Norms with a purpose: Examining the effects of descriptive norms and outcome expectations on muscular endurance
Bibliographic record
Abstract
Using normative messages to influence behaviour has been receiving increased attention in the activity setting, with descriptive norms (perception of what is commonly done) being associated with various forms of activity-related behaviour (Priebe & Spink, 2014; Spink et al., 2013). Most studies examined in the activity setting have used focus theory (Cialdini et al., 1990) as their conceptual underpinning. One of the main tenets of the theory suggests that norms will only influence behaviour if the norm is focal to the individual. While different procedures have been used to produce a normative focus (e.g., arousal), the use of a positive outcome expectation has not been examined. This study explored the influence of adding a positive outcome expectation to a descriptive norm on endurance behaviour. Thirty university students were randomly assigned to one of three conditions: control (n=10), descriptive norm (DN; n=10), or descriptive norm + outcome expectation (DN+OE, n=10), and asked to perform two plank exercises to maximal exertion separated by a 3-minute rest period. Between planks, those in the DN were given a message that most others actually held their second plank longer; DN+OE received the same message plus were told those who did so were the most motivated and committed; control received no message. Controlling for the initial plank time, ANCOVA results revealed a significant main effect for condition, p < 0.02, ηp2 = 0.27, in which control participants held their second plank less than those receiving the DN (adj Cohen’s d=.83) and DN+OE (adj d=1.29) message. Further, a medium effect was found between the DN+OE and DN condition (adj d=.43), with the former having the highest plank time. If replicated, these results suggest that adding a positive outcome expectation to a normative message may produce a stronger normative focus resulting in a larger influence on activity behaviour.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".