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The Role of Emotional Regulation in the Relationship between Nurses' Creative Style and Innovation Behaviors: A Cross-Sectional Study.

Ferdinando ToscanoDavide GiusinoRaffaello DianaTayebe Rahimi Pordanjani
Published in: Nursing reports (Pavia, Italy) (2023)
Innovation is crucial to an effective healthcare system, and nurses are key figures in the innovation process. A potential factor behind innovation in nursing is the creative style of nurses. Creativity is an essential component of innovation. However, the relationship between creative style and innovation is complex and involves many different factors. Among them, given the nature of the nursing profession, we propose emotional regulation, or the ability to effectively manage one's emotions. In this study, we hypothesize that two specific emotion-regulation strategies, positive reappraisal and putting into perspective, play a role in the relationship between nurses' creative style and innovative behaviors. We tested a moderated mediation model using cross-sectional data from 187 nurses working in 3 university hospitals in Bojnord, Iran, in 2019. Our results show that positive reappraisal completely mediates the relationship between creative style and innovative behaviors, while putting into perspective moderates the relationship between positive reappraisal and innovative behaviors. These results suggest that nurses with a flair for creativity may be able to implement innovative behaviors in the workplace due to their ability to understand work-related situations and events positively. This may be especially true for nurses who can adopt alternative viewpoints. Our study discusses these findings by highlighting the importance of emotional regulation mechanisms in transforming nurses' creativity into effective innovation. Finally, we provide suggestions for healthcare organizations to promote innovation as an added value in the healthcare environment and services provided.
Keyphrases
  • healthcare
  • mental health
  • cross sectional
  • social media
  • risk factors
  • deep learning