Analyzing the impact of paid, owned and earned media on consumer purchase behavior and the moderating role of age in the online food ordering landscape
Bibliographic record
Abstract
Purpose This study examines the impact of integrated paid, owned and earned media (POEM) on consumer purchase behavior and the moderating role of age in the online food ordering landscape. Design/methodology/approach The study employs the S-O-R framework to propose a conceptual model of integrated POEM. Data were collected from consumers using online food aggregators, and analysis was conducted using structural equation modeling (SEM). The moderation analysis was performed using the PROCESS macro in SPSS. Findings The SEM analysis revealed a significant positive impact of POEM on brand awareness, which subsequently affects brand knowledge and purchase intention, ultimately shaping consumer purchase behavior. The moderation analysis indicates that age moderates the relationship between POEM and brand awareness. Practical implications Practically, the study provides actionable insights for marketers in online food ordering platforms by demonstrating the impact of integrated POEM on consumer purchase behavior. Each stage of this journey is critical for brands to manage effectively. Brands should monitor and optimize the transition from awareness to knowledge, intention and final purchase. Identifying gaps where conversions are not occurring is crucial. By tracking and analyzing each stage of the consumer journey, brands can gain valuable insights into where customers drop off, understand the reasons for these gaps and assess the effectiveness of their strategies. The moderation analysis highlights the need for age-specific strategies to optimize the effectiveness of POEM marketing communication. Originality/value This study is the first to explore the integrated POEM concept in the context of asset-light food aggregators using the S-O-R framework. It investigates the effects of POEM on consumer purchase behavior within food aggregator platforms and analyzes how age moderates the relationship between POEM and brand awareness. Thus, this research enhances the understanding of the complex dynamics in the online food delivery industry.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".