In-store or online grocery shopping before and during the COVID-19 pandemic

Adrian Meister; Caroline Winkler; Basil Schmid; Kay Axhausen

doi:10.1016/j.tbs.2022.08.010

. 2022 Oct 18;30:291–301. doi: 10.1016/j.tbs.2022.08.010

In-store or online grocery shopping before and during the COVID-19 pandemic

Adrian Meister ^a,^⁎, Caroline Winkler ^a, Basil Schmid ^a, Kay Axhausen ^a

PMCID: PMC9576720 PMID: 36277679

Abstract

This paper presents results of a unique stated choice (SC) experiment to uncover the determinants of grocery shopping channel choice during the first wave of COVID-19 infections, where the most restrictive containment measures were in place. The choice sets were framed under regular and pandemic conditions, allowing for the estimation of pandemic-specific effects for each of the choice attributes. Our results show a significant overall increase of about 13%-points in online grocery shopping under pandemic conditions. Shopping and delivery costs were found to be the major decision drivers in both experimental settings, while the waiting time in front of the grocery store and risk of infection only played an secondary role. The value of delivery time savings (VDTS) decreases from about 10.8 CHF/day in the regular to 7.4 CHF/day in the pandemic case, indicating that respondents show an increased patience when waiting for the delivery of the ordered groceries. However, choice attributes related to the shopping trip, i.e. travel time and cost, do not show any notable effects. The COVID-19 death risk was valued rather low by the respondents and the relatively unrestricted Swiss containment measures are in line with the respondents’ average preferences, as shown by a relatively low value of statistical life (VSL) of about 800,000 CHF.

1. Introduction

Following the first death from the COVID-19 pandemic in Switzerland on March 5th, 2020, the Swiss Federal Council classified the outbreak of the SARS-COV-2 virus as an “extraordinary situation” under the Federal Epidemics Act (FDHA, 2020). This led to the closure of all non-essential businesses and public institutions, as well as the prohibition of gatherings of more than five people. Though no general curfew was put in place as in neighboring countries like France or Italy, the everyday life of the Swiss population has changed in unprecedented ways. For just over one month until mid-April 2020, Swiss residents were recommended to only leave the house for essential activities such as visiting a doctor or to go grocery shopping (FDHA, 2020). Hygienic regulations such as capacity constraints as well as the use of hand disinfectant and face masks were implemented at grocery stores in order to minimize the chances of customers infecting one another. The risk of becoming infected during a visit to the grocery store is especially relevant for high-risk individuals who represent a non-negligible segment of society and include adults of older age groups and those with medical preconditions (BAG, 2020).

Over the last decade, the online market for groceries has been growing steadily in Switzerland: in 2019 it accounted for 1.1 billion CHF yearly, representing 2.8% of the online shopping market, compared to only 1.8% in 2012 (VSV, 2020). Although there is a rich body of revealed-preference (RP) literature on shopping channel choice decisions, RP data is often problematic due to the high correlations between choice attributes and its limited trade-off information (e.g., Train, 2009, Schmid, 2019). Only little work has been done to date applying stated choice (SC) methods to explicitly model this decision process (Hsiao, 2009, Schmid et al., 2019). Sophisticated statistical methods like SC experiments and discrete choice models however allow to better understand the trade-offs individuals make in this regard, and allow to obtain meaningful attribute importance measures and willingness-to-pay indicators. To date, only Grashuis et al. (2020) have looked at these (similar) decisions under pandemic conditions. We designed a SC experiment to model the pandemic-related behavioral response regarding shopping channel choice for groceries in Switzerland. The experiment was performed with two different hypothetical settings to investigate a potential change in attribute sensitivities: one experiment is framing grocery shopping decisions during the COVID-19 pandemic with containment measures in place, and the other is considering shopping channel choice prior to the COVID-19 outbreak. We used an integrated choice and latent variable (ICLV) model including general risk aversion and shopping channel attitudes to obtain deeper behavioral insights on consumer heterogeneity. Our framework simultaneously estimates the pandemic-related effects on the decision-driving attributes, the direct and indirect effects of socio-demographic characteristics on these factors, as well as additional respondent heterogeneity that arises from different attitudes.

The reminder of this paper is structured as follows: Section 2 presents relevant work and the latest findings regarding behavioral models for online grocery shopping. Section 3 gives an overview about our survey tool, the experimental design and the modeling methodology. Section 4 describes the modeling results. Section 5 provides a conclusion and critical reflection.

2. Literature Review

Grocery shopping is a rather unique form of shopping for several reasons, and its effects on related demand for transportation and mobility have been studied thoroughly. Shopping for groceries is firstly the most common and frequent shopping trip purpose (Suel and Polak, 2017). Groceries are considered to be experience goods, as their physical attributes are best inspected in person. They do not require separate visits for information gathering and are therefore less likely to be bought online compared to search goods, such as electronics or furniture (Peterson et al., 1997, Rudolph et al., 2015, Schmid and Axhausen, 2019). The effects of information and communication technologies (ICT) have been of interest to the field of online shopping, especially in the last 25 years, and they are assumed to either substitute, complement, modify, or not at all alter travel behavior to physical stores (Mokhtarian, 2004). Since groceries are predominately still purchased in-store, online shopping can be seen as a shopping channel with great potential, implying possible substitution effects (Farag, 2006, Suel and Polak, 2018, Dias et al., 2019, Zhai et al., 2017).

Concerning in-store and online grocery shopping during the pandemic, many studies have reported substantial increases and large potentials in online market shares among different consumer segments (e.g., Bezirgani and Lachapelle, 2021, Guzman et al., 2021, Pawar et al., 2021, Colaço and Silva, 2022). In case of Switzerland, the Swiss Retailer Survey for 2020 shows a remarkable 75% increase in sales for online grocery shoppers (Zumstein and Oswald, 2020). The question that follows is what factors motivated this stark increase in online grocery shopping behaviors and what role pandemic effects play in this decision-making process. A shift toward online grocery shopping was even evident in Asian countries where the norm is to purchase fruits and vegetables daily at outdoor markets; the reason being, unsurprisingly, that individuals do not want to take the unnecessary risk of potentially becoming infected (Chang, 2020). According to Wang et al., 2020, the main reason individuals reported stockpiling groceries was actually to ”go out less” and not foremost out of worry that products would run out or that prices would increase dramatically. Their estimates of willingness to pay for fresh foods are in line with an increase of 60% during COVID-19. A similar increase was identified by Wang et al., 2021, who saw the number of people ordering groceries online grow by 113% during the pandemic, though only about 50% would continue to do so afterwards. Almost all studies that consider these choices pre-pandemic tend to simply highlight the importance and sensitivity of cost as opposed to time (e.g., (Schmid and Axhausen, 2019, Rossolov et al., 2021, Marcucci et al., 2021)). The pandemic clearly added new attributes into the choice situation that cannot be neglected when investigating shopping channel preferences.

Few studies to date have taken shopping channel choice behavior into a stated choice experiment survey. Hsiao, 2009 focused on travel time and delivery time valuation in the context of books, a typical search good, and found that physically visiting a bookstore to purchase a book provided by far more disutility than having to wait for a book to be delivered. Similarly, Schmid and Axhausen, 2019 used SC experiments to investigate how individuals trade off attributes related to shopping channel choice for both experience and search goods, in their case in a hypothetical setting in which private vehicles did not exist. A major conclusion drawn was that respondents with a higher education typically exhibit a more positive attitude toward online shopping and are hence those who more often choose the online alternative for grocery purchases. In another study out of Norway, Marcucci et al., 2021 also took a stated preference approach to understand consumer channel choice and found the main drivers steering this choice to again be related to cost: product price and service cost. With the dawn of the COVID-19 pandemic, Grashuis et al., 2020 implemented a SC experiment to investigate grocery shopping channel choice using an online panel with 900 participants. They looked at the attributes of purchasing method (online purchase, in-store pickup, or in-store purchase), fees, minimum order amount, and time window. They found that participants in the scenario in which COVID-19 rates in their area were increasing were the least willing to purchase groceries in-store and no preference for purchasing method in a scenario in which COVID-19 rates were decreasing in an area. The option to opt-out of going shopping in person shared similar importance to the purchasing method, and the fees associated with the shopping method contributed to respondents’ disutility to an even larger extent. Though these findings are important, their approach was limited as they did not account for potential effects of socio-demographic and attitudinal characteristics on these decisions.

3. Methods

3.1. Experimental Design

We implemented two different SC experiments, one positing regular (pre-COVID-19) conditions and the other in a pandemic context. The SC experiments were implemented in a within-subject design, i.e. both treatment conditions were shown to each respondent. The hypothetical scenario descriptions for both experiments were designed to ensure realistic and intuitive consequences of the decisions, as well as logical and understandable relationships between the traded goods (see Table A1, Table A2). Table 1 shows an overview of the attributes of the alternatives in both experiments. In the regular setting, the in-store alternative is described by shopping cost and shopping time, as well as travel cost and travel time. The online alternative is characterized by shopping cost and shopping time, as well as delivery cost and delivery time. In the pandemic setting, the in-store alternative additionally includes attributes of waiting time in front of the store and risk of infection.

Table 1.

Choice set composition and overview about the relevant attributes for both experiments.

attributes	treatment		alternatives		levels
	pandemic	regular	in-store	online
shopping cost [CHF]	x	x	x	x	Appendix, Table A3
shopping time [min]	x	x	x	x	Appendix, Table A4
travel cost [CHF]	x	x	x		Appendix, Table A7
travel time [min]	x	x	x		Appendix, Table A8
delivery cost [CHF]	x	x		x	10, 25, 50
delivery time [h]	x	x		x	6, 24, 48
waiting time [min]	x		x		0, 10, 20
risk [-]	x		x		low, mid, high

	$MIXL$	${MIXL}_{fac}$	$ICLV$
Reference alternative: In-store shopping (S)	Coef.	Coef.	Coef.
$β_{shopping_cost}$	${1.80}^{* * *}$	${1.88}^{* * *}$	${1.80}^{* * *}$
$γ_{shopping_cost, large_basket}$	${0.36}^{* *}$	${0.31}^{*}$	${0.34}^{* *}$
$β_{shopping_cost, P}$	$- 0.22$	$- {0.35}^{*}$	$- 0.32$
$β_{delivery_cost}$	${2.11}^{* * *}$	${1.84}^{* * *}$	${1.94}^{* * *}$
$γ_{delivery_cost, pt}$	$- {0.84}^{* *}$	$- {0.80}^{* *}$	$- {0.76}^{* * *}$
$β_{delivery_time}$	$- {0.80}^{* * *}$	$- {0.78}^{* * *}$	$- {0.80}^{* * *}$
$γ_{delivery_time, medium_basket}$	${0.23}^{* *}$	${0.24}^{* *}$	${0.23}^{* *}$
$γ_{delivery_time, pt}$	${0.48}^{* *}$	${0.49}^{* *}$	${0.51}^{* *}$
$β_{delivery_time, P}$	${0.43}^{* * *}$	${0.38}^{* *}$	${0.39}^{* *}$
$θ_{delivery_time, retired, P}$	${0.78}^{* *}$	${0.47}^{*}$	${0.60}^{* *}$
$β_{waiting_time, P}$	$- {2.02}^{* *}$	$- {1.53}^{* *}$	$- {1.63}^{* *}$
$γ_{waiting_time, car, P}$	$- {1.41}^{* * *}$	$- {1.27}^{* *}$	$- {1.31}^{* *}$
$θ_{waiting_time, hh_size, P}$	${2.16}^{* *}$	${1.73}^{*}$	${1.90}^{* *}$
$β_{risk_medium, P}$	$- {0.87}^{* * *}$	$- {0.76}^{* * *}$	$- {0.77}^{* * *}$
$θ_{risk_medium, med_educ, P}$	$- {0.76}^{* * *}$	$- {0.66}^{* *}$	$- {0.71}^{* * *}$
$θ_{risk_medium, high_educ, P}$	$- {1.76}^{* * *}$	$- {1.56}^{* * *}$	$- {1.64}^{* * *}$
$θ_{risk_medium, income, P}$	${0.76}^{* *}$	${0.82}^{* *}$	${0.79}^{* *}$
$θ_{risk_medium, retired, P}$	$- 0.06$	${0.72}^{*}$	$0.39$
$β_{risk_high, P}$	$- {4.05}^{* * *}$	$- {3.44}^{* * *}$	$- {3.56}^{* * *}$
$γ_{risk_high, pt, P}$	${2.90}^{* * *}$	${2.42}^{* * *}$	${2.51}^{* * *}$
$γ_{risk_high, car, P}$	${0.69}^{* * *}$	${0.59}^{* * *}$	${0.62}^{* * *}$
$θ_{risk_high, med_educ, P}$	$- {1.34}^{* * *}$	$- {1.14}^{* * *}$	$- {1.30}^{* * *}$
$θ_{risk_high, high_educ, P}$	$- {3.52}^{* * *}$	$- {3.25}^{* * *}$	$- {3.51}^{* * *}$
$θ_{risk_high, retired, P}$	$0.35$	${1.90}^{* *}$	$1.60$
$θ_{risk_high, swiss, P}$	$- {0.13}^{*}$	$- {0.12}^{*}$	$- {0.12}^{*}$
$β_{ASC}$	$- {1.80}^{* * *}$	$- {2.41}^{* * *}$	$- {2.25}^{* * *}$
$θ_{ASC, male}$	${0.41}^{* * *}$	${0.35}^{* *}$	${0.42}^{* * *}$
$θ_{ASC, age}$	$- {1.98}^{* * *}$	$- {1.83}^{* * *}$	$- {2.06}^{* * *}$
$θ_{ASC, swiss}$	$- {0.12}^{* * *}$	$- {0.13}^{* * *}$	$- {0.13}^{* * *}$
$θ_{ASC, income, P}$	${0.78}^{* * *}$	${0.74}^{* * *}$	${0.77}^{* * *}$
$σ_{shopping_cost}$	${0.67}^{* * *}$	${0.64}^{* * *}$	${0.71}^{* * *}$
$σ_{delivery_cost}$	${1.61}^{* * *}$	${1.16}^{* * *}$	${1.26}^{* * *}$
$σ_{delivery_time}$	${0.50}^{* * *}$	${0.48}^{*}$	${0.50}^{* * *}$
$σ_{waiting_time}$	${4.42}^{* * *}$	${4.48}^{* * *}$	${4.53}^{* * *}$
$σ_{risk_med}$	${1.58}^{* * *}$	${1.54}^{* * *}$	${1.56}^{* * *}$
$σ_{risk_high}$	${3.22}^{* * *}$	${3.04}^{* * *}$	${3.09}^{* * *}$
$σ_{ASC}$	${2.14}^{* * *}$	${2.36}^{* * *}$	${2.37}^{* * *}$
$μ_{shopping_cost, PO}$		0.03
$μ_{shopping_cost, PR}$		–0.05
$μ_{delivery_cost, PO}$		0.09
$μ_{delivery_cost, PR}$		–0.09
$μ_{delivery_time, PO}$		–0.04
$μ_{delivery_time, PR}$		–0.02
$μ_{waiting_time, PO}$		0.16
$μ_{waiting_time, PR}$		0.40
$μ_{risk_med, PO}$		–0.06
$μ_{risk_med, PR}$		0.21
$μ_{risk_high, PO}$		–0.20
$μ_{risk_high, PR}$		0.33 $*$
$μ_{ASC, PO}$		${1.02}^{* * *}$	${2.79}^{* * *}$
$μ_{ASC, PR}$		$0.14$
$ν_{PO, age}$			$- 0.03$
$ν_{PO, male}$			$- 0.01$
$ν_{PO, income}$			${0.07}^{* * *}$
$ν_{PO, working}$			$- 0.02$
$ν_{PO, retired}$			$- {0.09}^{* *}$
$ν_{PO, med_educ}$			$0.02$
$ν_{PO, high_educ}$			$0.07$
$σ_{PO}$			${0.27}^{* * *}$
$τ_{shop 2}$			$- {1.69}^{* * *}$
$τ_{shop 3}$			$- {2.31}^{* * *}$
$τ_{shop 4}$			$- {1.71}^{* * *}$
$τ_{shop 5}$			$- {1.23}^{* * *}$
$τ_{shop 7}$			$- {2.05}^{* * *}$
$τ_{shop 9}$			$- {1.17}^{* * *}$
$σ_{shop 1}$			${0.88}^{* * *}$
$σ_{shop 2}$			${0.69}^{* * *}$
$σ_{shop 3}$			${0.72}^{* * *}$
$σ_{shop 4}$			${0.68}^{* * *}$
$σ_{shop 5}$			${0.76}^{* * *}$
$σ_{shop 7}$			${0.74}^{* * *}$
$σ_{shop 9}$			${0.79}^{* * *}$
Estimated parameters	57	71	79
Respondents	1009	1009	1009
Choice observations	8072	8072	8072
Draws	5000	5000	5000
LL(null)	-5082.75	-5082.75	–
LL(choicemodel)	-2613.80	-2576.94	-2615.26
AICc(choicemodel)	5348.55	5306.79	5353.72

attribute	relative partworths [%]
	regular	pandemic
shopping cost	$47.4$	$40.1$
delivery cost	$44.9$	$45.4$
delivery time	$7.7$	$5.9$
waiting time		$0.2$
medium risk		$1.6$
high risk		$6.8$

	online choice increase [%-points]
	${MNL}_{soc}$	$ICLV$
pandemic (dummy)	$4.0$	$10.2$
basket size: medium (dummy)	$2.7$	$1.9$
basket size: large (dummy)	$2.2$	$1.4$
mode: public transport (dummy)	$6.1$	$6.7$
mode: car (dummy)	$3.1$	$3.0$
shopping cost, online (+10%)	$- 2.3$	$- 2.9$
shopping cost, in-store (+10%)	$3.3$	$3.9$
delivery cost (+10%)	$- 0.7$	$- 0.6$
delivery time (+10%)	$- 0.2$	$- 0.2$
waiting time (+10%)	$0.1$	$0.1$
medium risk (dummy)	$3.1$	$2.9$
high risk (dummy)	$14.0$	$13.3$
age (+10 years)	$- 2.0$	$- 2.2$
male (dummy)	$3.8$	$2.7$
income (+2k CHF)	$0.3$	$0.7$
medium education (dummy)	$1.0$	$1.3$
high education (dummy)	$1.8$	$2.4$
working (dummy)	$- 1.2$	$- 1.9$
retired (dummy)	$- 1.3$	$- 2.2$
swiss (dummy)	$- 7.0$	$- 6.7$

indicator/attribute		willingness-to-pay
		${MNL}_{soc}$	$ICLV$
delivery time (VDTS), regular	[CHF/day]	$18.7$	$10.8$
delivery time (VDTS), pandemic	[CHF/day]	$18.0$	$7.4$

waiting time	[CHF/h]	$33.1$	$10.9$

medium risk	[CHF]	$28.6$	$11.1$
high risk	[CHF]	$152.0$	$63.4$

scenarios	low: sporadic cases	moderate: clusters of cases	high: community transmission
reported infections	4,000	80,000	200,000
reported recoveries	500	4,000	80,000
risk of infection	0.1%	1%	5%

course	symptoms	probability
asymptomatic	none	80%
mild course	(including influenza-like symptoms like cough, fever, fatigue, headache, as well as mild pneumonia)	15%
critical course	(including severe pneumonia, respiratory failure and septic shocks)	4.85%
fatal course	death	0.15%

basket size	levels (in-store/online)
	( $μ - 10 %$ )	$μ$	( $μ + 10 %$ )
small [CHF]	36 / 32	40 / 36	44 / 40
medium [CHF]	72 / 65	80 / 72	88 / 79
large [CHF]	108 / 97	120 / 108	132 / 119

basket size	levels (in-store/online)
	( $μ - 5 \min$ )	$μ$	( $μ + 5 \min$ )
small [min]	10 / 5	15 / 10	20 / 15
medium [min]	25 / 20	30 / 25	35 / 30
large [min]	45 / 40	50 / 45	55 / 50

shop1	I often shop for products online (+)
shop2	Online shopping is associated with risk (-)
shop3	Credit card fraud is one of the reasons why I do not like to shop online (-)
shop4	The internet has more disadvantages than advantages (-)
shop5	A disadvantage of online shopping is that I cannot physically inspect the products (-)
shop6	Online shopping facilitates the comparison of products and prices (+)
shop7	Receiving the wrong product is one of the reasons why I do not like online shopping (-)
shop8	I like to follow the latest technological developments (+)
shop9	I find everything that I need in physical shops (-)

risk1	Would you drink more than five alcoholic drinks during one evening? (+)
risk2	Would you have unprotected intercourse with a stranger? (+)
risk3	Would you not wear your seat belt as sidecar passenger? (+)
risk4	Would you drive a motorbike without wearing a helmet? (+)
risk5	Would you expose yourself to the sun without using sun screen? (+)
risk6	Would you walk home alone at night through an unsafe part of town? (+)

variable	value	% MTMC	% dataset
age	20-29 years	11.3	18.5
*	30-39 years	14.6	17.8
	40-49 years	19.7	17.7
*	50-59 years	20.1	18.0
*	60-69 years	16.4	18.8
*	70-79 years	12.3	7.9
*	over 80 years	5.2	1.0
gender	female	48.2	50.5
*	male	51.7	48.9
*	other	-	0.4
education	compulsory education	14.9	4.7
*	further education	52.0	67.3
*	university	33.0	27.8
occupation	employed	63.6	60.2
*	student/apprentice	2.8	5.0
*	unemployed/household duties	30.5	11.0
*	searching for job	0.7	2.8
*	retired	2.2	17.6
household size	1	15.3	29.3
*	2	33.7	36.0
*	⩾3	50.9	34.5
household income	under 2,000 CHF	1.5	7.0
	2,001 - 4,000 CHF	9.6	18.0
	4,001 - 6,000 CHF	14.1	25.2
	6,001 - 8,000 CHF	13.6	20.1
	8,001 - 10,000 CHF	10.7	12.6
	10,001 - 16,000 CHF	15.6	14.3
	more than 16,000 CHF	5.4	2.6
	not provided	29.0	-
citizenship	swiss	-	90.0
	other	-	9.9
online shopping behavior	never	-	61.9
	once per week	-	8.4
	multiple times per week	-	1.6
	2-3 times per month	-	7.9
	once a month or less	-	19.9
shopping transport mode	PT	-	23.6
	car	-	42.2
	foot/bike	-	34.0
SARS-COV-2 infection	yes, tested positive	-	1.2

transport mode	levels
	( $μ - 20 %$ )	$μ$	( $μ + 20 %$ )
bike/foot [CHF]	0	0	0
public transport (with local subscription) [CHF]	0	0	0
public transport [CHF]	2.7	3.2	3.7
motorized individual transport [CHF]	2.5	3.5	4.5

transport mode	levels
	$(μ - 3 \min)$	$μ$	( $μ + 3 \min$ )
bike/foot [min]	7	10	13
public transport [min]	6	9	12
motorized individual transport [min]	5	8	11

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In-store or online grocery shopping before and during the COVID-19 pandemic

Adrian Meister

Caroline Winkler

Basil Schmid

Kay Axhausen

Abstract

1. Introduction

2. Literature Review

3. Methods

3.1. Experimental Design

Table 1.

3.2. Modelling Framework

Table 2.

4. Results

4.1. Descriptive Analysis of Choice Behavior

Fig. 1.

Fig. 2.

4.2. Model Estimation Results

4.3. Partworth Analysis

Table 3.

4.4. Marginal Probability Effects

Table 4.

4.5. Willingness-To-Pay (WTP) Indicators

Table 5.

5. Conclusion

Footnotes

Appendix A.

Table A1.

Table A2.

Table A3.

Table A4.

Table A5.

Table A6.

Table A7.

Table A8.

Table A9.

Table A10.

References

ACTIONS

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