Consumers’ Curbside Pickup and Home Delivery Shopping Behavior in the Post-Pandemic Era

Abstract

Consumers’ shopping channel options have moved beyond the traditional brick-and-mortar channel and into e-commerce channels such as curbside-pickup-from-store and home-delivery-from-store. The COVID-19 pandemic has substantially accelerated consumers’ adoption of these e-commerce channels, particularly in their grocery shopping. Even allowing for the pandemic’s end, grocery shoppers’ reliance on e-commerce will stay and continue growing, calling for an urgent understanding of consumers’ curbside pickup and home delivery shopping behavior in the post-pandemic era. This chapter offers the first comprehensive manuscript to help understand the status-quo and future of consumers’ curbside pickup and home delivery shopping behavior. The unique collaboration between an academic scholar and a practitioner with rich industry experience provides practical insights and points of view with rigorous scientific support. Note that this chapter is beyond a review—a new digital-era shopping channel typology and framework is proposed; a new data analysis that compares the time consumers spend on shopping grocery online vs. offline is conducted; multiple predictions about grocery shoppers’ new behavior in the curbside-pickup-from-store and home-delivery-from-store channels in the post-pandemic era are presented. While the manuscript focuses on the grocery industry, the insights can be applied to other retail sectors that also provide curbside-pickup-from-store and/or home-delivery-from-store services.

1. Introduction

Visiting a brick-and-mortar store (e.g., a local shopping mall or grocery store) used to be the only way for consumers to purchase goods. The advance of digital technology opened the door to online shopping, which has substantially reframed both people’s shopping behavior and the competitive landscape of the retail industry. According to the latest U.S. Census Bureau’s Annual Retail Trade Survey [1], the U.S. retail sales from e-commerce grew from 0.19% (=5/2582 billion) in 1998 to 3.60% (=142/3935 billion) in 2008 to 9.66% (=508/5255 billion) in 2018. Many cite e-commerce as the cause of death for many iconic physical retailers, from books to clothing to electronics, etc., for example, Blockbuster (Year 2010), Borders Books (Year 2011), CompUSA (Year 2012), Golfsmith (Year 2016), The Limited (Year 2017), RadioShack (Year 2017), Toys R Us (Year 2018), and Barneys (Year 2019), just to name a few [2].

One retail sector, though, had largely been immune from the impact of e-commerce before the COVID-19 pandemic: the supermarket and grocery industry. Grocery is a trillion-dollar business for the U.S., constituting almost 5% of its GDP [3]. The competition in the industry has always been extremely fierce. As CNBC [4] reported, “The profit margins traditionally have been low in this business… Grocery is among the thinnest margins out there in retail. The average grocer probably gets a 2-, 2.5-, to 3 % type operating margin. That is very slim margin, and that is before interest and taxes.”

However, the highly perishable nature of many grocery purchases and stickiness of grocery shoppers’ habits have softened and delayed the impact of e-commerce on it compared to other retail sectors. Although the entry of new, online, players into the industry, Amazon, in particular, urged traditional grocers to test the waters on online grocery shopping services in the last decade, most parts of the grocery business operated the same in 2019 as they did in 2009 or 1999. The COVID-19 pandemic dramatically rocked the boat and accelerated the adoption and development of e-grocery. In general, e-grocery services in the U.S. include both curbside pickup and home delivery. The adoption and usage of both of these services increased rapidly during the pandemic. As a result, in 2020 e-grocery’s percentage of the grocery market was 10.2%, up from 3.4% of the 2019 market [5]. E-grocery is expected to continue its growth after the pandemic. Redman predicted that e-grocery sales would climb to 21.5% of total grocery sales by 2025, calling for the immediacy of research on a deeper understanding of e-grocery, both curbside pickup and home delivery [6].

This chapter aims to provide a comprehensive document of consumers’ curbside pickup and home delivery grocery shopping behavior from both academic research and industry practice perspectives. Section 2 first proposes a new generic retail shopping channel typology and framework, of which curbside-pickup-from-store and home-delivery-from-store are two components. The framework is then applied to the supermarket and grocery industry, and after that, grocery shoppers’ behavior in the two channels is discussed. Section 3 presents how the COVID-19 pandemic has changed consumers’ online grocery shopping behavior. Section 4 predicts how grocery shoppers might behave in the post-pandemic world, and what topics future research efforts should be focused on.

2. Curbside pickup and home delivery shopping behavior

2.1 A full Spectrum of shopping channels

Roughly speaking, a “shopping channel” is a source from which consumers buy goods (or services) from sellers. In old days, consumers had a single, offline shopping channel: visiting a brick-and-mortar (B&M) store. A digital or online channel is the means through which consumers can buy goods or services from a seller over the Internet. Initially, shopping via an online channel involves a shipment from an online seller’s warehouse to a buyer’s home, which we refer to as “ship-to-home” (S2H) channel.

Over time, other channel formats located between the B&M channel and the S2H channel have emerged, including:

• Ship-to-store (S2S): A consumer places an order via a retailer’s website or app. The ordered product is shipped from the retailer’s warehouse to a local brick-and-mortar store of the retailer. The consumer must visit the local store to pick up the product. Typically, the consumer needs to enter the store for pickup. We call this sub-type “Ship-to-store-for-in-store-pickup” (S2SFISP). But some retailers (e.g., Home Depot and Target) also make “Ship-to-store-for-curbside-pickup” (S2SFCP) available. Generally, with curbside pickup, retailers allow customers to pick up products without leaving their cars, typically at designated zones in on-site or nearby parking spaces.

  One key difference between S2SFISP and S2SFCP from a retailer’s perspective is in the latter, the cross-sell and up-sell opportunities are nonexistent.

• Home-delivery-from-store (HDFS): A consumer orders online via the website/app of a retailer or a third-party service provider of a product that is available (and only available) in his/her selected local store. The product is delivered to the consumer’s home, typically within hours, from the selected local store.

  Note that HDFS is different from S2H in terms of where an order is fulfilled—in HDFS, an order is fulfilled by a local brick-and-mortar store, while S2H orders are fulfilled by a warehouse, which is, more often than not, not locally located.

• Pickup-from-store (PFS): A consumer orders online a product that is available (and only available) in his/her selected local store. Instead of having the product delivered to the home, the consumer picks it up from the local store where the product is located.

  The key difference between PFS and S2S is the same as the one between HDFS and S2H, that is, whether the product availability and order fulfillment involve a local store or not. Similar to S2S, PFS also has two variances, “curbside-pickup-from-store” (CPFS) or “in-store-pickup-from-store” (ISPFS). The former has become increasingly popular and is the dominant PFS format in the Supermarket and Grocery Stores industry.

• Ship-to-third-place (S23P): An order is fulfilled by, and shipped from a warehouse. However, instead of shipping to the consumer’s home (“first place”) or workplace (“second place”), the ordered product is shipped to an alternate location, which we also call a “third place”¹.

  Amazon’s locker is an example of S23P. It is estimated that by the time of the COVID-19 outbreak, Amazon had about 15,000 lockers located in U.S. convenience stores, apartments, and universities [7].

• Third-place-delivery-from-store (3PDFS): After a consumer places an order online, the ordered product is delivered from a local store to a third place, where the consumer can pick it up.

  For example, Lowes Foods, a high-end regional grocer serving the southern U.S., is testing to provide grocery pickup services at various third places in North and South Carolina [8].

Figure 1 is a tree diagram that summarizes all the channels from which consumers can shop in the digital era. To our best knowledge, this is the first time such a comprehensive digital-era channel typology and framework is proposed. Where an order is fulfilled is a key factor in pinpointing the shopping channel formats—In B&M, PFS, 3PDFS, and HDFS, orders are fulfilled by a local store; in S2H, S2TP, and S2S, orders are fulfilled by a warehouse. As such, we avoid using the seemingly popular acronyms BOPIS (buy-online-pick-up-in-store) and BOPAC (buy-online-pickup-at-curbside). While we believe that the two terms are equivalent to ISPFS (in-store-pickup-from-store) and CPFS (curbside-pickup-from-store), respectively, we would like to use the latter to emphasize the “from store” part of them, since that makes them sharply differentiated from S2SFISP (ship-to-store-for-in-store-pickup) or S2SFCP (ship-to-store-for-curbside-pickup). In addition, it is worth pointing out that a local store that fulfills an online order is not necessarily an active store that also serves in-store shoppers. Retailers can also use a local dark store to fulfill online orders. A good example is the “ghost” kitchen [9].

Figure 1.

A general retail shopping channel typology in the digital era. Source: built by the author.

In theory, all the general retail shopping channels in Figure 1 can apply to grocery shopping. In the context, it may be helpful to also view those rich types of channel formats from a two-dimensional perspective, as illustrated in Figure 2. In the figure, the y-axis represents the necessity of the seller’s local presence. As suggested in Figure 1, whether an order is fulfilled locally is a key factor in pinpointing today’s shopping channel formats. On the other hand, the x-axis is the travel distance of a consumer to obtain the grocery, another critical decision factor of grocery shopping. Note that the difference between B&M and PFS in the y-axis represents the existence of the dark store. Also note that in the figure, B&M and S2SFISP overlap and thus merge into the same box since they are identical in terms of both seller’s local presence and buyer’s travel distance.

Figure 2.

A two-dimensional view of today’s grocery shopping channels. Source: built by the author.

In Figure 2, the boxes’ color shade is also meaningful, which is used to represent consumers’ actual “shopping” effort and time. This refers to the effort and time that a consumer spends on shopping the grocery, such as moving a product from the shelf to the shopping cart and unloading a product from the car to the pantry. In the figure, the darker a box is, the more shopping effort and time a consumer is involved to complete one grocery shopping. Note that the shopping effort and time are fundamentally different from the effort and time it takes a consumer to travel to a store in that the former is a function of household size (the larger a household, the more grocery products to load into a car, upload from the car, etc.) but the latter is not. Because of that, in Figure 1, B&M and S2SFISP are represented by two different color shades with B&M’s one being darker, even though they are identical in both dimensions.

The discussion of the shopping effort and time above and Figure 2 reveals that in shopping for groceries, consumers can incur two types of costs, which vary with their locations and size of household:

• Travel Cost: The gas and time cost associated with a consumer traveling to a brick-and-more store.

  This cost is dependent on location but independent of household size. For example, the travel cost of a household with 10 people whose house is next to a grocery store is demonstrably lower than a single-member household whose nearest grocery store is five miles away.

• Shopping Cost: The efforts and time a consumer spends on actual shopping.

  Part of this cost, for example, physical loading or unloading of groceries, is independent of location but dependent on household size. When consumer shops for groceries in a store, although handling those bulky items such as toilet paper always requires effort, buying it for a household of 10 definitely requires more effort than for a single-member household.

E-commerce and any online-related shopping channels are able to transfer some of the time and effort costs from the shopper to the retailer. For example, any store pickup channels shift the loading-related shopping cost to a local store, and any home delivery channels shift both loading-related shopping and travel costs from the shopper to the retailer. Because of that, when applying to the grocery shopping context, it is also useful to reorganize the full spectrum of retail shopping channels in Figure 3, in which the x-axis and y-axis represent a consumer’s travel cost and shopping cost, respectively. In the figure, “In-Store Pickup” includes S2SFISP and ISPFS, since both of them are identical from a consumer’s perspective in terms of the two types of costs. Similarly, “Curbside Pickup” includes S2SFISP and CPFS, “Third-Place Pickup” includes S23P and 3PDFS, and “Home Delivery” includes S2H and HDFS. Li and Moul used this framework to examine the impact of e-commerce on grocery shoppers’ channel choice, shopping frequency, and basket composition [10].

Figure 3.

Another two-dimensional view of today’s grocery shopping channels. Source: built by the author.

We hope that our newly proposed frameworks of grocery shopping channels above can help scholars and practitioners to better systematically understand and think of the increasingly sophisticated landscape of grocery shopping. Given that CPFS and HDFS currently dominate shoppers’ choices, and are likely to remain so for the foreseeable future among all the e-commerce channels in the industry, we will focus on them for the rest of the chapter.

2.2 Old and new competitors

E-commerce has not only brought new, online-related shopping channels to the Supermarkets and Grocery Stores industry but also created new competitors for grocery stores, or grocers, the traditional players in the industry. In the U.S., grocery stores used to compete with each other in specific localities. Interestingly, grocery stores tend to be regional enterprises, with very few located throughout the entire country, resulting in a highly fragmented U.S. grocery market. Over time, grocery stores have had to compete with new rivals, such as supercenters (e.g., Walmart Supercenter), Warehouse Clubs (e.g., Costco), Dollar Stores (e.g., Dollar General), Specialty Stores (e.g., Trader Joe’s), and Limited Selection Discounter (e.g., Aldi), resulting in a highly competitive and saturated grocery market even before the advent of the Internet.

Using data from multiple sources², Table 1 shows the near-complete competitive landscape of the Carolinas (North Carolina and South Carolina) grocery market in the U.S. This offers a vivid snapshot of the highly competitive and highly segmented U.S. grocery market. Note that the companies included in the table are very much inclusive, resulting in a less than 1% accumulated market share from all unlisted companies (most of them are small, family-owned grocery stores). All the data points in the table were built upon all stores of the listed companies in the two states, rather than from a sample of stores.

Part 1
Store type		Main player	Number of stores
Traditional Grocery Store	Higher-end	Harris Teeter, Lowes Foods, Publix, The Fresh Market	345
	Lower-end	BI-LO, Food Lion, Piggly Wiggly	822
Supercenter		Walmart Supercenter, Super Target	180
Warehouse Club		Costco, Sam’s Club, BJ’s	62
Dollar Store		Dollar General, Dollar Tree, Family Dollar	2548
Specialty Store		Earth Fare, Sprouts Farmers Market, Trader Joe’s, Whole Foods Market	64
Limited Selection Discounter		Aldi, Lidl	148
		Total Number of Main Players: 21	Total: 4169

Part 2
Store type		In-store shopping behavior		Online shopping behavior
		Median travel distance (in mile)	Median Dwell Time (in Minute)	Median Online Grocery Shopping Duration
Traditional Grocery Store	Higher-end	3.14 (65)	17.22 (88)	15.42 (107)
	Lower-end	2.94 (61)	17.04 (87)	14.21 (99)
Supercenter		5.52 (114)	21.74 (111)	13.56 (94)
Warehouse Club		7.62 (158)	23.29 (119)	14.42 (100)
Dollar Store		4.08 (85)	15.58 (80)	14.03 (98)
Specialty Store		5.77 (120)	19.56 (100)	14.72 (103)
Limited Selection Discounter		4.65 (96)	22.18 (114)	14.13 (98)
		Average: 4.82 (100)	Average: 19.52 (100)	Average: 14.36 (100)

Table 1.

The near-complete competitive landscape of the Carolinas (North Carolina and South Carolina) grocery market.

Note: For each column, the number in a bracket represents the index (%) vs. the average of the column. For example, for the first column, (65) means the median travel distance (in miles) of the higher-end traditional grocery store is 65% of the average median travel distance across all store types.

Source: Built by the author.

Methodology: This subsection discusses the methodology and provides details of the Table 1 building. The number of stores information was collected from individual companies and then cross-checked with Reference USA [11], a leading database for business location data in the U.S. There are a vast number of dollar stores in the market; its market share in the grocery market can be even more surprising—in the Triad area of North Carolina, for example, the market share of Dollar General, Dollar Tree, and Family Dollar collective was more than 5% in 2019 [12]. Americans turning to dollar stores for groceries has been a national trend [13]. The information about travel distance to store from home and in-store dwell time was built upon the SafeGraph’s Patterns database [14], which tracks U.S. consumers’ visits to millions of points of interest (POIs) via their mobile devices. Specifically, “median travel distance” was calculated as the median of all distance (in miles) between a store and a shopper’s home, and “median dwell time” is defined as the median of the minimum time (in minutes) each individual shopper spends in a store. Dwell time is important to retailers—research shows a 1% increase in dwell time resulted in a 1.3% increase in sales [15]. As far as we know, this is the first time that a comprehensive comparison of consumers’ grocery shopping travel distance and dwell time across different types of stores from non-survey data is presented.

The data show that a warehouse club has the largest geographic coverage in terms of in-store shopping, more than double the coverage of a traditional grocery store. Among all the dwell time data points, what is most notable is that consumers on average spend a relatively long time in an Aldi or Lidl, even longer than they spend in a Walmart Supercenter or Super Target. This suggests that those limited selection discounter’s “treasure hunt strategy” (e.g., Lidl consistently rotates the availability and promotion of various nonperishable goods) has been quite successful [16]. Pooling those numbers together, we see that the Carolinas market has one store for every 3700 (=15.6 millions/4169) people where they can buy groceries³, and a consumer typically does not need to travel far (in U.S. standard) to obtain their groceries.

The competition in the Internet era has become even more intensive. The industry’s low margin did not stop the online retail giant Amazon from escalating the national food fight. The incumbent players in the industry have also evolved. By this time, all the store types aforementioned have offered pickup-from-store and home-delivery-from-store. This means that those companies are competing not only offline but also online. The last column of Table 1 shows “median online shopping duration,” the counterpart of dwell time when consumers shop online. To calculate it, we applied Semrush Pro, a popular paid online keyword research and competitor analysis tool, to a sampling of retailers from Table 1 that have online grocery shopping available via Instacart.com⁴. We chose to look at Instacart.com since not all consumers who visit the web or app properties of a mass retailer (e.g., Walmart.com or the Target app) are exclusively grocery shopping⁵. Semrush Pro tracks the real-time traffic of numerous websites including Instacart.com. One key factor in comparing online shopping duration to in-store dwell time is consumers tend to shop more quickly online. In fact, the data show a more than 25% (= (19.52–14.36)/19.52) time reduction. Given the strong correlation between time spent on shopping (whether dwell time in stores or online shopping duration) and firm performance, this should have a big implication to grocers. We will talk more about this when we discuss consumer behavior in the post-pandemic era.

To conclude, we have applied a creative method to conduct a new data analysis that compares the time consumers spend on shopping grocery online vs. offline. This is in addition to the new digital-era shopping channel typology and framework we proposed in Section 2.1. As such, this chapter is beyond a review—it not only provides a comprehensive review of the current advances of those important retail channel formats but also offers a set of new findings.

Competitor’s Geographical Impact in the Digital Era: Stores’ new pickup-from-store and/or home-delivery-from-store services also have a strong impact on their geographical coverage. A physical store’s geographical coverage used to be highly limited. Consistent to our findings in Table 1, Quelch and Carlson (2011) stated that a grocery store generally serves customers who live within a one- to three-mile radius [17]. Li and Moul used real sales and entry data from multiple grocery stores to estimate that the opening of a new store has a significant impact on the in-store sales of rivals located only within 1.5 miles [18]. A study by Ellickson and Grieco (2012) shows that even the impact of Walmart’s expansion into groceries is highly localized, the entry of a new Walmart Supercenter significantly affects existing grocery stores only within a two-mile radius of its location [19].

Now with pickup-from-store and home-delivery-from-store, consumers can order from a store that is farther from home (e.g., In the Charlotte, NC area, a consumer can order through home-delivery-from-store from a retailer located 33 miles away from their home and receive the order within an hour for an additional $5 “long-distance service fee.”). A study by Li and Moul shows that an existing store adding curbside-pickup-from-store has a significant geographic impact on the in-store sales of direct competitors’ stores up to eight miles [18].

It is worth emphasizing that in such an increasingly saturated and competitive market, grocers should neither underestimate nor overestimate who their direct competitor is. Underestimation is a less urgent issue, given grocers have a sense of the other B&M players in their regions and localities. However, they ought not to overestimate this competition. Taking the higher-end grocers in Table 1 as an example, while Walmart Supercenters certainly have sales impacts on their stores, we advocate that those higher-end grocery stores should compete to Walmart Supercenters differently from their direct competitors that belong to the same store-type category. In general, any company competing in this market should choose proper upper and lower bounds of their direct competitors, then focus their marketing dollars on competing with rivals in—and only in—the identified range. Retailers cannot, and should not, compete with everyone in the horizon, for example, Walmart’s execution of everyday low price (EDLP) is not a pricing model that every retailer can successfully execute. Better understanding the consumer segments (behavioral and demographic) in the market, properly identifying the business opportunity associated with these segments, and focusing resources on positioning the retailer with high opportunity segments, while basic, are still the fundamental principles of success in the Supermarket and Grocery industry.

2.3 E-commerce grocery shopper behavior

When purchasing a product, consumers go through a decision-making process, which calls for distinct phases. The number of phases actually entailed is dependent on the consumer’s level of involvement with the product. Kotler and Keller [20] and Dickson and Sawyer [21] show that, in general, consumers are “low-involved” in their grocery shopping, resulting in, say, spending less than 12 seconds per item when they are in a grocery store.

As suggested in Table 1, e-commerce and new online-related channels substantially impact consumers’ grocery shopping behavior. Ample research has been done to understand consumers’ online grocery shopping behavior. In this subsection, we will briefly review the research progress in this area.

Many studies have been conducted to understand the determinants of consumer purchase intention toward online grocery shopping among different demographics (e.g., [22, 23]), or in different countries, for example, China [24, 25], India [26], Germany [27], Korea [28], Malaysia [29], Netherlands [30], Pakistan [31], Portugal [32], Thailand [33], U.K. [34], U.S. [35], and Vietnam [36].

Price has consistently been one of the most important factors in a customer’s decision-making process of choosing where to do grocery shopping. Many American households shop at several grocery stores on a weekly basis to get the best prices for the items that they had on their shopping lists. Interestingly, while the search cost rationale predicts that consumers can be more price sensitive online than offline due to the low search cost, Chu et al. used an actual grocery shopping dataset to show that the same households exhibited lower price sensitivities when they shopped online than when they shopped offline [37]. The result was robust, holding for both large basket-share categories and small basket-share categories, for consumer-packaged goods and nonpackaged goods, for categories that are more likely to be purchased online because of their bulkiness or heaviness, and for categories that are more likely to be purchased offline because of their “sensory” nature. Given that the data used by Chu et al. [37] were from “the only successful online store” in the market back to early 2000’s, it warrants a study to verify the results using newer data.

The delivery and service fees associated with pickup-from-store and home-delivery-from-store can also affect consumer behavior. Prior to the pandemic, those associated fees deterred a high number of shoppers because their trips to the physical grocery store were not an inconvenience and could be easily integrated into a part of their regular routines. Whether it was driving to work, school, or other errands that the shoppers took daily, adding another stop, in the consumer’s decision-making process, was not valued in the same way as leaving their house only to go grocery shopping because the original driving is a sunk cost. Lewis (2006) empirically studied the effects of shipping fees on customer acquisition, customer retention, and average expenditures using data from an online grocer [38]. He found that shipping fees greatly influenced order incidence rates and graduated shipping fees significantly affected average expenditures. The various fee structures and resulting consumer behavior change may have other farther-reaching effects. Belavina et al. used a theoretical model to predict that grocery and food service subscriptions incentivize smaller and more frequent basket orders, which reduced food waste and created more value for the customer. In turn, the subscription model resulted in higher revenues, lower operational costs for the retailer, and potentially higher adoption rates for the grocers. These advantages, however, were countered by greater delivery-related travel and expenses, which were also moderated by area geography and routing-related scale economies. The subscription also led to lower food waste–related emissions but to higher delivery-related emissions. Pooling the two contradicting effects together, the paper suggests that, based on geographic and demographic data, the subscription model is almost always environmentally preferable because lower food waste emissions dominate higher delivery emissions [39].

Studies also focus on how and why consumers may choose different product alternatives when they shop online. Researchers are particularly interested in how online shopping alters consumers’ healthy food choices (e.g., [35, 40, 41, 42, 43]). For example, Huyghe et al. [40] suggested that consumers chose relatively fewer unhealthy alternatives in the online shopping channel. This is because the e-commerce channel presents products symbolically, which decreases the products’ sensory stimuli, which in turn diminishes consumers’ desire to seek instant gratification of those unhealthy alternatives. Zatz et al. [35] found something similar--consumers who ordered their groceries online spent less money on junk food compared to when they shopped in person.

Consumers’ different product choice behavior, when they shop online, can affect a retailer’s assortment strategy. For example, using data from an online grocery delivery service, Borle et al. [44] showed that a reduction in assortment reduced overall store sales, a result that contrasted with the conclusions of several previous academic and practitioner studies conducted offline that suggested grocery retailers could reduce product assortment with little or no loss in sales. Nenycz-Thiel et al. [45] found that the online environment was, in general, beneficial for private labels. Data from multiple product categories showed consumers tended to buy more private label products online than offline.

Most existing research, however, has not separately examined pickup-from-store and home-delivery-from-store. The data used by the research typically come from a home-delivery-from-store setting. The understanding of pickup-from-store is important but extremely lagging. Curbside-pickup-from-store is and will stay as the larger e-commerce channel in grocery. Analysis of the survey’s data found that while pandemic restrictions were in effect, 69% of online orders were fulfilled in the store via pickup and predicted that, post-COVID-19, a full 75% of online orders will be fulfilled through pickup services [5]. It is expected that delivery will take considerable time to surpass pickup and that the former would only overtake the latter in major markets. Our extensive literature search only yields two papers that specifically examine curbside-pickup-from-store. Gielens et al. (2021) found that the different ways of implementing home-delivery-from-store, in-store fulfillment (pickup at existing stores), near-store fulfillment (pickup at outlets adjoining stores), and stand-alone fulfillment (pickup at free-standing locations), could affect consumers’ spending [46]. Li and Moul built near-complete competitive environments in multiple markets and used brick-and-mortar store and curbside pickup service entry events in those markets to qualify the impact of curbside-pickup-from-store on competing retailers’ sales performance [18].

3. How did the pandemic change grocery shopper behaviors?

The unprecedented COVID-19 pandemic has altered how consumers behave in many ways (e.g., [47, 48]), and their grocery shopping behavior is no exception. In this section, we will discuss the impact of the pandemic on groery shopping behavior from a 5-W framework, where to buy, when to buy, who is buying, what products to purchase, and how much money and time is devoted.

Where: This change is obvious, as already noted in the first section. During the lockdown period of spring 2020, many households that were required to not go to their workplaces also avoided other places, even those deemed as “essential businesses,” such as the grocery store. Shopping for groceries online became a “healthy” decision for consumers because it meant that they did not have to physically visit a store and potentially expose themselves and their families to the virus to stock their pantries. It is reported that 45% of consumers shopped online for groceries more during the pandemic than before the pandemic—46% used home-delivery-from-store more, and 40% used curbside-pickup-from-store more [49].

In response, since the onset of COVID-19, businesses of all sizes, from start-up e-commerce to the largest retailer in the world, announced different types of delivery innovations—from ultrafast delivery (15 minutes or less) to delivering directly into a household’s refrigerator (Walmart). Equally notably, most, if not all, retailers rushed to get home-delivery-from-store and curbside-pickup-from-store services available to more of their stores. For example, Food Lion, a grocer that operates over 1000 stores in 10 Southeastern and Mid-Atlantic states, tripled its delivery footprint in 2020 alone [50].

After the U.S. stay-at-home orders were lifted and consumers returned to in-store shopping, studies (e.g., [49]) found that while grocery stores remained to be the top choice of store format for in-store grocery shoppers, other store types, such as warehouse clubs and limited selection discounters, gained popularity during the pandemic. For example, a survey study in Ref. [49] shows that around a third of the consumers polled said they were shopping less at grocery stores (30%) vs. pre-pandemic. This was likely due to some shoppers desiring fewer trips with bulk items and other shoppers desiring less “traffic” while they shop.

When: Prior to the pandemic, Saturdays are prime time for grocery shoppers. Approximately 41 million Americans choose this day to do their weekly shopping [51]. During the pandemic, the increased flexibility due to work-from-home and the safety concerns led to a much more diverse shopping time.

Consumers also shopped in stores significantly less frequently during the pandemic. Pre-COVID-19, an average U.S. household took 2.7 trips to the grocery store in a typical week. The 2020 Deloitte Fresh Food Consumer Survey suggested that the number of consumers who shopped for fresh food multiple times a week dropped by half from 2019 to 2020 [52].

Who: Pre-COVID-19, the average 2.7 trips a U.S. household took to the grocery store in a typical week were split between a primary shopper and a secondary shopper. Traditionally 65% of the primary shoppers are female [53]. Since the start of the pandemic, 36% of households made some change in who did the shopping. Of those, the majority (24%) reduced the number of shoppers going into the store to just one person [54]. According to Reiley [55], more men are claiming to be the primary shopper during the pandemic, and “they do buy different things and buy differently.”

Among those who adapted online grocery purchases due to the pandemic, two research papers, one using data from the U.S. [56] and the other using data from Finland [57], identify similar demographic and household characteristics of these adopters—higher income, bigger health concerns or constraints. Specifically, Shen et al. [56] and Eriksson and Stenius [57] respectively suggest that a household with earning USD100,000+ in U.S. or EUR50,000+ in Finland is more likely to be an adopter. Both papers are also consistent in that women may more likely be adopters than men.

What: The pandemic has generated a lot of supply chain issues. Even putting aside the behavior alteration caused by the supply chain shortages, we have observed other behavior changes regarding what to buy. Interestingly, two seemly contradicting behavior change patterns have been reported.

On one hand, it is observed that during the pandemic consumers tend to consider a smaller choice. In the COVID-19 period, consumers are more likely to have a list of critical tried-and-true items and are less inclined to browse and let serendipity guide them to something new. In response to that (as well as addressing the supply chain challenges), companies have chosen to produce and ship more of the top-selling products, and/or the top-selling varieties of a particular product, pushing off the launch of different flavors or spinoffs until sample stations can return. For example, Frito-Lay said that, when stay-at-home orders started going into effect in March 2020, it cut its number of unique stock-keeping units (SKUs) by about 21% to get more products into the market faster [55]. This “narrower range” phenomenon is not just a brick-and-mortar constriction. As the pandemic accelerated the shift to online shopping, the number of packaged food products available to purchase on the Internet fell 21% globally from January to May 2020. This phenomenon was not unique to the US, as nine out of the 10 biggest countries by retail sales saw a drop in the number of unique SKUs available online [55].

On the other hand, some research showed that consumers’ variety-seeking behavior increased during the pandemic. Specifically, by analyzing a panel dataset containing over 14 million household-level transactions from grocery stores across eight U.S. states, Choi et al. found that U.S. consumers’ variety-seeking behavior increased at UPC level, brand level, and manufacturer level for the dominant number of product categories (In addition to that, they also found that liberals and conservatives exhibited different variety-seeking change patterns during the pandemic) [58]. Note their results were obtained from multiple perishable product categories such as fresh eggs and produce, so stockpiling or hoarding should not jeopardize their conclusion. This puzzling result can be explained by the psychological reactance theory, which posits that an individual will engage in a strong emotional and behavioral attempt to restore freedom when their actions and choices are deprived [48, 59].

Another interesting observation about what shoppers bought during the pandemic is that consumers, budget-minded consumers in particular, embraced more-affordable private-label brands. In turn, shelf space at retailers became more competitive and presumably squeezed out shelf space for smaller and newer brands.

How Much: With consumer concerns on the rise, so are grocery bills. This has been confirmed by many studies (e.g., [52, 54, 55]). For example, as Reiley [55] pointed out, 44% of shoppers reported spending more money on groceries each visit as a result of COVID-19. Households’ average weekly grocery spend surged from $121 at the beginning of 2020 to $161 in late March. The spending fell to around $126 per week by April as shoppers eased on excessive buying, but was still higher than at the beginning of the year. The increased spending on groceries is consistent to the observation that home cooking became a popular alternative to restaurant services in many households. This was also probably due to the fact that many consumers were increasing the amount of money spent per trip and simultaneously decreasing the amount of time spent in the store.

4. Grocery shopping behavior predictions for the post-pandemic era and potential areas for future research

Some shopping behavior changes we discussed in the last section are temporary, but some will become permanent as they continue in the post-pandemic era. In this section, we will attempt to lay out some important behaviors that we expect to observe throughout the e-commerce grocery shopper journeys post the pandemic.

4.1 The continuing adoption/use of curbside pickup and home delivery services

Foremost, post-pandemic grocery shoppers will keep using or adopting the curbside-pickup-from-store and home-delivery-from-store services. While neither of these services were new, the pandemic has undoubtedly accelerated their adoption and built up the momentum for them to become a major part of the consumers’ grocery shopping journey. Redman predicted that in the U.S., e-grocery sales would climb to 21.5% of total grocery sales by 2025 [6]. Shopping via those e-commerce channels will be one major pandemic-driven service feature that will become a permanent and bigger part of grocery retailing. This will make Section 2 of this discussion highly relevant and valuable to understanding grocery shopping behavior in the post-pandemic era. Admittedly, all the research discussed in Section 2 uses pre-COVID data, which may lead to a more conservative estimated magnitude than in the post-pandemic era. As such, we call for new studies to use post-COVID data to validate their conclusions.

Both curbside-pickup-from-store and home-delivery-from-store will continue growing post-pandemic. The latter will continue to ripen at a fast pace. However, we believe that between the two, curbside-pickup-from-store will persist as the bigger e-commerce channel for grocers. The home-delivery-from-store volume will only surpass curbside-pickup-from-store in major markets. In addition to the high costs associated with delivering, which will be a nearly insurmountable hurdle for most markets in the U.S., consumers’ mobility patterns will also play a defining role. One key consumer behavior post-pandemic will be households traveling outside of the house again at near pre-pandemic rates. Not all shopper movement behavior will be the same, but we predict that households that are looking to travel outside of their home will be the determining factor for the stickiness of curbside pickup service.

4.2 Moving from 3rd-party service to 1st-party service

A large number of grocers had set up curbside-pickup-from-store and home-delivery-from-store services pre-pandemic. However, in many of those cases, grocers did not offer customers their own, 1st-party e-commerce shopping experience. The actual service was provided by 3rd-party marketplaces, with Instacart ultimately becoming the largest. That was primarily because grocers’ resources (internal employees, budget, etc.) prevented them from delivering an experience that was even remotely consistent to the quality that they delivered in their physical footprint. Additionally, pre-pandemic the e-commerce shares of many food retailers were well below 10% of their total sales, which resulted in these retailers not acting on the significant investment needed to establish a 1st party e-commerce shopping experience.

As the pandemic went on and kept being extended, grocers rushed to establish their own e-commerce shopping channels. Those that had not made investments in developing their own e-commerce shopping experience before were beholden to those marketplaces, not only for the technology and human capital infrastructure but also for the data. Grocers realized that they did not understand their own online customers since their agreements with 3rd party marketplaces resulted in the grocers not having access to those shopper’s purchasing and customer journey data. Moreover, because of the e-commerce channel outsourcing, a grocer lost the ability to bring forward their brand proposition and brand reach to their current or future online shoppers.

The pain point of not being able to access their customers’ data sped up many grocers’ pace to develop and implement proprietary e-commerce channels. At this time, many grocers offer dual e-commerce shopping experiences to their customers through 1st-party and 3rd-party shopping options. We predict that post the pandemic, grocers will “push” their customers to use their own e-commerce channels and will battle with those 3rd-party marketplaces about who owns customer data. The ability to measure and react to consumer behavior using customer data will be an extremely important part of retailers successfully navigating the post-pandemic era. To that end, the future will hold more shoppers transitioning away from 3rd-party marketplaces to the retailers that they were loyal to before the pandemic and new retailers that they trialed during the pandemic and developped repport.

4.3 Impulse and explorative shopping behavior in the e-commerce channels

The growth of curbside-pickup-from-store and home-delivery-from-store is a great opportunity for grocers. However, it also poses new challenges to them (and manufacturer brands) at the same time. As shown in Table 1, consumers spend significantly less time to complete grocery shopping online compared to offline. The less time they spend, the less money they spend. This is probably because, as was discussed in Section 3, consumers are more likely to have and stick to a shopping list when they shop grocery online. Without all the powerful in-store instruments, such as calculated floor plan design and sampling stations, grocers need to find innovative ways to encourage their customers to extend their time spent on the retailer’s website/app.

In a similar vein, consumers have done much less impulse purchasing when they do grocery shopping online. Grocers used to be big beneficiaries of consumers’ impulse purchasing behavior such as purchasing wine displayed on promotional end caps or adding a candy bar to the basket in the check-out lane. Before the pandemic, consumers made three impulse buys a week, and 70% identified food as the main category [60]. Even worse, at this point in either curbside-pickup-from-store or home-delivery-from-store cross-sell and up-sell opportunities are virtually nonexistent (which was one of the reasons why grocers hesitated to invest in them pre-pandemic). It would be a tremendous missed opportunity for grocers to neglect impulse sales and cross-sell/up-sell opportunities after investing millions of dollars to set up their curbside-pickup-from-store or home-delivery-from-store infrastructure. Without them, the brand experience, discovery, and personal connection would all disappear. The e-commerce channels may turn the store into just a warehouse. Because of that, we call for more research that can help grocers engage their consumers for more online impulse and explorative shopping behavior.

We believe that data and new development in technology will play a critical role here. The traditional search technique based on keywords and the traditional recommendation system based on, say, association rule technique, is not helpful. We need a new, smarter search engine or recommender that can not only “explore” but also “exploit.” Consumer data will be the building block for any of those developments, which provides additional rationales for why grocers would like to set up their own e-commerce channels.

4.4 (Un)healthy food choices in the e-commerce channels

As discussed, consumers buy fewer new brands and products when they shop grocery via an e-commerce channel. Consumers are more likely to order what they have ordered before. Using Instacart data, Chintala et al. have shown that e-commerce shoppers on Instacart tend to have a 95% chance of purchasing the exact same brand/item once it has been included in at least five previous baskets [61]. If a consumer has ordered healthy products before, they are more likely to order them again. But if a consumer has ordered unhealthy products before, their adverse effect can also be reinforced. We expect to see more disparities in healthy vs. unhealthy food choices post-pandemic.

The current marketing tools and techniques could make the disparity even worse. Taking targeted marketing as an example. If purchasing habits are healthful, a consumer will be targeted with products that fit that pattern; if purchases lean in the other direction, so will targeted marketing. While a technique such as collaborative filtering is very suitable to be used in Netflix’s recommendation system, which recommends movies based on a person’s tastes in movies predicted from his/her past movie consumption, it will only reinforce a consumer’s unhealthy eating patterns if used by a grocer in its recommendation system. The development and application of new techniques to grocery e-commerce channels are warranted.

4.5 Trip consolidation vs. disaggregation

Pre-pandemic, consumer grocery shopping trips are most frequently segmented by the number of items within the purchase, resulting in pantry stock-up trips, fill-in trips, special purpose trips, and quick trips [62]. Among them, pantry stock-up trips are grocery shopping trips with the largest total number of items within the basket. A pantry stock-up trip typically consists of products that are from categories and subcategories that are mostly defined as planned. A relevant example of this would be toilet paper. Most households do not increase or decrease the amount of toilet paper that they use as it is mostly dependent on the average number of people within a household over the course of any given day. Because of this, a household could estimate the amount of toilet paper that they may need over a period of time.

With the fast development of online grocery shopping, consumers will also develop their strategies of mixing up online and offline channels to optimize their grocery shopping experience, for example, using curbside-pickup-from-store and/or home-delivery-from-store services for bulky or other shelf-stable items, and using Brick-and-Mortar for fresh foods such as meats and produces. As a result, we expect to observe both trip consolidation and trip disaggregation at the same time in the post-pandemic future. For example, consumers are likely to continue pandemic buying stock-up behavior, leading to less-frequent stock-up trips with even larger number of items within a trip. At the same time, consumers will probably run other types of grocery shopping trips more frequently for their perishable goods needs.

In response to that, even before the pandemic, but increasing since then, service providers and retailers have started to offer some form of automated replenishment of these items. This trend has much bigger implications for the grocery industry post-pandemic. For example, this trend will have a big impact on the current grocery store floor plan, which typically includes a large center aisle section full of shelf-stable items. The size (most commonly measured in square feet) of the center store section will shrink because of the automated replenishment and those e-commerce channels. In addition, only sending specific items would most likely (depending on the total cost of the item) not be profitable to the retailer, and by excluding the consumer from the “purchase” removes the possibility for the consumer to add other products, either planned items or impulse purchases introduced along the e-commerce check out process to the basket that may make the overall basket’s profitability go from negative to positive. Because of that, in the post-pandemic future, it is important to look beyond auto replenishment as the product shows up on the consumer’s doorstep every x number of days/weeks.

4.6 Shopping behavior affected by fee structure or labor supply

As we have discussed in Section 2.3, the delivery and service fees associated with curbside-pickup-from-store and home-delivery-from-store can also affect consumer behavior. In fact, pre-pandemic many consumers avoided e-commerce groceries, especially home-delivery-from-store, because of the perceived high cost of service fees associated with inherent convenience, as well as the new phenomenon of tipping your delivery driver.

In the post-pandemic period, we expect that grocers will continue exploring various fee structures for their own e-commerce channels, while they also try to move their customers there from a 3rd-party channel. One way that some grocers have tried is to find a fee structure to alleviate the pressures created by an unstable labor supply, specifically around fulfilling consumers’ curbside and delivery orders. For example, some grocers have incentivized consumers to select different curbside pickup or delivery time slots based on the service fees associated with those slots. It is well-documented that consumers undervalue their time and overvalue the cost of a service/delivery/fulfillment fee that has historically not been a part of the total receipt of their shopping trip. Because of this, a consumer’s behavior of when they would like to have their order ready and what fulfillment methodology that they chose to utilize could be significantly influenced by the retailer or marketplace. In addition to the variability of the cost of various time slots across potential windows of fulfillment, some grocers have also begun to incentivize shoppers to increase their overall basket spend by offering free shipping on their shortest window of fulfillment. By doing this, customers are introduced to a new opportunity of impulse buying that does not have to be prefaced with the retailer prompting them with a set of items hoping that one of them will satisfy a need that they did not realize that they had prior to starting their shopping trip. The plethora of subscription models and fee structures that are emerging (and fast-changing) will be very much worth the research endeavor.

5. Conclusion

Accelerated by the COVID-19 pandemic, curbside-pickup-from-store and home-delivery-from-store are growing explosively into the new normal and the post-pandemic era beyond that. Curbside pickup will remain the larger option between the two, while home delivery is ripening quickly. This chapter not only provides a comprehensive review of the current advances of those important retail channel formats but also offers a set of new points of view, such as a set of new shopping channel typologies and frameworks, as well as making a list of predictions of their future directions. We hope that our discussion is of help to both scholars and practitioners to better understand the fast-evolving consumer behavior beyond the pandemic.

Thanks

The authors are indebted to Yian Li and Molly Fisher for their great support. This chapter has also benefited from valuable comments by Roger Beahm and Michelle Steward. The first author appreciates the financial support from Wake Forest University School of Business and Wake Forest University Open Access Publishing Fund.