Fostering consumer engagement with sustainability marketing using augmented reality (SMART): A climate change response
Bibliographic record
Abstract
• Investigates the increasing importance of sustainability in consumer engagement amid rising climate change concerns. • Provides an extended framework for AR-focused sustainability marketing and a scale to measure consumer perceptions. • Challenges traditional marketing approaches by showcasing the impact of technologies such as augmented reality (AR). • Compares consumer responses across different cultures through a mix of qualitative and quantitative methods. • Emphasizes integrating sustainability and AR for strategic business insights and climate-positive actions. As climate change concerns escalate, businesses increasingly realize the pivotal role of consumer engagement through sustainability practices in enhancing brand and firm performance. In a transformative landscape characterized by societal shifts towards environmentally conscious consumer behaviors, the re-evaluation of sustainability marketing strategies is crucial. This need is further amplified by the advent of technologies like augmented reality (AR), which are reshaping market dynamics. This study not only extends the conceptualization of sustainability marketing using AR (SMART) but also proposes a comprehensive measurement scale. This scale is designed to accurately measure consumers’ perceptions of AR-focused sustainability marketing efforts in fostering engagement. Our approach, a mix of qualitative and quantitative methods, involved five studies with n = 1072 consumers across the U.S., U.K., and South Africa. We conceptualize consumers’ perceptions of sustainability marketing using AR (SMART) across six dimensions: social equity, economic development, environmental protection, ethical considerations, regulatory measures, and technological innovation. The findings not only underscore the importance of integrating sustainability practices and new-age technologies (i.e., AR to foster climate-positive consumer engagement) but also offer strategic insights that can help businesses thrive in a climate-change era and meet their sustainability objectives.
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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 | 0.026 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".