Social Networking Brand Engagement using Creative Brand Content Experiences
Bibliographic record
Abstract
In today’s environment, societies are free to create and browse online content; marketers therefore to face vexing challenges in drawing expressive social media users to engage with their brands. This current group of users display postmodernism characteristics; i.e. need more for subjective experiences to achieve self-realization. Hence, the creation of consumer brand engagement among expressive social media societies is through content that should be able to provide these experiences. However, there is lack research study on this trend. The objective of this study was to evaluate passive experiential content that users perceived as novelty emotions (i.e. perceived creativity) that leads to intrinsic state of engagements (i.e. cognitive engagement and affective engagement) and to intentional engagement (the activation of willingness of brand clicking activities in social media). This, in turn, creates an advantage for the marketers because from this process if users engage the chances of building a long-term consumer brand engagement relationship is higher. This research study was done on 25-34 year-old Malaysian active expressive social media users who utilised expressive social media sites on a daily basis for at least 3 hours as the sample population from whom to collect data and administer a questionnaire survey with a still image as stimulus using social networking messaging platforms. Data analysis was conducted using IBM SPSS and IBM AMOS software and results showed positive significant relationship within all the 7 constructs which were functional appeal, emotional appeal, vividness, perceived creativity, cognitive engagement, affective engagement and intentional engagement.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".