An investigation of the relation between ownership structure and management control in professional service organisations
Bibliographic record
Abstract
Purpose This study aims to examine the interplay between ownership structure (organisational form) and management control system (MCS) design as governance structures within Australian primary health-care organisations (PHOs), seeking support for the suggestion that professional services will be most efficiently and effectively provided in organisations that have internal governance that is matched to their ownership form. Design/methodology/approach The analysis is based on a series of in-depth investigations into the MCS choices made by seven Australian PHOs. Arguing that the degree of information impactedness is inversely related to the level of general practitioner (GP) ownership, organisations where more than 50 per cent of the GPs working within the practice are owners are classified as “high ownership” (“low information impactedness”). The adoption by high-performing organisations of their predicted MCS archetype according to Speklé’s development is then interpreted as representing empirical support. Findings The findings provide uniform support for the importance of the match between ownership structure and internal governance mechanisms. As predicted, the two high-performing, high member-owned organisations reported MCS resembling exploratory archetypes, the three high-performing, low member-owned organisations reported MCS consistent with a boundary archetype and the two low-performing organisations reported little emphasis on any control. Research limitations/implications This study provides evidence of the importance of the appropriate match between ownership structure and internal governance mechanisms for PHOs. Practical implications This study has potential to assist managers, owners and advisors to optimise MCS design in professional services organisations where there is heterogeneous ownership by professionals. Originality/value This study is one of the few attempts to provide empirical support for the assertion of the importance of a match between ownership structure and MCS design. It also represents one of the few attempts to provide empirical support for Speklé’s (2001) control archetypes, here the boundary and exploratory archetypes, archetypes that are applicable within important sectors of the economy, notably the professional services sector.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".