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WEB APPENDIX – MANUSCRIPT ID JIBS-6145-2016-02-OM.R2

WEB APPENDIX

Explicit vs. Implicit Country Stereotypes as Predictors of Product Preferences: Insights from the Stereotype Content Model

A1. SURVEY SAMPLE

Table A1.1 Survey sample information (Study 1)

Data collection period December 2013

Completion time M = 834 secs (13 minutes and 54 seconds), SD = 310

Sample statistics

Age group nGender distribution (%

female)18‒24 45 56%25‒34 74 57%35‒44 81 47%45‒55 89 47%

M = 37.43 (SD = 10.48) N = 289 52%

Table A1.2 Survey sample information (Study 2)

Data collection period and location February/March 2014; (national)

Completion time M = 1217 secs (20 minutes 17 seconds), SD = 593

Sample statistics

Age group nGender distribution (%

female)18‒24 41 56%25‒34 68 57%35‒44 81 51%45‒55 90 43%

M = 37.72 (SD = 10.34) N = 280 49%

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A2. STUDY 1: APPLICATION OF SINGLE-CATEGORY IAT (Karpinski & Steinman, 2006)

1. General instructions

2. Practice block (3 trials)

Aim: Familiarize participants with the procedure and the response keys.

Note: The time participants took to correct their response served as a built-in penalty for false responses and was included in the final IAT scores (Greenwald, Nosek, & Banaji, 2003).

2

In the following task we ask you to assign words to one of two categories. The words are going to appear in the middle of the screen. The categories are going to appear in the left and right upper corner of the screen.

Use the “E” key (for left category) and the “I” key (for right category) to indicate to which category the word belongs.

If you make a mistake, a red “X” will appear in the middle of the screen.

You can only proceed once you have corrected your response.

Please note: In order to be able to respond as fast as possible, you should keep your fingers on the response keys throughout the whole task.

Please try to be as fast as possible.

It is OK to make mistakes.

Please put one finger of your left hand on the “E” key and one finger of your right hand on the “I” key of your keyboard.

If the word appearing in the middle belongs to the left category (flowers), please press the “E” key with your left hand.

If the word appearing in the middle belongs to the right category (trees), please press the “I” key with your right hand.

The words can only belong to one of the two categories.

If you make a mistake, a red “X” will appear in the middle of the screen.

You can correct your mistake by pressing the other response key.


3. Block 1: Learning task for attribute dimension warmth (30 trials)

Aim: Participants learn to categorize attribute dimensions warmth or competence (block 2).

4. Blocks 2 and 3: Double association task (30 trials each)

Aim: Assessing association strength between the two categories that require the same response. The underlying assumption is that responses will be facilitated when categories that are closely associated share a response, as compared to when they do not which reflects in lower reaction latencies (Greenwald et al., 2003).

5. Examples of SC-IAT tasks

Examples for the attribute dimension learning task (block 1) and the double association task (block 2 and 3). Blocks 4 to 6 repeated the procedure reversing the response keys for the target concept (target country). Subsequent to the completion of the IAT for the warmth dimension, the same procedure (blocks 1 to 6) was repeated for the competence dimension. Whether implicit warmth or implicit competence was assessed first was counterbalanced across participants.

3

Please assign the following words to the categories “cold” or “warm”.

You will notice that an additional category [target country] has been added.

Thus on one of the sides, you are going to see two categories.

Please assign the words in the middle to the respective category.

Press “E” if the word belongs to a category on the left and press “I” if the word belongs to a category on the right.


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A3. TARGET WORDS USED FOR SC-IAT (STUDY 1) AND IAT (STUDY 2)

Stereotype-related target words. Target words were based on the original pool of items provided by Fiske et al. (2002) and previous relevant research operationalizing the dimensions of the SCM using both positive and negative warmth and competence items (e.g., Judd et al., 2005; Kervyn et al., 2008).

Country-related target words. To ensure the appropriateness of the country-word combination, we presented all target words to 31 participants (Mage = 32.13, SDage = 13.53) and asked them to indicate which country (i.e., Germany, USA, Japan, France, Italy, Sweden) they associate each word with as well as to indicate how confident they are of their opinion (1 = not at all confident/ 7 = very confident). Results revealed an inter-rater-reliability of α = .998, while the average confidence rating (M = 5.74, SD = 0.18) was significantly higher than the scale midpoint (t(31) = 53.82, p < .001), confirming the suitability of word-country associations employed.

Table A3.1 Country- and stereotype-related target words for the SC-IAT (Study 1).

Dimension

Country of origin Target word

Sweden SwedishStockholmABBAFjordScandinaviaMooseMalmö

France FranceParisEiffel TowerBaguetteWineLouvreVersailles

Germany GermanyMunichBeerOktoberfestBerlinNeuschwansteinGoethe

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Italy ItalyPizzaPastaRomePisaEspressoDa Vinci

Japan JapanTokyoRiceSushiConfuciusBuddhaKarate

USA AmericaBurgerNew YorkJeansBaseballHollywoodWashington

Stereotype dimension Competence Warmth

Positive dimension high-performing likablesecure sincerecompetent benevolentcapable niceskillful friendlyqualified good-naturedefficient warm

Negative dimension insecure coldunder-performing gruffinefficient unlikableclumsy unfriendlyincapable cold-heartedincompetent harshunqualified unpleasant

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Table A3.2 Country- and stereotype-related target words for the IAT (Study 2).

Dimension

Country of origin Target word

Germany GermanyMunichBeerOktoberfestBerlinNeuschwansteinGoethe

USA AmericaBurgerNew YorkJeansBaseballHollywoodWashington

Stereotype dimension Competence Warmth

Positive dimension high-performing likablesecure sincerecompetent benevolentcapable niceskillful friendlyqualified good-naturedefficient warm

Negative dimension insecure coldunder-performing gruffinefficient unlikableclumsy unfriendlyincapable cold-heartedincompetent harshunqualified unpleasant

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A4. SC-IAT AND IAT SCORING PROCEDURE

The final IAT scores were calculated by using the scoring procedure proposed by Greenwald et al. (2003) and Bluemke and Friese (2008). First, we excluded those participants who showed more than 10% of reactions faster than 300ms and/or more than 30% errors in any of the combined categorization blocks. Second, the first trial of each block was excluded as it served as orientation trial for the task. Third, all single trials that revealed response latencies slower than 300ms or faster than 3000ms were removed from further computations. In order to calculate the IAT score, we computed a separate d-algorithm for each IAT task (warmth-IAT, competence-IAT) by calculating the difference score between the combined categorization tasks and dividing them by the standard deviations (see Greenwald et al., 2003).

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A5. STUDY 1: CMV AND MULTICOLLINEARITY ASSESSMENT

We controlled for common method variance (CMV) through both ex ante and ex post procedures (Chang, Van Witteloostuijn, & Eden, 2010). With regard to the ex ante steps, we ensured the anonymity and confidentiality of responses and emphasized that there were no right or wrong answers. We further controlled for CMV ex post by employing Harman’s single factor test and including all measurement items in an exploratory factor analysis. The unrotated factor solution extracted 5 factors (with 43.29% being the most variance explained by any one factor), thus providing no evidence for CMV. Moreover, we employed the marker variable approach proposed by Lindell and Whitney (2001). We used the item “How familiar are you with social networking sites like Facebook, Second Life, or LinkedIn?” (measured on a seven-point scale anchored at not at all familiar/very familiar) as a marker variable which, from a conceptual point of view, was unrelated to the constructs analyzed in our model. We performed a partial correlation analysis of the items measuring our constructs and assessed whether the significance of their zero-order correlations changed when the marker variable was partialled out. The significance of the resulting coefficients did not change, suggesting that CMV was not a problem in the analysis.

Finally, we investigated multicollinearity by first examining the bivariate correlations among the measurement items of the predictor variables in our model. The direction of the correlations was consistent with the relations found in the model, and the same applied to the bivariate relations among the latent variables acting as predictors. We also calculated variance inflation factors (VIFs) for each structural relation and found no evidence for multicollinearity. All VIFs were well below the threshold value of 10, which would be indicative of multicollinearity problems (Cohen et al., 2003).

A6. STUDY 1: ADDITIONAL ANALYSIS ON THE MEDIATING EFFECTS OF COUNTRY STEREOTYPES ON PURCHASE INTENTION THROUGH BRAND AFFECT

We also conducted bootstrap mediation analysis (PROCESS, Model 4 with 5000 bootstrap resamples; Hayes, 2013) to further corroborate the mediating effects of country stereotypes on purchase intention through brand affect, controlling for the effect of brand familiarity. The results were fully consistent with those produced in our structural model. Specifically, the indirect effect through the path competenceexplicit → brand affect → purchase intention was significant with an estimate of .139 and a 95% BC (bias-corrected) confidence interval (CI) between .0353 and .2713. In contrast, a nonsignificant indirect effect was found for explicit warmth judgments (warmthexplicit → brand affect → purchase intention = .082, BCCI: -.0225 – .1951). Similarly, mediation analysis on the implicit stereotypes revealed a significant indirect effect of competence (competenceimplicit → brand affect → purchase intention = .298, BCCI: .0303 – .6058) but no indirect effect of warmth (warmthimplicit → brand affect → purchase intention = .213, BCCI: -.0400 – .5283).

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A7. STUDY 2: APPLICATION OF IAT (Greenwald, McGhee, & Schwartz, 1998)

1. General Instructions (see A2)

2. Practice Block (see A2)

3. Block 1 and 2: Learning Phase

Aim: Participants learn to categorize the target concept target country (block 1) as well as the attribute dimensions warmth or competence (block 2).

1.1 Block 1: Target country (28 trials)

1.2 Block 2: Attribute dimension warmth (28 trials)

4. Blocks 3 and 4: Double association task (28 trials each)

Aim: Assessing association strength between the two categories that require the same response.

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Please assign the following words to the categories “Germany” and “USA”.

You will notice that the categories have changed. Please assign the following words to the categories “cold” and “warm”.

You will notice that all 4 categories are now going to appear at the same time.In the following, two of the categories are going to appear on the left and two categories are going to appear on the right side.

Please assign the words in the middle to the respective category.

Press “E” if the word belongs to a category on the left and press “I” if the word belongs to a category on the right.


5. Examples for experimental procedure (IAT)

Examples for the target concept learning task (block 1), attribute dimension learning task (block 2) and the double association task (block 3 and 4). Blocks 5 to 8 repeated the procedure reversing the response keys for the target concept (USA vs. Germany). Subsequent to the completion of the IAT for the warmth dimension the same procedure (blocks 1 to 8) was repeated for the competence dimension. Whether implicit warmth or implicit competence was assessed first was counterbalanced across participants.

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A8. STUDY 2: PRODUCT CHOICE CONDITIONS

Participants chose between pairs of products either spontaneously or deliberately. The first two choices (tablet PCs, external hard drive) served as warm-up to familiarize participants with the procedure. For the third choice, each choice option was labeled with different COO information (Germany vs. USA).

Spontaneous choice manipulation

Deliberate choice manipulation

Warm-up choice tasks

Tablet PCs

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On the following pages you are going to see three pairs of electronic products. For each pair, please indicate your choice preference for one of the choice alternatives using the scale provided.

We ask you to indicate your choice preference fast and spontaneously.

On the following pages you are going to see three pairs of electronic products. For each pair, please indicate your choice preference for one of the choice alternatives using the scale provided.

We ask you to carefully and thoroughly think about your choice preference and justify your decision afterwards.


Figure A6.1 Set-up for warm-up choice of tablet PCs. Choice likelihood was indicated on a six-point scale (anchored at most likely A/most likely B).

External hard drives

Figure A6.2 Set-up for warm-up choice of external hard drives. Choice likelihood was indicated on a six-point scale (anchored at most likely A/most likely B).

Target choice task

Digital camera

Figure A6.3 Set-up for product choice of products with different COO labels (Made in Germany vs. Made in USA). Choice likelihood was indicated on a six-point scale (anchored at most likely A/most likely B). We counterbalanced across participants whether (a) Made in Germany appeared on superior or inferior camera and (b) Germany/inferior or Germany/superior appeared on left or right side.

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A9. STUDY 2: ANALYSIS OF ORDER EFFECTS AND CORRELATION MATRIX FOR STUDY 2

To control for carryover effects, we counterbalanced (a) the order between explicit/implicit

country stereotype assessment and product choice task (beginning vs. end), (b) the order

between the dimension of warmth and competence for the assessment of implicit country

stereotypes (competence-warmth vs. warmth-competence), (c) the presentation of the two

target countries for the assessment of explicit country stereotypes (Germany-USA vs. USA-

Germany), and (d) the order between explicit measures of warmth and competence for each

country (competence-warmth vs. warmth-competence).

We ran a series of independent sample t-tests for each different task with choice

likelihood as the dependent variable; no significant differences were noted (see Table 1

below). In addition, to make sure that the product features of the experimental brands did not

confound with the effects of country stereotypes, we conducted a 2 (COO: German vs. USA)

× 2 (product features: superior vs. inferior) ANOVA on product choice likelihood; no

interaction between the two factors was obtained (F = 0.473, n.s.), indicating that the impact

of country stereotypes is not influenced by the product features.

Table 1 Analysis of order effects for Study 2

Condition M (SD) t-value df p

Product choice task order

Beginning 3.76 (1.79)0.437 278 .663

End 3.66 (1.84)

Implicit stereotype dimension order

warmth-competence 3.72 (1.80)0.126 278 .899

competence-warmth 3.70 (1.83)

Explicit measure country order

Germany–USA 3.56 (1.84)1.353 278 .177

USA–Germany 3.86 (1.79)

Explicit stereotype dimension order USA

warmth-competence 3.81 (1.72)0.844 278 .399

competence-warmth 3.63 (1.89)

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Explicit stereotype dimension order Germany

warmth-competence 3.71 (1.83)

0.012 278 .990competence-warmth 3.71 (1.80)

Table 2 Correlation matrix for Study 2

Measure 1 2 3 4 5

Deliberate Condition (n = 132)

1 Competenceexplicit -

2 Warmthexplicit .088 -

3 Competenceimplicit .199* .232* -

4 Warmthimplicit −.010 .200* .289* -

5 Product choice .264* .107 .181* .044 -

Spontaneous Condition (n = 150)

1 Competenceexplicit -

2 Warmthexplicit .235* -

3 Competenceimplicit .093 .198* -

4 Warmthimplicit .109 .199* .309** -

5 Product choice −.033 −.122 −.074 .178* -

* p < .05, ** p < .001

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A10. INTER-ITEM CORRELATIONS FOR EXPLICIT MEASURES IN STUDY 1 AND 2

Table A10.1 Inter-item correlations for “Competence” in Study 1

Correlations

Capable Competent Efficient Intelligent

Capable Pearson Correlation 1 .679** .783** .653**

Covariance 1.091 .704 .862 .689

N 289 289 289 289

Competent Pearson Correlation .679** 1 .672** .620**

Covariance .704 .984 .703 .622

N 289 289 289 289

Efficient Pearson Correlation .783** .672** 1 .663**

Covariance .862 .703 1.110 .706

N 289 289 289 289

Intelligent Pearson Correlation .653** .620** .663** 1

Covariance .689 .622 .706 1.021

N 289 289 289 289

Table A10.2 Inter-item correlations for “Warmth” in Study 1

Correlations

Friendly Good-natured Kind Warm

Friendly Pearson Correlation 1 .727** .703** .757**

Covariance 1.147 .761 .820 .862

N 289 289 289 289

Good-natured Pearson Correlation .727** 1 .609** .667**

Covariance .761 .958 .650 .695

N 289 289 289 289

Kind Pearson Correlation .703** .609** 1 .665**

Covariance .820 .650 1.187 .771

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N 289 289 289 289

Warm Pearson Correlation .757** .667** .665** 1

Covariance .862 .695 .771 1.132

N 289 289 289 289

Table A10.3 Inter-item correlations for “Competence” in Study 2

Correlations

Capable Competent Efficient Intelligent

Capable Pearson 1 .605** .744** .496**

Covariance 1.101 .669 .894 .535

N 289 289 289 289

Competent Pearson .605** 1 .587** .624**

Covariance .669 1.112 .709 .676

N 289 289 289 289

Efficient Pearson .744** .587** 1 .510**

Covariance .894 .709 1.312 .601

N 289 289 289 289

Intelligent Pearson .496** .624** .510** 1

Covariance .535 .676 .601 1.057

N 289 289 289 289

Table A10.4 Inter-item correlations for “Warmth” in Study 2

Correlations

Friendly Good-natured Kind Warm

Friendly Pearson Correlation 1 .494** .537** .706**

Covariance 1.333 .580 .711 .962

N 289 289 289 289

Good-natured Pearson Correlation .494** 1 .440** .481**

Covariance .580 1.035 .513 .577

N 289 289 289 289

Kind Pearson Correlation .537** .440** 1 .502**

Covariance .711 .513 1.316 .680

N 289 289 289 289

Warm Pearson Correlation .706** .481** .502** 1

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Covariance .962 .577 .680 1.394

N 289 289 289 289

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