Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Continuous advances in information technologies, such as Artificial intelligence (AI), are opening up new and exciting opportunities for value co-creation between economic actors. However, little is known about the mechanisms and the process of value co-creation enabled by AI. While scholars agree that AI technology significantly changes human activities and human resources, currently we do not have an adequate understanding of how humans and AI technology interact in value co-creation. This is the central phenomenon investigated in this article. Specifically, using Service-Dominant Logic (S-DL) as a lens, this study investigates the activities, roles and resources that are exchanged in Al-enabled value co-creation, using the creation of competitive intelligence as a research context. The analysis suggests that Al-enabled value co-creation processes are complex interactions between human and non-human actors who perform any of six different roles either jointly or independently. This article contributes to SD-L and provides a deeper understanding of the activities (the ‘how’), the actors (the ‘who’), and the resources (the ‘what’) in Al-enabled value co-creation, thus helping to close an identified gap in the literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it