Factors Hindering the Adoption of E-Marketing among Cable Manufacturers in Zambia, Based on Technology Acceptance Model (TAM)
Bibliographic record
Abstract
The study was looking at establishing factors that are hindering the adoption of electronic marketing among cable manufacturers in Zambia based on the Technology Acceptance Model (TAM). The study first looked at the major challenges faced by cable manufacturing companies in developing countries in the adoption of e-commerce in marketing their products. The study then proposed a model through a web-based e-commerce web portal that could be adopted in order to address the challenges identified in the study. The study was based on three cable manufacturing companies. The three companies had a total population of 115 respondents which was used in our study. These were from the various departments (Marketing, IT and Management) within their respective organizations. Both qualitative and quantitative data was collected from respondents using a structured questionnaire. The questionnaire was generated based on the TAM conceptual model. The response rate was 47% giving 51 questionnaire responses from the three companies. The Chi-Square test of independence was used to analyze the data using SPSS software. The results showed that age has an influence on the perceived usefulness of technology. The test had an observed P-Value of 0.024 against the significance level of 0.05.The results further showed that frequent use of the internet has an influence on perceived ease of use of technology. An observed P-Value of 0.014 was recorded against the significant level of 0.05. The Chi Square test again recorded an observed P-Value of 0.03 against the significance level of 0.05, revealing that Internet knowledge has an influence on perceived security of E-marketing. It was therefore, concluded that level of education has no influence on perceived security of E-Marketing. Based on these results, an e-commerce prototype was developed as a web portal to help address the challenges of technology adoption in cable marketing by the three companies in the study.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".