Exploring Buyer Motivation to Improve Management, Marketing, Sales, and Finance Practices in the Martial Arts Industry
Bibliographic record
Abstract
The martial arts industry is experiencing immense growth, creating a highly competitive environment where challenges in attracting and retaining customers cause substantial losses and an inability to compete effectively. Customer memberships are the primary revenue source for fitness firms. Understanding buyer motivation is essential for marketing message creation and product development to attract and retain customers. The purpose of this qualitative, exploratory, single-case study was to investigate parent purchase motivation for children’s martial arts classes and to document internal buying motives in order to address the problem of acquiring and retaining customers in the commercialized martial arts industry. The study sample consisted of seven parents, two instructors, and two owners. The data collection methods were semistructured interviews comprising open-ended questions. Interviews were analyzed using NVivo® qualitative analysis software to code and analyze themes. The semistructured interviews identified 10 themes. Three new themes emerged—ease of participation, alternative to team sports, and convenience. Study findings contribute to the theory of planned behavior and theories used to predict purchase behavior. Recommendations for practice include refinements of product offerings and marketing messages and the creation of a new market segment, resulting in customer alignment and increased ability to attract and retain customers. Future research is recommended to replicate this study in other geographies, to use the data gathered in this study to seed qualitative research studies, and to weigh the relative influence of the three types of behaviors influencing intention in the theory of planned behavior.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".