Determinants of Performance Measurement Practices: Toward a Contingency Framework
Bibliographic record
Abstract
Abstract-The issue of understanding the antecedent conditions that are necessary for the effective implementation of performance measurement system (PMS) is at the heart of the debate in the management accounting control systems (MACS) literature. This study intends to examine empirically the associations between one element of MACS i.e. PMS and some contextual factors, namely organizational culture, industry type, and firm size from contingency lens. The paper is based on the results of a study carried out in Iran through a questionnaire survey of Chief Financial Officers (CFOs) belong to 128 companies in Tehran Stock Exchange (TSE). SMARTPLS V2.0 M3, which using partial least squares (PLS), was utilized to analyze the data collected in this study. The results of the survey reveal that organizational culture and size are the contributing factors in the usage of certain PMS, i.e. the extent use of multidimensional performance measures, within Iranian public listed companies. This study extends the current management accounting literature in general and previous research on PMS in particular through offering an exhaustive conceptualization of PMS. Moreover, this study sheds light on the way in which practitioners and organizations may realize those antecedents that are pivotal to their effective usage of PMS with the ultimate purpose of taking full advantage of their PMS implementation. Such insight offers guidance as to the focus required in understanding necessary organizational traits as a basic phase of the procedure of PMS usage. Keywords- Performance measurement system (PMS); Multidimensional performance measures; Contingency theory; Iran 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".