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Record W2057103008 · doi:10.1016/j.jom.2011.01.002

What drives financial performance–resource efficiency or resource slack?

2011· article· en· W2057103008 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 Operations Management · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsExtant taxonStock (firearms)Resource (disambiguation)Resource efficiencyBusinessIndustrial organizationEconomicsResource-based viewEnvironmental economicsMicroeconomicsFinanceMarketingComputer scienceCompetitive advantage

Abstract

fetched live from OpenAlex

Abstract Extant research in operations management has revealed divergent insights into the value potential of resource efficiency. While one view relates efficiency with good operations management and asserts that slack resources are a form of waste that should be minimized, the other view suggests that limited resource slack can impose heavy costs on firms by making them brittle. In this research, the authors build on these views to investigate the relationship of inventory, production, and marketing resource efficiency of firms with three metrics of financial performance (i.e., Stock‐Returns, Tobin's Q, and Returns‐on‐Assets). The authors evaluate the theoretical framework using secondary information on all U.S. based publicly‐owned manufacturing firms across the 16‐year time period of 1991–2006. Analysis utilizing a mixed‐model approach reveals that a focus on resource efficiency is positively associated with firm financial performance. However, findings also support the arguments favoring slack, indicating that the financial gains from resource efficiency exhibit diminishing returns.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0010.004
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.225
Teacher spread0.193 · 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