Transformative sustainability marketing: catalyzing sustainable consumption and consumer well-being through environmental performance insights
Bibliographic record
Abstract
Purpose This paper aims to highlight the pivotal role of augmented reality (AR), a disruptive new-age technology, in fostering sustainability initiatives of multinational corporation (MNC) brands. Drawing on the transformative marketing framework and stakeholder theory, the authors propose a transformative global sustainability marketing framework. This framework facilitates the understanding of observed differences in consumers’ perceptions of sustainability marketing initiatives across diverse markets and their impact on consumer engagement, sustainable consumption and well-being. Design/methodology/approach The authors tested the model using survey data collected from the UK, a country known for its high environmental performance index (EPI) ranking, and South Africa, a nation grappling with environmental challenges and a low EPI score. The study used structural equation modeling and multigroup analysis to test the proposed relationships. Findings Sustainability marketing initiatives furnished through AR significantly influence consumers’ engagement with the firm’s app. This engagement, in turn, impacts their intention for sustainable consumption, ultimately enhancing consumers’ psychological and social well-being. The multigroup analysis reveals that EPI moderates the relationship between sustainability marketing initiatives (economic development, environmental protection and ethical considerations) and consumer engagement. The positive link between consumer engagement and sustainable consumption intention is significant but weaker for consumers in the UK (high EPI) than for consumers in South Africa (low EPI). Research limitations/implications This research extends the transformative marketing framework by illustrating how AR technology can be integrated into sustainability initiatives and provides a holistic perspective that encompasses critical dimensions of sustainability marketing. Practical implications The study highlights AR’s significant potential as a tool for sustainability marketing, capable of bridging the gap between MNCs’ sustainability efforts and consumer engagement. By understanding and harnessing the power of AR, transformative MNCs can not only communicate their commitment to sustainability more effectively but also motivate consumers toward sustainable behaviors, contributing to the overall well-being of society. Originality/value This paper offers a novel theoretical framework to understand how sustainability marketing initiatives furnished through AR can lead to well-being.
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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 | 0.007 | 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 | 0.002 |
| 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".