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Record W3134316087 · doi:10.5430/ijba.v12n2p64

Risk in Leadership and Management: Risk-seeking vs. Risk Averse

2021· article· en· W3134316087 on OpenAlexvenueno aff
Samuel L. Dunn, Joshua D. Jensen

Bibliographic record

VenueInternational Journal of Business Administration · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsnot available
Fundersnot available
KeywordsRisk managementEnterprise risk managementFinancial risk managementRisk-seekingIT riskBusinessIT risk managementRisk analysis (engineering)Perspective (graphical)Factor analysis of information riskWork (physics)Business risksRisk assessmentMarketingRisk financingActuarial scienceRisk management information systemsEconomicsComputer sciencePolitical scienceManagementFinanceEngineering

Abstract

fetched live from OpenAlex

The 21st century business environment is full of dangers and risk. At the same time, it is ripe with opportunities and rewards. As organizations continue to pursue opportunities and rewards with vigor, while minimizing unnecessary exposure to risk, leaders find themselves with one of two perspectives on risk: risk-seeking or risk averse. Leaders who are risk-seeking are not moved by the inherent dangers of risk, and understand risk to be a necessary part of the business landscape – and therefore engage risk head-on. Alternatively, leaders who are risk averse understand the implications of risk on the enterprise, and work to minimize, or even avoid risk altogether, or at least to the furthest extent possible. This paper examines risk from a leadership perspective. There are various types of risk that business leaders face, and those will be identified and described herein, along with a discussion of various approaches for addressing risk and how risk is manifested in the business environment. Finally, this paper will assist leaders in determining their own attitudes toward risk using various self-assessment tools.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.091
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.025
GPT teacher head0.247
Teacher spread0.222 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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