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Record W2060069078 · doi:10.5430/jms.v2n4p95

Effects of Reengineering in Banks on Employees Perception of Job Security

2011· article· en· W2060069078 on OpenAlexvenueno aff
Ademola B. Owolabi, Benjamin O. Omolayo

Bibliographic record

VenueJournal of Management and Strategy · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsJob insecurityJob securityPerceptionScale (ratio)Business process reengineeringJob attitudePsychologyBusinessJob performanceMarketingSocial psychologyJob satisfactionEngineeringWork (physics)Geography

Abstract

fetched live from OpenAlex

Banks in Nigeria are passing through a process of reorganizing how works are done in their organization with the aim of becoming more efficient and effective. This paper examines employees' perception of job security in response to re-engineering or technological changes in Banks. A job insecurity scale was use to measure employee perception to job insecurity. The scale was adapted from Ashford, Lee and Bobkos (1989) job insecurity scale and standardized by the researcher for the purpose of this research. A total of 150 participants (86 males and 64 females) were randomly drawn from two head office branches of Spring and Wema banks (Plc) respectively. Four hypotheses were tested. Findings revealed that there was no significant effect of re-engineering on perception of job insecurity among banks employee. Significant relationship was, however, observed between age and job insecurity of employee, there was also significant effect of job status on job insecurity. The result also revealed a significant effect of gender on perception of job insecurity among bankers. The implication of these findings is that when worker have a good understanding of the process of reengineering through proper education that it does not connote job loss. This study has shown that some negative attitude display by worker in an organization were largely due to lack of proper education by management.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.272

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 studies0.0000.000
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 head0.013
GPT teacher head0.213
Teacher spread0.200 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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