Narratives selves in the digital world: An empirical investigation
Bibliographic record
Abstract
Abstract The digital era has led to the extension of self into virtual space, resulting in changes to consumption patterns. The existing academic landscape in this area focuses on Western perspectives, in the context of early‐stage digital interventions. However, the dynamic digital world demands a constant exploration to understand the corresponding influences on consumer behavior across varied cultural contexts. This research focuses on unraveling newer dimensions of the digital self from non‐Western perspectives. We adopt an interpretive lens to understand the evolving nature of self through a grounded theory approach. The study establishes the presence of multiple independent narrative selves, co‐created with people, and technology. Each narrative addresses different segments of personal audiences, enabling new modes of self‐expression to overcome the challenges of digital expressions. Additionally, we highlight the exclusion of the digital presence of family in the formation of the narrative self. From a theoretical perspective, we extend and contrast the existing conceptualizations on self, such as dialogical selves, self‐extension and expansion, and the unified core self. Further, the practical implications emphasize the need for narrative analytic approaches to understanding consumers and avenues for brands to decode narratives, develop strategies to gain consumer attention, and become part of consumers' narrative selves.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".