how mobile self-scanning use influences consumers’ grocery

“How Mobile Self-Scanning Use Influences Consumers’ Grocery Purchases” © 2019 Maya

Vuegen, Anne Ter Braak, Lien Lamey, and Kusum L. Ailawadi

MSI working papers are distributed for the benefit of MSI corporate and academic members

and the general public. Reports are not to be reproduced or published in any form or by any

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Marketing Science Institute Working Paper Series 2019 Report No. 19-109

How Mobile Self-Scanning Use Influences Consumers’

Grocery Purchases

Maya Vuegen, Anne Ter Braak, Lien Lamey, and Kusum L. Ailawadi

How Mobile Self-Scanning Use Influences Consumers’ Grocery Purchases

Maya Vuegen

PhD Candidate Marketing KU Leuven, Faculty of Economics and Business (Antwerp)

Korte Nieuwstraat 33 2000 Antwerp, Belgium Phone: +32 3 201 18 67

Email: [email protected]

Anne Ter Braak

Associate Professor of Marketing KU Leuven, Faculty of Economics and Business (Leuven)

Naamsestraat 69 3000 Leuven, Belgium

Phone: +32 16 32 69 03 Email: [email protected]

Lien Lamey

Associate Professor of Marketing KU Leuven, Faculty of Economics and Business (Leuven)

Naamsestraat 69 3000 Leuven, Belgium

Phone: +32 16 32 69 54 Email: [email protected]

Kusum L. Ailawadi

Charles Jordan 1911 TU’12 Professor of Marketing Tuck School of Business

Dartmouth College 100 Tuck Hall

Hanover, NH 03104 Phone: 603 646 2845

Email: [email protected]

December 11, 2018

Acknowledgments: The authors are grateful to AiMark and GfK for providing the data used in this research. They also thank the Marketing Science Institute for research support through the Alden G. Clayton Competition.

Marketing Science Institute Working Paper Series

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How Mobile Self-Scanning Use Influences Consumers’ Grocery Purchases

With mobile self-scanning devices, consumers scan items while they shop so they can track

spending in real-time and check out quickly without having to unload their shopping. The

authors study the effect of self-scanner use on several aspects of grocery purchase behavior,

while controlling for its potential endogeneity. They use panel data from the Netherlands,

covering all major grocery retailers that offer the technology. The results show that the impact

of self-scanning on purchase behavior is mostly among shoppers who have not yet built up

experience with the device. Such shoppers respond to the spending feedback with less

effortful ways of curbing spending, i.e., simply buying fewer items. This holds in particular

for price conscious consumers and when they shop at a price-oriented chain. Among shoppers

who have experience with the device, there is a strong and significant increase of private label

buying. The authors also report several other results related to shoppers’ motivation, ability,

and opportunity to use the self-scanner.

Keywords: mobile self-scanning, self-scanner experience, spending feedback, grocery

purchase behavior


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Introduction

Retailers are experimenting with various technology-based services to try to improve

consumers’ shopping experience in their physical stores. For example, shoppers can use self-

checkouts to quickly pay for their purchases; they can use a mobile self-scanner to track

spending while they shop and then go through expedited check-out; beacons in the store can

connect to their mobile devices so that they can easily get help from an employee if needed,

find an item, or receive personalized information and targeted offers.

This paper studies a specific technology-based service that is increasingly being offered

by grocery retailers – mobile self-scanning. Mobile self-scanning devices are hand-held

devices generally available at the store entrance that allow shoppers to scan items as they pick

them from the shelves (PlanetRetail 2012). Sometimes the technology is also in the form of a

mobile phone app. In contrast with stationary self-scanning check-outs where shoppers unload

and scan their items at the end of their shopping trip, mobile self-scanning devices let

shoppers scan their items during their shopping trip and check out at designated lanes.

Shoppers equipped with a mobile self-scanner get real-time spending feedback as the device

shows prices of individual items being purchased as well as their total spending and they are

not required to unload all their purchases from the shopping cart at the check-out. Thus, the

technology allows shoppers to be more informed during their shopping while also offering

speed at check-out (Shopper Marketing Update 2013; Griffith-Greene 2016). In the remainder

of this paper, when we use the term “self-scanning” we are referring specifically to mobile

self-scanning (variously called “Mobile Scan and Go” by Walmart, “Scan, Bag, Go” by

Kroger or “Scan as you Shop” by Tesco).

Self-service technologies (SST), of which mobile self-scanning is an example, are

perceived as opportunities to improve consumers’ in-store experience (Rosenblum 2007)

while also improving retailer efficiency though lower labor costs, higher promotion ROI etc.


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Prior research has identified some antecedents of SST adoption and also self-scanner adoption

(see, for example, Blut, Wang, & Schoefer 2016 for a meta-analysis on SST adoption and

Weijters et al. 2007 for antecedents of self-scanner use specifically). However, we do not

have a good understanding of the impact of these services on consumers’ purchase behavior.

No wonder then, that retailers are taking a cautious approach in introducing the

technology. Carrefour and Tesco in Europe and Kroger and Stop & Shop in the U.S. offer the

service in some of their stores, but other leading retailers such as Mercadona in Europe and

Albertson in the U.S. have not introduced it, at least not yet. Discounters have been

particularly reluctant to invest in the technology, maintaining that self-service is not

necessarily more efficient than well-trained cashiers (Evans 2016). In the Netherlands, for

example, the two hard discounters, Aldi and Lidl, do not offer mobile self-scanners, and in the

U.S., Walmart recently discontinued its mobile self-scanning service, noting that consumers

felt it was a hassle and did not “embrace it the way we anticipated” (Boyle 2018). Uncertainty

about its impact on purchase behavior is also underscored by contradictory statements in the

business press. Some retailers claim that “self-scanning leads to higher total ticket amounts”

(Shopper Marketing Update 2013) and “shoppers are actually throwing more in their cart with

this new technology” (USA Today 2018), others state that “now that shoppers use self-

scanners […] revenues are under pressure” (Distrifood 2014).

Previous research has examined the impact on purchases of using a mobile phone while

shopping. Sciandra and Inman (2016) differentiate between task-related and task-unrelated

mobile phone use. They find that shoppers who use their phones for task-unrelated activities

make more unplanned purchases and forget to buy planned items due to the distraction. On

the other hand, task-related phone use is associated with “better decisions”, i.e., fewer

unplanned items and more efficient choices. Grewal et al. (2018) also study how mobile

phone use affects purchases, focusing on total items purchased and total spending. They find a


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positive effect, and their mediation analysis shows that this happens because phone use

distracts attention, making shoppers spend more time at the shelf and return to places they

have already been, thus increasing the length of their shopping trip.

But using a mobile self-scanner is different from using one’s phone. A shopper may

choose to intermittently use their phone for task-related (e.g. checking a shopping list or

calculating prices) or unrelated (e.g., personal texting or phone conversations) activities.

Grewal et al. (2018) document that shoppers who were asked to use their phones as they

customarily do, used it for an average of less than one minute or about 5% of their total time

in the store. In contrast, the use of a mobile self-scanner while shopping is by definition task-

related and continuous. Sciandra and Inman’s (2016) work suggests that the shopper would

not be distracted because the use is task-related. In that case, the mechanism documented by

Grewal et al. (2018), based on distraction, would not apply. However, continuous use of the

mobile self-scanner throughout the shopping trip does require cognitive effort, at least among

those who have limited prior experience with the device. It is therefore not clear how the

implications of these previous findings would play out in the case of a mobile self-scanner,

especially for those who are more versus less experienced with the device.

Further, the mobile self-scanner provides real-time spending feedback to the consumer,

which was not a factor in either of these two papers. One paper in particular is directly

relevant to this issue. Van Ittersum et al. (2013) experimentally examine whether and how

spending feedback received through a smart shopping cart, influences the purchase behavior

of budget constrained versus non-budget constrained shoppers. In their studies, subjects were

provided real-time spending feedback or not, and they had a budget constraint or not. The

authors find that spending feedback stimulates budget-constrained shoppers to spend more,

shop for more items and buy more national brand items by eliminating their spending

uncertainty but it reduces spending and increases private label purchases by shoppers who are


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not budget-constrained by increasing the salience of individual prices as well as their basket

price. However, the budget constraint is made highly salient in these studies and it is unclear

how these effects would play out in a real-life setting where consumers are going about their

normal shopping. Nor does the work provide insight on how the effects might differ based on

prior experience and therefore the cognitive effort involved in using a mobile self-scanner.

Evanschitzky et al. (2015) conduct a store-intercept of shoppers, some of whom chose

to use a Personal Shopping Assistant (PSA), which is similar to a mobile self-scan device as it

allows the shopper to self-scan items but it is not clear whether it provided spending feedback.

The authors find that users spend more than non-users on the shopping trip. However, they

only include a small set of consumer characteristics and do not control for unobservable

variables that may drive both the decision to use the PSA and the size of the shopping basket.

In sum, the key characteristics of the mobile self-scanner (i.e. continuous, task-related,

offering real-time spending feedback) comprise a unique combination and while previous

work provides valuable insights, we do not know how this technology, which many retailers

are experimenting with, affects purchase behavior. Our goal in this paper is to conduct an in-

depth empirical analysis, using field data on actual purchase behavior, of the impact of mobile

self-scanning technology use on consumers’ grocery purchases. First, we study the impact on

total spending and on the components of spending through which the impact might occur,

e.g., total number of items purchased, low-need purchases, promotional purchases, and private

label purchases. We do so while controlling for both observable and unobservable variables

that may be correlated with both the decision to use the device and purchase behavior.

Second, we study these effects separately for two segments of consumers – those with low

versus high experience in using the device. Third, we examine the extent to which this impact

may be moderated by factors identified in prior research that relate to consumers’ motivation,

ability, and opportunity to act on the spending feedback provided by the device.


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We first develop a conceptual framework incorporating (a) the impact of the spending

feedback on purchase behavior, (b) how that impact may vary with consumers’ experience

with the device, and (c) consumers’ motivation, ability, and opportunity to use the mobile

self-scanner and act on the real-time spending feedback it provides. We then conduct our

analysis, guided by the framework, on a unique data set covering four weeks of grocery

shopping by approximately 1,800 consumers across all the major grocery chains in the

Netherlands that offer mobile self-scanning technology. The analysis is not only important

from an academic perspective but it has practical relevance for retailers who must decide

whether to adopt mobile self-scanning, and for manufacturers who are also directly affected

by changes in consumers’ purchase behavior.

The rest of the paper is organized as follows. We first present our conceptual

framework, then we describe our data and modeling approach. We follow this up with our

results and conclude with a discussion of our findings and the managerial implications.

Conceptual Development

To reiterate, the mobile self-scanner we study is a hand-held device available at the

store entrance that shoppers can choose to use while shopping. Unlike mobile phones which

can be used for a variety of shopping related or unrelated activities, the use of this device i s

directly related to the shopping task, creates a continuous additional task throughout the

shopping trip which is likely to become easier with experience, and provides real-time

spending feedback. In addition, shoppers themselves decide, at the beginning of a shopping

trip, whether to use the mobile self-scanner during that trip. These features have important

implications for our conceptual development and empirical analysis and form the key building

blocks of the conceptual framework in Figure 1.


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Overview of Framework

Our primary interest is in the impact of using a self-scanner on purchase behavior.

Arguably, the most important characteristic of the device, that may underlie a causal effect of

its use on the amount and type of purchases, is the spending feedback it provides. Hence, the

outcomes we study are aspects of purchase behavior that have the potential to be influenced

by spending feedback – total items purchased, the share of purchase volume accounted for by

low-need, promotional, and private label products, all of which combine into total spending.

Changing some of these behaviors (e.g., promotional and private label use) may be more

effortful than others (e.g., simply buying fewer items especially in categories that are less

needed by the shopper). The second important characteristic of the device is that it must be

used continually during the trip, and that requires significant effort, at least for shoppers who

have not yet gained sufficient experience in using it. As a result, we expect the effect of

device use on purchase behavior to be different for two segments of consumers – those who

are relatively unfamiliar with it versus those who have accumulated sufficient experience in

using it. The bold middle portion in the middle of Figure 1 shows these focal elements.

Shoppers may use the mobile self-scanner on a given trip not just for spending

feedback but also because it makes their shopping experience more enjoyable. This too can

underlie the causal effect of the device use on purchase behavior. Shoppers may spend more

time traveling in the store and shopping because they enjoy their experience with the

technology and that is associated with buying more (Hui et al. 2013).

Shoppers may also use the device for quick check-out. For instance, they may be more

likely to use the device when they have to buy a lot and don’t want to unload their cart at

check-out. Thus, factors that are associated with the decision to use the device may also be

correlated with purchase behavior outcomes, which must be accounted for to ensure that the

effect we estimate is not biased by self-selection.


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Motivation, Ability, and Opportunity (MAO) are widely accepted as part of the

psychological core of consumer behavior that drives information processing and decision-

making, especially when these involve high effort. We use MAO to identify observable

variables that drive the decision to use the mobile self-scanner, especially those that may also

affect the purchase outcomes and include them in our conceptual framework and empirical

model. And we allow for the possibility that some of these factors may moderate the impact

of device use on purchase outcomes.

We also include other control variables that may affect device use or purchase

outcomes or both. And finally, we account for common unobservable variables which may

bias the estimated effects through the use of a valid instrumental variable. We now discuss

each of the elements of our framework in more detail.

Spending Feedback and Purchase Behavior

The mobile self-scanner provides real-time spending feedback on each individual item

and on the running total basket price which can help shoppers keep control of their budget

(Blut et al. 2016; Marzocchi & Zammit 2006; van Ittersum et al. 2013). It increases the

salience of prices as well as total spending and should encourage shoppers to find ways to

save money (Wathieu, Muthukrishnan, & Bronnenberg 2004).

Prior research has identified several strategies for reducing monetary costs of grocery

shopping: buying fewer items especially by curtailing purchases of low-need items, buying

more items on price promotion, and/or buying less expensive private label items (Ailawadi,

Neslin, & Gedenk 2001; Griffith et al. 2009; Ma et al. 2011). Promotions not only allow

shoppers to save money on products that they need but also provide shoppers stimulation and

help fulfill their need for exploration, thereby encouraging purchases of products that may not

be needed (Chandon, Wansink, & Laurent 2000; Ailawadi et al. 2001). To try to distinguish

between savings-driven and exploration-driven promotional purchases, we look at


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promotional purchases of categories that are likely to be high-need for the shopper (i.e.,

categories the shopper is likely to be running out of) versus low-need. Thus, we examine the

effect of mobile self-scanning on six purchase behaviors (i.e. total spending, number of items,

and the share of total purchase volume that is low-need, promotional low-need, promotional

high-need, and private label), all of which are important for both manufacturers and retailers

as they assess the potential impact of introducing the technology.

Some of these behaviors -- reducing the number of items purchased, especially those

that are in low-need categories -- are less cognitively effortful. In the face of spending

feedback, it is easy to simply skip certain items, especially if they are not needed right away.

Prior research has shown that limited attentional capacity increases reliance on low-effort

decision making (Masicampo & Baumeister 2008; Dhar & Gorlin 2013) and choice deferral is

more likely under cognitively demanding circumstances (Dhar 1997; Dhar & Simonson

2003). While choice deferral and forfeiting low-need items can be effective strategies to save

money at the present time, they are less sustainable as savings strategies unless consumers

start consuming less. In contrast, seeking out price promotions requires more effort and

switching to private labels from national brands also entails a more cognitively effortful trade-

off compared to deferral.1 Yet, switching to private label and making use of promotions is a

sustainable way to stretch one’s budget. These distinctions are important when predicting the

impact of self-scanner use for low versus high experience segments as we now discuss.

Continuous Task-Related Mobile Device Use and Prior Experience

As we noted in the introduction, Sciandra and Inman (2016) find that mobile phone use

unrelated to the shopping task can degrade decision-making by distracting the consumer, but

task-related phone use is associated with better decisions. The mobile self-scanner is certainly

1 We recognize that promotions serve as a heuristic for saving money and private labels are an easy way for value conscious consumers to reliably save money. However, when a shopper changes what they would otherwise do in response to real-time spending feedback, we argue that looking for promotions and making the trade-off from a preferred brand to private label is more cognitively effortful than simply not buying.


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task-related, so the implication would be that the spending feedback from mobile self-scanning

should encourage shoppers to make use of opportunities to save money (or stretch their budget

for budget-constrained shoppers). However, unlike mobile phone use, which is generally

intermittent, self-scanner use is continuous -- consumers who use the self-scanner have to

perform an additional task throughout their shopping trip, i.e., scan each individual item they

add to their shopping cart (see also Weijters et al., p. 9 (2007) for a description). Multitasking

can restrict consumers’ ability to focus on the shopping process due to limited attentional

capacity (Kahneman 1973, p. 136-155; Lee & Faber 2007). As more attentional capacity is

devoted to the task of scanning items, less will be available for processing information from the

store environment.

Shoppers who have a lot of experience with the self-scanner know how to use the device

so they do not need to devote as much attentional capacity to the scanning task. But, the self-

scanning task is more effortful and time consuming for shoppers who are not experienced with

the device, so they are likely to be distracted by it. Grewal et al. (2018) find that distraction

actually increases the number of items purchased and total spending because shoppers spend

more time in the store. However, there was no spending feedback provided in the studies by

Grewal et al. (2018). Given the real-time spending feedback from a self-scanner, we expect that

shoppers will try to save money. Those who have less experience with the self-scanner will do

so in less cognitively effortful ways, even if those money-saving actions, such as reducing

number of items purchased, are not sustainable. In contrast, those who are more experienced

with the device will have the attentional capacity to save money through means that may be

more cognitively effortful but also more sustainable. With experience, use of the device could

potentially even streamline cognitive functions to focus on task-relevant behaviors and avoid

distractions from shopping goals.

While the spending feedback encourages shoppers to save money, there are other


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motivations to use the self-scanner (Marzocchi & Zammit 2006; Weijters et al. 2007), like

saving time or making the shopping trip more enjoyable which may also influence a shopper’s

purchasing behavior (Evanschitzky et al. 2015). If shoppers use the device to save time, this

may reduce unplanned purchases (Inman, Winer, & Ferraro 2009), reinforcing the spending

feedback effect. If they use it because they enjoy shopping with the self-scanner, they may

spend more time in the store, and consequently buy more items and spend more (Grewal et al.

2018; Hui et al. 2013). To the extent that shoppers who are more experienced with the scanner

enjoy it more, self-scanner use may even increase the total number of items purchased.

Expected Impact of Self-Scanner Use on Purchase Behavior Outcomes

With these concepts in place, we now lay out our expectations for the impact of self-

scanner use on the six purchase behaviors, for low and high experience segments.

Total Number of Items and Low-Need Share. It is well established that, although

consumers have a general idea of the price of products, they do not recall the exact price of an

item, even immediately after they have purchased it (Dickson & Sawyer 1990). When the

exact price is provided by a self-scanner, however, it becomes more salient and shoppers are

more likely to postpone a purchase or look for a better price elsewhere (Hamilton & Chernev

2013). Similarly, increasing the salience of the total basket price also can decrease purchase

likelihood for additional items (Wathieu et al. 2004). When consumers get feedback on their

expenditures, they are also less likely to give in to impulse (Baumeister 2002) and buy items

that they don’t really need. Spending feedback, as such, serves a monitoring function. For

those with limited self-scanner experience, and therefore limited attentional capacity, we

expect that self-scanning will reduce the number of items purchased and low-need share,

because that is a low effort strategy for immediate savings, even if it is not sustainable. For

the high experience segment, we expect that both effects will be less negative because

foregoing purchases is not a sustainable strategy.


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Promoted Low-Need and Promoted High-Need Share. As discussed previously, we

examine the effect of self-scanner use on promotional purchases of high-need and low-need

categories separately to distinguish between savings-driven and exploration-driven

promotional purchases. For the more experienced segment, we expect the share of high-need

promotional purchases to increase. This is because promotions are a sustainable way to save

money and experienced users of the self-scanning device have the attentional capacity for

more effortful ways to stretch their budget. We expect the effect on share of low-need

promotional purchases to be less positive or non-existent. For the less experienced segment,

we expect that the share of low-need promotional purchases will decrease because the items

are not needed and it is easier to forego a purchase. The effect on share of high-need

promotional purchases is unclear because, on one hand, promotions are a good way to save

money, but on the other hand making use of them requires more effort.

Private Label Share. Private label products (with the exception of some premium

private labels) tend to be priced significantly lower than national brands and grocery retailers

offer private label products in a wide range of categories (Geyskens, Gielens, & Gijsbrechts

2010). So, buying private label is a sure shot sustained way of saving money. Therefore,

spending feedback should lead to an increase in private label purchases. However, making the

decision to switch from national brands to private label is more cognitively effortful, so it is

easier for the more experienced segment. Further, the positive affect associated with this

segment’s shopping enjoyment may spill over to the store and its private label. Therefore, we

expect that self-scanner use will increase private label purchases for the more experienced

segment and the effect will be smaller or non-existent for the less experienced segment.

Total Spending. Because total spending arises from a combination of the above

purchase behaviors, we expect that using a self-scanner will reduce total spending (primarily

through fewer low-need and total items) for the low experience segment, and the effect will


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be less negative for the high experience segment.

Observable Correlates of Self-Scanner Use and Purchase Behavior

The decision to use a self-scanner, if it is available in the store, is one that the shopper

makes at the beginning of the shopping trip. Some of the drivers of that decision are likely to

also be correlated with one or more of our purchase outcomes. It is important to control for

them in order to obtain a valid estimate of the effect of self-scanner use that is not confounded

by self-selection. We use Motivation, Ability, and Opportunity (MAO) to identify relevant

drivers of the decision to use the self-scanner so that they can be included in our model.

MAO-related drivers have been used in a variety of (high effort) contexts, such as processing

of brand advertising (Batra & Ray 1986; MacInnis, Moorman, & Jaworski 1991), engaging in

healthy behaviors (Moorman & Matulich 1993) or in environmentally conscious behaviors

(Ölander & Thøgersen 1995), and consumer’s “readiness” to use SST (Meuter et al. 2005).

Motivation is defined as goal-directed arousal, such that the motivated individual is

energized, ready, and willing to engage in a goal-relevant activity (Park & Mittal 1985;

Hoyer, MacInnis, & Pieters 2013, p. 45). The goals that may motivate a shopper to use the

mobile self-scanner are to control spending, save time, and/or enjoy the shopping experience.

The motivation to control spending is stronger among price conscious shoppers so they

may be more likely to use the self-scanner. The motivation to save time is likely to be

stronger among shoppers who are time-pressured, shop less frequently, have larger

households and therefore bigger shopping needs, and it is stronger on major shopping trips

than on smaller fill-in trips which are generally not time-consuming. Depending on how

shoppers trade-off the time spent on scanning items themselves versus the time saved in the

check-out line, time-pressured shoppers may be either less or more likely to use the mobile

self-scanner. Shoppers with large household needs, infrequent shoppers wo are likely to have

larger shopping baskets, and those who are on a major shopping trip may find the time saving


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in check-out to be more worthwhile, and therefore should be more motivated to use the self-

scanner. Finally, shoppers who try new things are more likely to have the goal of shopping

enjoyment, so they may be more motivated to use the self-scanner.

Motivation may not result in the consumer engaging in a behavior or processing

information if she does not have the ability or the opportunity to do so. Ability generally

refers to the consumer’s own financial, cognitive, emotional, physical, or social resources

while opportunity generally refers to external circumstances. Age and education are

commonly identified as correlates of ability. In particular, lack of higher education is

associated with lower cognitive ability, and old age is associated with lower cognitive and

physical ability to use technology and to process information (Grewal et al. 2018; Weijters et

al. 2007; Meuter et al. 2005). Thus, we expect that older and less educated shoppers will be

less likely to use the mobile self-scanner. Shoppers with financial constraints are concerned

about spending and therefore may use the scanner but they already economize on their

spending and thus may be less able to make use of the self-scanner’s spending feedback.

Finally, a more familiar store may provide a better opportunity for shoppers to use the

self-scanner because they feel more comfortable multi-tasking in a familiar environment. A

price-oriented chain store with frequent promotions has a more functional store environment,

and multiple cues for low prices and saving opportunities, which may stimulate more

utilitarian and goal-oriented shopping (Arnold & Reynolds 2009; Babin & Darden 1995) and

convince shoppers to adopt the self-scanner to control spending. And a crowded store may

provide more opportunities to save time increasing the likelihood of self-scanner use.

Clearly, many of these variables may also be correlated with purchases. For example,

price conscious shoppers may buy more private labels, use price promotions more, and spend

less in total. Time pressured shoppers may buy less and not take the time to seek out

promotions, preferring to get in and out of the store as quickly as possible. Those who are


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willing to try new things may be more likely to make unplanned purchases that are not needed

and crowding affects spending (Knoeferle, Paus, & Vossen 2017).

In addition to these variables, we will include controls such as day and time effects,

chain fixed effects, and the shopper’s purchase behaviors in an initialization period to account

for unobserved heterogeneity. Finally, we will use an instrumental variable to account for the

possibility that there are other unobservable variables that drive both the decision to use a

self-scanner and purchase behavior and therefore may generate a self-selection bias.

Moderators of the Impact of Spending Feedback

There may be heterogeneity in how shoppers respond to the real-time spending

feedback they get throughout the shopping trip. We focus our attention on three key factors

related to a consumer’s Motivation (i.e., price consciousness), Ability (i.e., financial

constraints) and Opportunity (i.e. whether the trip is to a price-oriented chain) to respond to

the spending feedback. Consumers who are price conscious work towards a goal of saving

money. This type of goal (e.g., buying at the lowest price possible) should motivate

consumers to process the spending feedback. If that is the case, we expect that the effect of

the spending feedback on the various purchase behavior outcomes will be particularly

prominent for price conscious consumers.

Consumers who have tight budgets and have trouble making ends meet already save

where they can. Their ability to save further based on spending feedback is probably limited.

Also, van Ittersum et al. (2013) find that financially constrained shoppers, who usually keep

mental track of their spending themselves, actually buy more when they receive real -time

spending feedback. This is because budget shoppers are concerned but uncertain about their

total basket price and therefore usually spend less than their budget. When the spending

feedback shows them that they have spending room, they treat it as a “windfall” and increase

spending, and also enjoy the shopping experience more. In contrast, spending feedback makes


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the basket price more salient for non-budget shoppers who otherwise don’t pay attention to

spending, and induces them to reduce spending. Based on this research, we expect financial

constraints to make the effect of self-scanning use weaker.

The third moderator is the type of retailer, in particular its price-orientation. Grewal et

al. (2018) note that it is important to study the effects of device use in different types of

retailers, as prices and price comparisons may play a different role and become more

important or easy in some than in others. One advantage of our empirical context is that it

spans multiple retailers, some of whom are more price-oriented. The shopper should find it

easier to act on the self-scanner’s spending feedback in a price-oriented chain because there is

likely to be an abundance of price cues to make it easier to identify savings opportunities.

As we have discussed earlier, the mobile self-scanner consumes cognitive resources

when shoppers do not have much experience with it, even though it is very much a task-

related device. Under low experience, therefore, it is particularly important that the shopper

has the motivation, ability, and opportunity to process and act on spending feedback, because

expending cognitive resources to use the device leaves less available to process in-store

information and act on the spending feedback. Under high experience, consumers do not face

limited attentional capacity and having the motivation, ability and opportunity to process

spending feedback may be less crucial. We therefore expect the moderators to have a stronger

effect in the low experience segment than in the high experience one.

Generalizability of Conceptual Framework

While we have discussed our conceptual framework in the context of the current

features of the mobile self-scanning device, we wish to highlight that the framework is

versatile enough to be applicable even if the features of the technology change. MAO, as we

have noted earlier, is a very generalizable approach to understanding consumer behavior, so it

is readily transferable by simply including variables relevant to motivation, ability, and


17

opportunity to use the modified technology and respond to its new features. But other parts of

the framework are also easily adapted. If the technology were to become much easier to use,

the difference between low and high experience groups would be reduced as would the

difference between less and more effortful behaviors. The opposite would be expected if

additional features were to make its use more complicated.

Further, the impact of specific features could be easily incorporated. If, for example,

the technology also provided location-based promotional offers on products in various parts

of the store, the key features of the device would include promotion notification in addition to

spending feedback, and we would expect promotion share, especially of high-need categories,

to increase even for those with low experience, since promotion use would be less cognitively

effortful. We would also expect strong endogeneity of self-scanner use when it comes to the

promotional purchase outcomes. Similarly, if the technology could incorporate a shopping list

and push out notifications when the shopper passed by products on his/her shopping list, those

who have a shopping list may be more motivated to use the device, and/or the impact on total

number of items may be negative (because the device reinforces planned purchases).

Data

Research Setting

We conduct our empirical analysis in the Dutch grocery market. Grocery chains in the

Netherlands were early triers of mobile self-scanning. After an initial failure in 1987 due to

flawed technology, the leading grocery chain Albert Heijn successfully re-introduced self-

scanning in 2005 (PlanetRetail 2012). Other Dutch grocery chains followed suit. In early

2015, the time of our data, all the major grocery chains, except for the two hard discounters

Aldi and Lidl, offered mobile self-scanning in at least some of their stores. Our analysis

encompasses all chains with market share of at least 2% that offer self-scanning at least in


18

some stores (see Table 1). We distinguish between different formats of the same chain (i.e.

Albert Heijn offers self-scanning in its regular AH stores as well as their hypermarkets called

AH XL).2 Since the technology has been available in the Dutch market for several years, its

use is somewhat more engrained than in other markets, including the U.S., where it has been

introduced only recently. In addition, all chains that offer self-scanning make use of the same

type of hand-held device (the Motorola brand) and have a similar interface. Hence, it offers a

particularly useful context in which to study the impact on purchase behavior.

Data Sources and Variable Operationalization

The data for our analysis are obtained in collaboration with AiMark and GfK and are

compiled from three main GfK sources: (i) GfK homescan panel data; (ii) a bi-annual

household survey conducted by GfK; and (iii) a special survey that GfK allowed us to conduct

for every shopping trip during a four week period. We provide details of the variables

obtained from each source below.

First, we obtained complete purchase records of the Dutch home-scan panel dataset

from GfK for the period from January 2014 to March 2015. The GfK panel consists of more

than 5,500 panel members, representing a stratified national sample of Dutch households and

is frequently used by researchers (e.g. Ailawadi, Pauwels, & Steenkamp 2008; van Lin &

Gijsbrechts 2014; ter Braak, Dekimpe, & Geyskens 2013). This dataset contains information

for each shopping trip by a panelist, including the chain (though not the specific store) they

shopped at, each item that was bought, the amount, and the price paid. Private label and

promotional items are also flagged. We use these data to compute our six outcome variables

at the panelist trip level as well as a number of independent variables in our models.

Second, GfK provided us with data on panelist demographic and shopping related

2 Please note that we do not include the convenience store format, e.g., Albert Heijn To Go stores in our analysis. This format carries only a small subset of grocery products and it does not offer mobile self -scanners.


19

variables from the December 2014 version of a bi-annual survey it administers (see Ailawadi

et al. 2008 and van Lin & Gijsbrechts 2014 for examples of other research that has used these

data). We extract from these data the variables that are relevant to the behaviors of interest to

us, as discussed in our conceptual framework.

Third, and most importantly for our research, GfK collaborated with us to field a brief

survey to the home-scan panelists during four weeks (from February 14 to March 13, 2015).

In the first week panelists were asked about their first shopping trip in that week. Specifically,

they reported whether they had used a mobile self-scanner during that trip and how crowded

the store was during their trip. In addition, they were also asked how pressured for time they

generally are while shopping and how experienced they perceive themselves to be in using a

self-scanner device. During the three subsequent weeks, panelists were asked, for each of

their shopping trips in the week, whether they had used a mobile self-scanner during that trip

and how crowded the store was during their trip.

Finally, we received information from GfK on store characteristics like the number of

self-scanning check-outs, floor size, and location (cf. van Lin & Gijsbrechts 2014).

We include all the shopping trips for which these panelists indicated whether they had

used a mobile self-scanner or not, in which they purchased more than one CPG category.3

This results in a sample of 8,082 shopping trips made by 1,668 panelists. We use 2014 as an

initialization period to compute controls for unobserved heterogeneity. The operationalization

and descriptive statistics of all variables are provided in Table 2.

Mobile Self-Scanning Use

Panelists used a self-scanner in 1,359, or about 17% of the shopping trips in our

sample. Around 26% of the 1,668 panelists use it at least once, which is consistent with the

3 We filtered out 267 shopping trips that consisted of only non-CPG purchases (e.g. flowers, plants, car parts, and books) and 674 shopping trips that only include 1 CPG category.


20

20% incidence reported by PlanetRetail (2012, p.6). Self-scanning is readily available to our

sample as 81% of the households have a store that offers self-scanning within a 5 kilometer

range of their home (the mean distance to a self-scan store is 2.9 kilometers). Panelists that

use self-scanning at a certain chain at least once during our observation period, use it for 69%

of the visits to that chain. In other words, panelists have the tendency to either always or never

use self-scanning at a specific chain - much of the variation in self-scanning use is across

panelists and chains, not shopping trips.

On the question regarding their perceived experience in using the self-scanner device,

panelists score themselves an average of 3.70 on a scale from 0 to 9 (Min = 0; Max = 9; SD =

3.76; Median = 2). We classify those with a median score or lower as the low experience

segment and those with a score above the median as the high experience segment (i.e. 843

versus 825 households, respectively).4

Methodology

Model Specification

We model six purchase behaviors: (i) total spending in euros (Spendinghct), (ii) number

of items (NbrItemshct), (iii) share of purchases that are low-need (LowNeedSharehct), (iv)

share of purchases that are low-need promotions (PromoLowNeedSharehct), (v) share of

purchases that are high-need promotions (PromoHighNeedSharehct), and (vi) share of

purchases that are private label (PLSharehct).5 We take the logarithm of spending and number

of items to correct for a skewed distribution and use a logit transformation for the four share

4 If we assign the median score to the high experience segment instead, results for the high experience segment are stable, results for the low experience segment are substantively the same although two interactions with the price-oriented chain dummy, while of the same sign and magnitude, become insignificant (p = .25 and p = .34 in the spending and number of items equation, respectively. 5 As shown in Table 2, the shares are computed as a percentage of total purchase volume. We also computed shares of total number of items purchased and found similar results.


21

variables so that the model predictions are within the [0,1] range (e.g., Ailawadi et al. 2008).6

(1) Purchasemhct=

[

log(Spendinghct)log(NbrItemshct)

logit(LowNeedSharehct)logit(PromoLowNeedSharehct)logit(PromoHighNeedSharehct)

logit(PLSharehct) ]

For notational ease, we denote the m-th purchase outcome for panelist h during

shopping trip t at chain c as Purchasemhct. Each purchase outcome is modeled as a function of

the variable of central interest, self-scanning use (Self-scanhct), which equals 1 if panelist h

uses self-scanning during shopping trip t at chain c and 0 otherwise. In addition, the model

includes (a) main effects of all the variables related to MAO that we identified in the

conceptual section and defined in Table 2; (b) interaction effects of our three moderating

variables (PriceConsch, FinancConstrh, Price-Orientc); (c) a set of control variables for floor

size, weekday/weekend, time of day, and chain fixed effects, as well as the average value of

the respective dependent variable for the panelist, appropriately log-transformed, during the

2014 initialization period.

Although all chains offer the same self-scanning device with similar interfaces, the

chain fixed effects control for differences in the availability of self-scanners across chains as

well as any unobserved reasons why the propensity of shoppers to use self-scanners or

purchase behaviors may differ across chains. The initialization period variables capture

unobserved heterogeneity in the purchase preferences of panelists.

For brevity, we do not list all the individual explanatory variables in equation (2)

below, and instead group them as characteristics of the shopper (Shopperh), the shopping trip

(Triphct), and the shopping environment (Environhc), and controls (Controlshct). The purchase

6 To avoid logs of zero or one, we add or subtract a small positive number (0.0001) to the share variables before the logit transformation. Using a different number, e.g. .00001 (Melis et al. 2016) or .01 (Cleeren, van Heerde, & Dekimpe 2013) does not change our results. Our results are also quite robust if we use tobit censored at 0 and 1 instead of the logit transformation.


22

equations are estimated separately for the low and high experience segments, and all

continuous explanatory variables are mean-centered before estimation for ease of

interpretation of the main effect of self-scanning.

(2) Purchasemhct= α0m + β0

m*Self-scanhct

+ Self-scanhct*(β1m*PriceConsch + β2

m*FinancConstrh+β3m*Price-Orientc)

+ ∑ γ1,sm *Shoppers,h

9

s=1

+ ∑ γ2,rm *Tripr,hct

2

r=1

+ ∑ γ3,em *Environe,hct

2

e=1

+∑ γ4,nm *Controlsn,hct

13

n=1

+ εhctm

Estimation

We need to take two main issues into account in estimating the model in Equation 2.

Endogeneity. Although we have a rich set of observed variables in our models, it is

possible that there are unobserved variables that are correlated with use of the device as well

as purchase behavior. This is more likely to be the case for the number of items bought and

total spending. For example, it is quite plausible that a shopper may choose whether or not to

use a self-scanner based on how much she expects to buy though less plausible that she will

make that choice based on how much private label she expects to buy. We do have variables

that account for a shopper’s needs during a trip, but they are not perfect. We therefore need a

suitable instrumental variable to account for potential self-selection due to unobservables. The

instrument must be relevant, i.e., be able to predict the mobile self-scan use strongly enough.

In addition, it should satisfy the exclusion restriction, i.e., it should not directly affect

purchase behavior once the self-scanning use variable and other observed variables are

controlled for.

The prevalence of self-scanning check-outs in stores close to a panelist’s residence

serves as an effective instrument. The more frequently a panelist encounters stores in his or

her neighborhood with special check-out lanes for mobile self-scan users, the more likely the


23

panelist should be to use the self-scanner.7 Thus, the instrument is relevant. However, there is

no reason to believe that the prevalence of such check-outs in the neighborhood should affect

the panelist’s purchases at any given shopping trip, other than through the self-scanner use

variable. Thus, the instrument satisfies the exclusion restriction.

We next formally assess the strength of the instrument by computing the incremental

R2 and F-stat in the first-stage self-scan equations estimated by OLS (Bound, Jaeger, & Baker

1995).8 The R2 increases by 57% (from around .11 to .17) and the incremental F-stat is about

33, confirming the strength of the instrument (Stock, Wright, & Yogo 2002).

Note that the interactions of self-scanning use with moderators are also potentially

endogenous. In a traditional 2SLS approach, the products of the instrument and the various

exogenous variables would serve as additional instruments for these interactions (Wooldridge

2002, p. 236). However, a control function approach is simpler because it requires just one

correction term to account for the endogeneity of the main and interaction variables (Papies,

Ebbes, & van Heerde 2016). That is the approach we take. In line with Danaher et al. (2015)

we use two-stage residual inclusion (2SRI) (see Terza, Basu, & Rathouz (2008). Specifically,

we (i) estimate a probit model for self-scanning use with our instrument and all the exogenous

variables for each of the dependent variables, (ii) compute, for each observation, the predicted

probability that self-scanning is used (i.e. P̂r(self-scanhct=1)), and (iii) subtract the predicted

probability from the actual self-scanning use decision (i.e. probit residual = self-scanhct –

P̂r(self-scanhct=1). Finally, we include this probit residual as an additional explanatory

variable in Equation 2). Because the probit residual is an estimated quantity, we use a

bootstrap approach with 100 runs to approximate the correct standard errors (Terza et al.

7 Note that this variable also accounts for whether the technology is available at the stores, since there will obviously not be any special self-scan checkout lanes in a store where the technology is not available. 8 We estimate a first-stage equation for each of the six purchase behavior outcomes because the initialization period heterogeneity control, and hence the exogenous variable set, is different for each. Of course, estimates and fit are almost identical across the six first-stage regressions since they only differ in one exogenous variable.


24

2008, footnote 8; Papies et al. 2016, p.13).

Error interdependency and heteroscedasticity. To account for correlated errors across

equations and possible heteroscedasticity, we use a generalized method of moments (GMM)

procedure to estimate the six purchase equations simultaneously (see, for example,

Chintagunta, Gopinath, & Venkataram 2010). To allow for heteroscedasticity, GMM uses the

cluster-robust covariance matrix (White 1984, p.134-142). The Pagan and Hall test statistic

reveals the presence of heteroscedasticity in four out of the six purchase equations (p’s <.01),

and most of the error term correlations are highly significant, confirming the value of joint

GMM estimation.

Results

Table 3 shows pairwise correlations of all the variables in our analysis. Two points are

worth noting. First, our instrumental variable, % Self-scan Checkouts in the panelists’

vicinity, has a strong positive correlation with self-scanning use. Second, none of the

correlations between explanatory variables are high enough to cause concerns with multi -

collinearity. None of the VIF statistics exceeded the recommended cut-off-value of 10 (Cohen

et al. 2003, p. 423), suggesting that multicollinearity is not a major concern.

Mobile Self-Scanning Use

Table 4 reports the estimates of the self-scanning use equation.9 Although these are not

of primary interest in our research, they do provide insights into the use of mobile self-

scanning that are worth summarizing.

9 Table 4 reports estimates of self-scan usage relevant to total spending, with its initialization period heterogeneity control. Results are very similar for all six versions of the self-scan usage equations and available from the authors upon request.


25

First, our instrumental variable (% of check-outs in the vicinity of the panelist that are

devoted to self-scanning) has, as expected, a strong positive effect (3.09; p < .01). Second,

time-pressured shoppers are less likely to use the mobile self-scanner (-.12, p < .05)

suggesting that they do not value the time saved in the check-out line more than the extra time

they would need to scan the items themselves. As we expected, those who shop less

frequently are more likely to use the scanner (-.05, p < .10), as are those with larger

households (.11, p < .01). With their larger shopping needs, the extra task is presumably

worth it as they are motivated to control spending and save time at check-out without

unloading their large baskets.

Third, we had argued that lack of higher education is associated with lower cognitive

ability, and old age is associated with lower cognitive and physical ability to use technology

and to process information. Indeed, we find that older shoppers are less likely to use a self-

scanner (-.01, p < .01) which is also in line with other research on SST adoption (Meuter et al.

2005). In addition, a mid-level of education increases use – those with a secondary education

are more likely to use self-scanning than low or highly educated shoppers (.24, p < .01).

Finally, consumers are also more likely to use the self-scanner in familiar stores presumably

because they feel more comfortable multi-tasking in a familiar environment (.54, p < .01).10

Mobile Self-Scanning Use and Purchase Behavior

Table 5 and Table 6 reports the GMM parameter estimates for all six purchase

equations for the low and high experience segments respectively. We discuss them below.

Endogeneity. Recall that the probit residuals, whose coefficients are listed at the bottom

of Table 5 and Table 6, are the endogeneity controls. The probit residual is only significant

for the number of items equation when consumers have low experience (.31, p < .10). As

10 In addition, most of the chains (except Jumbo and Plus) require a shopper to show a loyalty card to get access to the mobile self-scanner and consumers are more likely to have a loyalty card from their favorite stores.


26

mentioned before, we expected the size of the basket to be the most likely source of

endogeneity. While we control for a household’s needs, the significance of the probit residual

indicates that unobservable variables still exist that affect both the decision to use a self-

scanner and the number of items bought and thus that the endogeneity correction is needed.

The positive sign of the coefficient indicates that the self-scanning coefficients without the

correction would be biased upward and that, on average, (unobserved) factors that make a

consumer in this group more likely to use a self-scanner also increase the number of items

bought. The probit residual is not significant in the other equations (p > .10) but we err on the

side of caution and retain it in the model, even though that is likely to increase standard errors.

Main Effect of Self-Scanning Use. We first discuss the main effect of self-scanning use

for the low experience segment (see Table 5). The coefficient is significant in two of the

purchase equations. On average, the use of mobile self-scanning significantly decreases

spending (-.36; p < .05) and the number of items bought (-.42; p < .05), while having no

significant effect on the four share variables. This is largely consistent with our expectations.

As more cognitive resources are used in the self-scanning task by the low experience group,

they go for the more easy means to save money, namely choice deferral and, as a

consequence, buy less items and spend less. We did expect a reduction in low-need purchases

but there we find no significant effect. Perhaps making the distinction between high vs. low

need categories itself involves some cognitive effort for consumers.

For the high experience segment (see Table 6) we find that the use of a self-scanner, on

average, significantly increases the share of private labels (.99; p < .05), while having no

significant effect on the other five dependent variables. Consistent with our expectation, these

shoppers are able to make trade-offs that are more sustainable on the long run, stretching their

budget with private labels even though they don’t spend less in total. Counter to our

expectations, however, they do not increase their share of promotional purchases. Perhaps this


27

is because private label is a more reliable way of saving money than price promotions are -- it

is uncertain whether products will be on promotion when the shopper needs them.

Moderators of the Self-Scanning Effect. The coefficient estimates of our three

moderators can be seen in Tables 5 and 6. In addition, we conduct a spotlight analysis for

each significant interaction effect to provide a sense of the size and significance (Spiller et al.

2013). Specifically, we compute the effect of self-scanning for low (10th percentile, i.e.,

lowest decile) and high (90th percentile, i.e., highest decile) values of the moderator using the

parameters in Table 5 (see also Datta, Ailawadi, & van Heerde 2017). The spotlight results

are shown in Figures 2 and 3 for the low and high experience segments respectively.

We start with a discussion of the results for the low experience segment in Table 5 and

Figure 2. The three moderators have significant effects in some but not all purchase equations.

As we expected, price consciousness makes the impact of self-scanner use more negative for

total items (-.19, p < .05) and low-need share (-1.33, p < .05). This supports our reasoning that

price conscious shoppers are more motivated to save money and thus are more likely to act on

the spending feedback provided by the self-scanner. Directionally, the moderation is also

negative for total spending but it does not reach statistical significance.

The spotlight analysis shows that the effect of self-scanning use on total number of

items purchased is significantly negative for those at the highest decile of price consciousness

but insignificant at the lowest decile (p = .00 and p = .25 in Panel A1 of Figure 2). When it

comes to low-need share, the spotlight analysis shows that self-scanning use increases low-

need share among the lowest decile of price consciousness and decreases it among the highest

decile, although neither effect is individually significantly different from zero (p = .25 and p =

0.13 respectively in Panel A2 of Figure 2).

Second, the moderation effects of financial constraints are mixed. For total spending,


28

the interaction is positive, which is what was expected based the work of van Ittersum et al.

(2013), though it falls just short of significance (.11; p = .12). However, we find a

significantly negative interaction for low-need promotion share (-1.01; p < .01; see panel B).

Third, being in a price-oriented chain makes the effect of self-scanning on spending,

the number of items, and low-need promotion share more negative (-.19, p < .10; -.19, p <

.10; and -1.23, p < .05 respectively). This is as we expected. The spotlight analysis in Panel C

of Figure 2 shows that the effect of self-scanning on total spending is significantly negative in

price-oriented chains (p = .02) but the decrease is not significant in other chains (p = .37). A

similar pattern occurs for the number of items and for low-need promotion share. In both

cases, the effect of self-scanning is negative and significant for price-oriented chains but not

different from zero for other chains (see Panel C2 and C3). These results are in line with our

reasoning that it is easier to act on the self-scanner’s spending feedback in a price-oriented

chain, where there are more opportunities to save money.

As we expected, there are hardly any moderator effects in the high experience segment,

except for price consciousness, which negatively moderates the effect of self-scanning on

total spending and on both low-need and high-need promotions. However, the spotlight

analysis in Figure 3 shows that the self-scan effect is not significantly different from zero for

either the lowest or the highest decile of price consciousness.

Discussion

Summary

In-store mobile technologies are being introduced in grocery stores worldwide, and

have the potential to influence shopping behavior in several ways. To our knowledge, this is

the first large scale empirical study, based on actual marketplace data, that examines both the

shopper’s decision to use a mobile self-scanner and the impact that scanner use has on several

aspects of his/her purchase behavior.


29

Our research adds to the rich research on adoption of SST by identifying characteristics

that are and are not associated with the decision to use a mobile self-scanner. For example,

our finding that time pressured shoppers are actually less likely to use it suggests that the time

saved in the check-out line is not perceived as enough to offset the extra time needed to scan

items while shopping. So, convenience, which is an important driver of SST adoption, is not a

given with this technology. However, those with larger households are more likely to use the

device, presumably because it is worth their while to monitor spending and save time at

check-out. And those who are familiar with a chain are also more likely to use the device

there, perhaps because they know their way around and can more easily multi-task while

shopping. Finally, the business press reports that consumers find it “fun to use” (Hance 2018)

or state that “Scan & Go is pretty cool” and a technology “to modernize my shopping

experience” (Bradley 2017). This might explain why younger people and people with less

time pressure are more likely to use it (rather than the other way around). Of course, the fact

that younger and more educated people are more likely to use the scanner is consistent with

prior research showing that these demographics are more open to new technologies in general

(Meuter et al. 2005; Weijters et al. 2007).

The impact of self-scanning on purchase behavior is mostly limited to shoppers who

have not yet built up enough experience with the device. Such shoppers respond to the

spending feedback with less effortful ways to saving money, such as simply buying fewer

items. This holds in particular for price conscious consumers and when they shop at a price-

oriented chain. Once shoppers have gained experience with the device, however, we found no

deleterious effects on purchasing in the store. Indeed, the only effect we see is a strong and

significant increase of private label buying. The spending feedback combined with less

constrained cognitive resources (as consumers are more experienced users) encourages the

shopper to buy lower priced options.


30

Implications for Researchers

Our research has provided several new insights on the impact of mobile self-scanning

technology but it also suggests several areas for additional research.

First, our finding that promotional purchases do not increase with the scanner’s

spending feedback even among those who have accumulated experience with the device and

hence have the attentional capacity, whereas private label purchases do, deserves further

investigation. Indeed, the more price conscious among these experienced consumers are, if

anything, less likely to use promotions. It appears that such consumers think that private

labels are a more reliable way to save money and/or develop an overall positive affect towards

the chain which spills over into the chain’s private label. This is somewhat consistent with

Ailawadi et al.’s (2001) finding that there are substantial distinct segments of promotion users

and private label users and that the private label segment is more price conscious. However, it

would be worth investigating why spending feedback does not encourage promotion use.

Second, an alternative reason why we find so few effects for the experienced segment

may be that as experience accumulates and learning occurs from repetition, shopping reverts

to being largely habit driven. Repeated use may also result in wear-out of the spending

feedback effect. For instance, a study on the effect of real-time feedback on a household’s

energy consumption, found that over time the effect declines as the feedback fades into

normal household routines and practices (Hargreaves, Nye, & Burgess 2013). While we have

looked at the differential impact of self-scanner use for low versus high experience segments,

future research could examine whether and how the effects of mobile self-scanning use on

purchase behavior change over time within respondents across a longer timeframe. Our

dataset does not allow us to track longitudinal variation because we were only able to survey

shoppers about their use of the technology during a one-month period. As the technology is

integrated into a mobile app and retailers are able to automatically capture information on its


31

use on an ongoing basis, there will be richer opportunities to explore the impact over time as

well as reasons for variation across shoppers. Moreover, future research could examine the

dynamics of how the spending feedback affects decisions within the trip (e.g., Gilbride,

Inman, & Stilley 2015) by tracking not only the sequence of purchases but also whether and

when shoppers decide to “un-scan” specific items.

Third, our results on how financial constraints moderate the impact of spending

feedback are not fully consistent with van Ittersum et al. (2013). Directionally, we do find that

financial constraints make the impact of spending feedback on total spending more positive,

but, interestingly, we find that these constraints make the effect on low-need promotional

share more negative. Van Ittersum et al. did not assess the impact on this purchase outcome;

indeed, in two of their three studies, the participants were given a specific shopping list so

shoppers had to buy each category on the list. Future research could combine experimental

and field research to delve more deeply into this issue.

Finally, as we have noted, shoppers are more likely to save time and enjoy the

shopping experience with the scanner once they become familiar with the device, and that

positive affect may spill over to the store. It would therefore be worthwhile to study the

impact of using self-scan technology on future patronage of the retailer. Future research could

investigate this using observational data (to measure time spent in the store vs. at the check-

out), survey data (to measure shopping enjoyment), and actual purchases and share of wallet

at the chain over time. Also, our research focused specifically on those purchase outcomes

that are likely to be affected by the scanner’s spending feedback but other purchases related to

time saving (such as fresh produce and other non-packaged items that must be weighed before

scanning) and enjoyment (such as hedonic purchases) could be studied in future research.

Implications for Retailers and Manufacturers

For retailers, mobile self-scanning is considered an interesting technology as it is


32

reported to cost less than installing more self-checkouts while it reduces labor costs to a

similar extent (USA Today 2018). However, our findings on the drivers of self-scanning use

suggest that they should not simply assume that mobile self-scanners are viewed as a

convenience by their customers. Shoppers who are pressed for time are less likely to use

them, at least in their current design. This is in line with the experience of retailers like

Walmart who have found that shoppers have not embraced the technology. So, if the main

reason that a retailer is considering installing the technology, at least in its current form, is to

make shopping more convenient for its customers, the decision should be reconsidered,

especially since the costs of installation and ongoing maintenance are still significant, and

there is some evidence that self-scanning can increase theft (PlanetRetail 2012). Retailers

should try to improve the technology so that shoppers do not perceive it as a burden but as an

enjoyable experience instead. This is particularly so because relatively inexperienced

shoppers buy fewer items when they use the device. All store types, but in particular price-

oriented chains, could see a short-term decrease in basket size due to self-scanner use.

On the positive side, given that consumers who use the technology in a given chain,

tend to use it for the majority of their shopping trips, it appears that once the initial hump is

overcome, continued use is more likely. Retailers can take heart in the finding that, once they

have gained experience, shoppers do not buy less when they use the device. In fact the effect

on total spending and total items purchased by experienced shoppers is directionally positive,

though not statistically significant. Plus, retailers benefit from an increase in private-label

share which is important given the potential for higher margins and further store loyalty

(Ailawadi et al. 2008, ter Braak et al. 2013). Of course, this same finding is a cause for

concern for brand manufacturers.

These findings also reveal opportunities for improving the technology. First, the self-

scanner can be a useful vehicle to connect with the shopper and deliver targeted promotions


33

that are easy to avail of. If GPS is incorporated with the self-scanning device and integrated

with loyalty program data, behavior-based and/or location-based promotion notifications can

be offered to shoppers when they are in particular areas of the store. This would make it easier

for them to avail of the promotions, and brand manufacturers can partner with the retailer to

offer such targeted and easy to use promotions on their own brands. Of course, the challenge

is to make promotions relevant and accessible to encourage purchases but not so accessible

that everyone can use them and they do not result in incremental sales. Therefore, using

targeting algorithms that maximize incremental sales is important. Companies like Catalina

Marketing who already work with manufacturers and retailers to offer algorithm-based

targeted coupons at check-out can expand their services to the mobile self-scanner as well.

Second, the mobile self-scanner can be integrated with the shopper’s shopping list,

which may be easier if the technology is in the form of a mobile phone app rather than a

separate hand-held device. If this is done, notifications can be pushed out to shoppers as they

pass by areas of the store where products on their shopping lists, as well as products that are

complementary to those on their shopping lists, are located. The former would definitely be

viewed as a convenience though, as we noted previously, it may focus shopper attention only

on planned shopping and therefore reduce the size of the shopping basket, but the latter could

expand the shopping basket while still being perceived as a convenience. A caution is in order

here, however. If the technology is on a mobile app, the shopper may also be more likely to

use their phone to check prices in other stores or for tasks unrelated with shopping.

Of course, a truly convenient “just pick up and go” technology, such as “Amazon Go”

which is being tested by Amazon.com (Amazon 2016), without the need to even scan items,

may be able to offer most of the benefits to the retailer without the downsides of needing

effort or being spurred to curb spending by spending feedback. However, that technology is

far from easy to develop.


34

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TABLE 1

Descriptive Statistics of the Different Chains

Chain Market sharea

Number of trips in our sample

Total

With self-

scanning %

Albert Heijn 22.28% 2,781 329 11.83% Albert Heijn XL 2.54% 255 97 38.04% C1000 6.17% 336 15 4.46% DekaMarkt 2.19% 334 24 7.19% Dirk 3.30% 575 28 4.87% Emté 3.06% 484 79 16.32% Hoogvliet 3.05% 545 163 29.91% Jumbo 13.35% 1,952 500 25.61% Plus 5.56% 820 124 15.12% Total 61.50% 8,082 1,359 16.82%

aNational value market share based on GfK purchase data for 2014.


41

TABLE 2

Variable Operationalization and Descriptives Definition Mean SD Min Max

Dependent variables

Purchasinghct The six purchase behavior outcome measures: - Total spending amount (€)a - No. of itemsa - Low-need share (%):b Purchase volume in low-need categories / total trip volume - Promoted low-need share (%):b Purchase volume bought on promotion in low-need

categories / total trip volume - Promoted high-need share (%):b Purchase volume bought on promotion in high-need

categories / total trip volume - Private label share: Private label purchase volume / total trip volume (%)b

To aggregate purchase volumes across categories and different volume units at the trip-level (e.g. kilogram and liters) we use an average overall category price per equivalent unit volume (cf. Ma et al. 2011). The resultant purchase volume is in euro units and variation herein is only driven by volume changes and not by price differences over time and across chains.d

Based on a median split of household h’s category k needs at the start of trip t (CategNeedkht.), purchases of k are categorized as either low-need (below the median) or high-need (above the median). CategNeedkht is calculated by multiplying the ratio of the no. of days since k was last bought by h to the average no. of days between purchases of k by h in 2014 with the ratio of the last bought volume of k by h to the average volume bought of k by h in 2014. Before multiplying, the second ratio is reverse coded so that large values imply relatively low volumes of k bought and small values imply relatively large volumes of k bought.

20.69 18.20

.51

.13

.12

.38

19.26 17.42

.30

.20

.20

.28

.58

2.00 .00 .00

.00

.00

219.35 164.00

1.00 1.00

1.00

1.00

Independent variables Self-scanhct Dummy variable which is 1 if household h uses self-scanning in chain c during shopping

trip t and 0 if otherwise.

17%

.00 1.00


42

Moderators Price consciousnesshc “For me, price is decisive when I am buying a product”, “Price is important to me when I

choose a product”; and “I generally strive to buy products at the lowest price” (5-point Likert scale from 1 (“strongly disagree”) to 5 (“strongly agree”)) (cf. Ailawadi et al. 2008). (Cronbach’s α = .77)

3.68 .69 1.00 5.00

Financial constraintshc “My household budget is always tight” and “My household often has problems making ends meet” (5-point Likert scale from 1 (“strongly disagree”) to 5 (“strongly agree”)). (Cronbach’s α = .90)

2.83 .99 1.00 5.00

Price-oriented chaince Variable that equals 1 if chain is classified as “price-oriented” and -1 if chain is classified as “not price-oriented”. This is based on the median-split of the summated ratings that households, on average, give to two value dimensions (i.e. price and promotion attractiveness (cf. van Lin & Gijsbrechts 2014)). We classified as price-oriented chains Dirk, Hoogvliet, C1000, and Albert Heijn XL, and Albert Heijn, Jumbo, Plus, and DekaMarkt, and Emté as not price-oriented chains.

21% -1.00 1.00

Shopper characteristics Shopping frequencyhc The average number of shopping trips per week household h makes during the initializat ion

period (i.e. 2014) 3.59 1.98 .12 11.27

Willingness to try new productshc

“I am always one of the firsts of my friends to try new products or services”, “I like to buy new products and try them out”, and “Generally I’m one of the first to buy a new product that’s introduced in the market” (5-point Likert scale from 1 (“strongly disagree”) to 5 (“strongly agree”)). (Cronbach’s α = .82)

2.65 .79 1.00 5.00

Time pressurehc “I am always in a hurry when I go grocery shopping.” (5-point Likert scale from 1 (“strongly disagree”) to 5 (“strongly agree”)).

2.28 .86 1.00 5.00

Household sizehc The number of people in household h. 2.34 1.18 1.00 8.00 Agehc The age of the housewife in household h. 56.73 12.87 15.50 75.00 Educationh Education dummy variables which equal 1 if household h highest level of education is that

and 0 if otherwise: - Primary education (reference category) - Secondary education - Higher education

37% 33% 30%

.00

.00

.00

1.00 1.00 1.00

Shopping trip characteristics Trip needhtc Ratio of the no. of days since h’s last trip to the average no. of days between trips of h in

2014 multiplied by the ratio of the number of categories last bought by h to the average number of categories bought during a trip by h in 2014. Before multiplying the two ratio’s,

1.45 1.75 .00 30.28


43

the second ratio is reverse coded so that large values imply a relatively low number of categories bought and small values imply a relatively large number of categories bought.f

Crowdinghctc “How calm or busy did you think it was in the store?” (0 = very quiet, 9 = very crowded) 4.91 2.13 .00 9.00 Shopping environment characteristics Chain loyaltyhcc A household’s total spending (€) in chain c during 2014 as a percentage of household total

spending across all grocery chains during 2014. Chain loyalty is 0 when household h did not shop at chain c during 2014 (cf. Ailawadi et al. 2008).

.47 .31 .00 1.00

Promo intensityct Ratio of the number of categories in chain c where at least one item is on promotion during the week of trip t to the total number of categories in c.

.17 .05 .10 .30

Controls Average floor sizehcc The average floor size (in hundreds of m²) of the stores of the chosen chain c within 5 km

of household h’s home. For 4% (i.e.363) of the observations in our final sample, there are no stores of the chosen chain within 5 km. For these observations we impute the grand mean of all stores across chains.

12.91 4.80 2.50 52.00

Weekendt Dummy variable which is 1 if the shopping trip t is in the weekend and 0 if otherwise. 21% .00 1.00 Time of dayt Time of day dummy variables which equal 1 if shopping trip t is during that part of the day

and 0 if otherwise: - Morning (reference category) - Midday - Evening

43% 52% 5%

.00

.00

.00

1.00 1.00 1.00

Chain fixed effects Chain dummy variables which equal 1 if the shopping trip is at that chain and 0 otherwise: - Albert Heijn (reference category) - Albert Heijn XL - C1000 - DekaMarkt - Dirk - Emté - Hoogvliet - Jumbo - Plus

35% 3% 4% 4% 7% 6% 7%

24% 10%

.00

.00

.00

.00

.00

.00

.00

.00

.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Initial avg. purchasingh(a,c) Household h’s average value for the respective purchase measure across all chains and trips during the initialization period (cf. Ma et al. 2011)d: - Initial average total spending amount (€)a - Initial average no. of itemsa

19.34 17.67

11.73 10.40

1.81 1.91

129.02 101.93


44

- Initial average low-need share (%)a - Initial average promoted low-need share (%)a - Initial average promoted high-need share (%)a

- Initial average private label share (%)a

.52

.11

.09

.48

.04

.06

.05

.14

.28

.00

.00

.04

.78

.40

.36

.95 Instrument

Avg. % self-scan checkoutsh

Define a household’s consideration set of stores (subset S) as stores within 5 km of the household’s home address that belong to chains visited by the household at least once in initialization period (cf. van Lin & Gijsbrechts 2014). Then calculate the weighted-by-chain-loyalty average % of self-scan checkouts for household h:

∑

ChainLoyaltyhc * 1nc

∑ (# self-scan checkoutss

total # checkoutss)

nc

all s in S belonging to c

c

where nc is the number of stores belonging to c in S.

.11 .11 .00 .54

Self-scan Experienceh “How experienced are you with the use of self-scanner?” (0 = very inexperienced, 9 = very experienced). We classify households as high or low experience based on a median split (median = 2)

3.70 3.76 .00 9.00

Notes: we report the variable statistics before mean-centering and/or other transformations a before logistic transformation; b before logit transformation; c before mean-centering; d Purchases of non-CPG-categories are not included; e For the price-oriented chain variable, we report the percentage of observations having the value of one. f For example, on trip t, household h buys items out of 10 different product categories. The last time h went shopping was 8 days ago, whereas h, on average goes shopping every 15 days (based on 2014 data). Therefore, the first ratio is .53 (= 8/15). On average, h buys items across 20 product categories (based on 2014 data). Thus the second ratio is .50 (= 10/20), before reverse coding. Suppose that the minimum and maximum values of the second ratio for h are .10 and 2.50 respectively, then the reverse coded second ratio is 2.10 (= .10+2.50 -.50). Multiplying the first and the (reverse coded) second ratio gives the trip need of h at t: 1.12 (= .53 * 2.10).


45

TABLE 3

Correlation Matrix

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 1 Log(total spending amount) 1 2 Log(no. of items) .87 1 3 Logit(low-need share) -.10 -.10 1 4 Logit(promoted low-need share) .20 .20 .27 1 5 Logit(promoted high-need share) .27 .27 -.34 .13 1 6 Logit(private label share) .07 .08 -.04 -.09 -.05 1 7 Self-scan use .14 .13 -.02 -.03 -.01 .05 1 8 Price consciousness -.10 -.03 .00 .00 .00 .03 .01 1 9 Financial constraints .01 .03 .03 -.03 -.04 .00 .04 .37 1

10 Price-oriented chain -.03 .01 .02 .10 .07 -.10 .01 .06 .02 1 11 Shopping frequency -.38 -.33 -.02 -.06 -.05 -.07 -.06 .12 -.05 .03 1 12 Willingness to try new products .07 .08 .02 .04 .04 .02 .01 .05 .08 -.01 -.07 1 13 Time pressure -.04 -.03 .01 -.02 -.02 .01 -.04 .04 .07 .04 -.03 .07 1 14 Household size .16 .20 -.02 .01 .03 .04 .10 .17 .02 -.02 .16 .08 .05 1 15 Age -.11 -.15 .00 .01 .00 -.04 -.12 -.23 -.16 -.01 .10 -.22 -.08 -.40 1 16 Education: secondary -.05 -.03 -.03 -.03 -.02 -.01 .07 .05 .04 .03 .08 -.04 .02 .03 -.06 1 17 Education: higher .05 .04 .02 .03 .01 .02 .00 -.06 -.14 -.03 -.06 .10 .06 .08 -.23 -.46 1 18 Trip need .03 .03 -.25 -.11 .12 .02 .01 .01 -.02 -.01 .15 .02 .01 .05 -.01 .03 -.02 1 19 Crowding .08 .07 .01 .04 .03 .00 .00 -.05 -.01 .02 .00 .02 .03 -.10 .12 -.04 -.03 -.03 1 20 Chain loyalty .26 .22 -.05 -.02 .05 .11 .13 -.18 -.07 -.03 -.16 -.01 -.03 -.06 .03 -.07 .08 -.01 -.01 1 21 Promo intensity -.03 -.01 .01 .21 .20 -.05 -.06 .01 -.03 .69 .03 .02 .03 -.04 .00 -.01 .01 .00 .03 .07 1 22 Average floor size .05 .02 .00 .02 .00 .08 .14 .01 .05 .25 -.03 .00 .01 -.03 .00 .03 .01 .07 .02 .00 .06 1 23 Weekend .06 .08 .05 -.03 -.06 .02 .03 .03 .01 -.04 -.04 .04 .03 .00 -.08 -.05 .06 -.04 .14 .05 -.05 -.01 1 24 Time of day: midday -.08 -.11 .03 -.03 -.06 .00 -.04 -.01 .02 .00 -.01 .03 .02 -.05 -.02 .01 .00 -.04 .13 -.03 .01 .03 -.01 1 25 Time of day: evening .05 .06 .03 .03 .00 .00 .02 .01 .01 .01 -.06 .05 .04 .03 -.14 -.01 .09 .00 -.09 .03 .01 .01 -.05 -.24 1 26 Init. log(avg. total spending amount) .61 .53 -.03 .06 .11 .09 .10 -.15 -.03 -.14 -.58 .07 -.01 .28 -.16 -.07 .07 -.08 -.01 .22 -.12 .02 .04 -.02 .04 1 27 Init. log(avg. no. of items) .56 .54 -.03 .08 .11 .08 .11 -.04 .03 -.10 -.54 .09 -.01 .37 -.26 -.04 .05 -.09 -.03 .14 -.09 .00 .04 -.05 .04 .93 1 28 Init. log(avg. low-need share) .00 .00 .02 .03 .02 -.01 .00 .00 -.04 -.01 -.08 -.02 -.03 -.08 -.01 -.01 .02 .00 .00 .06 .02 -.01 .03 .01 .00 .01 .01 1 29 Init. log(avg. promoted low-need share) -.10 -.07 -.02 .26 .24 -.07 -.04 .06 -.08 .12 .14 .02 -.04 -.04 .12 -.02 .00 .03 .04 -.08 .26 -.02 -.06 -.04 -.02 -.17 -.12 .13 1 30 Init. log(avg. promoted high-need share) -.07 -.05 -.03 .22 .24 -.05 -.03 .04 -.09 .09 .16 .06 -.04 -.02 .10 -.02 -.01 .03 .05 -.06 .22 -.02 -.06 -.04 -.02 -.13 -.09 -.13 .81 1 31 Init. log(avg. private label share) -.09 -.06 .00 -.04 -.04 .19 -.05 .20 .06 .00 .05 .03 .02 .09 -.09 .04 .01 -.01 -.05 -.20 -.02 .04 .00 -.01 -.02 -.11 -.03 -.02 -.16 -.15 1 32 Avg. % self-scan checkouts .01 .00 -.01 -.02 -.01 .00 .33 -.02 .00 .03 .03 -.02 -.01 .03 -.04 .05 -.02 .02 -.01 .06 .00 .16 .01 -.03 -.02 .00 .00 .00 .01 -.02 -.10 1 33 Self-scan experience .06 .06 .01 -.01 -.01 .05 .38 .03 .06 .02 .00 .11 .00 .12 -.18 .04 .03 .00 .03 .06 -.01 .09 .03 .03 .05 .08 .10 .04 -.03 -.03 -.01 .23 1

Notes: The matrix shows the correlations between the variables before mean-centering. Bold figures indicate correlations that are significant at 10% significance level.


46

TABLE 4

Coefficient Estimates for Self-Scanner Use Model

(N=8,082) Est z-stat Shopper characteristics

Price consciousness -.036 -.590 Financial constraints .035 .810 Shopping frequency -.053 * -1.700

Willingness to try new products .000 .000 Time pressure -.124 ** -2.550

Household size .108 *** 2.780

Age -.011 *** -3.250

Education: Secondary .244 *** 2.630

Education: Higher .059 .550 Shopping trip characteristics

Trip need .006 .460 Crowding .023 1.630

Shopping environment characteristics Chain loyalty .538 *** 4.050

Promo intensity .892 1.080 Controls

Average floor size .017 1.510

Weekend .051 .980 Time of day: midday -.091 * -1.730

Time of day: evening .086 .810 Initial log(avg. total spending amount) .000 .000

IV: Avg. % self-scan checkouts 3.090 *** 10.270

Constant -1.321 *** -14.130

Pseudo-R² 18% *p<.10, **p<.05, ***p<.01 two-sided. Notes: Model includes chain dummies; we do not report them for simplicity of presentation.


47

TABLE 5

Coefficient Estimates for Purchase Behavior Models: Low Experience Segment

(N = 4,015) Total Spending

Amount No. of Items Low-Need Share Promoted Low-

Need Share Promoted High-

Need Share Private Label

Share

Est z-stat Est z-stat Est z-stat Est z-stat Est z-stat Est z-stat Self-scan use -.355 ** -1.960 -.416 ** -2.303 -.146 -.173 -1.014 -1.026 .762 .713 -1.398 -1.541 Interactions

*Price consciousness -.129 -1.215 -.189 ** -1.973 -1.325 ** -2.441 .514 1.165 .242 .420 -.152 -.315 *Financial constraints .113 1.572 .067 .903 .194 .716 -1.014 *** -3.916 -.387 -1.318 .488 1.415 *Price-oriented chaina -.190 * -1.775 -.189 * -1.875 -.204 -.305 -1.227 ** -2.106 -.373 -.532 -.254 -.410

Shopper characteristics Price consciousness -.027 -1.234 -.027 -1.097 -.055 -.582 .059 .492 .087 .665 .189 * 1.769 Financial constraints .031 ** 2.227 .020 1.298 .126 * 1.811 .082 1.019 -.069 -.855 -.129 * -1.734 Shopping frequency -.022 ** -2.393 -.038 *** -3.376 .009 .235 -.192 *** -4.483 -.174 *** -3.895 -.198 *** -4.268 Willingness to try new products .027 1.615 .041 ** 2.361 .022 .270 .096 1.026 .091 .995 -.040 -.500 Time pressure -.039 ** -2.414 -.026 -1.521 .065 .770 -.072 -.771 -.006 -.063 .071 .981 Household size .030 * 1.903 .053 *** 3.308 -.063 -1.026 .293 *** 3.985 .193 *** 2.810 .207 *** 3.091 Age -.002 * -1.788 -.002 -1.358 -.004 -.659 .006 .870 -.001 -.147 .001 .115 Education: Secondary -.011 -.341 .041 1.265 .014 .091 .090 .530 -.064 -.365 -.134 -.846 Education: Higher -.004 -.117 .003 .074 .175 1.104 .242 1.323 -.078 -.445 -.172 -1.102

Shopping trip characteristics Trip need .036 *** 4.414 .031 *** 3.882 -.545 *** -11.428 -.287 *** -9.020 .293 *** 6.186 .105 *** 3.358 Crowding .035 *** 6.179 .036 *** 5.966 -.016 -.545 .019 .625 .037 1.269 -.013 -.467

Shopping environment characteristics Chain loyalty .353 *** 7.177 .464 *** 8.847 -.662 *** -3.011 -.167 -.633 .650 ** 2.478 1.231 *** 4.874 Promo intensity .211 .456 1.043 ** 2.187 -1.723 -.658 8.583 *** 2.578 9.931 *** 3.350 .654 .274

Controls Average floor size .008 ** 1.998 .010 ** 2.328 -.025 -1.175 .052 ** 2.408 .026 1.072 -.007 -.361

Weekend .023 .775 .061 ** 1.996 .415 *** 3.086 -.164 -1.112 -.739 *** -4.697 .133 1.022 Time of day: midday -.112 *** -4.476 -.142 *** -5.133 .142 1.163 .011 .083 -.343 *** -2.592 .020 .166 Time of day: evening .078 1.365 .100 1.521 .518 * 1.773 .309 .942 -.286 -.803 -.306 -1.096 Initial log(avg. purchasing) .874 *** 21.825 .743 *** 18.854 -.467 -.578 1.204 *** 9.735 .937 *** 7.448 1.340 *** 5.840

Probit residual .235 1.486 .305 * 1.842 -.082 -.124 .318 .365 -.978 -1.110 .582 .727 Constant 2.741 *** 81.027 2.542 *** 70.450 -.115 -.717 -4.971 *** -24.582 -4.680 *** -25.747 .106 .614 Notes: Models include chain fixed effects which are not reported for simplicity of presentation. *p<.10, **p<.05, ***p<.01, two-sided. a The main effect of whether a chain is price-oriented or not is accounted for by chain fixed effects.


48

TABLE 6

Coefficient Estimates for Purchase Behavior Models: High Experience Segment

(N = 4,067) Total Spending

Amount No. of Items Low-Need Share Promoted Low-

Need Share Promoted High-

Need Share Private Label

Share

Est z-stat Est z-stat Est z-stat Est z-stat Est z-stat Est z-stat Self-scan use .130 1.069 .133 1.063 -.100 -.217 -.087 -.140 .376 .653 .994 ** 2.249 Interactions

*Price consciousness -.063 -1.457 -.077 * -1.724 -.026 -.153 -.532 ** -2.359 -.469 ** -2.163 .045 .250 *Financial constraints -.020 -.662 -.004 -.124 .107 .883 .179 1.098 -.046 -.318 -.128 -1.065 *Price-oriented chaina -.003 -.082 .019 .475 .132 .910 .205 1.101 .242 1.280 -.024 -.169

Shopper characteristics Price consciousness .034 1.381 .037 1.434 -.155 -1.306 .034 .273 .301 ** 2.072 .262 * 1.891 Financial constraints .022 1.198 .009 .465 .062 .735 -.165 -1.639 -.136 -1.321 .076 .825 Shopping frequency -.006 -.648 -.005 -.432 .061 * 1.948 -.148 *** -3.476 -.214 *** -5.487 -.026 -.753 Willingness to try new products .035 ** 2.127 .025 1.385 .141 * 1.822 .171 * 1.795 .081 .887 .007 .089 Time pressure -.040 ** -2.333 -.044 ** -2.386 -.019 -.278 -.140 -1.481 -.154 ** -2.058 .050 .682 Household size -.001 -.034 .006 .361 -.022 -.312 .116 1.632 .167 ** 2.349 .045 .772 Age .000 -.356 .000 -.207 .004 .724 .001 .107 .000 .009 .000 -.009 Education: Secondary .005 .138 .006 .162 -.228 -1.620 .094 .509 -.048 -.264 -.121 -.817 Education: Higher -.011 -.306 .019 .485 -.012 -.076 .267 1.336 -.015 -.080 -.146 -.977

Shopping trip characteristics Trip need .048 *** 5.922 .054 *** 6.749 -.471 *** -12.136 -.208 *** -6.069 .283 *** 7.304 .019 .594 Crowding .034 *** 5.698 .038 *** 5.931 .009 .327 .102 *** 3.280 .066 ** 2.070 -.006 -.188

Shopping environment characteristics Chain loyalty .380 *** 7.303 .440 *** 8.150 -.610 *** -2.922 -.602 ** -2.373 .152 .610 1.393 *** 6.660 Promo intensity -.181 -.376 .412 .814 .150 .054 4.458 1.408 12.180 *** 4.080 -6.473 ** -2.432

Controls Average floor size .002 .521 .002 .355 .006 .316 -.008 -.314 .009 .418 -.028 * -1.704

Weekend .089 *** 3.184 .144 *** 4.839 .406 *** 3.345 -.294 ** -2.017 -.333 ** -2.550 -.006 -.048 Time of day: midday -.116 *** -4.509 -.144 *** -5.139 .180 1.555 -.480 *** -3.544 -.474 *** -3.468 .008 .068 Time of day: evening .036 .615 .082 1.369 .884 *** 3.199 .276 .817 -.334 -1.142 -.134 -.563 Initial log(avg. purchasing) .913 *** 24.672 .861 *** 19.620 2.603 *** 3.872 1.208 *** 8.880 1.103 *** 9.070 1.624 *** 7.938

Probit residual .077 .625 .059 .459 -.071 -.149 .280 .457 .037 .062 -.653 -1.455 Constant 2.656 *** 72.104 2.460 *** 58.084 .080 .491 -4.801 *** -24.080 -4.747 *** -22.978 -.007 -.040 Notes: Models include chain dummies; we do not report them for simplicity of presentation. *p<.10, **p<.05, ***p<.01, two-sided. a The main effect of whether a chain is price-oriented or not is accounted for by chain fixed effects.


49

Figure 1

Conceptual Framework


50

FIGURE 2

Spotlight Analysis for Moderator Effects: Low Experience Segment

A. Moderator: Price Consciousness B. Moderator: Financial Constraints

C. Moderator: Price-Oriented Chain


51

FIGURE 3

Spotlight Analysis for Moderator Effects: High Experience Segment

A. Moderator: Price Consciousness


how mobile self-scanning use influences consumers’ grocery

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