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Record W2027909344 · doi:10.1108/11766090510635361

Value creation logics and the choice of management control systems

2005· article· en· W2027909344 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

VenueQualitative Research in Accounting & Management · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBalanced scorecardContingencyValue (mathematics)Service-dominant logicManagement control systemControl (management)Perspective (graphical)Value creationInstitutional logicService (business)EconomicsBusinessComputer scienceManagementSociologyIndustrial organizationEpistemologyMarketingArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Most, if not all, management control tools were formulated for firms employing an industrial value creation logic (i.e., Ford, McDonald’s, and Wal‐Mart). We argue that given the growth, both in number and importance, of firms employing a knowledge value creation logic (i.e., Accenture, Goldman Sachs, and Clifford Chance) and firms employing a network logic (i.e., Verizon, eBay, and Expedia) that these control tools should be revisited in light of this potentially critical contingency. This paper outlines the key characteristics of knowledge intensive firms and network service firms and then examines how these contingencies impact Simons’ (1995) Levers of Control and Kaplan and Norton’s (1996) Balanced Scorecard. We find that whilst each lever/perspective is still relevant for each value creation logic, the relative importance and thus intensity of use should vary between logics.

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)0.016
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.074
GPT teacher head0.409
Teacher spread0.336 · 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