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Incumbent Firm Adaptation: New Perspectives on Organizational Change

2014· article· en· W2331376493 on OpenAlexaboutno aff
Michael L. Tushman

Bibliographic record

VenueAcademy of Management Proceedings · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptation (eye)Organizational changeOrganizational studiesPower (physics)Organization developmentOrganizational learningOrganizational theoryBusinessCompetitive advantageOrganizational identityDynamic capabilitiesOrganizational architectureOrganizational commitmentKnowledge managementPublic relationsSociologyPolitical scienceManagementMarketingPsychologyEconomicsComputer science

Abstract

fetched live from OpenAlex

The ability of organizations to innovate and adapt to changes in the external environment is a critical component of competitive success. Research on incumbent firm adaptation has historically focused on the importance of developing new capabilities; increasingly, scholars are highlighting that adaptation also requires shifts in organizational characteristics and processes. The purpose of this symposium is to showcase current research that offers a range of perspectives on how incumbent firms engage in organizational change as their environments shift. Some of the papers draw attention to the implications of elements of organizations that have been given relatively little attention in the literature on incumbent adaptation—for example, the role of intra-organizational power struggles, routines, and organizational identity. Others consider the unique ways in which incumbents, versus new entrants, react to environmental change. The papers take a range of different theoretical and methodological approaches to the topic. Our symposium is intended to integrate these approaches to broaden our general understanding of organizational change. Truce Breaking and Remaking: The CEO's Role in Changing Organizational Routines Presenter: Sarah Kaplan; U. of Toronto

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.245
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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