An empirical study of the effect of brand \npersonality and consistency between \nmarketing channels on performance within \nthe UK higher education sector
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".