RESEARCH INTERESTS
Topics: Industry Foresight, Technology Strategy, Behavioral Strategy
Methods: Quantitative, NLP, Machine Learning, Forecasting Tournaments
PUBLICATIONS
Peering Into a Crystal Ball: Forecasting Behavior and Industry Foresight
(equal authorship with Rahul Kapoor)
Distinguished Paper Award in Behavioral Strategy, Process and Change at the 80th Academy of Management Annual Meeting
forthcoming Strategic Management Journal
ABSTRACT:
Research summary
What makes some managers and entrepreneurs better at forecasting the industry context than others? We argue that, regardless of experience or expertise, a learning-based forecasting behavior in which individuals attend to and incorporate new relevant information from the environment into an updated belief that aligns with the Bayesian belief updating process is likely to generate superior industry foresight. However, the effectiveness of such a cognitively demanding process diminishes under high levels of uncertainty. We find support for these arguments using an experimental design of forecasting tournaments in the managerially relevant context of the global automotive industry from 2016-2019. The study provides a novel account of individual-level forecasting behavior and its effectiveness in an evolving industry, and suggests important implications for managers and entrepreneurs.
Managerial summary
How a focal industry will evolve is a key forecasting problem faced by managers and entrepreneurs as they seek to identify opportunities and make strategic decisions. However, developing superior industry foresight in the face of significant change, and limited and often contradictory information, can be especially challenging. We study how individuals forecast the ongoing transformation of the global automotive industry with respect to electrification and autonomy, using a novel research design of forecasting tournaments. A forecasting process in which individuals update their beliefs by neither ignoring prior information nor overeacting to new information helps to generate superior industry foresight. There was a significant penalty to forecasting accuracy when individuals did not update their beliefs at all, or when they updated, but overreacted to new information.
KEYWORDS: Industry Evolution, Forecasting, Belief Updating, Automobile Industry, Bayesian Process
WORKING PAPERS
Driving Commitment: Evaluative Divergence and Strategic Decision-Making in the Auto Industry
Job Market Paper of Dissertation, which won The Outstanding Dissertation Award for my field of Strategy and Runner-Up award in the INFORMS/Organization Science Dissertation Proposal Competition
Under review
ABSTRACT: Firms navigating technological change face profound uncertainty, yet the role of internal beliefs in shaping investment decisions related to emerging technologies remains largely unexplored. This study introduces evaluative divergence—variation in managerial evaluations (positive, neutral, negative) regarding emerging technologies—and examines its impact on investment likelihood and strategy. Using a novel dataset of over 250,000 managerial quotes from the automotive industry, collected and analyzed via large-scale textual analysis, I find that high evaluative divergence significantly reduces investment likelihood, not only compared to firms with positive consensus, but also compared to firms with neutral consensus. Notably, negative consensus does not deter investment less than high divergence, challenging the assumption that all consensus facilitates action. Furthermore, among firms that invest, high divergence favors flexible investments over large, commitment-heavy investments. Post hoc analyses reveal that high evaluative divergence is uniquely associated with linguistic markers of heightened risk perception, anxiety, and tentativeness, suggesting that these psychological factors may mediate its impact on investment decisions. These findings illuminate the critical role of internal belief heterogeneity in shaping strategic choices under uncertainty and highlight the distinct forms of consensus as firms navigate technological change. The study also offers a useful template to extract and attribute quotes at scale, enabling a complementary approach to systematically study cognition in a variety of organizational contexts.
KEYWORDS: managerial cognition, evaluative divergence, flexibility, commitment, automotive industry, technological change
Forecasting as a Problem of Cognitive Search: Experimental Evidence from Forecasting Tournaments in the Context of the Auto Industry
(equal authorship with Rahul Kapoor)
Distinguished Paper Award in Strategic Leadership and Corporate Governance at Academy of Management Annual Meeting
Under review
ABSTRACT: Having superior industry foresight has been deemed as an important enabler for effective decision making. We explore the antecedents of superior industry foresight by conceptualizing the forecasting process as a problem-solving process. Individuals face the problem of forecasting the specific industry context under conditions of significant uncertainty and limited information by searching for relevant information and developing conjectures about the specific industry outcome. We argue that an individual’s ability to forecast accurately will depend on the problem’s complexity and structure. Forecasting accuracy would be highest for low-complexity well-structured problems, lowest for high-complexity ill-structured problems, and intermediate for high-complexity well-structured and low-complexity ill-structured problems. The data for the study were collected from two successive year-long forecasting tournaments conducted between 2017 and 2019, focusing on the evolution of the automotive industry shaped by the emergence of electric and autonomous vehicles. Evidence from more than 15,000 forecasts made by over 2,300 individuals who participate in a leading global forecasting platform, offers support for our arguments. We also explore how individuals may improve their forecasting by updating their beliefs, and for which types of forecasting problems the belief updating process is likely to be more effective.
KEYWORDS: Problem Solving, Cognitive Search, Problem Complexity, Ill-structured problems, Industry Trends
Unpacking the Foresight-Performance Paradox
(with Mark Packard)
Under review
ABSTRACT: The prevailing belief within the strategy and entrepreneurship fields is that superior foresight has a straightforward positive relationship with firm performance. However, we argue that this relationship is far more nuanced and context-dependent, leading to a "foresight-performance paradox" where foresight can both enhance and hinder organizational outcomes. To unpack this puzzle, we develop a novel theoretical framework that delineates foresight along two fundamental dimensions: scope (convergent vs. divergent) and orientation (internal vs. external), yielding four distinct types of foresight. We theorize that the impact of each foresight type on firm performance is contingent upon its alignment with the prevailing uncertainty context (ambiguity, environmental, creative, and absolute) and the firm's strategic emphasis on different innovation archetypes (incremental, radical, architectural, and disruptive). Drawing upon the Theory-Based View and integrating insights from the alignment and inertial perspectives, we develop eight propositions outlining the conditions under which each foresight type should enhance or hinder firm performance. This research offers a more comprehensive and contingent understanding of the foresight-performance relationship and provides a critical roadmap for future theoretical and empirical exploration, including the measurement of distinct foresight types, the investigation of their antecedents and dynamics, and the role of artificial intelligence in shaping foresight.
KEYWORDS: foresight-performance tradeoff, divergent foresight, convergent foresight, internal foresight, external foresight
The Long and Short of It: Individual-Level Industry Forecasting and Temporal Duality
(equal authorship with Rahul Kapoor)
Best Paper Prize, Behavioral Strategy at Strategic Management Society Annual Conference
Preparing for submission
ABSTRACT: Forecasting the industry context underpins much of the canonical perspectives within the field of Strategy. Additionally, many of the core tradeoffs illuminated in the field are premised upon different forward-looking expectations between short-term and long-term outcomes. While extant literature has begun to offer important clarity regarding antecedents toward superior foresight, the focus has largely been on understanding foresight around short-term outcomes, which is but half of the story. In this study, we complement existing literature by offering a perspective of how individuals navigate the complexities of forecasting across different temporal horizons: a duality between short- and long-term issues. Integrating the behavioral decision theory and industry evolution literatures, we argue that individuals tend to overreact in the short-term of industry evolution and underreact in the long term due to a fundamental difference between common linear cognitive processes and nonlinear (S-curve) industry outcomes. Utilizing a novel dataset of 7,569 forecasts by 505 participants who participated in parallel short-term and long-term industry forecasting tournaments related to the evolution of the automotive industry, we find support for our hypotheses.
KEYWORDS: long-term foresight, forecasting tournaments, automotive industry
RESEARCH IN PROGRESS
Project on managerial evaluations, structure, and decision making outcomes
(with Tomasz Obloj)
Data analysis stage
Project on counterfactual thinking and foresight
(with Rahul Kapoor & Phil Tetlock)
data analysis
Effortful Innovation Emergence: Evidence from the Nuclear Fusion Industry
(with Jax Kirtley)
data gathering stage
AWARDS & HONORS
Winner—Outstanding Dissertation Award, Strategy Division (2024)
Finalist—Industry Studies Association Dissertation Award (2024)
Best Paper Proceedings, Academy of Management (2024)
Behavioral Strategy Best Paper Prize, SMS (2023)
Runner-up of the INFORMS/Organization Science Dissertation Proposal Competition (2021)
Distinguished Paper Award—STR Division in Strategic Leadership and Corporate Governance (2021)
Best Paper Proceedings, Academy of Management (2021)
Distinguished Paper Award—STR Division in Behavioral Strategy, Process and Change (2020)
Best Paper Proceedings, Academy of Management (2020)
Gladiator Champion—1st place public speaking award for BYU MBA class of 2017 (2016)
PYTHON CODE
Factiva Articles Download to Dataframe
PURPOSE: This code takes html-formatted articles from Factiva search results and iteratively converts the html to a single dataframe.
News Article Quote Extraction and Attribution
PURPOSE: This code takes the dataframe from code above and extracts quotes and paraphrases and attributes each to the speaker (speaker's full name, speaker title/role, speaker affiliated organization). It also collects information associated with organizations.
INTERACTIVE AUTOMOTIVE INDUSTRY NETWORK DATA
Below is a sample of the inter-firm connections across the global automotive industry as of 2020. I gathered these data as part of my dissertation work with a mix of Pitchbook, SDC Platinum, Crunchbase, and Google. Each node (blue oval) represents a firm in the industry and each link (blue line) between nodes represents an investment of some kind (e.g., joint venture, alliance, minority investment, acquisition). Can share upon request.
The network is interactive, so feel free to zoom in, click and drag any node as you see fit.
NOTE: Given the complexity of the network, it may take a minute or two to show up on your browser.