The Study of Influential Integrated Marketing Communication on Iranian Consumer Buying Behavior for Imported Branded Cars: Datis Khodro
Bibliographic record
Abstract
The automobile industry especially imported cars are the most lucrative sector in Iranian market since the disposable income in both urban and rural are increasing and easy alternative finance being provided by all Iranian financial institutions are developing, furthermore imported cars are considered as a short-term investment among the consumer in Iranian market due the foreign currency fluctuations. This study is considered as the first research to review the consumer buying behavior about imported cars and how Datis Company advertise, promote and what is the best place to be advertised to convince customers to buy or try new cars in the Iranian market. Promotional mix have the consequence of creating brand images and symbolic appeals that can be the effective way to strike the responsive chord with consumers. The purpose of this paper is to examine the consumer buying behavior about the imported car through various IMC tools, discover most efficient place , most influential advertising message and how often consumers decide to change the car to better or new one in the Iranian market. A simple random sampling was selected as the sampling method. The customers of Datis Company (Previous Purchase) were sampled to respond to the online questionnaires and 197 questionnaires were returned providing an 89.5 % response rate. We initiated with conducting an exploratory research on Iranian consumer behavior to determine the most important attribute adopted by them. The regression method applied to understand the influence of independent variables (Advertising, WOM, Internet Marketing, Direct Marketing, Public Relations, and Sales Promotion) on the dependent variable (IMC) in Datis Company. Above all, online marketing Communication (OMC), web and social network is discovered as the most effective way of placing the advertisement for Datis Company in the Iranian market. The findings of this study provide managerial implications for marketers for the advertising practice of technologically advanced products. The ambiguous results of the analysis suggest that companies should put more emphasis on the selection of the communicated information content of their advertisements.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".