Cognitive Microfoundations of Industry Foresight and Strategic Investments: Evidence Within the Global Automotive Industry / Daniel H Wilde.

Format:

Book

Thesis/Dissertation

Author/Creator:

Wilde, Daniel H., author.

Contributor:

University of Pennsylvania. Management, degree granting institution.

Language:

English

Subjects (All):

Management--Penn dissertations.

Penn dissertations--Management.

Local Subjects:

Management--Penn dissertations.

Penn dissertations--Management.

Physical Description:

1 online resource (185 pages)

Distribution:

Ann Arbor : ProQuest Dissertations & Theses, 2023

Contained In:

Dissertations Abstracts International 84-12A.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2022.

Language Note:

English

Summary:

During industry change, managers and entrepreneurs often must make strategic investment decisions well in advance of knowing when and how new technologies and business models would reshape the industry landscape. Scholars have emphasized the important role of managers' forward-looking cognition amidst this uncertainty as a driver of decision-making and ultimately firm performance. However, extant literature is surprisingly unclear regarding how managers can generate superior forward-looking cognition (industry foresight) in the first place. Moreover, we lack a clear understanding of how the collection of individual-level cognitions within the firm may influence firm-level strategy or performance. This dissertation helps begin to fill this void by means of three chapters that utilize various novel datasets including over 21,000 forecasts made by over 3,500 individuals within forecasting tournaments from 2016-2019, over 2.5 million automotive industry-related quotes, investment and patent data of 56 major automobile manufacturers from 1990-2020, and a field experiment at a top automaker from 2018-2019. Chapter 1 finds evidence that Bayesian belief updating helps generate superior industry foresight, especially under moderate uncertainty, and that not updating beliefs at all, or updating but overreacting to new information significantly penalizes forecasting accuracy. Chapter 2 finds that the expected industry foresight or forecastability of a forecasting problem depends on the problem's complexity (high vs. low) and ambiguity (ill-structured vs. well-structured), with the highest for low-complexity well-structured problems and the lowest for high-complexity ill-structured problems, and that non-Bayesian belief updating is particularly ineffective for high-complexity ill-structured problems. Finally, Chapter 3 finds that higher variation in forward-looking beliefs among the firm's senior and middle managers (i.e., cognitive variation) regarding the emerging technology is associated with making smaller incremental investments (e.g., CVC) in the technology and that lower cognitive variation is associated with making larger investments (e.g., M&A) in the technology. Ultimately, this dissertation sheds important light on the micro- and macro-level cognitive microfoundations of navigating industry change and offers important contributions to theory, methodology, and practice.

Notes:

Source: Dissertations Abstracts International, Volume: 84-12, Section: A.

Advisors: Kapoor, Rahul; Committee members: Tetlock, Philip; MacDuffie, John Paul; Levinthal, Daniel.

Department: Management.

Ph.D. University of Pennsylvania 2023.

Local Notes:

School code: 0175

ISBN:

9798379750596

Access Restriction:

Restricted for use by site license.