Effect of Integrated Marketing Communications on Customer Satisfaction of Selected Private Universities in South-West Nigeria
Bibliographic record
Abstract
Integrated Marketing Communications (IMCs) is critical to the performance of an organisation. However, many of the previous studies have focussed on manufacturing firms with very few targeted at private universities. Moreover, empirical evidence revealed that private universities have not been able to effectively deploy IMCs to enhance their performance and have been confronted with the challenge of poor customer satisfaction. The study investigated the effect of IMCs on customer satisfaction of selected private universities in South-West Nigeria. The study adopted cross-sectional survey research design. The population of the study comprised 554 employees of the Institutional Marketing Department and ad-hoc staff of the Registry Department of the universities selected for the study. Total enumeration method was used. The instrument used was a validated adapted questionnaire. The Cronbach’s alpha reliability coefficients of the constructs ranged from 0.701 to 0.832. The response rate was 85%. Data were analysed using multiple linear regression. The results revealed that IMCs had positive and significant effect on customer satisfaction (Adj. R2 = 0.834, F (5, 467) = 475.554, p < 0.05). The relative effects of IMCs on customer satisfaction showed that advertising had a positive and significant effect (β = 0.132, t = 3.038, p < 0.05), service promotion had a positive and significant effect (β = 0.195, t = 4.502, p < 0.05), online marketing had a positive and significant effect (β = 0.269, t = 5.972, p < 0.05), public relations also had a positive and significant effect (β = 0.377, t = 7.415, p < 0.05). The effect of direct marketing was however insignificant though positive (β = 0.059, t = 1.130, p > 0.05). The study concluded that IMCs affect customer satisfaction. The study recommended that private universities should prioritise their promotional efforts on advertising, service promotion, online marketing and public relations rather than direct marketing in their bid to sustain and improve their customer satisfaction record.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".