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Record W2604998008 · doi:10.1177/0306307017690518

Using Porterian activity analysis to understand organizational capabilities

2017· article· en· W2604998008 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of General Management · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsArgument (complex analysis)Knowledge managementPoint (geometry)Resource (disambiguation)Organizational performanceDynamic capabilitiesResource-based viewComputer scienceSociologyBusinessManagement scienceProcess managementManagementCompetitive advantageMarketingEngineeringEconomicsMathematics

Abstract

fetched live from OpenAlex

The article argues that organizational capabilities are comprised of two fundamental components: resources and activities. The starting point of the argument is that resources are best conceptualized by Barney’s and others’ research on the resource-based view, while activities are best conceptualized by Porter’s writings on the activity-based view. Porterian activity analysis is becoming more accepted in the strategy literature, but no strategy scholar has explicitly used Porter’s activities, and particularly his concept of drivers, to understand and analyze organizational capabilities. Introducing Porterian activities into the discussion of capabilities improves strategy scholars’ understanding of the bases of capability heterogeneity, offers academics future directions for research, and provides managers with guidance to enhance their organizations’ capabilities.

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.

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 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 score0.886
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.051
GPT teacher head0.292
Teacher spread0.241 · 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