MétaCan
Menu
Back to cohort
Record W4323316614 · doi:10.1108/jbs-07-2022-0136

Strategic pivoting: how organizations can shift attention whatever their size

2023· article· en· W4323316614 on OpenAlexaff
Mark N. Wexler, Judy Oberlander

Bibliographic record

VenueJournal of Business Strategy · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsFraser InstituteSimon Fraser University
Fundersnot available
KeywordsContext (archaeology)Flexibility (engineering)OriginalityStakeholderBusinessOrganizational architectureValue (mathematics)AmbidexterityStrategic managementProcess managementMarketingPath (computing)Strategic planningConceptualizationDynamic capabilitiesDimension (graph theory)Computer scienceEconomicsKnowledge managementManagementSociology

Abstract

fetched live from OpenAlex

Purpose Strategic pivoting, the decision to invest in shifting the attention of an organization, is no longer limited to early-stage organizations and entrepreneurs but has, without a discussion of complications, been applied to large corporations and public agencies. Design/methodology/approach This conceptual paper defines strategic pivoting, highlights the centrality of pivoting in new and entrepreneurial organizations and critically examines its application as a strategy fostering organizational agility in corporations. Findings Pivoting in the corporate context complicates the ease of executing an attention shift by introducing a path-dependent momentum that requires modification of the time horizon, stakeholder strategy and the frequency of pivoting. Practical implications This comparative examination of pivoting highlights the importance of organizational size, complexity, degree of specialization and path-dependent history when deciding to pivot. Originality/value The present ease with which the strategic pivot is treated as an adaptive strategy to corporate leaders seeking greater flexibility overstates the ease of execution.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
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 score0.823
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.036
GPT teacher head0.229
Teacher spread0.194 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Business StrategySame topicInnovation and Knowledge ManagementFrench-language works237,207