Evaluating of the Effectiveness of Television Advertisement of Life Insurance and Investing in Ma Insurance Company
Bibliographic record
Abstract
Nowadays, corporates require purposeful and effective marketing activities to survive and sell their goods and services in this ever-growing competitive world. Marketing is an issue having a significant role in marketing process of a corporate and it is dedicated a huge amount of marketing budget. Measuring effectiveness can be implemented through considering three steps: advertising inputs (e.g. advertising intensity, advertising content, and advertising budget); mental processing of customers during and after the ads broadcast; and finally advertising outcomes (e.g. resulting change in sales, income, and profit). In addition to the measurement of ads effectiveness on the customers mind, in the present study the impact of such ads on the sales was also tackled based on the AIDA hierarchy of effects model. Four hypotheses were considered for testing the research model. After the reliability and validity confirmation by, respectively, Cronbach's alpha and content validity, 400 questionnaires were distributed amongst the customers of “Ma” insurance company in Tehran, chosen base on the simple random sampling. Due to the rejection of data normality hypothesis, two Wilcoxon non-parametric tests, namely sign test and signed-rank test, were used. After statistical tests, hypotheses 3 and 4 (i.e. ads effectiveness on individuals willingness and action) were rejected. Finally, following the interpretations of results obtained from data analysis, the practical recommendations were presented for corporates and advertisers.
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 | 0.002 | 0.009 |
| 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) | 0.003 | 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".