Experiential value of the augmented reality experience in business-to-business marketing: a stakeholder approach
Bibliographic record
Abstract
Purpose This study aims to offer a better understanding of the experiential value of augmented reality (AR) experiences in business-to-business (B2B) marketing and its context-specific dimensions. Design/methodology/approach The present research uses a stakeholder approach to offer a holistic vision of the experiential value of AR in B2B. Semi-structured interviews were conducted with 15 stakeholders (buyers, sellers with AR and AR providers) coming from various sectors. Findings The results suggest eight different dimensions of the experiential value of AR in B2B marketing. The research highlights the importance of considering the various components of the AR experiential value in B2B, by going beyond the cognitive dimension of the experience to design an experience that fosters affective, social and even sensorial responses. Practical implications This study provides key insights to help companies develop more efficient AR experiences by considering the comprehensive value of these experiences and aligning them with diverse goals. Additionally, it emphasizes how these experiences can be leveraged to foster deeper relationships between sellers and buyers through shared interactions, and highlights the key role of affective and sensorial responses. Originality/value This study is one of the first to empirically examine AR experiences on their own in a B2B setting. It contributes to the existing literature on the use of AR in B2B interactions by emphasizing its specific experiential values.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".