Crowdedness and Mobile Targeting Effectiveness · question with broad implications for mobile...

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Crowdedness and Mobile Targeting Effectiveness

Michelle ANDREWS, Xueming LUO, Zheng FANG, and Anindya GHOSE

March 2014

Advances in mobile technologies can provide novel measures of the crowdedness of a consumer’s

immediate social environment. Our paper seeks to understand how crowdedness affects consumer

response to mobile targeting. This unconventional but important question is broadly related to how

engagement with mobile content changes in contextual environments. We rely on a unique marketing

environment –subway trains– in which the crowdedness of the environment is observable to empirically

examine this question. With the cooperation of a leading mobile telecom provider, we provide targeted

messages to subway passengers. On the bases of multi-method field data, we find robust evidence that

response behavior to mobile targeting varies as a function of crowdedness in the trains. The evidence

relies on exploiting an exogenous shock due to a traffic intervention, establishing user homogeneity via

same-train-same-time commuters and propensity score matching, using a residual crowding approach and

multiple falsification tests, and conducting additional checks with field surveys. According to the field

surveys, consumers’ immersion in their mobile phones explain why crowdedness in subway trains affects

mobile involvement and ultimate purchases. Marketers may consider gauging the crowdedness of a

consumer’s social environment as a new way to boost mobile targeting effectiveness.

Key words: mobile targeting, crowdedness, field study, multi-method, new technology

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1. Introduction

Mobile marketers have a vested interest in learning how to optimize their targeting campaigns. As mobile

ad spending is projected to exceed $60 billion by 2017 (eMarketer 2013), marketers are keen on

understanding what impacts consumer responses to mobile promotions. The effectiveness of mobile

targeting relies on reaching consumers when and where they are most receptive to marketing messages.

Recent examples include geo-fencing (i.e., sending mobile coupons to people within the virtual perimeter

of a store) and iBeacon (i.e., transmitting geo-located deals to within-store devices). Several studies have

investigated the contextual factors that influence the effectiveness of mobile targeting strategies. Mobile

users’ internet search behavior (Ghose et al. 2013), cross platform synergies with web advertising (Ghose

et al. 2014), geographic mobility (Ghose and Han 2011), location and time at which they receive a

promotion (Luo et al. 2014), distance between the store and the user (Molitor et al 2014), the weather

(Molitor et al. 2013), and the product characteristics (Bart et al. 2014) play an important role in driving

mobile purchase likelihood.

This work seeks to understand how crowdedness affects response to mobile targeting in the context of

consumers’ social environment.1 We use a unique marketing environment –subway trains– in which the

crowdedness of the environment is directly observable via mobile technology. The subway environment

is a promising and relevant marketing environment because in most cities people spend a considerable

amount of time commuting, averaging 48 minutes each way, according to Census Bureau reports. More

specifically, the underground subway in the city of our study is mobile-equipped and allows passengers to

use their mobiles throughout their commute. With the cooperation of a leading mobile telecom provider,

we provided targeted short message services (SMSs) to consumers in subway trains. Recipients could

purchase the promoted service by responding to the SMS. The natural setting of subway trains with

different levels of crowdedness enables us to examine the effects of crowdedness. We use cellular

technology to record in real-time the number of mobile users located within each mobile-equipped

subway train. Our knowledge of the exact dimensions of the subway train enables us to determine

people’s spatial proximity to one another. We thus gauge crowdedness as the number of subway

passengers per square meter and test how crowdedness impacts mobile targeting effectiveness.

Our identification strategy relies on multi-faceted field data. Since it is extremely difficult to

manipulate crowdedness in a real world setting, multi-method approaches are critical to reduce the threats

of self-selection, endogeneity, and other potential confounds. Commuters may self-select into more or

less crowded trains as a function of their work schedule demands. The ideal test of the effects of

crowdedness would be to randomly assign commuters to crowded versus non-crowded trains, but this is

practically impossible to accomplish. Therefore, to reduce sample-selection concerns and establish

1 We define crowdedness as the physical social presence of others. We use crowdedness and crowding interchangeably.

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commuter homogeneity, we rely on five key strategies. First, our field data involves both peak and non-

peak hours with SMSs sent to different trains from the morning to the evening. Thus, we capture various

time cycles and control for systematic differences between different types of subway commuters. Second,

our field data comes from two different days: a weekday and the weekend. This is useful because

travelers during the weekdays may differ from those during the weekend, and this helps reduce concerns

of selection bias. Third, our corporate partner randomly sampled commuters from amongst our targeted

subway population using an instant computational and randomization procedure. This randomization

helps alleviate concerns about mobile users’ heterogeneity. Fourth, we demonstrate that in our field data

individuals are virtually similar in all aspects of mobile usage behavior. We do so by controlling for the

potential confounds of each individual’s mobile usage in terms of their monthly average revenue, call

time, messages, and data usage. In addition, we use same-train-same-time commuters and propensity

score matching methods to assure that we can isolate the impact of crowdedness conditional on commuter

homogeneity. Fifth, a further potential confound would be the existence of an unobserved variable that

drives both crowdedness and response to mobile targeting. To reduce this possible endogeneity threat, our

field study exploits an exogenous increase in crowdedness as a result of an unexpected traffic intervention

enforced by the government. This intervention was brought about by a high-security police escort for an

important politician, which created an exogenous spike in crowdedness within the subway trains. These

five steps help reduce sample selection threats and boost the confidence of the internal and external

validity of the results.

Therefore, these measures enable us to isolate the effect of crowdedness on mobile purchase behavior

when comparing mobile users in high versus low crowded environments. We find that consumer

responses vary by the level of crowdedness in the trains. In congested subway trains, purchase rates were

significantly higher than in uncongested ones. That is, consumers in more (versus less) crowded trains

have a higher likelihood of responding to targeted mobile promotions, conditional on commuter

homogeneity.

To furnish more robust evidence, we augment the field data with field surveys. Through its customer

call center, our corporate partner contacted mobile users who received and purchased the targeted

promotion, as well as those who received but did not purchase it. We matched the attitudinal surveys with

observed crowdedness and mobile purchase records. We not only confirm the effects of crowdedness on

purchases, but also explain that such effects arise at least partly due to mobile immersion. That is, in

crowded subway trains, commuters experience a loss of physical space. To psychologically cope with this

spatial loss and avoid accidental gazes, commuters escape into their personal mobile space (i.e., mobile

immersion). In turn, via this immersion, passengers become more involved in targeted mobile messages,

and consequently more likely to make a purchase. Also, the surveys help rule out alternative explanations

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(such as social anxiety, the desire to show off the latest smartphone devices, price sensitivity, and deal

proneness). Thus, we add further corroborating evidence with attitudinal measures that are unobserved in

the field data but observable in the field surveys.

Our contributions to the literature are two-fold. First, substantively, we investigate the unconventional

question of whether social crowding changes consumer behavior in the context of mobile targeting. To

our knowledge, no one has explored the effects of crowdedness on mobile purchases in the unique

marketing environment of subway trains. Our finding that crowdedness can positively affect mobile

purchase behavior is counter-intuitive. Indeed, much of the literature on crowdedness has largely

demonstrated negative effects, including avoidance behaviors and risk-aversion (Harrell et al. 1980;

Maeng et al. 2013). In the context of our subway setting, we find that targeting consumers in crowded

trains with mobile promotions may serve as a welcome relief. Hence, we enhance our understanding of

how crowdedness affects consumer receptivity to mobile promotions, which is an uncommon but pivotal

question with broad implications for mobile targeting in the context of consumers’ social environment.

Second, methodologically, we rely on a rigorous multi-method research design with field data and

attitudinal surveys. Traditionally, crowdedness is often difficult and impossible to measure in

conventional retail environments. Today, mobile technologies allow researchers to more precisely

measure crowdedness in real-time. Our field data is interesting due to the unique environment where

consumers can be individually targeted for promotions, and the level of crowdedness that can be

measured with a great deal of precision with modern mobile technology. We identity the effects of

crowdedness by controlling for self-selection across peak versus non-peak, weekday versus weekend, and

exogenous versus non-exogenous crowdedness. Also, our field surveys shed light on additional evidence

for why consumer response to mobile targeting varies as a function of crowdedness, evidence that is

based on consumer attitudes from the surveys and not directly observed in the field data.

Our findings have meaningful implications for mobile targeting practices. For managers, crowding

represents a novel consumer setting for gauging the effectiveness of context-sensitive mobile messages.

Because consumers may be more engaged with their devices in a crowd, such social environments may be

a good thing for marketers who can deliver information at the right moment. More specifically, public

transit commutes provide a unique marketing environment and thus a window of opportunity for

marketers to target mobile users. As cities facilitate mobile usage in underground subways (Flegenheimer

2013), and since consumers spend substantial amounts of time commuting during quotidian life,

marketing opportunities to target customers based on ambient factors such as crowdedness will abound.

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2. Literature on Mobile Marketing and Crowdedness

Our research builds on two streams of research. First, the literature on mobile marketing across

marketing, information systems, and management science is relatively nascent. Ghose et al. (2013) find

that consumers are more likely to click on links to stores located close to them and on higher-ranked links

on their mobile screens. This is due to consumers’ tendency to browse the mobile internet for their

immediate, local needs and the higher search costs associated with the smaller screen sizes of mobile

devices. Indeed, building on that study, in a study of location-based advertising with smartphone users,

Molitor et al. (2014) found that mobile coupon redemption rates increased the closer consumers were to a

store and higher the offer was displayed on the screen, conditional on the discount. Echoing this, Luo et

al. (2014) show how the location of consumers relative to a promoted venue and the time at which they

receive a promotion affects their mobile purchase likelihood due to the contextual benefits of having

enough planning and travel time. In terms of smartphone users’ geographic mobility, Ghose and Han

(2011) find that people are more likely to use mobile content than to generate it when they are travelling,

and that the variance of user’s traveling patterns is a stronger predictor of their propensity to engage with

the mobile device than the mean. Researchers have demonstrated how real-time mobile promotions

transmitted within grocery stores can increase shoppers’ travel distance and consequently boost their

unplanned spending (Hui et al. 2013). Scholars have also found that for mobile display advertising,

products higher on involvement and utilitarian dimensions generate more favorable attitudes and purchase

intentions (Bart et al. 2014). Another emerging stream of work uses randomized field experiments to

causally measure cross-platform synergies between web and mobile advertising (Ghose et al. 2014).

Researchers demonstrated that in-app advertisements decrease consumer demand for mobile apps (Ghose

and Han 2014). Environmental factors may also explain why consumers make mobile purchases. For

instance, Molitor et al. (2013) show how the weather impacts consumers’ mobile coupon choice.

Following this stream of research, we suggest the effectiveness of mobile targeting is context dependent.

A particular context in which mobile users may find themselves –often several times a day– is that of a

crowd. People may experience crowdedness while commuting, buying food, and shopping.

Second, our work is related to the literature on crowdedness. In sociology and psychology, studies

have linked crowdedness to physical and mental diseases and juvenile delinquency (Schmitt 1966).

Crowdedness can increase stress (Collette and Webb 1976), frustration (Sherrod 1974), and hostility

(Griffitt and Veitch 1971). Individuals may consider themselves more anonymous in crowds, which can

reduce their social interactions and fuel antisocial behavior (Zimbardo 1969). In more crowded areas, for

example, crime rates were found to spike due to the higher likelihood that criminals could avoid detection

(Jarrell and Howsen 1990). Scholars also suggest that in more crowded settings, people perceive less

control over their situations (Aiello et al. 1977). In the consumer behavior research, crowdedness has been

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found to induce avoidance behaviors and shorter shopping times (Harrell et al. 1980). Other research has

shown that crowding can threaten consumers’ sense of uniqueness, which may lead them to purchase

more distinctive products in order to restore their perceived individuality (Xu et al. 2012). Recently,

Maeng and colleagues (2013) demonstrated that crowded environments can spur shoppers to become

more risk averse and favor safety-oriented options such as visiting a pharmacy over a convenience store.

In our study, we integrate and advance these two streams of research on mobile marketing and

crowding. Specifically, our multi-method field data can precisely quantify crowdedness with mobile

technologies. Holding commuter type constant in terms of homogeneous mobile usage, we empirically

test whether consumer purchase behavior varies as a function of the level of crowdedness.

3. Field Data

To explore how crowdedness affects consumer response to mobile targeting, we use data from a field

study conducted in the subway system of a major city with the cooperation of a leading mobile telecom

provider (who wishes to remain anonymous). Our targeted population consisted of mobile users from a

city with a population of 20 million. Our field data comprises four parts of data, from 1) a business

weekday, 2) a weekend day, 3) exogenous crowdedness, and 4) follow-up surveys.

3.1 Method

In parts 1 and 2 of our field data, a total of 10,690 mobile users who rode the subway were randomly

selected to be sent a promotion through a text message and their responses were recorded. The SMS

advertised a missed call alert service, which if purchased would notify mobile users of the time and

number of the calls they missed. Our study took place in China in September 2013, where cell phone

plans are piecemeal rather than all inclusive. As such, mobile users must purchase a missed call alert

service separately in order to be notified of missed calls. The SMS read “Missed a call and want to know

from whom? Subscribe to [Wireless Service Provider’s] missed call alert service and be notified by SMS

of the calls you missed! Only ¥9 for 3 months! Get ¥3 off if you reply “Y” to this SMS within the next 20

minutes!” Our corporate partner offered a ¥3 rebate to incentivize mobile users to respond. For those who

responded, the cost was charged to their phone bills. In our study, since the average subway commute is

30 minutes, we restricted the reply time to 20 minutes to ensure that most commuters would respond to

the SMS while still in transit.

3.2 Measuring Crowdedness

We measured crowdedness as the number of subway passengers per square meters. We did so in step-by-

step fashion. First, in our study, the subway consisted of articulated trains modeled after accordion-style

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buses where the absence of doors between cars enables passengers to travel the length of the subway train

without restriction. Each subway train is composed of six articulated cars. We measured the volume of

mobile users in each subway train as the number of mobile users who are automatically connected to the

subway-tunnel cellular lines.2 Second, we calculated passenger volume. The mobile phone penetration

rate in major Chinese cities is very high. In the large city of our study, our corporate partner serves 70%

of the population. We thus derived passenger volume by dividing the mobile user volume by 0.7. Third,

we calculate crowdedness by dividing this passenger volume by the total area of each subway train. Each

subway car is 19 meters long and 2.6 meters wide, totaling 296.4m2 (6 cars x 19m length x 2.6m width).

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3.3 Threats to Internal Validity

An ideal way to test the effects of crowdedness would be to conduct a randomized field experiment.

However, this is virtually impossible because it would require randomly assigning commuters to more

versus less crowded subway trains. Commuters are not accustomed to being assigned which subway train

they may ride. In other words, it is quite difficult to manipulate the level of crowdedness in a field setting.

Thus, our identification relies on field data. However, the internal validity of results based on field data

are often threatened by alternative explanations. We discuss each threat in turn, along with how we

address them to provide greater confidence in the results.

3.3.1 Threat Due to Self-Selection

The self-selection threat arises when people self-select onto more or less crowded trains depending on

their work schedule demands. For instance, during hours when there is likely to be more crowding, there

may be a greater proportion of working professionals than during other times, when more seniors,

children, and homemakers use the subway. If this is the case, the observed effects might well reflect

differences in behavior between people who travel during crowded rush hours versus other times, than the

effect of crowdedness per se. That is, purchases may be driven by variations in commuter type rather than

crowdedness. Thus, to reduce the concern of self-selection we deal with this threat in four specific ways.

First, crowdedness is more likely to occur during peak hours of travel. To isolate the effects of

crowdedness from the peak hours, we have field data across both peak and non-peak hours of

crowdedness. We do so by sending SMSs from morning to evening. For each day of our study, we

2 The wireless service provider can identify all mobile users’ phone numbers, including those of phones that are off (due to

telecommunication company chip-tracking technology). Thus, both the phones that are on and those that are off are recognized

and recorded. According to our corporate partner, over 99.9% of phones are on during the day.

3 Since crowdedness differs from city to city and culture to culture (e.g., cities with a high population density such as Hong Kong

are very different from those with a low population density such as Auckland), tolerance for crowdedness may also vary. Because

the Chinese are more tolerant of crowdedness given their large population, the effects of crowdedness in our field study may be

more conservative.

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selected five different times to represent the five cycles people may experience during an average day.

Each time cycle represents a peak or non-peak hour of crowdedness and thereby potentially has a

different level of crowdedness. The first time cycle was from 07:30 - 08:30 and represents the morning

rush hour. The second was from 10:00 - 12:00, representing the lull after the morning rush hour traffic.

The third was from 14:00 - 16:00, the afternoon lull before the evening rush hour traffic. The fourth was

from 17:30 - 18:30, the evening rush hour. And the fifth was from 21:00 - 22:00, the after-dinner traffic.

Because time-cycles are nested within the trains, we control for subway train effects to obtain a more

finer-grained analysis. Thus, within the five time cycles, the corporate partner pushed SMSs to 14 subway

trains each day of the weekday and weekend parts of the study. These five time cycles nested in the 14

subway trains help control for the systematic differences between various types of subway commuters,

i.e., business commuters at rush hour versus non-business commuters at non-rush hours.

Second, crowding may differ across weekdays and weekends. On weekdays, work schedules might

force business travelers to self-select into crowded trains during rush hours, while non-business travelers

(e.g., retirees and homemakers) might self-select into non-crowded trains in lull hours. Also, on weekends,

leisure habits enable people to self-select into more or less crowded trains. Therefore, to isolate the effects

of crowdedness from different types of commuters across the weekday and weekend, we have field data

from two different days: a weekday (Wednesday) and a weekend (Saturday). These weekday and

weekend results help control for the systematic differences between weekday and weekend crowdedness

in our field data.

Third, although we do not have random treatment of crowdedness, in conjunction with our corporate

partner, we randomly selected subjects from among our targeted subway population in order to randomize

away user heterogeneity. Our targeted population had not previously subscribed to a missed call alert

plan, nor had it received a similar SMS from the corporate partner. This also helps to rule out potential

carryover effects of prior marketing campaigns. For example, if passengers have already received a

similar mobile promotion, it is possible that promotion repetition or fatigue drives the results. For each

user from this targeted subway population, we assigned a random number. We used SAS software’s

random number generator and ran the RANUNI function, which returns a random value from a uniform

distribution (Deng and Graz 2002). We then sorted the random numbers in sequence and extracted a

sample from the sequence. We integrated each of these steps into an algorithm in the wireless provider’s

IT system. This algorithm allowed us to compute and randomize users instantly in order to accurately

gauge the level of crowdedness while we sent the SMSs in real-time. Because the wireless service

provider maintains purchase records of all of its clients, we were able to know immediately whether a

given mobile user in the subway train had previously subscribed to the promoted service. Such dynamic

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but instant computation and randomization are difficult to execute and require privileged support from the

corporate partner, thereby representing a unique feature of our field data.

Fourth, crowdedness may impact passengers differently depending on their mobile usage habits. For

example, users with more expensive phone bills might be more likely to purchase the missed call alert

package since they might connect with more people. Since our targeted purchasing behavior is for a

missed call-alert package, mobile usage can be reasonably expected to serve as good controls.

Specifically, we control for each mobile user’s wireless behavior, based on their monthly bills, call

minutes used, SMSs sent and received, and internet data usage.4 In the cell phone industry, ARPU, MOU,

SMS, and GPRS comprise key indicators of wireless usage behavior. ARPU (the average revenue per

user) is the revenue that one customer’s cellular device generated. MOU (individual monthly minutes of

usage) is how much voice time a user spent on her mobile. ARPU and MOU can help control for

customer heterogeneity, since business travelers are more likely to have a higher ARPU and MOU. In

addition, SMS (short message service) is the amount of monthly text messages sent and received. SMS

can also help tease out the effects of consumer age as younger generations are more prone to SMS usage.

GPRS (general packet radio service) is a measure of the individual monthly volume of data used with the

wireless service provider. GPRS is useful in terms of controlling for traveler habits in using the mobile

web and downloading mobile content. Table 1 provides summary statistics of these variables in natural

log to reduce the non-normality of the data. Specifically, panel A provides the statistics for the full sample

of these variables, panel B provides them for the combined part 1 and part 2 data (weekday and weekend

sample), and panel C provides them for the part 3 data (exogenous crowdedness sample). Appendix A

presents a histogram of ARPU for the full sample.

[Insert Table 1 Here]

Furthermore, we adopt two approaches to correct for selection bias and validate commuter

homogeneity. First, we analyze the effect of crowdedness with a subsample of the same-train-same-time

users. It is reasonable to expect that users in the same train at the same time during the weekday are

homogenous in their work schedules, i.e., similar types of passengers. Different types of passengers

would select different trains and different times to board. If so, analyzing the same-train-same time

mobile users helps establish commuter homogeneity. In addition, we used propensity score matching. We

mirrored randomization by matching the probability of mobile users entering the exogenous crowd versus

the non-exogenous crowd. In other words, using propensity score matching, our field data would be

equivalent to experimental randomization by transforming the field data into a quasi-experiment design

(Huang, Nijs, Hansen, and Anderson 2012; Rubin 2006). Thus, matching the treatment group with the

4 Government regulation prohibits the wireless provider from revealing customers’ private information so we are not able to

identify user demographics such as disposable income. However, income is not relevant in our context because the promoted

product costs about 50 cents per month.

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control group in terms of their mobile usage behaviors enables us to better isolate the treatment effects of

crowdedness. This enables us to demonstrate that individuals who are virtually similar in all respects that

are measurable due to mobile usage behaviors vary their response to mobile targeting as a function of the

level of crowdedness.

3.3.2 Threat Due to Endogeneity

This threat occurs when the crowd is endogenous and when some unobserved variable may drive both

crowdedness and purchases. We deal with this threat by exploiting an exogenous shock to crowdedness.

Specifically, in part 3 of our field data, exogenous crowdedness resulted from a sudden variation in

crowdedness in the subway system. The local government temporarily controlled vehicular traffic for an

hour on a weekday. Because the traffic intervention was enforced to provide a high-security police escort

to government personnel, denizens were not forewarned. Thus, the temporary traffic intervention

exogenously changes the level of crowdedness within the subway trains, creating an exogenous spike in

crowdedness.5 Thus, our field data have consumer responses from both types of crowdedness; exogenous

and non-exogenous. If both types of crowdedness obtain directionally similar results, this would reveal

more evidence for the robustness of the crowdedness effect.

During the traffic intervention, the wireless provider sent SMSs to passengers on subway trains. We

promoted a different product category to help generalize the findings.6 The promoted service enabled

users to stream videos on their mobile devices. The SMS read “To watch the newest videos on your

mobile anytime, anywhere, for only 3¥ per month, reply KTV3 now and get 3¥ off of your next month’s

bill!” For mobile users who responded with the provided mobile short code, the cost was charged to their

phone bill. We measured crowdedness in the same manner and selected mobile users who had not

subscribed to this video service.

3.3.3 Threats Due to Other Factors

Several other factors may also threaten the validity of our results. We briefly explain them and how we

dealt with them. Differences in subway stations might present a threat. For example, some subway

stations have food stands and newspaper stands, while others do not. Also, passengers boarding at stations

5 By exogenous, we refer to the city’s denizens, rather than the corporate partner. In other words, the commuters did not know

ahead of time about the traffic intervention. An alternative way to derive exogenous crowdedness is to exploit train delays.

However, a single train delay would not be long enough to create a sufficient increase in crowdedness because every 3-5 minutes

a train enters a station. Also, a longer train delay is normally not feasible except for tunnel accidents or bomb threats (which

would create an opposite effect by forcing more people to use above-ground transit). Thus, while imperfect, this exogenous

crowdedness via the traffic intervention can also better control for self-section.

6 In the non-exogenously crowdedness sample, we promoted a missed call alert service. Yet, one may speculate that the presence

of many social others in the subway may prompt users to be more likely to subscribe to a missed call alert. Thus, in the sample of

exogenous crowdedness, we promoted a different service that has the same purchase price.

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farther along a subway line experience less commute time than those who board earlier along the route. If

we sent mobile promotions to commuters located at different stations and traveling in different directions,

it is possible that station differences and observable commute length would affect the results. To reduce

these concerns, we pushed the SMS from only the fourth stop along the subway line. This helped to

ensure that enough passengers had boarded the train since the first station, but that they also still had a

sufficient distance to travel to stations further along the line. Also, the direction in which passengers

travel may also have an effect. If we sent mobile promotions to commuters traveling in different

directions, it is possible that commute direction would affect the results. Thus, we sent the SMS to trains

travelling in a single direction towards the city center.

Taken together, the above steps help reduce concerns of selection, endogeneity, and other threats, and

thereby help strengthen the case that we measure the effects of crowdedness.

3.4 Model

Our model estimates the purchase likelihood of each mobile user as . We

model the unobserved likelihood of making a mobile purchase as a logit function of crowdedness in

subway trains. Following Agarwal et al. (2011, p. 1063) and Guadagni and Little (2008), we assume an

i.i.d. extreme value distribution of the error term in the logit model:

=

( )

Crowd

iU = l+ l × crowdednessi +

l × subway_traini + l× Xi + i1

l, (1)

where Crowd

iU denotes the utility of a mobile purchase, l accounts for the unobserved fixed effects in

mobile users’ preferences for subway trains, and Xi is a vector of mobile user controls (natural logs of

ARPU, MOU, SMS, and GPRS). i is comprised of the idiosyncratic error terms. tests the effects of

crowdedness on mobile purchase probability after controlling for mobile user behaviors and subway train

fixed effects. We separate the effects of crowdedness from time cycles of the day by using subway train

fixed effects. Because subway train effects provide a finer-grained analysis of crowdedness than time

cycles (each time cycle contains several subway trains), we control for the effects of subway trains rather

than time cycles. Nevertheless, additional analyses show our results are robust to controlling for time

cycles. Furthermore, besides the train effects, crowdedness can also be related to weekday or weekend

effects (weekends and weekdays are expected to have different crowdedness patterns), so we further

identify the effects of crowdedness by controlling for weekday effects(m):

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Crowd

iU = m+ m × crowdednessi + m× weekdayi + m

× subway_traini + m× Xi + i2 m

. (2)

Of key interest to us is the effect () of crowdedness on mobile purchase likelihood.

3.5 Results

3.5.1 Preliminary Evidence for the Effects of Crowdedness on Mobile Purchase Likelihood

We begin by reporting the response rate in our field data. Specifically, of the 10,690 mobile users who

received an SMS, 334 of them replied and purchased the promoted service, corresponding to a 3.22%

response rate in part 1 and 2 of our data. Of the 1,270 users who received an SMS during the traffic

intervention, 37 of them replied and purchased, equating to a 2.91% response rate in part 3 of our data.

These response rates seem low, but are fairly high compared to the 0.6% response rate for mobile

coupons in Asia (eMarketer 2012) and the 1.65% rate for the effectiveness of location-based mobile

coupons (Molitor et al. 2013). Appendix B presents the crowdedness and purchase rate per train.

A naïve way to test the effects of crowdedness on mobile purchase is to conduct analyses with

different samples. If the results for each sample of crowdedness in our data are statistically significant,

this provides initial evidence for the effect of crowdedness on mobile purchase. Table 2 presents this

preliminary evidence, after controlling for weekday versus weekend effects and peak hour versus non-

peak hour effects. In all models of Table 2, we first entered the four mobile usage covariates to control for

user heterogeneity in terms of observable mobile usage behaviors. We then entered the crowdedness of

each sample from our data in turn. As shown in Table 2, the effects of crowdedness are statistically

significant across the sample without the traffic intervention (non-exogenous weekday and weekend

crowdedness combined), the sample with the traffic intervention (exogenous crowdedness), and the full

sample consistently (all p < 0.05). As depicted in Figure 1, mobile purchase likelihood increases as a

function of crowdedness in the non-exogenous sample. As depicted in Figure 2, mobile purchase

likelihood increases as a function of crowdedness in the exogenous sample as well. This pattern gives

initial evidence for the effect of crowdedness.

[Insert Table 2, Figure 1, and Figure 2 Here]

3.5.2 Main Evidence for the Effects of Crowdedness on Mobile Purchase Likelihood

The preliminary evidence simply focuses on variations within the non-exogenously crowded commuters

alone, within the exogenously crowded commuters alone, or within the full sample of commuters. Yet,

the results may be confounded by sample selection and endogeneity threats. As commuters may self-

select into different trains, the crowd may be endogenously induced, and some unobserved variable may

drive both crowdedness and purchases and hence drive the results in all the samples. Thus, we provide

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evidence in dealing with these threats by exploiting an exogenous shock to crowdedness. Specifically, we

pool the exogenous traffic intervention sample and the non-exogenous sample, and then add the

interaction between the traffic intervention and crowdedness. This interaction identifies the difference in

behavior between commuters in the exogenously crowded trains and those in the non-exogenously

crowded trains. If this interaction is significant, that would be a stronger test of the impact of crowdedness

because this impact is driven by the exogenous crowdedness free from endogeneity bias. Thus we

developed the following model to identify that variations in mobile purchases stem from variations in

crowdedness that are exogenously determined:

Crowd

iU = o+ o × crowdednessi +

o× traffic interventioni +o× crowdednessi × traffic interventioni

+ o× weekdayi + o × subway_traini + o

× locationi + o× Xi + i3 o, (3)

where the traffic intervention is a dummy variable (coded as = 1 for users in the exogenous crowd in part

3 of our field data and = 0 for users in the non-exogenous crowd in parts 1 and 2 of our field data).

Because the traffic intervention created exogenous crowdedness in two specific subway stations more

than in other stations, we sent the SMS to subway passengers passing through these two affected stations.

Therefore, we added a location variable in model 3. We also checked that the average crowdedness with

this exogenous shock during the traffic intervention hour (14:00 - 15:00 during the business week on

Thursday) in part 3 was 3.257 passengers/m2. This is indeed substantially higher than the counterpart

(2.01 passengers/m2 in Appendix B) without the exogenous shock in part 1 in the same hour, as expected.

We report the results in Table 3. In model 1 we entered the mobile covariates to control for user

heterogeneity in terms of observable mobile usage behaviors. In model 2 we then entered the traffic

intervention variable to control for differences in commuters in terms of exogenous crowdedness. In

model 3 we entered the crowdedness variable, and in model 4 we entered the interaction between

crowdedness and the traffic intervention. As shown in Table 3, model 2, the traffic intervention dummy

was negative and insignificant, which makes sense because the traffic intervention itself should not lead

to a higher purchase likelihood. As shown in model 3 and model 4 of Table 3, crowdedness significantly

boosts mobile purchase likelihood, after controlling for mobile usage behaviors (p < 0.05). Most

importantly, the interaction between crowdedness and the traffic intervention was positively significant.

This confirms that variations in mobile purchases are indeed significantly driven by variations in

crowdedness that are exogenously induced.


Even with the exogenously induced crowd, we cannot rule out the possibility that mobile purchases

may be driven by different types of passengers in subway trains. In other words, to confirm that it is

crowdedness that drives mobile purchases, we have to rule out the alternative explanation of user

14

heterogeneity (or establish user homogeneity). An ideal test would be to have varying levels of

crowdedness within the same train and time periods with virtually similar commuters so that we can

isolate the crowdedness effect, conditional on user homogeneity. We do so via two approaches. First, we

compare the effect of crowdedness with passengers riding the same train in the same time periods across

two weekdays. We believe that same-train-same-time users during the weekday would be homogenous. In

other words, different commuters would select different trains and different times to board. Thus, by

pinpointing same-train-same-time users, we can control for commuter heterogeneity. Our field data had a

total of 1,550 same-train-same-time users (1,018 users from the non-exogenous weekday sample and 532

users from the exogenous traffic intervention sample that also happened during a weekday). We report the

results with the same-train-same-time homogenous sample in Table 4. In model 1 we entered the mobile

covariates to control for user heterogeneity in terms of observable mobile usage behaviors. In model 2 we

entered the traffic intervention variable to control for differences in commuters in terms of exogenous

crowdedness (traffic intervention dummy = 1 for same-train-same-time users in the exogenous crowd and

= 0 for the same-train-same-time users in the part 1 weekday sample of our field data). In model 3 we

entered the crowdedness variable, and in model 4 we entered the interaction between crowdedness and the

traffic intervention. As shown in Table 4, again, the traffic intervention dummy was not significant. Also,

we again find that crowdedness significantly affected mobile purchase, after controlling for mobile usage

behaviors (p < 0.05). Importantly, the interaction between crowdedness and mobile user behaviors was

significant. This confirms that variations in mobile purchases are indeed driven by crowdedness, when

considering the same-train-same-time homogenous users.


Still, one may worry that there might be systematic differences among the same-train-same time

passengers in terms of their mobile usage behaviors. Thus, we use propensity score matching to further

establish user homogeneity (Huang, Nijs, Hansen, and Anderson 2012; Rubin 2006). In our analyses, the

propensity score is the probability that a mobile user will enter an exogenously-crowded train (treatment

group) versus the probability that a mobile user will enter a non-exogenously crowded train (control

group) given the value of her observed mobile usage behavior covariates. That is, by using the covariates

of mobile usage behaviors, we can pair similar people in the exogenous crowd (treatment) with those in

the non-exogenous crowd (control). This helps to ensure that, accounting for mobile usage behavior

covariates, the assignment of commuters to treatment or control crowding is independent of crowdedness

outcomes (Rosenbaum and Rubin 1983). In other words, when the propensity scores of mobile users in

the two types of crowdedness (treatment and control) are identical, mobile users are equally likely to

experience treatment crowdedness, since the values of the confounding covariates indicate an equal

chance (Rubin 2006).

15

We derive propensity scores using logistic regression, in which the dependent variable = 1 if a

commuter enters an exogenously-crowded train and = 0 if otherwise (all commuters here are the same-

train-same-time users). We then match the propensity scores and are thus able to compare commuters

who are virtually similar to each other in the treatment group with those in the control group. In turn,

matching these two groups allows us to isolate the treatment effects of crowdedness conditional on user

homogeneity. Appendix C reports the logit estimates of the propensity score matching. We have a total of

782 matched passengers (391 matches of the treatment and control groups) from the 1,550 same-train-

same-time users, on the basis of the propensity score matching. As shown in Figure 3, before the nearest

neighbor matching, the propensity score distributions between the treatment and control group are quite

different in the histogram plots. However, after matching, the distributions are virtually identical between

the two groups as shown in Figure 4. Thus, propensity score matching assures that the commuters are

virtually similar to each other, suggesting that we have established user homogeneity to the extent

possible.

[Insert Figures 3 and 4 Here]

We then test our results of crowdedness effects. As shown in Table 5, model 1, we entered the mobile

covariates. In model 2 we entered the traffic intervention variable to control for differences in commuters

(traffic intervention dummy = 1 for the nearest neighbor who matched users in the exogenous

crowdedness and = 0 for matched users in the part 1 weekday sample of our field data). In model 3 we

entered the crowdedness variable, and in model 4 we entered the interaction between crowdedness and the

traffic intervention. Results in Table 5, again, confirm that the traffic intervention dummy was not

significant. Also, crowdedness significantly affected mobile purchase. Importantly, conditional on user

homogeneity, the interaction between crowdedness and mobile user behaviors was significant (p < 0.05).

As such, this adds more confirming evidence that variations in mobile purchases are indeed driven by

crowdedness, even after employing propensity score matching to derive virtually similar passengers.


3.5.3 Robust Evidence with Residual Crowdedness Approach

Additionally, we test the robustness of the results with a residual crowdedness, which provides a measure

of variation in crowdedness independent of any observed mobile usage behavior (see an example of

residual approach in Luo, Rindfleish, and Tse 2007). There are two additional models involved. In the

first model, we regress crowdedness on mobile usage and obtain the residual crowdedness as the error

term (i4 p).

iCrowd = p+ p ×ARPUi +

p× MOUi +p× SMSi +

p× GPRSi + i4 p. (4)

16

Because this residual crowdedness (i4 p) is orthogonal to mobile usage behavior, it is free from any

confounding bias of commuter heterogeneity in terms of mobile usage covariates. In the second model,

we use this residual crowdedness to determine mobile purchase likelihood.

Crowd

iU = q+q × residual crowdednessi +q× traffic interventioni +

q× residual crowdednessi ×

traffic interventioni + q× weekdayi + q

× subway_traini + q × locationi + q× Xi + i5

q. (5)

We report our results of residual crowdedness in Table 6. In panel A, we regress crowdedness on mobile

usage behaviors; crowdedness is thus our dependent variable of interest in that model. From this first

regression, we use the error term as the residual crowdedness in panel B. In panel B, our dependent

variable of interest is mobile purchase. In Panel B, model 1, we enter mobile usage behaviors. In model 2

we enter the traffic intervention and residual crowdedness variables, and in model 3 we enter the

interaction between residual crowdedness and traffic intervention. Finally, in model 4 we enter the

interactions between residual crowdedness and the weekend variable, and between residual crowdedness

and the peak hour variable. As shown in Table 6, model 2, residual crowdedness significantly affected

mobile purchase (p < 0.05). Also, as shown in model 3, the interaction between residual crowdedness and

traffic intervention was significant. Thus, again, we find that variations in mobile purchases are indeed

driven by crowdedness. Moreover, in Table 6, model 4, the results show no significance of the interaction

between residual crowdedness and either the weekend or the peak hour variable. These further confirm

that effects of crowdedness on purchases are not driven by either the weekend or peak hour variable, but

rather by the exogenous crowdedness induced by the traffic intervention. As such, this residual approach

provides further empirical evidence that after considering all observed variables to establish user

homogeneity, mobile purchase likelihood is a function of crowdedness.


3.5.4 Evidence with Falsification Tests

We conduct several checks of falsification tests for the results. First, if crowdedness has an effect on

mobile purchase likelihood, then we should only see this when crowdedness exceeds a certain threshold

level. For example, fewer than 2 passengers per square meter is not truly representative of crowdedness.

Thus, we should not see effects of crowdedness on mobile purchase for such a low threshold of

crowdedness. We report our results in Table 7. In model 1 we entered the mobile covariates to control for

user heterogeneity in terms of observable mobile usage behaviors. In model 2 we entered the crowdedness

variable, and in model 3 we entered the interaction between crowdedness and each mobile user behavior

17

covariate. As shown in Table 7, model 2, we indeed find no significant effect of crowdedness on mobile

purchase when crowdedness is less than 2 passengers per square meter, as expected. This suggests a lower

boundary threshold of crowdedness effects. Put differently, for crowdedness to have an effect, there must

be at least 2 or more passengers per square meter. In this sense, our findings passed the lower boundary-

based falsification test. Note that in our setting, subways were not packed to capacity (such as the human

sardine-type of commutes in Tokyo, with up to 11 passengers/m2). In that case, it is logical to speculate

an upper boundary effect of crowdedness: too dense a crowd would negatively affect mobile purchases

because congestion would restrict the ability to use a mobile phone. However, we checked the squared

and cubic terms of crowdedness, but fail to find either of them significant (p > 0.10).


The second falsification check we conduct is to enter other interactions to check if any interaction

other than the traffic intervention will have a significant impact on mobile purchase. For example, it is

possible that mobile users with higher monthly bills in exogenously crowded trains may be more prone to

make a mobile purchase. As shown in Table 7, model 3, we fail to find any significant interaction effect

between mobile usage behaviors and crowdedness on mobile purchases. Thus, variations in mobile usage

behaviors do not account for the effects of crowdedness on mobile purchase. Overall, these two checks

provide falsification test support for the robustness of our results.7

3.5.5 Additional Evidence and Robustness Checks with Field Surveys

To further establish results robustness, our corporate partner conducted follow-up telephone surveys.

Specifically, the wireless provider surveyed mobile users who had received the promotional SMS. The

key purpose of these field surveys is two-fold. First, per our multi-method research design, we can

complement the field data with measurable but unobserved factors such as deal proneness and price

consciousness that may also affect mobile purchase. Second, a field survey enables us to identify the

psychological reasons for why crowdedness affects mobile purchase, after matching the purchase records

and measured crowdedness from the field data with the survey responses.8

We ensured that the corporate partner took several steps to increase the credibility and validity of the

survey results. First, the wireless provider used its customer service call-center to conduct the telephone

surveys. Second, actual commuters who received the SMS promotion were surveyed. Third, the surveys

7 Besides the logit model, we ran a linear probability model, as well as generalized linear models (binomial family) with robust,

HAC, and Jackknife standard errors across iterated/reweighted least squares and maximum likelihood (Chen and Hitt 2002, Kim

et al. 2002). These additional analysis results suggest that crowdedness increases the likelihood of mobile purchases (p < 0.01).

Also, we used the bootstrapping method with 5,000 resampling of the full dataset. Again, the coefficient of crowdedness is

consistently positive and significant, supporting that the effect of crowdedness is robust to bootstrap resampling as well.

8 There are advantages of conducting the survey through our corporate partner. Respondents are more likely to respond to the

service provider, as opposed to researchers, because they are customers with a vested interest in providing honest answers.

18

were conducted on the day after the weekday and weekend (parts 1 and 2 of our field data). We selected

the following day because the field studies were completed after 22:00 at night, rendering it too late to

call respondents. Fourth, to avoid demand effects, the survey was presented as intending to assess

satisfaction with the subway wireless signal. In this vein, the survey contained satisfaction questions as

well. Similar to lab experiments that rely on crowdedness primes (Maeng et al. 2013), our field survey

relies on reminding commuters of the crowdedness of their environment when they made their mobile

purchases to elicit context-specific responses. Thus, we asked users to reflect on when they received and

purchased the SMS on the subway the preceding day in order to remind them of the crowdedness they

experienced. Respondents were asked to confirm whether they had received the promotional SMS while

in the subway and whether they had read and replied (or not) to it while in the subway. We randomly

sampled a total of 300 mobile users, of which 180 had received and purchased the promoted service, and

120 had received but had not purchased it.

Table 8 shows that 240 of 300 mobile users responded to the survey, an initial response rate of 80%.

Of these respondents, 235 acknowledged having read the SMS while in the subway train, as opposed to

outside the subway train. This represents a 78.3% valid response rate.


We report the results in Table 9. In model 1, we entered mobile usage behavior as covariates, as well

as survey responses to questions related to preference for the promotion, perceptions of missed call

frequency, a prevention focus, deal proneness, and price consciousness. In model 2 we entered the

crowdedness variable from our field data. In model 3 we entered the immersion, social mimicry, social

anxiety, downtime, and showoff variables from the survey responses. As shown in Table 9, model 1, only

preference for the package was significant (p < 0.05). This makes sense, since people who like a product

are more likely to make a mobile purchase, regardless of the crowdedness of their environment. This is an

important attitudinal based factor that is not controlled for in our in our field data (as it is not observable).

Thus, our survey design added further evidence beyond the field data. The results of model 2 show that

crowdedness had a significant effect on the likelihood of mobile purchase (p < 0.05). This corroborates

our field data evidence for the effect of crowdedness on mobile purchase likelihood.


Interestingly, as shown in model 3, mobile immersion had a significant impact on mobile purchase,

while crowdedness remains significant, albeit with lower magnitude in effect size (all p < 0.05). Also, via

bootstrap mediation tests (Preacher and Hayes 2004), we find that crowdedness is positively related to

mobile immersion (0.465, p < 0.01), which in turn is positively related to involvement (1.375, p < 0.01).

In model 4, involvement is positively related to mobile purchase (p < 0.05). These findings support the

mobile immersion explanation of the crowding effect. Specifically, in a crowd, commuters perceive a loss

19

of personal physical space, so they cope by escaping into their personal mobile space. Put differently, a

crowd reduces interpersonal distance and invades individual comfort zones. Commuters may compensate

for this threat to their feeling of ease by escaping into their personal world.9 Because mobiles devices are

an extension of people’s personal world (i.e. contain private, personalized content), commuters may seek

psychological immersion and experience higher involvement with their personal mobile devices. In this

sense, immersion into personal mobile space and higher involvement with their cell phones can help

commuters attain a sense of comfort and relaxation in a crowded subway. In turn, the more consumers are

involved with their personal mobiles, the more likely they will be to consider mobile promotions and

make a purchase (Petty et al. 1983).

Besides supporting this mobile immersion explanation, the surveys rule out several additional

alternative explanations for our findings. Specifically, we conducted in-depth interviews with our

corporate partner executives and a focus group to investigate other possible explanations. As reported in

models 3 and 4, we find no significance of these alternative explanations in terms of social anxiety,

prevention mindsets, price consciousness, deal proneness, and mobile usage behavior. Appendix D

presents the survey instrument adopted by our corporate partner.

In summary, both field data and survey results demonstrate a positive and significant effect of

crowdedness on purchases. This effect is robust to accounting for peak versus non-peak hours, weekday

and weekend effects, train fixed effects, mobile user behaviors, exogenous crowdedness, propensity score

matching, residual crowdedness, subway station effects, traffic direction effects, and attitudinal factors

unobserved in the field data but measured in surveys sponsored by the corporate partner.

4. Conclusion

This study investigates whether crowdedness affects consumer responses to targeted mobile messages.

Today, mobile technology provides novel measures of crowdedness and enables the ability to gauge a

consumer’s social environment in terms of crowdedness.

We collect field data by sending SMS messages to mobile users in subway trains. In the unique

marketing environment of our data, the underground subway was equipped with subway-specific cellular

lines that run along the tunnel walls. We measured crowdedness as the volume of mobile users in each

subway train who are automatically connected to this underground line. Our finding is based on

crowdedness that is exogenously impacted by a government-mandated traffic intervention. The temporary

traffic intervention created a natural variation in crowdedness for subway commuters. This enabled us to

identify whether purchase variation stemmed from passenger heterogeneity or variation in the level of

9 The psychology literature (Worchel and Teddlie 1976) notes that crowding decreases interpersonal distance, which boosts the

likelihood of accidental touching and catching others’ gazes, which produces stress or arousal (Evans and Wener 2007, Freeman

1975; Schaeffer and Patterson 1980).

20

crowdedness. We use two methods (same-train-same-time users and propensity score matched users) to

ensure that passengers were homogenous during the hour in question. Conditional on passenger

homogeneity, the traffic intervention proffers more robust evidence that purchase incidence is driven by

exogenous variation in crowdedness. Further analysis with field surveys by our corporate partner not only

validates our results with attitude-based measures but also reveals a mobile immersion account for why

crowdedness influences mobile purchases. That is, people may compensate for the loss of personal

physical space in a crowd by escaping into their personal mobile space. This then boosts consumer

involvement with mobile devices and, thus, mobile purchase likelihood. Thus, this multi-facet

identification strategy with the traffic intervention more precisely (1) accounts for self-selection and (2)

isolates the effects of crowdedness on mobile purchases. Because we find that crowdedness has a positive

effect on mobile purchase likelihood, subway crowds may be a good thing for mobile targeting.

Our results offer several theoretical and managerial implications. Theoretically, we help deepen

understanding of consumer reactions to crowdedness. The consumer behavior literature reveals how

people react to crowdedness. Crowdedness can trigger nervousness and decrease creativity (Maeng et al.

2013). In retail settings it can elicit negative attitudes towards the store (Hui and Bateson 1991).

Crowdedness has also been shown to induce avoidance behaviors and prompt shoppers to rely more on

familiar brands, skirt interaction with store employees and others, and reduce shopping time (Harrell et al.

1980). Overall, these studies have largely shown the negative effects of crowdedness. However, we find

that targeting consumers in crowded environments with mobile promotions may serve as a welcome

relief. That is, in the context of consumers’ social environment, our research reveals positive effects of

crowdedness on mobile promotions.

We also extend the mobile commerce literature by showing that social contexts such as crowdedness

in public transit are significant determinants of mobile marketing effectiveness. Specifically,

environmental crowdedness affects consumer receptivity to mobile messages. We advance prior research

on mobile targeting (Danaher et al. 2013, Dickinger and Kleijnen 2008) by revealing the new contextual

dimension of crowdedness, a novel way to target mobile users. For marketers, the ability to reach

consumers anytime, anywhere, offers substantial opportunities (Kim et al. 2011). The potency of mobile

messages hinges on understanding consumers’ real-time experiences and contextual environment (Kenny

and Marshall 2000). Indeed, marketers must comprehend “how customers behave with respect to the

mobile medium” (Shankar and Balasubramanian 2009, p. 128).

The average public transportation commute time for Americans is 48 minutes each way (McKenzie

and Rapino 2011). This considerable amount of consumer down time during daily commutes can be a

gold mine for marketers. Grocery retailers in countries such as South Korea have created virtual stores in

underground subways by superimposing product images with QR codes over the platform walls. While

21

waiting for their train, commuters can purchase groceries on their mobiles and have them conveniently

delivered (Solon 2011). In the Netherlands, infra-red sensors inside trains are used to send mobile alerts to

boarding passengers about each car’s occupancy in order to improve commuting efficiency (Wokke

2013). Indeed, marketers are adopting new mobile technologies such as iBeacon (a short-distance pulsing

device that operates on the Bluetooth low energy of any iOS7 device). Retailers can send within-store

mobile promotions to connect with shoppers in real-time. Sports stadium and entertainment venues have

also adopted iBeacon to help spectators find their seats and receive concession-stand offers (Grobart

2013). Understanding consumers’ quotidian routines and contextual activities enables marketers to deliver

relevant messages that cater to customer needs in the moment. Consumers are receptive to receiving

“messages from particular brands at specific times and under certain conditions” (Johnson 2013). To the

extent that consumers have greater mobile involvement in a crowd, mobile marketing might have higher

effectiveness in crowded settings with banner ads and apps (Bart et al. 2014).

However, there is a catch. Although crowdedness is an inherent part of everyday life, marketers

should acknowledge that personal space preferences and tolerance for crowdedness may vary by settings

(subway, concert hall, and restaurant). For example, crowded restaurants more likely comprise self-

selected attendance and can boost people’s feelings of excitement or signal quality (Knowles 1980, Tse et

al. 2002). In that context, targeting consumers on their personal devices may be construed as an annoying

and unwelcome invasion of privacy. In contrast, our setting less likely involves self-selecting into desired

crowds because public transit often denotes commutes with strangers without choice. Also, in our studies,

consumers could purchase and activate the promoted service instantly. Future research could investigate

different consumption settings to reveal more nuanced effects of crowdedness. Research could also study

whether differences in product conspicuousness matter when targeting consumers in crowded versus non-

crowded settings. Finally, future research may examine whether consumers can regulate their negative

moods such as stress and anxiety in crowded environments by purchasing hedonic goods or services.

In conclusion, this research serves as an initial step to quantify crowdedness with mobile technologies

and identify the effects of crowdedness on purchases. Managers may consider gauging the crowdedness

of a consumer’s social environment as a new way to boost mobile targeting effectiveness.

22

Table 1: Summary Statistics of Users’ Wireless Usage Behavior Panel A: Full Sample (N = 11,960)

Mean S.D.

Percentile

10% 20% 25% 30% 40% 50% 60% 70% 75% 80% 90%

Ln(ARPU) 3.9549 0.5634 3.2742 3.4689 3.5553 3.6326 3.7815 3.9198 4.0592 4.2203 4.3118 4.4182 4.6863

Ln(MOU) 5.8269 1.2013 4.5539 5.0814 5.2627 5.4116 5.6937 5.9402 6.1841 6.4378 6.5779 6.7332 7.1229

Ln(SMS) 5.4453 1.0043 4.4427 4.9712 5.1299 5.2627 5.4806 5.6490 5.7777 5.9135 5.9915 6.0776 6.3208

Ln(GPRS) 8.9496 2.5195 6.3989 7.8910 8.3028 8.6857 9.2930 9.7031 10.0607 10.3697 10.4589 10.5841 10.9690

Panel B: Weekday and Weekend Sample (N = 10,690)

Mean S.D.

Percentile

10% 20% 25% 30% 40% 50% 60% 70% 75% 80% 90%

Ln(ARPU) 3.9370 0.5538 3.2712 3.4610 3.5456 3.6193 3.7643 3.9020 4.0353 4.1958 4.2863 4.3911 4.6627

Ln(MOU) 5.8043 1.2250 4.5326 5.0689 5.2470 5.3936 5.6733 5.9243 6.1612 6.4184 6.5596 6.7178 7.1212

Ln(SMS) 5.4395 1.0169 4.4427 4.9698 5.1240 5.2575 5.4765 5.6454 5.7746 5.9108 5.9890 6.0730 6.3172

Ln(GPRS) 8.9614 2.5510 6.3723 7.9058 8.3243 8.7225 9.3289 9.7448 10.0864 10.3857 10.4698 10.6121 10.9818

Panel C: Traffic intervention Sample (N = 1,270)

Mean S.D.

Percentile

10% 20% 25% 30% 40% 50% 60% 70% 75% 80% 90%

Ln(ARPU) 4.1056 0.6184 3.2978 3.5649 3.6662 3.7659 3.9544 4.1000 4.2446 4.4243 4.5198 4.6062 4.8827

Ln(MOU) 6.0178 0.9593 4.7362 5.2386 5.4282 5.5958 5.8704 6.1203 6.3502 6.5693 6.7262 6.8382 7.1370

Ln(SMS) 5.4939 0.8908 4.4067 4.9904 5.1705 5.3048 5.5294 5.6836 5.8081 5.9269 6.0088 6.1225 6.3331

Ln(GPRS) 8.8505 2.2358 6.5089 7.7497 8.1521 8.4282 9.0002 9.4204 9.7947 10.1545 10.3388 10.4607 10.8110 Note: ARPU, MOU, SMS, and GPRS comprise key indicators of wireless usage behavior. ARPU (the average revenue per user) is the revenue that one customer’s cellular device generated. MOU

(individual monthly minutes of usage) is how much voice time a user spent on her mobile. SMS (short message service) is the amount of monthly text messages sent and received. GPRS (general

packet radio service) is a measure of the individual monthly volume of data used with the wireless service provider.

23

Table 2: Preliminary Evidence for Effect of Crowdedness on Mobile Purchase

Parameter

Sample Without

Traffic

Intervention

Sample Without

Traffic

Intervention

Sample With

Traffic

Intervention

Sample With

Traffic

Intervention Full Sample Full Sample

Crowdedness 0.114**

(0.042)

0.601**

(0.285)

0.125**

(0.041)

Ln(ARPU) 0.305**

(0.128) 0.305**

(0.128) 0.239

(0.339) 0.236

(0.337) 0.301**

(0.118)

0.299**

(0.118)

Ln(MOU) -0.043

(0.070) -0.044

(0.069) 0.020

(0.220) 0.018

(0.217) -0.043

(0.065)

-0.044

(0.065)

Ln(SMS) 0.003

(0.073) 0.006

(0.073) 0.146

(0.250) 0.124

(0.253) 0.014

(0.069)

0.015

(0.069)

Ln(GPRS) -0.001

(0.025) -0.001

(0.024) 0.004

(0.086) 0.008

(0.085) -0.001

(0.024)

0.000

(0.023)

Day Effects

(Weekend

Dummy)

Yes Yes Yes Yes Yes Yes

Time Effects

(Peak Hour

Dummy)

Yes Yes Yes Yes Yes Yes

Observations 10,690 10,690 1,270 1,270 11,960 11,960 Note: **p < 0.05; *p < 0.10; ARPU = average revenue per user, MOU = minutes of usage, SMS = number of texts sent and received per user, GPRS

= data usage with the wireless provider.

Table 3: Evidence for Effect of Crowdedness on Mobile Purchase

Parameter Model 1 Model 2 Model 3 Model 4

Crowdedness

X

Traffic Intervention

0.492**

(0.187)

Crowdedness 0.126**

(0.041)

0.114**

(0.042)

Traffic Intervention -0.120

(0.117)

-0.142

(0.177)

-1.887

(1.057)

Ln(ARPU) 0.301**

(0.118)

0.308**

(0.119)

0.308**

(0.119)

0.306**

(0.119)

Ln(MOU) -0.043

(0.065)

-0.043

(0.065)

-0.044

(0.065)

-0.044

(0.065)

Ln(SMS) 0.014

(0.069)

0.014

(0.069)

0.015

(0.069)

0.013

(0.069)

Ln(GPRS) -0.001

(0.024)

-0.001

(0.023)

-0.001

(0.023)

-0.001

(0.023)

Day Effects (Weekend

Dummy) Yes Yes Yes Yes

Time Effects (Peak Hour


Observations 11,960 11,960 11,960 11,960 Note: **p < 0.05; *p < 0.10; ARPU = average revenue per user, MOU = minutes of usage, SMS =

number of texts sent and received per user, GPRS = data usage with the wireless provider.

24

Table 4: Robustness Checks with Same-Train-Same-Time Homogenous Sample


Crowdedness

X


0.430***

(0.103)

Crowdedness 0.124**

(0.041)

0.116**

(0.042)


(0.081)

-0.073

(0.081)

-0.042

(0.083)

Ln(ARPU) 0.301**

(0.118)

0.309**

(0.118)

0.307**

(0.118)

0.305**

(0.118)

Ln(MOU) -0.043

(0.065)

-0.040

(0.065)

-0.041

(0.065)

-0.040

(0.065)

Ln(SMS) 0.014

(0.069)

0.000

(0.070)

0.002

(0.070)

-0.001

(0.070)

Ln(GPRS) -0.001

(0.024)

0.002

(0.024)

0.003

(0.024)

0.003

(0.024)

Observations 1,550 1,550 1,550 1,550 Note: **p < 0.05; *p < 0.10; ARPU = average revenue per user, MOU = minutes of usage, SMS = number

of texts sent and received per user, GPRS = data usage with the wireless provider.

Table 5: Robustness Checks with Propensity Score Matching-based Homogenous Sample


Crowdedness

X


0.318***

(0.075)

Crowdedness 0.131**

(0.052)

0.125**

(0.047)


(0.073)

-0.082

(0.069)

-0.095

(0.076)

Ln(ARPU) 0.133

(0.156)

0.134

(0.157)

0.130

(0.155)

0.133

(0.152)

Ln(MOU) -0.105

(0.083)

-0.107

(0.085)

-0.101

(0.083)

-0.103

(0.087)

Ln(SMS) 0.004

(0.086)

0.006

(0.082)

-0.003

(0.091)

-0.004

(0.093)

Ln(GPRS) -0.012

(0.030)

-0.015

(0.032)

-0.011

(0.031)

-0.016

(0.033)

Observations 782 782 782 782 Note: ***p < 0.01; **p < 0.05; *p < 0.10; ARPU = average revenue per user, MOU = minutes of usage, SMS =

number of texts sent and received per user, GPRS = data usage with the wireless provider.

25

Table 6: Residual Approach to Robustness Checks

Panel A: Crowdedness as Dependent Variable

Parameter Model 1

Ln(ARPU) 0.026

(0.027) Ln(MOU) 0.018

(0.015) Ln(SMS) -0.031**

(0.016) Ln(GPRS) -0.003

(0.005)

Observations 2,886

Panel B: Mobile Purchase as Dependent Variable


Ln(ARPU) 0.301**

(0.118)

0.310**

(0.118)

0.309**

(0.118)

0.307**

(0.128)

Ln(MOU) -0.043

(0.065)

-0.039

(0.065)

-0.038

(0.065) -0.039

(0.069)

Ln(SMS) 0.014

(0.069)

-0.002

(0.070)

-0.004

(0.071) -0.011

(0.077)

Ln(GPRS) -0.001

(0.024)

0.002

(0.024)

0.002

(0.024) 0.002

(0.025)


(0.081)

-0.056

(0.085) -0.067

(0.131)

Residual_Crowdedness 0.124**

(0.041)

0.128**

(0.042) 0.126**

(0.042)

Residual_Crowdedness X Traffic Intervention 0.416***

(0.132) 0.412**

(0.142)

Residual_Crowdedness X Weekend -0.045

(0.107)

Residual_Crowdedness X Peak Hour -0.001

(0.015)

Day Effects (Weekend Dummy) Yes Yes Yes Yes

Time Effects (Peak Hour Dummy) Yes Yes Yes Yes

Observations 11,960 11,960 11,960 11,960 Note: **p < 0.05; *p < 0.10; ARPU = average revenue per user, MOU = minutes of usage, SMS = number of texts sent and received per user,

GPRS = data usage with the wireless provider.

26

Table 7: Falsification Tests

Subsample with Low Crowdedness (under 2

passengers/m2), Lower Boundary

Full Sample with Other

Interactions Parameter Model 1 Model 2 Model 3

Ln(ARPU) 0.354

(0.267)

0.352

(0.265)

0.299

(0.321) Ln(MOU) -0.235

(0.142)

-0.231

(0.142)

-0.227

(0.179) Ln(SMS) 0.083

(0.146)

0.074

(0.148)

0.107

(0.193) Ln(GPRS) -0.028

(0.052)

-0.026

(0.053)

-0.003

(0.068)

Crowdedness -0.084

(0.270)

0.121**

(0.316)

Crowdedness x Ln(ARPU) 0.002

(0.088)

Crowdedness x Ln(MOU) 0.054

(0.049)

Crowdedness x Ln(SMS) -0.027

(0.054)

Crowdedness x Ln(GPRS) 0.001

(0.018)

Day Effects (Weekend Dummy) Yes Yes Yes

Time Effects (Peak Hour Dummy) Yes Yes Yes

Observations 2,886 2,886 11,960

Note: **p < 0.05; *p < 0.10; ARPU = average revenue per user, MOU = minutes of usage, SMS = number of texts sent and received per user, GPRS = data

usage with the wireless provider.

Table 8: Wireless Provider Telephone Survey Results

Sample

Size Respondents

Response

Rate

Read SMS in

Subway Cars

Valid Response

Rate

Survey One

(Weekday) 200 169 84.5% 165 82.5%

Survey Two

(Weekend) 100 71 71.0% 70 70.0%

Total 300 240 80.0% 235 78.3%

27

Table 9: Additional Evidence with Field Surveys


Ln(ARPU) 0.066

(0.364)

0.067

(0.367)

-0.116

(0.423)

0.033

(0.372)

Ln(MOU) -0.127

(0.162)

-0.135

(0.164)

-0.124

(0.185)

-0.128

(0.165)

Ln(SMS) -0.145

(0.190)

-0.132

(0.191)

0.228

(0.213)

-0.159

(0.196)

Ln(GPRS) -0.067

(0.067)

0.060

(0.068)

0.119

(0.075)

0.063

(0.068)

Missed Call Preference 0.741**

(0.122)

0.723**

(0.122)

0.766**

(0.134)

0.752**

(0.129)

Missed Call Frequency 0.023

(0.140)

0.026

(0.141)

0.125

(0.153)

0.029

(0.143)

Prevention Focus 0.061

(0.146)

-0.029

(0.171)

0.042

(0.214)

0.082

(0.202)

Price Consciousness -0.041

(0.145)

-0.047

(0.146)

-0.074

(0.162)

-0.016

(0.148)

Deal Proneness 0.080

(0.141)

0.060

(0.143)

0.112

(0.159)

0.116

(0.148)

Crowdedness 0.238**

(0.116)

0.219**

(0.118)

0.152*

(0.097)

Mobile Immersion 0.821**

(0.352)

0.725**

(0.358)

Social Mimicry -0.203

(0.245)

-0.165

(0.231)

Down Time 0.144

(0.143)

0.220

(0.134)

Social Anxiety -0.097

(0.160)

-0.059

(0.152)

Show Off -0.076

(0.161)

-0.186

(0.150)

Mobile Involvement 0.882**

(0.218)

Day Effects (Weekend


Time Effects (Peak Hour


Observations 235 235 235 235 Note: **p < 0.05; *p < 0.10; ARPU = average revenue per user, MOU = minutes of usage, SMS = number

of texts sent and received per user, GPRS = data usage with the wireless provider.

28

Figure 1: Crowdedness and Purchase Rates

(Parts 1 and 2, N = 10,690)

Figure 2: Purchase Rate by Exogenous Crowdedness

(Part 3, N = 1,270)

29

Figure 3: Propensity Score Distribution before Nearest Neighbor Matching

Figure 4: Propensity Score Distribution After Nearest Neighbor Matching

0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

Percent

N 391

Mean 0.39

Median 0.38

Mode .

Normal

Kernel Normal

0

-0.06 0 0.06 0.12 0.18 0.24 0.30 0.36 0.42 0.48 0.54 0.60 0.66 0.72 0.78 0.84 0.90 0.96

0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

Percent

N 391

Mean 0.39

Median 0.38

Mode .

Normal

Kernel Normal

1

ps

Traffic

30

Appendix A: Histogram of ARPU Mobile Usage Behavior Full Sample

Appendix B: Crowdedness and Purchase Rate per Train

Weekday Weekend

Time Train Crowdedness Purchase Total Ratio Crowdedness Purchase Total Ratio

7:30 - 8:30 1 4.18 14 471 0.0297 1.80 4 169 0.0237

2 3.93 30 907 0.0331 1.87 5 197 0.0254

10:00 - 12:00

3 2.99 18 609 0.0296 3.07 9 316 0.0285

4 2.93 19 617 0.0308 3.18 10 340 0.0294

5 2.91 18 609 0.0296 3.20 9 309 0.0291

14:00 - 16:00 6 2.01 11 385 0.0286 3.09 9 300 0.0300

7 1.91 16 633 0.0253 3.42 11 332 0.0331

17:30 - 18:30 8 5.36 44 1069 0.0412 4.67 14 386 0.0363

9 4.57 30 763 0.0393 4.54 14 391 0.0358

21:00 - 22:00

10 0.96 6 213 0.0282 1.51 3 131 0.0229

11 0.94 11 426 0.0258 1.62 3 128 0.0234

12 0.91 6 212 0.0283 1.64 3 128 0.0234

13 0.89 6 217 0.0276 1.65 3 129 0.0233

14 0.83 5 179 0.0279 1.72 3 124 0.0242

31

Appendix C: Logit Estimates for Propensity Score Matching

Parameter Estimate Pr > ChiSq

Ln(ARPU) -1.387 0.015

Ln(MOU) 1.642 0.002

Ln(GPRS) 0.303 0.000

Ln2(ARPU) 0.458 0.000

Ln2(MOU) -0.131 0.001

Ln2(SMS) -0.038 0.018

Ln2(GPRS) -0.045 0.000

Note: The predicted likelihood in this logit model is the propensity score P(X). After matching

P(X) with near neighbor scores, the sample T–tests of ARPU, MOU, SMS, and GPRS show no

statistical differences (p >0.10) between the treatment sample and the control sample.

Appendix D: Field Survey Items The corporate partner’s customer service call-center conducted the field surveys. The surveys were conducted on the day

after the first two parts of the field study. We selected the following day because the first two parts of the field data were

completed after 22:00, rendering it too late to call respondents. To avoid demand effects, the surveys were presented under

the guise of a customer satisfaction survey intended to assess satisfaction with the subway wireless signal.

Before answering the survey questions, mobile users were asked to recall when they received and purchased the SMS on the

subway the preceding day in order to remind them of the crowdedness they experienced. Respondents were asked to

confirm whether they had received the promotional SMS while in the subway and whether they had read and replied (or

not) to it while in the subway.

Mobile immersion (adapted from Burger 1995) When surrounded by a lot of people in the subway, I am usually eager to get away by myself.

During a subway ride, I would like to spend the time quietly.

Social mimicry (Tanner et al. 2008) When I see others looking at their mobiles, I usually look at my own mobile.

Passing down time I prefer to pass down time by fiddling with my mobile phone in the subway car.

Social anxiety (Fenigstein et al. 1975) Large groups in the subway make me nervous.

During the subway ride, it is embarrassing to look at strangers.

Show off (Richins and Dawson 1992) I like my cellular phone to be noticed by other people.

Mobile involvement (Olsen 2007, Warrington and Shim 2000) In the subway, I am involved with my cellphone.

I pay attention to incoming SMSs during the subway ride.

We test several alternative explanations to our results. First, commuters may wish to occupy themselves during the down time

of their travel. Crowdedness would restrict the activities commuters could engage in to make this down time productive (i.e.

it would be harder to use a laptop). Also, due to crowded environments, the invasion of personal space would mean an

increased likelihood of catching unwanted gazes, which can heighten social stress and embarrassment. We test for this with

a measure of social anxiety (Fenigstein et al. 1975). It is also possible that in crowded environments, there would be a

higher number of consumers who use their mobiles, which could visibly and subconsciously prompt others to do likewise.

Thus, we test for social mimicry (Chartrand and Bargh 1999, Tanner et al. 2008). In public environments, some people may

wish to flaunt their smart phones to others. We test this alternative explanation by measuring the desire to show off mobile

devices (Richins and Dawson 1992). All these explanations may account for why crowdedness may lead consumers to

become more involved with their mobile devices. To rule out additional explanations, we included several control variables

in the survey instrument. The covariates include deal proneness (Lichentenstein et al. 1995), price consciousness

(Dickerson and Gentry 1983), a prevention-focused mindset (per prior research that finds crowdedness triggers a

prevention-focused mindset (Maeng et al. 2103)), the perceived frequency of missed calls, and the preference for missed

call alerts. In addition, we control for mobile usage behavior with the company’s customer records.

32

References

Agarwal A, Hosanagar K, Smith MD (2011) Location, location, location: An analysis of profitability of position in online

advertising markets. J. Marketing Res. 48(6):1057-1073.

Aiello JR, DeRisi DT, Epstein YM, Karlin RA (1977) Crowdedness and the role of interpersonal distance preference. Sociometry

40:271-282.

Bart Y, Stephen A, Sarvary M (2014) Which products are best suited to mobile advertising? A field study of mobile display

advertising effects on consumer attitudes and intentions. J. Marketing Res. Forthcoming.

Burger J (1995) Individual differences in preference for solitude. J. of Res. in Personality 29: 85–108.

Chartrand TL, Bargh JA (1999) The chameleon effect: The perception–behavior link and social interaction. J. Personality and

Social Psychology 76(6):893-910.

Chen P-Y, Hitt LM (2001) Measuring switching costs and the determinants of customer retention in internet-enabled businesses:

A study of the online brokerage industry. Info. Sys. Res. 13(3):255-274.

Collette J, Webb SD (1976) Urban density, household crowdedness and stress reactions. J. Sociology 12:184-191.

Deng C, Graz J (2002) Generating randomization schedules using SAS programming. Proc. 27th Annual SAS Users Group

Internat. Conf. 267-270.

Dickerson MD, Gentry JW (1983) Characteristics of adopters and non-adopters of home computers. J. Consumer Res. 10(9):225-

235.

eMarketer (2012) Mobile banners continue to boost high click rates. Emarketer.com.

eMarketer (2013) Mobile devices to boost US holiday ecommerce sales growth. Emarketer.com.

Evans GW, Wener RE (2007) Crowdedness and personal space invasion on the train: Please don’t make me sit in the middle. J.

Environmental Psych. 27(1):90-94.

Fenigstein A, Scheier MF, Buss AH (1975) Public and private self-consciousness: Assessment and theory. J. Consulting and

Clinical Psychology 43(4):522-527.

Flegenheimer M (2013) Wi-fi and cell phone service on subway trains? M.T.A. Leader says it may happen. New York Times.

http://www.nytimes.com/2013/09/18/nyregion/mta-plans-wi-fi-and-phone-service-on-subway-trains.html?_r=0.

Freeman J (1975) Crowdedness and Behavior. San Francisco.

Ghose A, Goldfarb A, Han SP (2013) How is the mobile internet different? Search costs and local activities. Info. Sys. Res.

24:613–631.

Ghose A. Han S, Park, S (2014). Analyzing the interdependence between web and mobile advertising: A randomized field

experiment. Working paper, NYU.

Ghose A, Han SP (2011) An empirical analysis of user content generation and usage behavior on the mobile internet.

Management Sci. 57:1671–1691.

Ghose A, Han SP (2014) Estimating demand for mobile applications in the new economy. Management Sci. Forthcoming.

Ghose A, Ipeirotis P, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and

crowd-sourced content. Marketing Sci. 31(3):493-520.

Griffitt W, Veitch R (1971) Hot and crowded: Influence of population density and temperature on interpersonal affective

behavior. J. Personality and Social Psychology 17:92-98.

Grobart S (2013) Apple’s location-tracking iBeacon is poised for use in retail sales. Businessweek.

http://www.businessweek.com/articles/2013-10-24/apples-location-tracking-ibeacon-poised-for-retail-sales-use.

Guadagni PM, Little JDC (2008) A logit model of brand choice calibrated on scanner data. Marketing Sci. 27(1):29-48.

Harrell G, Hutt M, Anderson J (1980) Path analysis of buyer behavior under conditions of crowdedness. J. Marketing Res. 17:45–

51.

Huang, Q, Nijs VR, Hansen K, Anderson ET (2012) Wal-mart’s impact on supplier profits. J. Marketing Res. 49(2):131-143

Hui M, Bateson J (1991) Perceived control and the effects of crowdedness and consumer choice on the service experience. J.

Consumer Res. 18:174–184.

Hui SK, Inman JJ, Huang Y, Suher J (2013) The effect of in-store travel distance on unplanned spending: applications to mobile

promotion strategies. J. Marketing 77(2):1-16.

Jarrell S, Howsen R (1990) Transient crowdedness and crime: The more ‘strangers’ in an area, the more crime except for murder,

rape, and assault. J. Economics and Sociology 49(4):483-494.

Johnson L (2013) Consumers are driving need for contextual mobile experiences: Token exec. Mobile Commerce Daily.

http://www.mobilecommercedaily.com/proximity-presence-portability-preferences-are-new-pillars-of-mobile-marketing-token-

exec.

Kenny D, Marshall JF (2000) Contextual marketing: The real business of the internet. Harvard Bus. Rev. 78(6):119-125.

Kim JB, Albuquerque P, Bronnenberg BJ (2011) Mapping online consumer search.

33

Kim N, Han NK, Srivastava RK (2002) A dynamic IT adoption model for the SOHO market: PC generational decisions with

technological expectations. Management Sci. 48(2):222-240.

Knowles ES (1980) An affiliative conflict theory of personal and group spatial behavior. In Paulus PB (ed.) Psychology of Group

Influence Hillsdale, NJ.

Lichentenstein DR, Netemeyer RG, Burton S (1995) Assessing the domain specificity of deal proneness: A field study. J.

Consumer Res. 22(3):314-326.

Luo X, Andrews M, Fang Z, Phang CW (2014) Mobile targeting. Management Sci. Forthcoming.

Luo, X, Rindfleisch A, Tse D (2007) Working with Rivals: The Impact of Competitor Alliances on Financial Returns to

Competitor-Oriented Firms,” Journal of Marketing Research, 44(1): 73-83.

Maeng A, Tanner RJ, Soman D (2013) Conservative when crowded: Social crowdedness and consumer choice. J. Marketing Res.

50(6):739-752.

McKenzie B, Rapino M (2011) Commuting in the United States: 2009. U.S. Census Bureau, American Community Survey

Reports.

Molitor D, Reichhart P, Spann M (2013) Location-based advertising: measuring the impact of context-specific factors on

consumers’ choice behavior. Working paper, Munich School of Management.

Molitor D, Reichhart P, Spann M, Ghose A (2014) Measuring the effectiveness of location-based advertising: A randomized field

experiment. Working paper, NYU.

Olsen SO (2007) Repurchase loyalty: The role of involvement and satisfaction. Psychology and Marketing 24(4):315-341.

Petty R, Cacioppo JT, Schumann D (1983) Central and peripheral routes to advertising effectiveness: The moderating role of

involvement. J. Consumer Res. 10(2):135-146.

Preacher KJ, Hayes AF (2004) SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavioral

Res. Methods, Instruments, and Computers 36(4):717-731.

Richins ML, Dawson S (1992) A consumer values orientation for materialism and its measurement: Scale development and

validation. J. Consumer Res. 19(3):303-316.

Rosenbaum, PR and DB Rubin (1983) The central role of the propensity score in observational studies for causal effects.

Biometrika. 70(1):41-55.

Rubin DB (2006) Matched Sampling for Causal Effects. New York: Cambridge University Press.

Schaeffer GH, Patterson ML (1980) Intimacy, arousal, and small group crowdedness. J. Personality and Social Psychology

38(2):283-290.

Schmitt RC (1966) Density, health, and social disorganization. J. American Institute of Planners 32:38-40.

Shankar V, Balasubramanian (2009) Mobile marketing: a synthesis and prognosis. J. Interactive Marketing 23:118-129.

Shankar V, Venkatesh A, Hofacker C, Naik P (2010) Mobile marketing in the retailing environment: Current insights and future

research avenues. J. of Interactive Marketing 24(2):111–120.

Sherrod DR (1974) Crowdedness, perceived control and behavioral aftereffects. J. Applied Social Psychology 4:171-186.

Solon O (2011) Tesco brings the supermarket to time-poor commuters in South Korea. Wired.

http://www.wired.co.uk/news/archive/2011-06/30/tesco-home-plus-billboard-store.

Tanner R, Ferraro R, Chartrand T (2008) Of chameleons and consumption: The impact of mimicry on choice and preferences. J.

Consumer Res. 34(6):754–767.

Tse ACB, Sin L, Yim FH (2002) How a crowded restaurant affects consumers’ attribution behavior. International J. Hospitality

Management 21:449–454.

Warrington P, Shim S (2000) An empirical investigation of the relationship between product involvement and brand commitment.

Psychology and Marketing. 17(9):761-782.

Watercutter A (2013) How Oreo won the marketing Super Bowl with a timely blackout ad on Twitter. Wired.

http://www.wired.com/underwire/2013/02/oreo-twitter-super-bowl/.

Wokke, AD (2013) NS-APP Laat Realtime Drukte in Treinen Zien. http://tweakers.net/nieuws/87045/ns-app-laat-realtime-drukte-

in-treinen-zien.html.

Worchel S, Teddlie C (1976) The experience of crowdedness: A two-factor theory. J. Personality and Social Psychology 34:30–

40.

Xu J, Shen H, Wyer RS (2012) Does the distance between us matter? Influences of physical proximity to others on consumer

choice. J. Consumer Psychology 22:418–423.

Zimbardo P (1969) The human choice: Individuation, reason, and order versus deindividuation, impulse, and chaos. In Arnold W,

Levine D (Eds.), Nebraska Symposium on Motivation 17:237-307.

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