Predicting CSR authenticity in healthcare organizations: an integrated SEM-IPMA-ANN approach through the lens of...

As healthcare business organizations face mounting pressure from civil society to act responsibly, they are expanding and strategically restructuring their CSR activities. Under the umbrella of CSR, cause-related marketing (CRM) campaigns are a means of fundraising available to healthcare firms engaging in socially responsible activities to improve global health. While healthcare organizations increasingly emphasize CSR implementation, its precise nature within the context of CSR authenticity has not been systematically investigated. Adopting self-compassion theory as a platform, this study has demonstrated novel relationships for gaining deeper insights into the key predictors of CSR authenticity in healthcare organizations.

The authors collected 882 responses and adopted the hybrid SEM-IPMA-ANN (Structural Equation modelling- Importance Performance Map Analysis- Artificial Neural Network) method to confirm the proposed model's significant predictors influencing CSR authenticity.

The findings provide an extended knowledge of self-compassion theory, thus contributing to the extant literature on CSR domain and healthcare marketing. The ANN findings established that social connectedness was the strongest predictor of CSR authenticity, followed by trust in the CRM campaign, isolation, mindfulness, common humanity, self-kindness, over-identification, and self-judgement. The IPMA result, consistent with ANN findings, revealed that social connectedness and trust in CRM campaigns were of relatively higher importance when predicting CSR authenticity.

The study contributes to our knowledge, and the findings would have implications for CSR practitioners in developing effective CRM campaigns for healthcare organizations. The conclusions would offer invaluable intelligence to CRM marketers, healthcare service practitioners, ad-campaign business professionals, and non-profit managers to manage critical and informed decisions.