Budgetary control and risk management institutionalization: a field study of three state-owned enterprises in China
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the design and implementation of enterprise risk management (ERM) in three large Chinese state-owned enterprises and to develop propositions on integrating ERM, budgetary control system and cash flow stability approach. Design/methodology/approach This study adopts a field study approach to analyze the risk assessment and risk-return matching of ERM. A field study was carried out over three years from 2008 to 2011 in three Chinese state-owned enterprises. These companies were chosen because less attention has been given to the implementation of ERM in such firms. Findings First, the authors find that all three companies use budgetary control to identify risks, analyze each risk to determine the potential consequences, determine the acceptable levels of risk, develop a risk mitigation plan and monitor the activities in all business processes that may change the levels of risks continuously. Second, the companies focus on cash flow risks through budgetary control to ensure the stability of cash flows. Finally, the degree of intensity of using budgetary control institutionalization to design and implement ERM has a positive impact on the level of risk acceptance and risk assessment culture. Research limitations/implications The findings of this study, however, should be interpreted with caution because this study was conducted in three Chinese state-owned enterprises. To increase the generalizability of the findings, future research is encouraged to replicate this study in different industries, as well as in different countries. Furthermore, future research might also examine the authors’ propositions using a large-scale survey across other regions of the world. Practical implications Companies can minimize resistance to change by using budgetary control institutionalization when implementing the ERM. State-owned enterprises can initiate and implement a new risk management system by identifying the potential risks and by developing a risk mitigation plan. Social implications The results of this study will help companies, particularly state-owned enterprises, to improve their performance and become more competitive, which in turn will benefit the society as a whole by performing their risk driver identification, risk driver impact assessment, risk management actions and risk management optimization more effectively. Originality/value The authors investigate how the firms use a legitimate system, namely, budgetary control, that is widely accepted and used in China to foster the acceptance and use of ERM. The authors also develop testable propositions of ERM implementation and cash flow stability that will provide useful guidelines for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".