Combining CSR, Innovativeness and Per- formance in relation to Sustainability in the Pharmaceutical Industry: Challenges and Best Practices
Jacq, Inès (2025)
Jacq, Inès
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025060319632
https://urn.fi/URN:NBN:fi:amk-2025060319632
Tiivistelmä
Weaving social responsibility into strategy—through dedicated governance bodies—builds trust and loyalty, while digital tools in R&D, manufacturing, and supply chains drive efficiency and free up capacity for sustainability. Public–private research partnerships combined with digital SOP platforms accelerate innova- tion and reduce compliance risks. Specialized access-to-medicine programs extend patient support and bolster resilience in crises. By uniting CSR oversight, digital transformation, and eco-efficiency, companies can simultaneously achieve meaningful social impact and lasting financial gains. This study used a qualita- tive analysis through archival research based on secondary data from relevant sources. The analysis is ba- sed on a review of both academic and non-academic sources. This research follows an interpretivist philo- sophy with a descriptive purpose and an inductive research approach. It applies the mono-method design in the form of Archival research, longitudinal and based on collection of secondary sources for the period of the last forty years.
Achieving a sustainable business model in the pharmaceutical industry is possible when CSR, innovation, and profitability are treated not as separate objectives, but as interdependent pillars. To deepen and broaden the understanding of CSR–profit integration in pharmaceuticals I would use longitudinal causal inference, context-rich qualitative deep dives, and AI-powered foresight.
Achieving a sustainable business model in the pharmaceutical industry is possible when CSR, innovation, and profitability are treated not as separate objectives, but as interdependent pillars. To deepen and broaden the understanding of CSR–profit integration in pharmaceuticals I would use longitudinal causal inference, context-rich qualitative deep dives, and AI-powered foresight.