Pursuing fitness: how dialectic goal striving and intersubjectivity influence consumer outcomes
Bibliographic record
Abstract
Consumers increasingly engage expert service providers in their goal pursuits. While the literature focuses primarily on goal attainment, this presents just one stage of extended goal striving. Using Bagozzi and Dholakia’s [(1999). “Goal Setting and Goal Striving in Consumer Behavior.” Journal of Marketing 63 (Special Issue): 19–32] goal-striving framework as the foundation, this qualitative research examines the client–trainer interactions in the goal-striving process. We find that goal striving with the aid of expert service providers entails intersubjectivity. The consumer wrestles with multiple understandings of fitness to determine and pursue a goal. This considers the individual’s perceptions and desires, cultural and societal discourses, and trainer’s views. Effective goal pursuit requires shared understanding between client and trainer. It entails a moment of release when consumers accept their inability to translate goals into actions alone. This occurs at multiple stages of the process. By examining the influence of service providers on goal strivers, this research extends our understanding of goal striving as an accepted agreement between the Self and Other.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".