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Record W2102076036 · doi:10.5539/ibr.v8n10p66

A Research on the Relationship between Top Managers’ Intelligence and Their Ideas about Business Process Reengineering: Consideration of Emotionality and Spirituality

2015· article· en· W2102076036 on OpenAlexvenueno aff
Evren Ayrancı, Ayşegül Ertuğrul Ayrancı

Bibliographic record

VenueInternational Business Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness process reengineeringOperationalizationSpiritual intelligenceEmotional intelligenceQuality (philosophy)BusinessProcess (computing)Dimension (graph theory)MarketingProcess managementKnowledge managementPsychologyComputer scienceSocial psychologyEpistemologyLean manufacturing

Abstract

fetched live from OpenAlex

With its dramatic boosts to effectiveness and efficiency, Business Process Reengineering (BPR) is a crucial tool for business success, thus countless businesses in various industries are inspired to get its benefits. Though these benefits are heavily related to technical matters such as cost, speed and quality; there is also a human side associated with BPR. This study is interested in an odd dimension of this human side: top managers’ emotional and spiritual intelligence. More precisely, top managers’ emotional and spiritual intelligences are believed to be related with their ideas about the targets and critical success factors of BPR. This study not only scrutinizes this belief, but also fills in a great gap as the literature does not offer a similar research. The operationalization stage of the study includes data from top managers of businesses in İkitelli Organized Industrial Zone (OIZ) and the findings clearly point out that top managers’ spiritual intelligence is strongly and positively related with their ideas about BPR whereas there is no connection between their emotional capabilities and their mentioned ideas.

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)0.017
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.504
GPT teacher head0.494
Teacher spread0.010 · 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.

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
Published2015
Admission routes1
Has abstractyes

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