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This article explores how firms' unresponsiveness to Black customer feedback influences Black (vs. White) customers' perceived firm-based discrimination and brand engagement.

Two experimental studies (Study 1(N1) = 254) and Study 1(N2) = 484) are conducted to test the modeled relationships. The data are analyzed using ANOVA, PROCESS Model 4 and PROCESS Model 7.

The findings suggest that though perceived discrimination remains modest in all conditions, Black (vs. White) respondents report higher perceived discrimination when the firm fails to respond to a Black customer's negative or neutral (but not positive) brand-related feedback on social media. The results also indicate that Black (vs. White) customers exhibit lower engagement through perceived discrimination in the case of the firm's unresponsiveness to a Black customer's negative and neutral (but not positive) brand-related feedback regardless of the manager's race.

Prior research on intercultural service encounters and ethnic differences in consumer engagement on social media are combined to examine the relationship between customer race and perceived discrimination based on the firm's unresponsiveness to customers' social media posts.

Manipulations were created based on a fictitious e-tailer. Thus, it is recommend that future researchers examine the extent to which the findings hold for existing (r)etailers. In addition, future studies using secondary data could provide additional evidence for the findings.

Managerial attention is accentuated among customer feedback responsiveness, engagement and perceived firm discrimination. Managers are encouraged to adopt communication strategies that complement the firm's strategy and social media presence.

There is evidence that quality improvement projects developed with the participation of patients and family members are more likely to result in a sustainable change. To identify the intervening factors is an important step in promoting and supporting patient and family members’ engagement.

A survey was carried out with 90 hospitals. A total of 35 intervening factors were evaluated by the healthcare professionals from the quality area using a Likert scale. Factor analysis was applied to identify the relationship among the factors and cluster analysis and the standardized scores for each new latent variable were obtained to observe the association between them and hospitals profile. Cluster analysis allowed to group the hospitals with similar responses and to analyze whether there was any association with the profile of the institutions.

A total of ten intervening factors are identified: two in the financial dimension, five in the structural and three in the personal and cultural. The standardized scores of latent variables suggest that the financial factors could be affected by the hospital capacity. The structural factors could be impacted by the accreditation status, location (region) and administrative control (ownership). And the personal and cultural factors could be by the location and dominant organizational culture. All of factors are influenced by the performed quality management activities. The cluster analysis allowed the identification of three groups in the financial dimension, and four in the other two dimensions. Except for the accreditation status in the personal and cultural dimension, no evidence of association between the groups and the variables raised to characterize the profile of the hospitals was found.

The study contributed to identify the relationship among the intervening factors turning possible to simplify and reduce them more comprehensively than those originally identified in the literature and at the same time maintaining the representativeness of the original variables.