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Record W4301396585 · doi:10.1007/s10551-022-05241-8

Business Versus Ethics? Thoughts on the Future of Business Ethics

2022· article· en· W4301396585 on OpenAlexaff
M. Tina Dacin, Jeffrey S. Harrison, David Hess, Sheila Killian, Julia Roloff

Bibliographic record

VenueJournal of Business Ethics · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsBusiness ethicsMeta-ethicsInformation ethicsApplied ethicsSociologyNormative ethicsMoralityPhilosophy of businessCorporate governanceLawEnvironmental ethicsPolitical scienceManagementBusiness modelPhilosophyEconomics

Abstract

fetched live from OpenAlex

To commemorate 40 years since the founding of the Journal of Business Ethics, the editors in chief of the journal have invited the editors to provide commentaries on the future of business ethics. This essay comprises a selection of commentaries aimed at creating dialogue around the theme Business versus Ethics? (inspired by the title of the commentary by Jeffrey Harrison). The authors of these commentaries seek to transcend the age-old separation fallacy (Freeman in Bus Ethics Q 4(4):409–421, 1994) that juxtaposes business and ethics/society, posing a forced choice or trade off. Providing a contemporary take on the classical question “if it’s legal is it ethical?”, David Hess explores the role of the law in promoting or hindering stakeholder-oriented purpose and governance structure. Jeffrey Harrison encourages scholars to move beyond the presupposition that businesses are either strategic or ethical and explore important questions at the intersection of strategy and ethics. The proposition that business models might be inherently ethical or inherently unethical in their design is developed by Sheila Killian, who examines business systems, their morality, and who they serve. However, the conundrum that entrepreneurs are either lauded for their self-belief and risk-taking, or loathed for their self-belief and risk-taking, is discussed by M. Tina Dacin and Julia Roloff using the metaphor of taboos and totems. These commentaries seek to explore positions that advocate multiplicity and tensions in which business ethics is not either/or but both.

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)0.063
metaresearch head score (Gemma)0.130
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0630.130
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.012
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0020.018
Insufficient payload (model declined to judge)0.0010.000

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 head0.449
GPT teacher head0.447
Teacher spread0.002 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations60
Published2022
Admission routes1
Has abstractyes

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