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Enregistrement W146942385

Enriching Planning through Industry Analysis.

2009· article· en· W146942385 sur OpenAlexaboutno aff
Mario Martínez, Mimi Wolverton

Notice bibliographique

RevuePlanning for higher education · 2009
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueManagement and Marketing Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRivalryStrategic planningHigher educationBargaining powerMarketingEconomicsPublic relationsBusinessService (business)Industrial organizationPolitical scienceEconomic growthMicroeconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The authors perform an 'industry analysis' for higher education, using the five forces model of M.E. Porter. Although strategic planning has fallen out of favor in many business organizations (Jelinek 1979; Welch and Welch 2005), it remains the primary means for strategy making on virtually every college campus in the United States. The higher education literature on strategic planning ranges from the conceptual (Peterson, Dill, and Mets 1997) to the practical, including step-by-step instructions for completing the process (Rowley, Lujan, and Dolence 1997). Strategic planning is an important tool, but higher education's sole dependence on it has come at the expense of other useful instruments in the strategy making process. For instance. Porter's (1980) five forces model, which sets the standard for industry analysis, can complement strategic planning and thus contribute to a more comprehensive organizational strategy. An industry analysis using Porter's model pays particular attention to five forces that influence any industry: threat of new entrants, intensity of rivalry, threat of substitutes, bargaining power of buyers, and bargaining power of suppliers. Current examples of the model's application vary from exploring leadership and differentiation in professional service firms (Ou and Chai 2007) to understanding the Internet's role in changing markets and entire industries (Karagiannopoulos, Georgopoulos, and Nikolopoulos 2005). In light of the ongoing interest in the five forces model in diverse fields outside of higher education, we ask two questions. First, does industry analysis provide insight into higher education? Second, does Porter's emphasis on the five forces sufficiently describe the environment in which higher education institutions function? In addition to answering these questions, we highlight where industry analysis complements and/or compares to strategic planning throughout the article. Applications of the Five Forces Model Academicians from a variety of disciplines have used Porter's five forces model to describe different industries. Ondersteijn, Giesen, and Huirne (2006) conducted an industry analysis using Porter's model to interpret the external context of Dutch dairy farming. Fratto, Jones, and Cassili (2006) employed it to better understand apparel retailers and price competition within the apparel industry. Siaw and Yu (2004) were interested in the impact of the Internet on banking competition. Pines's (2006) application of the model to emergency medicine allowed him to develop a set of recommendations for how players within the field - including emergency departments and physicians might better work together to strengthen their services and ultimately offer improved care. In an article about building a firm's lobbying strategy, Vining, Shapiro, and Borges (2005) used Porter's model to identify the environmental context in which the firm operates. Dobni and Dobni (1996) provide one of the few examples of an industry analysis in higher education planning. They applied Porter's model to Canadian university-based business schools and uncovered pathologies that were incompatible with the realities for which they were supposedly preparing their students. The relative absence of industry analysis in college and university planning suggests a need for more serious consideration and application in the field. A New Era for the Higher and Postsecondary Education Industry Peterson and Dill (1997) defined three eras that characterize the evolution of the higher education industry: traditional higher education, mass higher education, and postsecondary education. We contend that higher education has moved into a fourth era that brings with it immense pressure from organizations that in the past offered little competition for students and resources. International institutions now compete with U.S. colleges and universities for students. Corporations and private companies deliver training, education, or a mixture of the two. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,028
Score d'incertitude au seuil0,093

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0120,026
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0080,008
Études des sciences et des technologies0,0030,004
Communication savante0,0100,013
Science ouverte0,0020,009
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0280,008

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,040
Tête enseignante GPT0,331
Écart entre enseignants0,290 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations17
Publié2009
Routes d'admission1
Résumé présentoui

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