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Record W4320503226 · doi:10.55603/jes.v1i2.a1

Identifying Managerial Awareness Level on Negotiation and Conflict Resolution in Nepalese Banking Sectors: Descriptive Cross-sectional Analysis

2022· article· en· W4320503226 on OpenAlexaff
Neha Kayastha, Niranjan Devkota, Sushanta Kumar, Ranjana Koirala, Udaya Raj, Seeprata Parajuli

Bibliographic record

VenueJournal of Economic Sciences · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsNegotiationBusinessConflict resolutionProcess (computing)Sample (material)Descriptive statisticsBanking industryDescriptive researchPublic relationsMarketingAccountingPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

These days conflict resolution and negotiation seem to be tough and challenging tasks for managerial-level employees. Conflict with various stakeholders in the banking industry can be considered a major aspect. The study employs a descriptive data analysis procedure that covers a sample of 267 managerial-level staff. The purpose of this study is to identify the managerial awareness level of negotiation and conflict resolution in Nepalese banking sectors. The results of the study illustrated that managerial levels at commercial banks in Kathmandu Valley have high (86.14%) negotiation skills. Also, 67.16% of managers in the banking sector focus on maintaining a good relationship with another party while trying to resolve conflict through negotiation. Managerial employees even agreed that they faced challenges in the process of negotiation and conflict resolution. One of the major challenges is the lack of timing among the employees at commercial banks, due to which proper negotiation rarely takes place.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.340
GPT teacher head0.435
Teacher spread0.095 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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