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Record W2167085188 · doi:10.1300/j162v06n03_07

An Investigation of the Factors Affecting Innovation Performance in Chain and Independent Hotels

2006· article· en· W2167085188 on OpenAlexaff
Michael C. Ottenbacher, Vivienne Shaw, Andrew Lockwood

Bibliographic record

VenueJournal of Quality Assurance in Hospitality & Tourism · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAttractivenessBusinessMarketingService (business)Service recoveryHospitality industryEmpowermentTertiary sector of the economyHospitalityService qualityTourismEconomics

Abstract

fetched live from OpenAlex

SUMMARY The failure rate of new service projects is high, because the knowledge about how innovations should be developed is limited. In the last decade, several studies have investigated the success factors associated with service innovations (e.g., Atuahene-Gima, 1996; de Brentani, 2001; Storey and Easingwood, 1998). However, no research in new service development (NSD) has addressed the question of whether chain affiliated and independently operated service firms have different approaches for developing successful innovations. The majority of past new service development (NSD) success studies have concentrated on the financial service sector, which is generally represented by large corporate organizations. The findings of this study indicate that the factors which impact on the performance of NSD depend on the organizational relationship of hotels-chain affiliation or independent operation. The study's results suggest that market attractiveness, process management, market responsiveness and empowerment predict NSD success within chain affiliated hotels. While empowerment and market attractiveness are also related to NSD success in independent hotels, this is also linked to effective marketing communication, employee commitment, behaviour based evaluation, training of employees and marketing synergy.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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 designObservational
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

Citations78
Published2006
Admission routes1
Has abstractyes

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