The Impacts of the Personality Attribute of Time and Money on Customer Engagement Behavior: A Self-concept Perspective
Bibliographic record
Abstract
Although recent studies have explored the antecedents of customer engagement behavior (CEB), few empirical studies have explored the mechanisms that connect these antecedents to CEB. From self-concept perspective, this research uses experimental and survey methods to explore the influence of the type of customer-invested resource (time vs. money) and customers’ regulatory focus (promotion-focused vs. prevention-focused) on CEB and the mechanisms that underlie these processes. The results of three studies show that promotion-focused customers initiate more recommendations and complaints when time (vs. money) spent in the shopping experience is emphasized, whereas this effect does not exist for prevention-focused customers. A self-concept connection mediates the moderating role of regulatory focus in the relationship between types of resources and recommendations, whereas this mediating role of self-concept connection does not exist with complaining behaviors. In summary, the influence of customer-invested resources on CEB varies according to a customer’s regulatory focus.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".