This study explores how the perceived unfairness of negative word of mouth (NWOM) leads to favorable attitudes toward a product using a model that accounts for the mediation of empathy. We extend the literature by including the influence of prior attitude and consumption orientation (utilitarian or hedonic) in the framework. Three experimental studies with different degrees of unfairness are conducted to calibrate the model using the multigroup structural equation modeling (MGSEM) approach. We confirm that perceived unfairness evokes empathetic responses in NWOM receivers. The latter subsequently induces favorable post attitudes toward a product but only for highly unfair NWOM. Furthermore, prior attitude reinforces this effect by increasing empathy. However, consumption orientation does not appear to affect the above relationships significantly. The implications for eliciting benefits from NWOM are discussed.
This study explored the conceptualization, dimensional structure, and measurement of artificial intelligence (AI) social responsibility in the consumer market. Data were collected through semi-structured in-depth interviews with 32 respondents. A grounded theory research approach was employed to construct a structural model of AI social responsibility that included the dimensions of ethics, safety, applicability, credibility, and reflexivity. Subsequently, an exploratory factor analysis was conducted on 305 questionnaire data collected through an online survey as well as a confirmatory factor analysis on 325 questionnaire data. The analyses led to the development of an AI social responsibility scale consisting of 18 items and demonstrating good reliability and validity. Moreover, using structural equation modeling, strong nomological validity was demonstrated. The results indicated that AI social responsibility and its dimensions significantly predicted flow experience and experience satisfaction. The findings enhance understanding of the conceptual meaning and dimensional structure of AI social responsibility in the consumer market, as well as provide a psychometrically reliable and valid measurement tool for use in future research. Furthermore, the findings not only facilitate the design and implementation of AI technologies, but they also offer crucial insights for companies and their stakeholders to devise and refine AI social responsibility strategies and other marketing tactics—thereby augmenting CSR 3.0 management practices.