Modeling Value Cocreation Processes and Outcomes in Knowledge-Intensive Business Services Engagements
Bibliographic record
Abstract
Knowledge-intensive business services (KIBS) are a distinct category of business-to-business services, with unique implications for current understandings of value within the field of service science. KIBS engagements often involve multiple stakeholders with sometimes differing assessments of value. Moreover, stakeholders’ assessment of value is not based solely on an engagement’s deliverables; it also takes into account the collaborative process of producing these deliverables and the indirect outcomes resulting from the integration of deliverables and process results as new resources in line with each stakeholder’s interests. Supporting the design of KIBS engagements thus needs to enable a multistakeholder and multilevel measurement of value. This article identifies requirements for modeling KIBS engagements in a manner that addresses their specific characteristics and can support their design; requirements were derived from a multiple-case study of value cocreation in this domain. The article also presents value cocreation modeling (VCM), a technique developed to fulfill these requirements. In VCM, indicators help measure and evaluate elements that support each stakeholder’s value assessment at the process, deliverable, and outcome levels. VCM can be used as a conceptual tool by KIBS professionals to establish and monitor KIBS engagements and take corrective actions as needed for successful outcomes.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".