Final answer:
Double-loop learning is characterized by a focus on continuous experimentation and feedback, and the reevaluation of organizational problem-solving frameworks. It involves insight learning and is integral to continuous development in rapidly changing environments.
Step-by-step explanation:
The primary characteristic of double-loop (generative) learning would best be described as emphasizing continuous experimentation and feedback in an ongoing examination of the very way organizations go about defining and solving problems. This approach involves a deep reflection on the underlying policies, assumptions, and objectives that frame problems and dictate how they are addressed. Unlike single-loop learning, which focuses on solving problems within a set of given parameters, double-loop learning challenges and reconfigures these parameters themselves.
Double-loop learning is pertinent to continuous development and insight learning, as it goes beyond mere application of existing knowledge. It requires the capability to reflect on one's actions and the results, and to then use this insight to modify the objectives and the strategies used to achieve them. This type of learning is proactive and dynamic, suitable for environments characterized by rapid change and complexity.