The Effect of the Marketing Mix on Customer Purchase Decision in the Mobile Telecommunication Industry in Sub-Sahara Africa
Bibliographic record
Abstract
This study determined the awareness of the marketing strategies of the mobile telecommunication companies; customers’ purchase decision of products and services provided by the mobile telecommunication companies, and finally the influence of customer awareness of marketing strategies on customer purchase decision. The study adopted a quantitative approach with 300 respondents. The study found that customers of the various mobile telecommunication companies had knowledge of the 7Ps marketing strategies of the mobile service providers. Furthermore, respondent’s intentions to patronize the services of their mobile networks were about the same for all customers. The study also found that customer awareness of the pricing strategy (r=.146, p=.011), product/service strategy (r=.120, p=.038), process strategy (r=.153, p=.008), and promotional strategies (r=.129, p=.025) all have significant positive correlation on customer intention to patronize the services of the telecommunication companies. However, customer awareness of the marketing strategy (marketing mix) played no significant role in predicting the decision of the customer to patronize the services of their mobile telecommunication companies.
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
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".