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Record W2965765894 · doi:10.33423/jabe.v21i4.2129

An Analytical Approach to Maximizing Stakeholders’ Value in Manufacturing Firms

2019· article· en· W2965765894 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Applied Business and Economics · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement Theory and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsMaximizationValue (mathematics)BusinessSocial value orientationsProcess (computing)Industrial organizationMarketingMicroeconomicsEconomicsComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Understanding how to maximize stakeholders’ value helps organizations improve the social good of society. This study empirically examined how an analytical approach can enhance stakeholders’ value maximization (economic and social values) using a structured questionnaire administered to 48 manufacturing firms in Rivers State, Nigeria. The results have correlation coefficients (r) of 0.895 and 0.776, respectively, with p< 0.05, indicating a strong and significant positive relationship exist between analytical orientation and stakeholders’ economic and social value maximization. Therefore, managers of manufacturing firms should apply analytical methodologies in their stakeholders’ value management process to support societal well-being.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.041
GPT teacher head0.220
Teacher spread0.179 · 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