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Record W4249858146 · doi:10.1177/001088040004100532

Managing for Excellence

2000· article· en· W4249858146 on OpenAlexaff
Laurette Dubé, Cathy A. Enz, Leo M. Renaghan, Judy A. Siguaw

Bibliographic record

VenueCornell Hotel and Restaurant Administration Quarterly · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsMcGill University
Fundersnot available
KeywordsMarketingBusinessExcellenceBest practiceProfitability indexLoyaltyProfit (economics)Value (mathematics)Customer serviceLoyalty business modelCustomer satisfactionService (business)Function (biology)Service qualityManagementEconomics

Abstract

fetched live from OpenAlex

A study of the U.S. lodging industry's best practices found that managers use innovation to create customer value. A resounding majority of best practices arose at the corporate level, with a minority at the property level. Of those coming from properties, most came from upscale, full-service hotels, rather than limited-service or budget properties. However, there is no evidence that innovation is inherently a function of corporate offices or upscale hotels. Relatively few best practices were in the areas of design or information technology, while many appeared in human resources, marketing, and operations. Virtually all innovations began with one person's idea and survived only because of that person's initiative. The idea must then be spread throughout and integrated with existing operations, which makes communication essential. Finally, lodging companies need to develop ways to measure the outcomes of their innovative practices. In many cases a given practice was thought to have improved employee morale, customer satisfaction, or profitability, but specific, outcome-related measurements (whether of profit or customer loyalty) were frequently unavailable.

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 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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.025
GPT teacher head0.243
Teacher spread0.217 · 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 teacher head, not a consensus.

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

Citations25
Published2000
Admission routes1
Has abstractyes

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