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Record W2950494966 · doi:10.5539/ibr.v12n7p1

The Impact of Environmental Sensing Strategies on the Organizational Effectiveness: Evidence from Jordan

2019· article· en· W2950494966 on OpenAlexvenueno aff
Eyad Taha Al Rawashdeh, Ghazy Ali Al-Badayneh

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)IncentiveBusinessWork (physics)Organizational performanceOrder (exchange)Knowledge managementEnvironmental resource managementOperations managementEnvironmental economicsProcess managementMarketingComputer scienceManagementEngineeringFinanceEconomics

Abstract

fetched live from OpenAlex

The purpose of study is to identifying the impact of environmental sensing strategies on the organizational effectiveness among Jordanian commercial banks. In order to achieve the study objective, a questionnaire was designed and distributed to 175-members study community from the managerial level (director, deputy director and head of department). The appropriate statistical methods were used to analyze the data.     The result of the study reveals that, the level of applying environmental sensing strategies in Jordanian commercial banks has reached a high level. The level of organizational effectiveness in Jordanian commercial banks, has also reached a high level. There is a statistically significant impact of environmental sensing strategies (flexibility strategy, containment strategy, and forecasting strategy) on the organizational effectiveness as a whole and on its dimensions, individually, (availability of information, stability).    The study reached a number of recommendations, the most important are: creating a system of incentives and rewards for the employees to motivate them to work and to demonstrate distinguished abilities to interpret and clarify the information in a way that serves the organization.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.037
GPT teacher head0.332
Teacher spread0.295 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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