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Record W1996823769 · doi:10.4236/ti.2015.61001

Kant’s Categorical Imperative and the “Business” of Profit Maximization: The Quest for Service Paradigm

2015· article· en· W1996823769 on OpenAlexvenueno aff
Godwyns Agbude, Joseph Kayode Ogunwede, Joy Godwyns-Agbude, Ikedinachi Ayodele Power Wogu, Excellence-Oluye Nchekwube

Bibliographic record

VenueTechnology and Investment · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsProfit maximizationAppealGlobeProfit (economics)EconomicsIntuitionCategorical imperativeMaximizationPositive economicsMarketingBusinessNeoclassical economicsEpistemologyMicroeconomicsPolitical sciencePsychologyLawMoralityPhilosophy

Abstract

fetched live from OpenAlex

The discourse in the business world has gone beyond the primary purpose of business. While some scholars would argue that the primary purpose of business is profit maximization, others are of the opinion that business, beyond maximizing profit, exists to promote and enhance the well-being of humanity. Between these two divides, this paper attempts to contribute robustly to this perennial dialogue by interjecting Kant’s categorical imperative in pursuing the argument that though profit maximization is essential for business expansion, nonetheless the value of the human persons—both customers and employees—is equally and primarily essentially. Within the scope of this study, the researchers appeal to literature as case studies were presented to underscore the various attempts at making profit and pursuing personal economic benefit by some entrepreneurs without taking cognizance of the importance of the human persons that buy their proposed products. At the end, this paper vehemently appeals to the moral consciousness of entrepreneurs across the globe to integrate moral values to their pursuit of business profit and economic expansion.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.041
GPT teacher head0.224
Teacher spread0.183 · 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 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

Citations5
Published2015
Admission routes1
Has abstractyes

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