheses, we develop three different item-level panel probit models (see Greene, 1998, pp. 454456). In each case, the independent variables in each model include those for which we hypothesized effects based on the external memory literature and the literature on planned and unplanned purchasing. In our models of

SHOPPING LISTS 357

p(purchase I product on list) and p(brand switch I brand on list), in addition to ana- lyzing the effects of the hypothesized variables, we include the age variables from the list writing models as covariates as these could potentially affect the probability that an item is purchased.

We use LIMDEP’s (Limited Dependent Variable Models) panel probit procedure (Greene, 1998, pp. 454-458) to estimate p(product on list I purchase), ~ (pur - chase I product on list), and p(brand switch I brand on list) as a function of the dependent variables shown in the following. The models are estimated using maxi- mum likelihood procedures.

P(product on list I purchase) =AMCoupon, Feature Adv.,Rate Cat, TStore, Hol-

P(purchase I product on list) = Apartkipate, HH Size, Holiday, TStore, Rate

P(brand switch I brand on list) =AParticipate, HH Size, Holiday, TStore, Rate

iday, TTrip, Price, Gender, Young, Old)

Cat, TTrip, MeanPrice, MCoupon, Feature Adv., Young, Old)

Cat, TTrip, MeanPrice, MCoupon, Feature Adv., Young, Old)

The specific independent variables are described in the Appendix. We estimate these models at the item level. Thus, each observation represents a single grocery item either on a shopping list or store receipt. Note that by doing so we pool both across and within households. This panel probit procedure takes into account cor- related errors over time. In interpreting the results that follow, it is important to note that the panel probit models only allow us to determine correlations between the independent and dependent variables and not test causal relations.

RESULTS

The List Writing Stage

Descriptive results. Over the 2 months of the study, panelists wrote a total of 2,694 items on their shopping lists. The average number of items per shopping list was 20.1. On average, a specific brand was indicated for 22.9% of the items on the shopping list, and only the category was indicated for the remaining 77.1% of items.

On average, 42.8% of the items purchased over the course of this study were on the shopping list. This is consistent with trade studies (Rickard, 1995) that have shown that consumers buy nearly twice the number of items on their shopping lists. If we assume that consumers write most items they plan to buy on their shopping lists, then the majority of consumers’ purchases may be unplanned. This result validates previous studies of planned and unplanned purchasing. For example, our finding is similar to one of Kollat and Willet (1967),


who found that 50.5% of consumers’ grocery purchases were unplanned. This result also suggests that consumers may not use external memory aids to avoid making unplanned purchases, or if they do, they are not effective for this pur- pose. We further found that only 9.4% of purchased items were written on a shopping list with a specific brand name. If shopping lists are an accurate measure of brand-level purchase intentions, this suggests that for a high proportion of the items purchased, consumers may be making brand choice purchasing decisions in the store. However, note that it is possible that consumers may have category- and brand-level purchase intentions that they do not record on shopping lists. Our methodology does not allow us to determine this.

The surveys the panelists filled out prior to each shopping trip provide some preliminary evidence concerning why an external memory aid was used for certain items. The results from the survey are described in Table 2. Not surprisingly, the primary reason items are written on a shopping list is that they are needed. The most frequent mention across panelists’ shopping trips was that items were written on a shopping list because they were used up at home (78.6%). For about one third of the shopping trips made, items were written down because they were needed for a special event such as a party. Manufacturers’ and retailers’ marketing efforts also

TABLE 2 Influences on List Writing and List Fulfillment

Reasons why items were written on shopping listss Wrote things down because they were used up. Wrote down things for which I had a coupon.

78.6% 46.8%

Wrote down things I saw advertised in the store circular or local newspaper. 42.8% 39.9%

1 bought the item because it was on sale. 44.8% 34.5%

I bought the item because it was on a special display and it got my anention. 28.7% I bought the item because someone who was not with me would like it. 26.4% I bought the item because someone who was with me wanted it. 16.1% I bought the item because I decided to buy something new. 13.2% I bought the item because of information on the label (e.g., nutritional information). 11.5%

Reasons why items that were on the list were not purchasedb The item was not available in the store. 35.1% I forgot. 14.4% The item was too expensive. 13.2% I realized I did not need the item. 12.6% I ran out of time. 7.5% A better item was available (price). 6.9% A better item was available (quality). 4.0% I did not buy it because of information on the label (e.g., nutritional information). 2.9%

an = 173 surveys. bn = 174 surveys.

Wrote down things in anticipation of a special future event like a party or guests.

I bought the item on an impulse.

Reasons why items that were not on the list were purchasedb

SHOPPING LISTS 359

TABLE 3 Summary of List Writing Stage Results

independent Variables ~~

Model I : P(Product on Lisr 1 Purchase)"

Intercept Financial and need-based incentives to remember

Manufacturer's coupon (MCoupon) Feature advertising (Feature Adv.) Frequency of purchase in category (Rate Cat)

Store familiarity (TStore) Holiday vs. nonholiday (Holiday) Major vs. fill-in trips (=rip)

Price Gender Young Old

Schema-based advantages to remembering

Planned purchasing factors

x2 UZ

-1.08**

.30**

.08*

.07*

.lo* -.16** -.18**

.a** 1.29** -.48** -.26**

574.06** 12.3

an = 3,396. *p < .05. **p < .01.

influence why items are written on shopping lists. Specifically, consumers wrote items down on their shopping lists because they had coupons (46.8%) or because of information contained in store circulars and local newspaper advertisements (42.8%).2

Hypotheses testing. We test the hypotheses using panel probit regressions that model the conditional probabilities that items were on the shopping list given they were purchased (Model 1). The coefficients and significance levels for the list writing stage model are reported in Table 3. The significance levels are based on one-tailed tests. Table 3 also reports the sample size for each model, the test statistic

2Because memory deteriorates win age, one could argue that older and younger consumers use shopping lists for different reasons. To investigate this, we divided the respondents into two age categories (younger than 50.50 and older) and compared the reasons external memory aids were used and the reasons there was deviation from the external memory aid across these younger versus older groups. These groups differed in only four reason categories @s < .05). Older consumers were more hkely to Write things down when they had coupons for the item and when they saw the item advertised in the store circular or local newspaper than younger consumers. Older consumers were also more likely than younger consumers to buy an unintended item because it was on sale and not to fulfill an intended item because it was unavailable in the store.


for testing that the slopes all equal zero (x2), and U2 for each model (i.e., 1 -log likelihood of the full modelllog likelihood of a null model with only an intercept term). In the following, we report the variables' aggregate-level elasticities of the conditional probabilities that purchased items were written on the shopping list. For the binary variables the elasticities represent the relative change in the conditional planning probabilities due to the variable. For the continuous variables the elasticities reported represent the change in the conditional planning probabilities due to a 1% change in the variable.

Financial and need-based incentives to remember. External memory aids are likely to be used when there is a premium on accurate remembering. We suggest that for grocery shopping, when the external memory aid is a shopping list, a premium might be in the form of cost savings from purchasing specific items. Thus, we hypothesized that the p(product on list I purchase) would be greater for products in categories in which coupons and features are frequently used. Our results support Hlist ,,+tingl. As predicted, the elasticities derived from these models show that the probability the item was on the list is higher for product categories in which manufacturers' coupons are frequently used (7.4% higher,p c .01). We also expected feature advertising to provide a cost advantage and thus an incentive to use an external memory aid for grocery shopping. As anticipated, the probability the item was on the list is higher for product categories in which feature advertising is frequently used (2.1 % higher, p c .05).

In Hfist w,jting~, we hypothesized that frequently used items are more likely to be written on a shopping list than infrequently used items. The results support this hypothesis. Elasticities derived from the models show the probability the item was on the list given it was purchased increases with how frequently items in the product category are purchased (.024% increase, p c .05).

Schema-based advantages to remembering. We suggested three instances of grocery shopping trips in which the consumer's shopping trip schema is not highly formed or highly accessible, and thus an external memory aid would provide specific advantages in overcoming memory interference. Specifically, in Hast writing3 we predicted that the conditional probability that an item is written on a shopping list given it is purchased is (a) greater when the consumer shops in less familiar stores rather than more familiar stores, (b) lower during a holiday period, and (c) greater for fill-in trips than major trips. Results support the last two parts of this hypothesis. We found that the probability the item was on the list is lower during holiday periods (3.9% lower,p c .01) and higher during fill-in trips than during major trips (4.5% higher,p c .01). Contrary to predictions, results indicate that the conditional probability an item was on the list given it was purchased is higher when the

SHOPPING LISTS 361

consumer shops in a more familiar store (2.6% higher, p < .05). Although we did not anticipate this result, it is consistent with some previous findings of unplanned purchasing. In unfamiliar stores, consumers may spend more time searching for items and therefore will have increased exposure to unplanned products and in-store promotions. This increased exposure may lead to more unplanned purchases (Bucklin & Lattin, 1991; Park et al., 1989).

Additional factors from planned purchasing. Consistent with Hlist writing4

and with prior studies of planned purchasing, our results indicate that when the price increases by 1 %, the probability that the item was on the list given it was purchased significantly increases (.041% increase, p < .01). Also consistent with the literature on planned purchasing, we found that the probability a purchased item was on the list is greater for women than for men (29.3% greater,p < .Ol), lower for younger consumers (1 1.8% lower,p < .Ol), and lower for older consumers (6.4% lower, p < .01) than for other consumers.3

Summary of feSUltS. In sum, we find that the probability an external memory aid was used for an item given the item was purchased is:

1. Greater in product categories in which manufacturers’ coupons are fre-

2. Greater in product categories in which feature advertising is frequently used. 3. Greater the more frequently the consumer makes purchases in the prod-

4. Greater if the consumer shopped in a familiar store. 5. Lower during holiday periods than nonholiday periods. 6. Greater during fill-in trips than during major trips.

quently used.

uct category.

3Because our shopping trip survey results indicated that there were a few differences between older and younger consumers in the reasons items were written on lists, we checked whether any interactions exist between age and the other independent variables in the model of p(product on list 1 purchase). Only two interactions were significant: store familiarity and holiday versus nonholiday trips. We had hypothesized that people are more likely to write purchaseditems on their shopping lists for shopping trips to an unfamiliar store, but we found a main effect indicating that the probability a purchased item was on the shopping list was greater during a shopping trip to a familiar store. This finding was contrary to our expectations. However, the interaction of age and store familiarity reveals younger consumers are more likely to write purchased items on their shopping lists during trips to unfamiliar stores than older consumers, suggesting that our theorizing may hold at least for younger consumers. In addition, the interaction of age and holiday reveals that younger consumers write purchased items on shopping lists for holiday shopping trips more than older consumers do. This makes sense, as younger consumers probably do not have as well-defined holiday shopping schemas as do older consumers. This is consistent with the literature on external memory aids and with our theorizing.

362 BLOCK AND MORWIlZ

7. Greater the more expensive the item. 8. Greater if the shopper is female. 9. Lower if the shopper is younger or older.

The Goal Fulfillment Stage

Descriptive results. Over the 2 months of our study our panelists purchased a total of 5,173 items. On average, panelists purchased 43 items per shopping trip and spent an average of $61. Once consumers wrote down their purchase intentions on their shopping lists, they were very likely to fulfill those intentions. Overall, 83.3% of the items on the shopping list were purchased. Thus, the data support our expectations that external memory aids are effective for helping consumers remember to make planned purchases.

Of those items included on the list with a brand name that were not purchased, 29.0% were replaced with a different brand in the product category. In the other cases, no purchase was made in the product category.

The survey data provide some explanations for why consumers deviated from their purchase intentions. During the postshopping surveys, panelists were asked to check off as many reasons as applied for each shopping trip for why they purchased items that were not on their shopping list and did not purchase items that were on their shopping list.

A summary of the survey data is provided in Table 2. Marketing efforts played a significant role in inducing impulse purchasing. Across shopping trips, in many cases panelists indicated that they purchased items that were not on their shopping lists because they were on sale (44.8%), because the item was on a special display (28.7%), or because of information provided on an item's label (1 1.5%). In other cases, preferences of different household members influenced unplanned purchasing. Some items were characterized as being purchased on an impulse (34.5%) or to try something new (13.2%), other items were purchased because someone at home would like them (26.4%) or because someone who accompanied the shopper wanted the item (16.1%).

Based on the survey data, the primary reason panelists did not fulfill their purchase intentions was that the item was not available at the retailer (35. l %). In-store influences also lead panelists to deviate from their purchase intentions. Some items were not purchased because the panelist thought they were too expensive (13.2%). In other cases, panelists deviated from their plans because a better product in terms of price (6.9%) or quality (4.0%) was available. In a few cases, panelists decided not to purchase a product because of information on the label (2.9%). Finally, the panelists themselves were the reasons why several intended items were not purchased. In some cases, the panelist simply forgot to purchase the item (14.4%) or ran out of time in the store (7.5%). In other cases, the panelist decided while in the store that the item was not needed (12.6%).

SHOPPING LISTS 363

Hypothesis testing. We test the hypotheses using panel probit regressions that model the conditional probabilities that items were purchased given they were on the shopping list (Model 2) and the conditional probability of brand switching given that the brand-level item was on the list (Model 3). The models are estimated only for purchase occasions in which a list was used. The first hypothesis, derived from the literature on external memory and social cognitive research, suggests a likely influence of three factors-participation in list writing, household size, and holiday versus nonholiday shopping-on list fulfillment and brand switching. The other hypotheses, derived from the literature on planned purchasing, suggest additional factors that might influence list fulfillment and brand switching (e.g., the availability of coupons). The two age variables were included as covariates in our analyses. The coefficients, significance levels (one-tailed for hypothesized effects and two-tailed for covariates), sample size, ~ 2 , and U2 for the two list fulfillment stage models are reported in Table 4. We report the elasticities in what follows.

TABLE 4 Summary of List Fulfillment Stage Results

Model 2: Model 3: P(Brand Independent Variables P(Purchase I Product on List)” Switch I Brand on Listjb

Intercept .40** -.99* External memory factors

Shopper participated in list writing

Household size (HH Size) Holiday vs. nonholiday (Holiday)

Store familiarity (TStore) Frequency of purchase in category

Major vs. fill-in trips (‘ITrip) Price (MeanPrice) Manufacturer’s coupon

(MCoupon) Feature advertising (Feature Adv.)

Covariates Young Old

(Participated)

Planned purchasing factors

(Rate Cat)

x‘ UZ

.37**

.20***

.14*

.06

.15**

.28*

.16* -.05*

-.15*

.24***

.54*** 71.18*** 4.8

3.70

-.I4 -.23

-.38* -.08

-.08 .25**

-.51*

-.I4

-.98** -5.34*** 3 1.22*** 13.6

% = 1,B11.bn=570. * p < .lo. **p < .05. ***p < .01.


In Hfulfillment1, we predicted that the conditional probability that the item was purchased given it was on the list is higher when the shopper participates in the list writing, increases with household size, and is greater for holiday versus nonholiday trips. Results support this prediction. The results from Model 2 indicate that the p(purchase I product on list) is 7.4% higher when the shopper participated in list writing than when the shopper did not participate in list writing (p < .05) and that the p(purchase I product on list) increases by .113% with a 1% increase in household size (p < .01). There is marginal support for greater list fulfillment during holiday trips (2.6% greater, p < .lo).

We also expected lower brand switching with participation in list writing, decreases in household size, and holiday versus nonholiday trips. Results do not support this hypothesis. Model 3 shows that the p(brand switch I brand on list) is 6.4% lower when the shopper participated in list writing (p > .lo), decreases by .328% with a 1 % increase in household size (p > .lo), and decreases by 1.21% when it is a holiday trip (p > .lo). However, these results are not sta- tistically significant.

In HfulfillmentZ, we expected consumers to be more likely to fulfill intentions and less likely to switch from their intended brand in more familiar stores, for items in a frequently purchased category, and for major trips. Results support the hypothesis for list fulfillment more than for brand switching. Results indicate that list fulfillment increases by .022% with a 1% increase in purchase frequency (p < .05), but there is no significant difference on brand switching (.05% decrease, p > .lo). There is marginal support for greater list fulfillment during major trips (2.5% greater, p < .lo), but we found no significant association between trip type and brand switching (.43% lower, p > .lo). Store familiarity, however, is marginally associated with lower brand switching (2.32% lower, p < .lo), but is not significantly associated with list fulfillment (1.1% greater, p > .lo).

In the final hypothesis, Hfulfillment3, we expected price-related factors to be associated with list fulfillment and brand switching. Results partially support this hypothesis. Results show list fulfillment decreases by .023% (p < .lo) and brand switching increases by .48% (p < .05) with a 1% increase in the price of the product. We found a marginally significant association between manufacturers’ coupons and list fulfillment (2.9% greater, p < .lo) and between manufacturers’ coupons and brand switching (2.6% lower, p < .lo). Interestingly, and contrary to our hypothesis, we found that list fulfillment is 2.7% lower in categories in which feature advertising is frequently used (p < .lo). Perhaps this indicates that for categories in which products are frequently on special, consumers will delay making a purchase if a deal is not offered on a particular shopping occasion. We found no significant association between feature advertising and brand switching (.779% lower, p > .lo).

SHOPPING LISTS 365

Covariates. We tested whether age is associated with list fulfillment and brand switching. Results indicate that list fulfillment is significantly higher for older consumers (9.2% higher,p < .01) and for younger consumers (4.2% higher,p < .01) than for other consumers. Brand switching is 4.1% lower for younger consumers @ < .05) and 15.1% lower for older consumers @ < .01).4

Summary of results. In sum, we find that the probability an item was purchased given an external aid was used for the item is:

1. 2. 3. 4.

5. 6. 7.

8. 9.

Greater if the shopper participated in list writing. Greater the larger the household size. Greater during holiday periods than nonholiday periods. Greater the more frequently the consumer makes purchases in the product category. Greater during major trips than fill-in trips. Lower the more expensive the item. Greater in categories in which manufacturers’ coupons are frequently available. Lower in categories in which feature advertising is frequently used. Greater for older and younger heads of household.

We also found that the probability a different brand was purchased given an external aid was used for an item at the brand level is:

I. Greater if the consumer shopped in a less familiar store. 2. Greater the more expensive the item. 3. Lower in categories in which manufacturers’ coupons are frequently used. 4. Lower for older and younger heads of household.

4We also checked whether any interactions exist between age andthe other independent variables for list fulfillment. Only two interactions were significant: participation in list writing and the price of the item. We hypothesized and found that the probability of list fulfillment is higher when the shopper participates in list writing. The interaction of age and participation suggests that this effect is stronger for younger rather than older consumers. Perhaps because older consumers have been involved in joint shopping decisions over a longer period of time and know the other household members’ preferences better than younger consumers, they may be less reliant on shopping lists prepared by other household members in determining which items to purchase. We also hypothesized and found that the probability of list fulfillment was lower for more expensive items rather than less expensive items. The interaction of age and price suggests that younger consumers are even more Likely to fulfill their intentions to buy cheaper rather than expensive items. Perhaps this result reflects younger consumers’ lower incomes.


DISCUSSION

Contribution to External Memory

Our study explores the use of shopping lists as an external memory aid for consumers’ grocery shopping. Prior studies on external memory have sought answers to the questions of when memory aids are used, when internal versus external memory aids are used, and who uses memory aids. We extend the literature on external memory aids by examining the factors that influence the content of the memory aid (list writing) and factors that influence the effectiveness of the list in aiding memory (list fulfillment). Prior studies have not addressed either of these issues (Intons-Peterson, 1993).

Our results indicate that consumers record on their lists approximately 40% of the items they ultimately purchase. We extend the prior research on external memory aids by showing that they are not effective for reducing impulse or unplanned activities. Consistent with the external memory literature, we find that the propensity to write an item on the list given it is purchased is higher when there are financial incentives to remember to purchase an item, such as manufacturers’ coupons and feature advertising, and when there are need-based incentives to remember, such as when the product is frequently used in the household. As predicted, we also find the propensity to write an item on the list is greater when the consumer is less likely to have an accessible schema or script to guide his or her behavior. Spe- cifically, we find items are more likely to be recorded on lists for fill-in shopping trips and during nonholiday periods.

Shopping lists function as external memory storage devices. Overall, our results indicate that shopping lists are an effective external memory aid for helping consumers make planned purchases. We find that when consumers use shopping lists as memory aids for items, they are very likely to purchase these items. Spe- cifically, approximately 80% of items recorded on shopping lists are purchased. Consistent with the external memory aid literature, we find that the likelihood of purchasing an item on the list is higher if the person who wrote the list also participated in shopping. We also found that the likelihood of purchasing a list item increased with household size and is marginally greater during holiday periods.

We used proxy variables relevant to consumer grocery shopping to test our hypotheses derived from the literature on external memory aids. We acknowledge that some of these variables are likely to be better proxies than others. For example, manufacturers’ coupons and feature advertising are two straightforward repre- sentations of a financial incentive for using a memory aid, whereas a consumer’s frequency of purchasing in a category is a less direct representation of a need-based incentive. Despite this, results support our theorizing, indicating that all three of these variables are proxies for financial and need-based incentives. By contrast, results on the effect of store familiarity were contrary to predictions. In

SHOPPING LISTS 367

this case, holiday shopping and major versus fill-in shopping trips are probably better proxies for schema-based advantages than store familiarity, which may represent something else, like geographic proximity.

Contribution to Research on Planned and Unplanned Purchasing

Previous studies used two different research paradigms, which we call store intercept studies and scannerpanel studies, to examine planned versus unplanned shopping behavior. In store intercept studies (e.g.. Cobb & Hoyer, 1986; Kollat & Wil- let, 1967), shoppers are asked about their purchase plans during a grocery shopping trip. These verbal intention statements are taken either immediately preceding or following a purchase decision. A purchase is considered to be planned when the consumer states that he or she intended to purchase the product prior to making the actual purchase decision. Both Cobb and Hoyer (1986) and Kollat and Willet (1967) provided insight into the purchasing of planned versus impulse grocery products. Our methodology allows us to both verify and extend the findings from these earlier studies. We extend the findings in two ways. First, our methodology enables us to examine the planning process for the entire basket of grocery products (e.g., Cobb & Hoyer examined two products). In addition, because intentions are measured before an item is selected, it is possible to study factors that influence deviations from the plan (e.g., Cobb & Hoyer’s methodology only allowed them to determine whether a purchased item was planned, but not whether a planned item was purchased).

In scanner panel studies (Bucklin & Lattin, 1991; Kahn & Schmittlein, 1992), scanner data are used to examine the effect of in-store promotions on shopping behavior and how the effect of promotions varies for planned versus unplanned purchases. In these studies, whether a purchase is planned or unplanned is inferred based on consumer behavior theory and available scanner data. For example, Bucklin and Lattin (1991) inferred that a purchase is planned if the item is purchased at a store that the consumer typically shops at, was not on special, and the household’s inventory of the product was low. Kahn and Schmittlein (1992) used type of shopping trip (major vs. fill-in) as a surrogate for planned and unplanned purchasing. These studies make good use of the limited constructs available in scanner data to operationalize planned purchasing. We extend these studies by using a methodology that allows us to more concretely determine whether an item was planned. By doing so, we not only can validate their findings, but we can also answer questions not possible with their methodology. For example, scanner panel studies do not distinguish between plans to make a category-level purchase versus plans to buy a particular brand. In Bucklin and Lattin (199 l), a purchase was considered planned only if the consumer had decided to purchase a particular brand.

368 BLOCK AND MORWI‘IZ

We considered a purchase to be planned if either a brand or a category was indicated on the list. Furthermore, the nondiscrete nature of the measures in these previous studies makes it difficult to study deviations from a plan. Finally, the scanner panel studies only examine planned versus unplanned purchasing in one or two product categories. The methodology in our study extends the scanner panel studies by examining planning for the entire basket of grocery purchases and deviations from these plans. Thus, we add to the body of literature on how consumers form grocery intentions by examining the factors that influence the set of items consumers write on their shopping lists.

Limitations and Future Research

Our conclusion that shopping lists are effective at helping consumers remember to make planned purchases but are less effective at helping consumers avoid unplanned purchases is based on the assumption that shopping lists are a reasonable measure of consumers’ planned market purchases. Although it seems reasonable to assume that items recorded on shopping lists are planned purchases, it is less clear that shopping lists represent the universe of planned items. Consumers may not al- ways record all of their shopping plans on their lists. For example, if a consumer purchases a quart of milk on every shopping trip, he or she may not bother to write this item on the list. Alternately, other strategies that provide the same functional benefits as a list might be used in planning-for example, leaving the empty milk carton out on the table as a reminder, or using the manufacturers’ coupon itself as a memory aid. Thus, our conclusion that shopping lists are less effective at helping consumers avoid unplanned purchases should be interpreted with caution.

Our brand switching results should also be interpreted with some caution. In some cases, consumers may write a prototypic brand name (e.g., Kleenex, Coke) on their shopping list when they intend to purchase any brand in that category rather than that specific brand (Spiggle, 1986). Although we do not examine such cases, it is also possible that consumers might only write down a product category on their list (e.g., soup), even though they intend to purchase a particular brand (e.g., Campbell’s). Similarly, consumers may treat items on the list as cues for categories of items. For example, the word turkey on a list may act as a cue for turkey-related items like cranberry sauce or stuffing. In this study, we cannot determine when planned category items are cues for related grocery products.

It is also possible that the planned market purchases of households that use shopping lists and households that do not use shopping lists differ. In our study, the panel members were predominantly list writers. Future research might extend this study to include a representative sample of list writers and those who do not write lists and to use additional methods to elicit the planned market purchases from all households. Furthermore, our data are correlational. Future research should at-

SHOPPING LISTS 369

tempt to randomly assign participants to a list versus no-list condition. By having a no-list control against which to compare our findings, we might be able to provide additional insights into the effectiveness of external memory aids. In addition, our overall sample size is small, and panel participants were not chosen using probability sampling techniques, thereby limiting the generalizability of our results. However, a comparison of our panel to the 1990 U.S. Census reveals encouraging similarities. Our panel was composed mainly of small (68% were one- or two-person households) and older (68% were headed by people in the 50 and older age group) households. According to the 1990 U.S. Census, 56% of households are one- or two-person households, and 51% are headed by people 45 or older (U.S. Census Bureau, 1990). Thus, although we did not use a probability sample of panelists, our panel households closely match the population households on age and size of household.

Our measures of the frequency of promotional activity for product categories are also somewhat limited. These aggregate measures obtained from Fader and Lodish (1990) apply only at the product category level and do not necessarily accurately represent the level of promotional activity for a given item, regional market, or time period. Because we were interested in specifically examining the contents of consumer shopping lists, we could not use scanner data and thus did not have access to information about in-store promotions, the prices of competing products, and other store-specific variables. This information could provide additional insight into the use and effectiveness of shopping lists as external memory aids.

Future research might extend this study to explore consumers’ sequential shopping behavior. One possibility might be to develop a model that simultaneously es- timates the probability that a consumer writes a list and then the probability that different products are purchased in these different cases (see Kardes, Kalyanaram, Chandrashekaran, & Domoff, 1993, for an example of a sequential multistage process model). Finally, because the interpurchase time for some grocery products ex- ceeds the time frame of our study, we encourage future research conducted over a longer time horizon.

Managerial Implications

Assuming that the set of items on consumers’ shopping lists is a reasonable representation of their intended market purchases, our results are consistent with prior studies on unplanned purchasing in that the majority of purchases made by panelists were unplanned. In particular, we found that only 43% of purchased items were written on shopping lists prior to shopping. This supports grocery marketers’ common focus on end-aisle displays, shelf tags, and other marketing tools designed to induce impulse buying or brand switching. In addition, OUT survey data indicate that

370 BLOCK AND MORWIlZ

the top reasons consumers report for making unplanned purchases involve marketing incentives. However, our results also indicate that approximately 80% of items that were indicated as planned prior to shopping on a shopping list were actually purchased. This has interesting implications for retailers and manufacturers. If manufacturers or retailers can induce a consumer to plan an item in advance of shopping, there is a high probability that item will be purchased. Marketers should seek creative tools to encourage advance planning. For example, peel-off stickers could be attached to packages so that, when the consumer notices the supply is low, the label can be peeled off and placed directly onto a shopping list.

Couponing is a commonly used marketing tool used to encourage advance planning. In our study, for almost half of the shopping trips, an item was written on the shopping list because the consumer had a coupon for it. Interestingly, consumer product companies like Rocter & Gamble have experimented with reducing or eliminating coupons. Although there may be other reasons to eliminate coupons, our research suggests that one potential negative consequence of eliminating coupons might be that some planned purchasing will decrease. We found that manufacturers' and store coupons were significantly associated with whether a purchased item was planned. However, it is also important to note that, given the correlational nature of our results, the association between writing an item on a list and the presence of coupons could indicate that consumers plan to buy a product because they have a coupon or that consumers use coupons for products they would be buying whether or not they had a coupon. Future studies should further examine consumers' planning process for couponed grocery items.

ACKNOWLEDGMENTS

We thank Frank Kardes, the three anonymous reviewers, Rick Breisch, Eric Green- leaf, Steve Hoch, Don Lehmann, and Geeta Menon for comments on earlier drafts of this article, and Joan Scattone and Julianne Shanblatt for their help in setting up the database. We particularly thank the two Ruths, A1 and Bernie, and the following shopping panel families for their conscientiousness and commitment to our study: Atlas, Bender, Bernstein, Braeunig, Buxbaum, Campisi, Chine, Chomor, Facey, Faktorow, Gaines, Gatley, Goren, Grimes, Hoch, Jones, Juros, Kurtzman, Lesser, Michels, Mortensen, Nelson, Russell, Stein, Steinberg, and Stempler.

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APPENDIX

Independent Variables Included in the Logistic Regression Models

MCoupon

Fader and Lodish (1990) reported segments of grocery items for which manufacturers’ coupons tend to be used. MCoupon is set to 1 for product categories with high manufacturer coupon use and is set to 0 for product categories with low manufacturer coupon use.

Feature Adv.

Based on Fader and Lodish (1990), Feature Adv. is set to 1 for product categories with high feature advertising use and is set to 0 for product categories with low feature advertising use.

TTrip

TTrip is computed at the shopping trip level for each household and indicates whether a trip is a major trip or a fill-in trip. The same procedure as used in Kahn and Schmittlein (1989) was used to classify the type of shopping trip. Kahn and Schmittlein (1992) briefly described this procedure as follows:

Specifically, we categorize each shopping trip as a filler or major trip depending upon the total amount spent on each trip relative to a household-level cutoff point. To determine the cutoff point we create histograms of the amounts spent for each household. If the histogram is unimodal, we use the mode as the cutoff point. If the histogram is bi-modal, we use the midpoint between the two modes as the cutoff point. (p. 298)

Price

Price is the actual price of a purchased item as indicated on the store receipt.

MeanPrice

Retail item price is not available in our data for items that were not purchased. Therefore, in Models 2 and 3 we include MeanPrice. MeanPrice is the average price per product in the product category for all purchases made across trips and panelists.


TStore

TStore represents the type of store and is coded at the shopping trip level for each household. TStore is set to 1 for shopping trips that occur in a household’s most frequently visited retail store and is set to 0 for all other shopping trips. This is the same definition used in Kahn and Schmittlein (1992).

Rate Cat

Rate Cat is computed at the product category level for each panel household. Rate Cat is equal to the number of items purchased in a product category divided by the total number of shopping trips made by the household over the 2-month study period.

Gender

Gender is coded at the shopping trip level. For Models 1,2, and 3, Gender is set to 1 if the female head of household wrote the shopping list (with or without other people); otherwise it is set to 0.

Holiday

Holiday is coded at the shopping trip level and is set to 1 during the week prior to Thanksgiving and Christmas and to 0 during all other periods.

Participate

Participate is coded at the shopping trip level and is set to 1 if anyone who shopped participated in list writing; otherwise it is set to 0.

HH Size

HH Size is coded at the household level and is equal to the total number of people living in the household on a full-time basis.

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Young

Young is coded at the household level and is set to 1 if the head of household is 35 years old or younger and to 0 for all other households.

Old

Old is coded at the household level and is set to 1 if the head of household is 65 years old or older and to 0 for all other households.

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