Understanding the Impact of the Brand Experience on Brand Reputation by the Moderating Role of Technology Turbulence
Bibliographic record
Abstract
Brand experience is the conceptualization of the brand’s design, identity, packaging and connection that will remind the brand’s perceptual, cognitive, emotional and behavioral reflections. In brand studies, mostly brand behavior, attitudes and feelings are analyzed. The brand experience arises not only under a general constraint or emotion situation but also in a more customized and detailed combination of feelings, perceptions, emotions and behaviors. This combination points out that brand consumers evaluate the brands and companies not only with the general sense or general behavior but also with more complex combinations. Brand’s organizational reputation arises not only from brand’s reputation of being a reliable brand, but also from emotional appeal, products and services, vision and leadership, workplace environment, which are also associated with factors such as social and environmental responsibility and financial performance. Brand experience influences the formation of this multifactorial brand reputation. The consumers in contact with the brand perceive the reputation of the brand they consume according to their experience in an environment where technology and market change constantly. This research examines the role of technological and market uncertainty which is a dimension of environmental uncertainty on the relationship between brand reputation and brand experience, which has emotional, behavioral and intellectual dimensions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".