On Process Management (PM) The applicability of Michael Hammer’s theory in Argentina
Bibliographic record
Abstract
This article explores the applicability of Hammer’s theories on Operational Innovation (OI), Operational Excellence (OE) and processes, on firms of different industrial sectors and sizes, located in Argentina. The hypothesis of this study -which was corroborated- suggests that the manager’s role is supported by what is called Process Management (PM), which deals with performance gaps in a given period of time. In this context, OE, OI and processes’ understanding becomes an important constituent of the management activity. Through the implementation of a holistic PM-based perspective in many more organizations it is possible to boost results, achieve superior levels of performance and offer the right customer value. Specifically, a process is represented by a sequence of activities. It allows installing, following and measuring an operation, and isbased on five enablers and four capabilities that are explained in this study. This is an exploratory and descriptive work, with a qualitative methodology. Also, this study has a not experimental/transversal design. It is based on Hammer’s theories on the matter, which were complemented with executives/managers' interviews of different multinationals and local firms located in Argentina. debt crowdfunding in Latin America and Mexico. Implications for lenders, researchers and policy-makers are also discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".