Berkun takes a careful look at innovation history, including the software andInternet Age, to reveal how ideas truly become successful innovations--truthsthat people can apply to today's challenges.
Debunks 10 common myths, including: the Eureka Myth; the Lone Creator Myth; the Incentive Myth; and The Brainstorming Myth Written by David Burkus, founder of popular leadership blog LDRLB For anyone who struggles with creativity, or who ...
SUMMARY: The Myths Of Innovation By Scott Berkun
In The Truth About Tesla, Christopher Cooper sets out to prove that the conventional story not only oversimplifies history, it denies credit to some of the true inventors behind many of the groundbreaking technologies now attributed to ...
They were forced to adapt to a confluence of multiple disruptions inextricably linked to a longer-term, ongoing digital disruption. This book shows that companies that use disruption as an opportunity for innovation emerge from it stronger.
This open access book examines how the social sciences can be integrated into the praxis of engineering and science, presenting unique perspectives on the interplay between engineering and social science.
This book explains to technical and non-technical readers alike what it takes to get through a large software or web development project. It does not cite specific methods, but focuses on philosophy and strategy.
List of Tables and Figures; List of Acronyms; Acknowledgements; Introduction: Thinking Big Again; Chapter 1: From Crisis Ideology to the Division of Innovative Labour; Chapter 2: Technology, Innovation and Growth; Chapter 3: Risk-Taking ...
This myth-busting book shows large companies can construct a strategy, system, and culture of innovation that creates sustained growth.
This book covers: Architecture and strategy: Adopt a strategic architectural mindset to make a meaningful material impact Creating your strategy: Define the components of your technology strategy using proven patterns Communicating the ...
AI reasons from statistical correlations across data sets, while common sense is based heavily on conjecture. Erik Larson argues that hyping existing methods will only hold us back from developing truly humanlike AI.