Integrating interaction into standardized public procurement: exploring the creation and distribution of relational frictions
Bibliographic record
Abstract
Purpose This paper aims to explore how using non-standardized relational interfaces in traditional public procurement of professional services leads to relational frictions among actors. It also examines coping strategies and how these frictions are distributed. Design/methodology/approach This paper conducted a nested case study of a public organization in a developing country, analyzing three public procurement cases. These cases illustrate efforts to procure research services interactively within a standardized procurement system. The study involved in-depth interviews with informants within the public organization and key suppliers from each project, as well as analyzing relevant documents. Findings The study explains how relational frictions arise from shifts in relational interfaces and manifest as misalignments in activity links, disruptions in resource ties and tensions in actor bonds within a system accustomed to standardized exchanges. The findings also highlight how actors use coping activities and how relational frictions spread directly and indirectly to other relational interfaces. Practical implications This research helps public organizations understand the emergence of frictions as challenges in adopting innovative approaches within traditional procurement systems. It offers strategies to manage these frictions and enhance value creation. Originality/value This research introduces relational friction at the actor level, conceptualizes its emergence and distribution due to changes in established relational interfaces, and examines coping activities for its management.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".