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Record W2075731536 · doi:10.1177/1096348005274775

The How and Who of Strategy Making: Models and Appropriateness for Firms in Hospitality and Tourism Industries

2005· article· en· W2075731536 on OpenAlexaff
Robert J. Harrington

Bibliographic record

VenueJournal of Hospitality & Tourism Research · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFranchising Strategies and Performance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDynamismHospitality industryHospitalitySalientMarketingAdaptabilityProcess (computing)TypologyTourismBusinessService (business)Flexibility (engineering)Affect (linguistics)Ideal (ethics)Knowledge managementIndustrial organizationProcess managementEconomicsComputer scienceManagementSociology

Abstract

fetched live from OpenAlex

A strategy-making process typology of ideal types is proposed describing salient dimensions from previous research. Two main dimensions are described as deliberate-emergent and individualistic-collective approaches. These dimensions address research questions of “how” strategies are formulated or implemented and “who” is involved in the process. It is proposed that, in general, firms adapt these dimensions in the strategy-making process to the level of dynamism and complexity in the environment. Further discussion provides insight into appropriate choices of strategy-making process models for firms in the food-service industry and expresses the need for researchers and managers to consider the degree and type of dynamism and complexity, firm size, level of analysis, level of strategy or tactic, culture, and institutional factors. It is suggested that service firms have a potential need to utilize multiple models simultaneously to affect conflicting objectives of control and adaptability.

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.011
Scholarly communication0.0140.011
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.328
Teacher spread0.252 · 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 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

Citations26
Published2005
Admission routes1
Has abstractyes

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Same venueJournal of Hospitality & Tourism ResearchSame topicFranchising Strategies and PerformanceFrench-language works237,207