Understanding E-Paper Engagement: The Interplay of Product, Promotion, Culture, and Social Contexts in the Digital News Market
Bibliographic record
Abstract
The rapid development of technology has compelled the media industry to shift toward digital-based platforms. Achieving success in this transition requires an effective strategy that considers consumer behavior variables. Given that consumers respond differently to technology, it is essential to identify behavioral factors that have the potential to significantly impact digital media access. This study aims to examine the influence of product and promotional strategies on the decision to purchase e-paper from Bisnis Indonesia, with cultural and social characteristics of consumers as intervening variables. Products and promotions are external stimuli that influence consumer behavior and are shaped by the strength of individual cultural and social contexts. An associative research design was employed to test the hypotheses, involving 155 e-paper subscribers of Bisnis Indonesia from ten representative cities. Primary data were collected through questionnaires, and Structural Equation Modeling (SEM) was used for analysis via LISREL software. The results indicate a positive and significant effect of product and promotional variables on purchasing decisions, which is further strengthened by cultural and social consumer characteristics. Overall, the model explains 89% of the variance in the purchase decision variable.
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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 | 0.004 |
| 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 | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".