Marketing and Environmental Sustainability in the Sport Sector: Developing a Research Agenda for Action
Bibliographic record
Abstract
Authoritative discourse within the literature indicates a realization that sport impacts the natural environment (Brooks, 2006; Cachay, 1993; Lenskyj, 1998) and that environmental sustainability (ES) in sport management is emerging as a topic of concern. The number of sport-related journal articles focusing on environmental sustainability (ES), however has been found to be insufficient (Mallen, Stevens and Adams, 2011). The purpose of this study, thus, was to complete a content analysis of management journals to reveal marketing-ES literature to underscore the development of a sport marketing-ES research agenda. The premise was that the understandings concerning the outlook, perceptions, opinions and viewpoints in the marketing-ES manuscripts will enhance the capacity of researchers in developing a research agenda in sport marketing-ES. The methods involved content analysis steps provided by Krippendorff (1980), Weber (1985), and Wolfe, Hoeber and Babiak (2002) including: establishing the sampling units, the unit of text, the coding themes and sub-themes, analytical factors and coding mode. The data analysis framework involved the use of key marketing concepts: the consumer perspective (Aaker, 1996) and the corporate perspective (Knapp, 2000), along with the concept of influencers (Davis & Dunn, 2002). In sum, the examination involved 49 journals published from 1999 to 2009. Key findings included 63 marketing-ES manuscripts and their concepts, themes and sub-themes, along with their perspectives and influencers that were applied to construct 30 questions to develop a sport marketing-ES research agenda. It is time for sport researchers to generate a robust research response to the marketing-ES questions.
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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 | 0.082 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.039 | 0.050 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".