Imagery makes social media captivating! Aesthetic value in a consumer-as-value-maximizer framework
Bibliographic record
Abstract
Purpose Social media bring about the imagery of people, places and products. Showing particular success in attracting women and millennials, these media (e.g. Instagram, Snapchat and Pinterest) are built around imagery consumption. This paper follows a qualitative theory building approach to extend the theory of consumption values and develop a framework based on the values social media deliver to consumers that explain their use outcomes. Design/methodology/approach The framework was analytically developed based on a review of the literature. In contrast to frameworks such as stimulus-organism-response (S-O-R), the framework proposes that people consume social media to maximize relevant values, namely, the aesthetic, social and learning value. Then, a study based on semi-structured interviews is performed to elaborate on the values and their undertakings. Findings The paper defines the consumption’s aesthetic value and underscores it as a focal driver of social media use and a key concept in social commerce. Data analysis suggests that aesthetic value engenders such responses as consumer’s inspiration, infinity sensation and habitual entertainment. Additional drivers of social media users are social and learning values. The social value engenders self-expression and social privacy, whereas the learning value engenders resourcefulness and parallel shopping. Originality/value This paper stipulates that people consume (i.e. use) social media to maximize relevant values, which, in turn, result in two groups of responses (inner and outer responses). The framework indicates that the relevant values mediate the relation between a stimulus (e.g. social media use) and response (e.g. entertainment, inspiration and behavioral intent). It highlights the centrality of aesthetic value in digital marketing and social commerce environments. The framework, thus, contrary to S-O-R, views the consumer as a maximizer of values rather than (a) processor of emotional and cognitive rejoinders.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".