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Combining big data and lean startup methods for business model evolution
Published on Dec 1, 2017
· DOI :10.1007/s13162-017-0104-9
Steven H. Seggie10
Estimated H-index: 10
(ESSEC Business School),
Emre Soyer5
Estimated H-index: 5
(Özyeğin University),
Koen Pauwels29
Estimated H-index: 29
(Northeastern University)
Abstract
The continued survival of firms depends on successful innovation. Yet, legacy firms are struggling to adapt their business models to successfully innovate in the face of greater competition from both local and global startups. The authors propose that firms should build on the lean startup methodology to help adapt their business models while at the same time leveraging the resource advantages that they have as legacy corporations. This paper provides an integrated process for corporate innovation learning through combining the lean startup methodology with big data. By themselves, the volume, variety and velocity of big data may trigger confirmation bias, communication problems and illusions of control. However, the lean startup methodology has the potential to alleviate these complications. Specifically, firms should evolve their business models through fast verification of managerial hypotheses, innovation accounting and the build-measure-learn-loop cycle. Such advice is especially valid for environments with high levels of technological and demand uncertainty.
  • References (61)
  • Cited By (2)
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References61
Published on Oct 1, 2012in Harvard Business Review 4.37
Andrew P. McAfee13
Estimated H-index: 13
,
Erik Brynjolfsson58
Estimated H-index: 58
Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than we...
1,341 Citations
Published on Jan 1, 1999in Sloan Management Review
Derek F. Abell1
Estimated H-index: 1
High-performing companies employ dual strategies: they maximize today's capabilities and simultaneously develop new capabilities for the future. In the past, most organizations could run and change their businesses using a single strategy; even today, most companies do not clearly discriminate between present and future. A single-strategy approach, however, cannot meet the challenges created by accelerating competition and change. Strategies for today ensure that functional and supply-chain part...
76 Citations
Published on Dec 1, 2008in Harvard Business Review 4.37
Mark W. Johnson6
Estimated H-index: 6
,
Clayton M. Christensen49
Estimated H-index: 49
,
Henning Kagermann2
Estimated H-index: 2
When does an established company need a new business model to capture a game-changing opportunity? Only companies that understand how - and why - their current model works can answer that question.
1,048 Citations
Published on Jul 13, 2010
Alexander Osterwalder16
Estimated H-index: 16
,
Yves Pigneur26
Estimated H-index: 26
Business Model Generation is a handbook for visionaries, game changers, and challengers striving to defy outmoded business models and design tomorrow's enterprises. If your organization needs to adapt to harsh new realities, but you don't yet have a strategy that will get you out in front of your competitors, you need Business Model Generation. Co-created by 470 "Business Model Canvas" practitioners from 45 countries, the book features a beautiful, highly visual, 4-color design that takes powerf...
1,663 Citations
Published on Sep 13, 2011
Eric Ries1
Estimated H-index: 1
In late 2011, The Lean Startup by Eric Ries became one of the top selling business books. A significant portion of the content is dedicated to the development of radically new products. Ries asserts that “Startup success can be engineered by following the right process, which means it can be learned, which means it can be taught” (p. 3). His stated mission is “to improve the success rate of new innovative products worldwide” (p. 8). The book is written for entrepreneurs around his definition of ...
769 Citations
Published on Jan 8, 2010
James G. March63
Estimated H-index: 63
173 Citations Source Cite
Published on Sep 1, 2013in Journal of Product Innovation Management 4.30
George Castellion2
Estimated H-index: 2
,
Stephen K. Markham13
Estimated H-index: 13
A persistent myth in product innovation and management is that the failure rate of new products is 80% or higher. How does this false idea continue to displace the conclusions of empirical studies since 1977 that the new product failure rate is 40% or less? We examine the influence of a fallacy that encourages people's unthinking acceptance of ideas on new product failure rates and whose appeal rests primarily on an emotional, rather than a reasoned, argument. Self-interest also plays a major ro...
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Published on Jan 1, 2006in Harvard Business Review 4.37
Jeffrey Pfeffer80
Estimated H-index: 80
,
Robert I. Sutton39
Estimated H-index: 39
Executives routinely dose their organizations with strategic snake oil: discredited nostrums, partial remedies, or untested management miracle cures. In many cases, the facts about what works are out there - so why don't managers use them?
509 Citations
Published on Jan 1, 1934
Karl R. Popper46
Estimated H-index: 46
Described by the philosopher A.J. Ayer as a work of 'great originality and power', this book revolutionized contemporary thinking on science and knowledge. Ideas such as the now legendary doctrine of 'falsificationism' electrified the scientific community, influencing even working scientists, as well as post-war philosophy. This astonishing work ranks alongside The Open Society and Its Enemies as one of Popper's most enduring books and contains insights and arguments that demand to be read to th...
11.1k Citations
Published on Jan 1, 1988
Jonathan Baron60
Estimated H-index: 60
Preface Part I. Thinking in General: 1. What is thinking? 2. The study of thinking 3. Good thinking: the nature of rationality 4. Logic Part II. Probability and Belief: 5. Normative theory of probability 6. Descriptive theory of probability judgment 7. Hypothesis testing 8. Judgment of correlation and contingency 9. Actively open minded thinking Part III. Decisions and Plans: 10. Normative theory of choice 11. Description of choice under uncertainty 12. Description of choice under certainty 13. ...
1,190 Citations
Cited By2
Published on Dec 1, 2017
Hubert Gatignon26
Estimated H-index: 26
(INSEAD),
Xavier Lecocq10
Estimated H-index: 10
(Lille Catholic University)
+ 1 AuthorsAlina Sorescu11
Estimated H-index: 11
(Texas A&M University)
2 Citations Source Cite
Published on Nov 9, 2018in Journal of Business & Industrial Marketing 1.83
Denis Klimanov (National Research University – Higher School of Economics), Olga A. Tretyak3
Estimated H-index: 3
(National Research University – Higher School of Economics)
Purpose The purpose of this paper is to establish a connection between the business model (BM) and B2B marketing research by developing a new approach to the BM analysis and improvement, which is based on inter-organizational networks and value chains. Design/methodology/approach The methodology is based on mutual enrichment of methods and results of BM and business-to-business marketing studies that are relatively isolated from each other, and on integration of them to the unified structured ap...
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