WEB APPENDIX Web Appendix A.

WEB APPENDIX Web Appendix A. Smoothing satisfaction data (study 1) Web Appendix B. Propensity score matching (PSM) (study 1)

Web Appendix C. Descriptive statistics and correlations of model variables (study 1) Web Appendix D. Model-free evidence (study 1)

Web Appendix E. Spotlight analysis for the non-linear quantile regression (study 1) Web Appendix F. Robustness check (study 1)

Web Appendix G. Overview of measurement constructs, manipulation checks, and attention checks (studies 2 and 3).

Web Appendix H. Manipulation for study 2

Web Appendix I. Supplementary analyses for study 2 Web Appendix J. Manipulation for study 3

Web Appendix K. Supplementary analyses for study 3

Web Appendix A. Smoothing satisfaction data (study 1)

YouGov measures brand perception indicators daily and uses different samples each day.

Therefore, results might be biased by the ‘noise’ of day-to-day measurement error. To rule out this effect, we follow Luo, Raithel and Wiles (2013) and apply a filter technique to smooth the time series. We used the Hodrick-Prescott filter method (Hodrick and Prescott 1997). This method separates the trend component from a cyclical component and can be used to ‘denoise’

high frequency time series data. In our scenario, the cyclical component (‘noise’) can be assumed to be the measurement error while the trend component (‘denoised data’) is assumed to measure non-random changes in satisfaction. The Hodrick-Prescott filter computes the linear trend (smoothed series) s of y by minimizing the variance of y around s, subject to a penalty that constrains the second difference of s. That is, the filter chooses the smoothed series s to minimize:

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 Penalty foroughness

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fit of Goodness

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The penalty parameter λ controls the smoothness of the series. The larger λ, the smoother the trend component is. Following the recommendation of Hodrick and Prescott, we chose λ = 100. Figure A1 shows the measured satisfaction metric and estimated trend component

(smoothed time series) for the brand “Valero” around the recall announcement day (the difference between measured metric and trend component represents the cyclical component, i.e., measurement error).

Figure A1. Application of Hodrick-Prescott filter to smooth satisfaction data (study 3)

-30 -25

-20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50 55 60 -.2

-.1 .0 .1 .2 .3

Measured Customer Satisfaction Smoothed Customer Satisfaction

Days before and after recall event (0 = Day of recall)

Score

Note: Exemplary times series shows customer satisfaction of Valero (recall date: March 12, 2009)

Web Appendix B. Propensity score matching (PSM) (study 1)

In short, the PSM approach matches each individual with similar propensities to receive the treatment T = 1. The propensity score λ(x) = pr(T = 1 | X = x) is a function of observed covariates X. Conditional on X respectively λ(x), the treatment assignment is randomized.

Rosenbaum and Rubin (1983) show that under unconfoundedness, independence of potential outcomes and treatment indicators also holds for conditioning solely on the propensity score λ(x). In sum, PSM is advantageous by reducing the dimensionality of the matching problem and provides consistent estimates for the treatment effect (Wooldridge 2010). In this study, the propensity score is the probability that a firm will offer full (vs. partial) remedy conditional the value of the covariates. This study uses various recall event specific variables to create matched controls with similar propensity scores. One should only incorporate variables in the PSM model that could potentially have a direct impact on both firm response and the outcome of interest.

Further, one should only use covariates which have been observed before the event and which could not have been affected by the firm response. Table B1 lists the covariates,

operationalization, and data source.

Table B1. Variables for the PSM Models

Variable Operationalization Source

Brand

equity Composite factor score of the averages of the six daily indicator scores measured on days (-30, -1) before the recall date (α = 0.93, AVE = 83.7%):

1) Satisfaction (“Are you a recent satisfied or dissatisfied customer?”) 2) Quality (“Is the brand of good or poor quality?”)

3) Impression (“Do you have a generally positive or negative feeling about the brand?”)

4) Value (“Do you associate the brand with good or poor value-for-money?”) 5) Recommendation (“Would you recommend the brand to a friend?”)

6) Workplace reputation (“Would you be proud or embarrassed to work for the brand?”).

Respondents either agree with the positive or the negative statement for each question.

For each brand and day, this study calculated the net rating scores of the six indicators by taking the differences of the number of respondents who agreed with the positive statement and the number of respondents who agreed with the negative statement, and divided the result by the total number of respondents, including neutral raters.

YouGov BrandIndex

Injuries Natural logarithm of the number of reported injuries of product users CPSC Incidents Natural logarithm of the number of reported product failures CPSC Severity Potential failure consequences for the customer, which can involve severe injury or

even death or only minor issues such as skin irritation. We code a binary variable with 1 = high risk of injury (e.g., fire, fire-related burns, explosion, death) and 0 = low risk (else).

CPSC

Volume Natural logarithm of the number of recalled units CPSC Price Natural logarithm of the product’s (minimum) retail price in U.S. dollars CPSC

A probit regression estimates the conditional probability of being in the treatment group.

Table B2 shows the results for the PSM models with remedy offer (full vs. partial) as dependent variable for the 159 events. McFadden Pseudo-R² is .299 indicates a moderate to good fit, which implies that the covariates correlate significantly with the remedy offer (p < .001). If the number

of incidents is high, firms more likely offer full remedy (b = .223, p = .053), but firms offer less full remedy if the number of recalled product is high (b = -.225, p < .010) or the price of the product is high (b = -.575, p < .001).

Table B2. Log Estimates for propensity score matching

Dependent Variable: Remedy (1: full; 0: partial)

Independent Variables Coeff. SE p-value

Brand equity .107 .126 .396

Injuries .130 .182 .475

Incidents .223^$ .116 .053

Hazard .226 .244 .355

Volume -.225^** .073 .002

Price -.575^*** .090 .000

Intercept 5.024^*** 1.026 .000

Model Fit

Log Likelihood -77.194

Wald χ² 65.890^***

Pseudo R² .299

N 159

Note: *** p < .001, ** p < .01, * p < .05, $ p < .10

Using above probit regression results, this study applies the one-to-one estimator to match each treatment observation (full remedy) with the control observations (partial remedy) that were selected by propensity score similarity (here: similar probability that a firm will offer full remedy). This study furthermore disregards any observation that falls outside the smallest connected area of common support. The analysis applies the Gaussian Kernel density estimator to obtain smooth densities of the propensity scores for treatment and control units and to identify the area of common support. To provide evidence that matching was successful and balancing properties are fulfilled, this study (1) reports graphs of Kernel density estimates of propensity scores for treatment and control units before and after matching (Figure B1) and (2) discusses t- tests and matching metrics such as Rubin’s B and Rubin’s R comparing covariates’ means and variances between treatment and control groups before and after matching (Table B3). Figure B1 (Panel A) shows the propensity score distributions for treatment (full remedy) and control group (partial remedy) before the matching procedure. This study disregards any treatment or control observation that fell outside the smallest connected area of common support (Heckman, Ichimura and Todd 1998). We had to exclude eight treatment observations (firms offered full remedy) because these observation fall outside the common support region. Accordingly, 73 treatment observations (firms offered full remedy) remain in the sample. Since each treatment observation is matched with one control observation, the sample size after matching is N = 146. with Figure B1 (Panel B) illustrates the kernel density estimates of propensity scores λ(x) after matching. The trimmed propensity score distributions of treatment and control groups are virtually identical.

Figure B1. Propensity score distribution before and after matching

Panel A: Before Matching

Panel B: After Matching

After matching, probit regression produces a fairly low Pseudo-R² (.022) and insignificant ²-value (4.510; p = .606), and t-tests comparing covariates’ means between

treatment and control groups are insignificant (p > .100; see Table B3). Further, Rubin’s R (ratio of treated to (matched) non-treated variances of the propensity score index) is .682 (should be

close to 1). Rubin’s B (absolute standardized difference of the means of the linear index of the propensity score in the treated and (matched) non-treated group) improved from 142.9 to 35.0, which indicates that matching reduced bias substantially (more than 75%). Matching treatment observations with a similar control observation enables us to avoid bias in the estimated

treatment effect that may occur when linking treatment units with potentially dissimilar control units (Smith 1997).

Table B3. Test of balancing properties (matched sample with N = 146)

Panel A: Before Matching

Covariates Mean

(Treatment) Mean

(Control) % Bias t-value p-value Variance ratio Brand equity -0.077 0.080 -15.700 -0.990 0.324 0.760 Injuries 0.449 0.194 32.300 2.020 0.045 4.020 Incidents 1.800 1.520 17.100 1.070 0.284 1.350 Hazard 0.605 0.551 10.800 0.680 0.496 1.000 Volume 10.378 9.613 29.700 1.870 0.063 0.970 Price 4.605 6.866 -131.50 -8.300 0.000 0.730 Overall Criteria

Likelihood ratio χ²-value 65.980 p-value 0.000 Pseudo R² 0.299 Mean Bias 39.500 Median Bias 23.400 Rubin's B 142.900 Rubin's R 1.000 Panel B: After Matching

Covariates Mean

(Treatment) Mean

(Control) % Bias % |Bias|

Red. t-value p-value Variance ratio Brand equity -.053 -.223 17.000 -8.200 1.000 .321 1.180

Injuries .405 .403 .300 99.000 .020 .985 1.970

Incidents 1.637 1.487 9.200 46.100 .570 .569 .860

Hazard .616 .630 -2.800 74.500 -.170 .866 .960

Volume 10.370 10.918 -21.200 28.400 -1.140 .256 1.020 Price 4.829 4.512 18.500 85.900 1.010 .312 1.000 Overall Criteria

Likelihood ratio χ²-value 4.520 p-value 0.606 Pseudo R² 0.022 Mean Bias 11.500

% Mean Bias Reduction 70.886 Median Bias 13.100

% Median Bias Reduction 44.017 Rubin's B 35.000 Rubin's R 0.682

Since the propensity score matching combines each treatment unit with one control unit

which has the most similar propensity score, some control units are used multiple times whereas some control units are never used as matches. In the final step of this analysis, we therefore extract these individual observations weights which we then use in the regression models (see Table 3 in the main manuscript). This re-weighting of treatment and control units allows drawing inferences (Morgan and Harding 2006) and is particularly useful in combining PSM with other methods such as regression (Nichols 2007).

Web Appendix C. Descriptive statistics and correlations of model variables (study 1)

The following tables show the descriptive statistics and correlations of model variables for the raw sample before matching (Table C1) and the weighted sample after matching (Table C2).

Table C1. Descriptive statistics and correlations of variables (raw sample with N = 159)

# Variable 1 2 3 4 5 6 7 8

1 SATmin 1.000

2 Trec -0.329 1.000

3 SATA 0.749 -0.427 1.000

4 SAT60 0.660 -0.365 0.489 1.000

5 Remedy (full = 1, partial =

0) 0.003 -0.038 0.086 -0.034 1.000

6 Brand Equity -0.148 0.048 -0.070 -0.059 -0.079 1.000

7 Brand Equity^2 0.082 -0.093 -0.074 -0.055 -0.097 -0.168 1.000

8 Severity (high = 1, low = 0) 0.027 -0.039 -0.003 0.152 0.054 -0.154 -0.095 1.000 Mean -0.089 45.925 -0.566 -0.014 0.509 0.000 0.994 0.579 SD 0.078 17.516 2.803 0.104 0.501 1.000 1.437 0.495 Min -0.510 0.000 -9.604 -0.510 0.000 -2.683 0.000 0.000 Max 0.014 60.000 9.505 0.263 1.000 2.291 7.199 1.000 Notes: *** p < 0.001 ** p < 0.01 * p < 0.05 $ p < 0.10

Table C2. Descriptive statistics and correlations of variables (matched sample with N = 146)

# Variable 1 2 3 4 5 6 7 8

1 SATmin 1.000

2 Trec -0.346 1.000

3 SATA 0.675 -0.494 1.000

4 SAT60 0.706 -0.452 0.567 1.000

5 Remedy (full = 1, partial =

0) -0.119 -0.087 -0.027 -0.074 1.000

6 Brand Equity -0.089 0.008 0.061 -0.130 0.083 1.000

7 Brand Equity^2 0.038 0.119 -0.283 -0.157 -0.081 -0.514 1.000

8 Severity (high = 1, low = 0) 0.065 -0.134 0.114 0.281 -0.014 0.065 -0.292 1.000 Mean -0.081 45.884 -0.318 -0.008 0.500 -0.138 1.070 0.623 SD 0.075 16.770 2.621 0.120 0.502 1.029 1.872 0.486 Min -0.510 0.000 -7.081 -0.510 0.000 -2.683 0.000 0.000 Max 0.014 60.000 9.505 0.263 1.000 2.291 7.199 1.000 Notes: *** p < 0.001 ** p < 0.01 * p < 0.05 $ p < 0.10

Web Appendix D. Model-free evidence (study 1)

We also provide model-evidence by using the self-selection unadjusted sample of 159 observations. Further, we divide the brand equity variable into three equally spaced percentiles:

NLow BE =53, NMedium BE =53, NHigh BE =53. Then, we estimate for each satisfaction metric an outlier robust linear regression model. To test the significance of regression coefficients, we report the bias-corrected and accelerated 90% confidence intervals based on 10,000 replications. Table D1 shows the results.

Table D1. Model-free evidence for longer-term satisfaction metrics using raw sample (study 1)

SATmin Trec SATA SAT60

Coeff. Lo CI Hi CI Coeff. Lo CI Hi CI Coeff. Lo CI Hi CI Coeff. Lo CI Hi CI Remedy (full = 1, partial = 0) ^{0.029 -0.052 0.093 -21.432*} -48.645 -10.847 2.624^* 0.666 10.561 0.070 -0.027 0.117 Brand Equity¹ (Benchmark: Low Brand

equity)

Medium Brand Equity 0.043 -0.081 0.106 -17.216^* -27.497 -0.527 0.343 -2.067 3.874 0.048 -0.034 0.122 High Brand Equity -0.002 -0.129 0.052 -20.456^* -43.219 -0.807 2.007^* 0.053 10.327 0.050 -0.044 0.098 Remedy*Brand Equity

Remedy*Medium Brand Equity -0.101^* -0.337 -0.034 38.110^* 31.685 76.634 -0.791 -4.877 2.644 -0.047 -0.136 0.047 Remedy*High Brand Equity -0.043 -0.141 0.051 42.256^* 30.515 74.907 -4.857^* -14.439 -3.343 -0.170^* -0.328 -0.089 Severity (high = 1, low = 0) 0.068 -0.026 0.130 -1.752 -14.928 64.294 2.352^* 0.333 10.812 0.063 -0.132 0.109 Severity*Remedy -0.085^* -0.204 -0.010 22.802^* 0.687 52.922 -2.822^* -9.294 -0.434 -0.052 -0.097 0.230 Severity*Brand Equity

Severity*Medium Brand Equity -0.048 -0.139 0.103 15.928 -14.916 43.409 -0.618 -6.109 2.446 -0.001 -0.059 0.227 Severity*High Brand Equity -0.051 -0.115 0.101 19.702 -8.288 55.569 -5.281^* -13.697 -3.878 -0.065 -0.141 0.117 Severity*Remedy*Brand Equity

Severity*Remedy*Medium Brand Equity 0.122^* 0.043 0.376 -52.147^* -88.794 -40.619 0.979 -3.587 6.944 -0.001 -0.174 0.083 Severity*Remedy*High Brand Equity 0.080 -0.042 0.207 0.000 0.000 39.229 6.205^* 4.649 15.425 0.169^* 0.030 0.360 Intercept -0.093 -0.130 0.029 59.632^* 12.707 59.956 -1.384 -3.469 0.733 -0.061^* -0.103 -0.039 Model fit

F-value 4.58 97.29 3.97 5.92

R² 0.010 0.004 0.034 0.054

N 159 159 159 159

Notes:

SATMin: Minimum Satisfaction after Recall; TRec: Satisfaction Recovery Time; SATA: Total Net Satisfaction Effect; SAT60: Satisfaction on Day 60 after Recall

1 Brand Equity divided into three equally spaced percentiles: NLow BE =53, NMedium BE =53, NHigh BE =53

* Bias-corrected and accelerated 90% confidence interval based on 10,000 replications of the outlier robust regression does not include zero

Web Appendix E. Spotlight analysis for the non-linear quantile regression (study 1)

Table E1 shows for the non-linear model in Table 3 in the manuscript the effects of full (vs. partial) remedy on the four satisfaction metrics conditional on brand equity and failure severity.

Table E1. Brand equity conditional effects for remedy and severity on satisfaction metrics Panel A: Severity = Low

Brand Equity

Effect of Remedy (full vs. partial) on Satisfaction Metrics

SATmin Trec SATA SAT60

Minimum 0.088^*** -61.188^*** 5.222^*** 0.096^***

(0.016) (3.806) (0.560) (0.020)

-2 SD 0.050^$ -41.534^*** 3.587^*** 0.067^*

(0.027) (3.857) (0.703) (0.029)

-1 SD 0.008 -19.537^*** 1.739 0.032

(0.046) (4.442) (1.055) (0.386)

Mean -0.018 -5.631 0.538 0.004

(0.044) (3.751) (0.996) (0.024)

+1 SD -0.027 0.193 -0.014 -0.017

(0.026) (2.061) (0.752) (0.130)

+ 2 SD -0.019 -2.064 0.081 -0.030

(0.047) (4.744) (1.465) (0.299)

Maximum -0.013 -4.236 0.230 -0.032

(0.064) (6.384) (1.880) (0.359)

Panel B: Severity = High

Brand Equity

Effect of Remedy (full vs. partial) on Satisfaction Metrics

SATmin Trec SATA SAT60

Minimum 0.277^** -155.109^*** 23.261^*** 0.986^***

(0.084) (15.947) (2.864) (0.202)

-2 SD 0.072^** -16.912^** 4.183^*** 0.185^***

(0.027) (5.794) (1.118) (0.023)

-1 SD -0.020 16.483^*** -1.633^$ -0.052^$

(0.017) (4.060) (0.734) (0.027)

Mean -0.027^$ 10.011^* -1.212 -0.032^*

(0.014) (4.825) (0.742) (0.012)

+1 SD 0.051^* -36.335^*** 5.446^*** 0.246^***

(0.022) (5.924) (0.764) (0.059)

+ 2 SD 0.214^** -122.556^*** 18.341^*** 0.782^***

(0.067) (12.792) (2.220) (0.164)

Maximum 0.277^** -155.109^*** 23.261^*** 0.986^***

(0.084) (15.947) (2.864) (0.202)

Notes: *** p < .01 ** p < .01 * p < .05 $ p < .10 SATMin: Minimum Satisfaction after Recall TRec: Satisfaction Recovery Time

SATA: Total Net Satisfaction Effect SAT60: Satisfaction on Day 60 after Recall

Web Appendix F. Robustness check (study 1)

We tested alternatives to the one-to-one matching method used to adjust the field data for self-selection bias. Instead of estimating the propensity score model using probit regression and one-to-one matching, we used logit regression and Kernel matching (uniform distribution) to rebalance treatment (full remedy) and control (partial remedy) observations. Kernel matching is a non-parametric technique that uses weighted averages of all individuals in the control group to construct the counterfactual outcome (Heckman et al. 1998). This method is more efficient than one-to-one matching, because it uses more information, but has the drawback that potentially more bad matches are used. Using the same propensity score model (see Web Appendix B), this analysis disregards ten control observations because they fall outside the smallest connected area of common support. 149 observations remain in the analysis. The control units are weighted proportional to the inverse of the propensity scores, i.e., control units with small (large) distance to any treatment unit receive higher (smaller) weight. We then use these weights to rebalance the observations in the next step of the analysis. Instead of estimating quantile regression (see manuscript), we estimated simultaneously a four-equation linear regression system and adjust standard errors of coefficients according to heteroscedasticity and cross-equation correlation of error terms. We estimate this system twice: once for the linear model and once for the non-linear model (see formulas (1) and (2) in the manuscript). As this approach estimates the outcome mean (instead of the median), the estimates are prone to the influence of outliers. The following Table F1 presents the estimation results for the four satisfaction metrics.

We find additional evidence for the hypotheses (Table F1 in Web Appendix F). Remedy choice matters less (more) for high (low) brand equity firms (H1: support for three of four satisfaction metrics in the linear and non-linear models). But there is no evidence for a non-linear relationship in case of low severity events (H2: no support). In case of high severity, full remedy becomes more important for high brand equity firms (H3a: support for four of four metrics).

Finally, we find a non-linear effect, i.e. low and high brand equity firms can restore and preserve satisfaction better if offering full remedy compared to medium brand equity firms (H3b: support for two of four metrics). Although this alternative model specification is inferior compared to the quantile regression discussed in the manuscript, the findings are generally in line with the quantile regression results.

Table F1. OLS regression results for longer-term satisfaction metrics using self-selection adjusted sample (study 1)

Linear Model Non-linear Model

SATmin Trec SATA SAT60 SATmin Trec SATA SAT60

Remedy (full = 1, partial = 0)

0.039 -6.995 1.117 0.054 0.074 -4.332 0.837 0.106^$

(0.034) (4.346) (0.681) (0.043) (0.048) (4.971) (0.825) (0.064)

Brand Equity -0.007 -2.886^* 0.615^** 0.004 0.025 -3.603 0.175 0.039

(0.009) (1.297) (0.229) (0.010) (0.020) (3.294) (0.415) (0.026)

Brand Equity^2 0.020 -0.442 -0.271 0.021

(0.013) (1.512) (0.202) (0.018)

Rem.*Brand Equity H1 (-) -0.008 H1 (+)¹ 8.725^* H1 (-) -0.921^$ H1 (-) -0.029 H1 (-) -0.040 H1 (+)¹ 8.313^$ H1 (-) -0.549 H1 (-) -0.068^*

(0.018) (4.241) (0.549) (0.021) (0.025) (4.853) (0.654) (0.033)

Rem.*Brand Equity^2 H2 (+) -0.020 H2 (-)¹ -3.161 H2 (+) 0.056 H2 (+) -0.035^$

(0.015) (2.478) (0.343) (0.020)

Severity (high = 1, low = 0)

0.053 -11.492^* 1.249 0.106^* 0.091^$ -14.588^* 1.166 0.164^*

(0.033) (5.529) (0.795) (0.051) (0.047) (6.179) (0.969) (0.071)

Hazard*Remedy -0.062 10.176 -1.238 -0.083 -0.108^* 9.772 -1.313 -0.148^$

(0.038) (6.925) (1.029) (0.057) (0.052) (7.574) (1.223) (0.077)

Sev.*Brand Equity -0.018 9.050^$ -1.627^** -0.075 -0.053^* 12.337^* -1.637^* -0.131^*

(0.013) (5.122) (0.605) (0.046) (0.022) (5.458) (0.799) (0.056)

Sev.*Brand Equity^2 -0.026^$ 5.360 -0.590 -0.063^*

(0.015) (3.708) (0.587) (0.031)

Sev.*Rem.*Brand Equity H3a (+) 0.028 H3a (-)¹ -16.226^* H3a (+) 1.800^* H3a (+) 0.085^$ H3a (+) 0.064^* H3a (-)¹ -18.377^* H3a (+) 1.871^$ H3a (+) 0.145^*

(0.024) (7.286) (0.858) (0.051) (0.029) (7.330) (1.008) (0.061)

Sev.*Rem.*Brand

Equity^2 H3b (+) 0.035^* H3b (-)¹ -1.704 H3b (+) 0.748 H3b (+) 0.069^*

(0.018) (4.677) (0.674) (0.033)

Intercept -0.123^*** 52.59^*** -1.477^** -0.085^* -0.159^*** 53.38^*** -0.990 -0.124^*

(0.031) (3.018) (0.483) (0.040) (0.046) (3.846) (0.643) (0.062)

Equation fit

R² 0.083 0.122 0.090 0.176 0.125 0.152 0.113 0.219

System of equations fit

Log pseudolikelihood -572.9 -550.9

Wald chi2 165.2^*** 675.9^***

N 149 149

Notes: *** p < 0.001 ** p < 0.01 * p < 0.05 $ p < 0.10 (heteroscedasticity-consistent standard errors in parentheses; cross-equation correlations not displayed) SATMin: Minimum Satisfaction after Recall; TRec: Satisfaction Recovery Time; SATA: Total Net Satisfaction Effect; SAT60: Satisfaction on Day 60 after Recall

1 Expected sign reversed as a less (more) negative impact of the recall on satisfaction corresponds with a shorter (longer) satisfaction recovery time

Web Appendix G. Overview of measurement constructs, manipulation checks, and attention checks (studies 2 and 3)

Construct Measurement Items

Brand equity

(adapted from Brady et al. 2008)

Study2 = .88, AVE = 70.22%

Study3 = .89, AVE = 71.82%

How loyal are you to (BRAND)?

(1 = not at all loyal, 7 = very loyal) What kind of attitude do you have about (BRAND)?

(1 = negative attitude, 7 = positive attitude) What kind of image does (BRAND) have?

(1 = negative image, 7 = positive image”) How would you rate the quality delivered by (BRAND)?

(1 = low quality, 7 = high quality)

Would you be willing to pay more for (BRAND) than you would another (BRAND)?

(1 = definitely not, 7 = definitely) Customer satisfaction

(adapted from Fornell et al. 1996) Pre-recall satisfaction:

Study2 = .86, AVE = 78.83%

Post-recall satisfaction:

Study2 = .95, AVE = 90.77%

Study3 = .91, AVE = 85.29%

For post-recall satisfaction, the following sentenced is added as introduction:

Think back to (BRAND)’s recent product recall.

After reading details about the recall,…

…what is your overall satisfaction with (BRAND)?

(1 = unsatisfied, 7 = satisfied)

…to what extent does (BRAND)’s product meet your expectations?

(1 = falls short of expectations, 7 = exceeds expectations)

…how well does (BRAND)’s notebook compare with the ideal notebook brand?

(1 = not very close to the ideal, 7 = very close to the ideal)

Product expertise

(adapted from Thompson et al. 2005)

Study2 = .92, AVE = 86.83%

Study3 = .91, AVE = 85.98%

I am very familiar with (PRODUCT) I know a lot about (PRODUCT)

Relative to others, I know a lot about (PRODUCT) (1 = totally disagree, 7 = totally agree)

Manipulation checks Severity

Based on the information in the recall

announcement, how severe would you rate this product recall?

(1 = not severe at all, 7 = very severe) Remedy

What remedy did (BRAND) offer?

 (BRAND) will arrange for a free inspection and full repair

 (BRAND) will send you a free do-it- yourself repair kit

Attention checks Hazard

What was the product hazard?

 Fire and burn

 Overheating Damages and Injuries

Has (BRAND) received any reports of injuries to people or property?

 Yes

 No

Survey Attention Check

If you have read this question and are paying attention to this survey, please click “2” on the scale

Web Appendix H. Manipulation for study 2

The recall announcement is based on an actual CPSC announcement. The survey software automatically populated “Brand” with the smartphone brand that participants selected at the beginning of the survey. The announcement below displays the full remedy manipulation.

[Brand] Recalls Smartphones Due to Fire and Burn Hazard

July 2, 2020 Release # 20-392

WASHINGTON, D.C. - The U.S. Consumer Safety Commission, in cooperation with the firm below, today announced a voluntary recall of the following consumer product.

Consumers should stop using recalled products immediately unless otherwise instructed.

Name of Product: Certain Smartphones Units: About 273,000

Manufacturer: [Brand]

Hazard: An electrical component in the smartphone can short circuit, posing a fire and burn hazard .

Remedy: Consumers should stop using the recalled smartphones immediately and contact [Brand] to determine if their smartphone is affected. [Brand] will send affected customers a free do-it-yourself repair kit (full remedy: free inspection and full repair).

Incidents/Injuries: [Brand] has received 127 reports of burns to people .

Description: Not all smartphones are affected. Consumers should contact [Brand] to determine if their smartphone is included in the recall.

Sold By: Via [Brand]'s website, and authorized electronics retailer nationwide from July 2017 through July 2020.

Manufactured: United States

Web Appendix I. Supplementary analyses for study 2

Table I1. Equivalence of experimental groups (full vs. partial remedy)

Variable F-value for metric and Chi2-value for nominal variables (p-value) Brand Equity (metric) 1.14 (0.228)

Pre-recall Satisfaction (metric) 1.94 (0.116)

Age (metric) 1.13 (0.228)

Gender (nominal) 3.03 (0.219)

Phone Age (metric) 1.44 (0.230)

Phone Price (ln) (metric) 1.30 (0.255)

Phone Brand (nominal) 7.72 (0.461)

Familiarity (metric) 0.28 (0.594)

Table I2. Descriptive statistics of model variables (n = 553) Change in

Satisfaction Remedy (full

vs. partial) Brand Equity Brand Equity^2 Change in Satisfaction 1.000

Remedy (full vs. partial) 0.271 1.000

Brand Equity 0.273 -0.052 1.000

Brand Equity^2 0.257 -0.048 0.988 1.000

Mean 0.854 1.505 5.644 33.143

SD 0.239 0.500 1.135 11.522

Min 0.000 1.000 1.400 1.960

Max 2.151 2.000 7.000 49.000

Median 0.045 2.000 6.000 36.000

Skewness -1.655 -0.018 -1.120 -0.549

Kurtosis 5.929 1.000 4.148 2.539

Figure I1. Distribution for change in satisfaction Histogram

Boxplot

Table I3. Variance ratio test for change of satisfaction by experimental groups Change in Satisfaction

Group N Mean SE SD

Full remedy 274 -0.148 0.011 0.178

Partial remedy 279 -0.060 0.007 0.113

combined 553 -0.104 0.007 0.155

Std.Dev.-Ratio 1.565

F-value (p-value) 2.245 (0.000)

Table I4. Change in satisfaction conditional on remedy and brand equity Change in Satisfaction

Remedy = full Remedy = partial

Brand Equity Effect 95% CI Effect 95% CI

Minimum -0.031 [-0.074, 0.011] -0.367 [-0.419, -0.315]

-2 SD -0.051 [-0.076, -0.026] -0.206 [-0.240, -0.171]

-1 SD -0.046 [-0.071, -0.021] -0.146 [-0.176, -0.117]

Mean -0.032 [-0.051, -0.014] -0.095 [-0.115, -0.075]

+1 SD -0.010 [-0.016, -0.004] -0.051 [-0.071, -0.031]

Maximum 0.000 [-0.008, 0.008] -0.037 [-0.065, -0.009]

Note: 95% confidence interval based on heteroscedasticity-robust standard errors

Table I5. Quantile regression results for 10%, 30%, 50%, 70%, and 90% quantiles Dependent Variable: Change in Satisfaction (Quantile)

Independent variables 10% 30% 50% 70% 90%

Remedy (full vs. partial) 0.263^*** 0.098^** 0.062^*** 0.032^* 0.024^**

(0.048) (0.030) (0.014) (0.014) (0.007)

Brand Equity 0.072 0.073 0.077^*** 0.050^*** 0.002

(0.065) (0.068) (0.02) (0.014) (0.009)

Brand Equity^2 0.006 -0.012 -0.012^* -0.002 0.003

(0.021) (0.024) (0.006) (0.023) (0.005)

Remedy*Brand Equity 0.004 -0.013 -0.030^** -0.025^** -0.002

(0.035) (0.035) (0.011) (0.008) (0.005)

Remedy*Brand Equity^2 0.001 0.003 0.008^* 0.001 -0.003

(0.012) (0.012) (0.004) (0.012) (0.003)

Intercept -0.711^*** -0.268^*** -0.157^*** -0.065^* -0.024^*

(0.090) (0.056) (0.022) (0.026) (0.012)

Model Fit

Pseudo-R² 0.184 0.125 0.076 0.033 0.012

N 553 553 553 553 553

Notes: *** p < 0.001 ** p < 0.01 * p < 0.05 $ p < 0.10 In parentheses: heteroscedasticity-consistent standard errors

Table I6. Results for outlier robust regression

Dependent Variable: Change in Satisfaction

Independent variables Coef. SE

Remedy (full vs. partial) 0.029^** 0.009

Brand Equity 0.048^*** 0.013

Brand Equity^2 -0.025^*** 0.006

Remedy* Brand Equity H1 (-) -0.017^* 0.008

Remedy* Brand Equity^2 H2 (+) 0.015^*** 0.004

Intercept -0.102^*** 0.014

Model Fit

F-value 31.11

R² 0.117

N 553

Notes: *** p < 0.001 ** p < 0.01 * p < 0.05 $ p < 0.10

Web Appendix J. Manipulation for study 3

The recall announcement is based on an actual CPSC announcement. The survey software automatically populated “Brand” with the laptop brand that participants selected at the beginning of the survey. The announcement below displays the full remedy manipulation.

[Brand] Recalls Laptops Due to Overheating

June 24, 2020 Release # 20-392

WASHINGTON, D.C. - The U.S. Consumer Safety Commission, in cooperation with the firm below, today announced a voluntary recall of the following consumer product.

Consumers should stop using recalled products immediately unless otherwise instructed.

Name of Product: Certain Laptops Units: About 73,000

Manufacturer: [Brand]

Hazard: Irregularly positioned wires near the laptop's battery can cause overheating (high severity: explosions). This can lead to permanent damage to the laptop (high severity: This poses a fire and burn hazard to consumers).

Remedy: Consumers should stop using the recalled laptops immediately and contact [Brand] to determine if their notebook is affected. [Brand] will arrange for a free do-it- yourself repair kit (full remedy: free inspection and full repair).

Incidents/Injuries: [Brand] has received no reports of injuries to people or damage to property (high severity: 47 reports of burns and injuries to people).

Description: Not all units are affected. Consumers should contact [Brand] to determine if their unit is included in the recall.

Sold By: Via [Brand]'s website, and authorized electronics retailers nationwide from July 2017 through June 2020.

Manufactured: United States

Web Appendix K. Supplementary analyses for study 3

Table K1. Equivalence of experimental groups (full/partial remedy X low/high severity) Variable F-value for metric and Chi2-value for

nominal variables (p-value) Brand Equity (metric) 0.570 (0.637)

Age (metric) 2.180 (0.089)

Gender (nominal) 9.534 (0.146)

Laptop Age (metric) 0.400 (0.750) Laptop Price (ln) (metric) 1.130 (0.338) Phone Brand (nominal) 39.019 (0.603)

Table K2. Descriptive statistics of model variables (n = 724)

Satisfaction Remedy (full vs. partial)

Severity (high vs.

low)

Brand Equity

Brand Equity^2 Satisfaction 1.000

Remedy (full vs. partial) 0.172 1.000

Severity (high vs. low) -0.111 -0.006 1.000

Brand Equity 0.600 0.031 -0.035 1.000

Brand Equity^2 0.586 0.028 -0.035 0.990 1.000

Mean 4.859 0.506 0.503 5.356 30.074

SD 1.437 0.500 0.500 1.181 11.852

Min 1.000 0.000 0.000 1.200 1.440

Max 7.000 1.000 1.000 7.000 49.000

Median 5.000 1.000 1.000 5.600 31.360

Skewness -0.627 -0.022 -0.011 -0.661 -0.181

Kurtosis 2.747 1.000 1.000 2.954 2.085

Figure K1. Distribution for satisfaction

Histogram

Boxplot

Shapiro-Wilk test: W = 0.970 (p = 0.000)

Table K3. Variance ratio test for satisfaction by experimental groups Satisfaction

Group N Mean SD

Severity low, Remedy partial 177 4.672 1.488

Severity low, Remedy full 183 5.353 1.113

Severity high, Remedy partial 181 4.547 1.567

Severity high, Remedy full 183 4.852 1.420

Levene’s Variance Equality Test Statistic

W 9.419

p-value 0.000

Table K4. Impact of remedy offer on satisfaction conditional on severity and brand equity Effect of Remedy (full vs. partial)

on Satisfaction

Brand Equity Severity = low Severity = high

Minimum 0.908 0.353

(0.771) (0.909)

-4 SD 0.933 0.319

(0.717) (0.837)

-3 SD 1.417^* 0.150

(0.369) (0.454)

-2 SD 1.023^*** 0.076

(0.200) (0.222)

-1 SD 0.909^*** 0.097

(0.089) (0.171)

Mean 0.688^*** 0.211

(0.094) (0.170)

+1 SD 0.362^** 0.420^**

(0.092) (0.133)

Maximum 0.095 0.605^**

(0.080) (0.162)

Notes: *** p < .01 ** p < .01 * p < .05 $ p < .10 (heteroscedasticity-robust standard errors in parentheses)

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