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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".