ABSTRACT
Learning is critical for land management agencies implementing new policies in the face of rapid social and ecological change. We investigated learning in the U.S. Forest Service as it implemented new planning regulations. Our research objectives were to: (1) identify collective learning processes and outcomes during this time, and (2) understand factors within the organization supporting or impeding learning. Based on participant observation and 25 interviews with planning personnel, we found evidence of collective learning on individual national forests and across the organization. Several factors helped the agency act as a ‘learning organization,’ including internal networks and tools for information sharing, and meetings for staff to exchange lessons learned. Learning was compromised by limited time and capacity, and lack of internal clarity about balancing the desire for innovation with the need to ensure legal compliance and meet deadlines. This work contributes to the empirical foundations of collective learning theory, allowing us to identify learning processes and outcomes at multiple levels in a public organization, and identifying topics for future research. Based on our exploration of organizational learning, we offer suggestions for how to effectively support learning during times of new policy implementation.
Acknowledgements
A special thanks to all of our interviewees, the US Forest Service Ecosystem Management Coordination staff for inviting us to participate in and study their learning processes, and everyone in the Public Lands Policy Group that helped with the Planners’ Meeting.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Gwendolyn Ricco completed her M.S. in Forest Sciences at Colorado State University in August of 2017.
Courtney A. Schultz is associate professor of forest and natural resource policy at Colorado State University.
ORCID
Gwendolyn Ricco http://orcid.org/0000-0001-8383-178X
Courtney A. Schultz http://orcid.org/0000-0002-9972-7802