Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resources
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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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".