Grocery Shopping for America: External vs. Internal Threats to … · 2018-12-12 · GROCERY...

Running Head: GROCERY SHOPPING FOR AMERICA 1

Grocery Shopping for America:

External vs. Internal Threats to National Identity

Sonal Pandya∗, Luca Cian†, & Raj Venkatesan†

October 29, 2018

∗Corresponding Author. Department of Politics, University of Virginia, P.O. Box 400787,Charlottesville, VA 22904-4787; Phone: 434-243-1573; Fax: 434-243-3359; [email protected]†Darden School of Business, University of Virginia

mailto:[email protected]

GROCERY SHOPPING FOR AMERICA 2

Abstract

Nationalist political strategies capitalize on the psychology of external threats

to justify harshness towards outgroups. We hypothesize that while external threats

strengthen national identification, harshness towards outgroups that degrades in-

group’s constituent values (internal threat) weakens national identification. We

test the causal effects of US war casualties (external threat) and Abu Ghraib

torture scandal (internal threat) on national identification using weekly sales of

American-sounding supermarket brands, a behavioral proxy for national identifi-

cation. In our sample spanning over 8,000 brands and 1,100 supermarkets, the

market share of American-sounding brands increased in stores following the death

of a solider from the same county. These same brands’ national market shares

declined during Abu Ghraib. A July 2018 lab experiment reveals that Chinese im-

port competition (external threat) strengthens Americans’ national identification

but backlash against refugee family border separations (internal threat) weakens

identification. Our findings suggest that nationalist political strategies can backfire

if pushed too far.


The rise of nationalism, evidenced by Brexit, the election of Donald Trump, white

supremacist rallies, and extremist political parties, suggests political strategies that build

on the psychological foundations of national identity. Such strategies emphasize exter-

nal threats—war, trade, immigration—to justify harsh treatment of the nation’s puta-

tive enemies. We compare the effects of external threats—threats to the nation from

outgroups—and internal threats—threats from within the nation—on Americans’ na-

tional identification.

Social identity theory (SIT) posits that individual identity derives partially from

group affiliations (Tajfel & Turner, 1979) via categorization (e.g., “I am American.”)

and self-enhancement (e.g., “I am proud to be American.”) (Hogg, 2006). SIT pos-

tulates a fundamental need for positive self-esteem, which is partially based on inter-

group comparisons and dynamics (Sedikides, 1993; Sedikides & Strube, 1997). As such,

external threats to the group may strengthen individual identification with the group

(Branscombe, Ellemers, Spears, & Doosje, 1999; Davies, Steele, & Markus, 2008), re-

flected in the adoption of prototypic group behaviors (Akerlof & Kranton, 2000). When

considering internal threats, instead, one common reaction consists of the rejection of

threatening ingroup member(s) (Elsbach & Bhattacharya, 2001), the easiest cognitive

strategy to preserve a positive self-view (Marques, Paez, & Abrams, 1998).

Do the dynamics of social identity give nationalist politicians carte blanche? We

propose a distinct type of internal threat, actions of ingroup members that degrade the

group’s constituent values. These system-wide threats render the common strategy of

rejecting threatening ingroup members less effective. We hypothesize that the same self-

enhancement mechanism that strengthens national identification in response to external

threats acts as a circuit breaker, weakening national identification in response to na-

tionalist excesses. Weaker identification is the easiest cognitive strategy to preserve a

positive self-view in these scenarios.

We test our hypotheses using war-related threats the US faced in 2004. By observing


the differential effects of external and internal threats, we can establish their opposite

effects on national identification. War casualties are an external threat: US soldiers

(ingroup members) die while defending against an outgroup. By the end of 2004 nearly

1500 American soldiers had died in Iraq and Afghanistan. The Abu Ghraib scandal

was an internal threat; US soldiers tortured members of the outgroup. In late April

2004, images of American soldiers torturing Iraqi prisoners emerged and were seen by

76% of Americans within two weeks (Pew Research Center, 2004). Abu Ghraib was

widely condemned as an affront to American ideals; a majority of Americans reported

being upset or angry about the incident (Greenberg, 2005).1 The scandal undermined

American democratic ideals, one of the main justifications for the Iraq War (Greenberg,

2005). For example, US Defense Secretary Rumsfeld described the incident as “un-

American” and “inconsistent with the values of our nation.”2

We expect that war casualties strengthened Americans’ national identification, re-

flecting well-established SIT mechanisms. By contrast, we argue that Abu Ghraib weak-

ened national identification.

Using Branscombe et al.’s (1999) formulation of social identity threats, war casual-

ties and Abu Ghraib are best interpreted as external and internal threats to national

identity, respectively. In early 2004, 70% of Americans described the Iraq War as going

“very/fairly well” (Pew Research Center, 2004), suggesting casualties were not a compe-

tence threat. Nor do the events reflect realistic vs. symbolic threats (Stephan, Ybarra, &

Morrison, 2009). War casualties embodied both the realistic and symbolic threats under-

lying war motives: prevention of terrorism and weapons proliferation, democratization,

human rights.3

Compelling empirical tests of our claims require comparing simultaneous internal

and external threats within a single design. Extant research relies on laboratory experi-

1See Supplemental Materials for in-depth discussion of public response to the Abu Ghraib scandal.2Hearing, Congressional Armed Services Committees, May 7, 2004.3US Congressional Joint Resolutions 107-40-September 18, 2001 and 107-243-October 22, 2002.


ments, which may lack external validity, or self-reported surveys, often subject to social

desirability bias and demand effects (Barbera, Jost, Nagler, Tucker, & Bonneau, 2015).

Real-time threats are difficult to be reproduced in the lab and fictionalized threats may

be unrealistic. Recall of past threats is vulnerable to ex post rationalization (Aronczyk,

2013; Brewer, 1984).

We measure shifts in Americans’ national identification using weekly supermarket

scanner data by taking American-sounding supermarket brands as symbols of American

identity. This approach capitalizes on tight links between social identity and super-

market purchases (Escalas & Bettman, 2005). Consumers prefer brands consistent with

their most salient social identities (Khan, Misra, & Singh, 2013), and brands that bol-

ster their self-worth (Shachar, Erdem, Cutright, & Fitzsimons, 2011). Compensation for

own/other’s ethical failings shapes consumption behavior (Mari, Andrighetto, Gabbia-

dini, Durante, & Volpato, 2010; Zhong, Liljenquist, & Cain, 2009).

Mass consumption is a behavioral proxy for psychological constructs. By testing the

observable implications of our claims for consumption, our empirical tests have a high

degree of external validity and overcome limitations of extant research designs (Webb,

Campbell, Schwartz, & Sechrest, 1966). Supermarket purchasing is a frequent, con-

sistent, and nearly universal behavior in the United States. The average household

purchases groceries weekly (Kahn & Schmittlein, 1989). We observe nearly real-time

responses to threats and can evaluate temporal shifts in the strength of national identifi-

cation. Supermarket scanner data also provide detailed contextual information including

price and selection. As compared to opinion surveys, purchases are less subject to social

desirability bias.

In Study 1, we analyze weekly sales of American-sounding brands for a representative

sample of over 1,100 stores in 50 geographic markets. Our data span over 8,000 brands

across 30 product categories. We measure brands’ perceived American origin using

surveys. For a given store, weekly casualty exposure is the count of war casualties in a


week whose hometowns are in the same county as the store. Internet search patterns

capture weekly variation in Abu Ghraib exposure.

Our estimates represent the causal effects of threats on the strength of national

identification. War casualties and Abu Ghraib were both exogenous shocks that influ-

enced market share only though their effects of consumers’ national identification. For

each store-week in 2004, we model the change in the weekly market share of American-

sounding brands in 2004 as compared to the same store-week in 2001. By analyzing

differences, we hold constant time invariant store characteristics including the ex ante

demand for American-sounding brands, customer demographics, and seasonal fluctua-

tions. The timing of a store’s exposure to local causalities is quasi-random, which holds

constant all national-level factors that may influence national identification such as politi-

cians’ rhetoric or war performance. We control for zip code-level military enlistment to

account for cross-sectional variation in potential casualty exposure. Abu Ghraib was

a common shock across all stores. We control for weekly zip code-level housing prices

in case time-varying economic conditions incidentally coincided with casualties or Abu

Ghraib.

We find that in 2004, the market share of American-sounding brands increased in

stores in the weeks they had a local casualty, reflecting stronger national identification.

During the Abu Ghraib scandal, by contrast, the average market share of American-

sounding brands declined nationwide, evidence of weaker national identification.

The Abu Ghraib-induced decline is robust to restricting the sample to stores that

experienced casualties before the scandal and measuring exposure as cumulative local

casualties since 2002. Indeed, we show that higher cumulative casualties produce a

larger rise in American-sounding brands’ market share. This finding suggests that, over

time, cumulative external threats magnify shifts in national identification. Our research

uniquely lends itself to assessing cumulative effects. Stores with ex ante stronger as-

sociations to national identity saw larger changes in market share in response to both


threats.

Study 2 confirms the hypothesized psychological processes with an experiment. Our

July 2018 study drew treatments from real-time nationalist policies: Chinese trade com-

petition (external threat) and public outrage over family separation at the US-Mexico

border (internal threat). Consistent with our hypotheses, external (internal) threats

strengthened (weakened) national identification, measured as personal identification with

American identity, pride in that identity, and America’s superiority.

Study 1: Observational Evidence

Method

Measurement. Our analysis requires measurement of three concepts: the perceived

American origin of brands; weekly supermarket purchases; and the exposure to local war

casualties and the Abu Ghraib scandal.

Perceived Brand Nationality. We measure perceived brand nationality based on

product brand names because names are a highly salient, readily available cue (Usunier

& Shaner, 2002). For American consumers, brand names based on foreign languages

frequently evoke associations with a foreign country through distinctive letter combina-

tions and special characters, such as umlauts and accent marks that do not occur in

English. By contrast, brands that incorporate geographic locations in the US or Ameri-

can cultural symbols imply American-made products. Survey and experimental evidence

shows consumers frequently misidentify the national origin of products because they in-

fer nationality from marketing cues, rather than searching for official country of origin

labels (Balabanis & Diamantopoulos, 2011; Samiee, Shimp, & Sharma, 2005). Brand

nationality is a cue that operates outside of consumers’ conscious awareness in a man-

ner analogous to social stereotypes (Liu & Johnson, 2005; Martin, Lee, & Lacey, 2011).


Consumers draw inferences based on prior associations between the implied country and

the product.

We administered surveys to assess the perceived nationality of brands via an online

US-based subject pool. 1203 participants received a randomly selected brand name and

its product category and asked “What nationality does this brand most make you think

of?” Ten possible responses included eight nationalities (American, Chinese, English,

French, German, Italian, Japanese, Spanish), “none,” and “other.” Participants were

paid for each brand evaluation and allowed a maximum of 20 evaluations to minimize

respondent fatigue. Each of the over 8,000 brands in our data had seven independent

evaluations.

Using these data we calculated AmericanScoreb , which ranges 0-7 reflecting the

number of respondents who deemed brand b to be American. Table 1 provides examples

of brands at each variable value. Brands with AmericanScoreb = 7 exhibit strong Amer-

ican nationality cues including geographic references and historical figures. Coca-Cola

is an example of a high profile brand perceived as American though without explicit

American branding cues. Lower-scoring brands have distinctively foreign elements in-

cluding words in other languages and foreign geographic references (see Table S1 in the

Supplemental Material for the distribution of American Score across product categories).

American-sounding brands are typically objectively American in as much as they are

brands owned by US-based firms and/or are trademarks owned by Americans.

Pre Test. In a pre test we verified that American-sounding brands indeed symbolize

American national identity:4 The brands that score high (low) on our American Score

should be strong (weak) symbols of America.

To measure American symbolism, we adapted a three-item scale from Steenkamp,

Batra, and Alden (2003; To me, this brand is a symbol of America; I associate this

brand with things that are American; To me, this brand represents American values.

4Preregistered at AsPredicted.org.


Table 1.Brand Examples Across American Score ValuesAmericanScoreb Brand Example (Product Category)

7Sam Adams Boston Lager (beer)Coca Cola (carb. beverages)

6Land O’ Lakes (margarine/butter)Phillies (hot dogs)

5Olde Cape Cod (spaghetti sauce)Swanson American Recipes (frozen dinners)

4New England (ketchup/mustard)Dad’s Root Beer (carb. beverages)

3Maple Leaf (hot dogs)Van De Kamps (frozen dinners)

2Life in Provence Aioli (mayonnaise)Jubilee (ketchup/mustard)

1Royal Scot (margarine/butter)World Trend (toothbrushes)

0Konig Ludwig Weiss (beer)Anna Mario’s (spaghetti sauce)

The rating options ranged from 1 to 7, with 1 meaning “I completely disagree with this

statement,” and 7 meaning “I completely agree with this statement.”). As such, we

expected a correlation between our American Score and the American Symbolism scales.

Given the massive number of brands (8,644) already evaluated with the American

Score scale, we decided to sample only a selection of them (40 brands) to test the corre-

lation between the American Score and the American Symbolism scales.

Brand Selection. We randomly selected five different product categories (beer, laun-

dry detergent, toothpaste, spaghetti sauce, and frozen dinner). For each one of these

categories, we needed to include brands belonging to different levels of the American

Score scale. As such, we randomly selected two brands with an American Score of zero,

three, five, and seven. Summarizing, the selection of these 40 brands was the result

of two randomly selected brands for each of four levels of the American Score in five

different brand categories.

Study design. We assessed that evaluating 40 brands in a single session could generate

survey fatigue. As such, we assigned participants to evaluate only 20 brands (instead of


40), in a random order. Each respondent evaluated a brand that scored zero, three, five,

and seven on each of the five different brand categories. This generated a mix-model

design, in which we had 20 brands, within-subjects (four American Score levels five brand

categories), and two randomly generated brand lists, between-subjects. To exemplify,

Respondent 1 evaluated 20 brands (one brand for four different American Score levels

five brand categories—what we called “List A”). Respondent 2 evaluated the other 20

brands (one brand for four different American Score levels five brand categories—what

we called “List B”). Respondent 3 evaluated List A, Respondent 4 evaluated List B,

and so on. Table 2 shows the 40 brands used and identifies to which of the two lists it

belonged.

Respondent sample. 400 US-based participants from an online pool participated in

this study in exchange for money.

Analysis. We analyzed the Cronbach’s alpha of the three items we used to measure

American symbolism. Given that the alpha was high (.96), we decided to average these

three items in an index (“American Symbolism scale”). We then performed a mix-model

ANOVA with the two Lists (A and B) as the between-subjects independent variable,

and the 20 brands evaluated by each person as the within-subjects dependent variable.

The means of each brand are reported in Table 2.

Table 2.American Symbolism means for each of the 40 brands tested

Category List Brand NameAmerican

Score

American

Symbolism

Scale (mean)

American

Symbolism

Scale (95% CI)

beer List A Guinness 0 2.82 2.57, 3.06

beer List B Tequiza 0 2.25 2.07, 2.43

beer List A Sierra Blanca 3 2.70 2.50, 2.91

beer List B Molson 3 2.99 2.78, 3.20

beer List A Red Wolf 5 3.81 3.59, 4.03

beer List BBrooklyn

Brewery5 5.09 4.87, 5.31


beer List A Budweiser 7 5.81 5.63, 5.99

beer List BGreat Lakes

Brewing7 5.18 4.98, 5.38

laundry

detergentList A Blanca Nieves 0 2.41 2.21, 2.60

laundry

detergentList B Paloma 0 2.81 2.62, 2.99

laundry

detergentList A Ariel 3 3.18 2.97, 3.39

laundry

detergentList B Citra Suds 3 3.11 2.91, 3.31

laundry

detergentList A Ajax 5 4.69 4.46, 4.91

laundry

detergentList B Method 5 4.08 3.86, 4.30

laundry

detergentList A

Arm &

Hammer7 5.56 5.39, 5.73

laundry

detergentList B Tide 7 5.52 5.33, 5.70

toothpaste List A Elgydium 0 2.49 2.29, 2.69

toothpaste List B Dabur 0 2.17 2.00, 2.34

toothpaste List A Butler 3 3.40 3.18, 3.62

toothpaste List B Shane 3 3.28 3.06, 3.50

toothpaste List A Mentadent 5 3.65 3.43, 3.87

toothpaste List B Choice 5 3.73 3.51, 3.95

toothpaste List A Colgate 7 5.38 5.20, 5.56

toothpaste List B Crest 7 5.44 5.26, 5.62

spaghetti

sauceList A Cucina Antica 0 2.41 2.21, 2.62

spaghetti

sauceList B

Anna

Mario’s0 2.94 2.75, 3.14

spaghetti

sauceList A Prego 3 3.41 3.19, 3.63

spaghetti

sauceList B Roland 3 3.32 3.12, 3.52


spaghetti

sauceList A

Sonoma

Gourmet5 4.34 4.12, 4.57

spaghetti

sauceList B Ragu 5 4.51 4.29, 4.74

spaghetti

sauceList A

California

Seasonings7 4.77 4.55, 4.99

spaghetti

sauceList B

Uncle

Dave’s7 4.43 4.21, 4.65

frozen

dinnerList A Ajinomoto 0 2.27 2.07, 2.46

frozen

dinnerList B

Gallina

Blanca0 2.31 2.13, 2.49

frozen

dinnerList A

Michelina’s

Signature3 3.65 3.43, 3.87

frozen

dinnerList B

Bobby

Salazars3 3.01 2.82, 3.19

frozen

dinnerList A

Healthy

Choice5 4.85 4.64, 5.06

frozen

dinnerList B

Seeds of

Change5 3.62 3.41, 3.84

frozen

dinnerList A

Boston

Market7 5.45 5.26, 5.64

frozen

dinnerList B Uncle Ben’s 7 5.20 5.00, 5.41

This mix-model ANOVA revealed a non-significant effect of the list (F(1, 398) = 1.30,

p = .254, ηp2 = .003 ) and significant main effect of the brands (different brand names

were evaluated differently on the American Symbolism scale; F(19, 7,562) = 348.15, p <

.001, ηp2 = .467). The interaction between the two factors was significant (F(19, 7,562)

= 19.63, p < .001, ηp2 = .047). A significant interaction was unexpected but simply

indicated that, within the same brand level, sometime the means were higher in List A

and some other times were higher in List B.

Given that there was no general main effect of the of the Lists, we pooled the data


and grouped the 20 brands of List A and 20 brands of List B together. We then ran

a correlation between American Score (0, 3, 5, 7) and American Symbolism scale. The

two scales were strongly and positively correlated, r(7,998) = .56, p < .001 (note that

7,998 degrees of freedom, n = 8,000, equals to 20 brands ∗ 400 people). Table 3 shows

how each subsequent level of the American Score corresponded to a higher rating on the

American Symbolism scale.

Table 3.Average American Symbolism Means for Each Level of AmericanScore

American ScoreAmerican Symbolism

Scale (mean)American Symbolism

Scale (95% CI for mean)0 2.49 2.43, 2.553 3.20 3.14, 3.275 4.24 4.17, 4.317 5.27 5.21, 5.34

Main Study. We measure consumer behavior using weekly supermarket sales data

supplied by Information Resources Inc. (IRI), a leading source of US supermarket scan-

ner data (Bronnenberg, Kruger, & Mela, 2008). These data cover a representative sample

of 1,145 supermarkets across 50 IRI-designated geographic markets.5

The 135 supermarket chains represented in the data collectively accounted for about

80% of US supermarket sales in 2004. During the sample approximately 70% of American

grocery purchases were in supermarkets.

We construct our store-level measure of consumer response using weekly unit sales for

8,644 brands across 30 product categories: beer, blades, carbonated beverages, cigarettes,

coffee, cold cereal, deodorant, diapers, facial tissue, frozen dinners, frozen pizza, house-

hold cleaners, hot dogs, laundry detergent, butter, mayonnaise, milk, mustard/ketchup,

paper towel, peanut butter, photo, razors, salty snacks, shampoo, soup, spaghetti sauce,

sugar substitutes, toilet tissue, toothbrush, yogurt. Major supermarket chains stock ma-

5See Figure S2 in the Supplemental Material for map.


ture brands and maintain a relatively stable portfolio of brands within each store. We

aggregate data across multiple stock keeping unit (SKU) codes of a single brand-product

category (e.g., six-pack of Coke, two-liter bottle of Coke) but not across distinct but

related brands (e.g., Coke and Diet Coke).

For each product category-store-week in our dataset, we model the change in market-

share growth rate between 2001 and 2004. Our outcome of interest is indexed by:

i: 8 American Score levels,

j: 1,154 supermarkets,

k: 30 product categories, and

t: 52 weeks.

A brand’s weekly store market share is the number of brand product units sold as a

percentage of all units in the product category sold in that store-week. For example, if

brand b in product category k (e.g., yogurt) had a 50% market share in a given store j for

week t, the brand accounted for half of all units of yogurt sold in that store in that week.

Measuring market share, as opposed to the total number of units sold, allows us to scale

that store’s sales of a brand relative to overall demand for that product category in that

store-week. Changes in market share also capture shifts in demand for brands distinct

from changes in demand for a particular product category. For each category-store-

week we calculate the average market share across brands at each of the eight levels of

AmericanScorei. This aggregation reflects our interest in change across AmericanScorei

levels rather than individual brands and reduces the sample to a computationally feasible

size. As compared to sampling a subset of stores, this approach minimizes computational

burden, maintains generalizability and utilizes variation in casualties across all stores.

For every American Score level-product category-store-week in our sample, we calcu-

late the change in market share between 2004 and 2001 (Share2004ijkt -Share2001

ijkt ). 2001 is

the first year for which scanner data are available. Measuring change in demand within

each store allows us to hold constant all time-invariant baseline characteristics of the


store’s customer base that influence sales, including ex ante customer preferences. If we

were to observe sales only in 2004, we could not differentiate between a change in demand

and preexisting low demand. We choose 2001, the first year for which scanner data are

available, as a baseline because it precedes almost all war casualties.6 For each store, we

retain only brands that were sold in all weeks of 2004 and 2001 so our results are not

biased by attrition and entry. We also hold constant seasonal fluctuations by comparing

2004 and 2001 shares in the same week. For example, sales of American-sounding brands

may rise around July 4th or Memorial Day.

External and Internal Threats to National Identity: War Casualties and

Abu Ghraib. From the perspective of given supermarket, a “local” casualty is the

death of a deployed US soldier whose hometown is in the same US county as the store.7

Casualty data are from US Defense Department press releases as compiled by the Asso-

ciated Press. We matched each casualty’s self-reported hometown to its corresponding

county and summed county-week casualties. Hometown is distinct from service unit.

For example, Fort Hood, Texas had 504 Iraq War casualties representing 427 unique US

hometowns. We measure casualties at the county level because it is the most conser-

vative measure of exposure we can accurately construct and is consistent with existing

studies of local casualty responses (Kriner & Shen, 2010). Figure 1 summarizes weekly

national casualty counts (denoted on left vertical axis) in 2004. In most weeks the US

had no more than twenty five casualties total.

Consumers are more likely to be aware of local casualties. Proximity increases the

likelihood of exposure to information about the casualty through local media and social

networks and a personal connection. Local casualties shift war support among non-

consumers of news media, suggesting they obtain information about casualties through

other conduits (Althaus, Bramlett, & Gimpel, 2012). Even in the absence of a tangible

6If 9/11 and/or the 11 US war casualties in 2001 increased sales of American-sounding brands, thiswould bias against our expected finding for 2004.

7See Table S2 and Figure S3 in the Supplemental Material for a distribution across stores.


Fig. 1. Weekly Trends: US War Casualties vs. Abu Ghraib

connection, consumers and casualties share identity rooted in a common place of origin.

Self-reported personal connection to someone injured or killed in Iraq War increases the

likelihood that Iraq War drives choice of political candidate (Gartner, 2009) and presi-

dential approval (Gartner, 2008). The relatively high percentage of survey respondents

who report knowing a casualty, implausible given the number of troops deployed, sug-

gest strong perceived connections to casualties (Gartner, 2009; Kriner & Shen, 2010).

Experimental evidence shows stronger opposition to war in response to casualties from

respondent’s own state independent of local news framing (Kriner & Shen, 2012). Our

focus on local casualties holds constant all national-level factors that may influence re-

sponses to external threats including current state of the two wars and priming by elected

official and/or national media.

We measure weekly variation in Americans’ exposure to Abu Ghraib based on the

volume of web searches for “Abu Ghraib.” Online search trends capture the revealed

salience of the event for the mass public. Data are from Google Trends, which is based


on US Google searches to produce a normalized score that can be compared across

weeks. The Abu Ghraib scandal came to light in the last week of April 2004, the

week scored as 100 on the right vertical axis of Figure 1. Intense search activity lasted

three to four weeks. The figure suggests that Abu Ghraib did not influence casualties

counts in subsequent weeks. See Abu Ghraib in the Supplemental Material for a detailed

discussion.

Empirical Model. We estimate a ordinary least squares model:

Share2004ijkt − Share2001ijkt = β0 + β1(AbuGt ∗ AmScoreijk) (1)

+ β2(Casjt ∗ AmScoreijk)

+ β3(HomePrice2004jt ∗ AmScoreijk)

+ β4(Enlistments2004j ∗ AmScoreijk)

+ β5AbuGt

+ β6Casjt

+ β7AmScoreijk

+ β8HomePrice2004jt

+ β9Enlistments2004j

+ β10(Price2004ijkt − Price2001ijkt )

+ β11(NumSkus2004ijkt −NumSkus2001ijkt ) + eijkt

The coefficients of interest are β1 and β2. They measure to what extent Abu Ghraib

and local casualties respectively shifted the market share of relatively more American-

sounding brands.


Control Variables. In order to interpret our findings as causal, we assume that

local casualties and Abu Ghraib influenced brand purchases only by shifting consumers’

identification with American national identity. Neither war casualties nor Abu Ghraib

produced explicit calls for Americans to change their consumption, or systematically

changed brand characteristics or availability. We control for store-level price and stock

changes in the same week, the only real-time responses available to retailers. Controlling

for military enlistment, a store’s exposure to casualties was quasi-random. For each

store, we control for zip code-level military enlistment to control for the non-random

component of local casualty exposure. In case unobserved economic shocks correlated

with response to local casualties and/or Abu Ghraib, we also control for weekly average

home prices by store zip code.

The model includes Enlistmentj, the sum of military enlistments for 2001-2003 in the

same zip code as store j. Enlistment data covers all military branches and are assigned

to zip codes based on enlistees’ home address. These are Department of Defense data

obtained via a Freedom of Information Act request. Conditional on military enlistment, a

store’s exposure to war casualty is quasi-random. The timing of local casualties is quasi-

random but enlistment is not because the US has an all-volunteer military. In 2004 all

zip codes in our data had at least one enlistment. To the extent that communities with

higher enlistment are systematically different, we can draw sound inferences about only

those communities.

The model also includes HomePricejt, average home price in store j’s zip code in

week t.8 These data are from zillow.com. The variable accounts for zip code-week wealth

shocks that may influence the propensity to respond to one or both threats. Local wealth

shocks in a given week are unlikely to correlate with weekly exposure to local casualties.

Prices of American-sounding brands are not systematically different such that wealth

shocks would influence consumer purchases independent of casualties.

8Results are unchanged if variable calculated as the 2004-2001 difference or if the interaction of marketshare in 2001 and American Score is included to predict market share in 2004.


As is standard in empirical marketing analyses, we control for two time-varying brand-

store characteristics that influence fluctuations in market share (Ataman, Van Heerde,

& Mela, 2010). ∆Price2004 -2001ijkt controls for exogenous price changes and the effect

of promotional, time-limited price discounts. Non-pricing responses, such as advertising,

were less likely because they require longer lead times to implement. Price promo-

tions are retailers’ fastest response to negative demand shocks. Retailers’ contracts with

manufacturers forbid changes to products’ shelf space allocation and location, so no

retailer-driven change in product supply or location is possible in the short run.

We also control for weekly changes in the average number of varieties across all the

brands within each American score level that is stocked by a store in a product category.

All else equal, consumers are more likely to purchase brands belonging to an American

score level if a store stocks more varieties. ∆NumVariants2004 -2001ijkt is the change in

the average number of SKUs across all brands with AmericanScorei for product category

k in store j from 2001 in week t.

Our controls for prices and number of product varieties stocked are relatively stable

across weeks, as is characteristic of sales in well-established grocery retailers.

Empirical Findings

Table 4 summarizes estimates of our baseline models.9 In Model 1, the interaction of

Abu Ghraib and American Score is negative and statistically significant (β1 = -4e-06, p

< .01), confirming our expectation that American consumers reduced their purchases of

American-sounding brands in the weeks that Abu Ghraib was most salient. This result

is unchanged when we add local casualties and its interaction with American Score

(Model 2). In Model 2, the interaction between casualty in store j in week t (Casjt) and

AmericanScorei in store j in category k (AmScoreijk) is positive and significant (β2 =

2.17e-04, p < .01). This result demonstrates strengthened national identity in response

9See Table S3 in the Supplemental Material for summary statistics.


to external threats.

Table 4.External vs. Internal Threats: Baseline ResultsVariable Model 1 Model 2

Intercept0.001***(.00012)

9.9e-04***(1.2e-04)

AbuGTt ∗ AmScoreijk-4e-06***(1e-06)

-4e-06***(1e-06)

Casjt ∗ AmScoreijk2.37e-04***(5.7e-05)

AbuGTt1e-06(3e-06)

2e-06(3e-06)

AmScoreijk-5.24e-04***(2.6e-05)

-5.2e-04***(2.6e-05)

Casjt-6.5e-04**(2.6e-04)

HomePricejt2.7e-05***(3e-06)

2.7e-05***(3e-06)

Enlistmentsjt-1.74e-03***(2.8e-04)

-1.68e-03***(2.8e-04)

HomePricejt ∗ AmScoreijk-5e-06***(1e-06)

-5e-06***(1e-06)

Enlistmentsjt ∗ AmScoreijk2.88e-04***(6.2e-05)

2.67e-04***(6.2e-05)

(Price2004ijkt − Price2001ijkt )-5.28e-04***(1e-05)

-5.27e-04***(1e-05)

(NumSkus2004ijkt −NumSku2001ijkt )0.0085***(9e-06)

0.0085***(9e-06)

R-square 12.7% 12.7%n 6,344,222 6,344,222

***p<0.01, **p<0.05, *p<0.10

The average change in market share between 2004 and 2001 across all brands and

stores in any week was -.08%. The estimated model predicts that in counties with a single

casualty, the change in market share for brands with an American Score = 2 increases by

65% and the change in market share for brands with an American Score = 5 increases by

154%. The model also predicts that the share of American Score = 2 brands decreases

further by 91% and the share of American Score = 5 brands decreases further by 227%

during Abu Ghraib.


These findings are robust to zip code-level controls for weekly average housing prices

and military enlistment. Shares of American-sounding brands decreased more in stores

located in zip codes with higher consumer wealth in 2004 (β3 = -5e-06, p < .01) and

increased more in stores with greater enlistments or likelihood of exposure to casualties

(β4 = 2.67e-04, p < .01). The effect of internal and external threats are hence also robust

to differences in consumer wealth in 2004 (as measured by the average home prices in a

zip code in 2004) and a zip code’s likelihood of exposure to casualties. Controls for price

and number of varieties perform as expected.

Table 5 summarizes estimates based on alternative measures of casualty exposure.

Model 1 restricts the sample to stores that experienced their first local casualty prior

to May 2004 (i.e., prior to Abu Ghraib). Focusing on this subsample of stores is a

more challenging test because all stores were previously exposed to external threats

that strengthen national identification. Our baseline results holds: sales of American-

sounding brands decline during Abu Ghraib but rise in response to local casualties.

Model 1 further shows the decline during Abu Ghraib happened even in stores that

were exposed to casualties beforehand (i.e., exposed to external threats that strengthen

national identity.) The effect size of the external threat is also higher among stores that

experienced an internal threat prior to the external threat.

Table 5, Model 2 is based on cumulative total casualties from the beginning of 2002

through week t. Responses to internal and external threats may vary based on the

frequency of external threats. CumulativeCasjt*AmScoreijk is positive and statistically

significant. Finding is robust to using log(CumulativeCasjt). See Tables S4 and S5 in

the Supplemental Material.

We use Models 3 and 4 to compare the marginal effect of a single casualty and cu-

mulative casualties on AmericanScorei = 4 and AmericanScorei = 7 brands. In Figure

2 for each of the two AmericanScorei levels, we plot the change in market share be-

tween 2004 and 2001 at three values of cumulative casualties: one standard deviation


Table 5.External vs. Internal Threats: Dimensions of Local CasualtyExposureVariable Model 1 Model 2

Intercept1.35e-03***(1.68e-04)

2.42e-03***(1.80e-04)

AbuGTt ∗ AmScoreijk-5.13e-06***(1.28e-06)

-4.61e-06***(1.27e-06)

Castj ∗ AmScoreijk3.21e-04***(8.23e-05)

CumulativeCasjt ∗ AmScoreijk4.02e-05***(3.56e-06)

AbuGTt-1.26e-06(5.84e-06)

-3.80e-06(5.80e-06)

AmScoreijk-5.20e-04***(2.52e-05)

-6.69e-04***(2.90e-05)

Casjt-1.22e-03***(3.75e-04)

CumulativeCasjt-3.20e-06(1.68e-05)

HomePricejt1.03e-05***(2.57e-06)

-1.37e-05***(2.92e-06)

Enlistmentsjt-1.21e-04(2.51e-04)

-1.36e-03***(2.61e-04)

(Price2004ijkt − Price2001ijkt )-7.54e-04***(2.10e-05)

-7.37e-04***(2.10e-05)

(NumSkus2004ijkt −NumSku2001ijkt )9.17e-03***(1.85e-05)

9.17e-03***(1.85e-05)

R-square 13.6% 13.6%n 1,605,300 1,605,300

***p<0.01, **p<0.05, *p<0.10

below the mean (“low”), mean (“medium”), and one standard deviation above the mean

(“high”). Comparing weekly casualties across the two AmericanScorei levels demon-

strates larger market share growth for more American-sounding brands in response to

local casualties in the same week. Comparing cumulative casualties, market share growth

of AmericanScorei = 7 brands is marked higher for stores with above average cumula-

tive casualties. This finding suggests that repeated exposure to external threat magnifies

the embrace of national identity.

10Based on Table 5, Column 2 Estimates


Fig. 2. Cumulative Casualties Generate Sharper Increase in High AmericanScore Market Share10

Finally, we assess how ex ante variation across stores in the strength of national

identification shapes responses to external and internal threats. We split the stores in

our sample into two groups: stores whose average market share of AmericanScorei >

3 brands in 2001 (prior to war) was above the sample median, and stores whose value

was below the median. We estimated equation 1 separately for each group. Figure 3

plots regression coefficients for war casualties (external threat) and Abu Ghraib (internal

threat) for each group (See Table S6 and S7 in the Supplemental Material for underlying

regression results.) For the above median sample—stores with stronger national identifi-

cation in 2001—effect sizes are smaller for both types of threats as compared to stores in

the below median sample. These pattern confirm existing findings that high identifiers

may be generally more immune to threat, and are therefore better able to rationalize

(internal or external) threats precisely because of the strength of their group identity

(Branscombe et al., 1999; Ellemers & Haaker, 1995).


Fig. 3. Response to External and Internal Threats by Ex Ante National Identification

Study 2: Experimental Evidence

Study 1 establishes the causal effects of external and internal threats on national iden-

tification with a high degree of external validity. Study 2, a preregistered experiment,

provides cross-methodological evidence to establish internal validity. The experiment,

conducted in July 2018, capitalizes on real time threats to American national identity:

Chinese trade competition (external threat) and public opposition to refugee family sep-

arations at the US-Mexico border (internal threat). A July 2018 Gallup poll found that

62% of Americans believe Chinese trade policies are unfair to the US.11 At the same time,

two-thirds of Americans opposed family separation and the policy was widely condemned

by leaders across the political spectrum.12 74% of Americans reported being bothered

by images of children separated from their parents.13 Our reliance on real and on-going

threats addresses the challenge of generating realistic threats in an experimental context

and avoids the risk of ex post rationalization in survey-based studies.

11https://news.gallup.com/poll/236843/americans-say-china-trade-unfair-trade-canada-fair.aspx12https://fivethirtyeight.com/features/separating-families-at-the-border-is-really-unpopular/13https://www.washingtonpost.com/politics/most-americans-oppose-key-elements-of-trump-

immigration-policy/2018/07/05/36124360-7e3d-11e8-b0ef-fffcabeff946 story.html


Method

Subjects. 352 Amazon MTurk participants based in the US (Mdnage bracket = 30-

49 years old, SDage bracket = .754; 48% female) completed this study in exchange for

monetary compensation.

Procedure. We employed a between-subjects design. In the internal threat condi-

tion, participants read an article about abuse of refugee children separated from their

parents at the US border. In the other condition (external threat), participants read an

article about the US imposition of import tariffs on Chinese goods that accused China

of protectionist practices that harm American businesses.14 Both issues were on-going

threats during the experiment.

After reading the article about the (external/internal) threat, participants were told,

“We will come back on this article later. In the meanwhile, please answer to the following

unrelated questions”, to lower the demand effect. Then, to assess participants’ national

identity, we used the National Attachment Scale proposed by Huddy and Khatib (2007).

The scale is composed of three sub-constructs, namely: National Identity, National Pride,

and Nationalism evaluated on a ten-point scale (for more details, see Study 2 Scales in

the Supplemental Material available online). Finally, participants reported their gender,

age, ethnicity, income, political affiliation, and rural/urban living.

Results

We conducted a series of one-way ANOVAs on both the scale as a whole and on each

of its sub-constructs. The first ANOVA on the National Attachment Scale (α = .943),

controlling for gender, age, ethnicity, income, political affiliation, and rural/urban living,

revealed a significant main effect of threat (F(1, 344) = 9.75, p = .002, ηp2 = .03). As

predicted, participants had a lower (higher) identification with national identity in the

14For more details, see Study 2 manipulations in the Supplemental Material available online


presence of an internal (external) threat (Minternal = 6.46, SDinternal = 2.10, CI [6.15,

6.71]) vs. Mexternal = 7.02, SDexternal = 1.77; CI [6.78, 7.33]). With respect to the

covariates, age (F(1, 344) = 9.12, p = .003, ηp2 = .03) and income (F(1, 344) = 16.90,

p < .001, ηp2 = .05) had a significant effect in the model. Ethnicity (F(1, 344) = .74, p

= .391, ηp2 < .00), rural/urban living (F(1, 344) = 1.67, p = .197, ηp2 < .00), political

orientation (F(1, 344) = .99, p = .320, ηp2 < .00), and gender F(1, 344) = 2.65, p =

.105, ηp2 = .01) had no significant effect. Without the covariates, the significant effect

of our manipulation still holds.

Additional Calculations. We also ran an ANOVA for each of the sub-constructs

of the scale. For ease of reading, we condensed the results in the tables below.

Table 6.Dependent variable: National Identity (α = .892)

An ANOVA on National Identity, controlling for gender, age, ethnicity, income, political affiliation,

and rural/urban living, revealed a significant main effect of threat (F(1, 344) = 6.99, p = .009,

ηp2 = .02). As predicted, participants had a lower (higher) identification with national identity

in presence of an internal (external) threat (Minternal = 6.88, SDinternal = 2.41, CI [6.52, 7.25])

vs. Mexternal = 7.41, SDexternal = 2.01; CI [7.12, 7.70]). Covariate measures are reported below.

Without including covariates, the significant main effect of our manipulation still holds.

Covariate df F p-value ηp2

gender F(1, 344) 0.04 .843 .00

age F(1, 344) 11.21 .001 .03

ethnicity F(1, 344) 0.14 .704 .00

income F(1, 344) 19.30 .000 .05

rural/urban living F(1, 344) 3.39 .067 .01

political orientation F(1, 344) 0.66 .417 .00


Table 7.Dependent variable: National Pride (α = .912)

An ANOVA on National Pride, controlling for gender, age, ethnicity, income, political affiliation,

and rural/urban living, revealed a significant main effect of threat (F(1, 344) = 15.05, p < .001,




Without including covariates, the significant main effect of our manipulation still holds.


gender F(1, 344) 7.41 .007 .02

age F(1, 344) 12.08 .001 .03

ethnicity F(1, 344) 2.16 .143 .01

income F(1, 344) 13.44 .000 .04



Table 8.Dependent variable: Nationalism (r = .80)

An ANOVA on Nationalism, controlling for gender, age, ethnicity, income, political affiliation,

and rural/urban living, revealed a significant main effect of threat (F(1, 344) = 4.30, p = .039,




Without including covariates, our manipulation has a marginally significant effect on Nationalism

F(1, 350) = 3.47, p = .063, ηp2 = .01).


gender F(1, 344) 2.61 .107 .01

age F(1, 344) 2.24 .135 .01

ethnicity F(1, 344) 0.32 .569 .00

income F(1, 344) 9.32 .002 .03




General Discussion

External and internal threats to social groups produce opposite shifts in national identi-

fication. Study 1 distinguishes between threats external—the death of US soldiers—and

internal threats—torture committed by US soldiers. Study 1 showed war casualties

strengthened Americans’ national identification, corresponding to greater market share

of American-sounding brands. The Abu Ghraib scandal reduced these same brands’

market share, indicative of weaker national identification. Study 2 leveraged real-time

threats to verify that external and internal threats produce opposite shifts in identifica-

tion.

Our work highlights a distinctive type of internal threat that weakens group iden-

tification. Similar threats can emerge for other dimensions of social identity. Consider

Catholic clergy sex abuse. As the magnitude of abuse and complicity becomes clearer,

Catholics must reconcile their need for self-enhancement with their religious identity.

Revelations about systematic sexual harassment and biases pose an analogous internal

threat across a broad spectrum of industries.

We note several possible extensions. More nuanced study of threats can establish

which types of internal threats weaken identification, the tipping point for weaker identi-

fication (Marques et al., 1998), and circumstances when internal criticism can be accepted

(Hornsey, Oppes, & Svensson, 2002). Also, the point at which internal threats weaken

national identification instead of scapegoating (Marques et al., 1998) may depend on

the exact context and the nature of the threat. Although not examined here, this is an

interesting avenue for future work.

The emotional underpinnings of threat responses—shame, guilt, rage—likely produce

distinctive responses and need further investigation. The boundary conditions of self-

enhancement’s effect merit more study. Individual-level moderators may also shape

threat responses. Future research can also explore which factors amplify the moderating


effect of internal threats across social identities.

Our empirical contribution is the use of real-time, high frequency, fine-grained be-

havioral proxies for psychological phenomena. Brand choice is a shift in identification

observable in an unrelated domain. Existing work on threat and national identity rely

on past events (e.g., Simon, Pantaleo, & Mummendey, 1995), which may differ from

real-time responses and risk ex post rationalization. Unobtrusive measures are more

challenging tests of individual-level psychological processes. Causal inference based on

observational data can better assess change over time such as cumulative effects of threat.

Our research highlights psychological foundations of democratic accountability. In

establishing that threat responses drive real-time mass behavior, we establish a tighter

link between individual responses to social threats and political behavior like protest

and voting. Our internal threat findings suggest that cognitive responses to internal

threats may guard against nationalist excesses. Consumption taps into a wide range

of politically and socially relevant identities. We hope that this work will spur further

exploration of this topic.


References

Akerlof, G. A., & Kranton, R. E. (2000). Economics and Identity. Quarterly Journal of

Economics , 115 (3), 715-753.

Althaus, S. L., Bramlett, B. H., & Gimpel, J. G. (2012). When War Hits Home: The

Geography of Military Losses and Support for War in Time and Space. Journal of

Conflict Resolution, 56 (3), 382-412.

Aronczyk, M. (2013). Branding the nation: The global business of national identity.

Oxford, England: Oxford University Press.

Ataman, M. B., Van Heerde, H. J., & Mela, C. F. (2010). The Long-Term Effect of

Marketing Strategy on Brand Sales. Journal of Marketing Research, 47 (5), 866-

882.

Balabanis, G., & Diamantopoulos, A. (2011). Gains and Losses from the Misperception of

Brand Origin: The Role of Brand Strength and Country-of-Origin Image. Journal

of International Marketing , 19 (2), 95-116.

Barbera, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting From

Left to Right: Is Online Political Communication More Than an Echo Chamber?

Psychological Science, 26 (10), 1531-1542.

Branscombe, N. R., Ellemers, N., Spears, R., & Doosje, B. (1999). The Context and

Content of Social Identity Threat. In Social identity: Context, commitment, content

(p. 35-58). Oxford, England: Blackwell Publishers Inc.

Brewer, J. D. (1984). Looking Back at Fascism: A Phenomenological Analysis of BUF

Membership. The Sociological Review , 32 (4), 742-760.

Bronnenberg, B. J., Kruger, M. W., & Mela, C. F. (2008). Database Paper: The IRI

Marketing Data Set. Marketing Science, 27 (4), 745-748.

Davies, P. G., Steele, C. M., & Markus, H. R. (2008). A nation challenged: The impact

of foreign threat on America’s tolerance for diversity. Journal of Personality and


Social Psychology , 95 (2), 308-318.

Ellemers, N., & Haaker, M. (1995). Feedback and self-esteem: Individual and group level

manipulations and responses. University of Amsterdam.

Elsbach, K. D., & Bhattacharya, C. B. (2001). Defining Who You Are By What You’re

Not: Organizational Disidentification and The National Rifle Association. Orga-

nization Science, 12 (4), 393-413.

Escalas, J. E., & Bettman, J. R. (2005). Self-Construal, Reference Groups, and Brand

Meaning. Journal of Consumer Research, 32 (3), 378-389.

Gartner, S. S. (2008). The Multiple Effects of Casualties on Public Support for War:

An Experimental Approach. American Political Science Review , 102 (1), 95-106.

Gartner, S. S. (2009). Evaluating Claims of Social Connection to International Conflict

Casualties. International Interactions , 35 (3), 352-364.

Greenberg, K. J. (Ed.). (2005). The Torture Debate in America. Cambridge University

Press.

Hogg, M. A. (2006). Social Identity Theory. In P. J. Burke (Ed.), Contemporary Social

Psychological Theories (p. 111-136). Stanford, CA: Stanford University Press.

Hornsey, M. J., Oppes, T., & Svensson, A. (2002). “It’s OK If We Say It, but You

Can’t”: Responses to intergroup and intragroup criticism. European Journal of

Social Psychology , 32 (3), 293-307.

Huddy, L., & Khatib, N. (2007). American Patriotism, National Identity, and Political

Involvement. American Journal of Political Science, 51 (1), 63-77.

Kahn, B. E., & Schmittlein, D. C. (1989). Shopping trip behavior: An empirical inves-

tigation. Marketing Letters , 1 (1), 55-69.

Khan, R., Misra, K., & Singh, V. (2013). Ideology and Brand Consumption. Psycholog-

ical Science, 24 (3), 326-333.

Kriner, D. L., & Shen, F. X. (2010). The Casualty Gap: The Causes and Consequences

of American Wartime Inequalities. Oxford, England: Oxford University Press.


Kriner, D. L., & Shen, F. X. (2012). How Citizens Respond to Combat Casualties: The

Differential Impact of Local Casualties on Support for the War in Afghanistan.

Public Opinion Quarterly , 76 (4), 761-770.

Liu, S. S., & Johnson, K. F. (2005). The Automatic Country-of-Origin Effects on Brand

Judgments. Journal of Advertising , 34 (1), 87-97.

Mari, S., Andrighetto, L., Gabbiadini, A., Durante, F., & Volpato, C. (2010). The

Shadow of the Italian Colonial Experience: The Impact of Collective Emotions on

Intentions to Help the Victims’ Descendants. International Journal of Conflict and

Violence, 4 (1), 17.

Marques, J. M., Paez, D., & Abrams, D. (1998). Social identity and intragroup differ-

entiation as subjective social control. In S. Worchel, J. F. Morales, D. Paez, &

J.-C. Deschamps (Eds.), Social identity: International perspectives (p. 124-141).

London: Sage Publications.

Martin, B. A., Lee, M. S. W., & Lacey, C. (2011). Countering negative country of origin

effects using imagery processing. Journal of Consumer Behaviour , 10 (2), 80-92.

Pew Research Center. (2004, May). 76% Have Seen Prison Pictures; Bush Ap-

proval Slips (Tech. Rep.). Washington, DC: Pew Research Center. Retrieved

from http://assets.pewresearch.org/wp-content/uploads/sites/5/legacy

-pdf/213.pdf

Samiee, S., Shimp, T. A., & Sharma, S. (2005). Brand origin recognition accuracy: Its

antecedents and consumers’ cognitive limitations. Journal of International Busi-

ness Studies , 36 (4), 379-397.

Sedikides, C. (1993). Assessment, enhancement, and verification determinants of the

self-evaluation process. Journal of Personality and Social Psychology , 65 (2), 317-

338.

Sedikides, C., & Strube, M. J. (1997). Self-evaluation: To thine own self be good, to

thine own self be sure, to thine own self be true, and to thine own self be better.

http://assets.pewresearch.org/wp-content/uploads/sites/5/legacy-pdf/213.pdf

http://assets.pewresearch.org/wp-content/uploads/sites/5/legacy-pdf/213.pdf


In Advances in Experimental Social Psychology (Vol. 29, p. 209-269).

Shachar, R., Erdem, T., Cutright, K. M., & Fitzsimons, G. J. (2011). Brands: The

Opiate of the Nonreligious Masses? Marketing Science, 30 (1), 92-110.

Simon, B., Pantaleo, G., & Mummendey, A. (1995). Unique Individual or Interchange-

able Group Member? The Accentuation of Intragroup Differences Versus Similar-

ities as an Indicator of the Individual Self Versus the Collective Self. Journal of

Personality and Social Psychology , 69 (1), 106-119.

Steenkamp, J.-B. E., Batra, R., & Alden, D. L. (2003). How perceived brand globalness

creates brand value. Journal of International Business Studies , 34 (1), 53-65.

Stephan, W. G., Ybarra, O., & Morrison, K. R. (2009). Intergroup Threat Theory.

In T. D. Nelson (Ed.), Handbook of Prejudice, Stereotyping, and Discrimination

(p. 43-59). New York, NY: Psychology Press, Taylor & Francis Group.

Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In

W. G. Austin & S. Worchel (Eds.), The Social Psychology of Intergroup Relations

(p. 33-47). Monterey, CA: Brooks/Cole Publishing Company.

Usunier, J.-C., & Shaner, J. (2002). Using linguistics for creating better international

brand names. Journal of Marketing Communications , 8 (4), 211-228.

Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966). Unobtrusive

measures: Nonreactive research in the social sciences. Oxford, England: Rand

Mcnally.

Zhong, C.-B., Liljenquist, K., & Cain, D. M. (2009). Moral self-regulation: Licensing

and compensation. In D. De Cremer (Ed.), Psychological Perspectives on Ethi-

cal Behavior and Decision Making (p. 75-89). Charlotte, NC: Information Age

Publishing.

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