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Research

Please see below for an overview of my research pipeline.

Publications (as of 2023)

Strategic Entrepreneurship Journal


Born Into Chaos: How Founding Conditions Shape Whether Ventures Survive or Thrive When Experiencing Environmental Change

D. Carrington Motley; Eesley, Charles; Koo, Wesley

Research Pipeline

Mind What Matters: The Locus of Negative Feedback and New Venture Performance

Working Paper

Motley, D. Carrington; Leatherbee, Michael; Katila, Riitta

Under review (paper details withheld)

Organizational Learning: How to Learn From Failure and Success

Working Paper

Motley, D. Carrington

Review paper covering organizational learning from performance feedback (specifically failure and success) in the context of entrepreneurship across three levels of analysis: indviduals, teams, and organizations.

Turning Failure Into Fuel: Lessons From Serial Entrepreneurship

Initial Draft

Motley, D. Carrington

What enables serial entrepreneurs to improve performance of subsequent ventures following a failed venture? Prior research on serial entrepreneurs tends to highlight the fact that entrepreneurs with failed startups in their history are less likely to found future successful ventures than their peers. However, entrepreneurs themselves and organizational learning research, both, tout the value of failure experience. I resolve the tension between these two sets of findings by establishing a series of boundary conditions within which experience with failed ventures can be an asset to entrepreneurs. In doing so, I aim to contribute to the literature on entrepreneurship by providing evidence that failure can, in fact, be beneficial to entrepreneurs in a similar fashion to failure in other contexts. Additionally, I will contribute to the literature on organizational learning by delineating a series of conditions that enhance the ability of individuals to learn from idiosyncratic failures.

On the Origins of Pivots: the Relationship Between Founding Teams, Founding Conditions, and the likelihood of Business Idea Change

Data Analysis

Motley, D. Carrington; Eesley, Charles

Recent research on entrepreneurs provides evidence that ventures face their own unique types of inertial forces and may not be as flexible as previously imagined. However, it is also clear that startups often implement business idea changes leaving open the question of what drives differences in ventures' ability to change course. We develop theory arguing that the nature of a venture's founding conditions and founding team are connected to where business idea changes will be implemented. Our hypotheses are tested using a unique data of over 1000 entrepreneurs from a survey of univesity alumni.

Never the Less They Persist: Entrepreneurs and the Venture Funding Gap

Data Collection

Motley, D. Carrington and Yimfor, Emmanuel

Received IRB approval to distribute survey to large pool of entrepreneurs to investigate the main barriers that underlie the funding gap for minority entrepreneurs seeking venture funding. Currently finalizing the structure of the survey for an initial pilot. After pilot is complete, we will refine the survey & distribute to the full sample of entrepreneurs. After survey data has been collected, the survey data set will be merged with a previously collected data set on venture fund raising figures for entrepreneurs of different ethnicities (collected from Crunchbase, Pitchbook, and LinkedIn).

Size Isn’t Everything: The Effects of Failure Size & Locus of Experience

Data Analysis

Motley, D. Carrington

This study seeks to reconcile the contradictory findings of prior research regarding whether it is easier to learn from small or large failures. By examining how failure size and locus of experience interact we will develop a better understanding of the optimal conditions for learning from failure. Relying on theory regarding attribution bias and indirect learning, we hypothesize that entrepreneurs will learn best from their own small failures and from other people's large ones. We test our hypotheses by analyzing the text-based communication data of entrepreneurial teams participating in a university-based accelerator class.

Additional Info

Professional skillset

Alumni surveys

Regression analysis: Hazard models, selection models, panel data

Industry partnerships

Text analysis

Collaborators

I really enjoy working on teams and learning from other people. Below is a list of people with whom I am currently developing papers.

Charles Eesley (Advisor) Stanford University – Management Science & Engineering

Riitta Katila Stanford University – Management Science & Engineering

Michael Leatherbee Pontificia Universidad Católica de Chile – Industrial and Systems Engineering

Daniel Armanios Carnegie Mellon University – Engineering and Public Policy

Natharat Mongkolsinh Carnegie Mellon University – Engineering and Public Policy

Jeremy Utley Stanford University – Stanford d.school

Perry Klebahn Stanford University – Stanford d.school

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