An empirical study of the effect of brand \npersonality and consistency between \nmarketing channels on performance within \nthe UK higher education sector
Notice bibliographique
Résumé
2/316 \nAbstract \nOver the past decade, increased pressure on Higher Education Institutions (“HEIs”) has \ncontributed to additional national and international competition for students and funding. This \nhas been compounded by policy decisions on the part of government. Such increasing \ncompetition has led to an increase in managerialism, with tools and practices traditionally \nassociated with the corporate sector now being adopted and utilised by HEIs. Marketing and \nbrand management has received special attention from such institutions, particularly in order \nto attract students and build reputation. Some authors argue that the concept of branding \ntransfers directly to the education sector, whilst others argue that HEIs are more complex with \nmore specialist approaches required. Research suggests UK universities do not consistently \ncommunicate across all audiences, whilst previous literature recognises brand consistency as \nimportant. However, this literature is based predominantly on anecdote, or on evidence from \nsingle cases. \nIn this study, sixty HEIs were selected to represent the full range of UK universities. For each \nHEI, a prospectus was obtained, and the websites and Twitter feeds of the institutions were \ndownloaded. This provided 18,956,366 words to analyse. Brand personality was measured \nusing Aaker’s brand personality scale and Opoku’s dictionary of synonyms. The frequency of \nwords was used to assess brand personality across Aaker’s five dimensions for each marketing \nchannel. The data was then analysed to test the research hypotheses, using statistical analysis \ntechniques. These looked for relationships between brand personality, strength, consistency, \nand performance. \nResults highlighted a positive correlation between brand personality consistency relating to the \nprospectus and website, and HEI research and recruitment performance. Those HEIs with a \nconsistent brand personality between these two marketing channels performed better on RAE, \nUCAS Demand and points. This agrees with the existing literature, which suggests that brands \nrepresent crucial aspects of success in mature markets, and that consistency can be a key driver \nin creating strong brands. This research shows that these findings extend into the HE context. \nOur findings provide empirical support to anecdotal literature which has stated that brands are \nimportant differential tools within higher education, and that an online brand’s synonymity and \nconsistency with its offline brand is crucial to performance. Social media participation and \nvalidation was also positively related to RAE and UCAS Points performance on all measures \nof Twitter and Facebook. Lastly, brand personality strength communicated via the prospectus \nwas significantly and positively related to performance in the dimension of Sophistication, but \nwas significantly and negatively related to performance upon the dimensions of Competence, \nExcitement, Ruggedness and Sincerity.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».