Causal Layered Analysis: An Integrative and Transformative Theory and Method
This report explores how causal layered analysis (CLA), a futures studies technique, can enhance organizational innovation by providing a deeper understanding of challenges and opportunities.
(Generated with the help of GPT-4)
| Quick Facts | |
|---|---|
| Report location: | source |
| Language: | English |
| Publisher: |
|
| Authors: | Prof A Roux, Hendrik Albertus Kotze, Sohail Inayatullah |
| Geographic focus: | Global |
Methods
The research method involved a literature review on innovation and futures studies, focusing on causal layered analysis (CLA). The study also included a post-analysis application of CLA to three successful innovations to test the hypothesis that addressing needs at deeper layers leads to more enduring innovations.
(Generated with the help of GPT-4)
Key Insights
The research investigates causal layered analysis (CLA) as a method to enrich organizational innovation processes. It examines the role of innovation in organizations, the knowledge economy, and the challenges faced during innovation. The study delves into futures studies and how they can address innovation challenges by creating knowledge and understanding. CLA is presented as a tool for deeper analysis, and its potential to improve innovation is assessed by applying it to successful innovations like the internet, personal computers, and mobile phones. The findings suggest that CLA can help organizations understand customer needs and identify opportunities, thereby increasing the chances of innovation success.
(Generated with the help of GPT-4)
Additional Viewpoints
Categories: English publication language | Global geographic scope | causal layered analysis (cla) | customer needs | futures | futures studies | idea selection | innovation | internet | knowledge economy | mobile phones | organizational culture | personal computers | technological advancements