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Record W4365796921 · doi:10.1504/ijaape.2022.10055538

The challenges of implementing enterprise risk management: a study on manufacturing companies in the Tehran Stock Exchange

2022· article· en· W4365796921 on OpenAlexaff
Alireza Rahmani, Nelson Waweru, Seyed Ali Hosseini, Setareh Fasihi

Bibliographic record

VenueInternational Journal of Accounting Auditing and Performance Evaluation · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsYork University
Fundersnot available
KeywordsBusinessStock exchangeEnterprise risk managementStock (firearms)Risk managementManufacturing engineeringOperations managementFinanceEngineering

Abstract

fetched live from OpenAlex

Implementing enterprise risk management (ERM) is one of the important solutions in reducing the uncertainty and survival of companies. The study aims to explore the challenges of implementing ERM and possible solutions that may address these challenges in the manufacturing companies listed in the Tehran Stock Exchange. In the study, semi-structured interviews with the ERM experts among selected Iranian manufacturing companies are used. The identified challenges related to implementing ERM include intra-organisational and extra-organisational challenges. Intra-organisational challenges include risk governance, risk culture, ERM process, and infrastructures. Besides, extra-organisational challenges include the roles of government and policymakers, political and economic conditions, international restrictions, and the lack of a competitive environment (exclusiveness). Our study found that establishment of a risk committee, strengthening risk culture through ERM training top management commitment to ERM and the provision of sufficient funds were the factors that may be used to mitigated ERM implementation challenges.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.285
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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