Breakthrough invention and problem complexity: Evidence from a quasi-experiment

Yuchen Zhang (First Author), Wei Yang (Participant Author)

Research output: Contribution to journalJournal

2 Citations (Web of Science)

Abstract

Research Summary: Problem formulation is central to recombinant invention, and problem complexity particularly shapes the process and outcome of knowledge recombination. However, research on the antecedents of problem complexity remains limited. This study examines how breakthrough inventions may serve as an important antecedent of the complexity of problems formulated by individual inventors. We propose that a breakthrough invention may facilitate inventors' appreciation of novel knowledge couplings and improve their overall comprehension of knowledge interdependence for problem formulation, thus increasing problem complexity. We further argue that inventors' prior search breadth and experimentation strengthen the above effect. By exploiting the unexpected victory of AlphaGo and tracking the questions posted on StackOverflow.com by developers interested in deep learning, we find empirical evidence that supports our hypotheses. Managerial Summary: Because formulating complex problems may result in novel and valuable inventions, it is helpful to understand what drives inventors to formulate problems of higher complexity. In this study, we propose that breakthrough inventions can serve as an important antecedent of problem complexity. In the context of deep learning, we find that developers formulate problems of higher complexity after the unexpected victory of AlphaGo, a widely acknowledged inventive breakthrough. Further, the above effect is more pronounced when developers have broad search experience in the past or engage frequently in experimentation. The insights of this study are not only relevant to managerial practices within the dynamic field of deep learning but also generalizable to broader technological contexts with breakthrough inventions.
Original languageEnglish
JournalStrategic Management Journal
Early online date14 Jun 2022
DOIs
Publication statusPublished - 2022

Corresponding author email

yzhang54@tulane.edu

Keywords

  • breakthrough invention
  • deep learning
  • knowledge recombination
  • machine learning
  • problem complexity
  • problem formulation

Indexed by

  • SSCI
  • ABDC-A*
  • FT

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