MétaCan
Menu
Back to cohort

Study on Approaches of Constructing Travel Agencies’ Sustained Competitive Advantage by Knowledge Management

2010· article· en· W1953312456 on OpenAlexvenueno aff
Zhenjia Zhang

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge economyCompetitive advantageAgency (philosophy)Knowledge managementBusinessCompetition (biology)Construct (python library)Knowledge value chainPersonal knowledge managementMarketingOrganizational learningComputer scienceSociology

Abstract

fetched live from OpenAlex

Knowledge Management (KM) is an emerging concept in the field of management and widely adopted in organizations of the developed countries for enhancing organizational performance. Nowadays, the competition among the travel agencies has been incandesced, and all of them are struggling to find methods to improve their comprehensive competitive power. Moreover, under the situation that “knowledge management” has turned out to be the global management upsurge and only the best knowledge management can make them keep up with the step of the time and get victories ceaselessly in the fierce market competition. To each travel agency, employees are not only the knowledge’s creators and users but also the actual participants of the knowledge movement. The successful actualization of the knowledge management system can make the travel enterprise improve the staff’s knowledge level with less cost, as well as establish a sufficient reserve team of capable people, so as to enhance the enterprise’s knowledge and economy level and competitive power. By studying on knowledge management theories, this paper focuses on the research of knowledge management’s appliance and actualization methods in travel agencies. Based on this, this paper puts forward some useful approaches that can be used by the travel enterprises to build effective knowledge management system, thereby to construct and improve their sustained competitive advantages. Key words: Knowledge Management; Travel agencies; Sustained Competitive advantage Resume: La gestion du savoir-faire est un concept emergent dans le domaine du management qui est generalement adopte pardles organisations dans les pays developpes pour ameliorer la performance organisationnelle. Aujourd'hui, la concurrence entre les agences de voyage est en incandescence, et tous d'entre elles peinent a trouver des methodes pour ameliorer leur competitivite globale. En outre, dans une situation ou la «gestion du savoir-faire» s'est revelee etre la recrudescence de la gestion globale, seule la meilleure gestion du savoir-faire peut faire suivre les allures du temps et obtenir des succes sans cesse face a la concurrence feroce du marche. Pour chaque agence, les employes ne sont pas seulement les createurs et les utilisateurs du savoir-faire, mais aussi les participants reels au mouvement du savoir-faire. L'actualisation reussie du systeme de gestion du savoir-faire peut inciter les agences de voyages a ameliorer le niveau de connaissances du personnel a un moindre cout, ainsi qu’a creer une equipe de reserve suffisante de personnel competent, a fin d’ameliorer le savoir-faire de l'entreprise, le niveau d’economie et le pouvoir concurrentiel. En etudiant sur des theories de gestion du savoir-faire, cet atricle met l'accent sur la recherche des outils de gestion du savoir-faire et sur l'actualisation des methodes dans les agences de voyage. Sur cette base, le present article met en avant certaines approches utiles qui pourraient etre utilisees par les entreprises de voyage pour construire un systeme efficace de gestion du savoir-faire, et de ce fait leur permet de construire et d'ameliorer leurs avantages concurrentiels durables. Mots-cles : gestion du savoir-faire; agences de voyages; avantage competitif durable

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0050.012
Scholarly communication0.0120.008
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.030
GPT teacher head0.257
Teacher spread0.227 · 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

Labeled directly by 2 models reading the full record.

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

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Theoretical or conceptual
Domainnot available
GenreEmpirical · Other

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

Citations2
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCanadian social scienceSame topicInnovation and Knowledge ManagementFrench-language works237,207