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Record W7064703363

The competitiveness of Canada's food processing industry, a resource-based approach

2000· dissertation· en· W7064703363 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2000
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Leverage (statistics)Context (archaeology)Product (mathematics)Order (exchange)ImitationNature versus nurture
DOInot available

Abstract

fetched live from OpenAlex

This dissertation has three broad objectives. The first objective is to test the applicability of the resource-based theory of the firm in the context of the Canadian food processing industry. The second objective is to provide insight into other internal factors that may be contributing to the competitiveness of Canadian food processing companies. The third objective is to validate two competitiveness hypotheses, namely that operational flexibility is a competency of Canadian food processing companies and that strategic flexibility is an attribute of these companies. In order to address these objectives, a theoretical model based on the resource-based theory of the firm is developed and validated using the case method of research. The results suggest that the resource-based theory of the firm is applicable to managers in that managers were found to develop, nurture and leverage their competencies. However, the results also challenge two assertions of the resource-based theory of the firm literature, namely that companies should invest in barriers to imitation to protect their competencies, but most particularly that they should invest in the creation of causal ambiguity. The results also suggest that although the companies developed, nurtured and leveraged their competencies, other internal factors were important to their performance and competitiveness, including managing customer relationships and product innovation. Furthermore, the results establish that it is important for companies to understand their competencies, as well as these other factors, and interrelationships between them. This research did not validate the operational flexibility hypothesis given that only two companies were found to have it as a competency. The results, however, did illustrate that several dimensions of flexibility, such as mix and new product flexibility, must be present for a company to be able to draw a competitive advantage from operational flexibility. This research did validate the strategic flexibility hypothesis given that companies were found to monitor developments in their external environment, respond quickly to changing competitive conditions, and reassess components of their strategies. However, this research also found that the accumulation of market knowledge by managers was important to their strategic flexibility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0080.005
Scholarly communication0.0110.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.194
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2000
Admission routes1
Has abstractyes

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