Inhibiting Factors of Inter-organizational Cost Management Complementary Study
Bibliographic record
Abstract
The research problem of this study is based on the discussion of inhibitors related Inter-organizational Cost Management (IOCM). Taking on an inductive logic, the study´s objective is exploratory by way of a qualitative approach, with the overall goal of analyzing which factors inhibit the applicability of Inter-organizational Cost Management. This study is a complement and completion of the debate initiated by Farias (2016). Fifty-four surveys retrieved from the literature were analyzed, which demonstrate the difficulties faced by companies in managing costs in a cooperative manner. Analysis on these studies could illustrate the perceptions held by different businesses, and list the difficulties faced by them, leading to the identification of 30 inhibiting factors. The diversity of the same highlights the interdisciplinary nature, as well as complexity, of the phenomenon in question. The study chose to divide the inhibiting factors into three groups, which relate to the developmental stages of inter-organizational relationships (planning, start of operations and maturation), with the inhibitors present in the three stages. Inhibitory factors related to People were found to be most predominant; the implementation of inter-organizational approaches requires not only changes in processes, but also in the adaptation of organizational behavior on part of those involved. Thus, the application of IOCM cannot be seen as a technical approach, guided by technology and management programs alone, and companies need to overcome internal barriers.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".