Increasing functional value resonance as addressing the relationship between social presence and brand loyalty for SUV automotive consumers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this research is to analyze the influence of social presence on brand loyalty through functional value resonance in automotive consumers. This is a quantitative research approach. The purpose of the study includes automotive consumers in North Sumatera, Indonesia. The sample selection method is non-probability sampling, which does not ensure that every member of the population is sampled equally. In this study, purposive sampling was used with 205 respondents. Data is analyzed using the Partial Least Squares (PLS) approach using SmartPLS. The results of this study show that social presence had a positive and significant effect on brand loyalty for SUV automotive consumers in Medan City. Functional value resonance positively and significantly affects brand loyalty for SUV automotive consumers. Brand loyalty is influenced favorably and significantly through functional value resonance in automotive SUV consumers. Increased resonance of functional values can be achieved by companies by giving the impression that causes echoes from users associated with the value of SUV functions, such as improving the quality of reliable engines and creating security features so that they do not compete with competitors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it