Startups that pivot without clear reasoning or strategic testing risk failure. New research unveils how AI and structured experimentation can help entrepreneurs make impactful, growth-driven pivots instead of reactive, ineffective ones.
Research: The Theory-Based View and Strategic Pivots: The Effects of Theorization and Experimentation on the Type and Nature of Pivots. Image Credit: Lightspring / Shutterstock
For startups, pivoting is challenging and risky. The big question is not "whether to pivot" but how to identify pivots that will create a meaningful difference in performance. Consider the origin of X/Twitter: podcast platform Odeo. iTunes and Apple's entry into this space prompted a pivot to microblogging. The rest is history.
But for every successful pivot like the one producing Twitter, countless founders and startups are forgotten or never heard of because they do not engage in purposeful pivots–those that are focused, coherent, and impactful. Instead, they abide by the "pivot often in response to customer feedback" mantra and undertake reactive or remedial pivots to make superficial changes in their business model, thus failing to unlock the value-creation potential of their underlying ideas.
New research co-authored by Rajshree Agarwal, the Rudolph Lamone Chair of Strategy and Entrepreneurship at the University of Maryland's Robert H. Smith School of Business, tackles the deficiency. Using AI tools such as large language models (LLMs), the article provides new evidence for how entrepreneurs pivot and why it matters.
Either a "bias for action" (acting without articulating reasons why) or "paralysis by analysis" (overthinking to the point of indecision) can stymie a startup's ability to engage in the right type of pivot, one that delivers on its survival and growth goals. To counter this, "we show how and why entrepreneurs who take the time to articulate the 'why' and utilize experiments to test their assumptions are more likely to make pivots that are purposeful and coherent," says Agarwal, who directs Smith's Ed Snider Center for Enterprise and Markets and co-authored the findings in the journal Strategy Science with Smith PhD student Jacob Valentine and Elena Novelli, professor of strategy at Bayes Business School (UK).
Agarwal says the paper "unpacks the two dimensions of theorization and experimentation by showing how they are complementary for pivots by mixing LLM techniques with business case analysis."
The researchers used these mixed methods of examining rich data from more than 1,600 interviews with 261 entrepreneurs in London over a nine-month period in 2019 and 2020.
More broadly, the work has created a publicly available AI-generated dictionary of words and algorithms for future research. The authors write that this has "important implications for practitioners (e.g., entrepreneurs, incubators/accelerators, and business leaders) and policymakers (e.g., government agencies, such as the National Science Foundation and the National Institutes of Health that sponsor entrepreneurial and strategic training programs). "
Read the study: "The Theory-Based View and Strategic Pivots: The Effects of Theorization and Experimentation on the Type and Nature of Pivots."